From f470246a55c14a9609d39a6c6a20048b6a97d9f6 Mon Sep 17 00:00:00 2001 From: Tian Gao Date: Mon, 18 Nov 2024 23:12:13 -0500 Subject: [PATCH] Lazy load unnecessary modules to improve start speed --- docs/source/custom_event.rst | 3 ++- docs/source/extra_log.rst | 3 ++- docs/source/global_tracer.rst | 6 ++++-- example/json/async_simple.json | 2 +- example/json/different_sorts.json | 2 +- example/json/function_args_return.json | 2 +- example/json/gradient_descent.json | 2 +- example/json/logging_integration.json | 2 +- example/json/mcts_game.json | 2 +- example/json/multi_process_pool.json | 2 +- example/json/multithread.json | 2 +- example/src/gradient_descent.py | 3 ++- example/src/logging_integration.py | 3 ++- pyproject.toml | 4 ++-- src/viztracer/__init__.py | 10 ---------- src/viztracer/main.py | 7 +++++-- src/viztracer/modules/snaptrace.c | 7 +------ src/viztracer/modules/snaptrace_member.c | 15 +++++++++++++++ src/viztracer/util.py | 2 +- src/viztracer/viztracer.py | 4 ++-- tests/data/vdb_basic.py | 4 +++- tests/test_logging.py | 3 ++- tests/test_regression.py | 4 +++- tests/test_vcompressor.py | 6 ++++-- tests/test_vizcounter.py | 3 ++- tests/test_vizobject.py | 3 ++- 26 files changed, 62 insertions(+), 44 deletions(-) diff --git a/docs/source/custom_event.rst b/docs/source/custom_event.rst index da2b7380..3d796a88 100644 --- a/docs/source/custom_event.rst +++ b/docs/source/custom_event.rst @@ -65,7 +65,8 @@ Custom Event .. code-block:: python - from viztracer import VizTracer, VizCounter + from viztracer import VizTracer + from viztracer.vizcounter import VizCounter tracer = VizTracer() tracer.start() counter = VizCounter(tracer, "counter name") diff --git a/docs/source/extra_log.rst b/docs/source/extra_log.rst index daeb124c..24fc698a 100644 --- a/docs/source/extra_log.rst +++ b/docs/source/extra_log.rst @@ -217,7 +217,8 @@ data as Instant Events. .. code-block:: python - from viztracer import VizTracer, VizLoggingHandler + from viztracer import VizTracer + from viztracer.vizlogging import VizLoggingHandler tracer = VizTracer() handler = VizLoggingHandler() diff --git a/docs/source/global_tracer.rst b/docs/source/global_tracer.rst index 614e4750..559f3cdd 100644 --- a/docs/source/global_tracer.rst +++ b/docs/source/global_tracer.rst @@ -26,7 +26,8 @@ You can do things like: .. code-block:: python - from viztracer import VizLoggingHandler, get_tracer + from viztracer import get_tracer + from viztracer.vizlogging import VizLoggingHandler handler = VizLoggingHandler() @@ -34,6 +35,7 @@ You can do things like: .. code-block:: python - from viztracer import get_tracer, VizObject + from viztracer import get_tracer + from viztracer.vizobject import VizObject obj = VizObject(get_tracer(), "my variable") diff --git a/example/json/async_simple.json b/example/json/async_simple.json index 928cc46f..a7af001a 100644 --- a/example/json/async_simple.json +++ b/example/json/async_simple.json @@ -1 +1 @@ -{"traceEvents": [{"ph": "M", "pid": 32511, "tid": 32511, "name": "process_name", "args": {"name": "MainProcess"}}, {"ph": "M", "pid": 32511, "tid": 32511, "name": "thread_name", "args": {"name": "MainThread"}}, {"pid": 32511, "tid": 32511, "ts": 25981274393.691, "dur": 1.3, "name": "_asyncio._get_running_loop", "ph": "X", "cat": "FEE"}, {"pid": 32511, "tid": 32511, "ts": 25981274396.991, "dur": 0.1, "name": "builtins.isinstance", "ph": "X", "cat": "FEE"}, {"pid": 32511, "tid": 32511, "ts": 25981274397.191, "dur": 0.1, "name": "builtins.len", "ph": "X", "cat": "FEE"}, {"pid": 32511, "tid": 32511, "ts": 25981274398.191, "dur": 0.1, "name": "set.add", "ph": "X", "cat": "FEE"}, {"pid": 32511, "tid": 32511, "ts": 25981274395.991, "dur": 2.5, "name": "iscoroutine (/usr/lib/python3.8/asyncio/coroutines.py:177)", "ph": "X", "cat": "FEE"}, {"pid": 32511, "tid": 32511, "ts": 25981274401.791, "dur": 0.3, "name": "str.rpartition", "ph": "X", "cat": "FEE"}, {"pid": 32511, "tid": 32511, "ts": 25981274401.391, "dur": 1.0, "name": 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'iscoroutinefunction', 'iscoroutine'\n\nimport collections.abc\nimport functools\nimport inspect\nimport os\nimport sys\nimport traceback\nimport types\nimport warnings\n\nfrom . import base_futures\nfrom . import constants\nfrom . import format_helpers\nfrom .log import logger\n\n\ndef _is_debug_mode():\n # If you set _DEBUG to true, @coroutine will wrap the resulting\n # generator objects in a CoroWrapper instance (defined below). That\n # instance will log a message when the generator is never iterated\n # over, which may happen when you forget to use \"await\" or \"yield from\"\n # with a coroutine call.\n # Note that the value of the _DEBUG flag is taken\n # when the decorator is used, so to be of any use it must be set\n # before you define your coroutines. A downside of using this feature\n # is that tracebacks show entries for the CoroWrapper.__next__ method\n # when _DEBUG is true.\n return sys.flags.dev_mode or (not sys.flags.ignore_environment and\n bool(os.environ.get('PYTHONASYNCIODEBUG')))\n\n\n_DEBUG = _is_debug_mode()\n\n\nclass CoroWrapper:\n # Wrapper for coroutine object in _DEBUG mode.\n\n def __init__(self, gen, func=None):\n assert inspect.isgenerator(gen) or inspect.iscoroutine(gen), gen\n self.gen = gen\n self.func = func # Used to unwrap @coroutine decorator\n self._source_traceback = format_helpers.extract_stack(sys._getframe(1))\n self.__name__ = getattr(gen, '__name__', None)\n self.__qualname__ = getattr(gen, '__qualname__', None)\n\n def __repr__(self):\n coro_repr = _format_coroutine(self)\n if self._source_traceback:\n frame = self._source_traceback[-1]\n coro_repr += f', created at {frame[0]}:{frame[1]}'\n\n return f'<{self.__class__.__name__} {coro_repr}>'\n\n def __iter__(self):\n return self\n\n def __next__(self):\n return self.gen.send(None)\n\n def send(self, value):\n return self.gen.send(value)\n\n def throw(self, type, value=None, traceback=None):\n return self.gen.throw(type, value, traceback)\n\n def close(self):\n return self.gen.close()\n\n @property\n def gi_frame(self):\n return self.gen.gi_frame\n\n @property\n def gi_running(self):\n return self.gen.gi_running\n\n @property\n def gi_code(self):\n return self.gen.gi_code\n\n def __await__(self):\n return self\n\n @property\n def gi_yieldfrom(self):\n return self.gen.gi_yieldfrom\n\n def __del__(self):\n # Be careful accessing self.gen.frame -- self.gen might not exist.\n gen = getattr(self, 'gen', None)\n frame = getattr(gen, 'gi_frame', None)\n if frame is not None and frame.f_lasti == -1:\n msg = f'{self!r} was never yielded from'\n tb = getattr(self, '_source_traceback', ())\n if tb:\n tb = ''.join(traceback.format_list(tb))\n msg += (f'\\nCoroutine object created at '\n f'(most recent call last, truncated to '\n f'{constants.DEBUG_STACK_DEPTH} last lines):\\n')\n msg += tb.rstrip()\n logger.error(msg)\n\n\ndef coroutine(func):\n \"\"\"Decorator to mark coroutines.\n\n If the coroutine is not yielded from before it is destroyed,\n an error message is logged.\n \"\"\"\n warnings.warn('\"@coroutine\" decorator is deprecated since Python 3.8, use \"async def\" instead',\n DeprecationWarning,\n stacklevel=2)\n if inspect.iscoroutinefunction(func):\n # In Python 3.5 that's all we need to do for coroutines\n # defined with \"async def\".\n return func\n\n if inspect.isgeneratorfunction(func):\n coro = func\n else:\n @functools.wraps(func)\n def coro(*args, **kw):\n res = func(*args, **kw)\n if (base_futures.isfuture(res) or inspect.isgenerator(res) or\n isinstance(res, CoroWrapper)):\n res = yield from res\n else:\n # If 'res' is an awaitable, run it.\n try:\n await_meth = res.__await__\n except AttributeError:\n pass\n else:\n if isinstance(res, collections.abc.Awaitable):\n res = yield from await_meth()\n return res\n\n coro = types.coroutine(coro)\n if not _DEBUG:\n wrapper = coro\n else:\n @functools.wraps(func)\n def wrapper(*args, **kwds):\n w = CoroWrapper(coro(*args, **kwds), func=func)\n if w._source_traceback:\n del w._source_traceback[-1]\n # Python < 3.5 does not implement __qualname__\n # on generator objects, so we set it manually.\n # We use getattr as some callables (such as\n # functools.partial may lack __qualname__).\n w.__name__ = getattr(func, '__name__', None)\n w.__qualname__ = getattr(func, '__qualname__', None)\n return w\n\n wrapper._is_coroutine = _is_coroutine # For iscoroutinefunction().\n return wrapper\n\n\n# A marker for iscoroutinefunction.\n_is_coroutine = object()\n\n\ndef iscoroutinefunction(func):\n \"\"\"Return True if func is a decorated coroutine function.\"\"\"\n return (inspect.iscoroutinefunction(func) or\n getattr(func, '_is_coroutine', None) is _is_coroutine)\n\n\n# Prioritize native coroutine check to speed-up\n# asyncio.iscoroutine.\n_COROUTINE_TYPES = (types.CoroutineType, types.GeneratorType,\n collections.abc.Coroutine, CoroWrapper)\n_iscoroutine_typecache = set()\n\n\ndef iscoroutine(obj):\n \"\"\"Return True if obj is a coroutine object.\"\"\"\n if type(obj) in _iscoroutine_typecache:\n return True\n\n if isinstance(obj, _COROUTINE_TYPES):\n # Just in case we don't want to cache more than 100\n # positive types. That shouldn't ever happen, unless\n # someone stressing the system on purpose.\n if len(_iscoroutine_typecache) < 100:\n _iscoroutine_typecache.add(type(obj))\n return True\n else:\n return False\n\n\ndef _format_coroutine(coro):\n assert iscoroutine(coro)\n\n is_corowrapper = isinstance(coro, CoroWrapper)\n\n def get_name(coro):\n # Coroutines compiled with Cython sometimes don't have\n # proper __qualname__ or __name__. While that is a bug\n # in Cython, asyncio shouldn't crash with an AttributeError\n # in its __repr__ functions.\n if is_corowrapper:\n return format_helpers._format_callback(coro.func, (), {})\n\n if hasattr(coro, '__qualname__') and coro.__qualname__:\n coro_name = coro.__qualname__\n elif hasattr(coro, '__name__') and coro.__name__:\n coro_name = coro.__name__\n else:\n # Stop masking Cython bugs, expose them in a friendly way.\n coro_name = f'<{type(coro).__name__} without __name__>'\n return f'{coro_name}()'\n\n def is_running(coro):\n try:\n return coro.cr_running\n except AttributeError:\n try:\n return coro.gi_running\n except AttributeError:\n return False\n\n coro_code = None\n if hasattr(coro, 'cr_code') and coro.cr_code:\n coro_code = coro.cr_code\n elif hasattr(coro, 'gi_code') and coro.gi_code:\n coro_code = coro.gi_code\n\n coro_name = get_name(coro)\n\n if not coro_code:\n # Built-in types might not have __qualname__ or __name__.\n if is_running(coro):\n return f'{coro_name} running'\n else:\n return coro_name\n\n coro_frame = None\n if hasattr(coro, 'gi_frame') and coro.gi_frame:\n coro_frame = coro.gi_frame\n elif hasattr(coro, 'cr_frame') and coro.cr_frame:\n coro_frame = coro.cr_frame\n\n # If Cython's coroutine has a fake code object without proper\n # co_filename -- expose that.\n filename = coro_code.co_filename or ''\n\n lineno = 0\n if (is_corowrapper and\n coro.func is not None and\n not inspect.isgeneratorfunction(coro.func)):\n source = format_helpers._get_function_source(coro.func)\n if source is not None:\n filename, lineno = source\n if coro_frame is None:\n coro_repr = f'{coro_name} done, defined at {filename}:{lineno}'\n else:\n coro_repr = f'{coro_name} running, defined at {filename}:{lineno}'\n\n elif coro_frame is not None:\n lineno = coro_frame.f_lineno\n coro_repr = f'{coro_name} running at {filename}:{lineno}'\n\n else:\n lineno = coro_code.co_firstlineno\n coro_repr = f'{coro_name} done, defined at {filename}:{lineno}'\n\n return coro_repr\n", 269], "/usr/lib/python3.8/asyncio/events.py": ["\"\"\"Event loop and event loop policy.\"\"\"\n\n__all__ = (\n 'AbstractEventLoopPolicy',\n 'AbstractEventLoop', 'AbstractServer',\n 'Handle', 'TimerHandle',\n 'get_event_loop_policy', 'set_event_loop_policy',\n 'get_event_loop', 'set_event_loop', 'new_event_loop',\n 'get_child_watcher', 'set_child_watcher',\n '_set_running_loop', 'get_running_loop',\n '_get_running_loop',\n)\n\nimport contextvars\nimport os\nimport socket\nimport subprocess\nimport sys\nimport threading\n\nfrom . import format_helpers\nfrom . import exceptions\n\n\nclass Handle:\n \"\"\"Object returned by callback registration methods.\"\"\"\n\n __slots__ = ('_callback', '_args', '_cancelled', '_loop',\n '_source_traceback', '_repr', '__weakref__',\n '_context')\n\n def __init__(self, callback, args, loop, context=None):\n if context is None:\n context = contextvars.copy_context()\n self._context = context\n self._loop = loop\n self._callback = callback\n self._args = args\n self._cancelled = False\n self._repr = None\n if self._loop.get_debug():\n self._source_traceback = format_helpers.extract_stack(\n sys._getframe(1))\n else:\n self._source_traceback = None\n\n def _repr_info(self):\n info = [self.__class__.__name__]\n if self._cancelled:\n info.append('cancelled')\n if self._callback is not None:\n info.append(format_helpers._format_callback_source(\n self._callback, self._args))\n if self._source_traceback:\n frame = self._source_traceback[-1]\n info.append(f'created at {frame[0]}:{frame[1]}')\n return info\n\n def __repr__(self):\n if self._repr is not None:\n return self._repr\n info = self._repr_info()\n return '<{}>'.format(' '.join(info))\n\n def cancel(self):\n if not self._cancelled:\n self._cancelled = True\n if self._loop.get_debug():\n # Keep a representation in debug mode to keep callback and\n # parameters. For example, to log the warning\n # \"Executing took 2.5 second\"\n self._repr = repr(self)\n self._callback = None\n self._args = None\n\n def cancelled(self):\n return self._cancelled\n\n def _run(self):\n try:\n self._context.run(self._callback, *self._args)\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n cb = format_helpers._format_callback_source(\n self._callback, self._args)\n msg = f'Exception in callback {cb}'\n context = {\n 'message': msg,\n 'exception': exc,\n 'handle': self,\n }\n if self._source_traceback:\n context['source_traceback'] = self._source_traceback\n self._loop.call_exception_handler(context)\n self = None # Needed to break cycles when an exception occurs.\n\n\nclass TimerHandle(Handle):\n \"\"\"Object returned by timed callback registration methods.\"\"\"\n\n __slots__ = ['_scheduled', '_when']\n\n def __init__(self, when, callback, args, loop, context=None):\n assert when is not None\n super().__init__(callback, args, loop, context)\n if self._source_traceback:\n del self._source_traceback[-1]\n self._when = when\n self._scheduled = False\n\n def _repr_info(self):\n info = super()._repr_info()\n pos = 2 if self._cancelled else 1\n info.insert(pos, f'when={self._when}')\n return info\n\n def __hash__(self):\n return hash(self._when)\n\n def __lt__(self, other):\n return self._when < other._when\n\n def __le__(self, other):\n if self._when < other._when:\n return True\n return self.__eq__(other)\n\n def __gt__(self, other):\n return self._when > other._when\n\n def __ge__(self, other):\n if self._when > other._when:\n return True\n return self.__eq__(other)\n\n def __eq__(self, other):\n if isinstance(other, TimerHandle):\n return (self._when == other._when and\n self._callback == other._callback and\n self._args == other._args and\n self._cancelled == other._cancelled)\n return NotImplemented\n\n def __ne__(self, other):\n equal = self.__eq__(other)\n return NotImplemented if equal is NotImplemented else not equal\n\n def cancel(self):\n if not self._cancelled:\n self._loop._timer_handle_cancelled(self)\n super().cancel()\n\n def when(self):\n \"\"\"Return a scheduled callback time.\n\n The time is an absolute timestamp, using the same time\n reference as loop.time().\n \"\"\"\n return self._when\n\n\nclass AbstractServer:\n \"\"\"Abstract server returned by create_server().\"\"\"\n\n def close(self):\n \"\"\"Stop serving. This leaves existing connections open.\"\"\"\n raise NotImplementedError\n\n def get_loop(self):\n \"\"\"Get the event loop the Server object is attached to.\"\"\"\n raise NotImplementedError\n\n def is_serving(self):\n \"\"\"Return True if the server is accepting connections.\"\"\"\n raise NotImplementedError\n\n async def start_serving(self):\n \"\"\"Start accepting connections.\n\n This method is idempotent, so it can be called when\n the server is already being serving.\n \"\"\"\n raise NotImplementedError\n\n async def serve_forever(self):\n \"\"\"Start accepting connections until the coroutine is cancelled.\n\n The server is closed when the coroutine is cancelled.\n \"\"\"\n raise NotImplementedError\n\n async def wait_closed(self):\n \"\"\"Coroutine to wait until service is closed.\"\"\"\n raise NotImplementedError\n\n async def __aenter__(self):\n return self\n\n async def __aexit__(self, *exc):\n self.close()\n await self.wait_closed()\n\n\nclass AbstractEventLoop:\n \"\"\"Abstract event loop.\"\"\"\n\n # Running and stopping the event loop.\n\n def run_forever(self):\n \"\"\"Run the event loop until stop() is called.\"\"\"\n raise NotImplementedError\n\n def run_until_complete(self, future):\n \"\"\"Run the event loop until a Future is done.\n\n Return the Future's result, or raise its exception.\n \"\"\"\n raise NotImplementedError\n\n def stop(self):\n \"\"\"Stop the event loop as soon as reasonable.\n\n Exactly how soon that is may depend on the implementation, but\n no more I/O callbacks should be scheduled.\n \"\"\"\n raise NotImplementedError\n\n def is_running(self):\n \"\"\"Return whether the event loop is currently running.\"\"\"\n raise NotImplementedError\n\n def is_closed(self):\n \"\"\"Returns True if the event loop was closed.\"\"\"\n raise NotImplementedError\n\n def close(self):\n \"\"\"Close the loop.\n\n The loop should not be running.\n\n This is idempotent and irreversible.\n\n No other methods should be called after this one.\n \"\"\"\n raise NotImplementedError\n\n async def shutdown_asyncgens(self):\n \"\"\"Shutdown all active asynchronous generators.\"\"\"\n raise NotImplementedError\n\n # Methods scheduling callbacks. All these return Handles.\n\n def _timer_handle_cancelled(self, handle):\n \"\"\"Notification that a TimerHandle has been cancelled.\"\"\"\n raise NotImplementedError\n\n def call_soon(self, callback, *args):\n return self.call_later(0, callback, *args)\n\n def call_later(self, delay, callback, *args):\n raise NotImplementedError\n\n def call_at(self, when, callback, *args):\n raise NotImplementedError\n\n def time(self):\n raise NotImplementedError\n\n def create_future(self):\n raise NotImplementedError\n\n # Method scheduling a coroutine object: create a task.\n\n def create_task(self, coro, *, name=None):\n raise NotImplementedError\n\n # Methods for interacting with threads.\n\n def call_soon_threadsafe(self, callback, *args):\n raise NotImplementedError\n\n def run_in_executor(self, executor, func, *args):\n raise NotImplementedError\n\n def set_default_executor(self, executor):\n raise NotImplementedError\n\n # Network I/O methods returning Futures.\n\n async def getaddrinfo(self, host, port, *,\n family=0, type=0, proto=0, flags=0):\n raise NotImplementedError\n\n async def getnameinfo(self, sockaddr, flags=0):\n raise NotImplementedError\n\n async def create_connection(\n self, protocol_factory, host=None, port=None,\n *, ssl=None, family=0, proto=0,\n flags=0, sock=None, local_addr=None,\n server_hostname=None,\n ssl_handshake_timeout=None,\n happy_eyeballs_delay=None, interleave=None):\n raise NotImplementedError\n\n async def create_server(\n self, protocol_factory, host=None, port=None,\n *, family=socket.AF_UNSPEC,\n flags=socket.AI_PASSIVE, sock=None, backlog=100,\n ssl=None, reuse_address=None, reuse_port=None,\n ssl_handshake_timeout=None,\n start_serving=True):\n \"\"\"A coroutine which creates a TCP server bound to host and port.\n\n The return value is a Server object which can be used to stop\n the service.\n\n If host is an empty string or None all interfaces are assumed\n and a list of multiple sockets will be returned (most likely\n one for IPv4 and another one for IPv6). The host parameter can also be\n a sequence (e.g. list) of hosts to bind to.\n\n family can be set to either AF_INET or AF_INET6 to force the\n socket to use IPv4 or IPv6. If not set it will be determined\n from host (defaults to AF_UNSPEC).\n\n flags is a bitmask for getaddrinfo().\n\n sock can optionally be specified in order to use a preexisting\n socket object.\n\n backlog is the maximum number of queued connections passed to\n listen() (defaults to 100).\n\n ssl can be set to an SSLContext to enable SSL over the\n accepted connections.\n\n reuse_address tells the kernel to reuse a local socket in\n TIME_WAIT state, without waiting for its natural timeout to\n expire. If not specified will automatically be set to True on\n UNIX.\n\n reuse_port tells the kernel to allow this endpoint to be bound to\n the same port as other existing endpoints are bound to, so long as\n they all set this flag when being created. This option is not\n supported on Windows.\n\n ssl_handshake_timeout is the time in seconds that an SSL server\n will wait for completion of the SSL handshake before aborting the\n connection. Default is 60s.\n\n start_serving set to True (default) causes the created server\n to start accepting connections immediately. When set to False,\n the user should await Server.start_serving() or Server.serve_forever()\n to make the server to start accepting connections.\n \"\"\"\n raise NotImplementedError\n\n async def sendfile(self, transport, file, offset=0, count=None,\n *, fallback=True):\n \"\"\"Send a file through a transport.\n\n Return an amount of sent bytes.\n \"\"\"\n raise NotImplementedError\n\n async def start_tls(self, transport, protocol, sslcontext, *,\n server_side=False,\n server_hostname=None,\n ssl_handshake_timeout=None):\n \"\"\"Upgrade a transport to TLS.\n\n Return a new transport that *protocol* should start using\n immediately.\n \"\"\"\n raise NotImplementedError\n\n async def create_unix_connection(\n self, protocol_factory, path=None, *,\n ssl=None, sock=None,\n server_hostname=None,\n ssl_handshake_timeout=None):\n raise NotImplementedError\n\n async def create_unix_server(\n self, protocol_factory, path=None, *,\n sock=None, backlog=100, ssl=None,\n ssl_handshake_timeout=None,\n start_serving=True):\n \"\"\"A coroutine which creates a UNIX Domain Socket server.\n\n The return value is a Server object, which can be used to stop\n the service.\n\n path is a str, representing a file systsem path to bind the\n server socket to.\n\n sock can optionally be specified in order to use a preexisting\n socket object.\n\n backlog is the maximum number of queued connections passed to\n listen() (defaults to 100).\n\n ssl can be set to an SSLContext to enable SSL over the\n accepted connections.\n\n ssl_handshake_timeout is the time in seconds that an SSL server\n will wait for the SSL handshake to complete (defaults to 60s).\n\n start_serving set to True (default) causes the created server\n to start accepting connections immediately. When set to False,\n the user should await Server.start_serving() or Server.serve_forever()\n to make the server to start accepting connections.\n \"\"\"\n raise NotImplementedError\n\n async def create_datagram_endpoint(self, protocol_factory,\n local_addr=None, remote_addr=None, *,\n family=0, proto=0, flags=0,\n reuse_address=None, reuse_port=None,\n allow_broadcast=None, sock=None):\n \"\"\"A coroutine which creates a datagram endpoint.\n\n This method will try to establish the endpoint in the background.\n When successful, the coroutine returns a (transport, protocol) pair.\n\n protocol_factory must be a callable returning a protocol instance.\n\n socket family AF_INET, socket.AF_INET6 or socket.AF_UNIX depending on\n host (or family if specified), socket type SOCK_DGRAM.\n\n reuse_address tells the kernel to reuse a local socket in\n TIME_WAIT state, without waiting for its natural timeout to\n expire. If not specified it will automatically be set to True on\n UNIX.\n\n reuse_port tells the kernel to allow this endpoint to be bound to\n the same port as other existing endpoints are bound to, so long as\n they all set this flag when being created. This option is not\n supported on Windows and some UNIX's. If the\n :py:data:`~socket.SO_REUSEPORT` constant is not defined then this\n capability is unsupported.\n\n allow_broadcast tells the kernel to allow this endpoint to send\n messages to the broadcast address.\n\n sock can optionally be specified in order to use a preexisting\n socket object.\n \"\"\"\n raise NotImplementedError\n\n # Pipes and subprocesses.\n\n async def connect_read_pipe(self, protocol_factory, pipe):\n \"\"\"Register read pipe in event loop. Set the pipe to non-blocking mode.\n\n protocol_factory should instantiate object with Protocol interface.\n pipe is a file-like object.\n Return pair (transport, protocol), where transport supports the\n ReadTransport interface.\"\"\"\n # The reason to accept file-like object instead of just file descriptor\n # is: we need to own pipe and close it at transport finishing\n # Can got complicated errors if pass f.fileno(),\n # close fd in pipe transport then close f and vise versa.\n raise NotImplementedError\n\n async def connect_write_pipe(self, protocol_factory, pipe):\n \"\"\"Register write pipe in event loop.\n\n protocol_factory should instantiate object with BaseProtocol interface.\n Pipe is file-like object already switched to nonblocking.\n Return pair (transport, protocol), where transport support\n WriteTransport interface.\"\"\"\n # The reason to accept file-like object instead of just file descriptor\n # is: we need to own pipe and close it at transport finishing\n # Can got complicated errors if pass f.fileno(),\n # close fd in pipe transport then close f and vise versa.\n raise NotImplementedError\n\n async def subprocess_shell(self, protocol_factory, cmd, *,\n stdin=subprocess.PIPE,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n **kwargs):\n raise NotImplementedError\n\n async def subprocess_exec(self, protocol_factory, *args,\n stdin=subprocess.PIPE,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n **kwargs):\n raise NotImplementedError\n\n # Ready-based callback registration methods.\n # The add_*() methods return None.\n # The remove_*() methods return True if something was removed,\n # False if there was nothing to delete.\n\n def add_reader(self, fd, callback, *args):\n raise NotImplementedError\n\n def remove_reader(self, fd):\n raise NotImplementedError\n\n def add_writer(self, fd, callback, *args):\n raise NotImplementedError\n\n def remove_writer(self, fd):\n raise NotImplementedError\n\n # Completion based I/O methods returning Futures.\n\n async def sock_recv(self, sock, nbytes):\n raise NotImplementedError\n\n async def sock_recv_into(self, sock, buf):\n raise NotImplementedError\n\n async def sock_sendall(self, sock, data):\n raise NotImplementedError\n\n async def sock_connect(self, sock, address):\n raise NotImplementedError\n\n async def sock_accept(self, sock):\n raise NotImplementedError\n\n async def sock_sendfile(self, sock, file, offset=0, count=None,\n *, fallback=None):\n raise NotImplementedError\n\n # Signal handling.\n\n def add_signal_handler(self, sig, callback, *args):\n raise NotImplementedError\n\n def remove_signal_handler(self, sig):\n raise NotImplementedError\n\n # Task factory.\n\n def set_task_factory(self, factory):\n raise NotImplementedError\n\n def get_task_factory(self):\n raise NotImplementedError\n\n # Error handlers.\n\n def get_exception_handler(self):\n raise NotImplementedError\n\n def set_exception_handler(self, handler):\n raise NotImplementedError\n\n def default_exception_handler(self, context):\n raise NotImplementedError\n\n def call_exception_handler(self, context):\n raise NotImplementedError\n\n # Debug flag management.\n\n def get_debug(self):\n raise NotImplementedError\n\n def set_debug(self, enabled):\n raise NotImplementedError\n\n\nclass AbstractEventLoopPolicy:\n \"\"\"Abstract policy for accessing the event loop.\"\"\"\n\n def get_event_loop(self):\n \"\"\"Get the event loop for the current context.\n\n Returns an event loop object implementing the BaseEventLoop interface,\n or raises an exception in case no event loop has been set for the\n current context and the current policy does not specify to create one.\n\n It should never return None.\"\"\"\n raise NotImplementedError\n\n def set_event_loop(self, loop):\n \"\"\"Set the event loop for the current context to loop.\"\"\"\n raise NotImplementedError\n\n def new_event_loop(self):\n \"\"\"Create and return a new event loop object according to this\n policy's rules. If there's need to set this loop as the event loop for\n the current context, set_event_loop must be called explicitly.\"\"\"\n raise NotImplementedError\n\n # Child processes handling (Unix only).\n\n def get_child_watcher(self):\n \"Get the watcher for child processes.\"\n raise NotImplementedError\n\n def set_child_watcher(self, watcher):\n \"\"\"Set the watcher for child processes.\"\"\"\n raise NotImplementedError\n\n\nclass BaseDefaultEventLoopPolicy(AbstractEventLoopPolicy):\n \"\"\"Default policy implementation for accessing the event loop.\n\n In this policy, each thread has its own event loop. However, we\n only automatically create an event loop by default for the main\n thread; other threads by default have no event loop.\n\n Other policies may have different rules (e.g. a single global\n event loop, or automatically creating an event loop per thread, or\n using some other notion of context to which an event loop is\n associated).\n \"\"\"\n\n _loop_factory = None\n\n class _Local(threading.local):\n _loop = None\n _set_called = False\n\n def __init__(self):\n self._local = self._Local()\n\n def get_event_loop(self):\n \"\"\"Get the event loop for the current context.\n\n Returns an instance of EventLoop or raises an exception.\n \"\"\"\n if (self._local._loop is None and\n not self._local._set_called and\n isinstance(threading.current_thread(), threading._MainThread)):\n self.set_event_loop(self.new_event_loop())\n\n if self._local._loop is None:\n raise RuntimeError('There is no current event loop in thread %r.'\n % threading.current_thread().name)\n\n return self._local._loop\n\n def set_event_loop(self, loop):\n \"\"\"Set the event loop.\"\"\"\n self._local._set_called = True\n assert loop is None or isinstance(loop, AbstractEventLoop)\n self._local._loop = loop\n\n def new_event_loop(self):\n \"\"\"Create a new event loop.\n\n You must call set_event_loop() to make this the current event\n loop.\n \"\"\"\n return self._loop_factory()\n\n\n# Event loop policy. The policy itself is always global, even if the\n# policy's rules say that there is an event loop per thread (or other\n# notion of context). The default policy is installed by the first\n# call to get_event_loop_policy().\n_event_loop_policy = None\n\n# Lock for protecting the on-the-fly creation of the event loop policy.\n_lock = threading.Lock()\n\n\n# A TLS for the running event loop, used by _get_running_loop.\nclass _RunningLoop(threading.local):\n loop_pid = (None, None)\n\n\n_running_loop = _RunningLoop()\n\n\ndef get_running_loop():\n \"\"\"Return the running event loop. Raise a RuntimeError if there is none.\n\n This function is thread-specific.\n \"\"\"\n # NOTE: this function is implemented in C (see _asynciomodule.c)\n loop = _get_running_loop()\n if loop is None:\n raise RuntimeError('no running event loop')\n return loop\n\n\ndef _get_running_loop():\n \"\"\"Return the running event loop or None.\n\n This is a low-level function intended to be used by event loops.\n This function is thread-specific.\n \"\"\"\n # NOTE: this function is implemented in C (see _asynciomodule.c)\n running_loop, pid = _running_loop.loop_pid\n if running_loop is not None and pid == os.getpid():\n return running_loop\n\n\ndef _set_running_loop(loop):\n \"\"\"Set the running event loop.\n\n This is a low-level function intended to be used by event loops.\n This function is thread-specific.\n \"\"\"\n # NOTE: this function is implemented in C (see _asynciomodule.c)\n _running_loop.loop_pid = (loop, os.getpid())\n\n\ndef _init_event_loop_policy():\n global _event_loop_policy\n with _lock:\n if _event_loop_policy is None: # pragma: no branch\n from . import DefaultEventLoopPolicy\n _event_loop_policy = DefaultEventLoopPolicy()\n\n\ndef get_event_loop_policy():\n \"\"\"Get the current event loop policy.\"\"\"\n if _event_loop_policy is None:\n _init_event_loop_policy()\n return _event_loop_policy\n\n\ndef set_event_loop_policy(policy):\n \"\"\"Set the current event loop policy.\n\n If policy is None, the default policy is restored.\"\"\"\n global _event_loop_policy\n assert policy is None or isinstance(policy, AbstractEventLoopPolicy)\n _event_loop_policy = policy\n\n\ndef get_event_loop():\n \"\"\"Return an asyncio event loop.\n\n When called from a coroutine or a callback (e.g. scheduled with call_soon\n or similar API), this function will always return the running event loop.\n\n If there is no running event loop set, the function will return\n the result of `get_event_loop_policy().get_event_loop()` call.\n \"\"\"\n # NOTE: this function is implemented in C (see _asynciomodule.c)\n current_loop = _get_running_loop()\n if current_loop is not None:\n return current_loop\n return get_event_loop_policy().get_event_loop()\n\n\ndef set_event_loop(loop):\n \"\"\"Equivalent to calling get_event_loop_policy().set_event_loop(loop).\"\"\"\n get_event_loop_policy().set_event_loop(loop)\n\n\ndef new_event_loop():\n \"\"\"Equivalent to calling get_event_loop_policy().new_event_loop().\"\"\"\n return get_event_loop_policy().new_event_loop()\n\n\ndef get_child_watcher():\n \"\"\"Equivalent to calling get_event_loop_policy().get_child_watcher().\"\"\"\n return get_event_loop_policy().get_child_watcher()\n\n\ndef set_child_watcher(watcher):\n \"\"\"Equivalent to calling\n get_event_loop_policy().set_child_watcher(watcher).\"\"\"\n return get_event_loop_policy().set_child_watcher(watcher)\n\n\n# Alias pure-Python implementations for testing purposes.\n_py__get_running_loop = _get_running_loop\n_py__set_running_loop = _set_running_loop\n_py_get_running_loop = get_running_loop\n_py_get_event_loop = get_event_loop\n\n\ntry:\n # get_event_loop() is one of the most frequently called\n # functions in asyncio. Pure Python implementation is\n # about 4 times slower than C-accelerated.\n from _asyncio import (_get_running_loop, _set_running_loop,\n get_running_loop, get_event_loop)\nexcept ImportError:\n pass\nelse:\n # Alias C implementations for testing purposes.\n _c__get_running_loop = _get_running_loop\n _c__set_running_loop = _set_running_loop\n _c_get_running_loop = get_running_loop\n _c_get_event_loop = get_event_loop\n", 792], "/usr/lib/python3.8/asyncio/unix_events.py": ["\"\"\"Selector event loop for Unix with signal handling.\"\"\"\n\nimport errno\nimport io\nimport itertools\nimport os\nimport selectors\nimport signal\nimport socket\nimport stat\nimport subprocess\nimport sys\nimport threading\nimport warnings\n\nfrom . import base_events\nfrom . import base_subprocess\nfrom . import constants\nfrom . import coroutines\nfrom . import events\nfrom . import exceptions\nfrom . import futures\nfrom . import selector_events\nfrom . import tasks\nfrom . import transports\nfrom .log import logger\n\n\n__all__ = (\n 'SelectorEventLoop',\n 'AbstractChildWatcher', 'SafeChildWatcher',\n 'FastChildWatcher',\n 'MultiLoopChildWatcher', 'ThreadedChildWatcher',\n 'DefaultEventLoopPolicy',\n)\n\n\nif sys.platform == 'win32': # pragma: no cover\n raise ImportError('Signals are not really supported on Windows')\n\n\ndef _sighandler_noop(signum, frame):\n \"\"\"Dummy signal handler.\"\"\"\n pass\n\n\nclass _UnixSelectorEventLoop(selector_events.BaseSelectorEventLoop):\n \"\"\"Unix event loop.\n\n Adds signal handling and UNIX Domain Socket support to SelectorEventLoop.\n \"\"\"\n\n def __init__(self, selector=None):\n super().__init__(selector)\n self._signal_handlers = {}\n\n def close(self):\n super().close()\n if not sys.is_finalizing():\n for sig in list(self._signal_handlers):\n self.remove_signal_handler(sig)\n else:\n if self._signal_handlers:\n warnings.warn(f\"Closing the loop {self!r} \"\n f\"on interpreter shutdown \"\n f\"stage, skipping signal handlers removal\",\n ResourceWarning,\n source=self)\n self._signal_handlers.clear()\n\n def _process_self_data(self, data):\n for signum in data:\n if not signum:\n # ignore null bytes written by _write_to_self()\n continue\n self._handle_signal(signum)\n\n def add_signal_handler(self, sig, callback, *args):\n \"\"\"Add a handler for a signal. UNIX only.\n\n Raise ValueError if the signal number is invalid or uncatchable.\n Raise RuntimeError if there is a problem setting up the handler.\n \"\"\"\n if (coroutines.iscoroutine(callback) or\n coroutines.iscoroutinefunction(callback)):\n raise TypeError(\"coroutines cannot be used \"\n \"with add_signal_handler()\")\n self._check_signal(sig)\n self._check_closed()\n try:\n # set_wakeup_fd() raises ValueError if this is not the\n # main thread. By calling it early we ensure that an\n # event loop running in another thread cannot add a signal\n # handler.\n signal.set_wakeup_fd(self._csock.fileno())\n except (ValueError, OSError) as exc:\n raise RuntimeError(str(exc))\n\n handle = events.Handle(callback, args, self, None)\n self._signal_handlers[sig] = handle\n\n try:\n # Register a dummy signal handler to ask Python to write the signal\n # number in the wakeup file descriptor. _process_self_data() will\n # read signal numbers from this file descriptor to handle signals.\n signal.signal(sig, _sighandler_noop)\n\n # Set SA_RESTART to limit EINTR occurrences.\n signal.siginterrupt(sig, False)\n except OSError as exc:\n del self._signal_handlers[sig]\n if not self._signal_handlers:\n try:\n signal.set_wakeup_fd(-1)\n except (ValueError, OSError) as nexc:\n logger.info('set_wakeup_fd(-1) failed: %s', nexc)\n\n if exc.errno == errno.EINVAL:\n raise RuntimeError(f'sig {sig} cannot be caught')\n else:\n raise\n\n def _handle_signal(self, sig):\n \"\"\"Internal helper that is the actual signal handler.\"\"\"\n handle = self._signal_handlers.get(sig)\n if handle is None:\n return # Assume it's some race condition.\n if handle._cancelled:\n self.remove_signal_handler(sig) # Remove it properly.\n else:\n self._add_callback_signalsafe(handle)\n\n def remove_signal_handler(self, sig):\n \"\"\"Remove a handler for a signal. UNIX only.\n\n Return True if a signal handler was removed, False if not.\n \"\"\"\n self._check_signal(sig)\n try:\n del self._signal_handlers[sig]\n except KeyError:\n return False\n\n if sig == signal.SIGINT:\n handler = signal.default_int_handler\n else:\n handler = signal.SIG_DFL\n\n try:\n signal.signal(sig, handler)\n except OSError as exc:\n if exc.errno == errno.EINVAL:\n raise RuntimeError(f'sig {sig} cannot be caught')\n else:\n raise\n\n if not self._signal_handlers:\n try:\n signal.set_wakeup_fd(-1)\n except (ValueError, OSError) as exc:\n logger.info('set_wakeup_fd(-1) failed: %s', exc)\n\n return True\n\n def _check_signal(self, sig):\n \"\"\"Internal helper to validate a signal.\n\n Raise ValueError if the signal number is invalid or uncatchable.\n Raise RuntimeError if there is a problem setting up the handler.\n \"\"\"\n if not isinstance(sig, int):\n raise TypeError(f'sig must be an int, not {sig!r}')\n\n if sig not in signal.valid_signals():\n raise ValueError(f'invalid signal number {sig}')\n\n def _make_read_pipe_transport(self, pipe, protocol, waiter=None,\n extra=None):\n return _UnixReadPipeTransport(self, pipe, protocol, waiter, extra)\n\n def _make_write_pipe_transport(self, pipe, protocol, waiter=None,\n extra=None):\n return _UnixWritePipeTransport(self, pipe, protocol, waiter, extra)\n\n async def _make_subprocess_transport(self, protocol, args, shell,\n stdin, stdout, stderr, bufsize,\n extra=None, **kwargs):\n with events.get_child_watcher() as watcher:\n if not watcher.is_active():\n # Check early.\n # Raising exception before process creation\n # prevents subprocess execution if the watcher\n # is not ready to handle it.\n raise RuntimeError(\"asyncio.get_child_watcher() is not activated, \"\n \"subprocess support is not installed.\")\n waiter = self.create_future()\n transp = _UnixSubprocessTransport(self, protocol, args, shell,\n stdin, stdout, stderr, bufsize,\n waiter=waiter, extra=extra,\n **kwargs)\n\n watcher.add_child_handler(transp.get_pid(),\n self._child_watcher_callback, transp)\n try:\n await waiter\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException:\n transp.close()\n await transp._wait()\n raise\n\n return transp\n\n def _child_watcher_callback(self, pid, returncode, transp):\n self.call_soon_threadsafe(transp._process_exited, returncode)\n\n async def create_unix_connection(\n self, protocol_factory, path=None, *,\n ssl=None, sock=None,\n server_hostname=None,\n ssl_handshake_timeout=None):\n assert server_hostname is None or isinstance(server_hostname, str)\n if ssl:\n if server_hostname is None:\n raise ValueError(\n 'you have to pass server_hostname when using ssl')\n else:\n if server_hostname is not None:\n raise ValueError('server_hostname is only meaningful with ssl')\n if ssl_handshake_timeout is not None:\n raise ValueError(\n 'ssl_handshake_timeout is only meaningful with ssl')\n\n if path is not None:\n if sock is not None:\n raise ValueError(\n 'path and sock can not be specified at the same time')\n\n path = os.fspath(path)\n sock = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM, 0)\n try:\n sock.setblocking(False)\n await self.sock_connect(sock, path)\n except:\n sock.close()\n raise\n\n else:\n if sock is None:\n raise ValueError('no path and sock were specified')\n if (sock.family != socket.AF_UNIX or\n sock.type != socket.SOCK_STREAM):\n raise ValueError(\n f'A UNIX Domain Stream Socket was expected, got {sock!r}')\n sock.setblocking(False)\n\n transport, protocol = await self._create_connection_transport(\n sock, protocol_factory, ssl, server_hostname,\n ssl_handshake_timeout=ssl_handshake_timeout)\n return transport, protocol\n\n async def create_unix_server(\n self, protocol_factory, path=None, *,\n sock=None, backlog=100, ssl=None,\n ssl_handshake_timeout=None,\n start_serving=True):\n if isinstance(ssl, bool):\n raise TypeError('ssl argument must be an SSLContext or None')\n\n if ssl_handshake_timeout is not None and not ssl:\n raise ValueError(\n 'ssl_handshake_timeout is only meaningful with ssl')\n\n if path is not None:\n if sock is not None:\n raise ValueError(\n 'path and sock can not be specified at the same time')\n\n path = os.fspath(path)\n sock = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM)\n\n # Check for abstract socket. `str` and `bytes` paths are supported.\n if path[0] not in (0, '\\x00'):\n try:\n if stat.S_ISSOCK(os.stat(path).st_mode):\n os.remove(path)\n except FileNotFoundError:\n pass\n except OSError as err:\n # Directory may have permissions only to create socket.\n logger.error('Unable to check or remove stale UNIX socket '\n '%r: %r', path, err)\n\n try:\n sock.bind(path)\n except OSError as exc:\n sock.close()\n if exc.errno == errno.EADDRINUSE:\n # Let's improve the error message by adding\n # with what exact address it occurs.\n msg = f'Address {path!r} is already in use'\n raise OSError(errno.EADDRINUSE, msg) from None\n else:\n raise\n except:\n sock.close()\n raise\n else:\n if sock is None:\n raise ValueError(\n 'path was not specified, and no sock specified')\n\n if (sock.family != socket.AF_UNIX or\n sock.type != socket.SOCK_STREAM):\n raise ValueError(\n f'A UNIX Domain Stream Socket was expected, got {sock!r}')\n\n sock.setblocking(False)\n server = base_events.Server(self, [sock], protocol_factory,\n ssl, backlog, ssl_handshake_timeout)\n if start_serving:\n server._start_serving()\n # Skip one loop iteration so that all 'loop.add_reader'\n # go through.\n await tasks.sleep(0, loop=self)\n\n return server\n\n async def _sock_sendfile_native(self, sock, file, offset, count):\n try:\n os.sendfile\n except AttributeError as exc:\n raise exceptions.SendfileNotAvailableError(\n \"os.sendfile() is not available\")\n try:\n fileno = file.fileno()\n except (AttributeError, io.UnsupportedOperation) as err:\n raise exceptions.SendfileNotAvailableError(\"not a regular file\")\n try:\n fsize = os.fstat(fileno).st_size\n except OSError as err:\n raise exceptions.SendfileNotAvailableError(\"not a regular file\")\n blocksize = count if count else fsize\n if not blocksize:\n return 0 # empty file\n\n fut = self.create_future()\n self._sock_sendfile_native_impl(fut, None, sock, fileno,\n offset, count, blocksize, 0)\n return await fut\n\n def _sock_sendfile_native_impl(self, fut, registered_fd, sock, fileno,\n offset, count, blocksize, total_sent):\n fd = sock.fileno()\n if registered_fd is not None:\n # Remove the callback early. It should be rare that the\n # selector says the fd is ready but the call still returns\n # EAGAIN, and I am willing to take a hit in that case in\n # order to simplify the common case.\n self.remove_writer(registered_fd)\n if fut.cancelled():\n self._sock_sendfile_update_filepos(fileno, offset, total_sent)\n return\n if count:\n blocksize = count - total_sent\n if blocksize <= 0:\n self._sock_sendfile_update_filepos(fileno, offset, total_sent)\n fut.set_result(total_sent)\n return\n\n try:\n sent = os.sendfile(fd, fileno, offset, blocksize)\n except (BlockingIOError, InterruptedError):\n if registered_fd is None:\n self._sock_add_cancellation_callback(fut, sock)\n self.add_writer(fd, self._sock_sendfile_native_impl, fut,\n fd, sock, fileno,\n offset, count, blocksize, total_sent)\n except OSError as exc:\n if (registered_fd is not None and\n exc.errno == errno.ENOTCONN and\n type(exc) is not ConnectionError):\n # If we have an ENOTCONN and this isn't a first call to\n # sendfile(), i.e. the connection was closed in the middle\n # of the operation, normalize the error to ConnectionError\n # to make it consistent across all Posix systems.\n new_exc = ConnectionError(\n \"socket is not connected\", errno.ENOTCONN)\n new_exc.__cause__ = exc\n exc = new_exc\n if total_sent == 0:\n # We can get here for different reasons, the main\n # one being 'file' is not a regular mmap(2)-like\n # file, in which case we'll fall back on using\n # plain send().\n err = exceptions.SendfileNotAvailableError(\n \"os.sendfile call failed\")\n self._sock_sendfile_update_filepos(fileno, offset, total_sent)\n fut.set_exception(err)\n else:\n self._sock_sendfile_update_filepos(fileno, offset, total_sent)\n fut.set_exception(exc)\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n self._sock_sendfile_update_filepos(fileno, offset, total_sent)\n fut.set_exception(exc)\n else:\n if sent == 0:\n # EOF\n self._sock_sendfile_update_filepos(fileno, offset, total_sent)\n fut.set_result(total_sent)\n else:\n offset += sent\n total_sent += sent\n if registered_fd is None:\n self._sock_add_cancellation_callback(fut, sock)\n self.add_writer(fd, self._sock_sendfile_native_impl, fut,\n fd, sock, fileno,\n offset, count, blocksize, total_sent)\n\n def _sock_sendfile_update_filepos(self, fileno, offset, total_sent):\n if total_sent > 0:\n os.lseek(fileno, offset, os.SEEK_SET)\n\n def _sock_add_cancellation_callback(self, fut, sock):\n def cb(fut):\n if fut.cancelled():\n fd = sock.fileno()\n if fd != -1:\n self.remove_writer(fd)\n fut.add_done_callback(cb)\n\n\nclass _UnixReadPipeTransport(transports.ReadTransport):\n\n max_size = 256 * 1024 # max bytes we read in one event loop iteration\n\n def __init__(self, loop, pipe, protocol, waiter=None, extra=None):\n super().__init__(extra)\n self._extra['pipe'] = pipe\n self._loop = loop\n self._pipe = pipe\n self._fileno = pipe.fileno()\n self._protocol = protocol\n self._closing = False\n self._paused = False\n\n mode = os.fstat(self._fileno).st_mode\n if not (stat.S_ISFIFO(mode) or\n stat.S_ISSOCK(mode) or\n stat.S_ISCHR(mode)):\n self._pipe = None\n self._fileno = None\n self._protocol = None\n raise ValueError(\"Pipe transport is for pipes/sockets only.\")\n\n os.set_blocking(self._fileno, False)\n\n self._loop.call_soon(self._protocol.connection_made, self)\n # only start reading when connection_made() has been called\n self._loop.call_soon(self._loop._add_reader,\n self._fileno, self._read_ready)\n if waiter is not None:\n # only wake up the waiter when connection_made() has been called\n self._loop.call_soon(futures._set_result_unless_cancelled,\n waiter, None)\n\n def __repr__(self):\n info = [self.__class__.__name__]\n if self._pipe is None:\n info.append('closed')\n elif self._closing:\n info.append('closing')\n info.append(f'fd={self._fileno}')\n selector = getattr(self._loop, '_selector', None)\n if self._pipe is not None and selector is not None:\n polling = selector_events._test_selector_event(\n selector, self._fileno, selectors.EVENT_READ)\n if polling:\n info.append('polling')\n else:\n info.append('idle')\n elif self._pipe is not None:\n info.append('open')\n else:\n info.append('closed')\n return '<{}>'.format(' '.join(info))\n\n def _read_ready(self):\n try:\n data = os.read(self._fileno, self.max_size)\n except (BlockingIOError, InterruptedError):\n pass\n except OSError as exc:\n self._fatal_error(exc, 'Fatal read error on pipe transport')\n else:\n if data:\n self._protocol.data_received(data)\n else:\n if self._loop.get_debug():\n logger.info(\"%r was closed by peer\", self)\n self._closing = True\n self._loop._remove_reader(self._fileno)\n self._loop.call_soon(self._protocol.eof_received)\n self._loop.call_soon(self._call_connection_lost, None)\n\n def pause_reading(self):\n if self._closing or self._paused:\n return\n self._paused = True\n self._loop._remove_reader(self._fileno)\n if self._loop.get_debug():\n logger.debug(\"%r pauses reading\", self)\n\n def resume_reading(self):\n if self._closing or not self._paused:\n return\n self._paused = False\n self._loop._add_reader(self._fileno, self._read_ready)\n if self._loop.get_debug():\n logger.debug(\"%r resumes reading\", self)\n\n def set_protocol(self, protocol):\n self._protocol = protocol\n\n def get_protocol(self):\n return self._protocol\n\n def is_closing(self):\n return self._closing\n\n def close(self):\n if not self._closing:\n self._close(None)\n\n def __del__(self, _warn=warnings.warn):\n if self._pipe is not None:\n _warn(f\"unclosed transport {self!r}\", ResourceWarning, source=self)\n self._pipe.close()\n\n def _fatal_error(self, exc, message='Fatal error on pipe transport'):\n # should be called by exception handler only\n if (isinstance(exc, OSError) and exc.errno == errno.EIO):\n if self._loop.get_debug():\n logger.debug(\"%r: %s\", self, message, exc_info=True)\n else:\n self._loop.call_exception_handler({\n 'message': message,\n 'exception': exc,\n 'transport': self,\n 'protocol': self._protocol,\n })\n self._close(exc)\n\n def _close(self, exc):\n self._closing = True\n self._loop._remove_reader(self._fileno)\n self._loop.call_soon(self._call_connection_lost, exc)\n\n def _call_connection_lost(self, exc):\n try:\n self._protocol.connection_lost(exc)\n finally:\n self._pipe.close()\n self._pipe = None\n self._protocol = None\n self._loop = None\n\n\nclass _UnixWritePipeTransport(transports._FlowControlMixin,\n transports.WriteTransport):\n\n def __init__(self, loop, pipe, protocol, waiter=None, extra=None):\n super().__init__(extra, loop)\n self._extra['pipe'] = pipe\n self._pipe = pipe\n self._fileno = pipe.fileno()\n self._protocol = protocol\n self._buffer = bytearray()\n self._conn_lost = 0\n self._closing = False # Set when close() or write_eof() called.\n\n mode = os.fstat(self._fileno).st_mode\n is_char = stat.S_ISCHR(mode)\n is_fifo = stat.S_ISFIFO(mode)\n is_socket = stat.S_ISSOCK(mode)\n if not (is_char or is_fifo or is_socket):\n self._pipe = None\n self._fileno = None\n self._protocol = None\n raise ValueError(\"Pipe transport is only for \"\n \"pipes, sockets and character devices\")\n\n os.set_blocking(self._fileno, False)\n self._loop.call_soon(self._protocol.connection_made, self)\n\n # On AIX, the reader trick (to be notified when the read end of the\n # socket is closed) only works for sockets. On other platforms it\n # works for pipes and sockets. (Exception: OS X 10.4? Issue #19294.)\n if is_socket or (is_fifo and not sys.platform.startswith(\"aix\")):\n # only start reading when connection_made() has been called\n self._loop.call_soon(self._loop._add_reader,\n self._fileno, self._read_ready)\n\n if waiter is not None:\n # only wake up the waiter when connection_made() has been called\n self._loop.call_soon(futures._set_result_unless_cancelled,\n waiter, None)\n\n def __repr__(self):\n info = [self.__class__.__name__]\n if self._pipe is None:\n info.append('closed')\n elif self._closing:\n info.append('closing')\n info.append(f'fd={self._fileno}')\n selector = getattr(self._loop, '_selector', None)\n if self._pipe is not None and selector is not None:\n polling = selector_events._test_selector_event(\n selector, self._fileno, selectors.EVENT_WRITE)\n if polling:\n info.append('polling')\n else:\n info.append('idle')\n\n bufsize = self.get_write_buffer_size()\n info.append(f'bufsize={bufsize}')\n elif self._pipe is not None:\n info.append('open')\n else:\n info.append('closed')\n return '<{}>'.format(' '.join(info))\n\n def get_write_buffer_size(self):\n return len(self._buffer)\n\n def _read_ready(self):\n # Pipe was closed by peer.\n if self._loop.get_debug():\n logger.info(\"%r was closed by peer\", self)\n if self._buffer:\n self._close(BrokenPipeError())\n else:\n self._close()\n\n def write(self, data):\n assert isinstance(data, (bytes, bytearray, memoryview)), repr(data)\n if isinstance(data, bytearray):\n data = memoryview(data)\n if not data:\n return\n\n if self._conn_lost or self._closing:\n if self._conn_lost >= constants.LOG_THRESHOLD_FOR_CONNLOST_WRITES:\n logger.warning('pipe closed by peer or '\n 'os.write(pipe, data) raised exception.')\n self._conn_lost += 1\n return\n\n if not self._buffer:\n # Attempt to send it right away first.\n try:\n n = os.write(self._fileno, data)\n except (BlockingIOError, InterruptedError):\n n = 0\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n self._conn_lost += 1\n self._fatal_error(exc, 'Fatal write error on pipe transport')\n return\n if n == len(data):\n return\n elif n > 0:\n data = memoryview(data)[n:]\n self._loop._add_writer(self._fileno, self._write_ready)\n\n self._buffer += data\n self._maybe_pause_protocol()\n\n def _write_ready(self):\n assert self._buffer, 'Data should not be empty'\n\n try:\n n = os.write(self._fileno, self._buffer)\n except (BlockingIOError, InterruptedError):\n pass\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n self._buffer.clear()\n self._conn_lost += 1\n # Remove writer here, _fatal_error() doesn't it\n # because _buffer is empty.\n self._loop._remove_writer(self._fileno)\n self._fatal_error(exc, 'Fatal write error on pipe transport')\n else:\n if n == len(self._buffer):\n self._buffer.clear()\n self._loop._remove_writer(self._fileno)\n self._maybe_resume_protocol() # May append to buffer.\n if self._closing:\n self._loop._remove_reader(self._fileno)\n self._call_connection_lost(None)\n return\n elif n > 0:\n del self._buffer[:n]\n\n def can_write_eof(self):\n return True\n\n def write_eof(self):\n if self._closing:\n return\n assert self._pipe\n self._closing = True\n if not self._buffer:\n self._loop._remove_reader(self._fileno)\n self._loop.call_soon(self._call_connection_lost, None)\n\n def set_protocol(self, protocol):\n self._protocol = protocol\n\n def get_protocol(self):\n return self._protocol\n\n def is_closing(self):\n return self._closing\n\n def close(self):\n if self._pipe is not None and not self._closing:\n # write_eof is all what we needed to close the write pipe\n self.write_eof()\n\n def __del__(self, _warn=warnings.warn):\n if self._pipe is not None:\n _warn(f\"unclosed transport {self!r}\", ResourceWarning, source=self)\n self._pipe.close()\n\n def abort(self):\n self._close(None)\n\n def _fatal_error(self, exc, message='Fatal error on pipe transport'):\n # should be called by exception handler only\n if isinstance(exc, OSError):\n if self._loop.get_debug():\n logger.debug(\"%r: %s\", self, message, exc_info=True)\n else:\n self._loop.call_exception_handler({\n 'message': message,\n 'exception': exc,\n 'transport': self,\n 'protocol': self._protocol,\n })\n self._close(exc)\n\n def _close(self, exc=None):\n self._closing = True\n if self._buffer:\n self._loop._remove_writer(self._fileno)\n self._buffer.clear()\n self._loop._remove_reader(self._fileno)\n self._loop.call_soon(self._call_connection_lost, exc)\n\n def _call_connection_lost(self, exc):\n try:\n self._protocol.connection_lost(exc)\n finally:\n self._pipe.close()\n self._pipe = None\n self._protocol = None\n self._loop = None\n\n\nclass _UnixSubprocessTransport(base_subprocess.BaseSubprocessTransport):\n\n def _start(self, args, shell, stdin, stdout, stderr, bufsize, **kwargs):\n stdin_w = None\n if stdin == subprocess.PIPE:\n # Use a socket pair for stdin, since not all platforms\n # support selecting read events on the write end of a\n # socket (which we use in order to detect closing of the\n # other end). Notably this is needed on AIX, and works\n # just fine on other platforms.\n stdin, stdin_w = socket.socketpair()\n try:\n self._proc = subprocess.Popen(\n args, shell=shell, stdin=stdin, stdout=stdout, stderr=stderr,\n universal_newlines=False, bufsize=bufsize, **kwargs)\n if stdin_w is not None:\n stdin.close()\n self._proc.stdin = open(stdin_w.detach(), 'wb', buffering=bufsize)\n stdin_w = None\n finally:\n if stdin_w is not None:\n stdin.close()\n stdin_w.close()\n\n\nclass AbstractChildWatcher:\n \"\"\"Abstract base class for monitoring child processes.\n\n Objects derived from this class monitor a collection of subprocesses and\n report their termination or interruption by a signal.\n\n New callbacks are registered with .add_child_handler(). Starting a new\n process must be done within a 'with' block to allow the watcher to suspend\n its activity until the new process if fully registered (this is needed to\n prevent a race condition in some implementations).\n\n Example:\n with watcher:\n proc = subprocess.Popen(\"sleep 1\")\n watcher.add_child_handler(proc.pid, callback)\n\n Notes:\n Implementations of this class must be thread-safe.\n\n Since child watcher objects may catch the SIGCHLD signal and call\n waitpid(-1), there should be only one active object per process.\n \"\"\"\n\n def add_child_handler(self, pid, callback, *args):\n \"\"\"Register a new child handler.\n\n Arrange for callback(pid, returncode, *args) to be called when\n process 'pid' terminates. Specifying another callback for the same\n process replaces the previous handler.\n\n Note: callback() must be thread-safe.\n \"\"\"\n raise NotImplementedError()\n\n def remove_child_handler(self, pid):\n \"\"\"Removes the handler for process 'pid'.\n\n The function returns True if the handler was successfully removed,\n False if there was nothing to remove.\"\"\"\n\n raise NotImplementedError()\n\n def attach_loop(self, loop):\n \"\"\"Attach the watcher to an event loop.\n\n If the watcher was previously attached to an event loop, then it is\n first detached before attaching to the new loop.\n\n Note: loop may be None.\n \"\"\"\n raise NotImplementedError()\n\n def close(self):\n \"\"\"Close the watcher.\n\n This must be called to make sure that any underlying resource is freed.\n \"\"\"\n raise NotImplementedError()\n\n def is_active(self):\n \"\"\"Return ``True`` if the watcher is active and is used by the event loop.\n\n Return True if the watcher is installed and ready to handle process exit\n notifications.\n\n \"\"\"\n raise NotImplementedError()\n\n def __enter__(self):\n \"\"\"Enter the watcher's context and allow starting new processes\n\n This function must return self\"\"\"\n raise NotImplementedError()\n\n def __exit__(self, a, b, c):\n \"\"\"Exit the watcher's context\"\"\"\n raise NotImplementedError()\n\n\ndef _compute_returncode(status):\n if os.WIFSIGNALED(status):\n # The child process died because of a signal.\n return -os.WTERMSIG(status)\n elif os.WIFEXITED(status):\n # The child process exited (e.g sys.exit()).\n return os.WEXITSTATUS(status)\n else:\n # The child exited, but we don't understand its status.\n # This shouldn't happen, but if it does, let's just\n # return that status; perhaps that helps debug it.\n return status\n\n\nclass BaseChildWatcher(AbstractChildWatcher):\n\n def __init__(self):\n self._loop = None\n self._callbacks = {}\n\n def close(self):\n self.attach_loop(None)\n\n def is_active(self):\n return self._loop is not None and self._loop.is_running()\n\n def _do_waitpid(self, expected_pid):\n raise NotImplementedError()\n\n def _do_waitpid_all(self):\n raise NotImplementedError()\n\n def attach_loop(self, loop):\n assert loop is None or isinstance(loop, events.AbstractEventLoop)\n\n if self._loop is not None and loop is None and self._callbacks:\n warnings.warn(\n 'A loop is being detached '\n 'from a child watcher with pending handlers',\n RuntimeWarning)\n\n if self._loop is not None:\n self._loop.remove_signal_handler(signal.SIGCHLD)\n\n self._loop = loop\n if loop is not None:\n loop.add_signal_handler(signal.SIGCHLD, self._sig_chld)\n\n # Prevent a race condition in case a child terminated\n # during the switch.\n self._do_waitpid_all()\n\n def _sig_chld(self):\n try:\n self._do_waitpid_all()\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n # self._loop should always be available here\n # as '_sig_chld' is added as a signal handler\n # in 'attach_loop'\n self._loop.call_exception_handler({\n 'message': 'Unknown exception in SIGCHLD handler',\n 'exception': exc,\n })\n\n\nclass SafeChildWatcher(BaseChildWatcher):\n \"\"\"'Safe' child watcher implementation.\n\n This implementation avoids disrupting other code spawning processes by\n polling explicitly each process in the SIGCHLD handler instead of calling\n os.waitpid(-1).\n\n This is a safe solution but it has a significant overhead when handling a\n big number of children (O(n) each time SIGCHLD is raised)\n \"\"\"\n\n def close(self):\n self._callbacks.clear()\n super().close()\n\n def __enter__(self):\n return self\n\n def __exit__(self, a, b, c):\n pass\n\n def add_child_handler(self, pid, callback, *args):\n self._callbacks[pid] = (callback, args)\n\n # Prevent a race condition in case the child is already terminated.\n self._do_waitpid(pid)\n\n def remove_child_handler(self, pid):\n try:\n del self._callbacks[pid]\n return True\n except KeyError:\n return False\n\n def _do_waitpid_all(self):\n\n for pid in list(self._callbacks):\n self._do_waitpid(pid)\n\n def _do_waitpid(self, expected_pid):\n assert expected_pid > 0\n\n try:\n pid, status = os.waitpid(expected_pid, os.WNOHANG)\n except ChildProcessError:\n # The child process is already reaped\n # (may happen if waitpid() is called elsewhere).\n pid = expected_pid\n returncode = 255\n logger.warning(\n \"Unknown child process pid %d, will report returncode 255\",\n pid)\n else:\n if pid == 0:\n # The child process is still alive.\n return\n\n returncode = _compute_returncode(status)\n if self._loop.get_debug():\n logger.debug('process %s exited with returncode %s',\n expected_pid, returncode)\n\n try:\n callback, args = self._callbacks.pop(pid)\n except KeyError: # pragma: no cover\n # May happen if .remove_child_handler() is called\n # after os.waitpid() returns.\n if self._loop.get_debug():\n logger.warning(\"Child watcher got an unexpected pid: %r\",\n pid, exc_info=True)\n else:\n callback(pid, returncode, *args)\n\n\nclass FastChildWatcher(BaseChildWatcher):\n \"\"\"'Fast' child watcher implementation.\n\n This implementation reaps every terminated processes by calling\n os.waitpid(-1) directly, possibly breaking other code spawning processes\n and waiting for their termination.\n\n There is no noticeable overhead when handling a big number of children\n (O(1) each time a child terminates).\n \"\"\"\n def __init__(self):\n super().__init__()\n self._lock = threading.Lock()\n self._zombies = {}\n self._forks = 0\n\n def close(self):\n self._callbacks.clear()\n self._zombies.clear()\n super().close()\n\n def __enter__(self):\n with self._lock:\n self._forks += 1\n\n return self\n\n def __exit__(self, a, b, c):\n with self._lock:\n self._forks -= 1\n\n if self._forks or not self._zombies:\n return\n\n collateral_victims = str(self._zombies)\n self._zombies.clear()\n\n logger.warning(\n \"Caught subprocesses termination from unknown pids: %s\",\n collateral_victims)\n\n def add_child_handler(self, pid, callback, *args):\n assert self._forks, \"Must use the context manager\"\n\n with self._lock:\n try:\n returncode = self._zombies.pop(pid)\n except KeyError:\n # The child is running.\n self._callbacks[pid] = callback, args\n return\n\n # The child is dead already. We can fire the callback.\n callback(pid, returncode, *args)\n\n def remove_child_handler(self, pid):\n try:\n del self._callbacks[pid]\n return True\n except KeyError:\n return False\n\n def _do_waitpid_all(self):\n # Because of signal coalescing, we must keep calling waitpid() as\n # long as we're able to reap a child.\n while True:\n try:\n pid, status = os.waitpid(-1, os.WNOHANG)\n except ChildProcessError:\n # No more child processes exist.\n return\n else:\n if pid == 0:\n # A child process is still alive.\n return\n\n returncode = _compute_returncode(status)\n\n with self._lock:\n try:\n callback, args = self._callbacks.pop(pid)\n except KeyError:\n # unknown child\n if self._forks:\n # It may not be registered yet.\n self._zombies[pid] = returncode\n if self._loop.get_debug():\n logger.debug('unknown process %s exited '\n 'with returncode %s',\n pid, returncode)\n continue\n callback = None\n else:\n if self._loop.get_debug():\n logger.debug('process %s exited with returncode %s',\n pid, returncode)\n\n if callback is None:\n logger.warning(\n \"Caught subprocess termination from unknown pid: \"\n \"%d -> %d\", pid, returncode)\n else:\n callback(pid, returncode, *args)\n\n\nclass MultiLoopChildWatcher(AbstractChildWatcher):\n \"\"\"A watcher that doesn't require running loop in the main thread.\n\n This implementation registers a SIGCHLD signal handler on\n instantiation (which may conflict with other code that\n install own handler for this signal).\n\n The solution is safe but it has a significant overhead when\n handling a big number of processes (*O(n)* each time a\n SIGCHLD is received).\n \"\"\"\n\n # Implementation note:\n # The class keeps compatibility with AbstractChildWatcher ABC\n # To achieve this it has empty attach_loop() method\n # and doesn't accept explicit loop argument\n # for add_child_handler()/remove_child_handler()\n # but retrieves the current loop by get_running_loop()\n\n def __init__(self):\n self._callbacks = {}\n self._saved_sighandler = None\n\n def is_active(self):\n return self._saved_sighandler is not None\n\n def close(self):\n self._callbacks.clear()\n if self._saved_sighandler is not None:\n handler = signal.getsignal(signal.SIGCHLD)\n if handler != self._sig_chld:\n logger.warning(\"SIGCHLD handler was changed by outside code\")\n else:\n signal.signal(signal.SIGCHLD, self._saved_sighandler)\n self._saved_sighandler = None\n\n def __enter__(self):\n return self\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n pass\n\n def add_child_handler(self, pid, callback, *args):\n loop = events.get_running_loop()\n self._callbacks[pid] = (loop, callback, args)\n\n # Prevent a race condition in case the child is already terminated.\n self._do_waitpid(pid)\n\n def remove_child_handler(self, pid):\n try:\n del self._callbacks[pid]\n return True\n except KeyError:\n return False\n\n def attach_loop(self, loop):\n # Don't save the loop but initialize itself if called first time\n # The reason to do it here is that attach_loop() is called from\n # unix policy only for the main thread.\n # Main thread is required for subscription on SIGCHLD signal\n if self._saved_sighandler is None:\n self._saved_sighandler = signal.signal(signal.SIGCHLD, self._sig_chld)\n if self._saved_sighandler is None:\n logger.warning(\"Previous SIGCHLD handler was set by non-Python code, \"\n \"restore to default handler on watcher close.\")\n self._saved_sighandler = signal.SIG_DFL\n\n # Set SA_RESTART to limit EINTR occurrences.\n signal.siginterrupt(signal.SIGCHLD, False)\n\n def _do_waitpid_all(self):\n for pid in list(self._callbacks):\n self._do_waitpid(pid)\n\n def _do_waitpid(self, expected_pid):\n assert expected_pid > 0\n\n try:\n pid, status = os.waitpid(expected_pid, os.WNOHANG)\n except ChildProcessError:\n # The child process is already reaped\n # (may happen if waitpid() is called elsewhere).\n pid = expected_pid\n returncode = 255\n logger.warning(\n \"Unknown child process pid %d, will report returncode 255\",\n pid)\n debug_log = False\n else:\n if pid == 0:\n # The child process is still alive.\n return\n\n returncode = _compute_returncode(status)\n debug_log = True\n try:\n loop, callback, args = self._callbacks.pop(pid)\n except KeyError: # pragma: no cover\n # May happen if .remove_child_handler() is called\n # after os.waitpid() returns.\n logger.warning(\"Child watcher got an unexpected pid: %r\",\n pid, exc_info=True)\n else:\n if loop.is_closed():\n logger.warning(\"Loop %r that handles pid %r is closed\", loop, pid)\n else:\n if debug_log and loop.get_debug():\n logger.debug('process %s exited with returncode %s',\n expected_pid, returncode)\n loop.call_soon_threadsafe(callback, pid, returncode, *args)\n\n def _sig_chld(self, signum, frame):\n try:\n self._do_waitpid_all()\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException:\n logger.warning('Unknown exception in SIGCHLD handler', exc_info=True)\n\n\nclass ThreadedChildWatcher(AbstractChildWatcher):\n \"\"\"Threaded child watcher implementation.\n\n The watcher uses a thread per process\n for waiting for the process finish.\n\n It doesn't require subscription on POSIX signal\n but a thread creation is not free.\n\n The watcher has O(1) complexity, its performance doesn't depend\n on amount of spawn processes.\n \"\"\"\n\n def __init__(self):\n self._pid_counter = itertools.count(0)\n self._threads = {}\n\n def is_active(self):\n return True\n\n def close(self):\n self._join_threads()\n\n def _join_threads(self):\n \"\"\"Internal: Join all non-daemon threads\"\"\"\n threads = [thread for thread in list(self._threads.values())\n if thread.is_alive() and not thread.daemon]\n for thread in threads:\n thread.join()\n\n def __enter__(self):\n return self\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n pass\n\n def __del__(self, _warn=warnings.warn):\n threads = [thread for thread in list(self._threads.values())\n if thread.is_alive()]\n if threads:\n _warn(f\"{self.__class__} has registered but not finished child processes\",\n ResourceWarning,\n source=self)\n\n def add_child_handler(self, pid, callback, *args):\n loop = events.get_running_loop()\n thread = threading.Thread(target=self._do_waitpid,\n name=f\"waitpid-{next(self._pid_counter)}\",\n args=(loop, pid, callback, args),\n daemon=True)\n self._threads[pid] = thread\n thread.start()\n\n def remove_child_handler(self, pid):\n # asyncio never calls remove_child_handler() !!!\n # The method is no-op but is implemented because\n # abstract base classe requires it\n return True\n\n def attach_loop(self, loop):\n pass\n\n def _do_waitpid(self, loop, expected_pid, callback, args):\n assert expected_pid > 0\n\n try:\n pid, status = os.waitpid(expected_pid, 0)\n except ChildProcessError:\n # The child process is already reaped\n # (may happen if waitpid() is called elsewhere).\n pid = expected_pid\n returncode = 255\n logger.warning(\n \"Unknown child process pid %d, will report returncode 255\",\n pid)\n else:\n returncode = _compute_returncode(status)\n if loop.get_debug():\n logger.debug('process %s exited with returncode %s',\n expected_pid, returncode)\n\n if loop.is_closed():\n logger.warning(\"Loop %r that handles pid %r is closed\", loop, pid)\n else:\n loop.call_soon_threadsafe(callback, pid, returncode, *args)\n\n self._threads.pop(expected_pid)\n\n\nclass _UnixDefaultEventLoopPolicy(events.BaseDefaultEventLoopPolicy):\n \"\"\"UNIX event loop policy with a watcher for child processes.\"\"\"\n _loop_factory = _UnixSelectorEventLoop\n\n def __init__(self):\n super().__init__()\n self._watcher = None\n\n def _init_watcher(self):\n with events._lock:\n if self._watcher is None: # pragma: no branch\n self._watcher = ThreadedChildWatcher()\n if isinstance(threading.current_thread(),\n threading._MainThread):\n self._watcher.attach_loop(self._local._loop)\n\n def set_event_loop(self, loop):\n \"\"\"Set the event loop.\n\n As a side effect, if a child watcher was set before, then calling\n .set_event_loop() from the main thread will call .attach_loop(loop) on\n the child watcher.\n \"\"\"\n\n super().set_event_loop(loop)\n\n if (self._watcher is not None and\n isinstance(threading.current_thread(), threading._MainThread)):\n self._watcher.attach_loop(loop)\n\n def get_child_watcher(self):\n \"\"\"Get the watcher for child processes.\n\n If not yet set, a ThreadedChildWatcher object is automatically created.\n \"\"\"\n if self._watcher is None:\n self._init_watcher()\n\n return self._watcher\n\n def set_child_watcher(self, watcher):\n \"\"\"Set the watcher for child processes.\"\"\"\n\n assert watcher is None or isinstance(watcher, AbstractChildWatcher)\n\n if self._watcher is not None:\n self._watcher.close()\n\n self._watcher = watcher\n\n\nSelectorEventLoop = _UnixSelectorEventLoop\nDefaultEventLoopPolicy = _UnixDefaultEventLoopPolicy\n", 1389], "/home/gaogaotiantian/programs/codesnap/venv3.8/lib/python3.8/os.py": ["r\"\"\"OS routines for NT or Posix depending on what system we're on.\n\nThis exports:\n - all functions from posix or nt, e.g. unlink, stat, etc.\n - os.path is either posixpath or ntpath\n - os.name is either 'posix' or 'nt'\n - os.curdir is a string representing the current directory (always '.')\n - os.pardir is a string representing the parent directory (always '..')\n - os.sep is the (or a most common) pathname separator ('/' or '\\\\')\n - os.extsep is the extension separator (always '.')\n - os.altsep is the alternate pathname separator (None or '/')\n - os.pathsep is the component separator used in $PATH etc\n - os.linesep is the line separator in text files ('\\r' or '\\n' or '\\r\\n')\n - os.defpath is the default search path for executables\n - os.devnull is the file path of the null device ('/dev/null', etc.)\n\nPrograms that import and use 'os' stand a better chance of being\nportable between different platforms. Of course, they must then\nonly use functions that are defined by all platforms (e.g., unlink\nand opendir), and leave all pathname manipulation to os.path\n(e.g., split and join).\n\"\"\"\n\n#'\nimport abc\nimport sys\nimport stat as st\n\nfrom _collections_abc import _check_methods\n\n_names = sys.builtin_module_names\n\n# Note: more names are added to __all__ later.\n__all__ = [\"altsep\", \"curdir\", \"pardir\", \"sep\", \"pathsep\", \"linesep\",\n \"defpath\", \"name\", \"path\", \"devnull\", \"SEEK_SET\", \"SEEK_CUR\",\n \"SEEK_END\", \"fsencode\", \"fsdecode\", \"get_exec_path\", \"fdopen\",\n \"popen\", \"extsep\"]\n\ndef _exists(name):\n return name in globals()\n\ndef _get_exports_list(module):\n try:\n return list(module.__all__)\n except AttributeError:\n return [n for n in dir(module) if n[0] != '_']\n\n# Any new dependencies of the os module and/or changes in path separator\n# requires updating importlib as well.\nif 'posix' in _names:\n name = 'posix'\n linesep = '\\n'\n from posix import *\n try:\n from posix import _exit\n __all__.append('_exit')\n except ImportError:\n pass\n import posixpath as path\n\n try:\n from posix import _have_functions\n except ImportError:\n pass\n\n import posix\n __all__.extend(_get_exports_list(posix))\n del posix\n\nelif 'nt' in _names:\n name = 'nt'\n linesep = '\\r\\n'\n from nt import *\n try:\n from nt import _exit\n __all__.append('_exit')\n except ImportError:\n pass\n import ntpath as path\n\n import nt\n __all__.extend(_get_exports_list(nt))\n del nt\n\n try:\n from nt import _have_functions\n except ImportError:\n pass\n\nelse:\n raise ImportError('no os specific module found')\n\nsys.modules['os.path'] = path\nfrom os.path import (curdir, pardir, sep, pathsep, defpath, extsep, altsep,\n devnull)\n\ndel _names\n\n\nif _exists(\"_have_functions\"):\n _globals = globals()\n def _add(str, fn):\n if (fn in _globals) and (str in _have_functions):\n _set.add(_globals[fn])\n\n _set = set()\n _add(\"HAVE_FACCESSAT\", \"access\")\n _add(\"HAVE_FCHMODAT\", \"chmod\")\n _add(\"HAVE_FCHOWNAT\", \"chown\")\n _add(\"HAVE_FSTATAT\", \"stat\")\n _add(\"HAVE_FUTIMESAT\", \"utime\")\n _add(\"HAVE_LINKAT\", \"link\")\n _add(\"HAVE_MKDIRAT\", \"mkdir\")\n _add(\"HAVE_MKFIFOAT\", \"mkfifo\")\n _add(\"HAVE_MKNODAT\", \"mknod\")\n _add(\"HAVE_OPENAT\", \"open\")\n _add(\"HAVE_READLINKAT\", \"readlink\")\n _add(\"HAVE_RENAMEAT\", \"rename\")\n _add(\"HAVE_SYMLINKAT\", \"symlink\")\n _add(\"HAVE_UNLINKAT\", \"unlink\")\n _add(\"HAVE_UNLINKAT\", \"rmdir\")\n _add(\"HAVE_UTIMENSAT\", \"utime\")\n supports_dir_fd = _set\n\n _set = set()\n _add(\"HAVE_FACCESSAT\", \"access\")\n supports_effective_ids = _set\n\n _set = set()\n _add(\"HAVE_FCHDIR\", \"chdir\")\n _add(\"HAVE_FCHMOD\", \"chmod\")\n _add(\"HAVE_FCHOWN\", \"chown\")\n _add(\"HAVE_FDOPENDIR\", \"listdir\")\n _add(\"HAVE_FDOPENDIR\", \"scandir\")\n _add(\"HAVE_FEXECVE\", \"execve\")\n _set.add(stat) # fstat always works\n _add(\"HAVE_FTRUNCATE\", \"truncate\")\n _add(\"HAVE_FUTIMENS\", \"utime\")\n _add(\"HAVE_FUTIMES\", \"utime\")\n _add(\"HAVE_FPATHCONF\", \"pathconf\")\n if _exists(\"statvfs\") and _exists(\"fstatvfs\"): # mac os x10.3\n _add(\"HAVE_FSTATVFS\", \"statvfs\")\n supports_fd = _set\n\n _set = set()\n _add(\"HAVE_FACCESSAT\", \"access\")\n # Some platforms don't support lchmod(). Often the function exists\n # anyway, as a stub that always returns ENOSUP or perhaps EOPNOTSUPP.\n # (No, I don't know why that's a good design.) ./configure will detect\n # this and reject it--so HAVE_LCHMOD still won't be defined on such\n # platforms. This is Very Helpful.\n #\n # However, sometimes platforms without a working lchmod() *do* have\n # fchmodat(). (Examples: Linux kernel 3.2 with glibc 2.15,\n # OpenIndiana 3.x.) And fchmodat() has a flag that theoretically makes\n # it behave like lchmod(). So in theory it would be a suitable\n # replacement for lchmod(). But when lchmod() doesn't work, fchmodat()'s\n # flag doesn't work *either*. Sadly ./configure isn't sophisticated\n # enough to detect this condition--it only determines whether or not\n # fchmodat() minimally works.\n #\n # Therefore we simply ignore fchmodat() when deciding whether or not\n # os.chmod supports follow_symlinks. Just checking lchmod() is\n # sufficient. After all--if you have a working fchmodat(), your\n # lchmod() almost certainly works too.\n #\n # _add(\"HAVE_FCHMODAT\", \"chmod\")\n _add(\"HAVE_FCHOWNAT\", \"chown\")\n _add(\"HAVE_FSTATAT\", \"stat\")\n _add(\"HAVE_LCHFLAGS\", \"chflags\")\n _add(\"HAVE_LCHMOD\", \"chmod\")\n if _exists(\"lchown\"): # mac os x10.3\n _add(\"HAVE_LCHOWN\", \"chown\")\n _add(\"HAVE_LINKAT\", \"link\")\n _add(\"HAVE_LUTIMES\", \"utime\")\n _add(\"HAVE_LSTAT\", \"stat\")\n _add(\"HAVE_FSTATAT\", \"stat\")\n _add(\"HAVE_UTIMENSAT\", \"utime\")\n _add(\"MS_WINDOWS\", \"stat\")\n supports_follow_symlinks = _set\n\n del _set\n del _have_functions\n del _globals\n del _add\n\n\n# Python uses fixed values for the SEEK_ constants; they are mapped\n# to native constants if necessary in posixmodule.c\n# Other possible SEEK values are directly imported from posixmodule.c\nSEEK_SET = 0\nSEEK_CUR = 1\nSEEK_END = 2\n\n# Super directory utilities.\n# (Inspired by Eric Raymond; the doc strings are mostly his)\n\ndef makedirs(name, mode=0o777, exist_ok=False):\n \"\"\"makedirs(name [, mode=0o777][, exist_ok=False])\n\n Super-mkdir; create a leaf directory and all intermediate ones. Works like\n mkdir, except that any intermediate path segment (not just the rightmost)\n will be created if it does not exist. If the target directory already\n exists, raise an OSError if exist_ok is False. Otherwise no exception is\n raised. This is recursive.\n\n \"\"\"\n head, tail = path.split(name)\n if not tail:\n head, tail = path.split(head)\n if head and tail and not path.exists(head):\n try:\n makedirs(head, exist_ok=exist_ok)\n except FileExistsError:\n # Defeats race condition when another thread created the path\n pass\n cdir = curdir\n if isinstance(tail, bytes):\n cdir = bytes(curdir, 'ASCII')\n if tail == cdir: # xxx/newdir/. exists if xxx/newdir exists\n return\n try:\n mkdir(name, mode)\n except OSError:\n # Cannot rely on checking for EEXIST, since the operating system\n # could give priority to other errors like EACCES or EROFS\n if not exist_ok or not path.isdir(name):\n raise\n\ndef removedirs(name):\n \"\"\"removedirs(name)\n\n Super-rmdir; remove a leaf directory and all empty intermediate\n ones. Works like rmdir except that, if the leaf directory is\n successfully removed, directories corresponding to rightmost path\n segments will be pruned away until either the whole path is\n consumed or an error occurs. Errors during this latter phase are\n ignored -- they generally mean that a directory was not empty.\n\n \"\"\"\n rmdir(name)\n head, tail = path.split(name)\n if not tail:\n head, tail = path.split(head)\n while head and tail:\n try:\n rmdir(head)\n except OSError:\n break\n head, tail = path.split(head)\n\ndef renames(old, new):\n \"\"\"renames(old, new)\n\n Super-rename; create directories as necessary and delete any left\n empty. Works like rename, except creation of any intermediate\n directories needed to make the new pathname good is attempted\n first. After the rename, directories corresponding to rightmost\n path segments of the old name will be pruned until either the\n whole path is consumed or a nonempty directory is found.\n\n Note: this function can fail with the new directory structure made\n if you lack permissions needed to unlink the leaf directory or\n file.\n\n \"\"\"\n head, tail = path.split(new)\n if head and tail and not path.exists(head):\n makedirs(head)\n rename(old, new)\n head, tail = path.split(old)\n if head and tail:\n try:\n removedirs(head)\n except OSError:\n pass\n\n__all__.extend([\"makedirs\", \"removedirs\", \"renames\"])\n\ndef walk(top, topdown=True, onerror=None, followlinks=False):\n \"\"\"Directory tree generator.\n\n For each directory in the directory tree rooted at top (including top\n itself, but excluding '.' and '..'), yields a 3-tuple\n\n dirpath, dirnames, filenames\n\n dirpath is a string, the path to the directory. dirnames is a list of\n the names of the subdirectories in dirpath (excluding '.' and '..').\n filenames is a list of the names of the non-directory files in dirpath.\n Note that the names in the lists are just names, with no path components.\n To get a full path (which begins with top) to a file or directory in\n dirpath, do os.path.join(dirpath, name).\n\n If optional arg 'topdown' is true or not specified, the triple for a\n directory is generated before the triples for any of its subdirectories\n (directories are generated top down). If topdown is false, the triple\n for a directory is generated after the triples for all of its\n subdirectories (directories are generated bottom up).\n\n When topdown is true, the caller can modify the dirnames list in-place\n (e.g., via del or slice assignment), and walk will only recurse into the\n subdirectories whose names remain in dirnames; this can be used to prune the\n search, or to impose a specific order of visiting. Modifying dirnames when\n topdown is false has no effect on the behavior of os.walk(), since the\n directories in dirnames have already been generated by the time dirnames\n itself is generated. No matter the value of topdown, the list of\n subdirectories is retrieved before the tuples for the directory and its\n subdirectories are generated.\n\n By default errors from the os.scandir() call are ignored. If\n optional arg 'onerror' is specified, it should be a function; it\n will be called with one argument, an OSError instance. It can\n report the error to continue with the walk, or raise the exception\n to abort the walk. Note that the filename is available as the\n filename attribute of the exception object.\n\n By default, os.walk does not follow symbolic links to subdirectories on\n systems that support them. In order to get this functionality, set the\n optional argument 'followlinks' to true.\n\n Caution: if you pass a relative pathname for top, don't change the\n current working directory between resumptions of walk. walk never\n changes the current directory, and assumes that the client doesn't\n either.\n\n Example:\n\n import os\n from os.path import join, getsize\n for root, dirs, files in os.walk('python/Lib/email'):\n print(root, \"consumes\", end=\"\")\n print(sum(getsize(join(root, name)) for name in files), end=\"\")\n print(\"bytes in\", len(files), \"non-directory files\")\n if 'CVS' in dirs:\n dirs.remove('CVS') # don't visit CVS directories\n\n \"\"\"\n top = fspath(top)\n dirs = []\n nondirs = []\n walk_dirs = []\n\n # We may not have read permission for top, in which case we can't\n # get a list of the files the directory contains. os.walk\n # always suppressed the exception then, rather than blow up for a\n # minor reason when (say) a thousand readable directories are still\n # left to visit. That logic is copied here.\n try:\n # Note that scandir is global in this module due\n # to earlier import-*.\n scandir_it = scandir(top)\n except OSError as error:\n if onerror is not None:\n onerror(error)\n return\n\n with scandir_it:\n while True:\n try:\n try:\n entry = next(scandir_it)\n except StopIteration:\n break\n except OSError as error:\n if onerror is not None:\n onerror(error)\n return\n\n try:\n is_dir = entry.is_dir()\n except OSError:\n # If is_dir() raises an OSError, consider that the entry is not\n # a directory, same behaviour than os.path.isdir().\n is_dir = False\n\n if is_dir:\n dirs.append(entry.name)\n else:\n nondirs.append(entry.name)\n\n if not topdown and is_dir:\n # Bottom-up: recurse into sub-directory, but exclude symlinks to\n # directories if followlinks is False\n if followlinks:\n walk_into = True\n else:\n try:\n is_symlink = entry.is_symlink()\n except OSError:\n # If is_symlink() raises an OSError, consider that the\n # entry is not a symbolic link, same behaviour than\n # os.path.islink().\n is_symlink = False\n walk_into = not is_symlink\n\n if walk_into:\n walk_dirs.append(entry.path)\n\n # Yield before recursion if going top down\n if topdown:\n yield top, dirs, nondirs\n\n # Recurse into sub-directories\n islink, join = path.islink, path.join\n for dirname in dirs:\n new_path = join(top, dirname)\n # Issue #23605: os.path.islink() is used instead of caching\n # entry.is_symlink() result during the loop on os.scandir() because\n # the caller can replace the directory entry during the \"yield\"\n # above.\n if followlinks or not islink(new_path):\n yield from walk(new_path, topdown, onerror, followlinks)\n else:\n # Recurse into sub-directories\n for new_path in walk_dirs:\n yield from walk(new_path, topdown, onerror, followlinks)\n # Yield after recursion if going bottom up\n yield top, dirs, nondirs\n\n__all__.append(\"walk\")\n\nif {open, stat} <= supports_dir_fd and {scandir, stat} <= supports_fd:\n\n def fwalk(top=\".\", topdown=True, onerror=None, *, follow_symlinks=False, dir_fd=None):\n \"\"\"Directory tree generator.\n\n This behaves exactly like walk(), except that it yields a 4-tuple\n\n dirpath, dirnames, filenames, dirfd\n\n `dirpath`, `dirnames` and `filenames` are identical to walk() output,\n and `dirfd` is a file descriptor referring to the directory `dirpath`.\n\n The advantage of fwalk() over walk() is that it's safe against symlink\n races (when follow_symlinks is False).\n\n If dir_fd is not None, it should be a file descriptor open to a directory,\n and top should be relative; top will then be relative to that directory.\n (dir_fd is always supported for fwalk.)\n\n Caution:\n Since fwalk() yields file descriptors, those are only valid until the\n next iteration step, so you should dup() them if you want to keep them\n for a longer period.\n\n Example:\n\n import os\n for root, dirs, files, rootfd in os.fwalk('python/Lib/email'):\n print(root, \"consumes\", end=\"\")\n print(sum(os.stat(name, dir_fd=rootfd).st_size for name in files),\n end=\"\")\n print(\"bytes in\", len(files), \"non-directory files\")\n if 'CVS' in dirs:\n dirs.remove('CVS') # don't visit CVS directories\n \"\"\"\n if not isinstance(top, int) or not hasattr(top, '__index__'):\n top = fspath(top)\n # Note: To guard against symlink races, we use the standard\n # lstat()/open()/fstat() trick.\n if not follow_symlinks:\n orig_st = stat(top, follow_symlinks=False, dir_fd=dir_fd)\n topfd = open(top, O_RDONLY, dir_fd=dir_fd)\n try:\n if (follow_symlinks or (st.S_ISDIR(orig_st.st_mode) and\n path.samestat(orig_st, stat(topfd)))):\n yield from _fwalk(topfd, top, isinstance(top, bytes),\n topdown, onerror, follow_symlinks)\n finally:\n close(topfd)\n\n def _fwalk(topfd, toppath, isbytes, topdown, onerror, follow_symlinks):\n # Note: This uses O(depth of the directory tree) file descriptors: if\n # necessary, it can be adapted to only require O(1) FDs, see issue\n # #13734.\n\n scandir_it = scandir(topfd)\n dirs = []\n nondirs = []\n entries = None if topdown or follow_symlinks else []\n for entry in scandir_it:\n name = entry.name\n if isbytes:\n name = fsencode(name)\n try:\n if entry.is_dir():\n dirs.append(name)\n if entries is not None:\n entries.append(entry)\n else:\n nondirs.append(name)\n except OSError:\n try:\n # Add dangling symlinks, ignore disappeared files\n if entry.is_symlink():\n nondirs.append(name)\n except OSError:\n pass\n\n if topdown:\n yield toppath, dirs, nondirs, topfd\n\n for name in dirs if entries is None else zip(dirs, entries):\n try:\n if not follow_symlinks:\n if topdown:\n orig_st = stat(name, dir_fd=topfd, follow_symlinks=False)\n else:\n assert entries is not None\n name, entry = name\n orig_st = entry.stat(follow_symlinks=False)\n dirfd = open(name, O_RDONLY, dir_fd=topfd)\n except OSError as err:\n if onerror is not None:\n onerror(err)\n continue\n try:\n if follow_symlinks or path.samestat(orig_st, stat(dirfd)):\n dirpath = path.join(toppath, name)\n yield from _fwalk(dirfd, dirpath, isbytes,\n topdown, onerror, follow_symlinks)\n finally:\n close(dirfd)\n\n if not topdown:\n yield toppath, dirs, nondirs, topfd\n\n __all__.append(\"fwalk\")\n\ndef execl(file, *args):\n \"\"\"execl(file, *args)\n\n Execute the executable file with argument list args, replacing the\n current process. \"\"\"\n execv(file, args)\n\ndef execle(file, *args):\n \"\"\"execle(file, *args, env)\n\n Execute the executable file with argument list args and\n environment env, replacing the current process. \"\"\"\n env = args[-1]\n execve(file, args[:-1], env)\n\ndef execlp(file, *args):\n \"\"\"execlp(file, *args)\n\n Execute the executable file (which is searched for along $PATH)\n with argument list args, replacing the current process. \"\"\"\n execvp(file, args)\n\ndef execlpe(file, *args):\n \"\"\"execlpe(file, *args, env)\n\n Execute the executable file (which is searched for along $PATH)\n with argument list args and environment env, replacing the current\n process. \"\"\"\n env = args[-1]\n execvpe(file, args[:-1], env)\n\ndef execvp(file, args):\n \"\"\"execvp(file, args)\n\n Execute the executable file (which is searched for along $PATH)\n with argument list args, replacing the current process.\n args may be a list or tuple of strings. \"\"\"\n _execvpe(file, args)\n\ndef execvpe(file, args, env):\n \"\"\"execvpe(file, args, env)\n\n Execute the executable file (which is searched for along $PATH)\n with argument list args and environment env, replacing the\n current process.\n args may be a list or tuple of strings. \"\"\"\n _execvpe(file, args, env)\n\n__all__.extend([\"execl\",\"execle\",\"execlp\",\"execlpe\",\"execvp\",\"execvpe\"])\n\ndef _execvpe(file, args, env=None):\n if env is not None:\n exec_func = execve\n argrest = (args, env)\n else:\n exec_func = execv\n argrest = (args,)\n env = environ\n\n if path.dirname(file):\n exec_func(file, *argrest)\n return\n saved_exc = None\n path_list = get_exec_path(env)\n if name != 'nt':\n file = fsencode(file)\n path_list = map(fsencode, path_list)\n for dir in path_list:\n fullname = path.join(dir, file)\n try:\n exec_func(fullname, *argrest)\n except (FileNotFoundError, NotADirectoryError) as e:\n last_exc = e\n except OSError as e:\n last_exc = e\n if saved_exc is None:\n saved_exc = e\n if saved_exc is not None:\n raise saved_exc\n raise last_exc\n\n\ndef get_exec_path(env=None):\n \"\"\"Returns the sequence of directories that will be searched for the\n named executable (similar to a shell) when launching a process.\n\n *env* must be an environment variable dict or None. If *env* is None,\n os.environ will be used.\n \"\"\"\n # Use a local import instead of a global import to limit the number of\n # modules loaded at startup: the os module is always loaded at startup by\n # Python. It may also avoid a bootstrap issue.\n import warnings\n\n if env is None:\n env = environ\n\n # {b'PATH': ...}.get('PATH') and {'PATH': ...}.get(b'PATH') emit a\n # BytesWarning when using python -b or python -bb: ignore the warning\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\", BytesWarning)\n\n try:\n path_list = env.get('PATH')\n except TypeError:\n path_list = None\n\n if supports_bytes_environ:\n try:\n path_listb = env[b'PATH']\n except (KeyError, TypeError):\n pass\n else:\n if path_list is not None:\n raise ValueError(\n \"env cannot contain 'PATH' and b'PATH' keys\")\n path_list = path_listb\n\n if path_list is not None and isinstance(path_list, bytes):\n path_list = fsdecode(path_list)\n\n if path_list is None:\n path_list = defpath\n return path_list.split(pathsep)\n\n\n# Change environ to automatically call putenv(), unsetenv if they exist.\nfrom _collections_abc import MutableMapping\n\nclass _Environ(MutableMapping):\n def __init__(self, data, encodekey, decodekey, encodevalue, decodevalue, putenv, unsetenv):\n self.encodekey = encodekey\n self.decodekey = decodekey\n self.encodevalue = encodevalue\n self.decodevalue = decodevalue\n self.putenv = putenv\n self.unsetenv = unsetenv\n self._data = data\n\n def __getitem__(self, key):\n try:\n value = self._data[self.encodekey(key)]\n except KeyError:\n # raise KeyError with the original key value\n raise KeyError(key) from None\n return self.decodevalue(value)\n\n def __setitem__(self, key, value):\n key = self.encodekey(key)\n value = self.encodevalue(value)\n self.putenv(key, value)\n self._data[key] = value\n\n def __delitem__(self, key):\n encodedkey = self.encodekey(key)\n self.unsetenv(encodedkey)\n try:\n del self._data[encodedkey]\n except KeyError:\n # raise KeyError with the original key value\n raise KeyError(key) from None\n\n def __iter__(self):\n # list() from dict object is an atomic operation\n keys = list(self._data)\n for key in keys:\n yield self.decodekey(key)\n\n def __len__(self):\n return len(self._data)\n\n def __repr__(self):\n return 'environ({{{}}})'.format(', '.join(\n ('{!r}: {!r}'.format(self.decodekey(key), self.decodevalue(value))\n for key, value in self._data.items())))\n\n def copy(self):\n return dict(self)\n\n def setdefault(self, key, value):\n if key not in self:\n self[key] = value\n return self[key]\n\ntry:\n _putenv = putenv\nexcept NameError:\n _putenv = lambda key, value: None\nelse:\n if \"putenv\" not in __all__:\n __all__.append(\"putenv\")\n\ntry:\n _unsetenv = unsetenv\nexcept NameError:\n _unsetenv = lambda key: _putenv(key, \"\")\nelse:\n if \"unsetenv\" not in __all__:\n __all__.append(\"unsetenv\")\n\ndef _createenviron():\n if name == 'nt':\n # Where Env Var Names Must Be UPPERCASE\n def check_str(value):\n if not isinstance(value, str):\n raise TypeError(\"str expected, not %s\" % type(value).__name__)\n return value\n encode = check_str\n decode = str\n def encodekey(key):\n return encode(key).upper()\n data = {}\n for key, value in environ.items():\n data[encodekey(key)] = value\n else:\n # Where Env Var Names Can Be Mixed Case\n encoding = sys.getfilesystemencoding()\n def encode(value):\n if not isinstance(value, str):\n raise TypeError(\"str expected, not %s\" % type(value).__name__)\n return value.encode(encoding, 'surrogateescape')\n def decode(value):\n return value.decode(encoding, 'surrogateescape')\n encodekey = encode\n data = environ\n return _Environ(data,\n encodekey, decode,\n encode, decode,\n _putenv, _unsetenv)\n\n# unicode environ\nenviron = _createenviron()\ndel _createenviron\n\n\ndef getenv(key, default=None):\n \"\"\"Get an environment variable, return None if it doesn't exist.\n The optional second argument can specify an alternate default.\n key, default and the result are str.\"\"\"\n return environ.get(key, default)\n\nsupports_bytes_environ = (name != 'nt')\n__all__.extend((\"getenv\", \"supports_bytes_environ\"))\n\nif supports_bytes_environ:\n def _check_bytes(value):\n if not isinstance(value, bytes):\n raise TypeError(\"bytes expected, not %s\" % type(value).__name__)\n return value\n\n # bytes environ\n environb = _Environ(environ._data,\n _check_bytes, bytes,\n _check_bytes, bytes,\n _putenv, _unsetenv)\n del _check_bytes\n\n def getenvb(key, default=None):\n \"\"\"Get an environment variable, return None if it doesn't exist.\n The optional second argument can specify an alternate default.\n key, default and the result are bytes.\"\"\"\n return environb.get(key, default)\n\n __all__.extend((\"environb\", \"getenvb\"))\n\ndef _fscodec():\n encoding = sys.getfilesystemencoding()\n errors = sys.getfilesystemencodeerrors()\n\n def fsencode(filename):\n \"\"\"Encode filename (an os.PathLike, bytes, or str) to the filesystem\n encoding with 'surrogateescape' error handler, return bytes unchanged.\n On Windows, use 'strict' error handler if the file system encoding is\n 'mbcs' (which is the default encoding).\n \"\"\"\n filename = fspath(filename) # Does type-checking of `filename`.\n if isinstance(filename, str):\n return filename.encode(encoding, errors)\n else:\n return filename\n\n def fsdecode(filename):\n \"\"\"Decode filename (an os.PathLike, bytes, or str) from the filesystem\n encoding with 'surrogateescape' error handler, return str unchanged. On\n Windows, use 'strict' error handler if the file system encoding is\n 'mbcs' (which is the default encoding).\n \"\"\"\n filename = fspath(filename) # Does type-checking of `filename`.\n if isinstance(filename, bytes):\n return filename.decode(encoding, errors)\n else:\n return filename\n\n return fsencode, fsdecode\n\nfsencode, fsdecode = _fscodec()\ndel _fscodec\n\n# Supply spawn*() (probably only for Unix)\nif _exists(\"fork\") and not _exists(\"spawnv\") and _exists(\"execv\"):\n\n P_WAIT = 0\n P_NOWAIT = P_NOWAITO = 1\n\n __all__.extend([\"P_WAIT\", \"P_NOWAIT\", \"P_NOWAITO\"])\n\n # XXX Should we support P_DETACH? I suppose it could fork()**2\n # and close the std I/O streams. Also, P_OVERLAY is the same\n # as execv*()?\n\n def _spawnvef(mode, file, args, env, func):\n # Internal helper; func is the exec*() function to use\n if not isinstance(args, (tuple, list)):\n raise TypeError('argv must be a tuple or a list')\n if not args or not args[0]:\n raise ValueError('argv first element cannot be empty')\n pid = fork()\n if not pid:\n # Child\n try:\n if env is None:\n func(file, args)\n else:\n func(file, args, env)\n except:\n _exit(127)\n else:\n # Parent\n if mode == P_NOWAIT:\n return pid # Caller is responsible for waiting!\n while 1:\n wpid, sts = waitpid(pid, 0)\n if WIFSTOPPED(sts):\n continue\n elif WIFSIGNALED(sts):\n return -WTERMSIG(sts)\n elif WIFEXITED(sts):\n return WEXITSTATUS(sts)\n else:\n raise OSError(\"Not stopped, signaled or exited???\")\n\n def spawnv(mode, file, args):\n \"\"\"spawnv(mode, file, args) -> integer\n\nExecute file with arguments from args in a subprocess.\nIf mode == P_NOWAIT return the pid of the process.\nIf mode == P_WAIT return the process's exit code if it exits normally;\notherwise return -SIG, where SIG is the signal that killed it. \"\"\"\n return _spawnvef(mode, file, args, None, execv)\n\n def spawnve(mode, file, args, env):\n \"\"\"spawnve(mode, file, args, env) -> integer\n\nExecute file with arguments from args in a subprocess with the\nspecified environment.\nIf mode == P_NOWAIT return the pid of the process.\nIf mode == P_WAIT return the process's exit code if it exits normally;\notherwise return -SIG, where SIG is the signal that killed it. \"\"\"\n return _spawnvef(mode, file, args, env, execve)\n\n # Note: spawnvp[e] isn't currently supported on Windows\n\n def spawnvp(mode, file, args):\n \"\"\"spawnvp(mode, file, args) -> integer\n\nExecute file (which is looked for along $PATH) with arguments from\nargs in a subprocess.\nIf mode == P_NOWAIT return the pid of the process.\nIf mode == P_WAIT return the process's exit code if it exits normally;\notherwise return -SIG, where SIG is the signal that killed it. \"\"\"\n return _spawnvef(mode, file, args, None, execvp)\n\n def spawnvpe(mode, file, args, env):\n \"\"\"spawnvpe(mode, file, args, env) -> integer\n\nExecute file (which is looked for along $PATH) with arguments from\nargs in a subprocess with the supplied environment.\nIf mode == P_NOWAIT return the pid of the process.\nIf mode == P_WAIT return the process's exit code if it exits normally;\notherwise return -SIG, where SIG is the signal that killed it. \"\"\"\n return _spawnvef(mode, file, args, env, execvpe)\n\n\n __all__.extend([\"spawnv\", \"spawnve\", \"spawnvp\", \"spawnvpe\"])\n\n\nif _exists(\"spawnv\"):\n # These aren't supplied by the basic Windows code\n # but can be easily implemented in Python\n\n def spawnl(mode, file, *args):\n \"\"\"spawnl(mode, file, *args) -> integer\n\nExecute file with arguments from args in a subprocess.\nIf mode == P_NOWAIT return the pid of the process.\nIf mode == P_WAIT return the process's exit code if it exits normally;\notherwise return -SIG, where SIG is the signal that killed it. \"\"\"\n return spawnv(mode, file, args)\n\n def spawnle(mode, file, *args):\n \"\"\"spawnle(mode, file, *args, env) -> integer\n\nExecute file with arguments from args in a subprocess with the\nsupplied environment.\nIf mode == P_NOWAIT return the pid of the process.\nIf mode == P_WAIT return the process's exit code if it exits normally;\notherwise return -SIG, where SIG is the signal that killed it. \"\"\"\n env = args[-1]\n return spawnve(mode, file, args[:-1], env)\n\n\n __all__.extend([\"spawnl\", \"spawnle\"])\n\n\nif _exists(\"spawnvp\"):\n # At the moment, Windows doesn't implement spawnvp[e],\n # so it won't have spawnlp[e] either.\n def spawnlp(mode, file, *args):\n \"\"\"spawnlp(mode, file, *args) -> integer\n\nExecute file (which is looked for along $PATH) with arguments from\nargs in a subprocess with the supplied environment.\nIf mode == P_NOWAIT return the pid of the process.\nIf mode == P_WAIT return the process's exit code if it exits normally;\notherwise return -SIG, where SIG is the signal that killed it. \"\"\"\n return spawnvp(mode, file, args)\n\n def spawnlpe(mode, file, *args):\n \"\"\"spawnlpe(mode, file, *args, env) -> integer\n\nExecute file (which is looked for along $PATH) with arguments from\nargs in a subprocess with the supplied environment.\nIf mode == P_NOWAIT return the pid of the process.\nIf mode == P_WAIT return the process's exit code if it exits normally;\notherwise return -SIG, where SIG is the signal that killed it. \"\"\"\n env = args[-1]\n return spawnvpe(mode, file, args[:-1], env)\n\n\n __all__.extend([\"spawnlp\", \"spawnlpe\"])\n\n\n# Supply os.popen()\ndef popen(cmd, mode=\"r\", buffering=-1):\n if not isinstance(cmd, str):\n raise TypeError(\"invalid cmd type (%s, expected string)\" % type(cmd))\n if mode not in (\"r\", \"w\"):\n raise ValueError(\"invalid mode %r\" % mode)\n if buffering == 0 or buffering is None:\n raise ValueError(\"popen() does not support unbuffered streams\")\n import subprocess, io\n if mode == \"r\":\n proc = subprocess.Popen(cmd,\n shell=True,\n stdout=subprocess.PIPE,\n bufsize=buffering)\n return _wrap_close(io.TextIOWrapper(proc.stdout), proc)\n else:\n proc = subprocess.Popen(cmd,\n shell=True,\n stdin=subprocess.PIPE,\n bufsize=buffering)\n return _wrap_close(io.TextIOWrapper(proc.stdin), proc)\n\n# Helper for popen() -- a proxy for a file whose close waits for the process\nclass _wrap_close:\n def __init__(self, stream, proc):\n self._stream = stream\n self._proc = proc\n def close(self):\n self._stream.close()\n returncode = self._proc.wait()\n if returncode == 0:\n return None\n if name == 'nt':\n return returncode\n else:\n return returncode << 8 # Shift left to match old behavior\n def __enter__(self):\n return self\n def __exit__(self, *args):\n self.close()\n def __getattr__(self, name):\n return getattr(self._stream, name)\n def __iter__(self):\n return iter(self._stream)\n\n# Supply os.fdopen()\ndef fdopen(fd, *args, **kwargs):\n if not isinstance(fd, int):\n raise TypeError(\"invalid fd type (%s, expected integer)\" % type(fd))\n import io\n return io.open(fd, *args, **kwargs)\n\n\n# For testing purposes, make sure the function is available when the C\n# implementation exists.\ndef _fspath(path):\n \"\"\"Return the path representation of a path-like object.\n\n If str or bytes is passed in, it is returned unchanged. Otherwise the\n os.PathLike interface is used to get the path representation. If the\n path representation is not str or bytes, TypeError is raised. If the\n provided path is not str, bytes, or os.PathLike, TypeError is raised.\n \"\"\"\n if isinstance(path, (str, bytes)):\n return path\n\n # Work from the object's type to match method resolution of other magic\n # methods.\n path_type = type(path)\n try:\n path_repr = path_type.__fspath__(path)\n except AttributeError:\n if hasattr(path_type, '__fspath__'):\n raise\n else:\n raise TypeError(\"expected str, bytes or os.PathLike object, \"\n \"not \" + path_type.__name__)\n if isinstance(path_repr, (str, bytes)):\n return path_repr\n else:\n raise TypeError(\"expected {}.__fspath__() to return str or bytes, \"\n \"not {}\".format(path_type.__name__,\n type(path_repr).__name__))\n\n# If there is no C implementation, make the pure Python version the\n# implementation as transparently as possible.\nif not _exists('fspath'):\n fspath = _fspath\n fspath.__name__ = \"fspath\"\n\n\nclass PathLike(abc.ABC):\n\n \"\"\"Abstract base class for implementing the file system path protocol.\"\"\"\n\n @abc.abstractmethod\n def __fspath__(self):\n \"\"\"Return the file system path representation of the object.\"\"\"\n raise NotImplementedError\n\n @classmethod\n def __subclasshook__(cls, subclass):\n if cls is PathLike:\n return _check_methods(subclass, '__fspath__')\n return NotImplemented\n\n\nif name == 'nt':\n class _AddedDllDirectory:\n def __init__(self, path, cookie, remove_dll_directory):\n self.path = path\n self._cookie = cookie\n self._remove_dll_directory = remove_dll_directory\n def close(self):\n self._remove_dll_directory(self._cookie)\n self.path = None\n def __enter__(self):\n return self\n def __exit__(self, *args):\n self.close()\n def __repr__(self):\n if self.path:\n return \"\".format(self.path)\n return \"\"\n\n def add_dll_directory(path):\n \"\"\"Add a path to the DLL search path.\n\n This search path is used when resolving dependencies for imported\n extension modules (the module itself is resolved through sys.path),\n and also by ctypes.\n\n Remove the directory by calling close() on the returned object or\n using it in a with statement.\n \"\"\"\n import nt\n cookie = nt._add_dll_directory(path)\n return _AddedDllDirectory(\n path,\n cookie,\n nt._remove_dll_directory\n )\n", 1114], "/home/gaogaotiantian/programs/codesnap/venv3.8/lib/python3.8/_collections_abc.py": ["# Copyright 2007 Google, Inc. All Rights Reserved.\n# Licensed to PSF under a Contributor Agreement.\n\n\"\"\"Abstract Base Classes (ABCs) for collections, according to PEP 3119.\n\nUnit tests are in test_collections.\n\"\"\"\n\nfrom abc import ABCMeta, abstractmethod\nimport sys\n\n__all__ = [\"Awaitable\", \"Coroutine\",\n \"AsyncIterable\", \"AsyncIterator\", \"AsyncGenerator\",\n \"Hashable\", \"Iterable\", \"Iterator\", \"Generator\", \"Reversible\",\n \"Sized\", \"Container\", \"Callable\", \"Collection\",\n \"Set\", \"MutableSet\",\n \"Mapping\", \"MutableMapping\",\n \"MappingView\", \"KeysView\", \"ItemsView\", \"ValuesView\",\n \"Sequence\", \"MutableSequence\",\n \"ByteString\",\n ]\n\n# This module has been renamed from collections.abc to _collections_abc to\n# speed up interpreter startup. Some of the types such as MutableMapping are\n# required early but collections module imports a lot of other modules.\n# See issue #19218\n__name__ = \"collections.abc\"\n\n# Private list of types that we want to register with the various ABCs\n# so that they will pass tests like:\n# it = iter(somebytearray)\n# assert isinstance(it, Iterable)\n# Note: in other implementations, these types might not be distinct\n# and they may have their own implementation specific types that\n# are not included on this list.\nbytes_iterator = type(iter(b''))\nbytearray_iterator = type(iter(bytearray()))\n#callable_iterator = ???\ndict_keyiterator = type(iter({}.keys()))\ndict_valueiterator = type(iter({}.values()))\ndict_itemiterator = type(iter({}.items()))\nlist_iterator = type(iter([]))\nlist_reverseiterator = type(iter(reversed([])))\nrange_iterator = type(iter(range(0)))\nlongrange_iterator = type(iter(range(1 << 1000)))\nset_iterator = type(iter(set()))\nstr_iterator = type(iter(\"\"))\ntuple_iterator = type(iter(()))\nzip_iterator = type(iter(zip()))\n## views ##\ndict_keys = type({}.keys())\ndict_values = type({}.values())\ndict_items = type({}.items())\n## misc ##\nmappingproxy = type(type.__dict__)\ngenerator = type((lambda: (yield))())\n## coroutine ##\nasync def _coro(): pass\n_coro = _coro()\ncoroutine = type(_coro)\n_coro.close() # Prevent ResourceWarning\ndel _coro\n## asynchronous generator ##\nasync def _ag(): yield\n_ag = _ag()\nasync_generator = type(_ag)\ndel _ag\n\n\n### ONE-TRICK PONIES ###\n\ndef _check_methods(C, *methods):\n mro = C.__mro__\n for method in methods:\n for B in mro:\n if method in B.__dict__:\n if B.__dict__[method] is None:\n return NotImplemented\n break\n else:\n return NotImplemented\n return True\n\nclass Hashable(metaclass=ABCMeta):\n\n __slots__ = ()\n\n @abstractmethod\n def __hash__(self):\n return 0\n\n @classmethod\n def __subclasshook__(cls, C):\n if cls is Hashable:\n return _check_methods(C, \"__hash__\")\n return NotImplemented\n\n\nclass Awaitable(metaclass=ABCMeta):\n\n __slots__ = ()\n\n @abstractmethod\n def __await__(self):\n yield\n\n @classmethod\n def __subclasshook__(cls, C):\n if cls is Awaitable:\n return _check_methods(C, \"__await__\")\n return NotImplemented\n\n\nclass Coroutine(Awaitable):\n\n __slots__ = ()\n\n @abstractmethod\n def send(self, value):\n \"\"\"Send a value into the coroutine.\n Return next yielded value or raise StopIteration.\n \"\"\"\n raise StopIteration\n\n @abstractmethod\n def throw(self, typ, val=None, tb=None):\n \"\"\"Raise an exception in the coroutine.\n Return next yielded value or raise StopIteration.\n \"\"\"\n if val is None:\n if tb is None:\n raise typ\n val = typ()\n if tb is not None:\n val = val.with_traceback(tb)\n raise val\n\n def close(self):\n \"\"\"Raise GeneratorExit inside coroutine.\n \"\"\"\n try:\n self.throw(GeneratorExit)\n except (GeneratorExit, StopIteration):\n pass\n else:\n raise RuntimeError(\"coroutine ignored GeneratorExit\")\n\n @classmethod\n def __subclasshook__(cls, C):\n if cls is Coroutine:\n return _check_methods(C, '__await__', 'send', 'throw', 'close')\n return NotImplemented\n\n\nCoroutine.register(coroutine)\n\n\nclass AsyncIterable(metaclass=ABCMeta):\n\n __slots__ = ()\n\n @abstractmethod\n def __aiter__(self):\n return AsyncIterator()\n\n @classmethod\n def __subclasshook__(cls, C):\n if cls is AsyncIterable:\n return _check_methods(C, \"__aiter__\")\n return NotImplemented\n\n\nclass AsyncIterator(AsyncIterable):\n\n __slots__ = ()\n\n @abstractmethod\n async def __anext__(self):\n \"\"\"Return the next item or raise StopAsyncIteration when exhausted.\"\"\"\n raise StopAsyncIteration\n\n def __aiter__(self):\n return self\n\n @classmethod\n def __subclasshook__(cls, C):\n if cls is AsyncIterator:\n return _check_methods(C, \"__anext__\", \"__aiter__\")\n return NotImplemented\n\n\nclass AsyncGenerator(AsyncIterator):\n\n __slots__ = ()\n\n async def __anext__(self):\n \"\"\"Return the next item from the asynchronous generator.\n When exhausted, raise StopAsyncIteration.\n \"\"\"\n return await self.asend(None)\n\n @abstractmethod\n async def asend(self, value):\n \"\"\"Send a value into the asynchronous generator.\n Return next yielded value or raise StopAsyncIteration.\n \"\"\"\n raise StopAsyncIteration\n\n @abstractmethod\n async def athrow(self, typ, val=None, tb=None):\n \"\"\"Raise an exception in the asynchronous generator.\n Return next yielded value or raise StopAsyncIteration.\n \"\"\"\n if val is None:\n if tb is None:\n raise typ\n val = typ()\n if tb is not None:\n val = val.with_traceback(tb)\n raise val\n\n async def aclose(self):\n \"\"\"Raise GeneratorExit inside coroutine.\n \"\"\"\n try:\n await self.athrow(GeneratorExit)\n except (GeneratorExit, StopAsyncIteration):\n pass\n else:\n raise RuntimeError(\"asynchronous generator ignored GeneratorExit\")\n\n @classmethod\n def __subclasshook__(cls, C):\n if cls is AsyncGenerator:\n return _check_methods(C, '__aiter__', '__anext__',\n 'asend', 'athrow', 'aclose')\n return NotImplemented\n\n\nAsyncGenerator.register(async_generator)\n\n\nclass Iterable(metaclass=ABCMeta):\n\n __slots__ = ()\n\n @abstractmethod\n def __iter__(self):\n while False:\n yield None\n\n @classmethod\n def __subclasshook__(cls, C):\n if cls is Iterable:\n return _check_methods(C, \"__iter__\")\n return NotImplemented\n\n\nclass Iterator(Iterable):\n\n __slots__ = ()\n\n @abstractmethod\n def __next__(self):\n 'Return the next item from the iterator. When exhausted, raise StopIteration'\n raise StopIteration\n\n def __iter__(self):\n return self\n\n @classmethod\n def __subclasshook__(cls, C):\n if cls is Iterator:\n return _check_methods(C, '__iter__', '__next__')\n return NotImplemented\n\nIterator.register(bytes_iterator)\nIterator.register(bytearray_iterator)\n#Iterator.register(callable_iterator)\nIterator.register(dict_keyiterator)\nIterator.register(dict_valueiterator)\nIterator.register(dict_itemiterator)\nIterator.register(list_iterator)\nIterator.register(list_reverseiterator)\nIterator.register(range_iterator)\nIterator.register(longrange_iterator)\nIterator.register(set_iterator)\nIterator.register(str_iterator)\nIterator.register(tuple_iterator)\nIterator.register(zip_iterator)\n\n\nclass Reversible(Iterable):\n\n __slots__ = ()\n\n @abstractmethod\n def __reversed__(self):\n while False:\n yield None\n\n @classmethod\n def __subclasshook__(cls, C):\n if cls is Reversible:\n return _check_methods(C, \"__reversed__\", \"__iter__\")\n return NotImplemented\n\n\nclass Generator(Iterator):\n\n __slots__ = ()\n\n def __next__(self):\n \"\"\"Return the next item from the generator.\n When exhausted, raise StopIteration.\n \"\"\"\n return self.send(None)\n\n @abstractmethod\n def send(self, value):\n \"\"\"Send a value into the generator.\n Return next yielded value or raise StopIteration.\n \"\"\"\n raise StopIteration\n\n @abstractmethod\n def throw(self, typ, val=None, tb=None):\n \"\"\"Raise an exception in the generator.\n Return next yielded value or raise StopIteration.\n \"\"\"\n if val is None:\n if tb is None:\n raise typ\n val = typ()\n if tb is not None:\n val = val.with_traceback(tb)\n raise val\n\n def close(self):\n \"\"\"Raise GeneratorExit inside generator.\n \"\"\"\n try:\n self.throw(GeneratorExit)\n except (GeneratorExit, StopIteration):\n pass\n else:\n raise RuntimeError(\"generator ignored GeneratorExit\")\n\n @classmethod\n def __subclasshook__(cls, C):\n if cls is Generator:\n return _check_methods(C, '__iter__', '__next__',\n 'send', 'throw', 'close')\n return NotImplemented\n\nGenerator.register(generator)\n\n\nclass Sized(metaclass=ABCMeta):\n\n __slots__ = ()\n\n @abstractmethod\n def __len__(self):\n return 0\n\n @classmethod\n def __subclasshook__(cls, C):\n if cls is Sized:\n return _check_methods(C, \"__len__\")\n return NotImplemented\n\n\nclass Container(metaclass=ABCMeta):\n\n __slots__ = ()\n\n @abstractmethod\n def __contains__(self, x):\n return False\n\n @classmethod\n def __subclasshook__(cls, C):\n if cls is Container:\n return _check_methods(C, \"__contains__\")\n return NotImplemented\n\nclass Collection(Sized, Iterable, Container):\n\n __slots__ = ()\n\n @classmethod\n def __subclasshook__(cls, C):\n if cls is Collection:\n return _check_methods(C, \"__len__\", \"__iter__\", \"__contains__\")\n return NotImplemented\n\nclass Callable(metaclass=ABCMeta):\n\n __slots__ = ()\n\n @abstractmethod\n def __call__(self, *args, **kwds):\n return False\n\n @classmethod\n def __subclasshook__(cls, C):\n if cls is Callable:\n return _check_methods(C, \"__call__\")\n return NotImplemented\n\n\n### SETS ###\n\n\nclass Set(Collection):\n\n \"\"\"A set is a finite, iterable container.\n\n This class provides concrete generic implementations of all\n methods except for __contains__, __iter__ and __len__.\n\n To override the comparisons (presumably for speed, as the\n semantics are fixed), redefine __le__ and __ge__,\n then the other operations will automatically follow suit.\n \"\"\"\n\n __slots__ = ()\n\n def __le__(self, other):\n if not isinstance(other, Set):\n return NotImplemented\n if len(self) > len(other):\n return False\n for elem in self:\n if elem not in other:\n return False\n return True\n\n def __lt__(self, other):\n if not isinstance(other, Set):\n return NotImplemented\n return len(self) < len(other) and self.__le__(other)\n\n def __gt__(self, other):\n if not isinstance(other, Set):\n return NotImplemented\n return len(self) > len(other) and self.__ge__(other)\n\n def __ge__(self, other):\n if not isinstance(other, Set):\n return NotImplemented\n if len(self) < len(other):\n return False\n for elem in other:\n if elem not in self:\n return False\n return True\n\n def __eq__(self, other):\n if not isinstance(other, Set):\n return NotImplemented\n return len(self) == len(other) and self.__le__(other)\n\n @classmethod\n def _from_iterable(cls, it):\n '''Construct an instance of the class from any iterable input.\n\n Must override this method if the class constructor signature\n does not accept an iterable for an input.\n '''\n return cls(it)\n\n def __and__(self, other):\n if not isinstance(other, Iterable):\n return NotImplemented\n return self._from_iterable(value for value in other if value in self)\n\n __rand__ = __and__\n\n def isdisjoint(self, other):\n 'Return True if two sets have a null intersection.'\n for value in other:\n if value in self:\n return False\n return True\n\n def __or__(self, other):\n if not isinstance(other, Iterable):\n return NotImplemented\n chain = (e for s in (self, other) for e in s)\n return self._from_iterable(chain)\n\n __ror__ = __or__\n\n def __sub__(self, other):\n if not isinstance(other, Set):\n if not isinstance(other, Iterable):\n return NotImplemented\n other = self._from_iterable(other)\n return self._from_iterable(value for value in self\n if value not in other)\n\n def __rsub__(self, other):\n if not isinstance(other, Set):\n if not isinstance(other, Iterable):\n return NotImplemented\n other = self._from_iterable(other)\n return self._from_iterable(value for value in other\n if value not in self)\n\n def __xor__(self, other):\n if not isinstance(other, Set):\n if not isinstance(other, Iterable):\n return NotImplemented\n other = self._from_iterable(other)\n return (self - other) | (other - self)\n\n __rxor__ = __xor__\n\n def _hash(self):\n \"\"\"Compute the hash value of a set.\n\n Note that we don't define __hash__: not all sets are hashable.\n But if you define a hashable set type, its __hash__ should\n call this function.\n\n This must be compatible __eq__.\n\n All sets ought to compare equal if they contain the same\n elements, regardless of how they are implemented, and\n regardless of the order of the elements; so there's not much\n freedom for __eq__ or __hash__. We match the algorithm used\n by the built-in frozenset type.\n \"\"\"\n MAX = sys.maxsize\n MASK = 2 * MAX + 1\n n = len(self)\n h = 1927868237 * (n + 1)\n h &= MASK\n for x in self:\n hx = hash(x)\n h ^= (hx ^ (hx << 16) ^ 89869747) * 3644798167\n h &= MASK\n h = h * 69069 + 907133923\n h &= MASK\n if h > MAX:\n h -= MASK + 1\n if h == -1:\n h = 590923713\n return h\n\nSet.register(frozenset)\n\n\nclass MutableSet(Set):\n \"\"\"A mutable set is a finite, iterable container.\n\n This class provides concrete generic implementations of all\n methods except for __contains__, __iter__, __len__,\n add(), and discard().\n\n To override the comparisons (presumably for speed, as the\n semantics are fixed), all you have to do is redefine __le__ and\n then the other operations will automatically follow suit.\n \"\"\"\n\n __slots__ = ()\n\n @abstractmethod\n def add(self, value):\n \"\"\"Add an element.\"\"\"\n raise NotImplementedError\n\n @abstractmethod\n def discard(self, value):\n \"\"\"Remove an element. Do not raise an exception if absent.\"\"\"\n raise NotImplementedError\n\n def remove(self, value):\n \"\"\"Remove an element. If not a member, raise a KeyError.\"\"\"\n if value not in self:\n raise KeyError(value)\n self.discard(value)\n\n def pop(self):\n \"\"\"Return the popped value. Raise KeyError if empty.\"\"\"\n it = iter(self)\n try:\n value = next(it)\n except StopIteration:\n raise KeyError from None\n self.discard(value)\n return value\n\n def clear(self):\n \"\"\"This is slow (creates N new iterators!) but effective.\"\"\"\n try:\n while True:\n self.pop()\n except KeyError:\n pass\n\n def __ior__(self, it):\n for value in it:\n self.add(value)\n return self\n\n def __iand__(self, it):\n for value in (self - it):\n self.discard(value)\n return self\n\n def __ixor__(self, it):\n if it is self:\n self.clear()\n else:\n if not isinstance(it, Set):\n it = self._from_iterable(it)\n for value in it:\n if value in self:\n self.discard(value)\n else:\n self.add(value)\n return self\n\n def __isub__(self, it):\n if it is self:\n self.clear()\n else:\n for value in it:\n self.discard(value)\n return self\n\nMutableSet.register(set)\n\n\n### MAPPINGS ###\n\n\nclass Mapping(Collection):\n\n __slots__ = ()\n\n \"\"\"A Mapping is a generic container for associating key/value\n pairs.\n\n This class provides concrete generic implementations of all\n methods except for __getitem__, __iter__, and __len__.\n\n \"\"\"\n\n @abstractmethod\n def __getitem__(self, key):\n raise KeyError\n\n def get(self, key, default=None):\n 'D.get(k[,d]) -> D[k] if k in D, else d. d defaults to None.'\n try:\n return self[key]\n except KeyError:\n return default\n\n def __contains__(self, key):\n try:\n self[key]\n except KeyError:\n return False\n else:\n return True\n\n def keys(self):\n \"D.keys() -> a set-like object providing a view on D's keys\"\n return KeysView(self)\n\n def items(self):\n \"D.items() -> a set-like object providing a view on D's items\"\n return ItemsView(self)\n\n def values(self):\n \"D.values() -> an object providing a view on D's values\"\n return ValuesView(self)\n\n def __eq__(self, other):\n if not isinstance(other, Mapping):\n return NotImplemented\n return dict(self.items()) == dict(other.items())\n\n __reversed__ = None\n\nMapping.register(mappingproxy)\n\n\nclass MappingView(Sized):\n\n __slots__ = '_mapping',\n\n def __init__(self, mapping):\n self._mapping = mapping\n\n def __len__(self):\n return len(self._mapping)\n\n def __repr__(self):\n return '{0.__class__.__name__}({0._mapping!r})'.format(self)\n\n\nclass KeysView(MappingView, Set):\n\n __slots__ = ()\n\n @classmethod\n def _from_iterable(self, it):\n return set(it)\n\n def __contains__(self, key):\n return key in self._mapping\n\n def __iter__(self):\n yield from self._mapping\n\nKeysView.register(dict_keys)\n\n\nclass ItemsView(MappingView, Set):\n\n __slots__ = ()\n\n @classmethod\n def _from_iterable(self, it):\n return set(it)\n\n def __contains__(self, item):\n key, value = item\n try:\n v = self._mapping[key]\n except KeyError:\n return False\n else:\n return v is value or v == value\n\n def __iter__(self):\n for key in self._mapping:\n yield (key, self._mapping[key])\n\nItemsView.register(dict_items)\n\n\nclass ValuesView(MappingView, Collection):\n\n __slots__ = ()\n\n def __contains__(self, value):\n for key in self._mapping:\n v = self._mapping[key]\n if v is value or v == value:\n return True\n return False\n\n def __iter__(self):\n for key in self._mapping:\n yield self._mapping[key]\n\nValuesView.register(dict_values)\n\n\nclass MutableMapping(Mapping):\n\n __slots__ = ()\n\n \"\"\"A MutableMapping is a generic container for associating\n key/value pairs.\n\n This class provides concrete generic implementations of all\n methods except for __getitem__, __setitem__, __delitem__,\n __iter__, and __len__.\n\n \"\"\"\n\n @abstractmethod\n def __setitem__(self, key, value):\n raise KeyError\n\n @abstractmethod\n def __delitem__(self, key):\n raise KeyError\n\n __marker = object()\n\n def pop(self, key, default=__marker):\n '''D.pop(k[,d]) -> v, remove specified key and return the corresponding value.\n If key is not found, d is returned if given, otherwise KeyError is raised.\n '''\n try:\n value = self[key]\n except KeyError:\n if default is self.__marker:\n raise\n return default\n else:\n del self[key]\n return value\n\n def popitem(self):\n '''D.popitem() -> (k, v), remove and return some (key, value) pair\n as a 2-tuple; but raise KeyError if D is empty.\n '''\n try:\n key = next(iter(self))\n except StopIteration:\n raise KeyError from None\n value = self[key]\n del self[key]\n return key, value\n\n def clear(self):\n 'D.clear() -> None. Remove all items from D.'\n try:\n while True:\n self.popitem()\n except KeyError:\n pass\n\n def update(self, other=(), /, **kwds):\n ''' D.update([E, ]**F) -> None. Update D from mapping/iterable E and F.\n If E present and has a .keys() method, does: for k in E: D[k] = E[k]\n If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v\n In either case, this is followed by: for k, v in F.items(): D[k] = v\n '''\n if isinstance(other, Mapping):\n for key in other:\n self[key] = other[key]\n elif hasattr(other, \"keys\"):\n for key in other.keys():\n self[key] = other[key]\n else:\n for key, value in other:\n self[key] = value\n for key, value in kwds.items():\n self[key] = value\n\n def setdefault(self, key, default=None):\n 'D.setdefault(k[,d]) -> D.get(k,d), also set D[k]=d if k not in D'\n try:\n return self[key]\n except KeyError:\n self[key] = default\n return default\n\nMutableMapping.register(dict)\n\n\n### SEQUENCES ###\n\n\nclass Sequence(Reversible, Collection):\n\n \"\"\"All the operations on a read-only sequence.\n\n Concrete subclasses must override __new__ or __init__,\n __getitem__, and __len__.\n \"\"\"\n\n __slots__ = ()\n\n @abstractmethod\n def __getitem__(self, index):\n raise IndexError\n\n def __iter__(self):\n i = 0\n try:\n while True:\n v = self[i]\n yield v\n i += 1\n except IndexError:\n return\n\n def __contains__(self, value):\n for v in self:\n if v is value or v == value:\n return True\n return False\n\n def __reversed__(self):\n for i in reversed(range(len(self))):\n yield self[i]\n\n def index(self, value, start=0, stop=None):\n '''S.index(value, [start, [stop]]) -> integer -- return first index of value.\n Raises ValueError if the value is not present.\n\n Supporting start and stop arguments is optional, but\n recommended.\n '''\n if start is not None and start < 0:\n start = max(len(self) + start, 0)\n if stop is not None and stop < 0:\n stop += len(self)\n\n i = start\n while stop is None or i < stop:\n try:\n v = self[i]\n if v is value or v == value:\n return i\n except IndexError:\n break\n i += 1\n raise ValueError\n\n def count(self, value):\n 'S.count(value) -> integer -- return number of occurrences of value'\n return sum(1 for v in self if v is value or v == value)\n\nSequence.register(tuple)\nSequence.register(str)\nSequence.register(range)\nSequence.register(memoryview)\n\n\nclass ByteString(Sequence):\n\n \"\"\"This unifies bytes and bytearray.\n\n XXX Should add all their methods.\n \"\"\"\n\n __slots__ = ()\n\nByteString.register(bytes)\nByteString.register(bytearray)\n\n\nclass MutableSequence(Sequence):\n\n __slots__ = ()\n\n \"\"\"All the operations on a read-write sequence.\n\n Concrete subclasses must provide __new__ or __init__,\n __getitem__, __setitem__, __delitem__, __len__, and insert().\n\n \"\"\"\n\n @abstractmethod\n def __setitem__(self, index, value):\n raise IndexError\n\n @abstractmethod\n def __delitem__(self, index):\n raise IndexError\n\n @abstractmethod\n def insert(self, index, value):\n 'S.insert(index, value) -- insert value before index'\n raise IndexError\n\n def append(self, value):\n 'S.append(value) -- append value to the end of the sequence'\n self.insert(len(self), value)\n\n def clear(self):\n 'S.clear() -> None -- remove all items from S'\n try:\n while True:\n self.pop()\n except IndexError:\n pass\n\n def reverse(self):\n 'S.reverse() -- reverse *IN PLACE*'\n n = len(self)\n for i in range(n//2):\n self[i], self[n-i-1] = self[n-i-1], self[i]\n\n def extend(self, values):\n 'S.extend(iterable) -- extend sequence by appending elements from the iterable'\n if values is self:\n values = list(values)\n for v in values:\n self.append(v)\n\n def pop(self, index=-1):\n '''S.pop([index]) -> item -- remove and return item at index (default last).\n Raise IndexError if list is empty or index is out of range.\n '''\n v = self[index]\n del self[index]\n return v\n\n def remove(self, value):\n '''S.remove(value) -- remove first occurrence of value.\n Raise ValueError if the value is not present.\n '''\n del self[self.index(value)]\n\n def __iadd__(self, values):\n self.extend(values)\n return self\n\nMutableSequence.register(list)\nMutableSequence.register(bytearray) # Multiply inheriting, see ByteString\n", 1004], "/usr/lib/python3.8/asyncio/base_events.py": ["\"\"\"Base implementation of event loop.\n\nThe event loop can be broken up into a multiplexer (the part\nresponsible for notifying us of I/O events) and the event loop proper,\nwhich wraps a multiplexer with functionality for scheduling callbacks,\nimmediately or at a given time in the future.\n\nWhenever a public API takes a callback, subsequent positional\narguments will be passed to the callback if/when it is called. This\navoids the proliferation of trivial lambdas implementing closures.\nKeyword arguments for the callback are not supported; this is a\nconscious design decision, leaving the door open for keyword arguments\nto modify the meaning of the API call itself.\n\"\"\"\n\nimport collections\nimport collections.abc\nimport concurrent.futures\nimport functools\nimport heapq\nimport itertools\nimport os\nimport socket\nimport stat\nimport subprocess\nimport threading\nimport time\nimport traceback\nimport sys\nimport warnings\nimport weakref\n\ntry:\n import ssl\nexcept ImportError: # pragma: no cover\n ssl = None\n\nfrom . import constants\nfrom . import coroutines\nfrom . import events\nfrom . import exceptions\nfrom . import futures\nfrom . import protocols\nfrom . import sslproto\nfrom . import staggered\nfrom . import tasks\nfrom . import transports\nfrom . import trsock\nfrom .log import logger\n\n\n__all__ = 'BaseEventLoop',\n\n\n# Minimum number of _scheduled timer handles before cleanup of\n# cancelled handles is performed.\n_MIN_SCHEDULED_TIMER_HANDLES = 100\n\n# Minimum fraction of _scheduled timer handles that are cancelled\n# before cleanup of cancelled handles is performed.\n_MIN_CANCELLED_TIMER_HANDLES_FRACTION = 0.5\n\n\n_HAS_IPv6 = hasattr(socket, 'AF_INET6')\n\n# Maximum timeout passed to select to avoid OS limitations\nMAXIMUM_SELECT_TIMEOUT = 24 * 3600\n\n# Used for deprecation and removal of `loop.create_datagram_endpoint()`'s\n# *reuse_address* parameter\n_unset = object()\n\n\ndef _format_handle(handle):\n cb = handle._callback\n if isinstance(getattr(cb, '__self__', None), tasks.Task):\n # format the task\n return repr(cb.__self__)\n else:\n return str(handle)\n\n\ndef _format_pipe(fd):\n if fd == subprocess.PIPE:\n return ''\n elif fd == subprocess.STDOUT:\n return ''\n else:\n return repr(fd)\n\n\ndef _set_reuseport(sock):\n if not hasattr(socket, 'SO_REUSEPORT'):\n raise ValueError('reuse_port not supported by socket module')\n else:\n try:\n sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEPORT, 1)\n except OSError:\n raise ValueError('reuse_port not supported by socket module, '\n 'SO_REUSEPORT defined but not implemented.')\n\n\ndef _ipaddr_info(host, port, family, type, proto, flowinfo=0, scopeid=0):\n # Try to skip getaddrinfo if \"host\" is already an IP. Users might have\n # handled name resolution in their own code and pass in resolved IPs.\n if not hasattr(socket, 'inet_pton'):\n return\n\n if proto not in {0, socket.IPPROTO_TCP, socket.IPPROTO_UDP} or \\\n host is None:\n return None\n\n if type == socket.SOCK_STREAM:\n proto = socket.IPPROTO_TCP\n elif type == socket.SOCK_DGRAM:\n proto = socket.IPPROTO_UDP\n else:\n return None\n\n if port is None:\n port = 0\n elif isinstance(port, bytes) and port == b'':\n port = 0\n elif isinstance(port, str) and port == '':\n port = 0\n else:\n # If port's a service name like \"http\", don't skip getaddrinfo.\n try:\n port = int(port)\n except (TypeError, ValueError):\n return None\n\n if family == socket.AF_UNSPEC:\n afs = [socket.AF_INET]\n if _HAS_IPv6:\n afs.append(socket.AF_INET6)\n else:\n afs = [family]\n\n if isinstance(host, bytes):\n host = host.decode('idna')\n if '%' in host:\n # Linux's inet_pton doesn't accept an IPv6 zone index after host,\n # like '::1%lo0'.\n return None\n\n for af in afs:\n try:\n socket.inet_pton(af, host)\n # The host has already been resolved.\n if _HAS_IPv6 and af == socket.AF_INET6:\n return af, type, proto, '', (host, port, flowinfo, scopeid)\n else:\n return af, type, proto, '', (host, port)\n except OSError:\n pass\n\n # \"host\" is not an IP address.\n return None\n\n\ndef _interleave_addrinfos(addrinfos, first_address_family_count=1):\n \"\"\"Interleave list of addrinfo tuples by family.\"\"\"\n # Group addresses by family\n addrinfos_by_family = collections.OrderedDict()\n for addr in addrinfos:\n family = addr[0]\n if family not in addrinfos_by_family:\n addrinfos_by_family[family] = []\n addrinfos_by_family[family].append(addr)\n addrinfos_lists = list(addrinfos_by_family.values())\n\n reordered = []\n if first_address_family_count > 1:\n reordered.extend(addrinfos_lists[0][:first_address_family_count - 1])\n del addrinfos_lists[0][:first_address_family_count - 1]\n reordered.extend(\n a for a in itertools.chain.from_iterable(\n itertools.zip_longest(*addrinfos_lists)\n ) if a is not None)\n return reordered\n\n\ndef _run_until_complete_cb(fut):\n if not fut.cancelled():\n exc = fut.exception()\n if isinstance(exc, (SystemExit, KeyboardInterrupt)):\n # Issue #22429: run_forever() already finished, no need to\n # stop it.\n return\n futures._get_loop(fut).stop()\n\n\nif hasattr(socket, 'TCP_NODELAY'):\n def _set_nodelay(sock):\n if (sock.family in {socket.AF_INET, socket.AF_INET6} and\n sock.type == socket.SOCK_STREAM and\n sock.proto == socket.IPPROTO_TCP):\n sock.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)\nelse:\n def _set_nodelay(sock):\n pass\n\n\nclass _SendfileFallbackProtocol(protocols.Protocol):\n def __init__(self, transp):\n if not isinstance(transp, transports._FlowControlMixin):\n raise TypeError(\"transport should be _FlowControlMixin instance\")\n self._transport = transp\n self._proto = transp.get_protocol()\n self._should_resume_reading = transp.is_reading()\n self._should_resume_writing = transp._protocol_paused\n transp.pause_reading()\n transp.set_protocol(self)\n if self._should_resume_writing:\n self._write_ready_fut = self._transport._loop.create_future()\n else:\n self._write_ready_fut = None\n\n async def drain(self):\n if self._transport.is_closing():\n raise ConnectionError(\"Connection closed by peer\")\n fut = self._write_ready_fut\n if fut is None:\n return\n await fut\n\n def connection_made(self, transport):\n raise RuntimeError(\"Invalid state: \"\n \"connection should have been established already.\")\n\n def connection_lost(self, exc):\n if self._write_ready_fut is not None:\n # Never happens if peer disconnects after sending the whole content\n # Thus disconnection is always an exception from user perspective\n if exc is None:\n self._write_ready_fut.set_exception(\n ConnectionError(\"Connection is closed by peer\"))\n else:\n self._write_ready_fut.set_exception(exc)\n self._proto.connection_lost(exc)\n\n def pause_writing(self):\n if self._write_ready_fut is not None:\n return\n self._write_ready_fut = self._transport._loop.create_future()\n\n def resume_writing(self):\n if self._write_ready_fut is None:\n return\n self._write_ready_fut.set_result(False)\n self._write_ready_fut = None\n\n def data_received(self, data):\n raise RuntimeError(\"Invalid state: reading should be paused\")\n\n def eof_received(self):\n raise RuntimeError(\"Invalid state: reading should be paused\")\n\n async def restore(self):\n self._transport.set_protocol(self._proto)\n if self._should_resume_reading:\n self._transport.resume_reading()\n if self._write_ready_fut is not None:\n # Cancel the future.\n # Basically it has no effect because protocol is switched back,\n # no code should wait for it anymore.\n self._write_ready_fut.cancel()\n if self._should_resume_writing:\n self._proto.resume_writing()\n\n\nclass Server(events.AbstractServer):\n\n def __init__(self, loop, sockets, protocol_factory, ssl_context, backlog,\n ssl_handshake_timeout):\n self._loop = loop\n self._sockets = sockets\n self._active_count = 0\n self._waiters = []\n self._protocol_factory = protocol_factory\n self._backlog = backlog\n self._ssl_context = ssl_context\n self._ssl_handshake_timeout = ssl_handshake_timeout\n self._serving = False\n self._serving_forever_fut = None\n\n def __repr__(self):\n return f'<{self.__class__.__name__} sockets={self.sockets!r}>'\n\n def _attach(self):\n assert self._sockets is not None\n self._active_count += 1\n\n def _detach(self):\n assert self._active_count > 0\n self._active_count -= 1\n if self._active_count == 0 and self._sockets is None:\n self._wakeup()\n\n def _wakeup(self):\n waiters = self._waiters\n self._waiters = None\n for waiter in waiters:\n if not waiter.done():\n waiter.set_result(waiter)\n\n def _start_serving(self):\n if self._serving:\n return\n self._serving = True\n for sock in self._sockets:\n sock.listen(self._backlog)\n self._loop._start_serving(\n self._protocol_factory, sock, self._ssl_context,\n self, self._backlog, self._ssl_handshake_timeout)\n\n def get_loop(self):\n return self._loop\n\n def is_serving(self):\n return self._serving\n\n @property\n def sockets(self):\n if self._sockets is None:\n return ()\n return tuple(trsock.TransportSocket(s) for s in self._sockets)\n\n def close(self):\n sockets = self._sockets\n if sockets is None:\n return\n self._sockets = None\n\n for sock in sockets:\n self._loop._stop_serving(sock)\n\n self._serving = False\n\n if (self._serving_forever_fut is not None and\n not self._serving_forever_fut.done()):\n self._serving_forever_fut.cancel()\n self._serving_forever_fut = None\n\n if self._active_count == 0:\n self._wakeup()\n\n async def start_serving(self):\n self._start_serving()\n # Skip one loop iteration so that all 'loop.add_reader'\n # go through.\n await tasks.sleep(0, loop=self._loop)\n\n async def serve_forever(self):\n if self._serving_forever_fut is not None:\n raise RuntimeError(\n f'server {self!r} is already being awaited on serve_forever()')\n if self._sockets is None:\n raise RuntimeError(f'server {self!r} is closed')\n\n self._start_serving()\n self._serving_forever_fut = self._loop.create_future()\n\n try:\n await self._serving_forever_fut\n except exceptions.CancelledError:\n try:\n self.close()\n await self.wait_closed()\n finally:\n raise\n finally:\n self._serving_forever_fut = None\n\n async def wait_closed(self):\n if self._sockets is None or self._waiters is None:\n return\n waiter = self._loop.create_future()\n self._waiters.append(waiter)\n await waiter\n\n\nclass BaseEventLoop(events.AbstractEventLoop):\n\n def __init__(self):\n self._timer_cancelled_count = 0\n self._closed = False\n self._stopping = False\n self._ready = collections.deque()\n self._scheduled = []\n self._default_executor = None\n self._internal_fds = 0\n # Identifier of the thread running the event loop, or None if the\n # event loop is not running\n self._thread_id = None\n self._clock_resolution = time.get_clock_info('monotonic').resolution\n self._exception_handler = None\n self.set_debug(coroutines._is_debug_mode())\n # In debug mode, if the execution of a callback or a step of a task\n # exceed this duration in seconds, the slow callback/task is logged.\n self.slow_callback_duration = 0.1\n self._current_handle = None\n self._task_factory = None\n self._coroutine_origin_tracking_enabled = False\n self._coroutine_origin_tracking_saved_depth = None\n\n # A weak set of all asynchronous generators that are\n # being iterated by the loop.\n self._asyncgens = weakref.WeakSet()\n # Set to True when `loop.shutdown_asyncgens` is called.\n self._asyncgens_shutdown_called = False\n\n def __repr__(self):\n return (\n f'<{self.__class__.__name__} running={self.is_running()} '\n f'closed={self.is_closed()} debug={self.get_debug()}>'\n )\n\n def create_future(self):\n \"\"\"Create a Future object attached to the loop.\"\"\"\n return futures.Future(loop=self)\n\n def create_task(self, coro, *, name=None):\n \"\"\"Schedule a coroutine object.\n\n Return a task object.\n \"\"\"\n self._check_closed()\n if self._task_factory is None:\n task = tasks.Task(coro, loop=self, name=name)\n if task._source_traceback:\n del task._source_traceback[-1]\n else:\n task = self._task_factory(self, coro)\n tasks._set_task_name(task, name)\n\n return task\n\n def set_task_factory(self, factory):\n \"\"\"Set a task factory that will be used by loop.create_task().\n\n If factory is None the default task factory will be set.\n\n If factory is a callable, it should have a signature matching\n '(loop, coro)', where 'loop' will be a reference to the active\n event loop, 'coro' will be a coroutine object. The callable\n must return a Future.\n \"\"\"\n if factory is not None and not callable(factory):\n raise TypeError('task factory must be a callable or None')\n self._task_factory = factory\n\n def get_task_factory(self):\n \"\"\"Return a task factory, or None if the default one is in use.\"\"\"\n return self._task_factory\n\n def _make_socket_transport(self, sock, protocol, waiter=None, *,\n extra=None, server=None):\n \"\"\"Create socket transport.\"\"\"\n raise NotImplementedError\n\n def _make_ssl_transport(\n self, rawsock, protocol, sslcontext, waiter=None,\n *, server_side=False, server_hostname=None,\n extra=None, server=None,\n ssl_handshake_timeout=None,\n call_connection_made=True):\n \"\"\"Create SSL transport.\"\"\"\n raise NotImplementedError\n\n def _make_datagram_transport(self, sock, protocol,\n address=None, waiter=None, extra=None):\n \"\"\"Create datagram transport.\"\"\"\n raise NotImplementedError\n\n def _make_read_pipe_transport(self, pipe, protocol, waiter=None,\n extra=None):\n \"\"\"Create read pipe transport.\"\"\"\n raise NotImplementedError\n\n def _make_write_pipe_transport(self, pipe, protocol, waiter=None,\n extra=None):\n \"\"\"Create write pipe transport.\"\"\"\n raise NotImplementedError\n\n async def _make_subprocess_transport(self, protocol, args, shell,\n stdin, stdout, stderr, bufsize,\n extra=None, **kwargs):\n \"\"\"Create subprocess transport.\"\"\"\n raise NotImplementedError\n\n def _write_to_self(self):\n \"\"\"Write a byte to self-pipe, to wake up the event loop.\n\n This may be called from a different thread.\n\n The subclass is responsible for implementing the self-pipe.\n \"\"\"\n raise NotImplementedError\n\n def _process_events(self, event_list):\n \"\"\"Process selector events.\"\"\"\n raise NotImplementedError\n\n def _check_closed(self):\n if self._closed:\n raise RuntimeError('Event loop is closed')\n\n def _asyncgen_finalizer_hook(self, agen):\n self._asyncgens.discard(agen)\n if not self.is_closed():\n self.call_soon_threadsafe(self.create_task, agen.aclose())\n\n def _asyncgen_firstiter_hook(self, agen):\n if self._asyncgens_shutdown_called:\n warnings.warn(\n f\"asynchronous generator {agen!r} was scheduled after \"\n f\"loop.shutdown_asyncgens() call\",\n ResourceWarning, source=self)\n\n self._asyncgens.add(agen)\n\n async def shutdown_asyncgens(self):\n \"\"\"Shutdown all active asynchronous generators.\"\"\"\n self._asyncgens_shutdown_called = True\n\n if not len(self._asyncgens):\n # If Python version is <3.6 or we don't have any asynchronous\n # generators alive.\n return\n\n closing_agens = list(self._asyncgens)\n self._asyncgens.clear()\n\n results = await tasks.gather(\n *[ag.aclose() for ag in closing_agens],\n return_exceptions=True,\n loop=self)\n\n for result, agen in zip(results, closing_agens):\n if isinstance(result, Exception):\n self.call_exception_handler({\n 'message': f'an error occurred during closing of '\n f'asynchronous generator {agen!r}',\n 'exception': result,\n 'asyncgen': agen\n })\n\n def _check_running(self):\n if self.is_running():\n raise RuntimeError('This event loop is already running')\n if events._get_running_loop() is not None:\n raise RuntimeError(\n 'Cannot run the event loop while another loop is running')\n\n def run_forever(self):\n \"\"\"Run until stop() is called.\"\"\"\n self._check_closed()\n self._check_running()\n self._set_coroutine_origin_tracking(self._debug)\n self._thread_id = threading.get_ident()\n\n old_agen_hooks = sys.get_asyncgen_hooks()\n sys.set_asyncgen_hooks(firstiter=self._asyncgen_firstiter_hook,\n finalizer=self._asyncgen_finalizer_hook)\n try:\n events._set_running_loop(self)\n while True:\n self._run_once()\n if self._stopping:\n break\n finally:\n self._stopping = False\n self._thread_id = None\n events._set_running_loop(None)\n self._set_coroutine_origin_tracking(False)\n sys.set_asyncgen_hooks(*old_agen_hooks)\n\n def run_until_complete(self, future):\n \"\"\"Run until the Future is done.\n\n If the argument is a coroutine, it is wrapped in a Task.\n\n WARNING: It would be disastrous to call run_until_complete()\n with the same coroutine twice -- it would wrap it in two\n different Tasks and that can't be good.\n\n Return the Future's result, or raise its exception.\n \"\"\"\n self._check_closed()\n self._check_running()\n\n new_task = not futures.isfuture(future)\n future = tasks.ensure_future(future, loop=self)\n if new_task:\n # An exception is raised if the future didn't complete, so there\n # is no need to log the \"destroy pending task\" message\n future._log_destroy_pending = False\n\n future.add_done_callback(_run_until_complete_cb)\n try:\n self.run_forever()\n except:\n if new_task and future.done() and not future.cancelled():\n # The coroutine raised a BaseException. Consume the exception\n # to not log a warning, the caller doesn't have access to the\n # local task.\n future.exception()\n raise\n finally:\n future.remove_done_callback(_run_until_complete_cb)\n if not future.done():\n raise RuntimeError('Event loop stopped before Future completed.')\n\n return future.result()\n\n def stop(self):\n \"\"\"Stop running the event loop.\n\n Every callback already scheduled will still run. This simply informs\n run_forever to stop looping after a complete iteration.\n \"\"\"\n self._stopping = True\n\n def close(self):\n \"\"\"Close the event loop.\n\n This clears the queues and shuts down the executor,\n but does not wait for the executor to finish.\n\n The event loop must not be running.\n \"\"\"\n if self.is_running():\n raise RuntimeError(\"Cannot close a running event loop\")\n if self._closed:\n return\n if self._debug:\n logger.debug(\"Close %r\", self)\n self._closed = True\n self._ready.clear()\n self._scheduled.clear()\n executor = self._default_executor\n if executor is not None:\n self._default_executor = None\n executor.shutdown(wait=False)\n\n def is_closed(self):\n \"\"\"Returns True if the event loop was closed.\"\"\"\n return self._closed\n\n def __del__(self, _warn=warnings.warn):\n if not self.is_closed():\n _warn(f\"unclosed event loop {self!r}\", ResourceWarning, source=self)\n if not self.is_running():\n self.close()\n\n def is_running(self):\n \"\"\"Returns True if the event loop is running.\"\"\"\n return (self._thread_id is not None)\n\n def time(self):\n \"\"\"Return the time according to the event loop's clock.\n\n This is a float expressed in seconds since an epoch, but the\n epoch, precision, accuracy and drift are unspecified and may\n differ per event loop.\n \"\"\"\n return time.monotonic()\n\n def call_later(self, delay, callback, *args, context=None):\n \"\"\"Arrange for a callback to be called at a given time.\n\n Return a Handle: an opaque object with a cancel() method that\n can be used to cancel the call.\n\n The delay can be an int or float, expressed in seconds. It is\n always relative to the current time.\n\n Each callback will be called exactly once. If two callbacks\n are scheduled for exactly the same time, it undefined which\n will be called first.\n\n Any positional arguments after the callback will be passed to\n the callback when it is called.\n \"\"\"\n timer = self.call_at(self.time() + delay, callback, *args,\n context=context)\n if timer._source_traceback:\n del timer._source_traceback[-1]\n return timer\n\n def call_at(self, when, callback, *args, context=None):\n \"\"\"Like call_later(), but uses an absolute time.\n\n Absolute time corresponds to the event loop's time() method.\n \"\"\"\n self._check_closed()\n if self._debug:\n self._check_thread()\n self._check_callback(callback, 'call_at')\n timer = events.TimerHandle(when, callback, args, self, context)\n if timer._source_traceback:\n del timer._source_traceback[-1]\n heapq.heappush(self._scheduled, timer)\n timer._scheduled = True\n return timer\n\n def call_soon(self, callback, *args, context=None):\n \"\"\"Arrange for a callback to be called as soon as possible.\n\n This operates as a FIFO queue: callbacks are called in the\n order in which they are registered. Each callback will be\n called exactly once.\n\n Any positional arguments after the callback will be passed to\n the callback when it is called.\n \"\"\"\n self._check_closed()\n if self._debug:\n self._check_thread()\n self._check_callback(callback, 'call_soon')\n handle = self._call_soon(callback, args, context)\n if handle._source_traceback:\n del handle._source_traceback[-1]\n return handle\n\n def _check_callback(self, callback, method):\n if (coroutines.iscoroutine(callback) or\n coroutines.iscoroutinefunction(callback)):\n raise TypeError(\n f\"coroutines cannot be used with {method}()\")\n if not callable(callback):\n raise TypeError(\n f'a callable object was expected by {method}(), '\n f'got {callback!r}')\n\n def _call_soon(self, callback, args, context):\n handle = events.Handle(callback, args, self, context)\n if handle._source_traceback:\n del handle._source_traceback[-1]\n self._ready.append(handle)\n return handle\n\n def _check_thread(self):\n \"\"\"Check that the current thread is the thread running the event loop.\n\n Non-thread-safe methods of this class make this assumption and will\n likely behave incorrectly when the assumption is violated.\n\n Should only be called when (self._debug == True). The caller is\n responsible for checking this condition for performance reasons.\n \"\"\"\n if self._thread_id is None:\n return\n thread_id = threading.get_ident()\n if thread_id != self._thread_id:\n raise RuntimeError(\n \"Non-thread-safe operation invoked on an event loop other \"\n \"than the current one\")\n\n def call_soon_threadsafe(self, callback, *args, context=None):\n \"\"\"Like call_soon(), but thread-safe.\"\"\"\n self._check_closed()\n if self._debug:\n self._check_callback(callback, 'call_soon_threadsafe')\n handle = self._call_soon(callback, args, context)\n if handle._source_traceback:\n del handle._source_traceback[-1]\n self._write_to_self()\n return handle\n\n def run_in_executor(self, executor, func, *args):\n self._check_closed()\n if self._debug:\n self._check_callback(func, 'run_in_executor')\n if executor is None:\n executor = self._default_executor\n if executor is None:\n executor = concurrent.futures.ThreadPoolExecutor()\n self._default_executor = executor\n return futures.wrap_future(\n executor.submit(func, *args), loop=self)\n\n def set_default_executor(self, executor):\n if not isinstance(executor, concurrent.futures.ThreadPoolExecutor):\n warnings.warn(\n 'Using the default executor that is not an instance of '\n 'ThreadPoolExecutor is deprecated and will be prohibited '\n 'in Python 3.9',\n DeprecationWarning, 2)\n self._default_executor = executor\n\n def _getaddrinfo_debug(self, host, port, family, type, proto, flags):\n msg = [f\"{host}:{port!r}\"]\n if family:\n msg.append(f'family={family!r}')\n if type:\n msg.append(f'type={type!r}')\n if proto:\n msg.append(f'proto={proto!r}')\n if flags:\n msg.append(f'flags={flags!r}')\n msg = ', '.join(msg)\n logger.debug('Get address info %s', msg)\n\n t0 = self.time()\n addrinfo = socket.getaddrinfo(host, port, family, type, proto, flags)\n dt = self.time() - t0\n\n msg = f'Getting address info {msg} took {dt * 1e3:.3f}ms: {addrinfo!r}'\n if dt >= self.slow_callback_duration:\n logger.info(msg)\n else:\n logger.debug(msg)\n return addrinfo\n\n async def getaddrinfo(self, host, port, *,\n family=0, type=0, proto=0, flags=0):\n if self._debug:\n getaddr_func = self._getaddrinfo_debug\n else:\n getaddr_func = socket.getaddrinfo\n\n return await self.run_in_executor(\n None, getaddr_func, host, port, family, type, proto, flags)\n\n async def getnameinfo(self, sockaddr, flags=0):\n return await self.run_in_executor(\n None, socket.getnameinfo, sockaddr, flags)\n\n async def sock_sendfile(self, sock, file, offset=0, count=None,\n *, fallback=True):\n if self._debug and sock.gettimeout() != 0:\n raise ValueError(\"the socket must be non-blocking\")\n self._check_sendfile_params(sock, file, offset, count)\n try:\n return await self._sock_sendfile_native(sock, file,\n offset, count)\n except exceptions.SendfileNotAvailableError as exc:\n if not fallback:\n raise\n return await self._sock_sendfile_fallback(sock, file,\n offset, count)\n\n async def _sock_sendfile_native(self, sock, file, offset, count):\n # NB: sendfile syscall is not supported for SSL sockets and\n # non-mmap files even if sendfile is supported by OS\n raise exceptions.SendfileNotAvailableError(\n f\"syscall sendfile is not available for socket {sock!r} \"\n \"and file {file!r} combination\")\n\n async def _sock_sendfile_fallback(self, sock, file, offset, count):\n if offset:\n file.seek(offset)\n blocksize = (\n min(count, constants.SENDFILE_FALLBACK_READBUFFER_SIZE)\n if count else constants.SENDFILE_FALLBACK_READBUFFER_SIZE\n )\n buf = bytearray(blocksize)\n total_sent = 0\n try:\n while True:\n if count:\n blocksize = min(count - total_sent, blocksize)\n if blocksize <= 0:\n break\n view = memoryview(buf)[:blocksize]\n read = await self.run_in_executor(None, file.readinto, view)\n if not read:\n break # EOF\n await self.sock_sendall(sock, view[:read])\n total_sent += read\n return total_sent\n finally:\n if total_sent > 0 and hasattr(file, 'seek'):\n file.seek(offset + total_sent)\n\n def _check_sendfile_params(self, sock, file, offset, count):\n if 'b' not in getattr(file, 'mode', 'b'):\n raise ValueError(\"file should be opened in binary mode\")\n if not sock.type == socket.SOCK_STREAM:\n raise ValueError(\"only SOCK_STREAM type sockets are supported\")\n if count is not None:\n if not isinstance(count, int):\n raise TypeError(\n \"count must be a positive integer (got {!r})\".format(count))\n if count <= 0:\n raise ValueError(\n \"count must be a positive integer (got {!r})\".format(count))\n if not isinstance(offset, int):\n raise TypeError(\n \"offset must be a non-negative integer (got {!r})\".format(\n offset))\n if offset < 0:\n raise ValueError(\n \"offset must be a non-negative integer (got {!r})\".format(\n offset))\n\n async def _connect_sock(self, exceptions, addr_info, local_addr_infos=None):\n \"\"\"Create, bind and connect one socket.\"\"\"\n my_exceptions = []\n exceptions.append(my_exceptions)\n family, type_, proto, _, address = addr_info\n sock = None\n try:\n sock = socket.socket(family=family, type=type_, proto=proto)\n sock.setblocking(False)\n if local_addr_infos is not None:\n for _, _, _, _, laddr in local_addr_infos:\n try:\n sock.bind(laddr)\n break\n except OSError as exc:\n msg = (\n f'error while attempting to bind on '\n f'address {laddr!r}: '\n f'{exc.strerror.lower()}'\n )\n exc = OSError(exc.errno, msg)\n my_exceptions.append(exc)\n else: # all bind attempts failed\n raise my_exceptions.pop()\n await self.sock_connect(sock, address)\n return sock\n except OSError as exc:\n my_exceptions.append(exc)\n if sock is not None:\n sock.close()\n raise\n except:\n if sock is not None:\n sock.close()\n raise\n\n async def create_connection(\n self, protocol_factory, host=None, port=None,\n *, ssl=None, family=0,\n proto=0, flags=0, sock=None,\n local_addr=None, server_hostname=None,\n ssl_handshake_timeout=None,\n happy_eyeballs_delay=None, interleave=None):\n \"\"\"Connect to a TCP server.\n\n Create a streaming transport connection to a given Internet host and\n port: socket family AF_INET or socket.AF_INET6 depending on host (or\n family if specified), socket type SOCK_STREAM. protocol_factory must be\n a callable returning a protocol instance.\n\n This method is a coroutine which will try to establish the connection\n in the background. When successful, the coroutine returns a\n (transport, protocol) pair.\n \"\"\"\n if server_hostname is not None and not ssl:\n raise ValueError('server_hostname is only meaningful with ssl')\n\n if server_hostname is None and ssl:\n # Use host as default for server_hostname. It is an error\n # if host is empty or not set, e.g. when an\n # already-connected socket was passed or when only a port\n # is given. To avoid this error, you can pass\n # server_hostname='' -- this will bypass the hostname\n # check. (This also means that if host is a numeric\n # IP/IPv6 address, we will attempt to verify that exact\n # address; this will probably fail, but it is possible to\n # create a certificate for a specific IP address, so we\n # don't judge it here.)\n if not host:\n raise ValueError('You must set server_hostname '\n 'when using ssl without a host')\n server_hostname = host\n\n if ssl_handshake_timeout is not None and not ssl:\n raise ValueError(\n 'ssl_handshake_timeout is only meaningful with ssl')\n\n if happy_eyeballs_delay is not None and interleave is None:\n # If using happy eyeballs, default to interleave addresses by family\n interleave = 1\n\n if host is not None or port is not None:\n if sock is not None:\n raise ValueError(\n 'host/port and sock can not be specified at the same time')\n\n infos = await self._ensure_resolved(\n (host, port), family=family,\n type=socket.SOCK_STREAM, proto=proto, flags=flags, loop=self)\n if not infos:\n raise OSError('getaddrinfo() returned empty list')\n\n if local_addr is not None:\n laddr_infos = await self._ensure_resolved(\n local_addr, family=family,\n type=socket.SOCK_STREAM, proto=proto,\n flags=flags, loop=self)\n if not laddr_infos:\n raise OSError('getaddrinfo() returned empty list')\n else:\n laddr_infos = None\n\n if interleave:\n infos = _interleave_addrinfos(infos, interleave)\n\n exceptions = []\n if happy_eyeballs_delay is None:\n # not using happy eyeballs\n for addrinfo in infos:\n try:\n sock = await self._connect_sock(\n exceptions, addrinfo, laddr_infos)\n break\n except OSError:\n continue\n else: # using happy eyeballs\n sock, _, _ = await staggered.staggered_race(\n (functools.partial(self._connect_sock,\n exceptions, addrinfo, laddr_infos)\n for addrinfo in infos),\n happy_eyeballs_delay, loop=self)\n\n if sock is None:\n exceptions = [exc for sub in exceptions for exc in sub]\n if len(exceptions) == 1:\n raise exceptions[0]\n else:\n # If they all have the same str(), raise one.\n model = str(exceptions[0])\n if all(str(exc) == model for exc in exceptions):\n raise exceptions[0]\n # Raise a combined exception so the user can see all\n # the various error messages.\n raise OSError('Multiple exceptions: {}'.format(\n ', '.join(str(exc) for exc in exceptions)))\n\n else:\n if sock is None:\n raise ValueError(\n 'host and port was not specified and no sock specified')\n if sock.type != socket.SOCK_STREAM:\n # We allow AF_INET, AF_INET6, AF_UNIX as long as they\n # are SOCK_STREAM.\n # We support passing AF_UNIX sockets even though we have\n # a dedicated API for that: create_unix_connection.\n # Disallowing AF_UNIX in this method, breaks backwards\n # compatibility.\n raise ValueError(\n f'A Stream Socket was expected, got {sock!r}')\n\n transport, protocol = await self._create_connection_transport(\n sock, protocol_factory, ssl, server_hostname,\n ssl_handshake_timeout=ssl_handshake_timeout)\n if self._debug:\n # Get the socket from the transport because SSL transport closes\n # the old socket and creates a new SSL socket\n sock = transport.get_extra_info('socket')\n logger.debug(\"%r connected to %s:%r: (%r, %r)\",\n sock, host, port, transport, protocol)\n return transport, protocol\n\n async def _create_connection_transport(\n self, sock, protocol_factory, ssl,\n server_hostname, server_side=False,\n ssl_handshake_timeout=None):\n\n sock.setblocking(False)\n\n protocol = protocol_factory()\n waiter = self.create_future()\n if ssl:\n sslcontext = None if isinstance(ssl, bool) else ssl\n transport = self._make_ssl_transport(\n sock, protocol, sslcontext, waiter,\n server_side=server_side, server_hostname=server_hostname,\n ssl_handshake_timeout=ssl_handshake_timeout)\n else:\n transport = self._make_socket_transport(sock, protocol, waiter)\n\n try:\n await waiter\n except:\n transport.close()\n raise\n\n return transport, protocol\n\n async def sendfile(self, transport, file, offset=0, count=None,\n *, fallback=True):\n \"\"\"Send a file to transport.\n\n Return the total number of bytes which were sent.\n\n The method uses high-performance os.sendfile if available.\n\n file must be a regular file object opened in binary mode.\n\n offset tells from where to start reading the file. If specified,\n count is the total number of bytes to transmit as opposed to\n sending the file until EOF is reached. File position is updated on\n return or also in case of error in which case file.tell()\n can be used to figure out the number of bytes\n which were sent.\n\n fallback set to True makes asyncio to manually read and send\n the file when the platform does not support the sendfile syscall\n (e.g. Windows or SSL socket on Unix).\n\n Raise SendfileNotAvailableError if the system does not support\n sendfile syscall and fallback is False.\n \"\"\"\n if transport.is_closing():\n raise RuntimeError(\"Transport is closing\")\n mode = getattr(transport, '_sendfile_compatible',\n constants._SendfileMode.UNSUPPORTED)\n if mode is constants._SendfileMode.UNSUPPORTED:\n raise RuntimeError(\n f\"sendfile is not supported for transport {transport!r}\")\n if mode is constants._SendfileMode.TRY_NATIVE:\n try:\n return await self._sendfile_native(transport, file,\n offset, count)\n except exceptions.SendfileNotAvailableError as exc:\n if not fallback:\n raise\n\n if not fallback:\n raise RuntimeError(\n f\"fallback is disabled and native sendfile is not \"\n f\"supported for transport {transport!r}\")\n\n return await self._sendfile_fallback(transport, file,\n offset, count)\n\n async def _sendfile_native(self, transp, file, offset, count):\n raise exceptions.SendfileNotAvailableError(\n \"sendfile syscall is not supported\")\n\n async def _sendfile_fallback(self, transp, file, offset, count):\n if offset:\n file.seek(offset)\n blocksize = min(count, 16384) if count else 16384\n buf = bytearray(blocksize)\n total_sent = 0\n proto = _SendfileFallbackProtocol(transp)\n try:\n while True:\n if count:\n blocksize = min(count - total_sent, blocksize)\n if blocksize <= 0:\n return total_sent\n view = memoryview(buf)[:blocksize]\n read = await self.run_in_executor(None, file.readinto, view)\n if not read:\n return total_sent # EOF\n await proto.drain()\n transp.write(view[:read])\n total_sent += read\n finally:\n if total_sent > 0 and hasattr(file, 'seek'):\n file.seek(offset + total_sent)\n await proto.restore()\n\n async def start_tls(self, transport, protocol, sslcontext, *,\n server_side=False,\n server_hostname=None,\n ssl_handshake_timeout=None):\n \"\"\"Upgrade transport to TLS.\n\n Return a new transport that *protocol* should start using\n immediately.\n \"\"\"\n if ssl is None:\n raise RuntimeError('Python ssl module is not available')\n\n if not isinstance(sslcontext, ssl.SSLContext):\n raise TypeError(\n f'sslcontext is expected to be an instance of ssl.SSLContext, '\n f'got {sslcontext!r}')\n\n if not getattr(transport, '_start_tls_compatible', False):\n raise TypeError(\n f'transport {transport!r} is not supported by start_tls()')\n\n waiter = self.create_future()\n ssl_protocol = sslproto.SSLProtocol(\n self, protocol, sslcontext, waiter,\n server_side, server_hostname,\n ssl_handshake_timeout=ssl_handshake_timeout,\n call_connection_made=False)\n\n # Pause early so that \"ssl_protocol.data_received()\" doesn't\n # have a chance to get called before \"ssl_protocol.connection_made()\".\n transport.pause_reading()\n\n transport.set_protocol(ssl_protocol)\n conmade_cb = self.call_soon(ssl_protocol.connection_made, transport)\n resume_cb = self.call_soon(transport.resume_reading)\n\n try:\n await waiter\n except BaseException:\n transport.close()\n conmade_cb.cancel()\n resume_cb.cancel()\n raise\n\n return ssl_protocol._app_transport\n\n async def create_datagram_endpoint(self, protocol_factory,\n local_addr=None, remote_addr=None, *,\n family=0, proto=0, flags=0,\n reuse_address=_unset, reuse_port=None,\n allow_broadcast=None, sock=None):\n \"\"\"Create datagram connection.\"\"\"\n if sock is not None:\n if sock.type != socket.SOCK_DGRAM:\n raise ValueError(\n f'A UDP Socket was expected, got {sock!r}')\n if (local_addr or remote_addr or\n family or proto or flags or\n reuse_port or allow_broadcast):\n # show the problematic kwargs in exception msg\n opts = dict(local_addr=local_addr, remote_addr=remote_addr,\n family=family, proto=proto, flags=flags,\n reuse_address=reuse_address, reuse_port=reuse_port,\n allow_broadcast=allow_broadcast)\n problems = ', '.join(f'{k}={v}' for k, v in opts.items() if v)\n raise ValueError(\n f'socket modifier keyword arguments can not be used '\n f'when sock is specified. ({problems})')\n sock.setblocking(False)\n r_addr = None\n else:\n if not (local_addr or remote_addr):\n if family == 0:\n raise ValueError('unexpected address family')\n addr_pairs_info = (((family, proto), (None, None)),)\n elif hasattr(socket, 'AF_UNIX') and family == socket.AF_UNIX:\n for addr in (local_addr, remote_addr):\n if addr is not None and not isinstance(addr, str):\n raise TypeError('string is expected')\n\n if local_addr and local_addr[0] not in (0, '\\x00'):\n try:\n if stat.S_ISSOCK(os.stat(local_addr).st_mode):\n os.remove(local_addr)\n except FileNotFoundError:\n pass\n except OSError as err:\n # Directory may have permissions only to create socket.\n logger.error('Unable to check or remove stale UNIX '\n 'socket %r: %r',\n local_addr, err)\n\n addr_pairs_info = (((family, proto),\n (local_addr, remote_addr)), )\n else:\n # join address by (family, protocol)\n addr_infos = {} # Using order preserving dict\n for idx, addr in ((0, local_addr), (1, remote_addr)):\n if addr is not None:\n assert isinstance(addr, tuple) and len(addr) == 2, (\n '2-tuple is expected')\n\n infos = await self._ensure_resolved(\n addr, family=family, type=socket.SOCK_DGRAM,\n proto=proto, flags=flags, loop=self)\n if not infos:\n raise OSError('getaddrinfo() returned empty list')\n\n for fam, _, pro, _, address in infos:\n key = (fam, pro)\n if key not in addr_infos:\n addr_infos[key] = [None, None]\n addr_infos[key][idx] = address\n\n # each addr has to have info for each (family, proto) pair\n addr_pairs_info = [\n (key, addr_pair) for key, addr_pair in addr_infos.items()\n if not ((local_addr and addr_pair[0] is None) or\n (remote_addr and addr_pair[1] is None))]\n\n if not addr_pairs_info:\n raise ValueError('can not get address information')\n\n exceptions = []\n\n # bpo-37228\n if reuse_address is not _unset:\n if reuse_address:\n raise ValueError(\"Passing `reuse_address=True` is no \"\n \"longer supported, as the usage of \"\n \"SO_REUSEPORT in UDP poses a significant \"\n \"security concern.\")\n else:\n warnings.warn(\"The *reuse_address* parameter has been \"\n \"deprecated as of 3.5.10 and is scheduled \"\n \"for removal in 3.11.\", DeprecationWarning,\n stacklevel=2)\n\n for ((family, proto),\n (local_address, remote_address)) in addr_pairs_info:\n sock = None\n r_addr = None\n try:\n sock = socket.socket(\n family=family, type=socket.SOCK_DGRAM, proto=proto)\n if reuse_port:\n _set_reuseport(sock)\n if allow_broadcast:\n sock.setsockopt(\n socket.SOL_SOCKET, socket.SO_BROADCAST, 1)\n sock.setblocking(False)\n\n if local_addr:\n sock.bind(local_address)\n if remote_addr:\n if not allow_broadcast:\n await self.sock_connect(sock, remote_address)\n r_addr = remote_address\n except OSError as exc:\n if sock is not None:\n sock.close()\n exceptions.append(exc)\n except:\n if sock is not None:\n sock.close()\n raise\n else:\n break\n else:\n raise exceptions[0]\n\n protocol = protocol_factory()\n waiter = self.create_future()\n transport = self._make_datagram_transport(\n sock, protocol, r_addr, waiter)\n if self._debug:\n if local_addr:\n logger.info(\"Datagram endpoint local_addr=%r remote_addr=%r \"\n \"created: (%r, %r)\",\n local_addr, remote_addr, transport, protocol)\n else:\n logger.debug(\"Datagram endpoint remote_addr=%r created: \"\n \"(%r, %r)\",\n remote_addr, transport, protocol)\n\n try:\n await waiter\n except:\n transport.close()\n raise\n\n return transport, protocol\n\n async def _ensure_resolved(self, address, *,\n family=0, type=socket.SOCK_STREAM,\n proto=0, flags=0, loop):\n host, port = address[:2]\n info = _ipaddr_info(host, port, family, type, proto, *address[2:])\n if info is not None:\n # \"host\" is already a resolved IP.\n return [info]\n else:\n return await loop.getaddrinfo(host, port, family=family, type=type,\n proto=proto, flags=flags)\n\n async def _create_server_getaddrinfo(self, host, port, family, flags):\n infos = await self._ensure_resolved((host, port), family=family,\n type=socket.SOCK_STREAM,\n flags=flags, loop=self)\n if not infos:\n raise OSError(f'getaddrinfo({host!r}) returned empty list')\n return infos\n\n async def create_server(\n self, protocol_factory, host=None, port=None,\n *,\n family=socket.AF_UNSPEC,\n flags=socket.AI_PASSIVE,\n sock=None,\n backlog=100,\n ssl=None,\n reuse_address=None,\n reuse_port=None,\n ssl_handshake_timeout=None,\n start_serving=True):\n \"\"\"Create a TCP server.\n\n The host parameter can be a string, in that case the TCP server is\n bound to host and port.\n\n The host parameter can also be a sequence of strings and in that case\n the TCP server is bound to all hosts of the sequence. If a host\n appears multiple times (possibly indirectly e.g. when hostnames\n resolve to the same IP address), the server is only bound once to that\n host.\n\n Return a Server object which can be used to stop the service.\n\n This method is a coroutine.\n \"\"\"\n if isinstance(ssl, bool):\n raise TypeError('ssl argument must be an SSLContext or None')\n\n if ssl_handshake_timeout is not None and ssl is None:\n raise ValueError(\n 'ssl_handshake_timeout is only meaningful with ssl')\n\n if host is not None or port is not None:\n if sock is not None:\n raise ValueError(\n 'host/port and sock can not be specified at the same time')\n\n if reuse_address is None:\n reuse_address = os.name == 'posix' and sys.platform != 'cygwin'\n sockets = []\n if host == '':\n hosts = [None]\n elif (isinstance(host, str) or\n not isinstance(host, collections.abc.Iterable)):\n hosts = [host]\n else:\n hosts = host\n\n fs = [self._create_server_getaddrinfo(host, port, family=family,\n flags=flags)\n for host in hosts]\n infos = await tasks.gather(*fs, loop=self)\n infos = set(itertools.chain.from_iterable(infos))\n\n completed = False\n try:\n for res in infos:\n af, socktype, proto, canonname, sa = res\n try:\n sock = socket.socket(af, socktype, proto)\n except socket.error:\n # Assume it's a bad family/type/protocol combination.\n if self._debug:\n logger.warning('create_server() failed to create '\n 'socket.socket(%r, %r, %r)',\n af, socktype, proto, exc_info=True)\n continue\n sockets.append(sock)\n if reuse_address:\n sock.setsockopt(\n socket.SOL_SOCKET, socket.SO_REUSEADDR, True)\n if reuse_port:\n _set_reuseport(sock)\n # Disable IPv4/IPv6 dual stack support (enabled by\n # default on Linux) which makes a single socket\n # listen on both address families.\n if (_HAS_IPv6 and\n af == socket.AF_INET6 and\n hasattr(socket, 'IPPROTO_IPV6')):\n sock.setsockopt(socket.IPPROTO_IPV6,\n socket.IPV6_V6ONLY,\n True)\n try:\n sock.bind(sa)\n except OSError as err:\n raise OSError(err.errno, 'error while attempting '\n 'to bind on address %r: %s'\n % (sa, err.strerror.lower())) from None\n completed = True\n finally:\n if not completed:\n for sock in sockets:\n sock.close()\n else:\n if sock is None:\n raise ValueError('Neither host/port nor sock were specified')\n if sock.type != socket.SOCK_STREAM:\n raise ValueError(f'A Stream Socket was expected, got {sock!r}')\n sockets = [sock]\n\n for sock in sockets:\n sock.setblocking(False)\n\n server = Server(self, sockets, protocol_factory,\n ssl, backlog, ssl_handshake_timeout)\n if start_serving:\n server._start_serving()\n # Skip one loop iteration so that all 'loop.add_reader'\n # go through.\n await tasks.sleep(0, loop=self)\n\n if self._debug:\n logger.info(\"%r is serving\", server)\n return server\n\n async def connect_accepted_socket(\n self, protocol_factory, sock,\n *, ssl=None,\n ssl_handshake_timeout=None):\n \"\"\"Handle an accepted connection.\n\n This is used by servers that accept connections outside of\n asyncio but that use asyncio to handle connections.\n\n This method is a coroutine. When completed, the coroutine\n returns a (transport, protocol) pair.\n \"\"\"\n if sock.type != socket.SOCK_STREAM:\n raise ValueError(f'A Stream Socket was expected, got {sock!r}')\n\n if ssl_handshake_timeout is not None and not ssl:\n raise ValueError(\n 'ssl_handshake_timeout is only meaningful with ssl')\n\n transport, protocol = await self._create_connection_transport(\n sock, protocol_factory, ssl, '', server_side=True,\n ssl_handshake_timeout=ssl_handshake_timeout)\n if self._debug:\n # Get the socket from the transport because SSL transport closes\n # the old socket and creates a new SSL socket\n sock = transport.get_extra_info('socket')\n logger.debug(\"%r handled: (%r, %r)\", sock, transport, protocol)\n return transport, protocol\n\n async def connect_read_pipe(self, protocol_factory, pipe):\n protocol = protocol_factory()\n waiter = self.create_future()\n transport = self._make_read_pipe_transport(pipe, protocol, waiter)\n\n try:\n await waiter\n except:\n transport.close()\n raise\n\n if self._debug:\n logger.debug('Read pipe %r connected: (%r, %r)',\n pipe.fileno(), transport, protocol)\n return transport, protocol\n\n async def connect_write_pipe(self, protocol_factory, pipe):\n protocol = protocol_factory()\n waiter = self.create_future()\n transport = self._make_write_pipe_transport(pipe, protocol, waiter)\n\n try:\n await waiter\n except:\n transport.close()\n raise\n\n if self._debug:\n logger.debug('Write pipe %r connected: (%r, %r)',\n pipe.fileno(), transport, protocol)\n return transport, protocol\n\n def _log_subprocess(self, msg, stdin, stdout, stderr):\n info = [msg]\n if stdin is not None:\n info.append(f'stdin={_format_pipe(stdin)}')\n if stdout is not None and stderr == subprocess.STDOUT:\n info.append(f'stdout=stderr={_format_pipe(stdout)}')\n else:\n if stdout is not None:\n info.append(f'stdout={_format_pipe(stdout)}')\n if stderr is not None:\n info.append(f'stderr={_format_pipe(stderr)}')\n logger.debug(' '.join(info))\n\n async def subprocess_shell(self, protocol_factory, cmd, *,\n stdin=subprocess.PIPE,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n universal_newlines=False,\n shell=True, bufsize=0,\n encoding=None, errors=None, text=None,\n **kwargs):\n if not isinstance(cmd, (bytes, str)):\n raise ValueError(\"cmd must be a string\")\n if universal_newlines:\n raise ValueError(\"universal_newlines must be False\")\n if not shell:\n raise ValueError(\"shell must be True\")\n if bufsize != 0:\n raise ValueError(\"bufsize must be 0\")\n if text:\n raise ValueError(\"text must be False\")\n if encoding is not None:\n raise ValueError(\"encoding must be None\")\n if errors is not None:\n raise ValueError(\"errors must be None\")\n\n protocol = protocol_factory()\n debug_log = None\n if self._debug:\n # don't log parameters: they may contain sensitive information\n # (password) and may be too long\n debug_log = 'run shell command %r' % cmd\n self._log_subprocess(debug_log, stdin, stdout, stderr)\n transport = await self._make_subprocess_transport(\n protocol, cmd, True, stdin, stdout, stderr, bufsize, **kwargs)\n if self._debug and debug_log is not None:\n logger.info('%s: %r', debug_log, transport)\n return transport, protocol\n\n async def subprocess_exec(self, protocol_factory, program, *args,\n stdin=subprocess.PIPE, stdout=subprocess.PIPE,\n stderr=subprocess.PIPE, universal_newlines=False,\n shell=False, bufsize=0,\n encoding=None, errors=None, text=None,\n **kwargs):\n if universal_newlines:\n raise ValueError(\"universal_newlines must be False\")\n if shell:\n raise ValueError(\"shell must be False\")\n if bufsize != 0:\n raise ValueError(\"bufsize must be 0\")\n if text:\n raise ValueError(\"text must be False\")\n if encoding is not None:\n raise ValueError(\"encoding must be None\")\n if errors is not None:\n raise ValueError(\"errors must be None\")\n\n popen_args = (program,) + args\n protocol = protocol_factory()\n debug_log = None\n if self._debug:\n # don't log parameters: they may contain sensitive information\n # (password) and may be too long\n debug_log = f'execute program {program!r}'\n self._log_subprocess(debug_log, stdin, stdout, stderr)\n transport = await self._make_subprocess_transport(\n protocol, popen_args, False, stdin, stdout, stderr,\n bufsize, **kwargs)\n if self._debug and debug_log is not None:\n logger.info('%s: %r', debug_log, transport)\n return transport, protocol\n\n def get_exception_handler(self):\n \"\"\"Return an exception handler, or None if the default one is in use.\n \"\"\"\n return self._exception_handler\n\n def set_exception_handler(self, handler):\n \"\"\"Set handler as the new event loop exception handler.\n\n If handler is None, the default exception handler will\n be set.\n\n If handler is a callable object, it should have a\n signature matching '(loop, context)', where 'loop'\n will be a reference to the active event loop, 'context'\n will be a dict object (see `call_exception_handler()`\n documentation for details about context).\n \"\"\"\n if handler is not None and not callable(handler):\n raise TypeError(f'A callable object or None is expected, '\n f'got {handler!r}')\n self._exception_handler = handler\n\n def default_exception_handler(self, context):\n \"\"\"Default exception handler.\n\n This is called when an exception occurs and no exception\n handler is set, and can be called by a custom exception\n handler that wants to defer to the default behavior.\n\n This default handler logs the error message and other\n context-dependent information. In debug mode, a truncated\n stack trace is also appended showing where the given object\n (e.g. a handle or future or task) was created, if any.\n\n The context parameter has the same meaning as in\n `call_exception_handler()`.\n \"\"\"\n message = context.get('message')\n if not message:\n message = 'Unhandled exception in event loop'\n\n exception = context.get('exception')\n if exception is not None:\n exc_info = (type(exception), exception, exception.__traceback__)\n else:\n exc_info = False\n\n if ('source_traceback' not in context and\n self._current_handle is not None and\n self._current_handle._source_traceback):\n context['handle_traceback'] = \\\n self._current_handle._source_traceback\n\n log_lines = [message]\n for key in sorted(context):\n if key in {'message', 'exception'}:\n continue\n value = context[key]\n if key == 'source_traceback':\n tb = ''.join(traceback.format_list(value))\n value = 'Object created at (most recent call last):\\n'\n value += tb.rstrip()\n elif key == 'handle_traceback':\n tb = ''.join(traceback.format_list(value))\n value = 'Handle created at (most recent call last):\\n'\n value += tb.rstrip()\n else:\n value = repr(value)\n log_lines.append(f'{key}: {value}')\n\n logger.error('\\n'.join(log_lines), exc_info=exc_info)\n\n def call_exception_handler(self, context):\n \"\"\"Call the current event loop's exception handler.\n\n The context argument is a dict containing the following keys:\n\n - 'message': Error message;\n - 'exception' (optional): Exception object;\n - 'future' (optional): Future instance;\n - 'task' (optional): Task instance;\n - 'handle' (optional): Handle instance;\n - 'protocol' (optional): Protocol instance;\n - 'transport' (optional): Transport instance;\n - 'socket' (optional): Socket instance;\n - 'asyncgen' (optional): Asynchronous generator that caused\n the exception.\n\n New keys maybe introduced in the future.\n\n Note: do not overload this method in an event loop subclass.\n For custom exception handling, use the\n `set_exception_handler()` method.\n \"\"\"\n if self._exception_handler is None:\n try:\n self.default_exception_handler(context)\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException:\n # Second protection layer for unexpected errors\n # in the default implementation, as well as for subclassed\n # event loops with overloaded \"default_exception_handler\".\n logger.error('Exception in default exception handler',\n exc_info=True)\n else:\n try:\n self._exception_handler(self, context)\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n # Exception in the user set custom exception handler.\n try:\n # Let's try default handler.\n self.default_exception_handler({\n 'message': 'Unhandled error in exception handler',\n 'exception': exc,\n 'context': context,\n })\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException:\n # Guard 'default_exception_handler' in case it is\n # overloaded.\n logger.error('Exception in default exception handler '\n 'while handling an unexpected error '\n 'in custom exception handler',\n exc_info=True)\n\n def _add_callback(self, handle):\n \"\"\"Add a Handle to _scheduled (TimerHandle) or _ready.\"\"\"\n assert isinstance(handle, events.Handle), 'A Handle is required here'\n if handle._cancelled:\n return\n assert not isinstance(handle, events.TimerHandle)\n self._ready.append(handle)\n\n def _add_callback_signalsafe(self, handle):\n \"\"\"Like _add_callback() but called from a signal handler.\"\"\"\n self._add_callback(handle)\n self._write_to_self()\n\n def _timer_handle_cancelled(self, handle):\n \"\"\"Notification that a TimerHandle has been cancelled.\"\"\"\n if handle._scheduled:\n self._timer_cancelled_count += 1\n\n def _run_once(self):\n \"\"\"Run one full iteration of the event loop.\n\n This calls all currently ready callbacks, polls for I/O,\n schedules the resulting callbacks, and finally schedules\n 'call_later' callbacks.\n \"\"\"\n\n sched_count = len(self._scheduled)\n if (sched_count > _MIN_SCHEDULED_TIMER_HANDLES and\n self._timer_cancelled_count / sched_count >\n _MIN_CANCELLED_TIMER_HANDLES_FRACTION):\n # Remove delayed calls that were cancelled if their number\n # is too high\n new_scheduled = []\n for handle in self._scheduled:\n if handle._cancelled:\n handle._scheduled = False\n else:\n new_scheduled.append(handle)\n\n heapq.heapify(new_scheduled)\n self._scheduled = new_scheduled\n self._timer_cancelled_count = 0\n else:\n # Remove delayed calls that were cancelled from head of queue.\n while self._scheduled and self._scheduled[0]._cancelled:\n self._timer_cancelled_count -= 1\n handle = heapq.heappop(self._scheduled)\n handle._scheduled = False\n\n timeout = None\n if self._ready or self._stopping:\n timeout = 0\n elif self._scheduled:\n # Compute the desired timeout.\n when = self._scheduled[0]._when\n timeout = min(max(0, when - self.time()), MAXIMUM_SELECT_TIMEOUT)\n\n event_list = self._selector.select(timeout)\n self._process_events(event_list)\n\n # Handle 'later' callbacks that are ready.\n end_time = self.time() + self._clock_resolution\n while self._scheduled:\n handle = self._scheduled[0]\n if handle._when >= end_time:\n break\n handle = heapq.heappop(self._scheduled)\n handle._scheduled = False\n self._ready.append(handle)\n\n # This is the only place where callbacks are actually *called*.\n # All other places just add them to ready.\n # Note: We run all currently scheduled callbacks, but not any\n # callbacks scheduled by callbacks run this time around --\n # they will be run the next time (after another I/O poll).\n # Use an idiom that is thread-safe without using locks.\n ntodo = len(self._ready)\n for i in range(ntodo):\n handle = self._ready.popleft()\n if handle._cancelled:\n continue\n if self._debug:\n try:\n self._current_handle = handle\n t0 = self.time()\n handle._run()\n dt = self.time() - t0\n if dt >= self.slow_callback_duration:\n logger.warning('Executing %s took %.3f seconds',\n _format_handle(handle), dt)\n finally:\n self._current_handle = None\n else:\n handle._run()\n handle = None # Needed to break cycles when an exception occurs.\n\n def _set_coroutine_origin_tracking(self, enabled):\n if bool(enabled) == bool(self._coroutine_origin_tracking_enabled):\n return\n\n if enabled:\n self._coroutine_origin_tracking_saved_depth = (\n sys.get_coroutine_origin_tracking_depth())\n sys.set_coroutine_origin_tracking_depth(\n constants.DEBUG_STACK_DEPTH)\n else:\n sys.set_coroutine_origin_tracking_depth(\n self._coroutine_origin_tracking_saved_depth)\n\n self._coroutine_origin_tracking_enabled = enabled\n\n def get_debug(self):\n return self._debug\n\n def set_debug(self, enabled):\n self._debug = enabled\n\n if self.is_running():\n self.call_soon_threadsafe(self._set_coroutine_origin_tracking, enabled)\n", 1884], "/home/gaogaotiantian/programs/codesnap/venv3.8/lib/python3.8/_weakrefset.py": ["# Access WeakSet through the weakref module.\n# This code is separated-out because it is needed\n# by abc.py to load everything else at startup.\n\nfrom _weakref import ref\n\n__all__ = ['WeakSet']\n\n\nclass _IterationGuard:\n # This context manager registers itself in the current iterators of the\n # weak container, such as to delay all removals until the context manager\n # exits.\n # This technique should be relatively thread-safe (since sets are).\n\n def __init__(self, weakcontainer):\n # Don't create cycles\n self.weakcontainer = ref(weakcontainer)\n\n def __enter__(self):\n w = self.weakcontainer()\n if w is not None:\n w._iterating.add(self)\n return self\n\n def __exit__(self, e, t, b):\n w = self.weakcontainer()\n if w is not None:\n s = w._iterating\n s.remove(self)\n if not s:\n w._commit_removals()\n\n\nclass WeakSet:\n def __init__(self, data=None):\n self.data = set()\n def _remove(item, selfref=ref(self)):\n self = selfref()\n if self is not None:\n if self._iterating:\n self._pending_removals.append(item)\n else:\n self.data.discard(item)\n self._remove = _remove\n # A list of keys to be removed\n self._pending_removals = []\n self._iterating = set()\n if data is not None:\n self.update(data)\n\n def _commit_removals(self):\n l = self._pending_removals\n discard = self.data.discard\n while l:\n discard(l.pop())\n\n def __iter__(self):\n with _IterationGuard(self):\n for itemref in self.data:\n item = itemref()\n if item is not None:\n # Caveat: the iterator will keep a strong reference to\n # `item` until it is resumed or closed.\n yield item\n\n def __len__(self):\n return len(self.data) - len(self._pending_removals)\n\n def __contains__(self, item):\n try:\n wr = ref(item)\n except TypeError:\n return False\n return wr in self.data\n\n def __reduce__(self):\n return (self.__class__, (list(self),),\n getattr(self, '__dict__', None))\n\n def add(self, item):\n if self._pending_removals:\n self._commit_removals()\n self.data.add(ref(item, self._remove))\n\n def clear(self):\n if self._pending_removals:\n self._commit_removals()\n self.data.clear()\n\n def copy(self):\n return self.__class__(self)\n\n def pop(self):\n if self._pending_removals:\n self._commit_removals()\n while True:\n try:\n itemref = self.data.pop()\n except KeyError:\n raise KeyError('pop from empty WeakSet') from None\n item = itemref()\n if item is not None:\n return item\n\n def remove(self, item):\n if self._pending_removals:\n self._commit_removals()\n self.data.remove(ref(item))\n\n def discard(self, item):\n if self._pending_removals:\n self._commit_removals()\n self.data.discard(ref(item))\n\n def update(self, other):\n if self._pending_removals:\n self._commit_removals()\n for element in other:\n self.add(element)\n\n def __ior__(self, other):\n self.update(other)\n return self\n\n def difference(self, other):\n newset = self.copy()\n newset.difference_update(other)\n return newset\n __sub__ = difference\n\n def difference_update(self, other):\n self.__isub__(other)\n def __isub__(self, other):\n if self._pending_removals:\n self._commit_removals()\n if self is other:\n self.data.clear()\n else:\n self.data.difference_update(ref(item) for item in other)\n return self\n\n def intersection(self, other):\n return self.__class__(item for item in other if item in self)\n __and__ = intersection\n\n def intersection_update(self, other):\n self.__iand__(other)\n def __iand__(self, other):\n if self._pending_removals:\n self._commit_removals()\n self.data.intersection_update(ref(item) for item in other)\n return self\n\n def issubset(self, other):\n return self.data.issubset(ref(item) for item in other)\n __le__ = issubset\n\n def __lt__(self, other):\n return self.data < set(map(ref, other))\n\n def issuperset(self, other):\n return self.data.issuperset(ref(item) for item in other)\n __ge__ = issuperset\n\n def __gt__(self, other):\n return self.data > set(map(ref, other))\n\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return NotImplemented\n return self.data == set(map(ref, other))\n\n def symmetric_difference(self, other):\n newset = self.copy()\n newset.symmetric_difference_update(other)\n return newset\n __xor__ = symmetric_difference\n\n def symmetric_difference_update(self, other):\n self.__ixor__(other)\n def __ixor__(self, other):\n if self._pending_removals:\n self._commit_removals()\n if self is other:\n self.data.clear()\n else:\n self.data.symmetric_difference_update(ref(item, self._remove) for item in other)\n return self\n\n def union(self, other):\n return self.__class__(e for s in (self, other) for e in s)\n __or__ = union\n\n def isdisjoint(self, other):\n return len(self.intersection(other)) == 0\n\n def __repr__(self):\n return repr(self.data)\n", 199], "/usr/lib/python3.8/selectors.py": ["\"\"\"Selectors module.\n\nThis module allows high-level and efficient I/O multiplexing, built upon the\n`select` module primitives.\n\"\"\"\n\n\nfrom abc import ABCMeta, abstractmethod\nfrom collections import namedtuple\nfrom collections.abc import Mapping\nimport math\nimport select\nimport sys\n\n\n# generic events, that must be mapped to implementation-specific ones\nEVENT_READ = (1 << 0)\nEVENT_WRITE = (1 << 1)\n\n\ndef _fileobj_to_fd(fileobj):\n \"\"\"Return a file descriptor from a file object.\n\n Parameters:\n fileobj -- file object or file descriptor\n\n Returns:\n corresponding file descriptor\n\n Raises:\n ValueError if the object is invalid\n \"\"\"\n if isinstance(fileobj, int):\n fd = fileobj\n else:\n try:\n fd = int(fileobj.fileno())\n except (AttributeError, TypeError, ValueError):\n raise ValueError(\"Invalid file object: \"\n \"{!r}\".format(fileobj)) from None\n if fd < 0:\n raise ValueError(\"Invalid file descriptor: {}\".format(fd))\n return fd\n\n\nSelectorKey = namedtuple('SelectorKey', ['fileobj', 'fd', 'events', 'data'])\n\nSelectorKey.__doc__ = \"\"\"SelectorKey(fileobj, fd, events, data)\n\n Object used to associate a file object to its backing\n file descriptor, selected event mask, and attached data.\n\"\"\"\nif sys.version_info >= (3, 5):\n SelectorKey.fileobj.__doc__ = 'File object registered.'\n SelectorKey.fd.__doc__ = 'Underlying file descriptor.'\n SelectorKey.events.__doc__ = 'Events that must be waited for on this file object.'\n SelectorKey.data.__doc__ = ('''Optional opaque data associated to this file object.\n For example, this could be used to store a per-client session ID.''')\n\nclass _SelectorMapping(Mapping):\n \"\"\"Mapping of file objects to selector keys.\"\"\"\n\n def __init__(self, selector):\n self._selector = selector\n\n def __len__(self):\n return len(self._selector._fd_to_key)\n\n def __getitem__(self, fileobj):\n try:\n fd = self._selector._fileobj_lookup(fileobj)\n return self._selector._fd_to_key[fd]\n except KeyError:\n raise KeyError(\"{!r} is not registered\".format(fileobj)) from None\n\n def __iter__(self):\n return iter(self._selector._fd_to_key)\n\n\nclass BaseSelector(metaclass=ABCMeta):\n \"\"\"Selector abstract base class.\n\n A selector supports registering file objects to be monitored for specific\n I/O events.\n\n A file object is a file descriptor or any object with a `fileno()` method.\n An arbitrary object can be attached to the file object, which can be used\n for example to store context information, a callback, etc.\n\n A selector can use various implementations (select(), poll(), epoll()...)\n depending on the platform. The default `Selector` class uses the most\n efficient implementation on the current platform.\n \"\"\"\n\n @abstractmethod\n def register(self, fileobj, events, data=None):\n \"\"\"Register a file object.\n\n Parameters:\n fileobj -- file object or file descriptor\n events -- events to monitor (bitwise mask of EVENT_READ|EVENT_WRITE)\n data -- attached data\n\n Returns:\n SelectorKey instance\n\n Raises:\n ValueError if events is invalid\n KeyError if fileobj is already registered\n OSError if fileobj is closed or otherwise is unacceptable to\n the underlying system call (if a system call is made)\n\n Note:\n OSError may or may not be raised\n \"\"\"\n raise NotImplementedError\n\n @abstractmethod\n def unregister(self, fileobj):\n \"\"\"Unregister a file object.\n\n Parameters:\n fileobj -- file object or file descriptor\n\n Returns:\n SelectorKey instance\n\n Raises:\n KeyError if fileobj is not registered\n\n Note:\n If fileobj is registered but has since been closed this does\n *not* raise OSError (even if the wrapped syscall does)\n \"\"\"\n raise NotImplementedError\n\n def modify(self, fileobj, events, data=None):\n \"\"\"Change a registered file object monitored events or attached data.\n\n Parameters:\n fileobj -- file object or file descriptor\n events -- events to monitor (bitwise mask of EVENT_READ|EVENT_WRITE)\n data -- attached data\n\n Returns:\n SelectorKey instance\n\n Raises:\n Anything that unregister() or register() raises\n \"\"\"\n self.unregister(fileobj)\n return self.register(fileobj, events, data)\n\n @abstractmethod\n def select(self, timeout=None):\n \"\"\"Perform the actual selection, until some monitored file objects are\n ready or a timeout expires.\n\n Parameters:\n timeout -- if timeout > 0, this specifies the maximum wait time, in\n seconds\n if timeout <= 0, the select() call won't block, and will\n report the currently ready file objects\n if timeout is None, select() will block until a monitored\n file object becomes ready\n\n Returns:\n list of (key, events) for ready file objects\n `events` is a bitwise mask of EVENT_READ|EVENT_WRITE\n \"\"\"\n raise NotImplementedError\n\n def close(self):\n \"\"\"Close the selector.\n\n This must be called to make sure that any underlying resource is freed.\n \"\"\"\n pass\n\n def get_key(self, fileobj):\n \"\"\"Return the key associated to a registered file object.\n\n Returns:\n SelectorKey for this file object\n \"\"\"\n mapping = self.get_map()\n if mapping is None:\n raise RuntimeError('Selector is closed')\n try:\n return mapping[fileobj]\n except KeyError:\n raise KeyError(\"{!r} is not registered\".format(fileobj)) from None\n\n @abstractmethod\n def get_map(self):\n \"\"\"Return a mapping of file objects to selector keys.\"\"\"\n raise NotImplementedError\n\n def __enter__(self):\n return self\n\n def __exit__(self, *args):\n self.close()\n\n\nclass _BaseSelectorImpl(BaseSelector):\n \"\"\"Base selector implementation.\"\"\"\n\n def __init__(self):\n # this maps file descriptors to keys\n self._fd_to_key = {}\n # read-only mapping returned by get_map()\n self._map = _SelectorMapping(self)\n\n def _fileobj_lookup(self, fileobj):\n \"\"\"Return a file descriptor from a file object.\n\n This wraps _fileobj_to_fd() to do an exhaustive search in case\n the object is invalid but we still have it in our map. This\n is used by unregister() so we can unregister an object that\n was previously registered even if it is closed. It is also\n used by _SelectorMapping.\n \"\"\"\n try:\n return _fileobj_to_fd(fileobj)\n except ValueError:\n # Do an exhaustive search.\n for key in self._fd_to_key.values():\n if key.fileobj is fileobj:\n return key.fd\n # Raise ValueError after all.\n raise\n\n def register(self, fileobj, events, data=None):\n if (not events) or (events & ~(EVENT_READ | EVENT_WRITE)):\n raise ValueError(\"Invalid events: {!r}\".format(events))\n\n key = SelectorKey(fileobj, self._fileobj_lookup(fileobj), events, data)\n\n if key.fd in self._fd_to_key:\n raise KeyError(\"{!r} (FD {}) is already registered\"\n .format(fileobj, key.fd))\n\n self._fd_to_key[key.fd] = key\n return key\n\n def unregister(self, fileobj):\n try:\n key = self._fd_to_key.pop(self._fileobj_lookup(fileobj))\n except KeyError:\n raise KeyError(\"{!r} is not registered\".format(fileobj)) from None\n return key\n\n def modify(self, fileobj, events, data=None):\n try:\n key = self._fd_to_key[self._fileobj_lookup(fileobj)]\n except KeyError:\n raise KeyError(\"{!r} is not registered\".format(fileobj)) from None\n if events != key.events:\n self.unregister(fileobj)\n key = self.register(fileobj, events, data)\n elif data != key.data:\n # Use a shortcut to update the data.\n key = key._replace(data=data)\n self._fd_to_key[key.fd] = key\n return key\n\n def close(self):\n self._fd_to_key.clear()\n self._map = None\n\n def get_map(self):\n return self._map\n\n def _key_from_fd(self, fd):\n \"\"\"Return the key associated to a given file descriptor.\n\n Parameters:\n fd -- file descriptor\n\n Returns:\n corresponding key, or None if not found\n \"\"\"\n try:\n return self._fd_to_key[fd]\n except KeyError:\n return None\n\n\nclass SelectSelector(_BaseSelectorImpl):\n \"\"\"Select-based selector.\"\"\"\n\n def __init__(self):\n super().__init__()\n self._readers = set()\n self._writers = set()\n\n def register(self, fileobj, events, data=None):\n key = super().register(fileobj, events, data)\n if events & EVENT_READ:\n self._readers.add(key.fd)\n if events & EVENT_WRITE:\n self._writers.add(key.fd)\n return key\n\n def unregister(self, fileobj):\n key = super().unregister(fileobj)\n self._readers.discard(key.fd)\n self._writers.discard(key.fd)\n return key\n\n if sys.platform == 'win32':\n def _select(self, r, w, _, timeout=None):\n r, w, x = select.select(r, w, w, timeout)\n return r, w + x, []\n else:\n _select = select.select\n\n def select(self, timeout=None):\n timeout = None if timeout is None else max(timeout, 0)\n ready = []\n try:\n r, w, _ = self._select(self._readers, self._writers, [], timeout)\n except InterruptedError:\n return ready\n r = set(r)\n w = set(w)\n for fd in r | w:\n events = 0\n if fd in r:\n events |= EVENT_READ\n if fd in w:\n events |= EVENT_WRITE\n\n key = self._key_from_fd(fd)\n if key:\n ready.append((key, events & key.events))\n return ready\n\n\nclass _PollLikeSelector(_BaseSelectorImpl):\n \"\"\"Base class shared between poll, epoll and devpoll selectors.\"\"\"\n _selector_cls = None\n _EVENT_READ = None\n _EVENT_WRITE = None\n\n def __init__(self):\n super().__init__()\n self._selector = self._selector_cls()\n\n def register(self, fileobj, events, data=None):\n key = super().register(fileobj, events, data)\n poller_events = 0\n if events & EVENT_READ:\n poller_events |= self._EVENT_READ\n if events & EVENT_WRITE:\n poller_events |= self._EVENT_WRITE\n try:\n self._selector.register(key.fd, poller_events)\n except:\n super().unregister(fileobj)\n raise\n return key\n\n def unregister(self, fileobj):\n key = super().unregister(fileobj)\n try:\n self._selector.unregister(key.fd)\n except OSError:\n # This can happen if the FD was closed since it\n # was registered.\n pass\n return key\n\n def modify(self, fileobj, events, data=None):\n try:\n key = self._fd_to_key[self._fileobj_lookup(fileobj)]\n except KeyError:\n raise KeyError(f\"{fileobj!r} is not registered\") from None\n\n changed = False\n if events != key.events:\n selector_events = 0\n if events & EVENT_READ:\n selector_events |= self._EVENT_READ\n if events & EVENT_WRITE:\n selector_events |= self._EVENT_WRITE\n try:\n self._selector.modify(key.fd, selector_events)\n except:\n super().unregister(fileobj)\n raise\n changed = True\n if data != key.data:\n changed = True\n\n if changed:\n key = key._replace(events=events, data=data)\n self._fd_to_key[key.fd] = key\n return key\n\n def select(self, timeout=None):\n # This is shared between poll() and epoll().\n # epoll() has a different signature and handling of timeout parameter.\n if timeout is None:\n timeout = None\n elif timeout <= 0:\n timeout = 0\n else:\n # poll() has a resolution of 1 millisecond, round away from\n # zero to wait *at least* timeout seconds.\n timeout = math.ceil(timeout * 1e3)\n ready = []\n try:\n fd_event_list = self._selector.poll(timeout)\n except InterruptedError:\n return ready\n for fd, event in fd_event_list:\n events = 0\n if event & ~self._EVENT_READ:\n events |= EVENT_WRITE\n if event & ~self._EVENT_WRITE:\n events |= EVENT_READ\n\n key = self._key_from_fd(fd)\n if key:\n ready.append((key, events & key.events))\n return ready\n\n\nif hasattr(select, 'poll'):\n\n class PollSelector(_PollLikeSelector):\n \"\"\"Poll-based selector.\"\"\"\n _selector_cls = select.poll\n _EVENT_READ = select.POLLIN\n _EVENT_WRITE = select.POLLOUT\n\n\nif hasattr(select, 'epoll'):\n\n class EpollSelector(_PollLikeSelector):\n \"\"\"Epoll-based selector.\"\"\"\n _selector_cls = select.epoll\n _EVENT_READ = select.EPOLLIN\n _EVENT_WRITE = select.EPOLLOUT\n\n def fileno(self):\n return self._selector.fileno()\n\n def select(self, timeout=None):\n if timeout is None:\n timeout = -1\n elif timeout <= 0:\n timeout = 0\n else:\n # epoll_wait() has a resolution of 1 millisecond, round away\n # from zero to wait *at least* timeout seconds.\n timeout = math.ceil(timeout * 1e3) * 1e-3\n\n # epoll_wait() expects `maxevents` to be greater than zero;\n # we want to make sure that `select()` can be called when no\n # FD is registered.\n max_ev = max(len(self._fd_to_key), 1)\n\n ready = []\n try:\n fd_event_list = self._selector.poll(timeout, max_ev)\n except InterruptedError:\n return ready\n for fd, event in fd_event_list:\n events = 0\n if event & ~select.EPOLLIN:\n events |= EVENT_WRITE\n if event & ~select.EPOLLOUT:\n events |= EVENT_READ\n\n key = self._key_from_fd(fd)\n if key:\n ready.append((key, events & key.events))\n return ready\n\n def close(self):\n self._selector.close()\n super().close()\n\n\nif hasattr(select, 'devpoll'):\n\n class DevpollSelector(_PollLikeSelector):\n \"\"\"Solaris /dev/poll selector.\"\"\"\n _selector_cls = select.devpoll\n _EVENT_READ = select.POLLIN\n _EVENT_WRITE = select.POLLOUT\n\n def fileno(self):\n return self._selector.fileno()\n\n def close(self):\n self._selector.close()\n super().close()\n\n\nif hasattr(select, 'kqueue'):\n\n class KqueueSelector(_BaseSelectorImpl):\n \"\"\"Kqueue-based selector.\"\"\"\n\n def __init__(self):\n super().__init__()\n self._selector = select.kqueue()\n\n def fileno(self):\n return self._selector.fileno()\n\n def register(self, fileobj, events, data=None):\n key = super().register(fileobj, events, data)\n try:\n if events & EVENT_READ:\n kev = select.kevent(key.fd, select.KQ_FILTER_READ,\n select.KQ_EV_ADD)\n self._selector.control([kev], 0, 0)\n if events & EVENT_WRITE:\n kev = select.kevent(key.fd, select.KQ_FILTER_WRITE,\n select.KQ_EV_ADD)\n self._selector.control([kev], 0, 0)\n except:\n super().unregister(fileobj)\n raise\n return key\n\n def unregister(self, fileobj):\n key = super().unregister(fileobj)\n if key.events & EVENT_READ:\n kev = select.kevent(key.fd, select.KQ_FILTER_READ,\n select.KQ_EV_DELETE)\n try:\n self._selector.control([kev], 0, 0)\n except OSError:\n # This can happen if the FD was closed since it\n # was registered.\n pass\n if key.events & EVENT_WRITE:\n kev = select.kevent(key.fd, select.KQ_FILTER_WRITE,\n select.KQ_EV_DELETE)\n try:\n self._selector.control([kev], 0, 0)\n except OSError:\n # See comment above.\n pass\n return key\n\n def select(self, timeout=None):\n timeout = None if timeout is None else max(timeout, 0)\n max_ev = len(self._fd_to_key)\n ready = []\n try:\n kev_list = self._selector.control(None, max_ev, timeout)\n except InterruptedError:\n return ready\n for kev in kev_list:\n fd = kev.ident\n flag = kev.filter\n events = 0\n if flag == select.KQ_FILTER_READ:\n events |= EVENT_READ\n if flag == select.KQ_FILTER_WRITE:\n events |= EVENT_WRITE\n\n key = self._key_from_fd(fd)\n if key:\n ready.append((key, events & key.events))\n return ready\n\n def close(self):\n self._selector.close()\n super().close()\n\n\n# Choose the best implementation, roughly:\n# epoll|kqueue|devpoll > poll > select.\n# select() also can't accept a FD > FD_SETSIZE (usually around 1024)\nif 'KqueueSelector' in globals():\n DefaultSelector = KqueueSelector\nelif 'EpollSelector' in globals():\n DefaultSelector = EpollSelector\nelif 'DevpollSelector' in globals():\n DefaultSelector = DevpollSelector\nelif 'PollSelector' in globals():\n DefaultSelector = PollSelector\nelse:\n DefaultSelector = SelectSelector\n", 592], "/usr/lib/python3.8/logging/__init__.py": ["# Copyright 2001-2017 by Vinay Sajip. All Rights Reserved.\n#\n# Permission to use, copy, modify, and distribute this software and its\n# documentation for any purpose and without fee is hereby granted,\n# provided that the above copyright notice appear in all copies and that\n# both that copyright notice and this permission notice appear in\n# supporting documentation, and that the name of Vinay Sajip\n# not be used in advertising or publicity pertaining to distribution\n# of the software without specific, written prior permission.\n# VINAY SAJIP DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING\n# ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL\n# VINAY SAJIP BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR\n# ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER\n# IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT\n# OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.\n\n\"\"\"\nLogging package for Python. Based on PEP 282 and comments thereto in\ncomp.lang.python.\n\nCopyright (C) 2001-2017 Vinay Sajip. All Rights Reserved.\n\nTo use, simply 'import logging' and log away!\n\"\"\"\n\nimport sys, os, time, io, re, traceback, warnings, weakref, collections.abc\n\nfrom string import Template\nfrom string import Formatter as StrFormatter\n\n\n__all__ = ['BASIC_FORMAT', 'BufferingFormatter', 'CRITICAL', 'DEBUG', 'ERROR',\n 'FATAL', 'FileHandler', 'Filter', 'Formatter', 'Handler', 'INFO',\n 'LogRecord', 'Logger', 'LoggerAdapter', 'NOTSET', 'NullHandler',\n 'StreamHandler', 'WARN', 'WARNING', 'addLevelName', 'basicConfig',\n 'captureWarnings', 'critical', 'debug', 'disable', 'error',\n 'exception', 'fatal', 'getLevelName', 'getLogger', 'getLoggerClass',\n 'info', 'log', 'makeLogRecord', 'setLoggerClass', 'shutdown',\n 'warn', 'warning', 'getLogRecordFactory', 'setLogRecordFactory',\n 'lastResort', 'raiseExceptions']\n\nimport threading\n\n__author__ = \"Vinay Sajip \"\n__status__ = \"production\"\n# The following module attributes are no longer updated.\n__version__ = \"0.5.1.2\"\n__date__ = \"07 February 2010\"\n\n#---------------------------------------------------------------------------\n# Miscellaneous module data\n#---------------------------------------------------------------------------\n\n#\n#_startTime is used as the base when calculating the relative time of events\n#\n_startTime = time.time()\n\n#\n#raiseExceptions is used to see if exceptions during handling should be\n#propagated\n#\nraiseExceptions = True\n\n#\n# If you don't want threading information in the log, set this to zero\n#\nlogThreads = True\n\n#\n# If you don't want multiprocessing information in the log, set this to zero\n#\nlogMultiprocessing = True\n\n#\n# If you don't want process information in the log, set this to zero\n#\nlogProcesses = True\n\n#---------------------------------------------------------------------------\n# Level related stuff\n#---------------------------------------------------------------------------\n#\n# Default levels and level names, these can be replaced with any positive set\n# of values having corresponding names. There is a pseudo-level, NOTSET, which\n# is only really there as a lower limit for user-defined levels. Handlers and\n# loggers are initialized with NOTSET so that they will log all messages, even\n# at user-defined levels.\n#\n\nCRITICAL = 50\nFATAL = CRITICAL\nERROR = 40\nWARNING = 30\nWARN = WARNING\nINFO = 20\nDEBUG = 10\nNOTSET = 0\n\n_levelToName = {\n CRITICAL: 'CRITICAL',\n ERROR: 'ERROR',\n WARNING: 'WARNING',\n INFO: 'INFO',\n DEBUG: 'DEBUG',\n NOTSET: 'NOTSET',\n}\n_nameToLevel = {\n 'CRITICAL': CRITICAL,\n 'FATAL': FATAL,\n 'ERROR': ERROR,\n 'WARN': WARNING,\n 'WARNING': WARNING,\n 'INFO': INFO,\n 'DEBUG': DEBUG,\n 'NOTSET': NOTSET,\n}\n\ndef getLevelName(level):\n \"\"\"\n Return the textual representation of logging level 'level'.\n\n If the level is one of the predefined levels (CRITICAL, ERROR, WARNING,\n INFO, DEBUG) then you get the corresponding string. If you have\n associated levels with names using addLevelName then the name you have\n associated with 'level' is returned.\n\n If a numeric value corresponding to one of the defined levels is passed\n in, the corresponding string representation is returned.\n\n Otherwise, the string \"Level %s\" % level is returned.\n \"\"\"\n # See Issues #22386, #27937 and #29220 for why it's this way\n result = _levelToName.get(level)\n if result is not None:\n return result\n result = _nameToLevel.get(level)\n if result is not None:\n return result\n return \"Level %s\" % level\n\ndef addLevelName(level, levelName):\n \"\"\"\n Associate 'levelName' with 'level'.\n\n This is used when converting levels to text during message formatting.\n \"\"\"\n _acquireLock()\n try: #unlikely to cause an exception, but you never know...\n _levelToName[level] = levelName\n _nameToLevel[levelName] = level\n finally:\n _releaseLock()\n\nif hasattr(sys, '_getframe'):\n currentframe = lambda: sys._getframe(3)\nelse: #pragma: no cover\n def currentframe():\n \"\"\"Return the frame object for the caller's stack frame.\"\"\"\n try:\n raise Exception\n except Exception:\n return sys.exc_info()[2].tb_frame.f_back\n\n#\n# _srcfile is used when walking the stack to check when we've got the first\n# caller stack frame, by skipping frames whose filename is that of this\n# module's source. It therefore should contain the filename of this module's\n# source file.\n#\n# Ordinarily we would use __file__ for this, but frozen modules don't always\n# have __file__ set, for some reason (see Issue #21736). Thus, we get the\n# filename from a handy code object from a function defined in this module.\n# (There's no particular reason for picking addLevelName.)\n#\n\n_srcfile = os.path.normcase(addLevelName.__code__.co_filename)\n\n# _srcfile is only used in conjunction with sys._getframe().\n# To provide compatibility with older versions of Python, set _srcfile\n# to None if _getframe() is not available; this value will prevent\n# findCaller() from being called. You can also do this if you want to avoid\n# the overhead of fetching caller information, even when _getframe() is\n# available.\n#if not hasattr(sys, '_getframe'):\n# _srcfile = None\n\n\ndef _checkLevel(level):\n if isinstance(level, int):\n rv = level\n elif str(level) == level:\n if level not in _nameToLevel:\n raise ValueError(\"Unknown level: %r\" % level)\n rv = _nameToLevel[level]\n else:\n raise TypeError(\"Level not an integer or a valid string: %r\" % level)\n return rv\n\n#---------------------------------------------------------------------------\n# Thread-related stuff\n#---------------------------------------------------------------------------\n\n#\n#_lock is used to serialize access to shared data structures in this module.\n#This needs to be an RLock because fileConfig() creates and configures\n#Handlers, and so might arbitrary user threads. Since Handler code updates the\n#shared dictionary _handlers, it needs to acquire the lock. But if configuring,\n#the lock would already have been acquired - so we need an RLock.\n#The same argument applies to Loggers and Manager.loggerDict.\n#\n_lock = threading.RLock()\n\ndef _acquireLock():\n \"\"\"\n Acquire the module-level lock for serializing access to shared data.\n\n This should be released with _releaseLock().\n \"\"\"\n if _lock:\n _lock.acquire()\n\ndef _releaseLock():\n \"\"\"\n Release the module-level lock acquired by calling _acquireLock().\n \"\"\"\n if _lock:\n _lock.release()\n\n\n# Prevent a held logging lock from blocking a child from logging.\n\nif not hasattr(os, 'register_at_fork'): # Windows and friends.\n def _register_at_fork_reinit_lock(instance):\n pass # no-op when os.register_at_fork does not exist.\nelse:\n # A collection of instances with a createLock method (logging.Handler)\n # to be called in the child after forking. The weakref avoids us keeping\n # discarded Handler instances alive. A set is used to avoid accumulating\n # duplicate registrations as createLock() is responsible for registering\n # a new Handler instance with this set in the first place.\n _at_fork_reinit_lock_weakset = weakref.WeakSet()\n\n def _register_at_fork_reinit_lock(instance):\n _acquireLock()\n try:\n _at_fork_reinit_lock_weakset.add(instance)\n finally:\n _releaseLock()\n\n def _after_at_fork_child_reinit_locks():\n # _acquireLock() was called in the parent before forking.\n for handler in _at_fork_reinit_lock_weakset:\n try:\n handler.createLock()\n except Exception as err:\n # Similar to what PyErr_WriteUnraisable does.\n print(\"Ignoring exception from logging atfork\", instance,\n \"._reinit_lock() method:\", err, file=sys.stderr)\n _releaseLock() # Acquired by os.register_at_fork(before=.\n\n\n os.register_at_fork(before=_acquireLock,\n after_in_child=_after_at_fork_child_reinit_locks,\n after_in_parent=_releaseLock)\n\n\n#---------------------------------------------------------------------------\n# The logging record\n#---------------------------------------------------------------------------\n\nclass LogRecord(object):\n \"\"\"\n A LogRecord instance represents an event being logged.\n\n LogRecord instances are created every time something is logged. They\n contain all the information pertinent to the event being logged. The\n main information passed in is in msg and args, which are combined\n using str(msg) % args to create the message field of the record. The\n record also includes information such as when the record was created,\n the source line where the logging call was made, and any exception\n information to be logged.\n \"\"\"\n def __init__(self, name, level, pathname, lineno,\n msg, args, exc_info, func=None, sinfo=None, **kwargs):\n \"\"\"\n Initialize a logging record with interesting information.\n \"\"\"\n ct = time.time()\n self.name = name\n self.msg = msg\n #\n # The following statement allows passing of a dictionary as a sole\n # argument, so that you can do something like\n # logging.debug(\"a %(a)d b %(b)s\", {'a':1, 'b':2})\n # Suggested by Stefan Behnel.\n # Note that without the test for args[0], we get a problem because\n # during formatting, we test to see if the arg is present using\n # 'if self.args:'. If the event being logged is e.g. 'Value is %d'\n # and if the passed arg fails 'if self.args:' then no formatting\n # is done. For example, logger.warning('Value is %d', 0) would log\n # 'Value is %d' instead of 'Value is 0'.\n # For the use case of passing a dictionary, this should not be a\n # problem.\n # Issue #21172: a request was made to relax the isinstance check\n # to hasattr(args[0], '__getitem__'). However, the docs on string\n # formatting still seem to suggest a mapping object is required.\n # Thus, while not removing the isinstance check, it does now look\n # for collections.abc.Mapping rather than, as before, dict.\n if (args and len(args) == 1 and isinstance(args[0], collections.abc.Mapping)\n and args[0]):\n args = args[0]\n self.args = args\n self.levelname = getLevelName(level)\n self.levelno = level\n self.pathname = pathname\n try:\n self.filename = os.path.basename(pathname)\n self.module = os.path.splitext(self.filename)[0]\n except (TypeError, ValueError, AttributeError):\n self.filename = pathname\n self.module = \"Unknown module\"\n self.exc_info = exc_info\n self.exc_text = None # used to cache the traceback text\n self.stack_info = sinfo\n self.lineno = lineno\n self.funcName = func\n self.created = ct\n self.msecs = (ct - int(ct)) * 1000\n self.relativeCreated = (self.created - _startTime) * 1000\n if logThreads:\n self.thread = threading.get_ident()\n self.threadName = threading.current_thread().name\n else: # pragma: no cover\n self.thread = None\n self.threadName = None\n if not logMultiprocessing: # pragma: no cover\n self.processName = None\n else:\n self.processName = 'MainProcess'\n mp = sys.modules.get('multiprocessing')\n if mp is not None:\n # Errors may occur if multiprocessing has not finished loading\n # yet - e.g. if a custom import hook causes third-party code\n # to run when multiprocessing calls import. See issue 8200\n # for an example\n try:\n self.processName = mp.current_process().name\n except Exception: #pragma: no cover\n pass\n if logProcesses and hasattr(os, 'getpid'):\n self.process = os.getpid()\n else:\n self.process = None\n\n def __repr__(self):\n return ''%(self.name, self.levelno,\n self.pathname, self.lineno, self.msg)\n\n def getMessage(self):\n \"\"\"\n Return the message for this LogRecord.\n\n Return the message for this LogRecord after merging any user-supplied\n arguments with the message.\n \"\"\"\n msg = str(self.msg)\n if self.args:\n msg = msg % self.args\n return msg\n\n#\n# Determine which class to use when instantiating log records.\n#\n_logRecordFactory = LogRecord\n\ndef setLogRecordFactory(factory):\n \"\"\"\n Set the factory to be used when instantiating a log record.\n\n :param factory: A callable which will be called to instantiate\n a log record.\n \"\"\"\n global _logRecordFactory\n _logRecordFactory = factory\n\ndef getLogRecordFactory():\n \"\"\"\n Return the factory to be used when instantiating a log record.\n \"\"\"\n\n return _logRecordFactory\n\ndef makeLogRecord(dict):\n \"\"\"\n Make a LogRecord whose attributes are defined by the specified dictionary,\n This function is useful for converting a logging event received over\n a socket connection (which is sent as a dictionary) into a LogRecord\n instance.\n \"\"\"\n rv = _logRecordFactory(None, None, \"\", 0, \"\", (), None, None)\n rv.__dict__.update(dict)\n return rv\n\n\n#---------------------------------------------------------------------------\n# Formatter classes and functions\n#---------------------------------------------------------------------------\n_str_formatter = StrFormatter()\ndel StrFormatter\n\n\nclass PercentStyle(object):\n\n default_format = '%(message)s'\n asctime_format = '%(asctime)s'\n asctime_search = '%(asctime)'\n validation_pattern = re.compile(r'%\\(\\w+\\)[#0+ -]*(\\*|\\d+)?(\\.(\\*|\\d+))?[diouxefgcrsa%]', re.I)\n\n def __init__(self, fmt):\n self._fmt = fmt or self.default_format\n\n def usesTime(self):\n return self._fmt.find(self.asctime_search) >= 0\n\n def validate(self):\n \"\"\"Validate the input format, ensure it matches the correct style\"\"\"\n if not self.validation_pattern.search(self._fmt):\n raise ValueError(\"Invalid format '%s' for '%s' style\" % (self._fmt, self.default_format[0]))\n\n def _format(self, record):\n return self._fmt % record.__dict__\n\n def format(self, record):\n try:\n return self._format(record)\n except KeyError as e:\n raise ValueError('Formatting field not found in record: %s' % e)\n\n\nclass StrFormatStyle(PercentStyle):\n default_format = '{message}'\n asctime_format = '{asctime}'\n asctime_search = '{asctime'\n\n fmt_spec = re.compile(r'^(.?[<>=^])?[+ -]?#?0?(\\d+|{\\w+})?[,_]?(\\.(\\d+|{\\w+}))?[bcdefgnosx%]?$', re.I)\n field_spec = re.compile(r'^(\\d+|\\w+)(\\.\\w+|\\[[^]]+\\])*$')\n\n def _format(self, record):\n return self._fmt.format(**record.__dict__)\n\n def validate(self):\n \"\"\"Validate the input format, ensure it is the correct string formatting style\"\"\"\n fields = set()\n try:\n for _, fieldname, spec, conversion in _str_formatter.parse(self._fmt):\n if fieldname:\n if not self.field_spec.match(fieldname):\n raise ValueError('invalid field name/expression: %r' % fieldname)\n fields.add(fieldname)\n if conversion and conversion not in 'rsa':\n raise ValueError('invalid conversion: %r' % conversion)\n if spec and not self.fmt_spec.match(spec):\n raise ValueError('bad specifier: %r' % spec)\n except ValueError as e:\n raise ValueError('invalid format: %s' % e)\n if not fields:\n raise ValueError('invalid format: no fields')\n\n\nclass StringTemplateStyle(PercentStyle):\n default_format = '${message}'\n asctime_format = '${asctime}'\n asctime_search = '${asctime}'\n\n def __init__(self, fmt):\n self._fmt = fmt or self.default_format\n self._tpl = Template(self._fmt)\n\n def usesTime(self):\n fmt = self._fmt\n return fmt.find('$asctime') >= 0 or fmt.find(self.asctime_format) >= 0\n\n def validate(self):\n pattern = Template.pattern\n fields = set()\n for m in pattern.finditer(self._fmt):\n d = m.groupdict()\n if d['named']:\n fields.add(d['named'])\n elif d['braced']:\n fields.add(d['braced'])\n elif m.group(0) == '$':\n raise ValueError('invalid format: bare \\'$\\' not allowed')\n if not fields:\n raise ValueError('invalid format: no fields')\n\n def _format(self, record):\n return self._tpl.substitute(**record.__dict__)\n\n\nBASIC_FORMAT = \"%(levelname)s:%(name)s:%(message)s\"\n\n_STYLES = {\n '%': (PercentStyle, BASIC_FORMAT),\n '{': (StrFormatStyle, '{levelname}:{name}:{message}'),\n '$': (StringTemplateStyle, '${levelname}:${name}:${message}'),\n}\n\nclass Formatter(object):\n \"\"\"\n Formatter instances are used to convert a LogRecord to text.\n\n Formatters need to know how a LogRecord is constructed. They are\n responsible for converting a LogRecord to (usually) a string which can\n be interpreted by either a human or an external system. The base Formatter\n allows a formatting string to be specified. If none is supplied, the\n the style-dependent default value, \"%(message)s\", \"{message}\", or\n \"${message}\", is used.\n\n The Formatter can be initialized with a format string which makes use of\n knowledge of the LogRecord attributes - e.g. the default value mentioned\n above makes use of the fact that the user's message and arguments are pre-\n formatted into a LogRecord's message attribute. Currently, the useful\n attributes in a LogRecord are described by:\n\n %(name)s Name of the logger (logging channel)\n %(levelno)s Numeric logging level for the message (DEBUG, INFO,\n WARNING, ERROR, CRITICAL)\n %(levelname)s Text logging level for the message (\"DEBUG\", \"INFO\",\n \"WARNING\", \"ERROR\", \"CRITICAL\")\n %(pathname)s Full pathname of the source file where the logging\n call was issued (if available)\n %(filename)s Filename portion of pathname\n %(module)s Module (name portion of filename)\n %(lineno)d Source line number where the logging call was issued\n (if available)\n %(funcName)s Function name\n %(created)f Time when the LogRecord was created (time.time()\n return value)\n %(asctime)s Textual time when the LogRecord was created\n %(msecs)d Millisecond portion of the creation time\n %(relativeCreated)d Time in milliseconds when the LogRecord was created,\n relative to the time the logging module was loaded\n (typically at application startup time)\n %(thread)d Thread ID (if available)\n %(threadName)s Thread name (if available)\n %(process)d Process ID (if available)\n %(message)s The result of record.getMessage(), computed just as\n the record is emitted\n \"\"\"\n\n converter = time.localtime\n\n def __init__(self, fmt=None, datefmt=None, style='%', validate=True):\n \"\"\"\n Initialize the formatter with specified format strings.\n\n Initialize the formatter either with the specified format string, or a\n default as described above. Allow for specialized date formatting with\n the optional datefmt argument. If datefmt is omitted, you get an\n ISO8601-like (or RFC 3339-like) format.\n\n Use a style parameter of '%', '{' or '$' to specify that you want to\n use one of %-formatting, :meth:`str.format` (``{}``) formatting or\n :class:`string.Template` formatting in your format string.\n\n .. versionchanged:: 3.2\n Added the ``style`` parameter.\n \"\"\"\n if style not in _STYLES:\n raise ValueError('Style must be one of: %s' % ','.join(\n _STYLES.keys()))\n self._style = _STYLES[style][0](fmt)\n if validate:\n self._style.validate()\n\n self._fmt = self._style._fmt\n self.datefmt = datefmt\n\n default_time_format = '%Y-%m-%d %H:%M:%S'\n default_msec_format = '%s,%03d'\n\n def formatTime(self, record, datefmt=None):\n \"\"\"\n Return the creation time of the specified LogRecord as formatted text.\n\n This method should be called from format() by a formatter which\n wants to make use of a formatted time. This method can be overridden\n in formatters to provide for any specific requirement, but the\n basic behaviour is as follows: if datefmt (a string) is specified,\n it is used with time.strftime() to format the creation time of the\n record. Otherwise, an ISO8601-like (or RFC 3339-like) format is used.\n The resulting string is returned. This function uses a user-configurable\n function to convert the creation time to a tuple. By default,\n time.localtime() is used; to change this for a particular formatter\n instance, set the 'converter' attribute to a function with the same\n signature as time.localtime() or time.gmtime(). To change it for all\n formatters, for example if you want all logging times to be shown in GMT,\n set the 'converter' attribute in the Formatter class.\n \"\"\"\n ct = self.converter(record.created)\n if datefmt:\n s = time.strftime(datefmt, ct)\n else:\n t = time.strftime(self.default_time_format, ct)\n s = self.default_msec_format % (t, record.msecs)\n return s\n\n def formatException(self, ei):\n \"\"\"\n Format and return the specified exception information as a string.\n\n This default implementation just uses\n traceback.print_exception()\n \"\"\"\n sio = io.StringIO()\n tb = ei[2]\n # See issues #9427, #1553375. Commented out for now.\n #if getattr(self, 'fullstack', False):\n # traceback.print_stack(tb.tb_frame.f_back, file=sio)\n traceback.print_exception(ei[0], ei[1], tb, None, sio)\n s = sio.getvalue()\n sio.close()\n if s[-1:] == \"\\n\":\n s = s[:-1]\n return s\n\n def usesTime(self):\n \"\"\"\n Check if the format uses the creation time of the record.\n \"\"\"\n return self._style.usesTime()\n\n def formatMessage(self, record):\n return self._style.format(record)\n\n def formatStack(self, stack_info):\n \"\"\"\n This method is provided as an extension point for specialized\n formatting of stack information.\n\n The input data is a string as returned from a call to\n :func:`traceback.print_stack`, but with the last trailing newline\n removed.\n\n The base implementation just returns the value passed in.\n \"\"\"\n return stack_info\n\n def format(self, record):\n \"\"\"\n Format the specified record as text.\n\n The record's attribute dictionary is used as the operand to a\n string formatting operation which yields the returned string.\n Before formatting the dictionary, a couple of preparatory steps\n are carried out. The message attribute of the record is computed\n using LogRecord.getMessage(). If the formatting string uses the\n time (as determined by a call to usesTime(), formatTime() is\n called to format the event time. If there is exception information,\n it is formatted using formatException() and appended to the message.\n \"\"\"\n record.message = record.getMessage()\n if self.usesTime():\n record.asctime = self.formatTime(record, self.datefmt)\n s = self.formatMessage(record)\n if record.exc_info:\n # Cache the traceback text to avoid converting it multiple times\n # (it's constant anyway)\n if not record.exc_text:\n record.exc_text = self.formatException(record.exc_info)\n if record.exc_text:\n if s[-1:] != \"\\n\":\n s = s + \"\\n\"\n s = s + record.exc_text\n if record.stack_info:\n if s[-1:] != \"\\n\":\n s = s + \"\\n\"\n s = s + self.formatStack(record.stack_info)\n return s\n\n#\n# The default formatter to use when no other is specified\n#\n_defaultFormatter = Formatter()\n\nclass BufferingFormatter(object):\n \"\"\"\n A formatter suitable for formatting a number of records.\n \"\"\"\n def __init__(self, linefmt=None):\n \"\"\"\n Optionally specify a formatter which will be used to format each\n individual record.\n \"\"\"\n if linefmt:\n self.linefmt = linefmt\n else:\n self.linefmt = _defaultFormatter\n\n def formatHeader(self, records):\n \"\"\"\n Return the header string for the specified records.\n \"\"\"\n return \"\"\n\n def formatFooter(self, records):\n \"\"\"\n Return the footer string for the specified records.\n \"\"\"\n return \"\"\n\n def format(self, records):\n \"\"\"\n Format the specified records and return the result as a string.\n \"\"\"\n rv = \"\"\n if len(records) > 0:\n rv = rv + self.formatHeader(records)\n for record in records:\n rv = rv + self.linefmt.format(record)\n rv = rv + self.formatFooter(records)\n return rv\n\n#---------------------------------------------------------------------------\n# Filter classes and functions\n#---------------------------------------------------------------------------\n\nclass Filter(object):\n \"\"\"\n Filter instances are used to perform arbitrary filtering of LogRecords.\n\n Loggers and Handlers can optionally use Filter instances to filter\n records as desired. The base filter class only allows events which are\n below a certain point in the logger hierarchy. For example, a filter\n initialized with \"A.B\" will allow events logged by loggers \"A.B\",\n \"A.B.C\", \"A.B.C.D\", \"A.B.D\" etc. but not \"A.BB\", \"B.A.B\" etc. If\n initialized with the empty string, all events are passed.\n \"\"\"\n def __init__(self, name=''):\n \"\"\"\n Initialize a filter.\n\n Initialize with the name of the logger which, together with its\n children, will have its events allowed through the filter. If no\n name is specified, allow every event.\n \"\"\"\n self.name = name\n self.nlen = len(name)\n\n def filter(self, record):\n \"\"\"\n Determine if the specified record is to be logged.\n\n Is the specified record to be logged? Returns 0 for no, nonzero for\n yes. If deemed appropriate, the record may be modified in-place.\n \"\"\"\n if self.nlen == 0:\n return True\n elif self.name == record.name:\n return True\n elif record.name.find(self.name, 0, self.nlen) != 0:\n return False\n return (record.name[self.nlen] == \".\")\n\nclass Filterer(object):\n \"\"\"\n A base class for loggers and handlers which allows them to share\n common code.\n \"\"\"\n def __init__(self):\n \"\"\"\n Initialize the list of filters to be an empty list.\n \"\"\"\n self.filters = []\n\n def addFilter(self, filter):\n \"\"\"\n Add the specified filter to this handler.\n \"\"\"\n if not (filter in self.filters):\n self.filters.append(filter)\n\n def removeFilter(self, filter):\n \"\"\"\n Remove the specified filter from this handler.\n \"\"\"\n if filter in self.filters:\n self.filters.remove(filter)\n\n def filter(self, record):\n \"\"\"\n Determine if a record is loggable by consulting all the filters.\n\n The default is to allow the record to be logged; any filter can veto\n this and the record is then dropped. Returns a zero value if a record\n is to be dropped, else non-zero.\n\n .. versionchanged:: 3.2\n\n Allow filters to be just callables.\n \"\"\"\n rv = True\n for f in self.filters:\n if hasattr(f, 'filter'):\n result = f.filter(record)\n else:\n result = f(record) # assume callable - will raise if not\n if not result:\n rv = False\n break\n return rv\n\n#---------------------------------------------------------------------------\n# Handler classes and functions\n#---------------------------------------------------------------------------\n\n_handlers = weakref.WeakValueDictionary() #map of handler names to handlers\n_handlerList = [] # added to allow handlers to be removed in reverse of order initialized\n\ndef _removeHandlerRef(wr):\n \"\"\"\n Remove a handler reference from the internal cleanup list.\n \"\"\"\n # This function can be called during module teardown, when globals are\n # set to None. It can also be called from another thread. So we need to\n # pre-emptively grab the necessary globals and check if they're None,\n # to prevent race conditions and failures during interpreter shutdown.\n acquire, release, handlers = _acquireLock, _releaseLock, _handlerList\n if acquire and release and handlers:\n acquire()\n try:\n if wr in handlers:\n handlers.remove(wr)\n finally:\n release()\n\ndef _addHandlerRef(handler):\n \"\"\"\n Add a handler to the internal cleanup list using a weak reference.\n \"\"\"\n _acquireLock()\n try:\n _handlerList.append(weakref.ref(handler, _removeHandlerRef))\n finally:\n _releaseLock()\n\nclass Handler(Filterer):\n \"\"\"\n Handler instances dispatch logging events to specific destinations.\n\n The base handler class. Acts as a placeholder which defines the Handler\n interface. Handlers can optionally use Formatter instances to format\n records as desired. By default, no formatter is specified; in this case,\n the 'raw' message as determined by record.message is logged.\n \"\"\"\n def __init__(self, level=NOTSET):\n \"\"\"\n Initializes the instance - basically setting the formatter to None\n and the filter list to empty.\n \"\"\"\n Filterer.__init__(self)\n self._name = None\n self.level = _checkLevel(level)\n self.formatter = None\n # Add the handler to the global _handlerList (for cleanup on shutdown)\n _addHandlerRef(self)\n self.createLock()\n\n def get_name(self):\n return self._name\n\n def set_name(self, name):\n _acquireLock()\n try:\n if self._name in _handlers:\n del _handlers[self._name]\n self._name = name\n if name:\n _handlers[name] = self\n finally:\n _releaseLock()\n\n name = property(get_name, set_name)\n\n def createLock(self):\n \"\"\"\n Acquire a thread lock for serializing access to the underlying I/O.\n \"\"\"\n self.lock = threading.RLock()\n _register_at_fork_reinit_lock(self)\n\n def acquire(self):\n \"\"\"\n Acquire the I/O thread lock.\n \"\"\"\n if self.lock:\n self.lock.acquire()\n\n def release(self):\n \"\"\"\n Release the I/O thread lock.\n \"\"\"\n if self.lock:\n self.lock.release()\n\n def setLevel(self, level):\n \"\"\"\n Set the logging level of this handler. level must be an int or a str.\n \"\"\"\n self.level = _checkLevel(level)\n\n def format(self, record):\n \"\"\"\n Format the specified record.\n\n If a formatter is set, use it. Otherwise, use the default formatter\n for the module.\n \"\"\"\n if self.formatter:\n fmt = self.formatter\n else:\n fmt = _defaultFormatter\n return fmt.format(record)\n\n def emit(self, record):\n \"\"\"\n Do whatever it takes to actually log the specified logging record.\n\n This version is intended to be implemented by subclasses and so\n raises a NotImplementedError.\n \"\"\"\n raise NotImplementedError('emit must be implemented '\n 'by Handler subclasses')\n\n def handle(self, record):\n \"\"\"\n Conditionally emit the specified logging record.\n\n Emission depends on filters which may have been added to the handler.\n Wrap the actual emission of the record with acquisition/release of\n the I/O thread lock. Returns whether the filter passed the record for\n emission.\n \"\"\"\n rv = self.filter(record)\n if rv:\n self.acquire()\n try:\n self.emit(record)\n finally:\n self.release()\n return rv\n\n def setFormatter(self, fmt):\n \"\"\"\n Set the formatter for this handler.\n \"\"\"\n self.formatter = fmt\n\n def flush(self):\n \"\"\"\n Ensure all logging output has been flushed.\n\n This version does nothing and is intended to be implemented by\n subclasses.\n \"\"\"\n pass\n\n def close(self):\n \"\"\"\n Tidy up any resources used by the handler.\n\n This version removes the handler from an internal map of handlers,\n _handlers, which is used for handler lookup by name. Subclasses\n should ensure that this gets called from overridden close()\n methods.\n \"\"\"\n #get the module data lock, as we're updating a shared structure.\n _acquireLock()\n try: #unlikely to raise an exception, but you never know...\n if self._name and self._name in _handlers:\n del _handlers[self._name]\n finally:\n _releaseLock()\n\n def handleError(self, record):\n \"\"\"\n Handle errors which occur during an emit() call.\n\n This method should be called from handlers when an exception is\n encountered during an emit() call. If raiseExceptions is false,\n exceptions get silently ignored. This is what is mostly wanted\n for a logging system - most users will not care about errors in\n the logging system, they are more interested in application errors.\n You could, however, replace this with a custom handler if you wish.\n The record which was being processed is passed in to this method.\n \"\"\"\n if raiseExceptions and sys.stderr: # see issue 13807\n t, v, tb = sys.exc_info()\n try:\n sys.stderr.write('--- Logging error ---\\n')\n traceback.print_exception(t, v, tb, None, sys.stderr)\n sys.stderr.write('Call stack:\\n')\n # Walk the stack frame up until we're out of logging,\n # so as to print the calling context.\n frame = tb.tb_frame\n while (frame and os.path.dirname(frame.f_code.co_filename) ==\n __path__[0]):\n frame = frame.f_back\n if frame:\n traceback.print_stack(frame, file=sys.stderr)\n else:\n # couldn't find the right stack frame, for some reason\n sys.stderr.write('Logged from file %s, line %s\\n' % (\n record.filename, record.lineno))\n # Issue 18671: output logging message and arguments\n try:\n sys.stderr.write('Message: %r\\n'\n 'Arguments: %s\\n' % (record.msg,\n record.args))\n except RecursionError: # See issue 36272\n raise\n except Exception:\n sys.stderr.write('Unable to print the message and arguments'\n ' - possible formatting error.\\nUse the'\n ' traceback above to help find the error.\\n'\n )\n except OSError: #pragma: no cover\n pass # see issue 5971\n finally:\n del t, v, tb\n\n def __repr__(self):\n level = getLevelName(self.level)\n return '<%s (%s)>' % (self.__class__.__name__, level)\n\nclass StreamHandler(Handler):\n \"\"\"\n A handler class which writes logging records, appropriately formatted,\n to a stream. Note that this class does not close the stream, as\n sys.stdout or sys.stderr may be used.\n \"\"\"\n\n terminator = '\\n'\n\n def __init__(self, stream=None):\n \"\"\"\n Initialize the handler.\n\n If stream is not specified, sys.stderr is used.\n \"\"\"\n Handler.__init__(self)\n if stream is None:\n stream = sys.stderr\n self.stream = stream\n\n def flush(self):\n \"\"\"\n Flushes the stream.\n \"\"\"\n self.acquire()\n try:\n if self.stream and hasattr(self.stream, \"flush\"):\n self.stream.flush()\n finally:\n self.release()\n\n def emit(self, record):\n \"\"\"\n Emit a record.\n\n If a formatter is specified, it is used to format the record.\n The record is then written to the stream with a trailing newline. If\n exception information is present, it is formatted using\n traceback.print_exception and appended to the stream. If the stream\n has an 'encoding' attribute, it is used to determine how to do the\n output to the stream.\n \"\"\"\n try:\n msg = self.format(record)\n stream = self.stream\n # issue 35046: merged two stream.writes into one.\n stream.write(msg + self.terminator)\n self.flush()\n except RecursionError: # See issue 36272\n raise\n except Exception:\n self.handleError(record)\n\n def setStream(self, stream):\n \"\"\"\n Sets the StreamHandler's stream to the specified value,\n if it is different.\n\n Returns the old stream, if the stream was changed, or None\n if it wasn't.\n \"\"\"\n if stream is self.stream:\n result = None\n else:\n result = self.stream\n self.acquire()\n try:\n self.flush()\n self.stream = stream\n finally:\n self.release()\n return result\n\n def __repr__(self):\n level = getLevelName(self.level)\n name = getattr(self.stream, 'name', '')\n # bpo-36015: name can be an int\n name = str(name)\n if name:\n name += ' '\n return '<%s %s(%s)>' % (self.__class__.__name__, name, level)\n\n\nclass FileHandler(StreamHandler):\n \"\"\"\n A handler class which writes formatted logging records to disk files.\n \"\"\"\n def __init__(self, filename, mode='a', encoding=None, delay=False):\n \"\"\"\n Open the specified file and use it as the stream for logging.\n \"\"\"\n # Issue #27493: add support for Path objects to be passed in\n filename = os.fspath(filename)\n #keep the absolute path, otherwise derived classes which use this\n #may come a cropper when the current directory changes\n self.baseFilename = os.path.abspath(filename)\n self.mode = mode\n self.encoding = encoding\n self.delay = delay\n if delay:\n #We don't open the stream, but we still need to call the\n #Handler constructor to set level, formatter, lock etc.\n Handler.__init__(self)\n self.stream = None\n else:\n StreamHandler.__init__(self, self._open())\n\n def close(self):\n \"\"\"\n Closes the stream.\n \"\"\"\n self.acquire()\n try:\n try:\n if self.stream:\n try:\n self.flush()\n finally:\n stream = self.stream\n self.stream = None\n if hasattr(stream, \"close\"):\n stream.close()\n finally:\n # Issue #19523: call unconditionally to\n # prevent a handler leak when delay is set\n StreamHandler.close(self)\n finally:\n self.release()\n\n def _open(self):\n \"\"\"\n Open the current base file with the (original) mode and encoding.\n Return the resulting stream.\n \"\"\"\n return open(self.baseFilename, self.mode, encoding=self.encoding)\n\n def emit(self, record):\n \"\"\"\n Emit a record.\n\n If the stream was not opened because 'delay' was specified in the\n constructor, open it before calling the superclass's emit.\n \"\"\"\n if self.stream is None:\n self.stream = self._open()\n StreamHandler.emit(self, record)\n\n def __repr__(self):\n level = getLevelName(self.level)\n return '<%s %s (%s)>' % (self.__class__.__name__, self.baseFilename, level)\n\n\nclass _StderrHandler(StreamHandler):\n \"\"\"\n This class is like a StreamHandler using sys.stderr, but always uses\n whatever sys.stderr is currently set to rather than the value of\n sys.stderr at handler construction time.\n \"\"\"\n def __init__(self, level=NOTSET):\n \"\"\"\n Initialize the handler.\n \"\"\"\n Handler.__init__(self, level)\n\n @property\n def stream(self):\n return sys.stderr\n\n\n_defaultLastResort = _StderrHandler(WARNING)\nlastResort = _defaultLastResort\n\n#---------------------------------------------------------------------------\n# Manager classes and functions\n#---------------------------------------------------------------------------\n\nclass PlaceHolder(object):\n \"\"\"\n PlaceHolder instances are used in the Manager logger hierarchy to take\n the place of nodes for which no loggers have been defined. This class is\n intended for internal use only and not as part of the public API.\n \"\"\"\n def __init__(self, alogger):\n \"\"\"\n Initialize with the specified logger being a child of this placeholder.\n \"\"\"\n self.loggerMap = { alogger : None }\n\n def append(self, alogger):\n \"\"\"\n Add the specified logger as a child of this placeholder.\n \"\"\"\n if alogger not in self.loggerMap:\n self.loggerMap[alogger] = None\n\n#\n# Determine which class to use when instantiating loggers.\n#\n\ndef setLoggerClass(klass):\n \"\"\"\n Set the class to be used when instantiating a logger. The class should\n define __init__() such that only a name argument is required, and the\n __init__() should call Logger.__init__()\n \"\"\"\n if klass != Logger:\n if not issubclass(klass, Logger):\n raise TypeError(\"logger not derived from logging.Logger: \"\n + klass.__name__)\n global _loggerClass\n _loggerClass = klass\n\ndef getLoggerClass():\n \"\"\"\n Return the class to be used when instantiating a logger.\n \"\"\"\n return _loggerClass\n\nclass Manager(object):\n \"\"\"\n There is [under normal circumstances] just one Manager instance, which\n holds the hierarchy of loggers.\n \"\"\"\n def __init__(self, rootnode):\n \"\"\"\n Initialize the manager with the root node of the logger hierarchy.\n \"\"\"\n self.root = rootnode\n self.disable = 0\n self.emittedNoHandlerWarning = False\n self.loggerDict = {}\n self.loggerClass = None\n self.logRecordFactory = None\n\n def getLogger(self, name):\n \"\"\"\n Get a logger with the specified name (channel name), creating it\n if it doesn't yet exist. This name is a dot-separated hierarchical\n name, such as \"a\", \"a.b\", \"a.b.c\" or similar.\n\n If a PlaceHolder existed for the specified name [i.e. the logger\n didn't exist but a child of it did], replace it with the created\n logger and fix up the parent/child references which pointed to the\n placeholder to now point to the logger.\n \"\"\"\n rv = None\n if not isinstance(name, str):\n raise TypeError('A logger name must be a string')\n _acquireLock()\n try:\n if name in self.loggerDict:\n rv = self.loggerDict[name]\n if isinstance(rv, PlaceHolder):\n ph = rv\n rv = (self.loggerClass or _loggerClass)(name)\n rv.manager = self\n self.loggerDict[name] = rv\n self._fixupChildren(ph, rv)\n self._fixupParents(rv)\n else:\n rv = (self.loggerClass or _loggerClass)(name)\n rv.manager = self\n self.loggerDict[name] = rv\n self._fixupParents(rv)\n finally:\n _releaseLock()\n return rv\n\n def setLoggerClass(self, klass):\n \"\"\"\n Set the class to be used when instantiating a logger with this Manager.\n \"\"\"\n if klass != Logger:\n if not issubclass(klass, Logger):\n raise TypeError(\"logger not derived from logging.Logger: \"\n + klass.__name__)\n self.loggerClass = klass\n\n def setLogRecordFactory(self, factory):\n \"\"\"\n Set the factory to be used when instantiating a log record with this\n Manager.\n \"\"\"\n self.logRecordFactory = factory\n\n def _fixupParents(self, alogger):\n \"\"\"\n Ensure that there are either loggers or placeholders all the way\n from the specified logger to the root of the logger hierarchy.\n \"\"\"\n name = alogger.name\n i = name.rfind(\".\")\n rv = None\n while (i > 0) and not rv:\n substr = name[:i]\n if substr not in self.loggerDict:\n self.loggerDict[substr] = PlaceHolder(alogger)\n else:\n obj = self.loggerDict[substr]\n if isinstance(obj, Logger):\n rv = obj\n else:\n assert isinstance(obj, PlaceHolder)\n obj.append(alogger)\n i = name.rfind(\".\", 0, i - 1)\n if not rv:\n rv = self.root\n alogger.parent = rv\n\n def _fixupChildren(self, ph, alogger):\n \"\"\"\n Ensure that children of the placeholder ph are connected to the\n specified logger.\n \"\"\"\n name = alogger.name\n namelen = len(name)\n for c in ph.loggerMap.keys():\n #The if means ... if not c.parent.name.startswith(nm)\n if c.parent.name[:namelen] != name:\n alogger.parent = c.parent\n c.parent = alogger\n\n def _clear_cache(self):\n \"\"\"\n Clear the cache for all loggers in loggerDict\n Called when level changes are made\n \"\"\"\n\n _acquireLock()\n for logger in self.loggerDict.values():\n if isinstance(logger, Logger):\n logger._cache.clear()\n self.root._cache.clear()\n _releaseLock()\n\n#---------------------------------------------------------------------------\n# Logger classes and functions\n#---------------------------------------------------------------------------\n\nclass Logger(Filterer):\n \"\"\"\n Instances of the Logger class represent a single logging channel. A\n \"logging channel\" indicates an area of an application. Exactly how an\n \"area\" is defined is up to the application developer. Since an\n application can have any number of areas, logging channels are identified\n by a unique string. Application areas can be nested (e.g. an area\n of \"input processing\" might include sub-areas \"read CSV files\", \"read\n XLS files\" and \"read Gnumeric files\"). To cater for this natural nesting,\n channel names are organized into a namespace hierarchy where levels are\n separated by periods, much like the Java or Python package namespace. So\n in the instance given above, channel names might be \"input\" for the upper\n level, and \"input.csv\", \"input.xls\" and \"input.gnu\" for the sub-levels.\n There is no arbitrary limit to the depth of nesting.\n \"\"\"\n def __init__(self, name, level=NOTSET):\n \"\"\"\n Initialize the logger with a name and an optional level.\n \"\"\"\n Filterer.__init__(self)\n self.name = name\n self.level = _checkLevel(level)\n self.parent = None\n self.propagate = True\n self.handlers = []\n self.disabled = False\n self._cache = {}\n\n def setLevel(self, level):\n \"\"\"\n Set the logging level of this logger. level must be an int or a str.\n \"\"\"\n self.level = _checkLevel(level)\n self.manager._clear_cache()\n\n def debug(self, msg, *args, **kwargs):\n \"\"\"\n Log 'msg % args' with severity 'DEBUG'.\n\n To pass exception information, use the keyword argument exc_info with\n a true value, e.g.\n\n logger.debug(\"Houston, we have a %s\", \"thorny problem\", exc_info=1)\n \"\"\"\n if self.isEnabledFor(DEBUG):\n self._log(DEBUG, msg, args, **kwargs)\n\n def info(self, msg, *args, **kwargs):\n \"\"\"\n Log 'msg % args' with severity 'INFO'.\n\n To pass exception information, use the keyword argument exc_info with\n a true value, e.g.\n\n logger.info(\"Houston, we have a %s\", \"interesting problem\", exc_info=1)\n \"\"\"\n if self.isEnabledFor(INFO):\n self._log(INFO, msg, args, **kwargs)\n\n def warning(self, msg, *args, **kwargs):\n \"\"\"\n Log 'msg % args' with severity 'WARNING'.\n\n To pass exception information, use the keyword argument exc_info with\n a true value, e.g.\n\n logger.warning(\"Houston, we have a %s\", \"bit of a problem\", exc_info=1)\n \"\"\"\n if self.isEnabledFor(WARNING):\n self._log(WARNING, msg, args, **kwargs)\n\n def warn(self, msg, *args, **kwargs):\n warnings.warn(\"The 'warn' method is deprecated, \"\n \"use 'warning' instead\", DeprecationWarning, 2)\n self.warning(msg, *args, **kwargs)\n\n def error(self, msg, *args, **kwargs):\n \"\"\"\n Log 'msg % args' with severity 'ERROR'.\n\n To pass exception information, use the keyword argument exc_info with\n a true value, e.g.\n\n logger.error(\"Houston, we have a %s\", \"major problem\", exc_info=1)\n \"\"\"\n if self.isEnabledFor(ERROR):\n self._log(ERROR, msg, args, **kwargs)\n\n def exception(self, msg, *args, exc_info=True, **kwargs):\n \"\"\"\n Convenience method for logging an ERROR with exception information.\n \"\"\"\n self.error(msg, *args, exc_info=exc_info, **kwargs)\n\n def critical(self, msg, *args, **kwargs):\n \"\"\"\n Log 'msg % args' with severity 'CRITICAL'.\n\n To pass exception information, use the keyword argument exc_info with\n a true value, e.g.\n\n logger.critical(\"Houston, we have a %s\", \"major disaster\", exc_info=1)\n \"\"\"\n if self.isEnabledFor(CRITICAL):\n self._log(CRITICAL, msg, args, **kwargs)\n\n fatal = critical\n\n def log(self, level, msg, *args, **kwargs):\n \"\"\"\n Log 'msg % args' with the integer severity 'level'.\n\n To pass exception information, use the keyword argument exc_info with\n a true value, e.g.\n\n logger.log(level, \"We have a %s\", \"mysterious problem\", exc_info=1)\n \"\"\"\n if not isinstance(level, int):\n if raiseExceptions:\n raise TypeError(\"level must be an integer\")\n else:\n return\n if self.isEnabledFor(level):\n self._log(level, msg, args, **kwargs)\n\n def findCaller(self, stack_info=False, stacklevel=1):\n \"\"\"\n Find the stack frame of the caller so that we can note the source\n file name, line number and function name.\n \"\"\"\n f = currentframe()\n #On some versions of IronPython, currentframe() returns None if\n #IronPython isn't run with -X:Frames.\n if f is not None:\n f = f.f_back\n orig_f = f\n while f and stacklevel > 1:\n f = f.f_back\n stacklevel -= 1\n if not f:\n f = orig_f\n rv = \"(unknown file)\", 0, \"(unknown function)\", None\n while hasattr(f, \"f_code\"):\n co = f.f_code\n filename = os.path.normcase(co.co_filename)\n if filename == _srcfile:\n f = f.f_back\n continue\n sinfo = None\n if stack_info:\n sio = io.StringIO()\n sio.write('Stack (most recent call last):\\n')\n traceback.print_stack(f, file=sio)\n sinfo = sio.getvalue()\n if sinfo[-1] == '\\n':\n sinfo = sinfo[:-1]\n sio.close()\n rv = (co.co_filename, f.f_lineno, co.co_name, sinfo)\n break\n return rv\n\n def makeRecord(self, name, level, fn, lno, msg, args, exc_info,\n func=None, extra=None, sinfo=None):\n \"\"\"\n A factory method which can be overridden in subclasses to create\n specialized LogRecords.\n \"\"\"\n rv = _logRecordFactory(name, level, fn, lno, msg, args, exc_info, func,\n sinfo)\n if extra is not None:\n for key in extra:\n if (key in [\"message\", \"asctime\"]) or (key in rv.__dict__):\n raise KeyError(\"Attempt to overwrite %r in LogRecord\" % key)\n rv.__dict__[key] = extra[key]\n return rv\n\n def _log(self, level, msg, args, exc_info=None, extra=None, stack_info=False,\n stacklevel=1):\n \"\"\"\n Low-level logging routine which creates a LogRecord and then calls\n all the handlers of this logger to handle the record.\n \"\"\"\n sinfo = None\n if _srcfile:\n #IronPython doesn't track Python frames, so findCaller raises an\n #exception on some versions of IronPython. We trap it here so that\n #IronPython can use logging.\n try:\n fn, lno, func, sinfo = self.findCaller(stack_info, stacklevel)\n except ValueError: # pragma: no cover\n fn, lno, func = \"(unknown file)\", 0, \"(unknown function)\"\n else: # pragma: no cover\n fn, lno, func = \"(unknown file)\", 0, \"(unknown function)\"\n if exc_info:\n if isinstance(exc_info, BaseException):\n exc_info = (type(exc_info), exc_info, exc_info.__traceback__)\n elif not isinstance(exc_info, tuple):\n exc_info = sys.exc_info()\n record = self.makeRecord(self.name, level, fn, lno, msg, args,\n exc_info, func, extra, sinfo)\n self.handle(record)\n\n def handle(self, record):\n \"\"\"\n Call the handlers for the specified record.\n\n This method is used for unpickled records received from a socket, as\n well as those created locally. Logger-level filtering is applied.\n \"\"\"\n if (not self.disabled) and self.filter(record):\n self.callHandlers(record)\n\n def addHandler(self, hdlr):\n \"\"\"\n Add the specified handler to this logger.\n \"\"\"\n _acquireLock()\n try:\n if not (hdlr in self.handlers):\n self.handlers.append(hdlr)\n finally:\n _releaseLock()\n\n def removeHandler(self, hdlr):\n \"\"\"\n Remove the specified handler from this logger.\n \"\"\"\n _acquireLock()\n try:\n if hdlr in self.handlers:\n self.handlers.remove(hdlr)\n finally:\n _releaseLock()\n\n def hasHandlers(self):\n \"\"\"\n See if this logger has any handlers configured.\n\n Loop through all handlers for this logger and its parents in the\n logger hierarchy. Return True if a handler was found, else False.\n Stop searching up the hierarchy whenever a logger with the \"propagate\"\n attribute set to zero is found - that will be the last logger which\n is checked for the existence of handlers.\n \"\"\"\n c = self\n rv = False\n while c:\n if c.handlers:\n rv = True\n break\n if not c.propagate:\n break\n else:\n c = c.parent\n return rv\n\n def callHandlers(self, record):\n \"\"\"\n Pass a record to all relevant handlers.\n\n Loop through all handlers for this logger and its parents in the\n logger hierarchy. If no handler was found, output a one-off error\n message to sys.stderr. Stop searching up the hierarchy whenever a\n logger with the \"propagate\" attribute set to zero is found - that\n will be the last logger whose handlers are called.\n \"\"\"\n c = self\n found = 0\n while c:\n for hdlr in c.handlers:\n found = found + 1\n if record.levelno >= hdlr.level:\n hdlr.handle(record)\n if not c.propagate:\n c = None #break out\n else:\n c = c.parent\n if (found == 0):\n if lastResort:\n if record.levelno >= lastResort.level:\n lastResort.handle(record)\n elif raiseExceptions and not self.manager.emittedNoHandlerWarning:\n sys.stderr.write(\"No handlers could be found for logger\"\n \" \\\"%s\\\"\\n\" % self.name)\n self.manager.emittedNoHandlerWarning = True\n\n def getEffectiveLevel(self):\n \"\"\"\n Get the effective level for this logger.\n\n Loop through this logger and its parents in the logger hierarchy,\n looking for a non-zero logging level. Return the first one found.\n \"\"\"\n logger = self\n while logger:\n if logger.level:\n return logger.level\n logger = logger.parent\n return NOTSET\n\n def isEnabledFor(self, level):\n \"\"\"\n Is this logger enabled for level 'level'?\n \"\"\"\n if self.disabled:\n return False\n\n try:\n return self._cache[level]\n except KeyError:\n _acquireLock()\n try:\n if self.manager.disable >= level:\n is_enabled = self._cache[level] = False\n else:\n is_enabled = self._cache[level] = (\n level >= self.getEffectiveLevel()\n )\n finally:\n _releaseLock()\n return is_enabled\n\n def getChild(self, suffix):\n \"\"\"\n Get a logger which is a descendant to this one.\n\n This is a convenience method, such that\n\n logging.getLogger('abc').getChild('def.ghi')\n\n is the same as\n\n logging.getLogger('abc.def.ghi')\n\n It's useful, for example, when the parent logger is named using\n __name__ rather than a literal string.\n \"\"\"\n if self.root is not self:\n suffix = '.'.join((self.name, suffix))\n return self.manager.getLogger(suffix)\n\n def __repr__(self):\n level = getLevelName(self.getEffectiveLevel())\n return '<%s %s (%s)>' % (self.__class__.__name__, self.name, level)\n\n def __reduce__(self):\n # In general, only the root logger will not be accessible via its name.\n # However, the root logger's class has its own __reduce__ method.\n if getLogger(self.name) is not self:\n import pickle\n raise pickle.PicklingError('logger cannot be pickled')\n return getLogger, (self.name,)\n\n\nclass RootLogger(Logger):\n \"\"\"\n A root logger is not that different to any other logger, except that\n it must have a logging level and there is only one instance of it in\n the hierarchy.\n \"\"\"\n def __init__(self, level):\n \"\"\"\n Initialize the logger with the name \"root\".\n \"\"\"\n Logger.__init__(self, \"root\", level)\n\n def __reduce__(self):\n return getLogger, ()\n\n_loggerClass = Logger\n\nclass LoggerAdapter(object):\n \"\"\"\n An adapter for loggers which makes it easier to specify contextual\n information in logging output.\n \"\"\"\n\n def __init__(self, logger, extra):\n \"\"\"\n Initialize the adapter with a logger and a dict-like object which\n provides contextual information. This constructor signature allows\n easy stacking of LoggerAdapters, if so desired.\n\n You can effectively pass keyword arguments as shown in the\n following example:\n\n adapter = LoggerAdapter(someLogger, dict(p1=v1, p2=\"v2\"))\n \"\"\"\n self.logger = logger\n self.extra = extra\n\n def process(self, msg, kwargs):\n \"\"\"\n Process the logging message and keyword arguments passed in to\n a logging call to insert contextual information. You can either\n manipulate the message itself, the keyword args or both. Return\n the message and kwargs modified (or not) to suit your needs.\n\n Normally, you'll only need to override this one method in a\n LoggerAdapter subclass for your specific needs.\n \"\"\"\n kwargs[\"extra\"] = self.extra\n return msg, kwargs\n\n #\n # Boilerplate convenience methods\n #\n def debug(self, msg, *args, **kwargs):\n \"\"\"\n Delegate a debug call to the underlying logger.\n \"\"\"\n self.log(DEBUG, msg, *args, **kwargs)\n\n def info(self, msg, *args, **kwargs):\n \"\"\"\n Delegate an info call to the underlying logger.\n \"\"\"\n self.log(INFO, msg, *args, **kwargs)\n\n def warning(self, msg, *args, **kwargs):\n \"\"\"\n Delegate a warning call to the underlying logger.\n \"\"\"\n self.log(WARNING, msg, *args, **kwargs)\n\n def warn(self, msg, *args, **kwargs):\n warnings.warn(\"The 'warn' method is deprecated, \"\n \"use 'warning' instead\", DeprecationWarning, 2)\n self.warning(msg, *args, **kwargs)\n\n def error(self, msg, *args, **kwargs):\n \"\"\"\n Delegate an error call to the underlying logger.\n \"\"\"\n self.log(ERROR, msg, *args, **kwargs)\n\n def exception(self, msg, *args, exc_info=True, **kwargs):\n \"\"\"\n Delegate an exception call to the underlying logger.\n \"\"\"\n self.log(ERROR, msg, *args, exc_info=exc_info, **kwargs)\n\n def critical(self, msg, *args, **kwargs):\n \"\"\"\n Delegate a critical call to the underlying logger.\n \"\"\"\n self.log(CRITICAL, msg, *args, **kwargs)\n\n def log(self, level, msg, *args, **kwargs):\n \"\"\"\n Delegate a log call to the underlying logger, after adding\n contextual information from this adapter instance.\n \"\"\"\n if self.isEnabledFor(level):\n msg, kwargs = self.process(msg, kwargs)\n self.logger.log(level, msg, *args, **kwargs)\n\n def isEnabledFor(self, level):\n \"\"\"\n Is this logger enabled for level 'level'?\n \"\"\"\n return self.logger.isEnabledFor(level)\n\n def setLevel(self, level):\n \"\"\"\n Set the specified level on the underlying logger.\n \"\"\"\n self.logger.setLevel(level)\n\n def getEffectiveLevel(self):\n \"\"\"\n Get the effective level for the underlying logger.\n \"\"\"\n return self.logger.getEffectiveLevel()\n\n def hasHandlers(self):\n \"\"\"\n See if the underlying logger has any handlers.\n \"\"\"\n return self.logger.hasHandlers()\n\n def _log(self, level, msg, args, exc_info=None, extra=None, stack_info=False):\n \"\"\"\n Low-level log implementation, proxied to allow nested logger adapters.\n \"\"\"\n return self.logger._log(\n level,\n msg,\n args,\n exc_info=exc_info,\n extra=extra,\n stack_info=stack_info,\n )\n\n @property\n def manager(self):\n return self.logger.manager\n\n @manager.setter\n def manager(self, value):\n self.logger.manager = value\n\n @property\n def name(self):\n return self.logger.name\n\n def __repr__(self):\n logger = self.logger\n level = getLevelName(logger.getEffectiveLevel())\n return '<%s %s (%s)>' % (self.__class__.__name__, logger.name, level)\n\nroot = RootLogger(WARNING)\nLogger.root = root\nLogger.manager = Manager(Logger.root)\n\n#---------------------------------------------------------------------------\n# Configuration classes and functions\n#---------------------------------------------------------------------------\n\ndef basicConfig(**kwargs):\n \"\"\"\n Do basic configuration for the logging system.\n\n This function does nothing if the root logger already has handlers\n configured, unless the keyword argument *force* is set to ``True``.\n It is a convenience method intended for use by simple scripts\n to do one-shot configuration of the logging package.\n\n The default behaviour is to create a StreamHandler which writes to\n sys.stderr, set a formatter using the BASIC_FORMAT format string, and\n add the handler to the root logger.\n\n A number of optional keyword arguments may be specified, which can alter\n the default behaviour.\n\n filename Specifies that a FileHandler be created, using the specified\n filename, rather than a StreamHandler.\n filemode Specifies the mode to open the file, if filename is specified\n (if filemode is unspecified, it defaults to 'a').\n format Use the specified format string for the handler.\n datefmt Use the specified date/time format.\n style If a format string is specified, use this to specify the\n type of format string (possible values '%', '{', '$', for\n %-formatting, :meth:`str.format` and :class:`string.Template`\n - defaults to '%').\n level Set the root logger level to the specified level.\n stream Use the specified stream to initialize the StreamHandler. Note\n that this argument is incompatible with 'filename' - if both\n are present, 'stream' is ignored.\n handlers If specified, this should be an iterable of already created\n handlers, which will be added to the root handler. Any handler\n in the list which does not have a formatter assigned will be\n assigned the formatter created in this function.\n force If this keyword is specified as true, any existing handlers\n attached to the root logger are removed and closed, before\n carrying out the configuration as specified by the other\n arguments.\n Note that you could specify a stream created using open(filename, mode)\n rather than passing the filename and mode in. However, it should be\n remembered that StreamHandler does not close its stream (since it may be\n using sys.stdout or sys.stderr), whereas FileHandler closes its stream\n when the handler is closed.\n\n .. versionchanged:: 3.8\n Added the ``force`` parameter.\n\n .. versionchanged:: 3.2\n Added the ``style`` parameter.\n\n .. versionchanged:: 3.3\n Added the ``handlers`` parameter. A ``ValueError`` is now thrown for\n incompatible arguments (e.g. ``handlers`` specified together with\n ``filename``/``filemode``, or ``filename``/``filemode`` specified\n together with ``stream``, or ``handlers`` specified together with\n ``stream``.\n \"\"\"\n # Add thread safety in case someone mistakenly calls\n # basicConfig() from multiple threads\n _acquireLock()\n try:\n force = kwargs.pop('force', False)\n if force:\n for h in root.handlers[:]:\n root.removeHandler(h)\n h.close()\n if len(root.handlers) == 0:\n handlers = kwargs.pop(\"handlers\", None)\n if handlers is None:\n if \"stream\" in kwargs and \"filename\" in kwargs:\n raise ValueError(\"'stream' and 'filename' should not be \"\n \"specified together\")\n else:\n if \"stream\" in kwargs or \"filename\" in kwargs:\n raise ValueError(\"'stream' or 'filename' should not be \"\n \"specified together with 'handlers'\")\n if handlers is None:\n filename = kwargs.pop(\"filename\", None)\n mode = kwargs.pop(\"filemode\", 'a')\n if filename:\n h = FileHandler(filename, mode)\n else:\n stream = kwargs.pop(\"stream\", None)\n h = StreamHandler(stream)\n handlers = [h]\n dfs = kwargs.pop(\"datefmt\", None)\n style = kwargs.pop(\"style\", '%')\n if style not in _STYLES:\n raise ValueError('Style must be one of: %s' % ','.join(\n _STYLES.keys()))\n fs = kwargs.pop(\"format\", _STYLES[style][1])\n fmt = Formatter(fs, dfs, style)\n for h in handlers:\n if h.formatter is None:\n h.setFormatter(fmt)\n root.addHandler(h)\n level = kwargs.pop(\"level\", None)\n if level is not None:\n root.setLevel(level)\n if kwargs:\n keys = ', '.join(kwargs.keys())\n raise ValueError('Unrecognised argument(s): %s' % keys)\n finally:\n _releaseLock()\n\n#---------------------------------------------------------------------------\n# Utility functions at module level.\n# Basically delegate everything to the root logger.\n#---------------------------------------------------------------------------\n\ndef getLogger(name=None):\n \"\"\"\n Return a logger with the specified name, creating it if necessary.\n\n If no name is specified, return the root logger.\n \"\"\"\n if name:\n return Logger.manager.getLogger(name)\n else:\n return root\n\ndef critical(msg, *args, **kwargs):\n \"\"\"\n Log a message with severity 'CRITICAL' on the root logger. If the logger\n has no handlers, call basicConfig() to add a console handler with a\n pre-defined format.\n \"\"\"\n if len(root.handlers) == 0:\n basicConfig()\n root.critical(msg, *args, **kwargs)\n\nfatal = critical\n\ndef error(msg, *args, **kwargs):\n \"\"\"\n Log a message with severity 'ERROR' on the root logger. If the logger has\n no handlers, call basicConfig() to add a console handler with a pre-defined\n format.\n \"\"\"\n if len(root.handlers) == 0:\n basicConfig()\n root.error(msg, *args, **kwargs)\n\ndef exception(msg, *args, exc_info=True, **kwargs):\n \"\"\"\n Log a message with severity 'ERROR' on the root logger, with exception\n information. If the logger has no handlers, basicConfig() is called to add\n a console handler with a pre-defined format.\n \"\"\"\n error(msg, *args, exc_info=exc_info, **kwargs)\n\ndef warning(msg, *args, **kwargs):\n \"\"\"\n Log a message with severity 'WARNING' on the root logger. If the logger has\n no handlers, call basicConfig() to add a console handler with a pre-defined\n format.\n \"\"\"\n if len(root.handlers) == 0:\n basicConfig()\n root.warning(msg, *args, **kwargs)\n\ndef warn(msg, *args, **kwargs):\n warnings.warn(\"The 'warn' function is deprecated, \"\n \"use 'warning' instead\", DeprecationWarning, 2)\n warning(msg, *args, **kwargs)\n\ndef info(msg, *args, **kwargs):\n \"\"\"\n Log a message with severity 'INFO' on the root logger. If the logger has\n no handlers, call basicConfig() to add a console handler with a pre-defined\n format.\n \"\"\"\n if len(root.handlers) == 0:\n basicConfig()\n root.info(msg, *args, **kwargs)\n\ndef debug(msg, *args, **kwargs):\n \"\"\"\n Log a message with severity 'DEBUG' on the root logger. If the logger has\n no handlers, call basicConfig() to add a console handler with a pre-defined\n format.\n \"\"\"\n if len(root.handlers) == 0:\n basicConfig()\n root.debug(msg, *args, **kwargs)\n\ndef log(level, msg, *args, **kwargs):\n \"\"\"\n Log 'msg % args' with the integer severity 'level' on the root logger. If\n the logger has no handlers, call basicConfig() to add a console handler\n with a pre-defined format.\n \"\"\"\n if len(root.handlers) == 0:\n basicConfig()\n root.log(level, msg, *args, **kwargs)\n\ndef disable(level=CRITICAL):\n \"\"\"\n Disable all logging calls of severity 'level' and below.\n \"\"\"\n root.manager.disable = level\n root.manager._clear_cache()\n\ndef shutdown(handlerList=_handlerList):\n \"\"\"\n Perform any cleanup actions in the logging system (e.g. flushing\n buffers).\n\n Should be called at application exit.\n \"\"\"\n for wr in reversed(handlerList[:]):\n #errors might occur, for example, if files are locked\n #we just ignore them if raiseExceptions is not set\n try:\n h = wr()\n if h:\n try:\n h.acquire()\n h.flush()\n h.close()\n except (OSError, ValueError):\n # Ignore errors which might be caused\n # because handlers have been closed but\n # references to them are still around at\n # application exit.\n pass\n finally:\n h.release()\n except: # ignore everything, as we're shutting down\n if raiseExceptions:\n raise\n #else, swallow\n\n#Let's try and shutdown automatically on application exit...\nimport atexit\natexit.register(shutdown)\n\n# Null handler\n\nclass NullHandler(Handler):\n \"\"\"\n This handler does nothing. It's intended to be used to avoid the\n \"No handlers could be found for logger XXX\" one-off warning. This is\n important for library code, which may contain code to log events. If a user\n of the library does not configure logging, the one-off warning might be\n produced; to avoid this, the library developer simply needs to instantiate\n a NullHandler and add it to the top-level logger of the library module or\n package.\n \"\"\"\n def handle(self, record):\n \"\"\"Stub.\"\"\"\n\n def emit(self, record):\n \"\"\"Stub.\"\"\"\n\n def createLock(self):\n self.lock = None\n\n# Warnings integration\n\n_warnings_showwarning = None\n\ndef _showwarning(message, category, filename, lineno, file=None, line=None):\n \"\"\"\n Implementation of showwarnings which redirects to logging, which will first\n check to see if the file parameter is None. If a file is specified, it will\n delegate to the original warnings implementation of showwarning. Otherwise,\n it will call warnings.formatwarning and will log the resulting string to a\n warnings logger named \"py.warnings\" with level logging.WARNING.\n \"\"\"\n if file is not None:\n if _warnings_showwarning is not None:\n _warnings_showwarning(message, category, filename, lineno, file, line)\n else:\n s = warnings.formatwarning(message, category, filename, lineno, line)\n logger = getLogger(\"py.warnings\")\n if not logger.handlers:\n logger.addHandler(NullHandler())\n logger.warning(\"%s\", s)\n\ndef captureWarnings(capture):\n \"\"\"\n If capture is true, redirect all warnings to the logging package.\n If capture is False, ensure that warnings are not redirected to logging\n but to their original destinations.\n \"\"\"\n global _warnings_showwarning\n if capture:\n if _warnings_showwarning is None:\n _warnings_showwarning = warnings.showwarning\n warnings.showwarning = _showwarning\n else:\n if _warnings_showwarning is not None:\n warnings.showwarning = _warnings_showwarning\n _warnings_showwarning = None\n", 2190], "/usr/lib/python3.8/socket.py": ["# Wrapper module for _socket, providing some additional facilities\n# implemented in Python.\n\n\"\"\"\\\nThis module provides socket operations and some related functions.\nOn Unix, it supports IP (Internet Protocol) and Unix domain sockets.\nOn other systems, it only supports IP. Functions specific for a\nsocket are available as methods of the socket object.\n\nFunctions:\n\nsocket() -- create a new socket object\nsocketpair() -- create a pair of new socket objects [*]\nfromfd() -- create a socket object from an open file descriptor [*]\nfromshare() -- create a socket object from data received from socket.share() [*]\ngethostname() -- return the current hostname\ngethostbyname() -- map a hostname to its IP number\ngethostbyaddr() -- map an IP number or hostname to DNS info\ngetservbyname() -- map a service name and a protocol name to a port number\ngetprotobyname() -- map a protocol name (e.g. 'tcp') to a number\nntohs(), ntohl() -- convert 16, 32 bit int from network to host byte order\nhtons(), htonl() -- convert 16, 32 bit int from host to network byte order\ninet_aton() -- convert IP addr string (123.45.67.89) to 32-bit packed format\ninet_ntoa() -- convert 32-bit packed format IP to string (123.45.67.89)\nsocket.getdefaulttimeout() -- get the default timeout value\nsocket.setdefaulttimeout() -- set the default timeout value\ncreate_connection() -- connects to an address, with an optional timeout and\n optional source address.\n\n [*] not available on all platforms!\n\nSpecial objects:\n\nSocketType -- type object for socket objects\nerror -- exception raised for I/O errors\nhas_ipv6 -- boolean value indicating if IPv6 is supported\n\nIntEnum constants:\n\nAF_INET, AF_UNIX -- socket domains (first argument to socket() call)\nSOCK_STREAM, SOCK_DGRAM, SOCK_RAW -- socket types (second argument)\n\nInteger constants:\n\nMany other constants may be defined; these may be used in calls to\nthe setsockopt() and getsockopt() methods.\n\"\"\"\n\nimport _socket\nfrom _socket import *\n\nimport os, sys, io, selectors\nfrom enum import IntEnum, IntFlag\n\ntry:\n import errno\nexcept ImportError:\n errno = None\nEBADF = getattr(errno, 'EBADF', 9)\nEAGAIN = getattr(errno, 'EAGAIN', 11)\nEWOULDBLOCK = getattr(errno, 'EWOULDBLOCK', 11)\n\n__all__ = [\"fromfd\", \"getfqdn\", \"create_connection\", \"create_server\",\n \"has_dualstack_ipv6\", \"AddressFamily\", \"SocketKind\"]\n__all__.extend(os._get_exports_list(_socket))\n\n# Set up the socket.AF_* socket.SOCK_* constants as members of IntEnums for\n# nicer string representations.\n# Note that _socket only knows about the integer values. The public interface\n# in this module understands the enums and translates them back from integers\n# where needed (e.g. .family property of a socket object).\n\nIntEnum._convert_(\n 'AddressFamily',\n __name__,\n lambda C: C.isupper() and C.startswith('AF_'))\n\nIntEnum._convert_(\n 'SocketKind',\n __name__,\n lambda C: C.isupper() and C.startswith('SOCK_'))\n\nIntFlag._convert_(\n 'MsgFlag',\n __name__,\n lambda C: C.isupper() and C.startswith('MSG_'))\n\nIntFlag._convert_(\n 'AddressInfo',\n __name__,\n lambda C: C.isupper() and C.startswith('AI_'))\n\n_LOCALHOST = '127.0.0.1'\n_LOCALHOST_V6 = '::1'\n\n\ndef _intenum_converter(value, enum_klass):\n \"\"\"Convert a numeric family value to an IntEnum member.\n\n If it's not a known member, return the numeric value itself.\n \"\"\"\n try:\n return enum_klass(value)\n except ValueError:\n return value\n\n_realsocket = socket\n\n# WSA error codes\nif sys.platform.lower().startswith(\"win\"):\n errorTab = {}\n errorTab[6] = \"Specified event object handle is invalid.\"\n errorTab[8] = \"Insufficient memory available.\"\n errorTab[87] = \"One or more parameters are invalid.\"\n errorTab[995] = \"Overlapped operation aborted.\"\n errorTab[996] = \"Overlapped I/O event object not in signaled state.\"\n errorTab[997] = \"Overlapped operation will complete later.\"\n errorTab[10004] = \"The operation was interrupted.\"\n errorTab[10009] = \"A bad file handle was passed.\"\n errorTab[10013] = \"Permission denied.\"\n errorTab[10014] = \"A fault occurred on the network??\" # WSAEFAULT\n errorTab[10022] = \"An invalid operation was attempted.\"\n errorTab[10024] = \"Too many open files.\"\n errorTab[10035] = \"The socket operation would block\"\n errorTab[10036] = \"A blocking operation is already in progress.\"\n errorTab[10037] = \"Operation already in progress.\"\n errorTab[10038] = \"Socket operation on nonsocket.\"\n errorTab[10039] = \"Destination address required.\"\n errorTab[10040] = \"Message too long.\"\n errorTab[10041] = \"Protocol wrong type for socket.\"\n errorTab[10042] = \"Bad protocol option.\"\n errorTab[10043] = \"Protocol not supported.\"\n errorTab[10044] = \"Socket type not supported.\"\n errorTab[10045] = \"Operation not supported.\"\n errorTab[10046] = \"Protocol family not supported.\"\n errorTab[10047] = \"Address family not supported by protocol family.\"\n errorTab[10048] = \"The network address is in use.\"\n errorTab[10049] = \"Cannot assign requested address.\"\n errorTab[10050] = \"Network is down.\"\n errorTab[10051] = \"Network is unreachable.\"\n errorTab[10052] = \"Network dropped connection on reset.\"\n errorTab[10053] = \"Software caused connection abort.\"\n errorTab[10054] = \"The connection has been reset.\"\n errorTab[10055] = \"No buffer space available.\"\n errorTab[10056] = \"Socket is already connected.\"\n errorTab[10057] = \"Socket is not connected.\"\n errorTab[10058] = \"The network has been shut down.\"\n errorTab[10059] = \"Too many references.\"\n errorTab[10060] = \"The operation timed out.\"\n errorTab[10061] = \"Connection refused.\"\n errorTab[10062] = \"Cannot translate name.\"\n errorTab[10063] = \"The name is too long.\"\n errorTab[10064] = \"The host is down.\"\n errorTab[10065] = \"The host is unreachable.\"\n errorTab[10066] = \"Directory not empty.\"\n errorTab[10067] = \"Too many processes.\"\n errorTab[10068] = \"User quota exceeded.\"\n errorTab[10069] = \"Disk quota exceeded.\"\n errorTab[10070] = \"Stale file handle reference.\"\n errorTab[10071] = \"Item is remote.\"\n errorTab[10091] = \"Network subsystem is unavailable.\"\n errorTab[10092] = \"Winsock.dll version out of range.\"\n errorTab[10093] = \"Successful WSAStartup not yet performed.\"\n errorTab[10101] = \"Graceful shutdown in progress.\"\n errorTab[10102] = \"No more results from WSALookupServiceNext.\"\n errorTab[10103] = \"Call has been canceled.\"\n errorTab[10104] = \"Procedure call table is invalid.\"\n errorTab[10105] = \"Service provider is invalid.\"\n errorTab[10106] = \"Service provider failed to initialize.\"\n errorTab[10107] = \"System call failure.\"\n errorTab[10108] = \"Service not found.\"\n errorTab[10109] = \"Class type not found.\"\n errorTab[10110] = \"No more results from WSALookupServiceNext.\"\n errorTab[10111] = \"Call was canceled.\"\n errorTab[10112] = \"Database query was refused.\"\n errorTab[11001] = \"Host not found.\"\n errorTab[11002] = \"Nonauthoritative host not found.\"\n errorTab[11003] = \"This is a nonrecoverable error.\"\n errorTab[11004] = \"Valid name, no data record requested type.\"\n errorTab[11005] = \"QoS receivers.\"\n errorTab[11006] = \"QoS senders.\"\n errorTab[11007] = \"No QoS senders.\"\n errorTab[11008] = \"QoS no receivers.\"\n errorTab[11009] = \"QoS request confirmed.\"\n errorTab[11010] = \"QoS admission error.\"\n errorTab[11011] = \"QoS policy failure.\"\n errorTab[11012] = \"QoS bad style.\"\n errorTab[11013] = \"QoS bad object.\"\n errorTab[11014] = \"QoS traffic control error.\"\n errorTab[11015] = \"QoS generic error.\"\n errorTab[11016] = \"QoS service type error.\"\n errorTab[11017] = \"QoS flowspec error.\"\n errorTab[11018] = \"Invalid QoS provider buffer.\"\n errorTab[11019] = \"Invalid QoS filter style.\"\n errorTab[11020] = \"Invalid QoS filter style.\"\n errorTab[11021] = \"Incorrect QoS filter count.\"\n errorTab[11022] = \"Invalid QoS object length.\"\n errorTab[11023] = \"Incorrect QoS flow count.\"\n errorTab[11024] = \"Unrecognized QoS object.\"\n errorTab[11025] = \"Invalid QoS policy object.\"\n errorTab[11026] = \"Invalid QoS flow descriptor.\"\n errorTab[11027] = \"Invalid QoS provider-specific flowspec.\"\n errorTab[11028] = \"Invalid QoS provider-specific filterspec.\"\n errorTab[11029] = \"Invalid QoS shape discard mode object.\"\n errorTab[11030] = \"Invalid QoS shaping rate object.\"\n errorTab[11031] = \"Reserved policy QoS element type.\"\n __all__.append(\"errorTab\")\n\n\nclass _GiveupOnSendfile(Exception): pass\n\n\nclass socket(_socket.socket):\n\n \"\"\"A subclass of _socket.socket adding the makefile() method.\"\"\"\n\n __slots__ = [\"__weakref__\", \"_io_refs\", \"_closed\"]\n\n def __init__(self, family=-1, type=-1, proto=-1, fileno=None):\n # For user code address family and type values are IntEnum members, but\n # for the underlying _socket.socket they're just integers. The\n # constructor of _socket.socket converts the given argument to an\n # integer automatically.\n if fileno is None:\n if family == -1:\n family = AF_INET\n if type == -1:\n type = SOCK_STREAM\n if proto == -1:\n proto = 0\n _socket.socket.__init__(self, family, type, proto, fileno)\n self._io_refs = 0\n self._closed = False\n\n def __enter__(self):\n return self\n\n def __exit__(self, *args):\n if not self._closed:\n self.close()\n\n def __repr__(self):\n \"\"\"Wrap __repr__() to reveal the real class name and socket\n address(es).\n \"\"\"\n closed = getattr(self, '_closed', False)\n s = \"<%s.%s%s fd=%i, family=%s, type=%s, proto=%i\" \\\n % (self.__class__.__module__,\n self.__class__.__qualname__,\n \" [closed]\" if closed else \"\",\n self.fileno(),\n self.family,\n self.type,\n self.proto)\n if not closed:\n try:\n laddr = self.getsockname()\n if laddr:\n s += \", laddr=%s\" % str(laddr)\n except error:\n pass\n try:\n raddr = self.getpeername()\n if raddr:\n s += \", raddr=%s\" % str(raddr)\n except error:\n pass\n s += '>'\n return s\n\n def __getstate__(self):\n raise TypeError(f\"cannot pickle {self.__class__.__name__!r} object\")\n\n def dup(self):\n \"\"\"dup() -> socket object\n\n Duplicate the socket. Return a new socket object connected to the same\n system resource. The new socket is non-inheritable.\n \"\"\"\n fd = dup(self.fileno())\n sock = self.__class__(self.family, self.type, self.proto, fileno=fd)\n sock.settimeout(self.gettimeout())\n return sock\n\n def accept(self):\n \"\"\"accept() -> (socket object, address info)\n\n Wait for an incoming connection. Return a new socket\n representing the connection, and the address of the client.\n For IP sockets, the address info is a pair (hostaddr, port).\n \"\"\"\n fd, addr = self._accept()\n sock = socket(self.family, self.type, self.proto, fileno=fd)\n # Issue #7995: if no default timeout is set and the listening\n # socket had a (non-zero) timeout, force the new socket in blocking\n # mode to override platform-specific socket flags inheritance.\n if getdefaulttimeout() is None and self.gettimeout():\n sock.setblocking(True)\n return sock, addr\n\n def makefile(self, mode=\"r\", buffering=None, *,\n encoding=None, errors=None, newline=None):\n \"\"\"makefile(...) -> an I/O stream connected to the socket\n\n The arguments are as for io.open() after the filename, except the only\n supported mode values are 'r' (default), 'w' and 'b'.\n \"\"\"\n # XXX refactor to share code?\n if not set(mode) <= {\"r\", \"w\", \"b\"}:\n raise ValueError(\"invalid mode %r (only r, w, b allowed)\" % (mode,))\n writing = \"w\" in mode\n reading = \"r\" in mode or not writing\n assert reading or writing\n binary = \"b\" in mode\n rawmode = \"\"\n if reading:\n rawmode += \"r\"\n if writing:\n rawmode += \"w\"\n raw = SocketIO(self, rawmode)\n self._io_refs += 1\n if buffering is None:\n buffering = -1\n if buffering < 0:\n buffering = io.DEFAULT_BUFFER_SIZE\n if buffering == 0:\n if not binary:\n raise ValueError(\"unbuffered streams must be binary\")\n return raw\n if reading and writing:\n buffer = io.BufferedRWPair(raw, raw, buffering)\n elif reading:\n buffer = io.BufferedReader(raw, buffering)\n else:\n assert writing\n buffer = io.BufferedWriter(raw, buffering)\n if binary:\n return buffer\n text = io.TextIOWrapper(buffer, encoding, errors, newline)\n text.mode = mode\n return text\n\n if hasattr(os, 'sendfile'):\n\n def _sendfile_use_sendfile(self, file, offset=0, count=None):\n self._check_sendfile_params(file, offset, count)\n sockno = self.fileno()\n try:\n fileno = file.fileno()\n except (AttributeError, io.UnsupportedOperation) as err:\n raise _GiveupOnSendfile(err) # not a regular file\n try:\n fsize = os.fstat(fileno).st_size\n except OSError as err:\n raise _GiveupOnSendfile(err) # not a regular file\n if not fsize:\n return 0 # empty file\n # Truncate to 1GiB to avoid OverflowError, see bpo-38319.\n blocksize = min(count or fsize, 2 ** 30)\n timeout = self.gettimeout()\n if timeout == 0:\n raise ValueError(\"non-blocking sockets are not supported\")\n # poll/select have the advantage of not requiring any\n # extra file descriptor, contrarily to epoll/kqueue\n # (also, they require a single syscall).\n if hasattr(selectors, 'PollSelector'):\n selector = selectors.PollSelector()\n else:\n selector = selectors.SelectSelector()\n selector.register(sockno, selectors.EVENT_WRITE)\n\n total_sent = 0\n # localize variable access to minimize overhead\n selector_select = selector.select\n os_sendfile = os.sendfile\n try:\n while True:\n if timeout and not selector_select(timeout):\n raise _socket.timeout('timed out')\n if count:\n blocksize = count - total_sent\n if blocksize <= 0:\n break\n try:\n sent = os_sendfile(sockno, fileno, offset, blocksize)\n except BlockingIOError:\n if not timeout:\n # Block until the socket is ready to send some\n # data; avoids hogging CPU resources.\n selector_select()\n continue\n except OSError as err:\n if total_sent == 0:\n # We can get here for different reasons, the main\n # one being 'file' is not a regular mmap(2)-like\n # file, in which case we'll fall back on using\n # plain send().\n raise _GiveupOnSendfile(err)\n raise err from None\n else:\n if sent == 0:\n break # EOF\n offset += sent\n total_sent += sent\n return total_sent\n finally:\n if total_sent > 0 and hasattr(file, 'seek'):\n file.seek(offset)\n else:\n def _sendfile_use_sendfile(self, file, offset=0, count=None):\n raise _GiveupOnSendfile(\n \"os.sendfile() not available on this platform\")\n\n def _sendfile_use_send(self, file, offset=0, count=None):\n self._check_sendfile_params(file, offset, count)\n if self.gettimeout() == 0:\n raise ValueError(\"non-blocking sockets are not supported\")\n if offset:\n file.seek(offset)\n blocksize = min(count, 8192) if count else 8192\n total_sent = 0\n # localize variable access to minimize overhead\n file_read = file.read\n sock_send = self.send\n try:\n while True:\n if count:\n blocksize = min(count - total_sent, blocksize)\n if blocksize <= 0:\n break\n data = memoryview(file_read(blocksize))\n if not data:\n break # EOF\n while True:\n try:\n sent = sock_send(data)\n except BlockingIOError:\n continue\n else:\n total_sent += sent\n if sent < len(data):\n data = data[sent:]\n else:\n break\n return total_sent\n finally:\n if total_sent > 0 and hasattr(file, 'seek'):\n file.seek(offset + total_sent)\n\n def _check_sendfile_params(self, file, offset, count):\n if 'b' not in getattr(file, 'mode', 'b'):\n raise ValueError(\"file should be opened in binary mode\")\n if not self.type & SOCK_STREAM:\n raise ValueError(\"only SOCK_STREAM type sockets are supported\")\n if count is not None:\n if not isinstance(count, int):\n raise TypeError(\n \"count must be a positive integer (got {!r})\".format(count))\n if count <= 0:\n raise ValueError(\n \"count must be a positive integer (got {!r})\".format(count))\n\n def sendfile(self, file, offset=0, count=None):\n \"\"\"sendfile(file[, offset[, count]]) -> sent\n\n Send a file until EOF is reached by using high-performance\n os.sendfile() and return the total number of bytes which\n were sent.\n *file* must be a regular file object opened in binary mode.\n If os.sendfile() is not available (e.g. Windows) or file is\n not a regular file socket.send() will be used instead.\n *offset* tells from where to start reading the file.\n If specified, *count* is the total number of bytes to transmit\n as opposed to sending the file until EOF is reached.\n File position is updated on return or also in case of error in\n which case file.tell() can be used to figure out the number of\n bytes which were sent.\n The socket must be of SOCK_STREAM type.\n Non-blocking sockets are not supported.\n \"\"\"\n try:\n return self._sendfile_use_sendfile(file, offset, count)\n except _GiveupOnSendfile:\n return self._sendfile_use_send(file, offset, count)\n\n def _decref_socketios(self):\n if self._io_refs > 0:\n self._io_refs -= 1\n if self._closed:\n self.close()\n\n def _real_close(self, _ss=_socket.socket):\n # This function should not reference any globals. See issue #808164.\n _ss.close(self)\n\n def close(self):\n # This function should not reference any globals. See issue #808164.\n self._closed = True\n if self._io_refs <= 0:\n self._real_close()\n\n def detach(self):\n \"\"\"detach() -> file descriptor\n\n Close the socket object without closing the underlying file descriptor.\n The object cannot be used after this call, but the file descriptor\n can be reused for other purposes. The file descriptor is returned.\n \"\"\"\n self._closed = True\n return super().detach()\n\n @property\n def family(self):\n \"\"\"Read-only access to the address family for this socket.\n \"\"\"\n return _intenum_converter(super().family, AddressFamily)\n\n @property\n def type(self):\n \"\"\"Read-only access to the socket type.\n \"\"\"\n return _intenum_converter(super().type, SocketKind)\n\n if os.name == 'nt':\n def get_inheritable(self):\n return os.get_handle_inheritable(self.fileno())\n def set_inheritable(self, inheritable):\n os.set_handle_inheritable(self.fileno(), inheritable)\n else:\n def get_inheritable(self):\n return os.get_inheritable(self.fileno())\n def set_inheritable(self, inheritable):\n os.set_inheritable(self.fileno(), inheritable)\n get_inheritable.__doc__ = \"Get the inheritable flag of the socket\"\n set_inheritable.__doc__ = \"Set the inheritable flag of the socket\"\n\ndef fromfd(fd, family, type, proto=0):\n \"\"\" fromfd(fd, family, type[, proto]) -> socket object\n\n Create a socket object from a duplicate of the given file\n descriptor. The remaining arguments are the same as for socket().\n \"\"\"\n nfd = dup(fd)\n return socket(family, type, proto, nfd)\n\nif hasattr(_socket.socket, \"share\"):\n def fromshare(info):\n \"\"\" fromshare(info) -> socket object\n\n Create a socket object from the bytes object returned by\n socket.share(pid).\n \"\"\"\n return socket(0, 0, 0, info)\n __all__.append(\"fromshare\")\n\nif hasattr(_socket, \"socketpair\"):\n\n def socketpair(family=None, type=SOCK_STREAM, proto=0):\n \"\"\"socketpair([family[, type[, proto]]]) -> (socket object, socket object)\n\n Create a pair of socket objects from the sockets returned by the platform\n socketpair() function.\n The arguments are the same as for socket() except the default family is\n AF_UNIX if defined on the platform; otherwise, the default is AF_INET.\n \"\"\"\n if family is None:\n try:\n family = AF_UNIX\n except NameError:\n family = AF_INET\n a, b = _socket.socketpair(family, type, proto)\n a = socket(family, type, proto, a.detach())\n b = socket(family, type, proto, b.detach())\n return a, b\n\nelse:\n\n # Origin: https://gist.github.com/4325783, by Geert Jansen. Public domain.\n def socketpair(family=AF_INET, type=SOCK_STREAM, proto=0):\n if family == AF_INET:\n host = _LOCALHOST\n elif family == AF_INET6:\n host = _LOCALHOST_V6\n else:\n raise ValueError(\"Only AF_INET and AF_INET6 socket address families \"\n \"are supported\")\n if type != SOCK_STREAM:\n raise ValueError(\"Only SOCK_STREAM socket type is supported\")\n if proto != 0:\n raise ValueError(\"Only protocol zero is supported\")\n\n # We create a connected TCP socket. Note the trick with\n # setblocking(False) that prevents us from having to create a thread.\n lsock = socket(family, type, proto)\n try:\n lsock.bind((host, 0))\n lsock.listen()\n # On IPv6, ignore flow_info and scope_id\n addr, port = lsock.getsockname()[:2]\n csock = socket(family, type, proto)\n try:\n csock.setblocking(False)\n try:\n csock.connect((addr, port))\n except (BlockingIOError, InterruptedError):\n pass\n csock.setblocking(True)\n ssock, _ = lsock.accept()\n except:\n csock.close()\n raise\n finally:\n lsock.close()\n return (ssock, csock)\n __all__.append(\"socketpair\")\n\nsocketpair.__doc__ = \"\"\"socketpair([family[, type[, proto]]]) -> (socket object, socket object)\nCreate a pair of socket objects from the sockets returned by the platform\nsocketpair() function.\nThe arguments are the same as for socket() except the default family is AF_UNIX\nif defined on the platform; otherwise, the default is AF_INET.\n\"\"\"\n\n_blocking_errnos = { EAGAIN, EWOULDBLOCK }\n\nclass SocketIO(io.RawIOBase):\n\n \"\"\"Raw I/O implementation for stream sockets.\n\n This class supports the makefile() method on sockets. It provides\n the raw I/O interface on top of a socket object.\n \"\"\"\n\n # One might wonder why not let FileIO do the job instead. There are two\n # main reasons why FileIO is not adapted:\n # - it wouldn't work under Windows (where you can't used read() and\n # write() on a socket handle)\n # - it wouldn't work with socket timeouts (FileIO would ignore the\n # timeout and consider the socket non-blocking)\n\n # XXX More docs\n\n def __init__(self, sock, mode):\n if mode not in (\"r\", \"w\", \"rw\", \"rb\", \"wb\", \"rwb\"):\n raise ValueError(\"invalid mode: %r\" % mode)\n io.RawIOBase.__init__(self)\n self._sock = sock\n if \"b\" not in mode:\n mode += \"b\"\n self._mode = mode\n self._reading = \"r\" in mode\n self._writing = \"w\" in mode\n self._timeout_occurred = False\n\n def readinto(self, b):\n \"\"\"Read up to len(b) bytes into the writable buffer *b* and return\n the number of bytes read. If the socket is non-blocking and no bytes\n are available, None is returned.\n\n If *b* is non-empty, a 0 return value indicates that the connection\n was shutdown at the other end.\n \"\"\"\n self._checkClosed()\n self._checkReadable()\n if self._timeout_occurred:\n raise OSError(\"cannot read from timed out object\")\n while True:\n try:\n return self._sock.recv_into(b)\n except timeout:\n self._timeout_occurred = True\n raise\n except error as e:\n if e.args[0] in _blocking_errnos:\n return None\n raise\n\n def write(self, b):\n \"\"\"Write the given bytes or bytearray object *b* to the socket\n and return the number of bytes written. This can be less than\n len(b) if not all data could be written. If the socket is\n non-blocking and no bytes could be written None is returned.\n \"\"\"\n self._checkClosed()\n self._checkWritable()\n try:\n return self._sock.send(b)\n except error as e:\n # XXX what about EINTR?\n if e.args[0] in _blocking_errnos:\n return None\n raise\n\n def readable(self):\n \"\"\"True if the SocketIO is open for reading.\n \"\"\"\n if self.closed:\n raise ValueError(\"I/O operation on closed socket.\")\n return self._reading\n\n def writable(self):\n \"\"\"True if the SocketIO is open for writing.\n \"\"\"\n if self.closed:\n raise ValueError(\"I/O operation on closed socket.\")\n return self._writing\n\n def seekable(self):\n \"\"\"True if the SocketIO is open for seeking.\n \"\"\"\n if self.closed:\n raise ValueError(\"I/O operation on closed socket.\")\n return super().seekable()\n\n def fileno(self):\n \"\"\"Return the file descriptor of the underlying socket.\n \"\"\"\n self._checkClosed()\n return self._sock.fileno()\n\n @property\n def name(self):\n if not self.closed:\n return self.fileno()\n else:\n return -1\n\n @property\n def mode(self):\n return self._mode\n\n def close(self):\n \"\"\"Close the SocketIO object. This doesn't close the underlying\n socket, except if all references to it have disappeared.\n \"\"\"\n if self.closed:\n return\n io.RawIOBase.close(self)\n self._sock._decref_socketios()\n self._sock = None\n\n\ndef getfqdn(name=''):\n \"\"\"Get fully qualified domain name from name.\n\n An empty argument is interpreted as meaning the local host.\n\n First the hostname returned by gethostbyaddr() is checked, then\n possibly existing aliases. In case no FQDN is available, hostname\n from gethostname() is returned.\n \"\"\"\n name = name.strip()\n if not name or name == '0.0.0.0':\n name = gethostname()\n try:\n hostname, aliases, ipaddrs = gethostbyaddr(name)\n except error:\n pass\n else:\n aliases.insert(0, hostname)\n for name in aliases:\n if '.' in name:\n break\n else:\n name = hostname\n return name\n\n\n_GLOBAL_DEFAULT_TIMEOUT = object()\n\ndef create_connection(address, timeout=_GLOBAL_DEFAULT_TIMEOUT,\n source_address=None):\n \"\"\"Connect to *address* and return the socket object.\n\n Convenience function. Connect to *address* (a 2-tuple ``(host,\n port)``) and return the socket object. Passing the optional\n *timeout* parameter will set the timeout on the socket instance\n before attempting to connect. If no *timeout* is supplied, the\n global default timeout setting returned by :func:`getdefaulttimeout`\n is used. If *source_address* is set it must be a tuple of (host, port)\n for the socket to bind as a source address before making the connection.\n A host of '' or port 0 tells the OS to use the default.\n \"\"\"\n\n host, port = address\n err = None\n for res in getaddrinfo(host, port, 0, SOCK_STREAM):\n af, socktype, proto, canonname, sa = res\n sock = None\n try:\n sock = socket(af, socktype, proto)\n if timeout is not _GLOBAL_DEFAULT_TIMEOUT:\n sock.settimeout(timeout)\n if source_address:\n sock.bind(source_address)\n sock.connect(sa)\n # Break explicitly a reference cycle\n err = None\n return sock\n\n except error as _:\n err = _\n if sock is not None:\n sock.close()\n\n if err is not None:\n try:\n raise err\n finally:\n # Break explicitly a reference cycle\n err = None\n else:\n raise error(\"getaddrinfo returns an empty list\")\n\n\ndef has_dualstack_ipv6():\n \"\"\"Return True if the platform supports creating a SOCK_STREAM socket\n which can handle both AF_INET and AF_INET6 (IPv4 / IPv6) connections.\n \"\"\"\n if not has_ipv6 \\\n or not hasattr(_socket, 'IPPROTO_IPV6') \\\n or not hasattr(_socket, 'IPV6_V6ONLY'):\n return False\n try:\n with socket(AF_INET6, SOCK_STREAM) as sock:\n sock.setsockopt(IPPROTO_IPV6, IPV6_V6ONLY, 0)\n return True\n except error:\n return False\n\n\ndef create_server(address, *, family=AF_INET, backlog=None, reuse_port=False,\n dualstack_ipv6=False):\n \"\"\"Convenience function which creates a SOCK_STREAM type socket\n bound to *address* (a 2-tuple (host, port)) and return the socket\n object.\n\n *family* should be either AF_INET or AF_INET6.\n *backlog* is the queue size passed to socket.listen().\n *reuse_port* dictates whether to use the SO_REUSEPORT socket option.\n *dualstack_ipv6*: if true and the platform supports it, it will\n create an AF_INET6 socket able to accept both IPv4 or IPv6\n connections. When false it will explicitly disable this option on\n platforms that enable it by default (e.g. Linux).\n\n >>> with create_server(('', 8000)) as server:\n ... while True:\n ... conn, addr = server.accept()\n ... # handle new connection\n \"\"\"\n if reuse_port and not hasattr(_socket, \"SO_REUSEPORT\"):\n raise ValueError(\"SO_REUSEPORT not supported on this platform\")\n if dualstack_ipv6:\n if not has_dualstack_ipv6():\n raise ValueError(\"dualstack_ipv6 not supported on this platform\")\n if family != AF_INET6:\n raise ValueError(\"dualstack_ipv6 requires AF_INET6 family\")\n sock = socket(family, SOCK_STREAM)\n try:\n # Note about Windows. We don't set SO_REUSEADDR because:\n # 1) It's unnecessary: bind() will succeed even in case of a\n # previous closed socket on the same address and still in\n # TIME_WAIT state.\n # 2) If set, another socket is free to bind() on the same\n # address, effectively preventing this one from accepting\n # connections. Also, it may set the process in a state where\n # it'll no longer respond to any signals or graceful kills.\n # See: msdn2.microsoft.com/en-us/library/ms740621(VS.85).aspx\n if os.name not in ('nt', 'cygwin') and \\\n hasattr(_socket, 'SO_REUSEADDR'):\n try:\n sock.setsockopt(SOL_SOCKET, SO_REUSEADDR, 1)\n except error:\n # Fail later on bind(), for platforms which may not\n # support this option.\n pass\n if reuse_port:\n sock.setsockopt(SOL_SOCKET, SO_REUSEPORT, 1)\n if has_ipv6 and family == AF_INET6:\n if dualstack_ipv6:\n sock.setsockopt(IPPROTO_IPV6, IPV6_V6ONLY, 0)\n elif hasattr(_socket, \"IPV6_V6ONLY\") and \\\n hasattr(_socket, \"IPPROTO_IPV6\"):\n sock.setsockopt(IPPROTO_IPV6, IPV6_V6ONLY, 1)\n try:\n sock.bind(address)\n except error as err:\n msg = '%s (while attempting to bind on address %r)' % \\\n (err.strerror, address)\n raise error(err.errno, msg) from None\n if backlog is None:\n sock.listen()\n else:\n sock.listen(backlog)\n return sock\n except error:\n sock.close()\n raise\n\n\ndef getaddrinfo(host, port, family=0, type=0, proto=0, flags=0):\n \"\"\"Resolve host and port into list of address info entries.\n\n Translate the host/port argument into a sequence of 5-tuples that contain\n all the necessary arguments for creating a socket connected to that service.\n host is a domain name, a string representation of an IPv4/v6 address or\n None. port is a string service name such as 'http', a numeric port number or\n None. By passing None as the value of host and port, you can pass NULL to\n the underlying C API.\n\n The family, type and proto arguments can be optionally specified in order to\n narrow the list of addresses returned. Passing zero as a value for each of\n these arguments selects the full range of results.\n \"\"\"\n # We override this function since we want to translate the numeric family\n # and socket type values to enum constants.\n addrlist = []\n for res in _socket.getaddrinfo(host, port, family, type, proto, flags):\n af, socktype, proto, canonname, sa = res\n addrlist.append((_intenum_converter(af, AddressFamily),\n _intenum_converter(socktype, SocketKind),\n proto, canonname, sa))\n return addrlist\n", 923], "/usr/lib/python3.8/asyncio/selector_events.py": ["\"\"\"Event loop using a selector and related classes.\n\nA selector is a \"notify-when-ready\" multiplexer. For a subclass which\nalso includes support for signal handling, see the unix_events sub-module.\n\"\"\"\n\n__all__ = 'BaseSelectorEventLoop',\n\nimport collections\nimport errno\nimport functools\nimport selectors\nimport socket\nimport warnings\nimport weakref\ntry:\n import ssl\nexcept ImportError: # pragma: no cover\n ssl = None\n\nfrom . import base_events\nfrom . import constants\nfrom . import events\nfrom . import futures\nfrom . import protocols\nfrom . import sslproto\nfrom . import transports\nfrom . import trsock\nfrom .log import logger\n\n\ndef _test_selector_event(selector, fd, event):\n # Test if the selector is monitoring 'event' events\n # for the file descriptor 'fd'.\n try:\n key = selector.get_key(fd)\n except KeyError:\n return False\n else:\n return bool(key.events & event)\n\n\ndef _check_ssl_socket(sock):\n if ssl is not None and isinstance(sock, ssl.SSLSocket):\n raise TypeError(\"Socket cannot be of type SSLSocket\")\n\n\nclass BaseSelectorEventLoop(base_events.BaseEventLoop):\n \"\"\"Selector event loop.\n\n See events.EventLoop for API specification.\n \"\"\"\n\n def __init__(self, selector=None):\n super().__init__()\n\n if selector is None:\n selector = selectors.DefaultSelector()\n logger.debug('Using selector: %s', selector.__class__.__name__)\n self._selector = selector\n self._make_self_pipe()\n self._transports = weakref.WeakValueDictionary()\n\n def _make_socket_transport(self, sock, protocol, waiter=None, *,\n extra=None, server=None):\n return _SelectorSocketTransport(self, sock, protocol, waiter,\n extra, server)\n\n def _make_ssl_transport(\n self, rawsock, protocol, sslcontext, waiter=None,\n *, server_side=False, server_hostname=None,\n extra=None, server=None,\n ssl_handshake_timeout=constants.SSL_HANDSHAKE_TIMEOUT):\n ssl_protocol = sslproto.SSLProtocol(\n self, protocol, sslcontext, waiter,\n server_side, server_hostname,\n ssl_handshake_timeout=ssl_handshake_timeout)\n _SelectorSocketTransport(self, rawsock, ssl_protocol,\n extra=extra, server=server)\n return ssl_protocol._app_transport\n\n def _make_datagram_transport(self, sock, protocol,\n address=None, waiter=None, extra=None):\n return _SelectorDatagramTransport(self, sock, protocol,\n address, waiter, extra)\n\n def close(self):\n if self.is_running():\n raise RuntimeError(\"Cannot close a running event loop\")\n if self.is_closed():\n return\n self._close_self_pipe()\n super().close()\n if self._selector is not None:\n self._selector.close()\n self._selector = None\n\n def _close_self_pipe(self):\n self._remove_reader(self._ssock.fileno())\n self._ssock.close()\n self._ssock = None\n self._csock.close()\n self._csock = None\n self._internal_fds -= 1\n\n def _make_self_pipe(self):\n # A self-socket, really. :-)\n self._ssock, self._csock = socket.socketpair()\n self._ssock.setblocking(False)\n self._csock.setblocking(False)\n self._internal_fds += 1\n self._add_reader(self._ssock.fileno(), self._read_from_self)\n\n def _process_self_data(self, data):\n pass\n\n def _read_from_self(self):\n while True:\n try:\n data = self._ssock.recv(4096)\n if not data:\n break\n self._process_self_data(data)\n except InterruptedError:\n continue\n except BlockingIOError:\n break\n\n def _write_to_self(self):\n # This may be called from a different thread, possibly after\n # _close_self_pipe() has been called or even while it is\n # running. Guard for self._csock being None or closed. When\n # a socket is closed, send() raises OSError (with errno set to\n # EBADF, but let's not rely on the exact error code).\n csock = self._csock\n if csock is None:\n return\n\n try:\n csock.send(b'\\0')\n except OSError:\n if self._debug:\n logger.debug(\"Fail to write a null byte into the \"\n \"self-pipe socket\",\n exc_info=True)\n\n def _start_serving(self, protocol_factory, sock,\n sslcontext=None, server=None, backlog=100,\n ssl_handshake_timeout=constants.SSL_HANDSHAKE_TIMEOUT):\n self._add_reader(sock.fileno(), self._accept_connection,\n protocol_factory, sock, sslcontext, server, backlog,\n ssl_handshake_timeout)\n\n def _accept_connection(\n self, protocol_factory, sock,\n sslcontext=None, server=None, backlog=100,\n ssl_handshake_timeout=constants.SSL_HANDSHAKE_TIMEOUT):\n # This method is only called once for each event loop tick where the\n # listening socket has triggered an EVENT_READ. There may be multiple\n # connections waiting for an .accept() so it is called in a loop.\n # See https://bugs.python.org/issue27906 for more details.\n for _ in range(backlog):\n try:\n conn, addr = sock.accept()\n if self._debug:\n logger.debug(\"%r got a new connection from %r: %r\",\n server, addr, conn)\n conn.setblocking(False)\n except (BlockingIOError, InterruptedError, ConnectionAbortedError):\n # Early exit because the socket accept buffer is empty.\n return None\n except OSError as exc:\n # There's nowhere to send the error, so just log it.\n if exc.errno in (errno.EMFILE, errno.ENFILE,\n errno.ENOBUFS, errno.ENOMEM):\n # Some platforms (e.g. Linux keep reporting the FD as\n # ready, so we remove the read handler temporarily.\n # We'll try again in a while.\n self.call_exception_handler({\n 'message': 'socket.accept() out of system resource',\n 'exception': exc,\n 'socket': trsock.TransportSocket(sock),\n })\n self._remove_reader(sock.fileno())\n self.call_later(constants.ACCEPT_RETRY_DELAY,\n self._start_serving,\n protocol_factory, sock, sslcontext, server,\n backlog, ssl_handshake_timeout)\n else:\n raise # The event loop will catch, log and ignore it.\n else:\n extra = {'peername': addr}\n accept = self._accept_connection2(\n protocol_factory, conn, extra, sslcontext, server,\n ssl_handshake_timeout)\n self.create_task(accept)\n\n async def _accept_connection2(\n self, protocol_factory, conn, extra,\n sslcontext=None, server=None,\n ssl_handshake_timeout=constants.SSL_HANDSHAKE_TIMEOUT):\n protocol = None\n transport = None\n try:\n protocol = protocol_factory()\n waiter = self.create_future()\n if sslcontext:\n transport = self._make_ssl_transport(\n conn, protocol, sslcontext, waiter=waiter,\n server_side=True, extra=extra, server=server,\n ssl_handshake_timeout=ssl_handshake_timeout)\n else:\n transport = self._make_socket_transport(\n conn, protocol, waiter=waiter, extra=extra,\n server=server)\n\n try:\n await waiter\n except BaseException:\n transport.close()\n raise\n # It's now up to the protocol to handle the connection.\n\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n if self._debug:\n context = {\n 'message':\n 'Error on transport creation for incoming connection',\n 'exception': exc,\n }\n if protocol is not None:\n context['protocol'] = protocol\n if transport is not None:\n context['transport'] = transport\n self.call_exception_handler(context)\n\n def _ensure_fd_no_transport(self, fd):\n fileno = fd\n if not isinstance(fileno, int):\n try:\n fileno = int(fileno.fileno())\n except (AttributeError, TypeError, ValueError):\n # This code matches selectors._fileobj_to_fd function.\n raise ValueError(f\"Invalid file object: {fd!r}\") from None\n try:\n transport = self._transports[fileno]\n except KeyError:\n pass\n else:\n if not transport.is_closing():\n raise RuntimeError(\n f'File descriptor {fd!r} is used by transport '\n f'{transport!r}')\n\n def _add_reader(self, fd, callback, *args):\n self._check_closed()\n handle = events.Handle(callback, args, self, None)\n try:\n key = self._selector.get_key(fd)\n except KeyError:\n self._selector.register(fd, selectors.EVENT_READ,\n (handle, None))\n else:\n mask, (reader, writer) = key.events, key.data\n self._selector.modify(fd, mask | selectors.EVENT_READ,\n (handle, writer))\n if reader is not None:\n reader.cancel()\n\n def _remove_reader(self, fd):\n if self.is_closed():\n return False\n try:\n key = self._selector.get_key(fd)\n except KeyError:\n return False\n else:\n mask, (reader, writer) = key.events, key.data\n mask &= ~selectors.EVENT_READ\n if not mask:\n self._selector.unregister(fd)\n else:\n self._selector.modify(fd, mask, (None, writer))\n\n if reader is not None:\n reader.cancel()\n return True\n else:\n return False\n\n def _add_writer(self, fd, callback, *args):\n self._check_closed()\n handle = events.Handle(callback, args, self, None)\n try:\n key = self._selector.get_key(fd)\n except KeyError:\n self._selector.register(fd, selectors.EVENT_WRITE,\n (None, handle))\n else:\n mask, (reader, writer) = key.events, key.data\n self._selector.modify(fd, mask | selectors.EVENT_WRITE,\n (reader, handle))\n if writer is not None:\n writer.cancel()\n\n def _remove_writer(self, fd):\n \"\"\"Remove a writer callback.\"\"\"\n if self.is_closed():\n return False\n try:\n key = self._selector.get_key(fd)\n except KeyError:\n return False\n else:\n mask, (reader, writer) = key.events, key.data\n # Remove both writer and connector.\n mask &= ~selectors.EVENT_WRITE\n if not mask:\n self._selector.unregister(fd)\n else:\n self._selector.modify(fd, mask, (reader, None))\n\n if writer is not None:\n writer.cancel()\n return True\n else:\n return False\n\n def add_reader(self, fd, callback, *args):\n \"\"\"Add a reader callback.\"\"\"\n self._ensure_fd_no_transport(fd)\n return self._add_reader(fd, callback, *args)\n\n def remove_reader(self, fd):\n \"\"\"Remove a reader callback.\"\"\"\n self._ensure_fd_no_transport(fd)\n return self._remove_reader(fd)\n\n def add_writer(self, fd, callback, *args):\n \"\"\"Add a writer callback..\"\"\"\n self._ensure_fd_no_transport(fd)\n return self._add_writer(fd, callback, *args)\n\n def remove_writer(self, fd):\n \"\"\"Remove a writer callback.\"\"\"\n self._ensure_fd_no_transport(fd)\n return self._remove_writer(fd)\n\n async def sock_recv(self, sock, n):\n \"\"\"Receive data from the socket.\n\n The return value is a bytes object representing the data received.\n The maximum amount of data to be received at once is specified by\n nbytes.\n \"\"\"\n _check_ssl_socket(sock)\n if self._debug and sock.gettimeout() != 0:\n raise ValueError(\"the socket must be non-blocking\")\n try:\n return sock.recv(n)\n except (BlockingIOError, InterruptedError):\n pass\n fut = self.create_future()\n fd = sock.fileno()\n self.add_reader(fd, self._sock_recv, fut, sock, n)\n fut.add_done_callback(\n functools.partial(self._sock_read_done, fd))\n return await fut\n\n def _sock_read_done(self, fd, fut):\n self.remove_reader(fd)\n\n def _sock_recv(self, fut, sock, n):\n # _sock_recv() can add itself as an I/O callback if the operation can't\n # be done immediately. Don't use it directly, call sock_recv().\n if fut.done():\n return\n try:\n data = sock.recv(n)\n except (BlockingIOError, InterruptedError):\n return # try again next time\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n fut.set_exception(exc)\n else:\n fut.set_result(data)\n\n async def sock_recv_into(self, sock, buf):\n \"\"\"Receive data from the socket.\n\n The received data is written into *buf* (a writable buffer).\n The return value is the number of bytes written.\n \"\"\"\n _check_ssl_socket(sock)\n if self._debug and sock.gettimeout() != 0:\n raise ValueError(\"the socket must be non-blocking\")\n try:\n return sock.recv_into(buf)\n except (BlockingIOError, InterruptedError):\n pass\n fut = self.create_future()\n fd = sock.fileno()\n self.add_reader(fd, self._sock_recv_into, fut, sock, buf)\n fut.add_done_callback(\n functools.partial(self._sock_read_done, fd))\n return await fut\n\n def _sock_recv_into(self, fut, sock, buf):\n # _sock_recv_into() can add itself as an I/O callback if the operation\n # can't be done immediately. Don't use it directly, call\n # sock_recv_into().\n if fut.done():\n return\n try:\n nbytes = sock.recv_into(buf)\n except (BlockingIOError, InterruptedError):\n return # try again next time\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n fut.set_exception(exc)\n else:\n fut.set_result(nbytes)\n\n async def sock_sendall(self, sock, data):\n \"\"\"Send data to the socket.\n\n The socket must be connected to a remote socket. This method continues\n to send data from data until either all data has been sent or an\n error occurs. None is returned on success. On error, an exception is\n raised, and there is no way to determine how much data, if any, was\n successfully processed by the receiving end of the connection.\n \"\"\"\n _check_ssl_socket(sock)\n if self._debug and sock.gettimeout() != 0:\n raise ValueError(\"the socket must be non-blocking\")\n try:\n n = sock.send(data)\n except (BlockingIOError, InterruptedError):\n n = 0\n\n if n == len(data):\n # all data sent\n return\n\n fut = self.create_future()\n fd = sock.fileno()\n fut.add_done_callback(\n functools.partial(self._sock_write_done, fd))\n # use a trick with a list in closure to store a mutable state\n self.add_writer(fd, self._sock_sendall, fut, sock,\n memoryview(data), [n])\n return await fut\n\n def _sock_sendall(self, fut, sock, view, pos):\n if fut.done():\n # Future cancellation can be scheduled on previous loop iteration\n return\n start = pos[0]\n try:\n n = sock.send(view[start:])\n except (BlockingIOError, InterruptedError):\n return\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n fut.set_exception(exc)\n return\n\n start += n\n\n if start == len(view):\n fut.set_result(None)\n else:\n pos[0] = start\n\n async def sock_connect(self, sock, address):\n \"\"\"Connect to a remote socket at address.\n\n This method is a coroutine.\n \"\"\"\n _check_ssl_socket(sock)\n if self._debug and sock.gettimeout() != 0:\n raise ValueError(\"the socket must be non-blocking\")\n\n if not hasattr(socket, 'AF_UNIX') or sock.family != socket.AF_UNIX:\n resolved = await self._ensure_resolved(\n address, family=sock.family, proto=sock.proto, loop=self)\n _, _, _, _, address = resolved[0]\n\n fut = self.create_future()\n self._sock_connect(fut, sock, address)\n return await fut\n\n def _sock_connect(self, fut, sock, address):\n fd = sock.fileno()\n try:\n sock.connect(address)\n except (BlockingIOError, InterruptedError):\n # Issue #23618: When the C function connect() fails with EINTR, the\n # connection runs in background. We have to wait until the socket\n # becomes writable to be notified when the connection succeed or\n # fails.\n fut.add_done_callback(\n functools.partial(self._sock_write_done, fd))\n self.add_writer(fd, self._sock_connect_cb, fut, sock, address)\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n fut.set_exception(exc)\n else:\n fut.set_result(None)\n\n def _sock_write_done(self, fd, fut):\n self.remove_writer(fd)\n\n def _sock_connect_cb(self, fut, sock, address):\n if fut.done():\n return\n\n try:\n err = sock.getsockopt(socket.SOL_SOCKET, socket.SO_ERROR)\n if err != 0:\n # Jump to any except clause below.\n raise OSError(err, f'Connect call failed {address}')\n except (BlockingIOError, InterruptedError):\n # socket is still registered, the callback will be retried later\n pass\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n fut.set_exception(exc)\n else:\n fut.set_result(None)\n\n async def sock_accept(self, sock):\n \"\"\"Accept a connection.\n\n The socket must be bound to an address and listening for connections.\n The return value is a pair (conn, address) where conn is a new socket\n object usable to send and receive data on the connection, and address\n is the address bound to the socket on the other end of the connection.\n \"\"\"\n _check_ssl_socket(sock)\n if self._debug and sock.gettimeout() != 0:\n raise ValueError(\"the socket must be non-blocking\")\n fut = self.create_future()\n self._sock_accept(fut, False, sock)\n return await fut\n\n def _sock_accept(self, fut, registered, sock):\n fd = sock.fileno()\n if registered:\n self.remove_reader(fd)\n if fut.done():\n return\n try:\n conn, address = sock.accept()\n conn.setblocking(False)\n except (BlockingIOError, InterruptedError):\n self.add_reader(fd, self._sock_accept, fut, True, sock)\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n fut.set_exception(exc)\n else:\n fut.set_result((conn, address))\n\n async def _sendfile_native(self, transp, file, offset, count):\n del self._transports[transp._sock_fd]\n resume_reading = transp.is_reading()\n transp.pause_reading()\n await transp._make_empty_waiter()\n try:\n return await self.sock_sendfile(transp._sock, file, offset, count,\n fallback=False)\n finally:\n transp._reset_empty_waiter()\n if resume_reading:\n transp.resume_reading()\n self._transports[transp._sock_fd] = transp\n\n def _process_events(self, event_list):\n for key, mask in event_list:\n fileobj, (reader, writer) = key.fileobj, key.data\n if mask & selectors.EVENT_READ and reader is not None:\n if reader._cancelled:\n self._remove_reader(fileobj)\n else:\n self._add_callback(reader)\n if mask & selectors.EVENT_WRITE and writer is not None:\n if writer._cancelled:\n self._remove_writer(fileobj)\n else:\n self._add_callback(writer)\n\n def _stop_serving(self, sock):\n self._remove_reader(sock.fileno())\n sock.close()\n\n\nclass _SelectorTransport(transports._FlowControlMixin,\n transports.Transport):\n\n max_size = 256 * 1024 # Buffer size passed to recv().\n\n _buffer_factory = bytearray # Constructs initial value for self._buffer.\n\n # Attribute used in the destructor: it must be set even if the constructor\n # is not called (see _SelectorSslTransport which may start by raising an\n # exception)\n _sock = None\n\n def __init__(self, loop, sock, protocol, extra=None, server=None):\n super().__init__(extra, loop)\n self._extra['socket'] = trsock.TransportSocket(sock)\n try:\n self._extra['sockname'] = sock.getsockname()\n except OSError:\n self._extra['sockname'] = None\n if 'peername' not in self._extra:\n try:\n self._extra['peername'] = sock.getpeername()\n except socket.error:\n self._extra['peername'] = None\n self._sock = sock\n self._sock_fd = sock.fileno()\n\n self._protocol_connected = False\n self.set_protocol(protocol)\n\n self._server = server\n self._buffer = self._buffer_factory()\n self._conn_lost = 0 # Set when call to connection_lost scheduled.\n self._closing = False # Set when close() called.\n if self._server is not None:\n self._server._attach()\n loop._transports[self._sock_fd] = self\n\n def __repr__(self):\n info = [self.__class__.__name__]\n if self._sock is None:\n info.append('closed')\n elif self._closing:\n info.append('closing')\n info.append(f'fd={self._sock_fd}')\n # test if the transport was closed\n if self._loop is not None and not self._loop.is_closed():\n polling = _test_selector_event(self._loop._selector,\n self._sock_fd, selectors.EVENT_READ)\n if polling:\n info.append('read=polling')\n else:\n info.append('read=idle')\n\n polling = _test_selector_event(self._loop._selector,\n self._sock_fd,\n selectors.EVENT_WRITE)\n if polling:\n state = 'polling'\n else:\n state = 'idle'\n\n bufsize = self.get_write_buffer_size()\n info.append(f'write=<{state}, bufsize={bufsize}>')\n return '<{}>'.format(' '.join(info))\n\n def abort(self):\n self._force_close(None)\n\n def set_protocol(self, protocol):\n self._protocol = protocol\n self._protocol_connected = True\n\n def get_protocol(self):\n return self._protocol\n\n def is_closing(self):\n return self._closing\n\n def close(self):\n if self._closing:\n return\n self._closing = True\n self._loop._remove_reader(self._sock_fd)\n if not self._buffer:\n self._conn_lost += 1\n self._loop._remove_writer(self._sock_fd)\n self._loop.call_soon(self._call_connection_lost, None)\n\n def __del__(self, _warn=warnings.warn):\n if self._sock is not None:\n _warn(f\"unclosed transport {self!r}\", ResourceWarning, source=self)\n self._sock.close()\n\n def _fatal_error(self, exc, message='Fatal error on transport'):\n # Should be called from exception handler only.\n if isinstance(exc, OSError):\n if self._loop.get_debug():\n logger.debug(\"%r: %s\", self, message, exc_info=True)\n else:\n self._loop.call_exception_handler({\n 'message': message,\n 'exception': exc,\n 'transport': self,\n 'protocol': self._protocol,\n })\n self._force_close(exc)\n\n def _force_close(self, exc):\n if self._conn_lost:\n return\n if self._buffer:\n self._buffer.clear()\n self._loop._remove_writer(self._sock_fd)\n if not self._closing:\n self._closing = True\n self._loop._remove_reader(self._sock_fd)\n self._conn_lost += 1\n self._loop.call_soon(self._call_connection_lost, exc)\n\n def _call_connection_lost(self, exc):\n try:\n if self._protocol_connected:\n self._protocol.connection_lost(exc)\n finally:\n self._sock.close()\n self._sock = None\n self._protocol = None\n self._loop = None\n server = self._server\n if server is not None:\n server._detach()\n self._server = None\n\n def get_write_buffer_size(self):\n return len(self._buffer)\n\n def _add_reader(self, fd, callback, *args):\n if self._closing:\n return\n\n self._loop._add_reader(fd, callback, *args)\n\n\nclass _SelectorSocketTransport(_SelectorTransport):\n\n _start_tls_compatible = True\n _sendfile_compatible = constants._SendfileMode.TRY_NATIVE\n\n def __init__(self, loop, sock, protocol, waiter=None,\n extra=None, server=None):\n\n self._read_ready_cb = None\n super().__init__(loop, sock, protocol, extra, server)\n self._eof = False\n self._paused = False\n self._empty_waiter = None\n\n # Disable the Nagle algorithm -- small writes will be\n # sent without waiting for the TCP ACK. This generally\n # decreases the latency (in some cases significantly.)\n base_events._set_nodelay(self._sock)\n\n self._loop.call_soon(self._protocol.connection_made, self)\n # only start reading when connection_made() has been called\n self._loop.call_soon(self._add_reader,\n self._sock_fd, self._read_ready)\n if waiter is not None:\n # only wake up the waiter when connection_made() has been called\n self._loop.call_soon(futures._set_result_unless_cancelled,\n waiter, None)\n\n def set_protocol(self, protocol):\n if isinstance(protocol, protocols.BufferedProtocol):\n self._read_ready_cb = self._read_ready__get_buffer\n else:\n self._read_ready_cb = self._read_ready__data_received\n\n super().set_protocol(protocol)\n\n def is_reading(self):\n return not self._paused and not self._closing\n\n def pause_reading(self):\n if self._closing or self._paused:\n return\n self._paused = True\n self._loop._remove_reader(self._sock_fd)\n if self._loop.get_debug():\n logger.debug(\"%r pauses reading\", self)\n\n def resume_reading(self):\n if self._closing or not self._paused:\n return\n self._paused = False\n self._add_reader(self._sock_fd, self._read_ready)\n if self._loop.get_debug():\n logger.debug(\"%r resumes reading\", self)\n\n def _read_ready(self):\n self._read_ready_cb()\n\n def _read_ready__get_buffer(self):\n if self._conn_lost:\n return\n\n try:\n buf = self._protocol.get_buffer(-1)\n if not len(buf):\n raise RuntimeError('get_buffer() returned an empty buffer')\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n self._fatal_error(\n exc, 'Fatal error: protocol.get_buffer() call failed.')\n return\n\n try:\n nbytes = self._sock.recv_into(buf)\n except (BlockingIOError, InterruptedError):\n return\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n self._fatal_error(exc, 'Fatal read error on socket transport')\n return\n\n if not nbytes:\n self._read_ready__on_eof()\n return\n\n try:\n self._protocol.buffer_updated(nbytes)\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n self._fatal_error(\n exc, 'Fatal error: protocol.buffer_updated() call failed.')\n\n def _read_ready__data_received(self):\n if self._conn_lost:\n return\n try:\n data = self._sock.recv(self.max_size)\n except (BlockingIOError, InterruptedError):\n return\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n self._fatal_error(exc, 'Fatal read error on socket transport')\n return\n\n if not data:\n self._read_ready__on_eof()\n return\n\n try:\n self._protocol.data_received(data)\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n self._fatal_error(\n exc, 'Fatal error: protocol.data_received() call failed.')\n\n def _read_ready__on_eof(self):\n if self._loop.get_debug():\n logger.debug(\"%r received EOF\", self)\n\n try:\n keep_open = self._protocol.eof_received()\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n self._fatal_error(\n exc, 'Fatal error: protocol.eof_received() call failed.')\n return\n\n if keep_open:\n # We're keeping the connection open so the\n # protocol can write more, but we still can't\n # receive more, so remove the reader callback.\n self._loop._remove_reader(self._sock_fd)\n else:\n self.close()\n\n def write(self, data):\n if not isinstance(data, (bytes, bytearray, memoryview)):\n raise TypeError(f'data argument must be a bytes-like object, '\n f'not {type(data).__name__!r}')\n if self._eof:\n raise RuntimeError('Cannot call write() after write_eof()')\n if self._empty_waiter is not None:\n raise RuntimeError('unable to write; sendfile is in progress')\n if not data:\n return\n\n if self._conn_lost:\n if self._conn_lost >= constants.LOG_THRESHOLD_FOR_CONNLOST_WRITES:\n logger.warning('socket.send() raised exception.')\n self._conn_lost += 1\n return\n\n if not self._buffer:\n # Optimization: try to send now.\n try:\n n = self._sock.send(data)\n except (BlockingIOError, InterruptedError):\n pass\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n self._fatal_error(exc, 'Fatal write error on socket transport')\n return\n else:\n data = data[n:]\n if not data:\n return\n # Not all was written; register write handler.\n self._loop._add_writer(self._sock_fd, self._write_ready)\n\n # Add it to the buffer.\n self._buffer.extend(data)\n self._maybe_pause_protocol()\n\n def _write_ready(self):\n assert self._buffer, 'Data should not be empty'\n\n if self._conn_lost:\n return\n try:\n n = self._sock.send(self._buffer)\n except (BlockingIOError, InterruptedError):\n pass\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n self._loop._remove_writer(self._sock_fd)\n self._buffer.clear()\n self._fatal_error(exc, 'Fatal write error on socket transport')\n if self._empty_waiter is not None:\n self._empty_waiter.set_exception(exc)\n else:\n if n:\n del self._buffer[:n]\n self._maybe_resume_protocol() # May append to buffer.\n if not self._buffer:\n self._loop._remove_writer(self._sock_fd)\n if self._empty_waiter is not None:\n self._empty_waiter.set_result(None)\n if self._closing:\n self._call_connection_lost(None)\n elif self._eof:\n self._sock.shutdown(socket.SHUT_WR)\n\n def write_eof(self):\n if self._closing or self._eof:\n return\n self._eof = True\n if not self._buffer:\n self._sock.shutdown(socket.SHUT_WR)\n\n def can_write_eof(self):\n return True\n\n def _call_connection_lost(self, exc):\n super()._call_connection_lost(exc)\n if self._empty_waiter is not None:\n self._empty_waiter.set_exception(\n ConnectionError(\"Connection is closed by peer\"))\n\n def _make_empty_waiter(self):\n if self._empty_waiter is not None:\n raise RuntimeError(\"Empty waiter is already set\")\n self._empty_waiter = self._loop.create_future()\n if not self._buffer:\n self._empty_waiter.set_result(None)\n return self._empty_waiter\n\n def _reset_empty_waiter(self):\n self._empty_waiter = None\n\n\nclass _SelectorDatagramTransport(_SelectorTransport):\n\n _buffer_factory = collections.deque\n\n def __init__(self, loop, sock, protocol, address=None,\n waiter=None, extra=None):\n super().__init__(loop, sock, protocol, extra)\n self._address = address\n self._loop.call_soon(self._protocol.connection_made, self)\n # only start reading when connection_made() has been called\n self._loop.call_soon(self._add_reader,\n self._sock_fd, self._read_ready)\n if waiter is not None:\n # only wake up the waiter when connection_made() has been called\n self._loop.call_soon(futures._set_result_unless_cancelled,\n waiter, None)\n\n def get_write_buffer_size(self):\n return sum(len(data) for data, _ in self._buffer)\n\n def _read_ready(self):\n if self._conn_lost:\n return\n try:\n data, addr = self._sock.recvfrom(self.max_size)\n except (BlockingIOError, InterruptedError):\n pass\n except OSError as exc:\n self._protocol.error_received(exc)\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n self._fatal_error(exc, 'Fatal read error on datagram transport')\n else:\n self._protocol.datagram_received(data, addr)\n\n def sendto(self, data, addr=None):\n if not isinstance(data, (bytes, bytearray, memoryview)):\n raise TypeError(f'data argument must be a bytes-like object, '\n f'not {type(data).__name__!r}')\n if not data:\n return\n\n if self._address:\n if addr not in (None, self._address):\n raise ValueError(\n f'Invalid address: must be None or {self._address}')\n addr = self._address\n\n if self._conn_lost and self._address:\n if self._conn_lost >= constants.LOG_THRESHOLD_FOR_CONNLOST_WRITES:\n logger.warning('socket.send() raised exception.')\n self._conn_lost += 1\n return\n\n if not self._buffer:\n # Attempt to send it right away first.\n try:\n if self._extra['peername']:\n self._sock.send(data)\n else:\n self._sock.sendto(data, addr)\n return\n except (BlockingIOError, InterruptedError):\n self._loop._add_writer(self._sock_fd, self._sendto_ready)\n except OSError as exc:\n self._protocol.error_received(exc)\n return\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n self._fatal_error(\n exc, 'Fatal write error on datagram transport')\n return\n\n # Ensure that what we buffer is immutable.\n self._buffer.append((bytes(data), addr))\n self._maybe_pause_protocol()\n\n def _sendto_ready(self):\n while self._buffer:\n data, addr = self._buffer.popleft()\n try:\n if self._extra['peername']:\n self._sock.send(data)\n else:\n self._sock.sendto(data, addr)\n except (BlockingIOError, InterruptedError):\n self._buffer.appendleft((data, addr)) # Try again later.\n break\n except OSError as exc:\n self._protocol.error_received(exc)\n return\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n self._fatal_error(\n exc, 'Fatal write error on datagram transport')\n return\n\n self._maybe_resume_protocol() # May append to buffer.\n if not self._buffer:\n self._loop._remove_writer(self._sock_fd)\n if self._closing:\n self._call_connection_lost(None)\n", 1091], "/home/gaogaotiantian/programs/codesnap/venv3.8/lib/python3.8/weakref.py": ["\"\"\"Weak reference support for Python.\n\nThis module is an implementation of PEP 205:\n\nhttp://www.python.org/dev/peps/pep-0205/\n\"\"\"\n\n# Naming convention: Variables named \"wr\" are weak reference objects;\n# they are called this instead of \"ref\" to avoid name collisions with\n# the module-global ref() function imported from _weakref.\n\nfrom _weakref import (\n getweakrefcount,\n getweakrefs,\n ref,\n proxy,\n CallableProxyType,\n ProxyType,\n ReferenceType,\n _remove_dead_weakref)\n\nfrom _weakrefset import WeakSet, _IterationGuard\n\nimport _collections_abc # Import after _weakref to avoid circular import.\nimport sys\nimport itertools\n\nProxyTypes = (ProxyType, CallableProxyType)\n\n__all__ = [\"ref\", \"proxy\", \"getweakrefcount\", \"getweakrefs\",\n \"WeakKeyDictionary\", \"ReferenceType\", \"ProxyType\",\n \"CallableProxyType\", \"ProxyTypes\", \"WeakValueDictionary\",\n \"WeakSet\", \"WeakMethod\", \"finalize\"]\n\n\nclass WeakMethod(ref):\n \"\"\"\n A custom `weakref.ref` subclass which simulates a weak reference to\n a bound method, working around the lifetime problem of bound methods.\n \"\"\"\n\n __slots__ = \"_func_ref\", \"_meth_type\", \"_alive\", \"__weakref__\"\n\n def __new__(cls, meth, callback=None):\n try:\n obj = meth.__self__\n func = meth.__func__\n except AttributeError:\n raise TypeError(\"argument should be a bound method, not {}\"\n .format(type(meth))) from None\n def _cb(arg):\n # The self-weakref trick is needed to avoid creating a reference\n # cycle.\n self = self_wr()\n if self._alive:\n self._alive = False\n if callback is not None:\n callback(self)\n self = ref.__new__(cls, obj, _cb)\n self._func_ref = ref(func, _cb)\n self._meth_type = type(meth)\n self._alive = True\n self_wr = ref(self)\n return self\n\n def __call__(self):\n obj = super().__call__()\n func = self._func_ref()\n if obj is None or func is None:\n return None\n return self._meth_type(func, obj)\n\n def __eq__(self, other):\n if isinstance(other, WeakMethod):\n if not self._alive or not other._alive:\n return self is other\n return ref.__eq__(self, other) and self._func_ref == other._func_ref\n return False\n\n def __ne__(self, other):\n if isinstance(other, WeakMethod):\n if not self._alive or not other._alive:\n return self is not other\n return ref.__ne__(self, other) or self._func_ref != other._func_ref\n return True\n\n __hash__ = ref.__hash__\n\n\nclass WeakValueDictionary(_collections_abc.MutableMapping):\n \"\"\"Mapping class that references values weakly.\n\n Entries in the dictionary will be discarded when no strong\n reference to the value exists anymore\n \"\"\"\n # We inherit the constructor without worrying about the input\n # dictionary; since it uses our .update() method, we get the right\n # checks (if the other dictionary is a WeakValueDictionary,\n # objects are unwrapped on the way out, and we always wrap on the\n # way in).\n\n def __init__(self, other=(), /, **kw):\n def remove(wr, selfref=ref(self), _atomic_removal=_remove_dead_weakref):\n self = selfref()\n if self is not None:\n if self._iterating:\n self._pending_removals.append(wr.key)\n else:\n # Atomic removal is necessary since this function\n # can be called asynchronously by the GC\n _atomic_removal(self.data, wr.key)\n self._remove = remove\n # A list of keys to be removed\n self._pending_removals = []\n self._iterating = set()\n self.data = {}\n self.update(other, **kw)\n\n def _commit_removals(self):\n l = self._pending_removals\n d = self.data\n # We shouldn't encounter any KeyError, because this method should\n # always be called *before* mutating the dict.\n while l:\n key = l.pop()\n _remove_dead_weakref(d, key)\n\n def __getitem__(self, key):\n if self._pending_removals:\n self._commit_removals()\n o = self.data[key]()\n if o is None:\n raise KeyError(key)\n else:\n return o\n\n def __delitem__(self, key):\n if self._pending_removals:\n self._commit_removals()\n del self.data[key]\n\n def __len__(self):\n if self._pending_removals:\n self._commit_removals()\n return len(self.data)\n\n def __contains__(self, key):\n if self._pending_removals:\n self._commit_removals()\n try:\n o = self.data[key]()\n except KeyError:\n return False\n return o is not None\n\n def __repr__(self):\n return \"<%s at %#x>\" % (self.__class__.__name__, id(self))\n\n def __setitem__(self, key, value):\n if self._pending_removals:\n self._commit_removals()\n self.data[key] = KeyedRef(value, self._remove, key)\n\n def copy(self):\n if self._pending_removals:\n self._commit_removals()\n new = WeakValueDictionary()\n with _IterationGuard(self):\n for key, wr in self.data.items():\n o = wr()\n if o is not None:\n new[key] = o\n return new\n\n __copy__ = copy\n\n def __deepcopy__(self, memo):\n from copy import deepcopy\n if self._pending_removals:\n self._commit_removals()\n new = self.__class__()\n with _IterationGuard(self):\n for key, wr in self.data.items():\n o = wr()\n if o is not None:\n new[deepcopy(key, memo)] = o\n return new\n\n def get(self, key, default=None):\n if self._pending_removals:\n self._commit_removals()\n try:\n wr = self.data[key]\n except KeyError:\n return default\n else:\n o = wr()\n if o is None:\n # This should only happen\n return default\n else:\n return o\n\n def items(self):\n if self._pending_removals:\n self._commit_removals()\n with _IterationGuard(self):\n for k, wr in self.data.items():\n v = wr()\n if v is not None:\n yield k, v\n\n def keys(self):\n if self._pending_removals:\n self._commit_removals()\n with _IterationGuard(self):\n for k, wr in self.data.items():\n if wr() is not None:\n yield k\n\n __iter__ = keys\n\n def itervaluerefs(self):\n \"\"\"Return an iterator that yields the weak references to the values.\n\n The references are not guaranteed to be 'live' at the time\n they are used, so the result of calling the references needs\n to be checked before being used. This can be used to avoid\n creating references that will cause the garbage collector to\n keep the values around longer than needed.\n\n \"\"\"\n if self._pending_removals:\n self._commit_removals()\n with _IterationGuard(self):\n yield from self.data.values()\n\n def values(self):\n if self._pending_removals:\n self._commit_removals()\n with _IterationGuard(self):\n for wr in self.data.values():\n obj = wr()\n if obj is not None:\n yield obj\n\n def popitem(self):\n if self._pending_removals:\n self._commit_removals()\n while True:\n key, wr = self.data.popitem()\n o = wr()\n if o is not None:\n return key, o\n\n def pop(self, key, *args):\n if self._pending_removals:\n self._commit_removals()\n try:\n o = self.data.pop(key)()\n except KeyError:\n o = None\n if o is None:\n if args:\n return args[0]\n else:\n raise KeyError(key)\n else:\n return o\n\n def setdefault(self, key, default=None):\n try:\n o = self.data[key]()\n except KeyError:\n o = None\n if o is None:\n if self._pending_removals:\n self._commit_removals()\n self.data[key] = KeyedRef(default, self._remove, key)\n return default\n else:\n return o\n\n def update(self, other=None, /, **kwargs):\n if self._pending_removals:\n self._commit_removals()\n d = self.data\n if other is not None:\n if not hasattr(other, \"items\"):\n other = dict(other)\n for key, o in other.items():\n d[key] = KeyedRef(o, self._remove, key)\n for key, o in kwargs.items():\n d[key] = KeyedRef(o, self._remove, key)\n\n def valuerefs(self):\n \"\"\"Return a list of weak references to the values.\n\n The references are not guaranteed to be 'live' at the time\n they are used, so the result of calling the references needs\n to be checked before being used. This can be used to avoid\n creating references that will cause the garbage collector to\n keep the values around longer than needed.\n\n \"\"\"\n if self._pending_removals:\n self._commit_removals()\n return list(self.data.values())\n\n\nclass KeyedRef(ref):\n \"\"\"Specialized reference that includes a key corresponding to the value.\n\n This is used in the WeakValueDictionary to avoid having to create\n a function object for each key stored in the mapping. A shared\n callback object can use the 'key' attribute of a KeyedRef instead\n of getting a reference to the key from an enclosing scope.\n\n \"\"\"\n\n __slots__ = \"key\",\n\n def __new__(type, ob, callback, key):\n self = ref.__new__(type, ob, callback)\n self.key = key\n return self\n\n def __init__(self, ob, callback, key):\n super().__init__(ob, callback)\n\n\nclass WeakKeyDictionary(_collections_abc.MutableMapping):\n \"\"\" Mapping class that references keys weakly.\n\n Entries in the dictionary will be discarded when there is no\n longer a strong reference to the key. This can be used to\n associate additional data with an object owned by other parts of\n an application without adding attributes to those objects. This\n can be especially useful with objects that override attribute\n accesses.\n \"\"\"\n\n def __init__(self, dict=None):\n self.data = {}\n def remove(k, selfref=ref(self)):\n self = selfref()\n if self is not None:\n if self._iterating:\n self._pending_removals.append(k)\n else:\n del self.data[k]\n self._remove = remove\n # A list of dead weakrefs (keys to be removed)\n self._pending_removals = []\n self._iterating = set()\n self._dirty_len = False\n if dict is not None:\n self.update(dict)\n\n def _commit_removals(self):\n # NOTE: We don't need to call this method before mutating the dict,\n # because a dead weakref never compares equal to a live weakref,\n # even if they happened to refer to equal objects.\n # However, it means keys may already have been removed.\n l = self._pending_removals\n d = self.data\n while l:\n try:\n del d[l.pop()]\n except KeyError:\n pass\n\n def _scrub_removals(self):\n d = self.data\n self._pending_removals = [k for k in self._pending_removals if k in d]\n self._dirty_len = False\n\n def __delitem__(self, key):\n self._dirty_len = True\n del self.data[ref(key)]\n\n def __getitem__(self, key):\n return self.data[ref(key)]\n\n def __len__(self):\n if self._dirty_len and self._pending_removals:\n # self._pending_removals may still contain keys which were\n # explicitly removed, we have to scrub them (see issue #21173).\n self._scrub_removals()\n return len(self.data) - len(self._pending_removals)\n\n def __repr__(self):\n return \"<%s at %#x>\" % (self.__class__.__name__, id(self))\n\n def __setitem__(self, key, value):\n self.data[ref(key, self._remove)] = value\n\n def copy(self):\n new = WeakKeyDictionary()\n with _IterationGuard(self):\n for key, value in self.data.items():\n o = key()\n if o is not None:\n new[o] = value\n return new\n\n __copy__ = copy\n\n def __deepcopy__(self, memo):\n from copy import deepcopy\n new = self.__class__()\n with _IterationGuard(self):\n for key, value in self.data.items():\n o = key()\n if o is not None:\n new[o] = deepcopy(value, memo)\n return new\n\n def get(self, key, default=None):\n return self.data.get(ref(key),default)\n\n def __contains__(self, key):\n try:\n wr = ref(key)\n except TypeError:\n return False\n return wr in self.data\n\n def items(self):\n with _IterationGuard(self):\n for wr, value in self.data.items():\n key = wr()\n if key is not None:\n yield key, value\n\n def keys(self):\n with _IterationGuard(self):\n for wr in self.data:\n obj = wr()\n if obj is not None:\n yield obj\n\n __iter__ = keys\n\n def values(self):\n with _IterationGuard(self):\n for wr, value in self.data.items():\n if wr() is not None:\n yield value\n\n def keyrefs(self):\n \"\"\"Return a list of weak references to the keys.\n\n The references are not guaranteed to be 'live' at the time\n they are used, so the result of calling the references needs\n to be checked before being used. This can be used to avoid\n creating references that will cause the garbage collector to\n keep the keys around longer than needed.\n\n \"\"\"\n return list(self.data)\n\n def popitem(self):\n self._dirty_len = True\n while True:\n key, value = self.data.popitem()\n o = key()\n if o is not None:\n return o, value\n\n def pop(self, key, *args):\n self._dirty_len = True\n return self.data.pop(ref(key), *args)\n\n def setdefault(self, key, default=None):\n return self.data.setdefault(ref(key, self._remove),default)\n\n def update(self, dict=None, /, **kwargs):\n d = self.data\n if dict is not None:\n if not hasattr(dict, \"items\"):\n dict = type({})(dict)\n for key, value in dict.items():\n d[ref(key, self._remove)] = value\n if len(kwargs):\n self.update(kwargs)\n\n\nclass finalize:\n \"\"\"Class for finalization of weakrefable objects\n\n finalize(obj, func, *args, **kwargs) returns a callable finalizer\n object which will be called when obj is garbage collected. The\n first time the finalizer is called it evaluates func(*arg, **kwargs)\n and returns the result. After this the finalizer is dead, and\n calling it just returns None.\n\n When the program exits any remaining finalizers for which the\n atexit attribute is true will be run in reverse order of creation.\n By default atexit is true.\n \"\"\"\n\n # Finalizer objects don't have any state of their own. They are\n # just used as keys to lookup _Info objects in the registry. This\n # ensures that they cannot be part of a ref-cycle.\n\n __slots__ = ()\n _registry = {}\n _shutdown = False\n _index_iter = itertools.count()\n _dirty = False\n _registered_with_atexit = False\n\n class _Info:\n __slots__ = (\"weakref\", \"func\", \"args\", \"kwargs\", \"atexit\", \"index\")\n\n def __init__(*args, **kwargs):\n if len(args) >= 3:\n self, obj, func, *args = args\n elif not args:\n raise TypeError(\"descriptor '__init__' of 'finalize' object \"\n \"needs an argument\")\n else:\n if 'func' not in kwargs:\n raise TypeError('finalize expected at least 2 positional '\n 'arguments, got %d' % (len(args)-1))\n func = kwargs.pop('func')\n if len(args) >= 2:\n self, obj, *args = args\n import warnings\n warnings.warn(\"Passing 'func' as keyword argument is deprecated\",\n DeprecationWarning, stacklevel=2)\n else:\n if 'obj' not in kwargs:\n raise TypeError('finalize expected at least 2 positional '\n 'arguments, got %d' % (len(args)-1))\n obj = kwargs.pop('obj')\n self, *args = args\n import warnings\n warnings.warn(\"Passing 'obj' as keyword argument is deprecated\",\n DeprecationWarning, stacklevel=2)\n args = tuple(args)\n\n if not self._registered_with_atexit:\n # We may register the exit function more than once because\n # of a thread race, but that is harmless\n import atexit\n atexit.register(self._exitfunc)\n finalize._registered_with_atexit = True\n info = self._Info()\n info.weakref = ref(obj, self)\n info.func = func\n info.args = args\n info.kwargs = kwargs or None\n info.atexit = True\n info.index = next(self._index_iter)\n self._registry[self] = info\n finalize._dirty = True\n __init__.__text_signature__ = '($self, obj, func, /, *args, **kwargs)'\n\n def __call__(self, _=None):\n \"\"\"If alive then mark as dead and return func(*args, **kwargs);\n otherwise return None\"\"\"\n info = self._registry.pop(self, None)\n if info and not self._shutdown:\n return info.func(*info.args, **(info.kwargs or {}))\n\n def detach(self):\n \"\"\"If alive then mark as dead and return (obj, func, args, kwargs);\n otherwise return None\"\"\"\n info = self._registry.get(self)\n obj = info and info.weakref()\n if obj is not None and self._registry.pop(self, None):\n return (obj, info.func, info.args, info.kwargs or {})\n\n def peek(self):\n \"\"\"If alive then return (obj, func, args, kwargs);\n otherwise return None\"\"\"\n info = self._registry.get(self)\n obj = info and info.weakref()\n if obj is not None:\n return (obj, info.func, info.args, info.kwargs or {})\n\n @property\n def alive(self):\n \"\"\"Whether finalizer is alive\"\"\"\n return self in self._registry\n\n @property\n def atexit(self):\n \"\"\"Whether finalizer should be called at exit\"\"\"\n info = self._registry.get(self)\n return bool(info) and info.atexit\n\n @atexit.setter\n def atexit(self, value):\n info = self._registry.get(self)\n if info:\n info.atexit = bool(value)\n\n def __repr__(self):\n info = self._registry.get(self)\n obj = info and info.weakref()\n if obj is None:\n return '<%s object at %#x; dead>' % (type(self).__name__, id(self))\n else:\n return '<%s object at %#x; for %r at %#x>' % \\\n (type(self).__name__, id(self), type(obj).__name__, id(obj))\n\n @classmethod\n def _select_for_exit(cls):\n # Return live finalizers marked for exit, oldest first\n L = [(f,i) for (f,i) in cls._registry.items() if i.atexit]\n L.sort(key=lambda item:item[1].index)\n return [f for (f,i) in L]\n\n @classmethod\n def _exitfunc(cls):\n # At shutdown invoke finalizers for which atexit is true.\n # This is called once all other non-daemonic threads have been\n # joined.\n reenable_gc = False\n try:\n if cls._registry:\n import gc\n if gc.isenabled():\n reenable_gc = True\n gc.disable()\n pending = None\n while True:\n if pending is None or finalize._dirty:\n pending = cls._select_for_exit()\n finalize._dirty = False\n if not pending:\n break\n f = pending.pop()\n try:\n # gc is disabled, so (assuming no daemonic\n # threads) the following is the only line in\n # this function which might trigger creation\n # of a new finalizer\n f()\n except Exception:\n sys.excepthook(*sys.exc_info())\n assert f not in cls._registry\n finally:\n # prevent any more finalizers from executing during shutdown\n finalize._shutdown = True\n if reenable_gc:\n gc.enable()\n", 650], "/usr/lib/python3.8/asyncio/base_futures.py": ["__all__ = ()\n\nimport reprlib\n\nfrom . import format_helpers\n\n# States for Future.\n_PENDING = 'PENDING'\n_CANCELLED = 'CANCELLED'\n_FINISHED = 'FINISHED'\n\n\ndef isfuture(obj):\n \"\"\"Check for a Future.\n\n This returns True when obj is a Future instance or is advertising\n itself as duck-type compatible by setting _asyncio_future_blocking.\n See comment in Future for more details.\n \"\"\"\n return (hasattr(obj.__class__, '_asyncio_future_blocking') and\n obj._asyncio_future_blocking is not None)\n\n\ndef _format_callbacks(cb):\n \"\"\"helper function for Future.__repr__\"\"\"\n size = len(cb)\n if not size:\n cb = ''\n\n def format_cb(callback):\n return format_helpers._format_callback_source(callback, ())\n\n if size == 1:\n cb = format_cb(cb[0][0])\n elif size == 2:\n cb = '{}, {}'.format(format_cb(cb[0][0]), format_cb(cb[1][0]))\n elif size > 2:\n cb = '{}, <{} more>, {}'.format(format_cb(cb[0][0]),\n size - 2,\n format_cb(cb[-1][0]))\n return f'cb=[{cb}]'\n\n\ndef _future_repr_info(future):\n # (Future) -> str\n \"\"\"helper function for Future.__repr__\"\"\"\n info = [future._state.lower()]\n if future._state == _FINISHED:\n if future._exception is not None:\n info.append(f'exception={future._exception!r}')\n else:\n # use reprlib to limit the length of the output, especially\n # for very long strings\n result = reprlib.repr(future._result)\n info.append(f'result={result}')\n if future._callbacks:\n info.append(_format_callbacks(future._callbacks))\n if future._source_traceback:\n frame = future._source_traceback[-1]\n info.append(f'created at {frame[0]}:{frame[1]}')\n return info\n", 61], "/usr/lib/python3.8/asyncio/tasks.py": ["\"\"\"Support for tasks, coroutines and the scheduler.\"\"\"\n\n__all__ = (\n 'Task', 'create_task',\n 'FIRST_COMPLETED', 'FIRST_EXCEPTION', 'ALL_COMPLETED',\n 'wait', 'wait_for', 'as_completed', 'sleep',\n 'gather', 'shield', 'ensure_future', 'run_coroutine_threadsafe',\n 'current_task', 'all_tasks',\n '_register_task', '_unregister_task', '_enter_task', '_leave_task',\n)\n\nimport concurrent.futures\nimport contextvars\nimport functools\nimport inspect\nimport itertools\nimport types\nimport warnings\nimport weakref\n\nfrom . import base_tasks\nfrom . import coroutines\nfrom . import events\nfrom . import exceptions\nfrom . import futures\nfrom .coroutines import _is_coroutine\n\n# Helper to generate new task names\n# This uses itertools.count() instead of a \"+= 1\" operation because the latter\n# is not thread safe. See bpo-11866 for a longer explanation.\n_task_name_counter = itertools.count(1).__next__\n\n\ndef current_task(loop=None):\n \"\"\"Return a currently executed task.\"\"\"\n if loop is None:\n loop = events.get_running_loop()\n return _current_tasks.get(loop)\n\n\ndef all_tasks(loop=None):\n \"\"\"Return a set of all tasks for the loop.\"\"\"\n if loop is None:\n loop = events.get_running_loop()\n # Looping over a WeakSet (_all_tasks) isn't safe as it can be updated from another\n # thread while we do so. Therefore we cast it to list prior to filtering. The list\n # cast itself requires iteration, so we repeat it several times ignoring\n # RuntimeErrors (which are not very likely to occur). See issues 34970 and 36607 for\n # details.\n i = 0\n while True:\n try:\n tasks = list(_all_tasks)\n except RuntimeError:\n i += 1\n if i >= 1000:\n raise\n else:\n break\n return {t for t in tasks\n if futures._get_loop(t) is loop and not t.done()}\n\n\ndef _all_tasks_compat(loop=None):\n # Different from \"all_task()\" by returning *all* Tasks, including\n # the completed ones. Used to implement deprecated \"Tasks.all_task()\"\n # method.\n if loop is None:\n loop = events.get_event_loop()\n # Looping over a WeakSet (_all_tasks) isn't safe as it can be updated from another\n # thread while we do so. Therefore we cast it to list prior to filtering. The list\n # cast itself requires iteration, so we repeat it several times ignoring\n # RuntimeErrors (which are not very likely to occur). See issues 34970 and 36607 for\n # details.\n i = 0\n while True:\n try:\n tasks = list(_all_tasks)\n except RuntimeError:\n i += 1\n if i >= 1000:\n raise\n else:\n break\n return {t for t in tasks if futures._get_loop(t) is loop}\n\n\ndef _set_task_name(task, name):\n if name is not None:\n try:\n set_name = task.set_name\n except AttributeError:\n pass\n else:\n set_name(name)\n\n\nclass Task(futures._PyFuture): # Inherit Python Task implementation\n # from a Python Future implementation.\n\n \"\"\"A coroutine wrapped in a Future.\"\"\"\n\n # An important invariant maintained while a Task not done:\n #\n # - Either _fut_waiter is None, and _step() is scheduled;\n # - or _fut_waiter is some Future, and _step() is *not* scheduled.\n #\n # The only transition from the latter to the former is through\n # _wakeup(). When _fut_waiter is not None, one of its callbacks\n # must be _wakeup().\n\n # If False, don't log a message if the task is destroyed whereas its\n # status is still pending\n _log_destroy_pending = True\n\n @classmethod\n def current_task(cls, loop=None):\n \"\"\"Return the currently running task in an event loop or None.\n\n By default the current task for the current event loop is returned.\n\n None is returned when called not in the context of a Task.\n \"\"\"\n warnings.warn(\"Task.current_task() is deprecated since Python 3.7, \"\n \"use asyncio.current_task() instead\",\n DeprecationWarning,\n stacklevel=2)\n if loop is None:\n loop = events.get_event_loop()\n return current_task(loop)\n\n @classmethod\n def all_tasks(cls, loop=None):\n \"\"\"Return a set of all tasks for an event loop.\n\n By default all tasks for the current event loop are returned.\n \"\"\"\n warnings.warn(\"Task.all_tasks() is deprecated since Python 3.7, \"\n \"use asyncio.all_tasks() instead\",\n DeprecationWarning,\n stacklevel=2)\n return _all_tasks_compat(loop)\n\n def __init__(self, coro, *, loop=None, name=None):\n super().__init__(loop=loop)\n if self._source_traceback:\n del self._source_traceback[-1]\n if not coroutines.iscoroutine(coro):\n # raise after Future.__init__(), attrs are required for __del__\n # prevent logging for pending task in __del__\n self._log_destroy_pending = False\n raise TypeError(f\"a coroutine was expected, got {coro!r}\")\n\n if name is None:\n self._name = f'Task-{_task_name_counter()}'\n else:\n self._name = str(name)\n\n self._must_cancel = False\n self._fut_waiter = None\n self._coro = coro\n self._context = contextvars.copy_context()\n\n self._loop.call_soon(self.__step, context=self._context)\n _register_task(self)\n\n def __del__(self):\n if self._state == futures._PENDING and self._log_destroy_pending:\n context = {\n 'task': self,\n 'message': 'Task was destroyed but it is pending!',\n }\n if self._source_traceback:\n context['source_traceback'] = self._source_traceback\n self._loop.call_exception_handler(context)\n super().__del__()\n\n def _repr_info(self):\n return base_tasks._task_repr_info(self)\n\n def get_coro(self):\n return self._coro\n\n def get_name(self):\n return self._name\n\n def set_name(self, value):\n self._name = str(value)\n\n def set_result(self, result):\n raise RuntimeError('Task does not support set_result operation')\n\n def set_exception(self, exception):\n raise RuntimeError('Task does not support set_exception operation')\n\n def get_stack(self, *, limit=None):\n \"\"\"Return the list of stack frames for this task's coroutine.\n\n If the coroutine is not done, this returns the stack where it is\n suspended. If the coroutine has completed successfully or was\n cancelled, this returns an empty list. If the coroutine was\n terminated by an exception, this returns the list of traceback\n frames.\n\n The frames are always ordered from oldest to newest.\n\n The optional limit gives the maximum number of frames to\n return; by default all available frames are returned. Its\n meaning differs depending on whether a stack or a traceback is\n returned: the newest frames of a stack are returned, but the\n oldest frames of a traceback are returned. (This matches the\n behavior of the traceback module.)\n\n For reasons beyond our control, only one stack frame is\n returned for a suspended coroutine.\n \"\"\"\n return base_tasks._task_get_stack(self, limit)\n\n def print_stack(self, *, limit=None, file=None):\n \"\"\"Print the stack or traceback for this task's coroutine.\n\n This produces output similar to that of the traceback module,\n for the frames retrieved by get_stack(). The limit argument\n is passed to get_stack(). The file argument is an I/O stream\n to which the output is written; by default output is written\n to sys.stderr.\n \"\"\"\n return base_tasks._task_print_stack(self, limit, file)\n\n def cancel(self):\n \"\"\"Request that this task cancel itself.\n\n This arranges for a CancelledError to be thrown into the\n wrapped coroutine on the next cycle through the event loop.\n The coroutine then has a chance to clean up or even deny\n the request using try/except/finally.\n\n Unlike Future.cancel, this does not guarantee that the\n task will be cancelled: the exception might be caught and\n acted upon, delaying cancellation of the task or preventing\n cancellation completely. The task may also return a value or\n raise a different exception.\n\n Immediately after this method is called, Task.cancelled() will\n not return True (unless the task was already cancelled). A\n task will be marked as cancelled when the wrapped coroutine\n terminates with a CancelledError exception (even if cancel()\n was not called).\n \"\"\"\n self._log_traceback = False\n if self.done():\n return False\n if self._fut_waiter is not None:\n if self._fut_waiter.cancel():\n # Leave self._fut_waiter; it may be a Task that\n # catches and ignores the cancellation so we may have\n # to cancel it again later.\n return True\n # It must be the case that self.__step is already scheduled.\n self._must_cancel = True\n return True\n\n def __step(self, exc=None):\n if self.done():\n raise exceptions.InvalidStateError(\n f'_step(): already done: {self!r}, {exc!r}')\n if self._must_cancel:\n if not isinstance(exc, exceptions.CancelledError):\n exc = exceptions.CancelledError()\n self._must_cancel = False\n coro = self._coro\n self._fut_waiter = None\n\n _enter_task(self._loop, self)\n # Call either coro.throw(exc) or coro.send(None).\n try:\n if exc is None:\n # We use the `send` method directly, because coroutines\n # don't have `__iter__` and `__next__` methods.\n result = coro.send(None)\n else:\n result = coro.throw(exc)\n except StopIteration as exc:\n if self._must_cancel:\n # Task is cancelled right before coro stops.\n self._must_cancel = False\n super().cancel()\n else:\n super().set_result(exc.value)\n except exceptions.CancelledError:\n super().cancel() # I.e., Future.cancel(self).\n except (KeyboardInterrupt, SystemExit) as exc:\n super().set_exception(exc)\n raise\n except BaseException as exc:\n super().set_exception(exc)\n else:\n blocking = getattr(result, '_asyncio_future_blocking', None)\n if blocking is not None:\n # Yielded Future must come from Future.__iter__().\n if futures._get_loop(result) is not self._loop:\n new_exc = RuntimeError(\n f'Task {self!r} got Future '\n f'{result!r} attached to a different loop')\n self._loop.call_soon(\n self.__step, new_exc, context=self._context)\n elif blocking:\n if result is self:\n new_exc = RuntimeError(\n f'Task cannot await on itself: {self!r}')\n self._loop.call_soon(\n self.__step, new_exc, context=self._context)\n else:\n result._asyncio_future_blocking = False\n result.add_done_callback(\n self.__wakeup, context=self._context)\n self._fut_waiter = result\n if self._must_cancel:\n if self._fut_waiter.cancel():\n self._must_cancel = False\n else:\n new_exc = RuntimeError(\n f'yield was used instead of yield from '\n f'in task {self!r} with {result!r}')\n self._loop.call_soon(\n self.__step, new_exc, context=self._context)\n\n elif result is None:\n # Bare yield relinquishes control for one event loop iteration.\n self._loop.call_soon(self.__step, context=self._context)\n elif inspect.isgenerator(result):\n # Yielding a generator is just wrong.\n new_exc = RuntimeError(\n f'yield was used instead of yield from for '\n f'generator in task {self!r} with {result!r}')\n self._loop.call_soon(\n self.__step, new_exc, context=self._context)\n else:\n # Yielding something else is an error.\n new_exc = RuntimeError(f'Task got bad yield: {result!r}')\n self._loop.call_soon(\n self.__step, new_exc, context=self._context)\n finally:\n _leave_task(self._loop, self)\n self = None # Needed to break cycles when an exception occurs.\n\n def __wakeup(self, future):\n try:\n future.result()\n except BaseException as exc:\n # This may also be a cancellation.\n self.__step(exc)\n else:\n # Don't pass the value of `future.result()` explicitly,\n # as `Future.__iter__` and `Future.__await__` don't need it.\n # If we call `_step(value, None)` instead of `_step()`,\n # Python eval loop would use `.send(value)` method call,\n # instead of `__next__()`, which is slower for futures\n # that return non-generator iterators from their `__iter__`.\n self.__step()\n self = None # Needed to break cycles when an exception occurs.\n\n\n_PyTask = Task\n\n\ntry:\n import _asyncio\nexcept ImportError:\n pass\nelse:\n # _CTask is needed for tests.\n Task = _CTask = _asyncio.Task\n\n\ndef create_task(coro, *, name=None):\n \"\"\"Schedule the execution of a coroutine object in a spawn task.\n\n Return a Task object.\n \"\"\"\n loop = events.get_running_loop()\n task = loop.create_task(coro)\n _set_task_name(task, name)\n return task\n\n\n# wait() and as_completed() similar to those in PEP 3148.\n\nFIRST_COMPLETED = concurrent.futures.FIRST_COMPLETED\nFIRST_EXCEPTION = concurrent.futures.FIRST_EXCEPTION\nALL_COMPLETED = concurrent.futures.ALL_COMPLETED\n\n\nasync def wait(fs, *, loop=None, timeout=None, return_when=ALL_COMPLETED):\n \"\"\"Wait for the Futures and coroutines given by fs to complete.\n\n The sequence futures must not be empty.\n\n Coroutines will be wrapped in Tasks.\n\n Returns two sets of Future: (done, pending).\n\n Usage:\n\n done, pending = await asyncio.wait(fs)\n\n Note: This does not raise TimeoutError! Futures that aren't done\n when the timeout occurs are returned in the second set.\n \"\"\"\n if futures.isfuture(fs) or coroutines.iscoroutine(fs):\n raise TypeError(f\"expect a list of futures, not {type(fs).__name__}\")\n if not fs:\n raise ValueError('Set of coroutines/Futures is empty.')\n if return_when not in (FIRST_COMPLETED, FIRST_EXCEPTION, ALL_COMPLETED):\n raise ValueError(f'Invalid return_when value: {return_when}')\n\n if loop is None:\n loop = events.get_running_loop()\n else:\n warnings.warn(\"The loop argument is deprecated since Python 3.8, \"\n \"and scheduled for removal in Python 3.10.\",\n DeprecationWarning, stacklevel=2)\n\n fs = {ensure_future(f, loop=loop) for f in set(fs)}\n\n return await _wait(fs, timeout, return_when, loop)\n\n\ndef _release_waiter(waiter, *args):\n if not waiter.done():\n waiter.set_result(None)\n\n\nasync def wait_for(fut, timeout, *, loop=None):\n \"\"\"Wait for the single Future or coroutine to complete, with timeout.\n\n Coroutine will be wrapped in Task.\n\n Returns result of the Future or coroutine. When a timeout occurs,\n it cancels the task and raises TimeoutError. To avoid the task\n cancellation, wrap it in shield().\n\n If the wait is cancelled, the task is also cancelled.\n\n This function is a coroutine.\n \"\"\"\n if loop is None:\n loop = events.get_running_loop()\n else:\n warnings.warn(\"The loop argument is deprecated since Python 3.8, \"\n \"and scheduled for removal in Python 3.10.\",\n DeprecationWarning, stacklevel=2)\n\n if timeout is None:\n return await fut\n\n if timeout <= 0:\n fut = ensure_future(fut, loop=loop)\n\n if fut.done():\n return fut.result()\n\n await _cancel_and_wait(fut, loop=loop)\n try:\n fut.result()\n except exceptions.CancelledError as exc:\n raise exceptions.TimeoutError() from exc\n else:\n raise exceptions.TimeoutError()\n\n waiter = loop.create_future()\n timeout_handle = loop.call_later(timeout, _release_waiter, waiter)\n cb = functools.partial(_release_waiter, waiter)\n\n fut = ensure_future(fut, loop=loop)\n fut.add_done_callback(cb)\n\n try:\n # wait until the future completes or the timeout\n try:\n await waiter\n except exceptions.CancelledError:\n if fut.done():\n return fut.result()\n else:\n fut.remove_done_callback(cb)\n fut.cancel()\n raise\n\n if fut.done():\n return fut.result()\n else:\n fut.remove_done_callback(cb)\n # We must ensure that the task is not running\n # after wait_for() returns.\n # See https://bugs.python.org/issue32751\n await _cancel_and_wait(fut, loop=loop)\n raise exceptions.TimeoutError()\n finally:\n timeout_handle.cancel()\n\n\nasync def _wait(fs, timeout, return_when, loop):\n \"\"\"Internal helper for wait().\n\n The fs argument must be a collection of Futures.\n \"\"\"\n assert fs, 'Set of Futures is empty.'\n waiter = loop.create_future()\n timeout_handle = None\n if timeout is not None:\n timeout_handle = loop.call_later(timeout, _release_waiter, waiter)\n counter = len(fs)\n\n def _on_completion(f):\n nonlocal counter\n counter -= 1\n if (counter <= 0 or\n return_when == FIRST_COMPLETED or\n return_when == FIRST_EXCEPTION and (not f.cancelled() and\n f.exception() is not None)):\n if timeout_handle is not None:\n timeout_handle.cancel()\n if not waiter.done():\n waiter.set_result(None)\n\n for f in fs:\n f.add_done_callback(_on_completion)\n\n try:\n await waiter\n finally:\n if timeout_handle is not None:\n timeout_handle.cancel()\n for f in fs:\n f.remove_done_callback(_on_completion)\n\n done, pending = set(), set()\n for f in fs:\n if f.done():\n done.add(f)\n else:\n pending.add(f)\n return done, pending\n\n\nasync def _cancel_and_wait(fut, loop):\n \"\"\"Cancel the *fut* future or task and wait until it completes.\"\"\"\n\n waiter = loop.create_future()\n cb = functools.partial(_release_waiter, waiter)\n fut.add_done_callback(cb)\n\n try:\n fut.cancel()\n # We cannot wait on *fut* directly to make\n # sure _cancel_and_wait itself is reliably cancellable.\n await waiter\n finally:\n fut.remove_done_callback(cb)\n\n\n# This is *not* a @coroutine! It is just an iterator (yielding Futures).\ndef as_completed(fs, *, loop=None, timeout=None):\n \"\"\"Return an iterator whose values are coroutines.\n\n When waiting for the yielded coroutines you'll get the results (or\n exceptions!) of the original Futures (or coroutines), in the order\n in which and as soon as they complete.\n\n This differs from PEP 3148; the proper way to use this is:\n\n for f in as_completed(fs):\n result = await f # The 'await' may raise.\n # Use result.\n\n If a timeout is specified, the 'await' will raise\n TimeoutError when the timeout occurs before all Futures are done.\n\n Note: The futures 'f' are not necessarily members of fs.\n \"\"\"\n if futures.isfuture(fs) or coroutines.iscoroutine(fs):\n raise TypeError(f\"expect a list of futures, not {type(fs).__name__}\")\n\n from .queues import Queue # Import here to avoid circular import problem.\n done = Queue(loop=loop)\n\n if loop is None:\n loop = events.get_event_loop()\n else:\n warnings.warn(\"The loop argument is deprecated since Python 3.8, \"\n \"and scheduled for removal in Python 3.10.\",\n DeprecationWarning, stacklevel=2)\n todo = {ensure_future(f, loop=loop) for f in set(fs)}\n timeout_handle = None\n\n def _on_timeout():\n for f in todo:\n f.remove_done_callback(_on_completion)\n done.put_nowait(None) # Queue a dummy value for _wait_for_one().\n todo.clear() # Can't do todo.remove(f) in the loop.\n\n def _on_completion(f):\n if not todo:\n return # _on_timeout() was here first.\n todo.remove(f)\n done.put_nowait(f)\n if not todo and timeout_handle is not None:\n timeout_handle.cancel()\n\n async def _wait_for_one():\n f = await done.get()\n if f is None:\n # Dummy value from _on_timeout().\n raise exceptions.TimeoutError\n return f.result() # May raise f.exception().\n\n for f in todo:\n f.add_done_callback(_on_completion)\n if todo and timeout is not None:\n timeout_handle = loop.call_later(timeout, _on_timeout)\n for _ in range(len(todo)):\n yield _wait_for_one()\n\n\n@types.coroutine\ndef __sleep0():\n \"\"\"Skip one event loop run cycle.\n\n This is a private helper for 'asyncio.sleep()', used\n when the 'delay' is set to 0. It uses a bare 'yield'\n expression (which Task.__step knows how to handle)\n instead of creating a Future object.\n \"\"\"\n yield\n\n\nasync def sleep(delay, result=None, *, loop=None):\n \"\"\"Coroutine that completes after a given time (in seconds).\"\"\"\n if delay <= 0:\n await __sleep0()\n return result\n\n if loop is None:\n loop = events.get_running_loop()\n else:\n warnings.warn(\"The loop argument is deprecated since Python 3.8, \"\n \"and scheduled for removal in Python 3.10.\",\n DeprecationWarning, stacklevel=2)\n\n future = loop.create_future()\n h = loop.call_later(delay,\n futures._set_result_unless_cancelled,\n future, result)\n try:\n return await future\n finally:\n h.cancel()\n\n\ndef ensure_future(coro_or_future, *, loop=None):\n \"\"\"Wrap a coroutine or an awaitable in a future.\n\n If the argument is a Future, it is returned directly.\n \"\"\"\n if coroutines.iscoroutine(coro_or_future):\n if loop is None:\n loop = events.get_event_loop()\n task = loop.create_task(coro_or_future)\n if task._source_traceback:\n del task._source_traceback[-1]\n return task\n elif futures.isfuture(coro_or_future):\n if loop is not None and loop is not futures._get_loop(coro_or_future):\n raise ValueError('The future belongs to a different loop than '\n 'the one specified as the loop argument')\n return coro_or_future\n elif inspect.isawaitable(coro_or_future):\n return ensure_future(_wrap_awaitable(coro_or_future), loop=loop)\n else:\n raise TypeError('An asyncio.Future, a coroutine or an awaitable is '\n 'required')\n\n\n@types.coroutine\ndef _wrap_awaitable(awaitable):\n \"\"\"Helper for asyncio.ensure_future().\n\n Wraps awaitable (an object with __await__) into a coroutine\n that will later be wrapped in a Task by ensure_future().\n \"\"\"\n return (yield from awaitable.__await__())\n\n_wrap_awaitable._is_coroutine = _is_coroutine\n\n\nclass _GatheringFuture(futures.Future):\n \"\"\"Helper for gather().\n\n This overrides cancel() to cancel all the children and act more\n like Task.cancel(), which doesn't immediately mark itself as\n cancelled.\n \"\"\"\n\n def __init__(self, children, *, loop=None):\n super().__init__(loop=loop)\n self._children = children\n self._cancel_requested = False\n\n def cancel(self):\n if self.done():\n return False\n ret = False\n for child in self._children:\n if child.cancel():\n ret = True\n if ret:\n # If any child tasks were actually cancelled, we should\n # propagate the cancellation request regardless of\n # *return_exceptions* argument. See issue 32684.\n self._cancel_requested = True\n return ret\n\n\ndef gather(*coros_or_futures, loop=None, return_exceptions=False):\n \"\"\"Return a future aggregating results from the given coroutines/futures.\n\n Coroutines will be wrapped in a future and scheduled in the event\n loop. They will not necessarily be scheduled in the same order as\n passed in.\n\n All futures must share the same event loop. If all the tasks are\n done successfully, the returned future's result is the list of\n results (in the order of the original sequence, not necessarily\n the order of results arrival). If *return_exceptions* is True,\n exceptions in the tasks are treated the same as successful\n results, and gathered in the result list; otherwise, the first\n raised exception will be immediately propagated to the returned\n future.\n\n Cancellation: if the outer Future is cancelled, all children (that\n have not completed yet) are also cancelled. If any child is\n cancelled, this is treated as if it raised CancelledError --\n the outer Future is *not* cancelled in this case. (This is to\n prevent the cancellation of one child to cause other children to\n be cancelled.)\n\n If *return_exceptions* is False, cancelling gather() after it\n has been marked done won't cancel any submitted awaitables.\n For instance, gather can be marked done after propagating an\n exception to the caller, therefore, calling ``gather.cancel()``\n after catching an exception (raised by one of the awaitables) from\n gather won't cancel any other awaitables.\n \"\"\"\n if not coros_or_futures:\n if loop is None:\n loop = events.get_event_loop()\n else:\n warnings.warn(\"The loop argument is deprecated since Python 3.8, \"\n \"and scheduled for removal in Python 3.10.\",\n DeprecationWarning, stacklevel=2)\n outer = loop.create_future()\n outer.set_result([])\n return outer\n\n def _done_callback(fut):\n nonlocal nfinished\n nfinished += 1\n\n if outer.done():\n if not fut.cancelled():\n # Mark exception retrieved.\n fut.exception()\n return\n\n if not return_exceptions:\n if fut.cancelled():\n # Check if 'fut' is cancelled first, as\n # 'fut.exception()' will *raise* a CancelledError\n # instead of returning it.\n exc = exceptions.CancelledError()\n outer.set_exception(exc)\n return\n else:\n exc = fut.exception()\n if exc is not None:\n outer.set_exception(exc)\n return\n\n if nfinished == nfuts:\n # All futures are done; create a list of results\n # and set it to the 'outer' future.\n results = []\n\n for fut in children:\n if fut.cancelled():\n # Check if 'fut' is cancelled first, as\n # 'fut.exception()' will *raise* a CancelledError\n # instead of returning it.\n res = exceptions.CancelledError()\n else:\n res = fut.exception()\n if res is None:\n res = fut.result()\n results.append(res)\n\n if outer._cancel_requested:\n # If gather is being cancelled we must propagate the\n # cancellation regardless of *return_exceptions* argument.\n # See issue 32684.\n outer.set_exception(exceptions.CancelledError())\n else:\n outer.set_result(results)\n\n arg_to_fut = {}\n children = []\n nfuts = 0\n nfinished = 0\n for arg in coros_or_futures:\n if arg not in arg_to_fut:\n fut = ensure_future(arg, loop=loop)\n if loop is None:\n loop = futures._get_loop(fut)\n if fut is not arg:\n # 'arg' was not a Future, therefore, 'fut' is a new\n # Future created specifically for 'arg'. Since the caller\n # can't control it, disable the \"destroy pending task\"\n # warning.\n fut._log_destroy_pending = False\n\n nfuts += 1\n arg_to_fut[arg] = fut\n fut.add_done_callback(_done_callback)\n\n else:\n # There's a duplicate Future object in coros_or_futures.\n fut = arg_to_fut[arg]\n\n children.append(fut)\n\n outer = _GatheringFuture(children, loop=loop)\n return outer\n\n\ndef shield(arg, *, loop=None):\n \"\"\"Wait for a future, shielding it from cancellation.\n\n The statement\n\n res = await shield(something())\n\n is exactly equivalent to the statement\n\n res = await something()\n\n *except* that if the coroutine containing it is cancelled, the\n task running in something() is not cancelled. From the POV of\n something(), the cancellation did not happen. But its caller is\n still cancelled, so the yield-from expression still raises\n CancelledError. Note: If something() is cancelled by other means\n this will still cancel shield().\n\n If you want to completely ignore cancellation (not recommended)\n you can combine shield() with a try/except clause, as follows:\n\n try:\n res = await shield(something())\n except CancelledError:\n res = None\n \"\"\"\n if loop is not None:\n warnings.warn(\"The loop argument is deprecated since Python 3.8, \"\n \"and scheduled for removal in Python 3.10.\",\n DeprecationWarning, stacklevel=2)\n inner = ensure_future(arg, loop=loop)\n if inner.done():\n # Shortcut.\n return inner\n loop = futures._get_loop(inner)\n outer = loop.create_future()\n\n def _inner_done_callback(inner):\n if outer.cancelled():\n if not inner.cancelled():\n # Mark inner's result as retrieved.\n inner.exception()\n return\n\n if inner.cancelled():\n outer.cancel()\n else:\n exc = inner.exception()\n if exc is not None:\n outer.set_exception(exc)\n else:\n outer.set_result(inner.result())\n\n\n def _outer_done_callback(outer):\n if not inner.done():\n inner.remove_done_callback(_inner_done_callback)\n\n inner.add_done_callback(_inner_done_callback)\n outer.add_done_callback(_outer_done_callback)\n return outer\n\n\ndef run_coroutine_threadsafe(coro, loop):\n \"\"\"Submit a coroutine object to a given event loop.\n\n Return a concurrent.futures.Future to access the result.\n \"\"\"\n if not coroutines.iscoroutine(coro):\n raise TypeError('A coroutine object is required')\n future = concurrent.futures.Future()\n\n def callback():\n try:\n futures._chain_future(ensure_future(coro, loop=loop), future)\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n if future.set_running_or_notify_cancel():\n future.set_exception(exc)\n raise\n\n loop.call_soon_threadsafe(callback)\n return future\n\n\n# WeakSet containing all alive tasks.\n_all_tasks = weakref.WeakSet()\n\n# Dictionary containing tasks that are currently active in\n# all running event loops. {EventLoop: Task}\n_current_tasks = {}\n\n\ndef _register_task(task):\n \"\"\"Register a new task in asyncio as executed by loop.\"\"\"\n _all_tasks.add(task)\n\n\ndef _enter_task(loop, task):\n current_task = _current_tasks.get(loop)\n if current_task is not None:\n raise RuntimeError(f\"Cannot enter into task {task!r} while another \"\n f\"task {current_task!r} is being executed.\")\n _current_tasks[loop] = task\n\n\ndef _leave_task(loop, task):\n current_task = _current_tasks.get(loop)\n if current_task is not task:\n raise RuntimeError(f\"Leaving task {task!r} does not match \"\n f\"the current task {current_task!r}.\")\n del _current_tasks[loop]\n\n\ndef _unregister_task(task):\n \"\"\"Unregister a task.\"\"\"\n _all_tasks.discard(task)\n\n\n_py_register_task = _register_task\n_py_unregister_task = _unregister_task\n_py_enter_task = _enter_task\n_py_leave_task = _leave_task\n\n\ntry:\n from _asyncio import (_register_task, _unregister_task,\n _enter_task, _leave_task,\n _all_tasks, _current_tasks)\nexcept ImportError:\n pass\nelse:\n _c_register_task = _register_task\n _c_unregister_task = _unregister_task\n _c_enter_task = _enter_task\n _c_leave_task = _leave_task\n", 981], "/home/gaogaotiantian/programs/codesnap/example/src/async_simple.py": ["import asyncio\n\nasync def io_task():\n await asyncio.sleep(0.01)\n\nasync def main():\n t1 = asyncio.create_task(io_task())\n t2 = asyncio.create_task(io_task())\n t3 = asyncio.create_task(io_task())\n\n await t1\n await t2\n await t3\n\n\nif __name__ == \"__main__\":\n asyncio.run(main())\n", 17], "/usr/lib/python3.8/asyncio/futures.py": ["\"\"\"A Future class similar to the one in PEP 3148.\"\"\"\n\n__all__ = (\n 'Future', 'wrap_future', 'isfuture',\n)\n\nimport concurrent.futures\nimport contextvars\nimport logging\nimport sys\n\nfrom . import base_futures\nfrom . import events\nfrom . import exceptions\nfrom . import format_helpers\n\n\nisfuture = base_futures.isfuture\n\n\n_PENDING = base_futures._PENDING\n_CANCELLED = base_futures._CANCELLED\n_FINISHED = base_futures._FINISHED\n\n\nSTACK_DEBUG = logging.DEBUG - 1 # heavy-duty debugging\n\n\nclass Future:\n \"\"\"This class is *almost* compatible with concurrent.futures.Future.\n\n Differences:\n\n - This class is not thread-safe.\n\n - result() and exception() do not take a timeout argument and\n raise an exception when the future isn't done yet.\n\n - Callbacks registered with add_done_callback() are always called\n via the event loop's call_soon().\n\n - This class is not compatible with the wait() and as_completed()\n methods in the concurrent.futures package.\n\n (In Python 3.4 or later we may be able to unify the implementations.)\n \"\"\"\n\n # Class variables serving as defaults for instance variables.\n _state = _PENDING\n _result = None\n _exception = None\n _loop = None\n _source_traceback = None\n\n # This field is used for a dual purpose:\n # - Its presence is a marker to declare that a class implements\n # the Future protocol (i.e. is intended to be duck-type compatible).\n # The value must also be not-None, to enable a subclass to declare\n # that it is not compatible by setting this to None.\n # - It is set by __iter__() below so that Task._step() can tell\n # the difference between\n # `await Future()` or`yield from Future()` (correct) vs.\n # `yield Future()` (incorrect).\n _asyncio_future_blocking = False\n\n __log_traceback = False\n\n def __init__(self, *, loop=None):\n \"\"\"Initialize the future.\n\n The optional event_loop argument allows explicitly setting the event\n loop object used by the future. If it's not provided, the future uses\n the default event loop.\n \"\"\"\n if loop is None:\n self._loop = events.get_event_loop()\n else:\n self._loop = loop\n self._callbacks = []\n if self._loop.get_debug():\n self._source_traceback = format_helpers.extract_stack(\n sys._getframe(1))\n\n _repr_info = base_futures._future_repr_info\n\n def __repr__(self):\n return '<{} {}>'.format(self.__class__.__name__,\n ' '.join(self._repr_info()))\n\n def __del__(self):\n if not self.__log_traceback:\n # set_exception() was not called, or result() or exception()\n # has consumed the exception\n return\n exc = self._exception\n context = {\n 'message':\n f'{self.__class__.__name__} exception was never retrieved',\n 'exception': exc,\n 'future': self,\n }\n if self._source_traceback:\n context['source_traceback'] = self._source_traceback\n self._loop.call_exception_handler(context)\n\n @property\n def _log_traceback(self):\n return self.__log_traceback\n\n @_log_traceback.setter\n def _log_traceback(self, val):\n if bool(val):\n raise ValueError('_log_traceback can only be set to False')\n self.__log_traceback = False\n\n def get_loop(self):\n \"\"\"Return the event loop the Future is bound to.\"\"\"\n loop = self._loop\n if loop is None:\n raise RuntimeError(\"Future object is not initialized.\")\n return loop\n\n def cancel(self):\n \"\"\"Cancel the future and schedule callbacks.\n\n If the future is already done or cancelled, return False. Otherwise,\n change the future's state to cancelled, schedule the callbacks and\n return True.\n \"\"\"\n self.__log_traceback = False\n if self._state != _PENDING:\n return False\n self._state = _CANCELLED\n self.__schedule_callbacks()\n return True\n\n def __schedule_callbacks(self):\n \"\"\"Internal: Ask the event loop to call all callbacks.\n\n The callbacks are scheduled to be called as soon as possible. Also\n clears the callback list.\n \"\"\"\n callbacks = self._callbacks[:]\n if not callbacks:\n return\n\n self._callbacks[:] = []\n for callback, ctx in callbacks:\n self._loop.call_soon(callback, self, context=ctx)\n\n def cancelled(self):\n \"\"\"Return True if the future was cancelled.\"\"\"\n return self._state == _CANCELLED\n\n # Don't implement running(); see http://bugs.python.org/issue18699\n\n def done(self):\n \"\"\"Return True if the future is done.\n\n Done means either that a result / exception are available, or that the\n future was cancelled.\n \"\"\"\n return self._state != _PENDING\n\n def result(self):\n \"\"\"Return the result this future represents.\n\n If the future has been cancelled, raises CancelledError. If the\n future's result isn't yet available, raises InvalidStateError. If\n the future is done and has an exception set, this exception is raised.\n \"\"\"\n if self._state == _CANCELLED:\n raise exceptions.CancelledError\n if self._state != _FINISHED:\n raise exceptions.InvalidStateError('Result is not ready.')\n self.__log_traceback = False\n if self._exception is not None:\n raise self._exception\n return self._result\n\n def exception(self):\n \"\"\"Return the exception that was set on this future.\n\n The exception (or None if no exception was set) is returned only if\n the future is done. If the future has been cancelled, raises\n CancelledError. If the future isn't done yet, raises\n InvalidStateError.\n \"\"\"\n if self._state == _CANCELLED:\n raise exceptions.CancelledError\n if self._state != _FINISHED:\n raise exceptions.InvalidStateError('Exception is not set.')\n self.__log_traceback = False\n return self._exception\n\n def add_done_callback(self, fn, *, context=None):\n \"\"\"Add a callback to be run when the future becomes done.\n\n The callback is called with a single argument - the future object. If\n the future is already done when this is called, the callback is\n scheduled with call_soon.\n \"\"\"\n if self._state != _PENDING:\n self._loop.call_soon(fn, self, context=context)\n else:\n if context is None:\n context = contextvars.copy_context()\n self._callbacks.append((fn, context))\n\n # New method not in PEP 3148.\n\n def remove_done_callback(self, fn):\n \"\"\"Remove all instances of a callback from the \"call when done\" list.\n\n Returns the number of callbacks removed.\n \"\"\"\n filtered_callbacks = [(f, ctx)\n for (f, ctx) in self._callbacks\n if f != fn]\n removed_count = len(self._callbacks) - len(filtered_callbacks)\n if removed_count:\n self._callbacks[:] = filtered_callbacks\n return removed_count\n\n # So-called internal methods (note: no set_running_or_notify_cancel()).\n\n def set_result(self, result):\n \"\"\"Mark the future done and set its result.\n\n If the future is already done when this method is called, raises\n InvalidStateError.\n \"\"\"\n if self._state != _PENDING:\n raise exceptions.InvalidStateError(f'{self._state}: {self!r}')\n self._result = result\n self._state = _FINISHED\n self.__schedule_callbacks()\n\n def set_exception(self, exception):\n \"\"\"Mark the future done and set an exception.\n\n If the future is already done when this method is called, raises\n InvalidStateError.\n \"\"\"\n if self._state != _PENDING:\n raise exceptions.InvalidStateError(f'{self._state}: {self!r}')\n if isinstance(exception, type):\n exception = exception()\n if type(exception) is StopIteration:\n raise TypeError(\"StopIteration interacts badly with generators \"\n \"and cannot be raised into a Future\")\n self._exception = exception\n self._state = _FINISHED\n self.__schedule_callbacks()\n self.__log_traceback = True\n\n def __await__(self):\n if not self.done():\n self._asyncio_future_blocking = True\n yield self # This tells Task to wait for completion.\n if not self.done():\n raise RuntimeError(\"await wasn't used with future\")\n return self.result() # May raise too.\n\n __iter__ = __await__ # make compatible with 'yield from'.\n\n\n# Needed for testing purposes.\n_PyFuture = Future\n\n\ndef _get_loop(fut):\n # Tries to call Future.get_loop() if it's available.\n # Otherwise fallbacks to using the old '_loop' property.\n try:\n get_loop = fut.get_loop\n except AttributeError:\n pass\n else:\n return get_loop()\n return fut._loop\n\n\ndef _set_result_unless_cancelled(fut, result):\n \"\"\"Helper setting the result only if the future was not cancelled.\"\"\"\n if fut.cancelled():\n return\n fut.set_result(result)\n\n\ndef _convert_future_exc(exc):\n exc_class = type(exc)\n if exc_class is concurrent.futures.CancelledError:\n return exceptions.CancelledError(*exc.args)\n elif exc_class is concurrent.futures.TimeoutError:\n return exceptions.TimeoutError(*exc.args)\n elif exc_class is concurrent.futures.InvalidStateError:\n return exceptions.InvalidStateError(*exc.args)\n else:\n return exc\n\n\ndef _set_concurrent_future_state(concurrent, source):\n \"\"\"Copy state from a future to a concurrent.futures.Future.\"\"\"\n assert source.done()\n if source.cancelled():\n concurrent.cancel()\n if not concurrent.set_running_or_notify_cancel():\n return\n exception = source.exception()\n if exception is not None:\n concurrent.set_exception(_convert_future_exc(exception))\n else:\n result = source.result()\n concurrent.set_result(result)\n\n\ndef _copy_future_state(source, dest):\n \"\"\"Internal helper to copy state from another Future.\n\n The other Future may be a concurrent.futures.Future.\n \"\"\"\n assert source.done()\n if dest.cancelled():\n return\n assert not dest.done()\n if source.cancelled():\n dest.cancel()\n else:\n exception = source.exception()\n if exception is not None:\n dest.set_exception(_convert_future_exc(exception))\n else:\n result = source.result()\n dest.set_result(result)\n\n\ndef _chain_future(source, destination):\n \"\"\"Chain two futures so that when one completes, so does the other.\n\n The result (or exception) of source will be copied to destination.\n If destination is cancelled, source gets cancelled too.\n Compatible with both asyncio.Future and concurrent.futures.Future.\n \"\"\"\n if not isfuture(source) and not isinstance(source,\n concurrent.futures.Future):\n raise TypeError('A future is required for source argument')\n if not isfuture(destination) and not isinstance(destination,\n concurrent.futures.Future):\n raise TypeError('A future is required for destination argument')\n source_loop = _get_loop(source) if isfuture(source) else None\n dest_loop = _get_loop(destination) if isfuture(destination) else None\n\n def _set_state(future, other):\n if isfuture(future):\n _copy_future_state(other, future)\n else:\n _set_concurrent_future_state(future, other)\n\n def _call_check_cancel(destination):\n if destination.cancelled():\n if source_loop is None or source_loop is dest_loop:\n source.cancel()\n else:\n source_loop.call_soon_threadsafe(source.cancel)\n\n def _call_set_state(source):\n if (destination.cancelled() and\n dest_loop is not None and dest_loop.is_closed()):\n return\n if dest_loop is None or dest_loop is source_loop:\n _set_state(destination, source)\n else:\n dest_loop.call_soon_threadsafe(_set_state, destination, source)\n\n destination.add_done_callback(_call_check_cancel)\n source.add_done_callback(_call_set_state)\n\n\ndef wrap_future(future, *, loop=None):\n \"\"\"Wrap concurrent.futures.Future object.\"\"\"\n if isfuture(future):\n return future\n assert isinstance(future, concurrent.futures.Future), \\\n f'concurrent.futures.Future is expected, got {future!r}'\n if loop is None:\n loop = events.get_event_loop()\n new_future = loop.create_future()\n _chain_future(future, new_future)\n return new_future\n\n\ntry:\n import _asyncio\nexcept ImportError:\n pass\nelse:\n # _CFuture is needed for tests.\n Future = _CFuture = _asyncio.Future\n", 399], "/usr/lib/python3.8/asyncio/runners.py": ["__all__ = 'run',\n\nfrom . import coroutines\nfrom . import events\nfrom . import tasks\n\n\ndef run(main, *, debug=None):\n \"\"\"Execute the coroutine and return the result.\n\n This function runs the passed coroutine, taking care of\n managing the asyncio event loop and finalizing asynchronous\n generators.\n\n This function cannot be called when another asyncio event loop is\n running in the same thread.\n\n If debug is True, the event loop will be run in debug mode.\n\n This function always creates a new event loop and closes it at the end.\n It should be used as a main entry point for asyncio programs, and should\n ideally only be called once.\n\n Example:\n\n async def main():\n await asyncio.sleep(1)\n print('hello')\n\n asyncio.run(main())\n \"\"\"\n if events._get_running_loop() is not None:\n raise RuntimeError(\n \"asyncio.run() cannot be called from a running event loop\")\n\n if not coroutines.iscoroutine(main):\n raise ValueError(\"a coroutine was expected, got {!r}\".format(main))\n\n loop = events.new_event_loop()\n try:\n events.set_event_loop(loop)\n if debug is not None:\n loop.set_debug(debug)\n return loop.run_until_complete(main)\n finally:\n try:\n _cancel_all_tasks(loop)\n loop.run_until_complete(loop.shutdown_asyncgens())\n finally:\n events.set_event_loop(None)\n loop.close()\n\n\ndef _cancel_all_tasks(loop):\n to_cancel = tasks.all_tasks(loop)\n if not to_cancel:\n return\n\n for task in to_cancel:\n task.cancel()\n\n loop.run_until_complete(\n tasks.gather(*to_cancel, loop=loop, return_exceptions=True))\n\n for task in to_cancel:\n if task.cancelled():\n continue\n if task.exception() is not None:\n loop.call_exception_handler({\n 'message': 'unhandled exception during asyncio.run() shutdown',\n 'exception': task.exception(),\n 'task': task,\n })\n", 73]}, "functions": {"iscoroutine (/usr/lib/python3.8/asyncio/coroutines.py:177)": ["/usr/lib/python3.8/asyncio/coroutines.py", 177], "__init__ (/usr/lib/python3.8/asyncio/events.py:625)": ["/usr/lib/python3.8/asyncio/events.py", 625], "__init__ (/usr/lib/python3.8/asyncio/unix_events.py:1341)": ["/usr/lib/python3.8/asyncio/unix_events.py", 1341], "_init_event_loop_policy (/usr/lib/python3.8/asyncio/events.py:711)": ["/usr/lib/python3.8/asyncio/events.py", 711], "get_event_loop_policy (/usr/lib/python3.8/asyncio/events.py:719)": ["/usr/lib/python3.8/asyncio/events.py", 719], "encode (/home/gaogaotiantian/programs/codesnap/venv3.8/lib/python3.8/os.py:748)": ["/home/gaogaotiantian/programs/codesnap/venv3.8/lib/python3.8/os.py", 748], "__getitem__ (/home/gaogaotiantian/programs/codesnap/venv3.8/lib/python3.8/os.py:670)": ["/home/gaogaotiantian/programs/codesnap/venv3.8/lib/python3.8/os.py", 670], "get (/home/gaogaotiantian/programs/codesnap/venv3.8/lib/python3.8/_collections_abc.py:657)": ["/home/gaogaotiantian/programs/codesnap/venv3.8/lib/python3.8/_collections_abc.py", 657], "_is_debug_mode (/usr/lib/python3.8/asyncio/coroutines.py:18)": ["/usr/lib/python3.8/asyncio/coroutines.py", 18], "is_running (/usr/lib/python3.8/asyncio/base_events.py:658)": ["/usr/lib/python3.8/asyncio/base_events.py", 658], "set_debug (/usr/lib/python3.8/asyncio/base_events.py:1880)": ["/usr/lib/python3.8/asyncio/base_events.py", 1880], "__init__ (/home/gaogaotiantian/programs/codesnap/venv3.8/lib/python3.8/_weakrefset.py:36)": ["/home/gaogaotiantian/programs/codesnap/venv3.8/lib/python3.8/_weakrefset.py", 36], "__init__ (/usr/lib/python3.8/asyncio/base_events.py:386)": ["/usr/lib/python3.8/asyncio/base_events.py", 386], "__init__ (/usr/lib/python3.8/selectors.py:63)": ["/usr/lib/python3.8/selectors.py", 63], "__init__ (/usr/lib/python3.8/selectors.py:209)": ["/usr/lib/python3.8/selectors.py", 209], "__init__ (/usr/lib/python3.8/selectors.py:347)": ["/usr/lib/python3.8/selectors.py", 347], "_acquireLock (/usr/lib/python3.8/logging/__init__.py:214)": ["/usr/lib/python3.8/logging/__init__.py", 214], "getEffectiveLevel (/usr/lib/python3.8/logging/__init__.py:1663)": ["/usr/lib/python3.8/logging/__init__.py", 1663], "_releaseLock (/usr/lib/python3.8/logging/__init__.py:223)": ["/usr/lib/python3.8/logging/__init__.py", 223], "isEnabledFor (/usr/lib/python3.8/logging/__init__.py:1677)": ["/usr/lib/python3.8/logging/__init__.py", 1677], "debug (/usr/lib/python3.8/logging/__init__.py:1412)": ["/usr/lib/python3.8/logging/__init__.py", 1412], "__init__ (/usr/lib/python3.8/socket.py:219)": ["/usr/lib/python3.8/socket.py", 219], "socketpair (/usr/lib/python3.8/socket.py:558)": ["/usr/lib/python3.8/socket.py", 558], "_check_closed (/usr/lib/python3.8/asyncio/base_events.py:506)": ["/usr/lib/python3.8/asyncio/base_events.py", 506], "get_debug (/usr/lib/python3.8/asyncio/base_events.py:1877)": ["/usr/lib/python3.8/asyncio/base_events.py", 1877], "__init__ (/usr/lib/python3.8/asyncio/events.py:32)": ["/usr/lib/python3.8/asyncio/events.py", 32], "get_map (/usr/lib/python3.8/selectors.py:272)": ["/usr/lib/python3.8/selectors.py", 272], "_fileobj_to_fd (/usr/lib/python3.8/selectors.py:21)": ["/usr/lib/python3.8/selectors.py", 21], "_fileobj_lookup (/usr/lib/python3.8/selectors.py:215)": ["/usr/lib/python3.8/selectors.py", 215], "__getitem__ (/usr/lib/python3.8/selectors.py:69)": ["/usr/lib/python3.8/selectors.py", 69], "get_key (/usr/lib/python3.8/selectors.py:180)": ["/usr/lib/python3.8/selectors.py", 180], "register (/usr/lib/python3.8/selectors.py:234)": ["/usr/lib/python3.8/selectors.py", 234], "register (/usr/lib/python3.8/selectors.py:351)": ["/usr/lib/python3.8/selectors.py", 351], "_add_reader (/usr/lib/python3.8/asyncio/selector_events.py:257)": ["/usr/lib/python3.8/asyncio/selector_events.py", 257], "_make_self_pipe (/usr/lib/python3.8/asyncio/selector_events.py:106)": ["/usr/lib/python3.8/asyncio/selector_events.py", 106], "update (/home/gaogaotiantian/programs/codesnap/venv3.8/lib/python3.8/weakref.py:284)": ["/home/gaogaotiantian/programs/codesnap/venv3.8/lib/python3.8/weakref.py", 284], "__init__ (/home/gaogaotiantian/programs/codesnap/venv3.8/lib/python3.8/weakref.py:102)": ["/home/gaogaotiantian/programs/codesnap/venv3.8/lib/python3.8/weakref.py", 102], "__init__ (/usr/lib/python3.8/asyncio/selector_events.py:54)": ["/usr/lib/python3.8/asyncio/selector_events.py", 54], "__init__ (/usr/lib/python3.8/asyncio/unix_events.py:53)": ["/usr/lib/python3.8/asyncio/unix_events.py", 53], "new_event_loop (/usr/lib/python3.8/asyncio/events.py:650)": ["/usr/lib/python3.8/asyncio/events.py", 650], "new_event_loop (/usr/lib/python3.8/asyncio/events.py:756)": ["/usr/lib/python3.8/asyncio/events.py", 756], "set_event_loop (/usr/lib/python3.8/asyncio/events.py:644)": ["/usr/lib/python3.8/asyncio/events.py", 644], "set_event_loop (/usr/lib/python3.8/asyncio/unix_events.py:1353)": ["/usr/lib/python3.8/asyncio/unix_events.py", 1353], "set_event_loop (/usr/lib/python3.8/asyncio/events.py:751)": ["/usr/lib/python3.8/asyncio/events.py", 751], "_check_running (/usr/lib/python3.8/asyncio/base_events.py:550)": ["/usr/lib/python3.8/asyncio/base_events.py", 550], "isfuture (/usr/lib/python3.8/asyncio/base_futures.py:13)": ["/usr/lib/python3.8/asyncio/base_futures.py", 13], "_call_soon (/usr/lib/python3.8/asyncio/base_events.py:738)": ["/usr/lib/python3.8/asyncio/base_events.py", 738], "call_soon (/usr/lib/python3.8/asyncio/base_events.py:709)": ["/usr/lib/python3.8/asyncio/base_events.py", 709], "add (/home/gaogaotiantian/programs/codesnap/venv3.8/lib/python3.8/_weakrefset.py:81)": ["/home/gaogaotiantian/programs/codesnap/venv3.8/lib/python3.8/_weakrefset.py", 81], "create_task (/usr/lib/python3.8/asyncio/base_events.py:424)": ["/usr/lib/python3.8/asyncio/base_events.py", 424], "ensure_future (/usr/lib/python3.8/asyncio/tasks.py:661)": ["/usr/lib/python3.8/asyncio/tasks.py", 661], "_set_coroutine_origin_tracking (/usr/lib/python3.8/asyncio/base_events.py:1862)": ["/usr/lib/python3.8/asyncio/base_events.py", 1862], "select (/usr/lib/python3.8/selectors.py:451)": ["/usr/lib/python3.8/selectors.py", 451], "_process_events (/usr/lib/python3.8/asyncio/selector_events.py:586)": ["/usr/lib/python3.8/asyncio/selector_events.py", 586], "time (/usr/lib/python3.8/asyncio/base_events.py:662)": ["/usr/lib/python3.8/asyncio/base_events.py", 662], "_set_task_name (/usr/lib/python3.8/asyncio/tasks.py:88)": ["/usr/lib/python3.8/asyncio/tasks.py", 88], "create_task (/usr/lib/python3.8/asyncio/tasks.py:376)": ["/usr/lib/python3.8/asyncio/tasks.py", 376], "main (/home/gaogaotiantian/programs/codesnap/example/src/async_simple.py:6)": ["/home/gaogaotiantian/programs/codesnap/example/src/async_simple.py", 6], "_run (/usr/lib/python3.8/asyncio/events.py:79)": ["/usr/lib/python3.8/asyncio/events.py", 79], "_run_once (/usr/lib/python3.8/asyncio/base_events.py:1784)": ["/usr/lib/python3.8/asyncio/base_events.py", 1784], "create_future (/usr/lib/python3.8/asyncio/base_events.py:420)": ["/usr/lib/python3.8/asyncio/base_events.py", 420], "__init__ (/usr/lib/python3.8/asyncio/events.py:104)": ["/usr/lib/python3.8/asyncio/events.py", 104], "call_at (/usr/lib/python3.8/asyncio/base_events.py:693)": ["/usr/lib/python3.8/asyncio/base_events.py", 693], "call_later (/usr/lib/python3.8/asyncio/base_events.py:671)": ["/usr/lib/python3.8/asyncio/base_events.py", 671], "sleep (/usr/lib/python3.8/asyncio/tasks.py:638)": ["/usr/lib/python3.8/asyncio/tasks.py", 638], "io_task (/home/gaogaotiantian/programs/codesnap/example/src/async_simple.py:3)": ["/home/gaogaotiantian/programs/codesnap/example/src/async_simple.py", 3], "__lt__ (/usr/lib/python3.8/asyncio/events.py:121)": ["/usr/lib/python3.8/asyncio/events.py", 121], "_set_result_unless_cancelled (/usr/lib/python3.8/asyncio/futures.py:284)": ["/usr/lib/python3.8/asyncio/futures.py", 284], "_timer_handle_cancelled (/usr/lib/python3.8/asyncio/base_events.py:1779)": ["/usr/lib/python3.8/asyncio/base_events.py", 1779], "cancel (/usr/lib/python3.8/asyncio/events.py:65)": ["/usr/lib/python3.8/asyncio/events.py", 65], "cancel (/usr/lib/python3.8/asyncio/events.py:149)": ["/usr/lib/python3.8/asyncio/events.py", 149], "_get_loop (/usr/lib/python3.8/asyncio/futures.py:272)": ["/usr/lib/python3.8/asyncio/futures.py", 272], "stop (/usr/lib/python3.8/asyncio/base_events.py:618)": ["/usr/lib/python3.8/asyncio/base_events.py", 618], "_run_until_complete_cb (/usr/lib/python3.8/asyncio/base_events.py:184)": ["/usr/lib/python3.8/asyncio/base_events.py", 184], "run_forever (/usr/lib/python3.8/asyncio/base_events.py:557)": ["/usr/lib/python3.8/asyncio/base_events.py", 557], "run_until_complete (/usr/lib/python3.8/asyncio/base_events.py:580)": ["/usr/lib/python3.8/asyncio/base_events.py", 580], "__len__ (/home/gaogaotiantian/programs/codesnap/venv3.8/lib/python3.8/_weakrefset.py:67)": ["/home/gaogaotiantian/programs/codesnap/venv3.8/lib/python3.8/_weakrefset.py", 67], "__init__ (/home/gaogaotiantian/programs/codesnap/venv3.8/lib/python3.8/_weakrefset.py:16)": ["/home/gaogaotiantian/programs/codesnap/venv3.8/lib/python3.8/_weakrefset.py", 16], "__enter__ (/home/gaogaotiantian/programs/codesnap/venv3.8/lib/python3.8/_weakrefset.py:20)": ["/home/gaogaotiantian/programs/codesnap/venv3.8/lib/python3.8/_weakrefset.py", 20], "__iter__ (/home/gaogaotiantian/programs/codesnap/venv3.8/lib/python3.8/_weakrefset.py:58)": ["/home/gaogaotiantian/programs/codesnap/venv3.8/lib/python3.8/_weakrefset.py", 58], "_commit_removals (/home/gaogaotiantian/programs/codesnap/venv3.8/lib/python3.8/_weakrefset.py:52)": ["/home/gaogaotiantian/programs/codesnap/venv3.8/lib/python3.8/_weakrefset.py", 52], "__exit__ (/home/gaogaotiantian/programs/codesnap/venv3.8/lib/python3.8/_weakrefset.py:26)": ["/home/gaogaotiantian/programs/codesnap/venv3.8/lib/python3.8/_weakrefset.py", 26], " (/usr/lib/python3.8/asyncio/tasks.py:60)": ["/usr/lib/python3.8/asyncio/tasks.py", 60], "all_tasks (/usr/lib/python3.8/asyncio/tasks.py:41)": ["/usr/lib/python3.8/asyncio/tasks.py", 41], "_cancel_all_tasks (/usr/lib/python3.8/asyncio/runners.py:54)": ["/usr/lib/python3.8/asyncio/runners.py", 54], "shutdown_asyncgens (/usr/lib/python3.8/asyncio/base_events.py:524)": ["/usr/lib/python3.8/asyncio/base_events.py", 524], "is_closed (/usr/lib/python3.8/asyncio/base_events.py:648)": ["/usr/lib/python3.8/asyncio/base_events.py", 648], "unregister (/usr/lib/python3.8/selectors.py:247)": ["/usr/lib/python3.8/selectors.py", 247], "unregister (/usr/lib/python3.8/selectors.py:365)": ["/usr/lib/python3.8/selectors.py", 365], "_remove_reader (/usr/lib/python3.8/asyncio/selector_events.py:272)": ["/usr/lib/python3.8/asyncio/selector_events.py", 272], "_real_close (/usr/lib/python3.8/socket.py:492)": ["/usr/lib/python3.8/socket.py", 492], "close (/usr/lib/python3.8/socket.py:496)": ["/usr/lib/python3.8/socket.py", 496], "_close_self_pipe (/usr/lib/python3.8/asyncio/selector_events.py:98)": ["/usr/lib/python3.8/asyncio/selector_events.py", 98], "close (/usr/lib/python3.8/asyncio/base_events.py:626)": ["/usr/lib/python3.8/asyncio/base_events.py", 626], "close (/usr/lib/python3.8/selectors.py:268)": ["/usr/lib/python3.8/selectors.py", 268], "close (/usr/lib/python3.8/selectors.py:483)": ["/usr/lib/python3.8/selectors.py", 483], "close (/usr/lib/python3.8/asyncio/selector_events.py:87)": ["/usr/lib/python3.8/asyncio/selector_events.py", 87], "close (/usr/lib/python3.8/asyncio/unix_events.py:57)": ["/usr/lib/python3.8/asyncio/unix_events.py", 57], "run (/usr/lib/python3.8/asyncio/runners.py:8)": ["/usr/lib/python3.8/asyncio/runners.py", 8], " (/home/gaogaotiantian/programs/codesnap/example/src/async_simple.py:1)": ["/home/gaogaotiantian/programs/codesnap/example/src/async_simple.py", 1]}}} \ No newline at end of file +{"traceEvents": [{"ph": "M", "pid": 30327, "tid": 30327, "name": "process_name", "args": {"name": 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"thread_name", "args": {"name": "Task-1"}}, {"ph": "M", "pid": 30327, "tid": 16329104, "name": "thread_name", "args": {"name": "Task-2"}}, {"ph": "M", "pid": 30327, "tid": 16329280, "name": "thread_name", "args": {"name": "Task-3"}}, {"ph": "M", "pid": 30327, "tid": 16329456, "name": "thread_name", "args": {"name": "Task-4"}}, {"ph": "M", "pid": 30327, "tid": 16329984, "name": "thread_name", "args": {"name": "Task-5"}}, {"ph": "M", "pid": 30327, "tid": 16329808, "name": "thread_name", "args": {"name": "Task-6"}}], "viztracer_metadata": {"overflow": false, "version": "0.17.1"}, "file_info": {"files": {"/usr/lib/python3.13/asyncio/runners.py": ["__all__ = ('Runner', 'run')\n\nimport contextvars\nimport enum\nimport functools\nimport threading\nimport signal\nfrom . import coroutines\nfrom . import events\nfrom . import exceptions\nfrom . import tasks\nfrom . import constants\n\nclass _State(enum.Enum):\n CREATED = \"created\"\n INITIALIZED = \"initialized\"\n CLOSED = \"closed\"\n\n\nclass Runner:\n \"\"\"A context manager that controls event loop life cycle.\n\n The context manager always creates a new event loop,\n allows to run async functions inside it,\n and properly finalizes the loop at the context manager exit.\n\n If debug is True, the event loop will be run in debug mode.\n If loop_factory is passed, it is used for new event loop creation.\n\n asyncio.run(main(), debug=True)\n\n is a shortcut for\n\n with asyncio.Runner(debug=True) as runner:\n runner.run(main())\n\n The run() method can be called multiple times within the runner's context.\n\n This can be useful for interactive console (e.g. IPython),\n unittest runners, console tools, -- everywhere when async code\n is called from existing sync framework and where the preferred single\n asyncio.run() call doesn't work.\n\n \"\"\"\n\n # Note: the class is final, it is not intended for inheritance.\n\n def __init__(self, *, debug=None, loop_factory=None):\n self._state = _State.CREATED\n self._debug = debug\n self._loop_factory = loop_factory\n self._loop = None\n self._context = None\n self._interrupt_count = 0\n self._set_event_loop = False\n\n def __enter__(self):\n self._lazy_init()\n return self\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n self.close()\n\n def close(self):\n \"\"\"Shutdown and close event loop.\"\"\"\n if self._state is not _State.INITIALIZED:\n return\n try:\n loop = self._loop\n _cancel_all_tasks(loop)\n loop.run_until_complete(loop.shutdown_asyncgens())\n loop.run_until_complete(\n loop.shutdown_default_executor(constants.THREAD_JOIN_TIMEOUT))\n finally:\n if self._set_event_loop:\n events.set_event_loop(None)\n loop.close()\n self._loop = None\n self._state = _State.CLOSED\n\n def get_loop(self):\n \"\"\"Return embedded event loop.\"\"\"\n self._lazy_init()\n return self._loop\n\n def run(self, coro, *, context=None):\n \"\"\"Run a coroutine inside the embedded event loop.\"\"\"\n if not coroutines.iscoroutine(coro):\n raise ValueError(\"a coroutine was expected, got {!r}\".format(coro))\n\n if events._get_running_loop() is not None:\n # fail fast with short traceback\n raise RuntimeError(\n \"Runner.run() cannot be called from a running event loop\")\n\n self._lazy_init()\n\n if context is None:\n context = self._context\n task = self._loop.create_task(coro, context=context)\n\n if (threading.current_thread() is threading.main_thread()\n and signal.getsignal(signal.SIGINT) is signal.default_int_handler\n ):\n sigint_handler = functools.partial(self._on_sigint, main_task=task)\n try:\n signal.signal(signal.SIGINT, sigint_handler)\n except ValueError:\n # `signal.signal` may throw if `threading.main_thread` does\n # not support signals (e.g. embedded interpreter with signals\n # not registered - see gh-91880)\n sigint_handler = None\n else:\n sigint_handler = None\n\n self._interrupt_count = 0\n try:\n return self._loop.run_until_complete(task)\n except exceptions.CancelledError:\n if self._interrupt_count > 0:\n uncancel = getattr(task, \"uncancel\", None)\n if uncancel is not None and uncancel() == 0:\n raise KeyboardInterrupt()\n raise # CancelledError\n finally:\n if (sigint_handler is not None\n and signal.getsignal(signal.SIGINT) is sigint_handler\n ):\n signal.signal(signal.SIGINT, signal.default_int_handler)\n\n def _lazy_init(self):\n if self._state is _State.CLOSED:\n raise RuntimeError(\"Runner is closed\")\n if self._state is _State.INITIALIZED:\n return\n if self._loop_factory is None:\n self._loop = events.new_event_loop()\n if not self._set_event_loop:\n # Call set_event_loop only once to avoid calling\n # attach_loop multiple times on child watchers\n events.set_event_loop(self._loop)\n self._set_event_loop = True\n else:\n self._loop = self._loop_factory()\n if self._debug is not None:\n self._loop.set_debug(self._debug)\n self._context = contextvars.copy_context()\n self._state = _State.INITIALIZED\n\n def _on_sigint(self, signum, frame, main_task):\n self._interrupt_count += 1\n if self._interrupt_count == 1 and not main_task.done():\n main_task.cancel()\n # wakeup loop if it is blocked by select() with long timeout\n self._loop.call_soon_threadsafe(lambda: None)\n return\n raise KeyboardInterrupt()\n\n\ndef run(main, *, debug=None, loop_factory=None):\n \"\"\"Execute the coroutine and return the result.\n\n This function runs the passed coroutine, taking care of\n managing the asyncio event loop, finalizing asynchronous\n generators and closing the default executor.\n\n This function cannot be called when another asyncio event loop is\n running in the same thread.\n\n If debug is True, the event loop will be run in debug mode.\n\n This function always creates a new event loop and closes it at the end.\n It should be used as a main entry point for asyncio programs, and should\n ideally only be called once.\n\n The executor is given a timeout duration of 5 minutes to shutdown.\n If the executor hasn't finished within that duration, a warning is\n emitted and the executor is closed.\n\n Example:\n\n async def main():\n await asyncio.sleep(1)\n print('hello')\n\n asyncio.run(main())\n \"\"\"\n if events._get_running_loop() is not None:\n # fail fast with short traceback\n raise RuntimeError(\n \"asyncio.run() cannot be called from a running event loop\")\n\n with Runner(debug=debug, loop_factory=loop_factory) as runner:\n return runner.run(main)\n\n\ndef _cancel_all_tasks(loop):\n to_cancel = tasks.all_tasks(loop)\n if not to_cancel:\n return\n\n for task in to_cancel:\n task.cancel()\n\n loop.run_until_complete(tasks.gather(*to_cancel, return_exceptions=True))\n\n for task in to_cancel:\n if task.cancelled():\n continue\n if task.exception() is not None:\n loop.call_exception_handler({\n 'message': 'unhandled exception during asyncio.run() shutdown',\n 'exception': task.exception(),\n 'task': task,\n })\n", 215], "/usr/lib/python3.13/asyncio/events.py": ["\"\"\"Event loop and event loop policy.\"\"\"\n\n# Contains code from https://github.com/MagicStack/uvloop/tree/v0.16.0\n# SPDX-License-Identifier: PSF-2.0 AND (MIT OR Apache-2.0)\n# SPDX-FileCopyrightText: Copyright (c) 2015-2021 MagicStack Inc. http://magic.io\n\n__all__ = (\n 'AbstractEventLoopPolicy',\n 'AbstractEventLoop', 'AbstractServer',\n 'Handle', 'TimerHandle',\n 'get_event_loop_policy', 'set_event_loop_policy',\n 'get_event_loop', 'set_event_loop', 'new_event_loop',\n 'get_child_watcher', 'set_child_watcher',\n '_set_running_loop', 'get_running_loop',\n '_get_running_loop',\n)\n\nimport contextvars\nimport os\nimport signal\nimport socket\nimport subprocess\nimport sys\nimport threading\n\nfrom . import format_helpers\n\n\nclass Handle:\n \"\"\"Object returned by callback registration methods.\"\"\"\n\n __slots__ = ('_callback', '_args', '_cancelled', '_loop',\n '_source_traceback', '_repr', '__weakref__',\n '_context')\n\n def __init__(self, callback, args, loop, context=None):\n if context is None:\n context = contextvars.copy_context()\n self._context = context\n self._loop = loop\n self._callback = callback\n self._args = args\n self._cancelled = False\n self._repr = None\n if self._loop.get_debug():\n self._source_traceback = format_helpers.extract_stack(\n sys._getframe(1))\n else:\n self._source_traceback = None\n\n def _repr_info(self):\n info = [self.__class__.__name__]\n if self._cancelled:\n info.append('cancelled')\n if self._callback is not None:\n info.append(format_helpers._format_callback_source(\n self._callback, self._args,\n debug=self._loop.get_debug()))\n if self._source_traceback:\n frame = self._source_traceback[-1]\n info.append(f'created at {frame[0]}:{frame[1]}')\n return info\n\n def __repr__(self):\n if self._repr is not None:\n return self._repr\n info = self._repr_info()\n return '<{}>'.format(' '.join(info))\n\n def get_context(self):\n return self._context\n\n def cancel(self):\n if not self._cancelled:\n self._cancelled = True\n if self._loop.get_debug():\n # Keep a representation in debug mode to keep callback and\n # parameters. For example, to log the warning\n # \"Executing took 2.5 second\"\n self._repr = repr(self)\n self._callback = None\n self._args = None\n\n def cancelled(self):\n return self._cancelled\n\n def _run(self):\n try:\n self._context.run(self._callback, *self._args)\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n cb = format_helpers._format_callback_source(\n self._callback, self._args,\n debug=self._loop.get_debug())\n msg = f'Exception in callback {cb}'\n context = {\n 'message': msg,\n 'exception': exc,\n 'handle': self,\n }\n if self._source_traceback:\n context['source_traceback'] = self._source_traceback\n self._loop.call_exception_handler(context)\n self = None # Needed to break cycles when an exception occurs.\n\n\nclass TimerHandle(Handle):\n \"\"\"Object returned by timed callback registration methods.\"\"\"\n\n __slots__ = ['_scheduled', '_when']\n\n def __init__(self, when, callback, args, loop, context=None):\n super().__init__(callback, args, loop, context)\n if self._source_traceback:\n del self._source_traceback[-1]\n self._when = when\n self._scheduled = False\n\n def _repr_info(self):\n info = super()._repr_info()\n pos = 2 if self._cancelled else 1\n info.insert(pos, f'when={self._when}')\n return info\n\n def __hash__(self):\n return hash(self._when)\n\n def __lt__(self, other):\n if isinstance(other, TimerHandle):\n return self._when < other._when\n return NotImplemented\n\n def __le__(self, other):\n if isinstance(other, TimerHandle):\n return self._when < other._when or self.__eq__(other)\n return NotImplemented\n\n def __gt__(self, other):\n if isinstance(other, TimerHandle):\n return self._when > other._when\n return NotImplemented\n\n def __ge__(self, other):\n if isinstance(other, TimerHandle):\n return self._when > other._when or self.__eq__(other)\n return NotImplemented\n\n def __eq__(self, other):\n if isinstance(other, TimerHandle):\n return (self._when == other._when and\n self._callback == other._callback and\n self._args == other._args and\n self._cancelled == other._cancelled)\n return NotImplemented\n\n def cancel(self):\n if not self._cancelled:\n self._loop._timer_handle_cancelled(self)\n super().cancel()\n\n def when(self):\n \"\"\"Return a scheduled callback time.\n\n The time is an absolute timestamp, using the same time\n reference as loop.time().\n \"\"\"\n return self._when\n\n\nclass AbstractServer:\n \"\"\"Abstract server returned by create_server().\"\"\"\n\n def close(self):\n \"\"\"Stop serving. This leaves existing connections open.\"\"\"\n raise NotImplementedError\n\n def close_clients(self):\n \"\"\"Close all active connections.\"\"\"\n raise NotImplementedError\n\n def abort_clients(self):\n \"\"\"Close all active connections immediately.\"\"\"\n raise NotImplementedError\n\n def get_loop(self):\n \"\"\"Get the event loop the Server object is attached to.\"\"\"\n raise NotImplementedError\n\n def is_serving(self):\n \"\"\"Return True if the server is accepting connections.\"\"\"\n raise NotImplementedError\n\n async def start_serving(self):\n \"\"\"Start accepting connections.\n\n This method is idempotent, so it can be called when\n the server is already being serving.\n \"\"\"\n raise NotImplementedError\n\n async def serve_forever(self):\n \"\"\"Start accepting connections until the coroutine is cancelled.\n\n The server is closed when the coroutine is cancelled.\n \"\"\"\n raise NotImplementedError\n\n async def wait_closed(self):\n \"\"\"Coroutine to wait until service is closed.\"\"\"\n raise NotImplementedError\n\n async def __aenter__(self):\n return self\n\n async def __aexit__(self, *exc):\n self.close()\n await self.wait_closed()\n\n\nclass AbstractEventLoop:\n \"\"\"Abstract event loop.\"\"\"\n\n # Running and stopping the event loop.\n\n def run_forever(self):\n \"\"\"Run the event loop until stop() is called.\"\"\"\n raise NotImplementedError\n\n def run_until_complete(self, future):\n \"\"\"Run the event loop until a Future is done.\n\n Return the Future's result, or raise its exception.\n \"\"\"\n raise NotImplementedError\n\n def stop(self):\n \"\"\"Stop the event loop as soon as reasonable.\n\n Exactly how soon that is may depend on the implementation, but\n no more I/O callbacks should be scheduled.\n \"\"\"\n raise NotImplementedError\n\n def is_running(self):\n \"\"\"Return whether the event loop is currently running.\"\"\"\n raise NotImplementedError\n\n def is_closed(self):\n \"\"\"Returns True if the event loop was closed.\"\"\"\n raise NotImplementedError\n\n def close(self):\n \"\"\"Close the loop.\n\n The loop should not be running.\n\n This is idempotent and irreversible.\n\n No other methods should be called after this one.\n \"\"\"\n raise NotImplementedError\n\n async def shutdown_asyncgens(self):\n \"\"\"Shutdown all active asynchronous generators.\"\"\"\n raise NotImplementedError\n\n async def shutdown_default_executor(self):\n \"\"\"Schedule the shutdown of the default executor.\"\"\"\n raise NotImplementedError\n\n # Methods scheduling callbacks. All these return Handles.\n\n def _timer_handle_cancelled(self, handle):\n \"\"\"Notification that a TimerHandle has been cancelled.\"\"\"\n raise NotImplementedError\n\n def call_soon(self, callback, *args, context=None):\n return self.call_later(0, callback, *args, context=context)\n\n def call_later(self, delay, callback, *args, context=None):\n raise NotImplementedError\n\n def call_at(self, when, callback, *args, context=None):\n raise NotImplementedError\n\n def time(self):\n raise NotImplementedError\n\n def create_future(self):\n raise NotImplementedError\n\n # Method scheduling a coroutine object: create a task.\n\n def create_task(self, coro, *, name=None, context=None):\n raise NotImplementedError\n\n # Methods for interacting with threads.\n\n def call_soon_threadsafe(self, callback, *args, context=None):\n raise NotImplementedError\n\n def run_in_executor(self, executor, func, *args):\n raise NotImplementedError\n\n def set_default_executor(self, executor):\n raise NotImplementedError\n\n # Network I/O methods returning Futures.\n\n async def getaddrinfo(self, host, port, *,\n family=0, type=0, proto=0, flags=0):\n raise NotImplementedError\n\n async def getnameinfo(self, sockaddr, flags=0):\n raise NotImplementedError\n\n async def create_connection(\n self, protocol_factory, host=None, port=None,\n *, ssl=None, family=0, proto=0,\n flags=0, sock=None, local_addr=None,\n server_hostname=None,\n ssl_handshake_timeout=None,\n ssl_shutdown_timeout=None,\n happy_eyeballs_delay=None, interleave=None):\n raise NotImplementedError\n\n async def create_server(\n self, protocol_factory, host=None, port=None,\n *, family=socket.AF_UNSPEC,\n flags=socket.AI_PASSIVE, sock=None, backlog=100,\n ssl=None, reuse_address=None, reuse_port=None,\n keep_alive=None,\n ssl_handshake_timeout=None,\n ssl_shutdown_timeout=None,\n start_serving=True):\n \"\"\"A coroutine which creates a TCP server bound to host and port.\n\n The return value is a Server object which can be used to stop\n the service.\n\n If host is an empty string or None all interfaces are assumed\n and a list of multiple sockets will be returned (most likely\n one for IPv4 and another one for IPv6). The host parameter can also be\n a sequence (e.g. list) of hosts to bind to.\n\n family can be set to either AF_INET or AF_INET6 to force the\n socket to use IPv4 or IPv6. If not set it will be determined\n from host (defaults to AF_UNSPEC).\n\n flags is a bitmask for getaddrinfo().\n\n sock can optionally be specified in order to use a preexisting\n socket object.\n\n backlog is the maximum number of queued connections passed to\n listen() (defaults to 100).\n\n ssl can be set to an SSLContext to enable SSL over the\n accepted connections.\n\n reuse_address tells the kernel to reuse a local socket in\n TIME_WAIT state, without waiting for its natural timeout to\n expire. If not specified will automatically be set to True on\n UNIX.\n\n reuse_port tells the kernel to allow this endpoint to be bound to\n the same port as other existing endpoints are bound to, so long as\n they all set this flag when being created. This option is not\n supported on Windows.\n\n keep_alive set to True keeps connections active by enabling the\n periodic transmission of messages.\n\n ssl_handshake_timeout is the time in seconds that an SSL server\n will wait for completion of the SSL handshake before aborting the\n connection. Default is 60s.\n\n ssl_shutdown_timeout is the time in seconds that an SSL server\n will wait for completion of the SSL shutdown procedure\n before aborting the connection. Default is 30s.\n\n start_serving set to True (default) causes the created server\n to start accepting connections immediately. When set to False,\n the user should await Server.start_serving() or Server.serve_forever()\n to make the server to start accepting connections.\n \"\"\"\n raise NotImplementedError\n\n async def sendfile(self, transport, file, offset=0, count=None,\n *, fallback=True):\n \"\"\"Send a file through a transport.\n\n Return an amount of sent bytes.\n \"\"\"\n raise NotImplementedError\n\n async def start_tls(self, transport, protocol, sslcontext, *,\n server_side=False,\n server_hostname=None,\n ssl_handshake_timeout=None,\n ssl_shutdown_timeout=None):\n \"\"\"Upgrade a transport to TLS.\n\n Return a new transport that *protocol* should start using\n immediately.\n \"\"\"\n raise NotImplementedError\n\n async def create_unix_connection(\n self, protocol_factory, path=None, *,\n ssl=None, sock=None,\n server_hostname=None,\n ssl_handshake_timeout=None,\n ssl_shutdown_timeout=None):\n raise NotImplementedError\n\n async def create_unix_server(\n self, protocol_factory, path=None, *,\n sock=None, backlog=100, ssl=None,\n ssl_handshake_timeout=None,\n ssl_shutdown_timeout=None,\n start_serving=True):\n \"\"\"A coroutine which creates a UNIX Domain Socket server.\n\n The return value is a Server object, which can be used to stop\n the service.\n\n path is a str, representing a file system path to bind the\n server socket to.\n\n sock can optionally be specified in order to use a preexisting\n socket object.\n\n backlog is the maximum number of queued connections passed to\n listen() (defaults to 100).\n\n ssl can be set to an SSLContext to enable SSL over the\n accepted connections.\n\n ssl_handshake_timeout is the time in seconds that an SSL server\n will wait for the SSL handshake to complete (defaults to 60s).\n\n ssl_shutdown_timeout is the time in seconds that an SSL server\n will wait for the SSL shutdown to finish (defaults to 30s).\n\n start_serving set to True (default) causes the created server\n to start accepting connections immediately. When set to False,\n the user should await Server.start_serving() or Server.serve_forever()\n to make the server to start accepting connections.\n \"\"\"\n raise NotImplementedError\n\n async def connect_accepted_socket(\n self, protocol_factory, sock,\n *, ssl=None,\n ssl_handshake_timeout=None,\n ssl_shutdown_timeout=None):\n \"\"\"Handle an accepted connection.\n\n This is used by servers that accept connections outside of\n asyncio, but use asyncio to handle connections.\n\n This method is a coroutine. When completed, the coroutine\n returns a (transport, protocol) pair.\n \"\"\"\n raise NotImplementedError\n\n async def create_datagram_endpoint(self, protocol_factory,\n local_addr=None, remote_addr=None, *,\n family=0, proto=0, flags=0,\n reuse_address=None, reuse_port=None,\n allow_broadcast=None, sock=None):\n \"\"\"A coroutine which creates a datagram endpoint.\n\n This method will try to establish the endpoint in the background.\n When successful, the coroutine returns a (transport, protocol) pair.\n\n protocol_factory must be a callable returning a protocol instance.\n\n socket family AF_INET, socket.AF_INET6 or socket.AF_UNIX depending on\n host (or family if specified), socket type SOCK_DGRAM.\n\n reuse_address tells the kernel to reuse a local socket in\n TIME_WAIT state, without waiting for its natural timeout to\n expire. If not specified it will automatically be set to True on\n UNIX.\n\n reuse_port tells the kernel to allow this endpoint to be bound to\n the same port as other existing endpoints are bound to, so long as\n they all set this flag when being created. This option is not\n supported on Windows and some UNIX's. If the\n :py:data:`~socket.SO_REUSEPORT` constant is not defined then this\n capability is unsupported.\n\n allow_broadcast tells the kernel to allow this endpoint to send\n messages to the broadcast address.\n\n sock can optionally be specified in order to use a preexisting\n socket object.\n \"\"\"\n raise NotImplementedError\n\n # Pipes and subprocesses.\n\n async def connect_read_pipe(self, protocol_factory, pipe):\n \"\"\"Register read pipe in event loop. Set the pipe to non-blocking mode.\n\n protocol_factory should instantiate object with Protocol interface.\n pipe is a file-like object.\n Return pair (transport, protocol), where transport supports the\n ReadTransport interface.\"\"\"\n # The reason to accept file-like object instead of just file descriptor\n # is: we need to own pipe and close it at transport finishing\n # Can got complicated errors if pass f.fileno(),\n # close fd in pipe transport then close f and vice versa.\n raise NotImplementedError\n\n async def connect_write_pipe(self, protocol_factory, pipe):\n \"\"\"Register write pipe in event loop.\n\n protocol_factory should instantiate object with BaseProtocol interface.\n Pipe is file-like object already switched to nonblocking.\n Return pair (transport, protocol), where transport support\n WriteTransport interface.\"\"\"\n # The reason to accept file-like object instead of just file descriptor\n # is: we need to own pipe and close it at transport finishing\n # Can got complicated errors if pass f.fileno(),\n # close fd in pipe transport then close f and vice versa.\n raise NotImplementedError\n\n async def subprocess_shell(self, protocol_factory, cmd, *,\n stdin=subprocess.PIPE,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n **kwargs):\n raise NotImplementedError\n\n async def subprocess_exec(self, protocol_factory, *args,\n stdin=subprocess.PIPE,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n **kwargs):\n raise NotImplementedError\n\n # Ready-based callback registration methods.\n # The add_*() methods return None.\n # The remove_*() methods return True if something was removed,\n # False if there was nothing to delete.\n\n def add_reader(self, fd, callback, *args):\n raise NotImplementedError\n\n def remove_reader(self, fd):\n raise NotImplementedError\n\n def add_writer(self, fd, callback, *args):\n raise NotImplementedError\n\n def remove_writer(self, fd):\n raise NotImplementedError\n\n # Completion based I/O methods returning Futures.\n\n async def sock_recv(self, sock, nbytes):\n raise NotImplementedError\n\n async def sock_recv_into(self, sock, buf):\n raise NotImplementedError\n\n async def sock_recvfrom(self, sock, bufsize):\n raise NotImplementedError\n\n async def sock_recvfrom_into(self, sock, buf, nbytes=0):\n raise NotImplementedError\n\n async def sock_sendall(self, sock, data):\n raise NotImplementedError\n\n async def sock_sendto(self, sock, data, address):\n raise NotImplementedError\n\n async def sock_connect(self, sock, address):\n raise NotImplementedError\n\n async def sock_accept(self, sock):\n raise NotImplementedError\n\n async def sock_sendfile(self, sock, file, offset=0, count=None,\n *, fallback=None):\n raise NotImplementedError\n\n # Signal handling.\n\n def add_signal_handler(self, sig, callback, *args):\n raise NotImplementedError\n\n def remove_signal_handler(self, sig):\n raise NotImplementedError\n\n # Task factory.\n\n def set_task_factory(self, factory):\n raise NotImplementedError\n\n def get_task_factory(self):\n raise NotImplementedError\n\n # Error handlers.\n\n def get_exception_handler(self):\n raise NotImplementedError\n\n def set_exception_handler(self, handler):\n raise NotImplementedError\n\n def default_exception_handler(self, context):\n raise NotImplementedError\n\n def call_exception_handler(self, context):\n raise NotImplementedError\n\n # Debug flag management.\n\n def get_debug(self):\n raise NotImplementedError\n\n def set_debug(self, enabled):\n raise NotImplementedError\n\n\nclass AbstractEventLoopPolicy:\n \"\"\"Abstract policy for accessing the event loop.\"\"\"\n\n def get_event_loop(self):\n \"\"\"Get the event loop for the current context.\n\n Returns an event loop object implementing the AbstractEventLoop interface,\n or raises an exception in case no event loop has been set for the\n current context and the current policy does not specify to create one.\n\n It should never return None.\"\"\"\n raise NotImplementedError\n\n def set_event_loop(self, loop):\n \"\"\"Set the event loop for the current context to loop.\"\"\"\n raise NotImplementedError\n\n def new_event_loop(self):\n \"\"\"Create and return a new event loop object according to this\n policy's rules. If there's need to set this loop as the event loop for\n the current context, set_event_loop must be called explicitly.\"\"\"\n raise NotImplementedError\n\n # Child processes handling (Unix only).\n\n def get_child_watcher(self):\n \"Get the watcher for child processes.\"\n raise NotImplementedError\n\n def set_child_watcher(self, watcher):\n \"\"\"Set the watcher for child processes.\"\"\"\n raise NotImplementedError\n\n\nclass BaseDefaultEventLoopPolicy(AbstractEventLoopPolicy):\n \"\"\"Default policy implementation for accessing the event loop.\n\n In this policy, each thread has its own event loop. However, we\n only automatically create an event loop by default for the main\n thread; other threads by default have no event loop.\n\n Other policies may have different rules (e.g. a single global\n event loop, or automatically creating an event loop per thread, or\n using some other notion of context to which an event loop is\n associated).\n \"\"\"\n\n _loop_factory = None\n\n class _Local(threading.local):\n _loop = None\n _set_called = False\n\n def __init__(self):\n self._local = self._Local()\n\n def get_event_loop(self):\n \"\"\"Get the event loop for the current context.\n\n Returns an instance of EventLoop or raises an exception.\n \"\"\"\n if (self._local._loop is None and\n not self._local._set_called and\n threading.current_thread() is threading.main_thread()):\n stacklevel = 2\n try:\n f = sys._getframe(1)\n except AttributeError:\n pass\n else:\n # Move up the call stack so that the warning is attached\n # to the line outside asyncio itself.\n while f:\n module = f.f_globals.get('__name__')\n if not (module == 'asyncio' or module.startswith('asyncio.')):\n break\n f = f.f_back\n stacklevel += 1\n import warnings\n warnings.warn('There is no current event loop',\n DeprecationWarning, stacklevel=stacklevel)\n self.set_event_loop(self.new_event_loop())\n\n if self._local._loop is None:\n raise RuntimeError('There is no current event loop in thread %r.'\n % threading.current_thread().name)\n\n return self._local._loop\n\n def set_event_loop(self, loop):\n \"\"\"Set the event loop.\"\"\"\n self._local._set_called = True\n if loop is not None and not isinstance(loop, AbstractEventLoop):\n raise TypeError(f\"loop must be an instance of AbstractEventLoop or None, not '{type(loop).__name__}'\")\n self._local._loop = loop\n\n def new_event_loop(self):\n \"\"\"Create a new event loop.\n\n You must call set_event_loop() to make this the current event\n loop.\n \"\"\"\n return self._loop_factory()\n\n\n# Event loop policy. The policy itself is always global, even if the\n# policy's rules say that there is an event loop per thread (or other\n# notion of context). The default policy is installed by the first\n# call to get_event_loop_policy().\n_event_loop_policy = None\n\n# Lock for protecting the on-the-fly creation of the event loop policy.\n_lock = threading.Lock()\n\n\n# A TLS for the running event loop, used by _get_running_loop.\nclass _RunningLoop(threading.local):\n loop_pid = (None, None)\n\n\n_running_loop = _RunningLoop()\n\n\ndef get_running_loop():\n \"\"\"Return the running event loop. Raise a RuntimeError if there is none.\n\n This function is thread-specific.\n \"\"\"\n # NOTE: this function is implemented in C (see _asynciomodule.c)\n loop = _get_running_loop()\n if loop is None:\n raise RuntimeError('no running event loop')\n return loop\n\n\ndef _get_running_loop():\n \"\"\"Return the running event loop or None.\n\n This is a low-level function intended to be used by event loops.\n This function is thread-specific.\n \"\"\"\n # NOTE: this function is implemented in C (see _asynciomodule.c)\n running_loop, pid = _running_loop.loop_pid\n if running_loop is not None and pid == os.getpid():\n return running_loop\n\n\ndef _set_running_loop(loop):\n \"\"\"Set the running event loop.\n\n This is a low-level function intended to be used by event loops.\n This function is thread-specific.\n \"\"\"\n # NOTE: this function is implemented in C (see _asynciomodule.c)\n _running_loop.loop_pid = (loop, os.getpid())\n\n\ndef _init_event_loop_policy():\n global _event_loop_policy\n with _lock:\n if _event_loop_policy is None: # pragma: no branch\n from . import DefaultEventLoopPolicy\n _event_loop_policy = DefaultEventLoopPolicy()\n\n\ndef get_event_loop_policy():\n \"\"\"Get the current event loop policy.\"\"\"\n if _event_loop_policy is None:\n _init_event_loop_policy()\n return _event_loop_policy\n\n\ndef set_event_loop_policy(policy):\n \"\"\"Set the current event loop policy.\n\n If policy is None, the default policy is restored.\"\"\"\n global _event_loop_policy\n if policy is not None and not isinstance(policy, AbstractEventLoopPolicy):\n raise TypeError(f\"policy must be an instance of AbstractEventLoopPolicy or None, not '{type(policy).__name__}'\")\n _event_loop_policy = policy\n\n\ndef get_event_loop():\n \"\"\"Return an asyncio event loop.\n\n When called from a coroutine or a callback (e.g. scheduled with call_soon\n or similar API), this function will always return the running event loop.\n\n If there is no running event loop set, the function will return\n the result of `get_event_loop_policy().get_event_loop()` call.\n \"\"\"\n # NOTE: this function is implemented in C (see _asynciomodule.c)\n current_loop = _get_running_loop()\n if current_loop is not None:\n return current_loop\n return get_event_loop_policy().get_event_loop()\n\n\ndef set_event_loop(loop):\n \"\"\"Equivalent to calling get_event_loop_policy().set_event_loop(loop).\"\"\"\n get_event_loop_policy().set_event_loop(loop)\n\n\ndef new_event_loop():\n \"\"\"Equivalent to calling get_event_loop_policy().new_event_loop().\"\"\"\n return get_event_loop_policy().new_event_loop()\n\n\ndef get_child_watcher():\n \"\"\"Equivalent to calling get_event_loop_policy().get_child_watcher().\"\"\"\n return get_event_loop_policy().get_child_watcher()\n\n\ndef set_child_watcher(watcher):\n \"\"\"Equivalent to calling\n get_event_loop_policy().set_child_watcher(watcher).\"\"\"\n return get_event_loop_policy().set_child_watcher(watcher)\n\n\n# Alias pure-Python implementations for testing purposes.\n_py__get_running_loop = _get_running_loop\n_py__set_running_loop = _set_running_loop\n_py_get_running_loop = get_running_loop\n_py_get_event_loop = get_event_loop\n\n\ntry:\n # get_event_loop() is one of the most frequently called\n # functions in asyncio. Pure Python implementation is\n # about 4 times slower than C-accelerated.\n from _asyncio import (_get_running_loop, _set_running_loop,\n get_running_loop, get_event_loop)\nexcept ImportError:\n pass\nelse:\n # Alias C implementations for testing purposes.\n _c__get_running_loop = _get_running_loop\n _c__set_running_loop = _set_running_loop\n _c_get_running_loop = get_running_loop\n _c_get_event_loop = get_event_loop\n\n\nif hasattr(os, 'fork'):\n def on_fork():\n # Reset the loop and wakeupfd in the forked child process.\n if _event_loop_policy is not None:\n _event_loop_policy._local = BaseDefaultEventLoopPolicy._Local()\n _set_running_loop(None)\n signal.set_wakeup_fd(-1)\n\n os.register_at_fork(after_in_child=on_fork)\n", 882], "/usr/lib/python3.13/asyncio/unix_events.py": ["\"\"\"Selector event loop for Unix with signal handling.\"\"\"\n\nimport errno\nimport io\nimport itertools\nimport os\nimport selectors\nimport signal\nimport socket\nimport stat\nimport subprocess\nimport sys\nimport threading\nimport warnings\n\nfrom . import base_events\nfrom . import base_subprocess\nfrom . import constants\nfrom . import coroutines\nfrom . import events\nfrom . import exceptions\nfrom . import futures\nfrom . import selector_events\nfrom . import tasks\nfrom . import transports\nfrom .log import logger\n\n\n__all__ = (\n 'SelectorEventLoop',\n 'AbstractChildWatcher', 'SafeChildWatcher',\n 'FastChildWatcher', 'PidfdChildWatcher',\n 'MultiLoopChildWatcher', 'ThreadedChildWatcher',\n 'DefaultEventLoopPolicy',\n 'EventLoop',\n)\n\n\nif sys.platform == 'win32': # pragma: no cover\n raise ImportError('Signals are not really supported on Windows')\n\n\ndef _sighandler_noop(signum, frame):\n \"\"\"Dummy signal handler.\"\"\"\n pass\n\n\ndef waitstatus_to_exitcode(status):\n try:\n return os.waitstatus_to_exitcode(status)\n except ValueError:\n # The child exited, but we don't understand its status.\n # This shouldn't happen, but if it does, let's just\n # return that status; perhaps that helps debug it.\n return status\n\n\nclass _UnixSelectorEventLoop(selector_events.BaseSelectorEventLoop):\n \"\"\"Unix event loop.\n\n Adds signal handling and UNIX Domain Socket support to SelectorEventLoop.\n \"\"\"\n\n def __init__(self, selector=None):\n super().__init__(selector)\n self._signal_handlers = {}\n self._unix_server_sockets = {}\n\n def close(self):\n super().close()\n if not sys.is_finalizing():\n for sig in list(self._signal_handlers):\n self.remove_signal_handler(sig)\n else:\n if self._signal_handlers:\n warnings.warn(f\"Closing the loop {self!r} \"\n f\"on interpreter shutdown \"\n f\"stage, skipping signal handlers removal\",\n ResourceWarning,\n source=self)\n self._signal_handlers.clear()\n\n def _process_self_data(self, data):\n for signum in data:\n if not signum:\n # ignore null bytes written by _write_to_self()\n continue\n self._handle_signal(signum)\n\n def add_signal_handler(self, sig, callback, *args):\n \"\"\"Add a handler for a signal. UNIX only.\n\n Raise ValueError if the signal number is invalid or uncatchable.\n Raise RuntimeError if there is a problem setting up the handler.\n \"\"\"\n if (coroutines.iscoroutine(callback) or\n coroutines.iscoroutinefunction(callback)):\n raise TypeError(\"coroutines cannot be used \"\n \"with add_signal_handler()\")\n self._check_signal(sig)\n self._check_closed()\n try:\n # set_wakeup_fd() raises ValueError if this is not the\n # main thread. By calling it early we ensure that an\n # event loop running in another thread cannot add a signal\n # handler.\n signal.set_wakeup_fd(self._csock.fileno())\n except (ValueError, OSError) as exc:\n raise RuntimeError(str(exc))\n\n handle = events.Handle(callback, args, self, None)\n self._signal_handlers[sig] = handle\n\n try:\n # Register a dummy signal handler to ask Python to write the signal\n # number in the wakeup file descriptor. _process_self_data() will\n # read signal numbers from this file descriptor to handle signals.\n signal.signal(sig, _sighandler_noop)\n\n # Set SA_RESTART to limit EINTR occurrences.\n signal.siginterrupt(sig, False)\n except OSError as exc:\n del self._signal_handlers[sig]\n if not self._signal_handlers:\n try:\n signal.set_wakeup_fd(-1)\n except (ValueError, OSError) as nexc:\n logger.info('set_wakeup_fd(-1) failed: %s', nexc)\n\n if exc.errno == errno.EINVAL:\n raise RuntimeError(f'sig {sig} cannot be caught')\n else:\n raise\n\n def _handle_signal(self, sig):\n \"\"\"Internal helper that is the actual signal handler.\"\"\"\n handle = self._signal_handlers.get(sig)\n if handle is None:\n return # Assume it's some race condition.\n if handle._cancelled:\n self.remove_signal_handler(sig) # Remove it properly.\n else:\n self._add_callback_signalsafe(handle)\n\n def remove_signal_handler(self, sig):\n \"\"\"Remove a handler for a signal. UNIX only.\n\n Return True if a signal handler was removed, False if not.\n \"\"\"\n self._check_signal(sig)\n try:\n del self._signal_handlers[sig]\n except KeyError:\n return False\n\n if sig == signal.SIGINT:\n handler = signal.default_int_handler\n else:\n handler = signal.SIG_DFL\n\n try:\n signal.signal(sig, handler)\n except OSError as exc:\n if exc.errno == errno.EINVAL:\n raise RuntimeError(f'sig {sig} cannot be caught')\n else:\n raise\n\n if not self._signal_handlers:\n try:\n signal.set_wakeup_fd(-1)\n except (ValueError, OSError) as exc:\n logger.info('set_wakeup_fd(-1) failed: %s', exc)\n\n return True\n\n def _check_signal(self, sig):\n \"\"\"Internal helper to validate a signal.\n\n Raise ValueError if the signal number is invalid or uncatchable.\n Raise RuntimeError if there is a problem setting up the handler.\n \"\"\"\n if not isinstance(sig, int):\n raise TypeError(f'sig must be an int, not {sig!r}')\n\n if sig not in signal.valid_signals():\n raise ValueError(f'invalid signal number {sig}')\n\n def _make_read_pipe_transport(self, pipe, protocol, waiter=None,\n extra=None):\n return _UnixReadPipeTransport(self, pipe, protocol, waiter, extra)\n\n def _make_write_pipe_transport(self, pipe, protocol, waiter=None,\n extra=None):\n return _UnixWritePipeTransport(self, pipe, protocol, waiter, extra)\n\n async def _make_subprocess_transport(self, protocol, args, shell,\n stdin, stdout, stderr, bufsize,\n extra=None, **kwargs):\n with warnings.catch_warnings():\n warnings.simplefilter('ignore', DeprecationWarning)\n watcher = events.get_child_watcher()\n\n with watcher:\n if not watcher.is_active():\n # Check early.\n # Raising exception before process creation\n # prevents subprocess execution if the watcher\n # is not ready to handle it.\n raise RuntimeError(\"asyncio.get_child_watcher() is not activated, \"\n \"subprocess support is not installed.\")\n waiter = self.create_future()\n transp = _UnixSubprocessTransport(self, protocol, args, shell,\n stdin, stdout, stderr, bufsize,\n waiter=waiter, extra=extra,\n **kwargs)\n watcher.add_child_handler(transp.get_pid(),\n self._child_watcher_callback, transp)\n try:\n await waiter\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException:\n transp.close()\n await transp._wait()\n raise\n\n return transp\n\n def _child_watcher_callback(self, pid, returncode, transp):\n self.call_soon_threadsafe(transp._process_exited, returncode)\n\n async def create_unix_connection(\n self, protocol_factory, path=None, *,\n ssl=None, sock=None,\n server_hostname=None,\n ssl_handshake_timeout=None,\n ssl_shutdown_timeout=None):\n assert server_hostname is None or isinstance(server_hostname, str)\n if ssl:\n if server_hostname is None:\n raise ValueError(\n 'you have to pass server_hostname when using ssl')\n else:\n if server_hostname is not None:\n raise ValueError('server_hostname is only meaningful with ssl')\n if ssl_handshake_timeout is not None:\n raise ValueError(\n 'ssl_handshake_timeout is only meaningful with ssl')\n if ssl_shutdown_timeout is not None:\n raise ValueError(\n 'ssl_shutdown_timeout is only meaningful with ssl')\n\n if path is not None:\n if sock is not None:\n raise ValueError(\n 'path and sock can not be specified at the same time')\n\n path = os.fspath(path)\n sock = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM, 0)\n try:\n sock.setblocking(False)\n await self.sock_connect(sock, path)\n except:\n sock.close()\n raise\n\n else:\n if sock is None:\n raise ValueError('no path and sock were specified')\n if (sock.family != socket.AF_UNIX or\n sock.type != socket.SOCK_STREAM):\n raise ValueError(\n f'A UNIX Domain Stream Socket was expected, got {sock!r}')\n sock.setblocking(False)\n\n transport, protocol = await self._create_connection_transport(\n sock, protocol_factory, ssl, server_hostname,\n ssl_handshake_timeout=ssl_handshake_timeout,\n ssl_shutdown_timeout=ssl_shutdown_timeout)\n return transport, protocol\n\n async def create_unix_server(\n self, protocol_factory, path=None, *,\n sock=None, backlog=100, ssl=None,\n ssl_handshake_timeout=None,\n ssl_shutdown_timeout=None,\n start_serving=True, cleanup_socket=True):\n if isinstance(ssl, bool):\n raise TypeError('ssl argument must be an SSLContext or None')\n\n if ssl_handshake_timeout is not None and not ssl:\n raise ValueError(\n 'ssl_handshake_timeout is only meaningful with ssl')\n\n if ssl_shutdown_timeout is not None and not ssl:\n raise ValueError(\n 'ssl_shutdown_timeout is only meaningful with ssl')\n\n if path is not None:\n if sock is not None:\n raise ValueError(\n 'path and sock can not be specified at the same time')\n\n path = os.fspath(path)\n sock = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM)\n\n # Check for abstract socket. `str` and `bytes` paths are supported.\n if path[0] not in (0, '\\x00'):\n try:\n if stat.S_ISSOCK(os.stat(path).st_mode):\n os.remove(path)\n except FileNotFoundError:\n pass\n except OSError as err:\n # Directory may have permissions only to create socket.\n logger.error('Unable to check or remove stale UNIX socket '\n '%r: %r', path, err)\n\n try:\n sock.bind(path)\n except OSError as exc:\n sock.close()\n if exc.errno == errno.EADDRINUSE:\n # Let's improve the error message by adding\n # with what exact address it occurs.\n msg = f'Address {path!r} is already in use'\n raise OSError(errno.EADDRINUSE, msg) from None\n else:\n raise\n except:\n sock.close()\n raise\n else:\n if sock is None:\n raise ValueError(\n 'path was not specified, and no sock specified')\n\n if (sock.family != socket.AF_UNIX or\n sock.type != socket.SOCK_STREAM):\n raise ValueError(\n f'A UNIX Domain Stream Socket was expected, got {sock!r}')\n\n if cleanup_socket:\n path = sock.getsockname()\n # Check for abstract socket. `str` and `bytes` paths are supported.\n if path[0] not in (0, '\\x00'):\n try:\n self._unix_server_sockets[sock] = os.stat(path).st_ino\n except FileNotFoundError:\n pass\n\n sock.setblocking(False)\n server = base_events.Server(self, [sock], protocol_factory,\n ssl, backlog, ssl_handshake_timeout,\n ssl_shutdown_timeout)\n if start_serving:\n server._start_serving()\n # Skip one loop iteration so that all 'loop.add_reader'\n # go through.\n await tasks.sleep(0)\n\n return server\n\n async def _sock_sendfile_native(self, sock, file, offset, count):\n try:\n os.sendfile\n except AttributeError:\n raise exceptions.SendfileNotAvailableError(\n \"os.sendfile() is not available\")\n try:\n fileno = file.fileno()\n except (AttributeError, io.UnsupportedOperation) as err:\n raise exceptions.SendfileNotAvailableError(\"not a regular file\")\n try:\n fsize = os.fstat(fileno).st_size\n except OSError:\n raise exceptions.SendfileNotAvailableError(\"not a regular file\")\n blocksize = count if count else fsize\n if not blocksize:\n return 0 # empty file\n\n fut = self.create_future()\n self._sock_sendfile_native_impl(fut, None, sock, fileno,\n offset, count, blocksize, 0)\n return await fut\n\n def _sock_sendfile_native_impl(self, fut, registered_fd, sock, fileno,\n offset, count, blocksize, total_sent):\n fd = sock.fileno()\n if registered_fd is not None:\n # Remove the callback early. It should be rare that the\n # selector says the fd is ready but the call still returns\n # EAGAIN, and I am willing to take a hit in that case in\n # order to simplify the common case.\n self.remove_writer(registered_fd)\n if fut.cancelled():\n self._sock_sendfile_update_filepos(fileno, offset, total_sent)\n return\n if count:\n blocksize = count - total_sent\n if blocksize <= 0:\n self._sock_sendfile_update_filepos(fileno, offset, total_sent)\n fut.set_result(total_sent)\n return\n\n # On 32-bit architectures truncate to 1GiB to avoid OverflowError\n blocksize = min(blocksize, sys.maxsize//2 + 1)\n\n try:\n sent = os.sendfile(fd, fileno, offset, blocksize)\n except (BlockingIOError, InterruptedError):\n if registered_fd is None:\n self._sock_add_cancellation_callback(fut, sock)\n self.add_writer(fd, self._sock_sendfile_native_impl, fut,\n fd, sock, fileno,\n offset, count, blocksize, total_sent)\n except OSError as exc:\n if (registered_fd is not None and\n exc.errno == errno.ENOTCONN and\n type(exc) is not ConnectionError):\n # If we have an ENOTCONN and this isn't a first call to\n # sendfile(), i.e. the connection was closed in the middle\n # of the operation, normalize the error to ConnectionError\n # to make it consistent across all Posix systems.\n new_exc = ConnectionError(\n \"socket is not connected\", errno.ENOTCONN)\n new_exc.__cause__ = exc\n exc = new_exc\n if total_sent == 0:\n # We can get here for different reasons, the main\n # one being 'file' is not a regular mmap(2)-like\n # file, in which case we'll fall back on using\n # plain send().\n err = exceptions.SendfileNotAvailableError(\n \"os.sendfile call failed\")\n self._sock_sendfile_update_filepos(fileno, offset, total_sent)\n fut.set_exception(err)\n else:\n self._sock_sendfile_update_filepos(fileno, offset, total_sent)\n fut.set_exception(exc)\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n self._sock_sendfile_update_filepos(fileno, offset, total_sent)\n fut.set_exception(exc)\n else:\n if sent == 0:\n # EOF\n self._sock_sendfile_update_filepos(fileno, offset, total_sent)\n fut.set_result(total_sent)\n else:\n offset += sent\n total_sent += sent\n if registered_fd is None:\n self._sock_add_cancellation_callback(fut, sock)\n self.add_writer(fd, self._sock_sendfile_native_impl, fut,\n fd, sock, fileno,\n offset, count, blocksize, total_sent)\n\n def _sock_sendfile_update_filepos(self, fileno, offset, total_sent):\n if total_sent > 0:\n os.lseek(fileno, offset, os.SEEK_SET)\n\n def _sock_add_cancellation_callback(self, fut, sock):\n def cb(fut):\n if fut.cancelled():\n fd = sock.fileno()\n if fd != -1:\n self.remove_writer(fd)\n fut.add_done_callback(cb)\n\n def _stop_serving(self, sock):\n # Is this a unix socket that needs cleanup?\n if sock in self._unix_server_sockets:\n path = sock.getsockname()\n else:\n path = None\n\n super()._stop_serving(sock)\n\n if path is not None:\n prev_ino = self._unix_server_sockets[sock]\n del self._unix_server_sockets[sock]\n try:\n if os.stat(path).st_ino == prev_ino:\n os.unlink(path)\n except FileNotFoundError:\n pass\n except OSError as err:\n logger.error('Unable to clean up listening UNIX socket '\n '%r: %r', path, err)\n\n\nclass _UnixReadPipeTransport(transports.ReadTransport):\n\n max_size = 256 * 1024 # max bytes we read in one event loop iteration\n\n def __init__(self, loop, pipe, protocol, waiter=None, extra=None):\n super().__init__(extra)\n self._extra['pipe'] = pipe\n self._loop = loop\n self._pipe = pipe\n self._fileno = pipe.fileno()\n self._protocol = protocol\n self._closing = False\n self._paused = False\n\n mode = os.fstat(self._fileno).st_mode\n if not (stat.S_ISFIFO(mode) or\n stat.S_ISSOCK(mode) or\n stat.S_ISCHR(mode)):\n self._pipe = None\n self._fileno = None\n self._protocol = None\n raise ValueError(\"Pipe transport is for pipes/sockets only.\")\n\n os.set_blocking(self._fileno, False)\n\n self._loop.call_soon(self._protocol.connection_made, self)\n # only start reading when connection_made() has been called\n self._loop.call_soon(self._add_reader,\n self._fileno, self._read_ready)\n if waiter is not None:\n # only wake up the waiter when connection_made() has been called\n self._loop.call_soon(futures._set_result_unless_cancelled,\n waiter, None)\n\n def _add_reader(self, fd, callback):\n if not self.is_reading():\n return\n self._loop._add_reader(fd, callback)\n\n def is_reading(self):\n return not self._paused and not self._closing\n\n def __repr__(self):\n info = [self.__class__.__name__]\n if self._pipe is None:\n info.append('closed')\n elif self._closing:\n info.append('closing')\n info.append(f'fd={self._fileno}')\n selector = getattr(self._loop, '_selector', None)\n if self._pipe is not None and selector is not None:\n polling = selector_events._test_selector_event(\n selector, self._fileno, selectors.EVENT_READ)\n if polling:\n info.append('polling')\n else:\n info.append('idle')\n elif self._pipe is not None:\n info.append('open')\n else:\n info.append('closed')\n return '<{}>'.format(' '.join(info))\n\n def _read_ready(self):\n try:\n data = os.read(self._fileno, self.max_size)\n except (BlockingIOError, InterruptedError):\n pass\n except OSError as exc:\n self._fatal_error(exc, 'Fatal read error on pipe transport')\n else:\n if data:\n self._protocol.data_received(data)\n else:\n if self._loop.get_debug():\n logger.info(\"%r was closed by peer\", self)\n self._closing = True\n self._loop._remove_reader(self._fileno)\n self._loop.call_soon(self._protocol.eof_received)\n self._loop.call_soon(self._call_connection_lost, None)\n\n def pause_reading(self):\n if not self.is_reading():\n return\n self._paused = True\n self._loop._remove_reader(self._fileno)\n if self._loop.get_debug():\n logger.debug(\"%r pauses reading\", self)\n\n def resume_reading(self):\n if self._closing or not self._paused:\n return\n self._paused = False\n self._loop._add_reader(self._fileno, self._read_ready)\n if self._loop.get_debug():\n logger.debug(\"%r resumes reading\", self)\n\n def set_protocol(self, protocol):\n self._protocol = protocol\n\n def get_protocol(self):\n return self._protocol\n\n def is_closing(self):\n return self._closing\n\n def close(self):\n if not self._closing:\n self._close(None)\n\n def __del__(self, _warn=warnings.warn):\n if self._pipe is not None:\n _warn(f\"unclosed transport {self!r}\", ResourceWarning, source=self)\n self._pipe.close()\n\n def _fatal_error(self, exc, message='Fatal error on pipe transport'):\n # should be called by exception handler only\n if (isinstance(exc, OSError) and exc.errno == errno.EIO):\n if self._loop.get_debug():\n logger.debug(\"%r: %s\", self, message, exc_info=True)\n else:\n self._loop.call_exception_handler({\n 'message': message,\n 'exception': exc,\n 'transport': self,\n 'protocol': self._protocol,\n })\n self._close(exc)\n\n def _close(self, exc):\n self._closing = True\n self._loop._remove_reader(self._fileno)\n self._loop.call_soon(self._call_connection_lost, exc)\n\n def _call_connection_lost(self, exc):\n try:\n self._protocol.connection_lost(exc)\n finally:\n self._pipe.close()\n self._pipe = None\n self._protocol = None\n self._loop = None\n\n\nclass _UnixWritePipeTransport(transports._FlowControlMixin,\n transports.WriteTransport):\n\n def __init__(self, loop, pipe, protocol, waiter=None, extra=None):\n super().__init__(extra, loop)\n self._extra['pipe'] = pipe\n self._pipe = pipe\n self._fileno = pipe.fileno()\n self._protocol = protocol\n self._buffer = bytearray()\n self._conn_lost = 0\n self._closing = False # Set when close() or write_eof() called.\n\n mode = os.fstat(self._fileno).st_mode\n is_char = stat.S_ISCHR(mode)\n is_fifo = stat.S_ISFIFO(mode)\n is_socket = stat.S_ISSOCK(mode)\n if not (is_char or is_fifo or is_socket):\n self._pipe = None\n self._fileno = None\n self._protocol = None\n raise ValueError(\"Pipe transport is only for \"\n \"pipes, sockets and character devices\")\n\n os.set_blocking(self._fileno, False)\n self._loop.call_soon(self._protocol.connection_made, self)\n\n # On AIX, the reader trick (to be notified when the read end of the\n # socket is closed) only works for sockets. On other platforms it\n # works for pipes and sockets. (Exception: OS X 10.4? Issue #19294.)\n if is_socket or (is_fifo and not sys.platform.startswith(\"aix\")):\n # only start reading when connection_made() has been called\n self._loop.call_soon(self._loop._add_reader,\n self._fileno, self._read_ready)\n\n if waiter is not None:\n # only wake up the waiter when connection_made() has been called\n self._loop.call_soon(futures._set_result_unless_cancelled,\n waiter, None)\n\n def __repr__(self):\n info = [self.__class__.__name__]\n if self._pipe is None:\n info.append('closed')\n elif self._closing:\n info.append('closing')\n info.append(f'fd={self._fileno}')\n selector = getattr(self._loop, '_selector', None)\n if self._pipe is not None and selector is not None:\n polling = selector_events._test_selector_event(\n selector, self._fileno, selectors.EVENT_WRITE)\n if polling:\n info.append('polling')\n else:\n info.append('idle')\n\n bufsize = self.get_write_buffer_size()\n info.append(f'bufsize={bufsize}')\n elif self._pipe is not None:\n info.append('open')\n else:\n info.append('closed')\n return '<{}>'.format(' '.join(info))\n\n def get_write_buffer_size(self):\n return len(self._buffer)\n\n def _read_ready(self):\n # Pipe was closed by peer.\n if self._loop.get_debug():\n logger.info(\"%r was closed by peer\", self)\n if self._buffer:\n self._close(BrokenPipeError())\n else:\n self._close()\n\n def write(self, data):\n assert isinstance(data, (bytes, bytearray, memoryview)), repr(data)\n if isinstance(data, bytearray):\n data = memoryview(data)\n if not data:\n return\n\n if self._conn_lost or self._closing:\n if self._conn_lost >= constants.LOG_THRESHOLD_FOR_CONNLOST_WRITES:\n logger.warning('pipe closed by peer or '\n 'os.write(pipe, data) raised exception.')\n self._conn_lost += 1\n return\n\n if not self._buffer:\n # Attempt to send it right away first.\n try:\n n = os.write(self._fileno, data)\n except (BlockingIOError, InterruptedError):\n n = 0\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n self._conn_lost += 1\n self._fatal_error(exc, 'Fatal write error on pipe transport')\n return\n if n == len(data):\n return\n elif n > 0:\n data = memoryview(data)[n:]\n self._loop._add_writer(self._fileno, self._write_ready)\n\n self._buffer += data\n self._maybe_pause_protocol()\n\n def _write_ready(self):\n assert self._buffer, 'Data should not be empty'\n\n try:\n n = os.write(self._fileno, self._buffer)\n except (BlockingIOError, InterruptedError):\n pass\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n self._buffer.clear()\n self._conn_lost += 1\n # Remove writer here, _fatal_error() doesn't it\n # because _buffer is empty.\n self._loop._remove_writer(self._fileno)\n self._fatal_error(exc, 'Fatal write error on pipe transport')\n else:\n if n == len(self._buffer):\n self._buffer.clear()\n self._loop._remove_writer(self._fileno)\n self._maybe_resume_protocol() # May append to buffer.\n if self._closing:\n self._loop._remove_reader(self._fileno)\n self._call_connection_lost(None)\n return\n elif n > 0:\n del self._buffer[:n]\n\n def can_write_eof(self):\n return True\n\n def write_eof(self):\n if self._closing:\n return\n assert self._pipe\n self._closing = True\n if not self._buffer:\n self._loop._remove_reader(self._fileno)\n self._loop.call_soon(self._call_connection_lost, None)\n\n def set_protocol(self, protocol):\n self._protocol = protocol\n\n def get_protocol(self):\n return self._protocol\n\n def is_closing(self):\n return self._closing\n\n def close(self):\n if self._pipe is not None and not self._closing:\n # write_eof is all what we needed to close the write pipe\n self.write_eof()\n\n def __del__(self, _warn=warnings.warn):\n if self._pipe is not None:\n _warn(f\"unclosed transport {self!r}\", ResourceWarning, source=self)\n self._pipe.close()\n\n def abort(self):\n self._close(None)\n\n def _fatal_error(self, exc, message='Fatal error on pipe transport'):\n # should be called by exception handler only\n if isinstance(exc, OSError):\n if self._loop.get_debug():\n logger.debug(\"%r: %s\", self, message, exc_info=True)\n else:\n self._loop.call_exception_handler({\n 'message': message,\n 'exception': exc,\n 'transport': self,\n 'protocol': self._protocol,\n })\n self._close(exc)\n\n def _close(self, exc=None):\n self._closing = True\n if self._buffer:\n self._loop._remove_writer(self._fileno)\n self._buffer.clear()\n self._loop._remove_reader(self._fileno)\n self._loop.call_soon(self._call_connection_lost, exc)\n\n def _call_connection_lost(self, exc):\n try:\n self._protocol.connection_lost(exc)\n finally:\n self._pipe.close()\n self._pipe = None\n self._protocol = None\n self._loop = None\n\n\nclass _UnixSubprocessTransport(base_subprocess.BaseSubprocessTransport):\n\n def _start(self, args, shell, stdin, stdout, stderr, bufsize, **kwargs):\n stdin_w = None\n if stdin == subprocess.PIPE and sys.platform.startswith('aix'):\n # Use a socket pair for stdin on AIX, since it does not\n # support selecting read events on the write end of a\n # socket (which we use in order to detect closing of the\n # other end).\n stdin, stdin_w = socket.socketpair()\n try:\n self._proc = subprocess.Popen(\n args, shell=shell, stdin=stdin, stdout=stdout, stderr=stderr,\n universal_newlines=False, bufsize=bufsize, **kwargs)\n if stdin_w is not None:\n stdin.close()\n self._proc.stdin = open(stdin_w.detach(), 'wb', buffering=bufsize)\n stdin_w = None\n finally:\n if stdin_w is not None:\n stdin.close()\n stdin_w.close()\n\n\nclass AbstractChildWatcher:\n \"\"\"Abstract base class for monitoring child processes.\n\n Objects derived from this class monitor a collection of subprocesses and\n report their termination or interruption by a signal.\n\n New callbacks are registered with .add_child_handler(). Starting a new\n process must be done within a 'with' block to allow the watcher to suspend\n its activity until the new process if fully registered (this is needed to\n prevent a race condition in some implementations).\n\n Example:\n with watcher:\n proc = subprocess.Popen(\"sleep 1\")\n watcher.add_child_handler(proc.pid, callback)\n\n Notes:\n Implementations of this class must be thread-safe.\n\n Since child watcher objects may catch the SIGCHLD signal and call\n waitpid(-1), there should be only one active object per process.\n \"\"\"\n\n def __init_subclass__(cls) -> None:\n if cls.__module__ != __name__:\n warnings._deprecated(\"AbstractChildWatcher\",\n \"{name!r} is deprecated as of Python 3.12 and will be \"\n \"removed in Python {remove}.\",\n remove=(3, 14))\n\n def add_child_handler(self, pid, callback, *args):\n \"\"\"Register a new child handler.\n\n Arrange for callback(pid, returncode, *args) to be called when\n process 'pid' terminates. Specifying another callback for the same\n process replaces the previous handler.\n\n Note: callback() must be thread-safe.\n \"\"\"\n raise NotImplementedError()\n\n def remove_child_handler(self, pid):\n \"\"\"Removes the handler for process 'pid'.\n\n The function returns True if the handler was successfully removed,\n False if there was nothing to remove.\"\"\"\n\n raise NotImplementedError()\n\n def attach_loop(self, loop):\n \"\"\"Attach the watcher to an event loop.\n\n If the watcher was previously attached to an event loop, then it is\n first detached before attaching to the new loop.\n\n Note: loop may be None.\n \"\"\"\n raise NotImplementedError()\n\n def close(self):\n \"\"\"Close the watcher.\n\n This must be called to make sure that any underlying resource is freed.\n \"\"\"\n raise NotImplementedError()\n\n def is_active(self):\n \"\"\"Return ``True`` if the watcher is active and is used by the event loop.\n\n Return True if the watcher is installed and ready to handle process exit\n notifications.\n\n \"\"\"\n raise NotImplementedError()\n\n def __enter__(self):\n \"\"\"Enter the watcher's context and allow starting new processes\n\n This function must return self\"\"\"\n raise NotImplementedError()\n\n def __exit__(self, a, b, c):\n \"\"\"Exit the watcher's context\"\"\"\n raise NotImplementedError()\n\n\nclass PidfdChildWatcher(AbstractChildWatcher):\n \"\"\"Child watcher implementation using Linux's pid file descriptors.\n\n This child watcher polls process file descriptors (pidfds) to await child\n process termination. In some respects, PidfdChildWatcher is a \"Goldilocks\"\n child watcher implementation. It doesn't require signals or threads, doesn't\n interfere with any processes launched outside the event loop, and scales\n linearly with the number of subprocesses launched by the event loop. The\n main disadvantage is that pidfds are specific to Linux, and only work on\n recent (5.3+) kernels.\n \"\"\"\n\n def __enter__(self):\n return self\n\n def __exit__(self, exc_type, exc_value, exc_traceback):\n pass\n\n def is_active(self):\n return True\n\n def close(self):\n pass\n\n def attach_loop(self, loop):\n pass\n\n def add_child_handler(self, pid, callback, *args):\n loop = events.get_running_loop()\n pidfd = os.pidfd_open(pid)\n loop._add_reader(pidfd, self._do_wait, pid, pidfd, callback, args)\n\n def _do_wait(self, pid, pidfd, callback, args):\n loop = events.get_running_loop()\n loop._remove_reader(pidfd)\n try:\n _, status = os.waitpid(pid, 0)\n except ChildProcessError:\n # The child process is already reaped\n # (may happen if waitpid() is called elsewhere).\n returncode = 255\n logger.warning(\n \"child process pid %d exit status already read: \"\n \" will report returncode 255\",\n pid)\n else:\n returncode = waitstatus_to_exitcode(status)\n\n os.close(pidfd)\n callback(pid, returncode, *args)\n\n def remove_child_handler(self, pid):\n # asyncio never calls remove_child_handler() !!!\n # The method is no-op but is implemented because\n # abstract base classes require it.\n return True\n\n\nclass BaseChildWatcher(AbstractChildWatcher):\n\n def __init__(self):\n self._loop = None\n self._callbacks = {}\n\n def close(self):\n self.attach_loop(None)\n\n def is_active(self):\n return self._loop is not None and self._loop.is_running()\n\n def _do_waitpid(self, expected_pid):\n raise NotImplementedError()\n\n def _do_waitpid_all(self):\n raise NotImplementedError()\n\n def attach_loop(self, loop):\n assert loop is None or isinstance(loop, events.AbstractEventLoop)\n\n if self._loop is not None and loop is None and self._callbacks:\n warnings.warn(\n 'A loop is being detached '\n 'from a child watcher with pending handlers',\n RuntimeWarning)\n\n if self._loop is not None:\n self._loop.remove_signal_handler(signal.SIGCHLD)\n\n self._loop = loop\n if loop is not None:\n loop.add_signal_handler(signal.SIGCHLD, self._sig_chld)\n\n # Prevent a race condition in case a child terminated\n # during the switch.\n self._do_waitpid_all()\n\n def _sig_chld(self):\n try:\n self._do_waitpid_all()\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n # self._loop should always be available here\n # as '_sig_chld' is added as a signal handler\n # in 'attach_loop'\n self._loop.call_exception_handler({\n 'message': 'Unknown exception in SIGCHLD handler',\n 'exception': exc,\n })\n\n\nclass SafeChildWatcher(BaseChildWatcher):\n \"\"\"'Safe' child watcher implementation.\n\n This implementation avoids disrupting other code spawning processes by\n polling explicitly each process in the SIGCHLD handler instead of calling\n os.waitpid(-1).\n\n This is a safe solution but it has a significant overhead when handling a\n big number of children (O(n) each time SIGCHLD is raised)\n \"\"\"\n\n def __init__(self):\n super().__init__()\n warnings._deprecated(\"SafeChildWatcher\",\n \"{name!r} is deprecated as of Python 3.12 and will be \"\n \"removed in Python {remove}.\",\n remove=(3, 14))\n\n def close(self):\n self._callbacks.clear()\n super().close()\n\n def __enter__(self):\n return self\n\n def __exit__(self, a, b, c):\n pass\n\n def add_child_handler(self, pid, callback, *args):\n self._callbacks[pid] = (callback, args)\n\n # Prevent a race condition in case the child is already terminated.\n self._do_waitpid(pid)\n\n def remove_child_handler(self, pid):\n try:\n del self._callbacks[pid]\n return True\n except KeyError:\n return False\n\n def _do_waitpid_all(self):\n\n for pid in list(self._callbacks):\n self._do_waitpid(pid)\n\n def _do_waitpid(self, expected_pid):\n assert expected_pid > 0\n\n try:\n pid, status = os.waitpid(expected_pid, os.WNOHANG)\n except ChildProcessError:\n # The child process is already reaped\n # (may happen if waitpid() is called elsewhere).\n pid = expected_pid\n returncode = 255\n logger.warning(\n \"Unknown child process pid %d, will report returncode 255\",\n pid)\n else:\n if pid == 0:\n # The child process is still alive.\n return\n\n returncode = waitstatus_to_exitcode(status)\n if self._loop.get_debug():\n logger.debug('process %s exited with returncode %s',\n expected_pid, returncode)\n\n try:\n callback, args = self._callbacks.pop(pid)\n except KeyError: # pragma: no cover\n # May happen if .remove_child_handler() is called\n # after os.waitpid() returns.\n if self._loop.get_debug():\n logger.warning(\"Child watcher got an unexpected pid: %r\",\n pid, exc_info=True)\n else:\n callback(pid, returncode, *args)\n\n\nclass FastChildWatcher(BaseChildWatcher):\n \"\"\"'Fast' child watcher implementation.\n\n This implementation reaps every terminated processes by calling\n os.waitpid(-1) directly, possibly breaking other code spawning processes\n and waiting for their termination.\n\n There is no noticeable overhead when handling a big number of children\n (O(1) each time a child terminates).\n \"\"\"\n def __init__(self):\n super().__init__()\n self._lock = threading.Lock()\n self._zombies = {}\n self._forks = 0\n warnings._deprecated(\"FastChildWatcher\",\n \"{name!r} is deprecated as of Python 3.12 and will be \"\n \"removed in Python {remove}.\",\n remove=(3, 14))\n\n def close(self):\n self._callbacks.clear()\n self._zombies.clear()\n super().close()\n\n def __enter__(self):\n with self._lock:\n self._forks += 1\n\n return self\n\n def __exit__(self, a, b, c):\n with self._lock:\n self._forks -= 1\n\n if self._forks or not self._zombies:\n return\n\n collateral_victims = str(self._zombies)\n self._zombies.clear()\n\n logger.warning(\n \"Caught subprocesses termination from unknown pids: %s\",\n collateral_victims)\n\n def add_child_handler(self, pid, callback, *args):\n assert self._forks, \"Must use the context manager\"\n\n with self._lock:\n try:\n returncode = self._zombies.pop(pid)\n except KeyError:\n # The child is running.\n self._callbacks[pid] = callback, args\n return\n\n # The child is dead already. We can fire the callback.\n callback(pid, returncode, *args)\n\n def remove_child_handler(self, pid):\n try:\n del self._callbacks[pid]\n return True\n except KeyError:\n return False\n\n def _do_waitpid_all(self):\n # Because of signal coalescing, we must keep calling waitpid() as\n # long as we're able to reap a child.\n while True:\n try:\n pid, status = os.waitpid(-1, os.WNOHANG)\n except ChildProcessError:\n # No more child processes exist.\n return\n else:\n if pid == 0:\n # A child process is still alive.\n return\n\n returncode = waitstatus_to_exitcode(status)\n\n with self._lock:\n try:\n callback, args = self._callbacks.pop(pid)\n except KeyError:\n # unknown child\n if self._forks:\n # It may not be registered yet.\n self._zombies[pid] = returncode\n if self._loop.get_debug():\n logger.debug('unknown process %s exited '\n 'with returncode %s',\n pid, returncode)\n continue\n callback = None\n else:\n if self._loop.get_debug():\n logger.debug('process %s exited with returncode %s',\n pid, returncode)\n\n if callback is None:\n logger.warning(\n \"Caught subprocess termination from unknown pid: \"\n \"%d -> %d\", pid, returncode)\n else:\n callback(pid, returncode, *args)\n\n\nclass MultiLoopChildWatcher(AbstractChildWatcher):\n \"\"\"A watcher that doesn't require running loop in the main thread.\n\n This implementation registers a SIGCHLD signal handler on\n instantiation (which may conflict with other code that\n install own handler for this signal).\n\n The solution is safe but it has a significant overhead when\n handling a big number of processes (*O(n)* each time a\n SIGCHLD is received).\n \"\"\"\n\n # Implementation note:\n # The class keeps compatibility with AbstractChildWatcher ABC\n # To achieve this it has empty attach_loop() method\n # and doesn't accept explicit loop argument\n # for add_child_handler()/remove_child_handler()\n # but retrieves the current loop by get_running_loop()\n\n def __init__(self):\n self._callbacks = {}\n self._saved_sighandler = None\n warnings._deprecated(\"MultiLoopChildWatcher\",\n \"{name!r} is deprecated as of Python 3.12 and will be \"\n \"removed in Python {remove}.\",\n remove=(3, 14))\n\n def is_active(self):\n return self._saved_sighandler is not None\n\n def close(self):\n self._callbacks.clear()\n if self._saved_sighandler is None:\n return\n\n handler = signal.getsignal(signal.SIGCHLD)\n if handler != self._sig_chld:\n logger.warning(\"SIGCHLD handler was changed by outside code\")\n else:\n signal.signal(signal.SIGCHLD, self._saved_sighandler)\n self._saved_sighandler = None\n\n def __enter__(self):\n return self\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n pass\n\n def add_child_handler(self, pid, callback, *args):\n loop = events.get_running_loop()\n self._callbacks[pid] = (loop, callback, args)\n\n # Prevent a race condition in case the child is already terminated.\n self._do_waitpid(pid)\n\n def remove_child_handler(self, pid):\n try:\n del self._callbacks[pid]\n return True\n except KeyError:\n return False\n\n def attach_loop(self, loop):\n # Don't save the loop but initialize itself if called first time\n # The reason to do it here is that attach_loop() is called from\n # unix policy only for the main thread.\n # Main thread is required for subscription on SIGCHLD signal\n if self._saved_sighandler is not None:\n return\n\n self._saved_sighandler = signal.signal(signal.SIGCHLD, self._sig_chld)\n if self._saved_sighandler is None:\n logger.warning(\"Previous SIGCHLD handler was set by non-Python code, \"\n \"restore to default handler on watcher close.\")\n self._saved_sighandler = signal.SIG_DFL\n\n # Set SA_RESTART to limit EINTR occurrences.\n signal.siginterrupt(signal.SIGCHLD, False)\n\n def _do_waitpid_all(self):\n for pid in list(self._callbacks):\n self._do_waitpid(pid)\n\n def _do_waitpid(self, expected_pid):\n assert expected_pid > 0\n\n try:\n pid, status = os.waitpid(expected_pid, os.WNOHANG)\n except ChildProcessError:\n # The child process is already reaped\n # (may happen if waitpid() is called elsewhere).\n pid = expected_pid\n returncode = 255\n logger.warning(\n \"Unknown child process pid %d, will report returncode 255\",\n pid)\n debug_log = False\n else:\n if pid == 0:\n # The child process is still alive.\n return\n\n returncode = waitstatus_to_exitcode(status)\n debug_log = True\n try:\n loop, callback, args = self._callbacks.pop(pid)\n except KeyError: # pragma: no cover\n # May happen if .remove_child_handler() is called\n # after os.waitpid() returns.\n logger.warning(\"Child watcher got an unexpected pid: %r\",\n pid, exc_info=True)\n else:\n if loop.is_closed():\n logger.warning(\"Loop %r that handles pid %r is closed\", loop, pid)\n else:\n if debug_log and loop.get_debug():\n logger.debug('process %s exited with returncode %s',\n expected_pid, returncode)\n loop.call_soon_threadsafe(callback, pid, returncode, *args)\n\n def _sig_chld(self, signum, frame):\n try:\n self._do_waitpid_all()\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException:\n logger.warning('Unknown exception in SIGCHLD handler', exc_info=True)\n\n\nclass ThreadedChildWatcher(AbstractChildWatcher):\n \"\"\"Threaded child watcher implementation.\n\n The watcher uses a thread per process\n for waiting for the process finish.\n\n It doesn't require subscription on POSIX signal\n but a thread creation is not free.\n\n The watcher has O(1) complexity, its performance doesn't depend\n on amount of spawn processes.\n \"\"\"\n\n def __init__(self):\n self._pid_counter = itertools.count(0)\n self._threads = {}\n\n def is_active(self):\n return True\n\n def close(self):\n pass\n\n def __enter__(self):\n return self\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n pass\n\n def __del__(self, _warn=warnings.warn):\n threads = [thread for thread in list(self._threads.values())\n if thread.is_alive()]\n if threads:\n _warn(f\"{self.__class__} has registered but not finished child processes\",\n ResourceWarning,\n source=self)\n\n def add_child_handler(self, pid, callback, *args):\n loop = events.get_running_loop()\n thread = threading.Thread(target=self._do_waitpid,\n name=f\"asyncio-waitpid-{next(self._pid_counter)}\",\n args=(loop, pid, callback, args),\n daemon=True)\n self._threads[pid] = thread\n thread.start()\n\n def remove_child_handler(self, pid):\n # asyncio never calls remove_child_handler() !!!\n # The method is no-op but is implemented because\n # abstract base classes require it.\n return True\n\n def attach_loop(self, loop):\n pass\n\n def _do_waitpid(self, loop, expected_pid, callback, args):\n assert expected_pid > 0\n\n try:\n pid, status = os.waitpid(expected_pid, 0)\n except ChildProcessError:\n # The child process is already reaped\n # (may happen if waitpid() is called elsewhere).\n pid = expected_pid\n returncode = 255\n logger.warning(\n \"Unknown child process pid %d, will report returncode 255\",\n pid)\n else:\n returncode = waitstatus_to_exitcode(status)\n if loop.get_debug():\n logger.debug('process %s exited with returncode %s',\n expected_pid, returncode)\n\n if loop.is_closed():\n logger.warning(\"Loop %r that handles pid %r is closed\", loop, pid)\n else:\n loop.call_soon_threadsafe(callback, pid, returncode, *args)\n\n self._threads.pop(expected_pid)\n\ndef can_use_pidfd():\n if not hasattr(os, 'pidfd_open'):\n return False\n try:\n pid = os.getpid()\n os.close(os.pidfd_open(pid, 0))\n except OSError:\n # blocked by security policy like SECCOMP\n return False\n return True\n\n\nclass _UnixDefaultEventLoopPolicy(events.BaseDefaultEventLoopPolicy):\n \"\"\"UNIX event loop policy with a watcher for child processes.\"\"\"\n _loop_factory = _UnixSelectorEventLoop\n\n def __init__(self):\n super().__init__()\n self._watcher = None\n\n def _init_watcher(self):\n with events._lock:\n if self._watcher is None: # pragma: no branch\n if can_use_pidfd():\n self._watcher = PidfdChildWatcher()\n else:\n self._watcher = ThreadedChildWatcher()\n\n def set_event_loop(self, loop):\n \"\"\"Set the event loop.\n\n As a side effect, if a child watcher was set before, then calling\n .set_event_loop() from the main thread will call .attach_loop(loop) on\n the child watcher.\n \"\"\"\n\n super().set_event_loop(loop)\n\n if (self._watcher is not None and\n threading.current_thread() is threading.main_thread()):\n self._watcher.attach_loop(loop)\n\n def get_child_watcher(self):\n \"\"\"Get the watcher for child processes.\n\n If not yet set, a ThreadedChildWatcher object is automatically created.\n \"\"\"\n if self._watcher is None:\n self._init_watcher()\n\n warnings._deprecated(\"get_child_watcher\",\n \"{name!r} is deprecated as of Python 3.12 and will be \"\n \"removed in Python {remove}.\", remove=(3, 14))\n return self._watcher\n\n def set_child_watcher(self, watcher):\n \"\"\"Set the watcher for child processes.\"\"\"\n\n assert watcher is None or isinstance(watcher, AbstractChildWatcher)\n\n if self._watcher is not None:\n self._watcher.close()\n\n self._watcher = watcher\n warnings._deprecated(\"set_child_watcher\",\n \"{name!r} is deprecated as of Python 3.12 and will be \"\n \"removed in Python {remove}.\", remove=(3, 14))\n\n\nSelectorEventLoop = _UnixSelectorEventLoop\nDefaultEventLoopPolicy = _UnixDefaultEventLoopPolicy\nEventLoop = SelectorEventLoop\n", 1536], "/usr/lib/python3.13/asyncio/coroutines.py": ["__all__ = 'iscoroutinefunction', 'iscoroutine'\n\nimport collections.abc\nimport inspect\nimport os\nimport sys\nimport types\n\n\ndef _is_debug_mode():\n # See: https://docs.python.org/3/library/asyncio-dev.html#asyncio-debug-mode.\n return sys.flags.dev_mode or (not sys.flags.ignore_environment and\n bool(os.environ.get('PYTHONASYNCIODEBUG')))\n\n\n# A marker for iscoroutinefunction.\n_is_coroutine = object()\n\n\ndef iscoroutinefunction(func):\n \"\"\"Return True if func is a decorated coroutine function.\"\"\"\n return (inspect.iscoroutinefunction(func) or\n getattr(func, '_is_coroutine', None) is _is_coroutine)\n\n\n# Prioritize native coroutine check to speed-up\n# asyncio.iscoroutine.\n_COROUTINE_TYPES = (types.CoroutineType, collections.abc.Coroutine)\n_iscoroutine_typecache = set()\n\n\ndef iscoroutine(obj):\n \"\"\"Return True if obj is a coroutine object.\"\"\"\n if type(obj) in _iscoroutine_typecache:\n return True\n\n if isinstance(obj, _COROUTINE_TYPES):\n # Just in case we don't want to cache more than 100\n # positive types. That shouldn't ever happen, unless\n # someone stressing the system on purpose.\n if len(_iscoroutine_typecache) < 100:\n _iscoroutine_typecache.add(type(obj))\n return True\n else:\n return False\n\n\ndef _format_coroutine(coro):\n assert iscoroutine(coro)\n\n def get_name(coro):\n # Coroutines compiled with Cython sometimes don't have\n # proper __qualname__ or __name__. While that is a bug\n # in Cython, asyncio shouldn't crash with an AttributeError\n # in its __repr__ functions.\n if hasattr(coro, '__qualname__') and coro.__qualname__:\n coro_name = coro.__qualname__\n elif hasattr(coro, '__name__') and coro.__name__:\n coro_name = coro.__name__\n else:\n # Stop masking Cython bugs, expose them in a friendly way.\n coro_name = f'<{type(coro).__name__} without __name__>'\n return f'{coro_name}()'\n\n def is_running(coro):\n try:\n return coro.cr_running\n except AttributeError:\n try:\n return coro.gi_running\n except AttributeError:\n return False\n\n coro_code = None\n if hasattr(coro, 'cr_code') and coro.cr_code:\n coro_code = coro.cr_code\n elif hasattr(coro, 'gi_code') and coro.gi_code:\n coro_code = coro.gi_code\n\n coro_name = get_name(coro)\n\n if not coro_code:\n # Built-in types might not have __qualname__ or __name__.\n if is_running(coro):\n return f'{coro_name} running'\n else:\n return coro_name\n\n coro_frame = None\n if hasattr(coro, 'gi_frame') and coro.gi_frame:\n coro_frame = coro.gi_frame\n elif hasattr(coro, 'cr_frame') and coro.cr_frame:\n coro_frame = coro.cr_frame\n\n # If Cython's coroutine has a fake code object without proper\n # co_filename -- expose that.\n filename = coro_code.co_filename or ''\n\n lineno = 0\n\n if coro_frame is not None:\n lineno = coro_frame.f_lineno\n coro_repr = f'{coro_name} running at {filename}:{lineno}'\n\n else:\n lineno = coro_code.co_firstlineno\n coro_repr = f'{coro_name} done, defined at {filename}:{lineno}'\n\n return coro_repr\n", 109], "/usr/lib/python3.13/asyncio/base_events.py": ["\"\"\"Base implementation of event loop.\n\nThe event loop can be broken up into a multiplexer (the part\nresponsible for notifying us of I/O events) and the event loop proper,\nwhich wraps a multiplexer with functionality for scheduling callbacks,\nimmediately or at a given time in the future.\n\nWhenever a public API takes a callback, subsequent positional\narguments will be passed to the callback if/when it is called. This\navoids the proliferation of trivial lambdas implementing closures.\nKeyword arguments for the callback are not supported; this is a\nconscious design decision, leaving the door open for keyword arguments\nto modify the meaning of the API call itself.\n\"\"\"\n\nimport collections\nimport collections.abc\nimport concurrent.futures\nimport errno\nimport functools\nimport heapq\nimport itertools\nimport os\nimport socket\nimport stat\nimport subprocess\nimport threading\nimport time\nimport traceback\nimport sys\nimport warnings\nimport weakref\n\ntry:\n import ssl\nexcept ImportError: # pragma: no cover\n ssl = None\n\nfrom . import constants\nfrom . import coroutines\nfrom . import events\nfrom . import exceptions\nfrom . import futures\nfrom . import protocols\nfrom . import sslproto\nfrom . import staggered\nfrom . import tasks\nfrom . import timeouts\nfrom . import transports\nfrom . import trsock\nfrom .log import logger\n\n\n__all__ = 'BaseEventLoop','Server',\n\n\n# Minimum number of _scheduled timer handles before cleanup of\n# cancelled handles is performed.\n_MIN_SCHEDULED_TIMER_HANDLES = 100\n\n# Minimum fraction of _scheduled timer handles that are cancelled\n# before cleanup of cancelled handles is performed.\n_MIN_CANCELLED_TIMER_HANDLES_FRACTION = 0.5\n\n\n_HAS_IPv6 = hasattr(socket, 'AF_INET6')\n\n# Maximum timeout passed to select to avoid OS limitations\nMAXIMUM_SELECT_TIMEOUT = 24 * 3600\n\n\ndef _format_handle(handle):\n cb = handle._callback\n if isinstance(getattr(cb, '__self__', None), tasks.Task):\n # format the task\n return repr(cb.__self__)\n else:\n return str(handle)\n\n\ndef _format_pipe(fd):\n if fd == subprocess.PIPE:\n return ''\n elif fd == subprocess.STDOUT:\n return ''\n else:\n return repr(fd)\n\n\ndef _set_reuseport(sock):\n if not hasattr(socket, 'SO_REUSEPORT'):\n raise ValueError('reuse_port not supported by socket module')\n else:\n try:\n sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEPORT, 1)\n except OSError:\n raise ValueError('reuse_port not supported by socket module, '\n 'SO_REUSEPORT defined but not implemented.')\n\n\ndef _ipaddr_info(host, port, family, type, proto, flowinfo=0, scopeid=0):\n # Try to skip getaddrinfo if \"host\" is already an IP. Users might have\n # handled name resolution in their own code and pass in resolved IPs.\n if not hasattr(socket, 'inet_pton'):\n return\n\n if proto not in {0, socket.IPPROTO_TCP, socket.IPPROTO_UDP} or \\\n host is None:\n return None\n\n if type == socket.SOCK_STREAM:\n proto = socket.IPPROTO_TCP\n elif type == socket.SOCK_DGRAM:\n proto = socket.IPPROTO_UDP\n else:\n return None\n\n if port is None:\n port = 0\n elif isinstance(port, bytes) and port == b'':\n port = 0\n elif isinstance(port, str) and port == '':\n port = 0\n else:\n # If port's a service name like \"http\", don't skip getaddrinfo.\n try:\n port = int(port)\n except (TypeError, ValueError):\n return None\n\n if family == socket.AF_UNSPEC:\n afs = [socket.AF_INET]\n if _HAS_IPv6:\n afs.append(socket.AF_INET6)\n else:\n afs = [family]\n\n if isinstance(host, bytes):\n host = host.decode('idna')\n if '%' in host:\n # Linux's inet_pton doesn't accept an IPv6 zone index after host,\n # like '::1%lo0'.\n return None\n\n for af in afs:\n try:\n socket.inet_pton(af, host)\n # The host has already been resolved.\n if _HAS_IPv6 and af == socket.AF_INET6:\n return af, type, proto, '', (host, port, flowinfo, scopeid)\n else:\n return af, type, proto, '', (host, port)\n except OSError:\n pass\n\n # \"host\" is not an IP address.\n return None\n\n\ndef _interleave_addrinfos(addrinfos, first_address_family_count=1):\n \"\"\"Interleave list of addrinfo tuples by family.\"\"\"\n # Group addresses by family\n addrinfos_by_family = collections.OrderedDict()\n for addr in addrinfos:\n family = addr[0]\n if family not in addrinfos_by_family:\n addrinfos_by_family[family] = []\n addrinfos_by_family[family].append(addr)\n addrinfos_lists = list(addrinfos_by_family.values())\n\n reordered = []\n if first_address_family_count > 1:\n reordered.extend(addrinfos_lists[0][:first_address_family_count - 1])\n del addrinfos_lists[0][:first_address_family_count - 1]\n reordered.extend(\n a for a in itertools.chain.from_iterable(\n itertools.zip_longest(*addrinfos_lists)\n ) if a is not None)\n return reordered\n\n\ndef _run_until_complete_cb(fut):\n if not fut.cancelled():\n exc = fut.exception()\n if isinstance(exc, (SystemExit, KeyboardInterrupt)):\n # Issue #22429: run_forever() already finished, no need to\n # stop it.\n return\n futures._get_loop(fut).stop()\n\n\nif hasattr(socket, 'TCP_NODELAY'):\n def _set_nodelay(sock):\n if (sock.family in {socket.AF_INET, socket.AF_INET6} and\n sock.type == socket.SOCK_STREAM and\n sock.proto == socket.IPPROTO_TCP):\n sock.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)\nelse:\n def _set_nodelay(sock):\n pass\n\n\ndef _check_ssl_socket(sock):\n if ssl is not None and isinstance(sock, ssl.SSLSocket):\n raise TypeError(\"Socket cannot be of type SSLSocket\")\n\n\nclass _SendfileFallbackProtocol(protocols.Protocol):\n def __init__(self, transp):\n if not isinstance(transp, transports._FlowControlMixin):\n raise TypeError(\"transport should be _FlowControlMixin instance\")\n self._transport = transp\n self._proto = transp.get_protocol()\n self._should_resume_reading = transp.is_reading()\n self._should_resume_writing = transp._protocol_paused\n transp.pause_reading()\n transp.set_protocol(self)\n if self._should_resume_writing:\n self._write_ready_fut = self._transport._loop.create_future()\n else:\n self._write_ready_fut = None\n\n async def drain(self):\n if self._transport.is_closing():\n raise ConnectionError(\"Connection closed by peer\")\n fut = self._write_ready_fut\n if fut is None:\n return\n await fut\n\n def connection_made(self, transport):\n raise RuntimeError(\"Invalid state: \"\n \"connection should have been established already.\")\n\n def connection_lost(self, exc):\n if self._write_ready_fut is not None:\n # Never happens if peer disconnects after sending the whole content\n # Thus disconnection is always an exception from user perspective\n if exc is None:\n self._write_ready_fut.set_exception(\n ConnectionError(\"Connection is closed by peer\"))\n else:\n self._write_ready_fut.set_exception(exc)\n self._proto.connection_lost(exc)\n\n def pause_writing(self):\n if self._write_ready_fut is not None:\n return\n self._write_ready_fut = self._transport._loop.create_future()\n\n def resume_writing(self):\n if self._write_ready_fut is None:\n return\n self._write_ready_fut.set_result(False)\n self._write_ready_fut = None\n\n def data_received(self, data):\n raise RuntimeError(\"Invalid state: reading should be paused\")\n\n def eof_received(self):\n raise RuntimeError(\"Invalid state: reading should be paused\")\n\n async def restore(self):\n self._transport.set_protocol(self._proto)\n if self._should_resume_reading:\n self._transport.resume_reading()\n if self._write_ready_fut is not None:\n # Cancel the future.\n # Basically it has no effect because protocol is switched back,\n # no code should wait for it anymore.\n self._write_ready_fut.cancel()\n if self._should_resume_writing:\n self._proto.resume_writing()\n\n\nclass Server(events.AbstractServer):\n\n def __init__(self, loop, sockets, protocol_factory, ssl_context, backlog,\n ssl_handshake_timeout, ssl_shutdown_timeout=None):\n self._loop = loop\n self._sockets = sockets\n # Weak references so we don't break Transport's ability to\n # detect abandoned transports\n self._clients = weakref.WeakSet()\n self._waiters = []\n self._protocol_factory = protocol_factory\n self._backlog = backlog\n self._ssl_context = ssl_context\n self._ssl_handshake_timeout = ssl_handshake_timeout\n self._ssl_shutdown_timeout = ssl_shutdown_timeout\n self._serving = False\n self._serving_forever_fut = None\n\n def __repr__(self):\n return f'<{self.__class__.__name__} sockets={self.sockets!r}>'\n\n def _attach(self, transport):\n assert self._sockets is not None\n self._clients.add(transport)\n\n def _detach(self, transport):\n self._clients.discard(transport)\n if len(self._clients) == 0 and self._sockets is None:\n self._wakeup()\n\n def _wakeup(self):\n waiters = self._waiters\n self._waiters = None\n for waiter in waiters:\n if not waiter.done():\n waiter.set_result(None)\n\n def _start_serving(self):\n if self._serving:\n return\n self._serving = True\n for sock in self._sockets:\n sock.listen(self._backlog)\n self._loop._start_serving(\n self._protocol_factory, sock, self._ssl_context,\n self, self._backlog, self._ssl_handshake_timeout,\n self._ssl_shutdown_timeout)\n\n def get_loop(self):\n return self._loop\n\n def is_serving(self):\n return self._serving\n\n @property\n def sockets(self):\n if self._sockets is None:\n return ()\n return tuple(trsock.TransportSocket(s) for s in self._sockets)\n\n def close(self):\n sockets = self._sockets\n if sockets is None:\n return\n self._sockets = None\n\n for sock in sockets:\n self._loop._stop_serving(sock)\n\n self._serving = False\n\n if (self._serving_forever_fut is not None and\n not self._serving_forever_fut.done()):\n self._serving_forever_fut.cancel()\n self._serving_forever_fut = None\n\n if len(self._clients) == 0:\n self._wakeup()\n\n def close_clients(self):\n for transport in self._clients.copy():\n transport.close()\n\n def abort_clients(self):\n for transport in self._clients.copy():\n transport.abort()\n\n async def start_serving(self):\n self._start_serving()\n # Skip one loop iteration so that all 'loop.add_reader'\n # go through.\n await tasks.sleep(0)\n\n async def serve_forever(self):\n if self._serving_forever_fut is not None:\n raise RuntimeError(\n f'server {self!r} is already being awaited on serve_forever()')\n if self._sockets is None:\n raise RuntimeError(f'server {self!r} is closed')\n\n self._start_serving()\n self._serving_forever_fut = self._loop.create_future()\n\n try:\n await self._serving_forever_fut\n except exceptions.CancelledError:\n try:\n self.close()\n await self.wait_closed()\n finally:\n raise\n finally:\n self._serving_forever_fut = None\n\n async def wait_closed(self):\n \"\"\"Wait until server is closed and all connections are dropped.\n\n - If the server is not closed, wait.\n - If it is closed, but there are still active connections, wait.\n\n Anyone waiting here will be unblocked once both conditions\n (server is closed and all connections have been dropped)\n have become true, in either order.\n\n Historical note: In 3.11 and before, this was broken, returning\n immediately if the server was already closed, even if there\n were still active connections. An attempted fix in 3.12.0 was\n still broken, returning immediately if the server was still\n open and there were no active connections. Hopefully in 3.12.1\n we have it right.\n \"\"\"\n # Waiters are unblocked by self._wakeup(), which is called\n # from two places: self.close() and self._detach(), but only\n # when both conditions have become true. To signal that this\n # has happened, self._wakeup() sets self._waiters to None.\n if self._waiters is None:\n return\n waiter = self._loop.create_future()\n self._waiters.append(waiter)\n await waiter\n\n\nclass BaseEventLoop(events.AbstractEventLoop):\n\n def __init__(self):\n self._timer_cancelled_count = 0\n self._closed = False\n self._stopping = False\n self._ready = collections.deque()\n self._scheduled = []\n self._default_executor = None\n self._internal_fds = 0\n # Identifier of the thread running the event loop, or None if the\n # event loop is not running\n self._thread_id = None\n self._clock_resolution = time.get_clock_info('monotonic').resolution\n self._exception_handler = None\n self.set_debug(coroutines._is_debug_mode())\n # The preserved state of async generator hooks.\n self._old_agen_hooks = None\n # In debug mode, if the execution of a callback or a step of a task\n # exceed this duration in seconds, the slow callback/task is logged.\n self.slow_callback_duration = 0.1\n self._current_handle = None\n self._task_factory = None\n self._coroutine_origin_tracking_enabled = False\n self._coroutine_origin_tracking_saved_depth = None\n\n # A weak set of all asynchronous generators that are\n # being iterated by the loop.\n self._asyncgens = weakref.WeakSet()\n # Set to True when `loop.shutdown_asyncgens` is called.\n self._asyncgens_shutdown_called = False\n # Set to True when `loop.shutdown_default_executor` is called.\n self._executor_shutdown_called = False\n\n def __repr__(self):\n return (\n f'<{self.__class__.__name__} running={self.is_running()} '\n f'closed={self.is_closed()} debug={self.get_debug()}>'\n )\n\n def create_future(self):\n \"\"\"Create a Future object attached to the loop.\"\"\"\n return futures.Future(loop=self)\n\n def create_task(self, coro, *, name=None, context=None):\n \"\"\"Schedule a coroutine object.\n\n Return a task object.\n \"\"\"\n self._check_closed()\n if self._task_factory is None:\n task = tasks.Task(coro, loop=self, name=name, context=context)\n if task._source_traceback:\n del task._source_traceback[-1]\n else:\n if context is None:\n # Use legacy API if context is not needed\n task = self._task_factory(self, coro)\n else:\n task = self._task_factory(self, coro, context=context)\n\n task.set_name(name)\n\n return task\n\n def set_task_factory(self, factory):\n \"\"\"Set a task factory that will be used by loop.create_task().\n\n If factory is None the default task factory will be set.\n\n If factory is a callable, it should have a signature matching\n '(loop, coro)', where 'loop' will be a reference to the active\n event loop, 'coro' will be a coroutine object. The callable\n must return a Future.\n \"\"\"\n if factory is not None and not callable(factory):\n raise TypeError('task factory must be a callable or None')\n self._task_factory = factory\n\n def get_task_factory(self):\n \"\"\"Return a task factory, or None if the default one is in use.\"\"\"\n return self._task_factory\n\n def _make_socket_transport(self, sock, protocol, waiter=None, *,\n extra=None, server=None):\n \"\"\"Create socket transport.\"\"\"\n raise NotImplementedError\n\n def _make_ssl_transport(\n self, rawsock, protocol, sslcontext, waiter=None,\n *, server_side=False, server_hostname=None,\n extra=None, server=None,\n ssl_handshake_timeout=None,\n ssl_shutdown_timeout=None,\n call_connection_made=True):\n \"\"\"Create SSL transport.\"\"\"\n raise NotImplementedError\n\n def _make_datagram_transport(self, sock, protocol,\n address=None, waiter=None, extra=None):\n \"\"\"Create datagram transport.\"\"\"\n raise NotImplementedError\n\n def _make_read_pipe_transport(self, pipe, protocol, waiter=None,\n extra=None):\n \"\"\"Create read pipe transport.\"\"\"\n raise NotImplementedError\n\n def _make_write_pipe_transport(self, pipe, protocol, waiter=None,\n extra=None):\n \"\"\"Create write pipe transport.\"\"\"\n raise NotImplementedError\n\n async def _make_subprocess_transport(self, protocol, args, shell,\n stdin, stdout, stderr, bufsize,\n extra=None, **kwargs):\n \"\"\"Create subprocess transport.\"\"\"\n raise NotImplementedError\n\n def _write_to_self(self):\n \"\"\"Write a byte to self-pipe, to wake up the event loop.\n\n This may be called from a different thread.\n\n The subclass is responsible for implementing the self-pipe.\n \"\"\"\n raise NotImplementedError\n\n def _process_events(self, event_list):\n \"\"\"Process selector events.\"\"\"\n raise NotImplementedError\n\n def _check_closed(self):\n if self._closed:\n raise RuntimeError('Event loop is closed')\n\n def _check_default_executor(self):\n if self._executor_shutdown_called:\n raise RuntimeError('Executor shutdown has been called')\n\n def _asyncgen_finalizer_hook(self, agen):\n self._asyncgens.discard(agen)\n if not self.is_closed():\n self.call_soon_threadsafe(self.create_task, agen.aclose())\n\n def _asyncgen_firstiter_hook(self, agen):\n if self._asyncgens_shutdown_called:\n warnings.warn(\n f\"asynchronous generator {agen!r} was scheduled after \"\n f\"loop.shutdown_asyncgens() call\",\n ResourceWarning, source=self)\n\n self._asyncgens.add(agen)\n\n async def shutdown_asyncgens(self):\n \"\"\"Shutdown all active asynchronous generators.\"\"\"\n self._asyncgens_shutdown_called = True\n\n if not len(self._asyncgens):\n # If Python version is <3.6 or we don't have any asynchronous\n # generators alive.\n return\n\n closing_agens = list(self._asyncgens)\n self._asyncgens.clear()\n\n results = await tasks.gather(\n *[ag.aclose() for ag in closing_agens],\n return_exceptions=True)\n\n for result, agen in zip(results, closing_agens):\n if isinstance(result, Exception):\n self.call_exception_handler({\n 'message': f'an error occurred during closing of '\n f'asynchronous generator {agen!r}',\n 'exception': result,\n 'asyncgen': agen\n })\n\n async def shutdown_default_executor(self, timeout=None):\n \"\"\"Schedule the shutdown of the default executor.\n\n The timeout parameter specifies the amount of time the executor will\n be given to finish joining. The default value is None, which means\n that the executor will be given an unlimited amount of time.\n \"\"\"\n self._executor_shutdown_called = True\n if self._default_executor is None:\n return\n future = self.create_future()\n thread = threading.Thread(target=self._do_shutdown, args=(future,))\n thread.start()\n try:\n async with timeouts.timeout(timeout):\n await future\n except TimeoutError:\n warnings.warn(\"The executor did not finishing joining \"\n f\"its threads within {timeout} seconds.\",\n RuntimeWarning, stacklevel=2)\n self._default_executor.shutdown(wait=False)\n else:\n thread.join()\n\n def _do_shutdown(self, future):\n try:\n self._default_executor.shutdown(wait=True)\n if not self.is_closed():\n self.call_soon_threadsafe(futures._set_result_unless_cancelled,\n future, None)\n except Exception as ex:\n if not self.is_closed() and not future.cancelled():\n self.call_soon_threadsafe(future.set_exception, ex)\n\n def _check_running(self):\n if self.is_running():\n raise RuntimeError('This event loop is already running')\n if events._get_running_loop() is not None:\n raise RuntimeError(\n 'Cannot run the event loop while another loop is running')\n\n def _run_forever_setup(self):\n \"\"\"Prepare the run loop to process events.\n\n This method exists so that custom custom event loop subclasses (e.g., event loops\n that integrate a GUI event loop with Python's event loop) have access to all the\n loop setup logic.\n \"\"\"\n self._check_closed()\n self._check_running()\n self._set_coroutine_origin_tracking(self._debug)\n\n self._old_agen_hooks = sys.get_asyncgen_hooks()\n self._thread_id = threading.get_ident()\n sys.set_asyncgen_hooks(\n firstiter=self._asyncgen_firstiter_hook,\n finalizer=self._asyncgen_finalizer_hook\n )\n\n events._set_running_loop(self)\n\n def _run_forever_cleanup(self):\n \"\"\"Clean up after an event loop finishes the looping over events.\n\n This method exists so that custom custom event loop subclasses (e.g., event loops\n that integrate a GUI event loop with Python's event loop) have access to all the\n loop cleanup logic.\n \"\"\"\n self._stopping = False\n self._thread_id = None\n events._set_running_loop(None)\n self._set_coroutine_origin_tracking(False)\n # Restore any pre-existing async generator hooks.\n if self._old_agen_hooks is not None:\n sys.set_asyncgen_hooks(*self._old_agen_hooks)\n self._old_agen_hooks = None\n\n def run_forever(self):\n \"\"\"Run until stop() is called.\"\"\"\n try:\n self._run_forever_setup()\n while True:\n self._run_once()\n if self._stopping:\n break\n finally:\n self._run_forever_cleanup()\n\n def run_until_complete(self, future):\n \"\"\"Run until the Future is done.\n\n If the argument is a coroutine, it is wrapped in a Task.\n\n WARNING: It would be disastrous to call run_until_complete()\n with the same coroutine twice -- it would wrap it in two\n different Tasks and that can't be good.\n\n Return the Future's result, or raise its exception.\n \"\"\"\n self._check_closed()\n self._check_running()\n\n new_task = not futures.isfuture(future)\n future = tasks.ensure_future(future, loop=self)\n if new_task:\n # An exception is raised if the future didn't complete, so there\n # is no need to log the \"destroy pending task\" message\n future._log_destroy_pending = False\n\n future.add_done_callback(_run_until_complete_cb)\n try:\n self.run_forever()\n except:\n if new_task and future.done() and not future.cancelled():\n # The coroutine raised a BaseException. Consume the exception\n # to not log a warning, the caller doesn't have access to the\n # local task.\n future.exception()\n raise\n finally:\n future.remove_done_callback(_run_until_complete_cb)\n if not future.done():\n raise RuntimeError('Event loop stopped before Future completed.')\n\n return future.result()\n\n def stop(self):\n \"\"\"Stop running the event loop.\n\n Every callback already scheduled will still run. This simply informs\n run_forever to stop looping after a complete iteration.\n \"\"\"\n self._stopping = True\n\n def close(self):\n \"\"\"Close the event loop.\n\n This clears the queues and shuts down the executor,\n but does not wait for the executor to finish.\n\n The event loop must not be running.\n \"\"\"\n if self.is_running():\n raise RuntimeError(\"Cannot close a running event loop\")\n if self._closed:\n return\n if self._debug:\n logger.debug(\"Close %r\", self)\n self._closed = True\n self._ready.clear()\n self._scheduled.clear()\n self._executor_shutdown_called = True\n executor = self._default_executor\n if executor is not None:\n self._default_executor = None\n executor.shutdown(wait=False)\n\n def is_closed(self):\n \"\"\"Returns True if the event loop was closed.\"\"\"\n return self._closed\n\n def __del__(self, _warn=warnings.warn):\n if not self.is_closed():\n _warn(f\"unclosed event loop {self!r}\", ResourceWarning, source=self)\n if not self.is_running():\n self.close()\n\n def is_running(self):\n \"\"\"Returns True if the event loop is running.\"\"\"\n return (self._thread_id is not None)\n\n def time(self):\n \"\"\"Return the time according to the event loop's clock.\n\n This is a float expressed in seconds since an epoch, but the\n epoch, precision, accuracy and drift are unspecified and may\n differ per event loop.\n \"\"\"\n return time.monotonic()\n\n def call_later(self, delay, callback, *args, context=None):\n \"\"\"Arrange for a callback to be called at a given time.\n\n Return a Handle: an opaque object with a cancel() method that\n can be used to cancel the call.\n\n The delay can be an int or float, expressed in seconds. It is\n always relative to the current time.\n\n Each callback will be called exactly once. If two callbacks\n are scheduled for exactly the same time, it is undefined which\n will be called first.\n\n Any positional arguments after the callback will be passed to\n the callback when it is called.\n \"\"\"\n if delay is None:\n raise TypeError('delay must not be None')\n timer = self.call_at(self.time() + delay, callback, *args,\n context=context)\n if timer._source_traceback:\n del timer._source_traceback[-1]\n return timer\n\n def call_at(self, when, callback, *args, context=None):\n \"\"\"Like call_later(), but uses an absolute time.\n\n Absolute time corresponds to the event loop's time() method.\n \"\"\"\n if when is None:\n raise TypeError(\"when cannot be None\")\n self._check_closed()\n if self._debug:\n self._check_thread()\n self._check_callback(callback, 'call_at')\n timer = events.TimerHandle(when, callback, args, self, context)\n if timer._source_traceback:\n del timer._source_traceback[-1]\n heapq.heappush(self._scheduled, timer)\n timer._scheduled = True\n return timer\n\n def call_soon(self, callback, *args, context=None):\n \"\"\"Arrange for a callback to be called as soon as possible.\n\n This operates as a FIFO queue: callbacks are called in the\n order in which they are registered. Each callback will be\n called exactly once.\n\n Any positional arguments after the callback will be passed to\n the callback when it is called.\n \"\"\"\n self._check_closed()\n if self._debug:\n self._check_thread()\n self._check_callback(callback, 'call_soon')\n handle = self._call_soon(callback, args, context)\n if handle._source_traceback:\n del handle._source_traceback[-1]\n return handle\n\n def _check_callback(self, callback, method):\n if (coroutines.iscoroutine(callback) or\n coroutines.iscoroutinefunction(callback)):\n raise TypeError(\n f\"coroutines cannot be used with {method}()\")\n if not callable(callback):\n raise TypeError(\n f'a callable object was expected by {method}(), '\n f'got {callback!r}')\n\n def _call_soon(self, callback, args, context):\n handle = events.Handle(callback, args, self, context)\n if handle._source_traceback:\n del handle._source_traceback[-1]\n self._ready.append(handle)\n return handle\n\n def _check_thread(self):\n \"\"\"Check that the current thread is the thread running the event loop.\n\n Non-thread-safe methods of this class make this assumption and will\n likely behave incorrectly when the assumption is violated.\n\n Should only be called when (self._debug == True). The caller is\n responsible for checking this condition for performance reasons.\n \"\"\"\n if self._thread_id is None:\n return\n thread_id = threading.get_ident()\n if thread_id != self._thread_id:\n raise RuntimeError(\n \"Non-thread-safe operation invoked on an event loop other \"\n \"than the current one\")\n\n def call_soon_threadsafe(self, callback, *args, context=None):\n \"\"\"Like call_soon(), but thread-safe.\"\"\"\n self._check_closed()\n if self._debug:\n self._check_callback(callback, 'call_soon_threadsafe')\n handle = self._call_soon(callback, args, context)\n if handle._source_traceback:\n del handle._source_traceback[-1]\n self._write_to_self()\n return handle\n\n def run_in_executor(self, executor, func, *args):\n self._check_closed()\n if self._debug:\n self._check_callback(func, 'run_in_executor')\n if executor is None:\n executor = self._default_executor\n # Only check when the default executor is being used\n self._check_default_executor()\n if executor is None:\n executor = concurrent.futures.ThreadPoolExecutor(\n thread_name_prefix='asyncio'\n )\n self._default_executor = executor\n return futures.wrap_future(\n executor.submit(func, *args), loop=self)\n\n def set_default_executor(self, executor):\n if not isinstance(executor, concurrent.futures.ThreadPoolExecutor):\n raise TypeError('executor must be ThreadPoolExecutor instance')\n self._default_executor = executor\n\n def _getaddrinfo_debug(self, host, port, family, type, proto, flags):\n msg = [f\"{host}:{port!r}\"]\n if family:\n msg.append(f'family={family!r}')\n if type:\n msg.append(f'type={type!r}')\n if proto:\n msg.append(f'proto={proto!r}')\n if flags:\n msg.append(f'flags={flags!r}')\n msg = ', '.join(msg)\n logger.debug('Get address info %s', msg)\n\n t0 = self.time()\n addrinfo = socket.getaddrinfo(host, port, family, type, proto, flags)\n dt = self.time() - t0\n\n msg = f'Getting address info {msg} took {dt * 1e3:.3f}ms: {addrinfo!r}'\n if dt >= self.slow_callback_duration:\n logger.info(msg)\n else:\n logger.debug(msg)\n return addrinfo\n\n async def getaddrinfo(self, host, port, *,\n family=0, type=0, proto=0, flags=0):\n if self._debug:\n getaddr_func = self._getaddrinfo_debug\n else:\n getaddr_func = socket.getaddrinfo\n\n return await self.run_in_executor(\n None, getaddr_func, host, port, family, type, proto, flags)\n\n async def getnameinfo(self, sockaddr, flags=0):\n return await self.run_in_executor(\n None, socket.getnameinfo, sockaddr, flags)\n\n async def sock_sendfile(self, sock, file, offset=0, count=None,\n *, fallback=True):\n if self._debug and sock.gettimeout() != 0:\n raise ValueError(\"the socket must be non-blocking\")\n _check_ssl_socket(sock)\n self._check_sendfile_params(sock, file, offset, count)\n try:\n return await self._sock_sendfile_native(sock, file,\n offset, count)\n except exceptions.SendfileNotAvailableError as exc:\n if not fallback:\n raise\n return await self._sock_sendfile_fallback(sock, file,\n offset, count)\n\n async def _sock_sendfile_native(self, sock, file, offset, count):\n # NB: sendfile syscall is not supported for SSL sockets and\n # non-mmap files even if sendfile is supported by OS\n raise exceptions.SendfileNotAvailableError(\n f\"syscall sendfile is not available for socket {sock!r} \"\n f\"and file {file!r} combination\")\n\n async def _sock_sendfile_fallback(self, sock, file, offset, count):\n if offset:\n file.seek(offset)\n blocksize = (\n min(count, constants.SENDFILE_FALLBACK_READBUFFER_SIZE)\n if count else constants.SENDFILE_FALLBACK_READBUFFER_SIZE\n )\n buf = bytearray(blocksize)\n total_sent = 0\n try:\n while True:\n if count:\n blocksize = min(count - total_sent, blocksize)\n if blocksize <= 0:\n break\n view = memoryview(buf)[:blocksize]\n read = await self.run_in_executor(None, file.readinto, view)\n if not read:\n break # EOF\n await self.sock_sendall(sock, view[:read])\n total_sent += read\n return total_sent\n finally:\n if total_sent > 0 and hasattr(file, 'seek'):\n file.seek(offset + total_sent)\n\n def _check_sendfile_params(self, sock, file, offset, count):\n if 'b' not in getattr(file, 'mode', 'b'):\n raise ValueError(\"file should be opened in binary mode\")\n if not sock.type == socket.SOCK_STREAM:\n raise ValueError(\"only SOCK_STREAM type sockets are supported\")\n if count is not None:\n if not isinstance(count, int):\n raise TypeError(\n \"count must be a positive integer (got {!r})\".format(count))\n if count <= 0:\n raise ValueError(\n \"count must be a positive integer (got {!r})\".format(count))\n if not isinstance(offset, int):\n raise TypeError(\n \"offset must be a non-negative integer (got {!r})\".format(\n offset))\n if offset < 0:\n raise ValueError(\n \"offset must be a non-negative integer (got {!r})\".format(\n offset))\n\n async def _connect_sock(self, exceptions, addr_info, local_addr_infos=None):\n \"\"\"Create, bind and connect one socket.\"\"\"\n my_exceptions = []\n exceptions.append(my_exceptions)\n family, type_, proto, _, address = addr_info\n sock = None\n try:\n sock = socket.socket(family=family, type=type_, proto=proto)\n sock.setblocking(False)\n if local_addr_infos is not None:\n for lfamily, _, _, _, laddr in local_addr_infos:\n # skip local addresses of different family\n if lfamily != family:\n continue\n try:\n sock.bind(laddr)\n break\n except OSError as exc:\n msg = (\n f'error while attempting to bind on '\n f'address {laddr!r}: {str(exc).lower()}'\n )\n exc = OSError(exc.errno, msg)\n my_exceptions.append(exc)\n else: # all bind attempts failed\n if my_exceptions:\n raise my_exceptions.pop()\n else:\n raise OSError(f\"no matching local address with {family=} found\")\n await self.sock_connect(sock, address)\n return sock\n except OSError as exc:\n my_exceptions.append(exc)\n if sock is not None:\n sock.close()\n raise\n except:\n if sock is not None:\n sock.close()\n raise\n finally:\n exceptions = my_exceptions = None\n\n async def create_connection(\n self, protocol_factory, host=None, port=None,\n *, ssl=None, family=0,\n proto=0, flags=0, sock=None,\n local_addr=None, server_hostname=None,\n ssl_handshake_timeout=None,\n ssl_shutdown_timeout=None,\n happy_eyeballs_delay=None, interleave=None,\n all_errors=False):\n \"\"\"Connect to a TCP server.\n\n Create a streaming transport connection to a given internet host and\n port: socket family AF_INET or socket.AF_INET6 depending on host (or\n family if specified), socket type SOCK_STREAM. protocol_factory must be\n a callable returning a protocol instance.\n\n This method is a coroutine which will try to establish the connection\n in the background. When successful, the coroutine returns a\n (transport, protocol) pair.\n \"\"\"\n if server_hostname is not None and not ssl:\n raise ValueError('server_hostname is only meaningful with ssl')\n\n if server_hostname is None and ssl:\n # Use host as default for server_hostname. It is an error\n # if host is empty or not set, e.g. when an\n # already-connected socket was passed or when only a port\n # is given. To avoid this error, you can pass\n # server_hostname='' -- this will bypass the hostname\n # check. (This also means that if host is a numeric\n # IP/IPv6 address, we will attempt to verify that exact\n # address; this will probably fail, but it is possible to\n # create a certificate for a specific IP address, so we\n # don't judge it here.)\n if not host:\n raise ValueError('You must set server_hostname '\n 'when using ssl without a host')\n server_hostname = host\n\n if ssl_handshake_timeout is not None and not ssl:\n raise ValueError(\n 'ssl_handshake_timeout is only meaningful with ssl')\n\n if ssl_shutdown_timeout is not None and not ssl:\n raise ValueError(\n 'ssl_shutdown_timeout is only meaningful with ssl')\n\n if sock is not None:\n _check_ssl_socket(sock)\n\n if happy_eyeballs_delay is not None and interleave is None:\n # If using happy eyeballs, default to interleave addresses by family\n interleave = 1\n\n if host is not None or port is not None:\n if sock is not None:\n raise ValueError(\n 'host/port and sock can not be specified at the same time')\n\n infos = await self._ensure_resolved(\n (host, port), family=family,\n type=socket.SOCK_STREAM, proto=proto, flags=flags, loop=self)\n if not infos:\n raise OSError('getaddrinfo() returned empty list')\n\n if local_addr is not None:\n laddr_infos = await self._ensure_resolved(\n local_addr, family=family,\n type=socket.SOCK_STREAM, proto=proto,\n flags=flags, loop=self)\n if not laddr_infos:\n raise OSError('getaddrinfo() returned empty list')\n else:\n laddr_infos = None\n\n if interleave:\n infos = _interleave_addrinfos(infos, interleave)\n\n exceptions = []\n if happy_eyeballs_delay is None:\n # not using happy eyeballs\n for addrinfo in infos:\n try:\n sock = await self._connect_sock(\n exceptions, addrinfo, laddr_infos)\n break\n except OSError:\n continue\n else: # using happy eyeballs\n sock, _, _ = await staggered.staggered_race(\n (functools.partial(self._connect_sock,\n exceptions, addrinfo, laddr_infos)\n for addrinfo in infos),\n happy_eyeballs_delay, loop=self)\n\n if sock is None:\n exceptions = [exc for sub in exceptions for exc in sub]\n try:\n if all_errors:\n raise ExceptionGroup(\"create_connection failed\", exceptions)\n if len(exceptions) == 1:\n raise exceptions[0]\n else:\n # If they all have the same str(), raise one.\n model = str(exceptions[0])\n if all(str(exc) == model for exc in exceptions):\n raise exceptions[0]\n # Raise a combined exception so the user can see all\n # the various error messages.\n raise OSError('Multiple exceptions: {}'.format(\n ', '.join(str(exc) for exc in exceptions)))\n finally:\n exceptions = None\n\n else:\n if sock is None:\n raise ValueError(\n 'host and port was not specified and no sock specified')\n if sock.type != socket.SOCK_STREAM:\n # We allow AF_INET, AF_INET6, AF_UNIX as long as they\n # are SOCK_STREAM.\n # We support passing AF_UNIX sockets even though we have\n # a dedicated API for that: create_unix_connection.\n # Disallowing AF_UNIX in this method, breaks backwards\n # compatibility.\n raise ValueError(\n f'A Stream Socket was expected, got {sock!r}')\n\n transport, protocol = await self._create_connection_transport(\n sock, protocol_factory, ssl, server_hostname,\n ssl_handshake_timeout=ssl_handshake_timeout,\n ssl_shutdown_timeout=ssl_shutdown_timeout)\n if self._debug:\n # Get the socket from the transport because SSL transport closes\n # the old socket and creates a new SSL socket\n sock = transport.get_extra_info('socket')\n logger.debug(\"%r connected to %s:%r: (%r, %r)\",\n sock, host, port, transport, protocol)\n return transport, protocol\n\n async def _create_connection_transport(\n self, sock, protocol_factory, ssl,\n server_hostname, server_side=False,\n ssl_handshake_timeout=None,\n ssl_shutdown_timeout=None):\n\n sock.setblocking(False)\n\n protocol = protocol_factory()\n waiter = self.create_future()\n if ssl:\n sslcontext = None if isinstance(ssl, bool) else ssl\n transport = self._make_ssl_transport(\n sock, protocol, sslcontext, waiter,\n server_side=server_side, server_hostname=server_hostname,\n ssl_handshake_timeout=ssl_handshake_timeout,\n ssl_shutdown_timeout=ssl_shutdown_timeout)\n else:\n transport = self._make_socket_transport(sock, protocol, waiter)\n\n try:\n await waiter\n except:\n transport.close()\n raise\n\n return transport, protocol\n\n async def sendfile(self, transport, file, offset=0, count=None,\n *, fallback=True):\n \"\"\"Send a file to transport.\n\n Return the total number of bytes which were sent.\n\n The method uses high-performance os.sendfile if available.\n\n file must be a regular file object opened in binary mode.\n\n offset tells from where to start reading the file. If specified,\n count is the total number of bytes to transmit as opposed to\n sending the file until EOF is reached. File position is updated on\n return or also in case of error in which case file.tell()\n can be used to figure out the number of bytes\n which were sent.\n\n fallback set to True makes asyncio to manually read and send\n the file when the platform does not support the sendfile syscall\n (e.g. Windows or SSL socket on Unix).\n\n Raise SendfileNotAvailableError if the system does not support\n sendfile syscall and fallback is False.\n \"\"\"\n if transport.is_closing():\n raise RuntimeError(\"Transport is closing\")\n mode = getattr(transport, '_sendfile_compatible',\n constants._SendfileMode.UNSUPPORTED)\n if mode is constants._SendfileMode.UNSUPPORTED:\n raise RuntimeError(\n f\"sendfile is not supported for transport {transport!r}\")\n if mode is constants._SendfileMode.TRY_NATIVE:\n try:\n return await self._sendfile_native(transport, file,\n offset, count)\n except exceptions.SendfileNotAvailableError as exc:\n if not fallback:\n raise\n\n if not fallback:\n raise RuntimeError(\n f\"fallback is disabled and native sendfile is not \"\n f\"supported for transport {transport!r}\")\n\n return await self._sendfile_fallback(transport, file,\n offset, count)\n\n async def _sendfile_native(self, transp, file, offset, count):\n raise exceptions.SendfileNotAvailableError(\n \"sendfile syscall is not supported\")\n\n async def _sendfile_fallback(self, transp, file, offset, count):\n if offset:\n file.seek(offset)\n blocksize = min(count, 16384) if count else 16384\n buf = bytearray(blocksize)\n total_sent = 0\n proto = _SendfileFallbackProtocol(transp)\n try:\n while True:\n if count:\n blocksize = min(count - total_sent, blocksize)\n if blocksize <= 0:\n return total_sent\n view = memoryview(buf)[:blocksize]\n read = await self.run_in_executor(None, file.readinto, view)\n if not read:\n return total_sent # EOF\n await proto.drain()\n transp.write(view[:read])\n total_sent += read\n finally:\n if total_sent > 0 and hasattr(file, 'seek'):\n file.seek(offset + total_sent)\n await proto.restore()\n\n async def start_tls(self, transport, protocol, sslcontext, *,\n server_side=False,\n server_hostname=None,\n ssl_handshake_timeout=None,\n ssl_shutdown_timeout=None):\n \"\"\"Upgrade transport to TLS.\n\n Return a new transport that *protocol* should start using\n immediately.\n \"\"\"\n if ssl is None:\n raise RuntimeError('Python ssl module is not available')\n\n if not isinstance(sslcontext, ssl.SSLContext):\n raise TypeError(\n f'sslcontext is expected to be an instance of ssl.SSLContext, '\n f'got {sslcontext!r}')\n\n if not getattr(transport, '_start_tls_compatible', False):\n raise TypeError(\n f'transport {transport!r} is not supported by start_tls()')\n\n waiter = self.create_future()\n ssl_protocol = sslproto.SSLProtocol(\n self, protocol, sslcontext, waiter,\n server_side, server_hostname,\n ssl_handshake_timeout=ssl_handshake_timeout,\n ssl_shutdown_timeout=ssl_shutdown_timeout,\n call_connection_made=False)\n\n # Pause early so that \"ssl_protocol.data_received()\" doesn't\n # have a chance to get called before \"ssl_protocol.connection_made()\".\n transport.pause_reading()\n\n transport.set_protocol(ssl_protocol)\n conmade_cb = self.call_soon(ssl_protocol.connection_made, transport)\n resume_cb = self.call_soon(transport.resume_reading)\n\n try:\n await waiter\n except BaseException:\n transport.close()\n conmade_cb.cancel()\n resume_cb.cancel()\n raise\n\n return ssl_protocol._app_transport\n\n async def create_datagram_endpoint(self, protocol_factory,\n local_addr=None, remote_addr=None, *,\n family=0, proto=0, flags=0,\n reuse_port=None,\n allow_broadcast=None, sock=None):\n \"\"\"Create datagram connection.\"\"\"\n if sock is not None:\n if sock.type == socket.SOCK_STREAM:\n raise ValueError(\n f'A datagram socket was expected, got {sock!r}')\n if (local_addr or remote_addr or\n family or proto or flags or\n reuse_port or allow_broadcast):\n # show the problematic kwargs in exception msg\n opts = dict(local_addr=local_addr, remote_addr=remote_addr,\n family=family, proto=proto, flags=flags,\n reuse_port=reuse_port,\n allow_broadcast=allow_broadcast)\n problems = ', '.join(f'{k}={v}' for k, v in opts.items() if v)\n raise ValueError(\n f'socket modifier keyword arguments can not be used '\n f'when sock is specified. ({problems})')\n sock.setblocking(False)\n r_addr = None\n else:\n if not (local_addr or remote_addr):\n if family == 0:\n raise ValueError('unexpected address family')\n addr_pairs_info = (((family, proto), (None, None)),)\n elif hasattr(socket, 'AF_UNIX') and family == socket.AF_UNIX:\n for addr in (local_addr, remote_addr):\n if addr is not None and not isinstance(addr, str):\n raise TypeError('string is expected')\n\n if local_addr and local_addr[0] not in (0, '\\x00'):\n try:\n if stat.S_ISSOCK(os.stat(local_addr).st_mode):\n os.remove(local_addr)\n except FileNotFoundError:\n pass\n except OSError as err:\n # Directory may have permissions only to create socket.\n logger.error('Unable to check or remove stale UNIX '\n 'socket %r: %r',\n local_addr, err)\n\n addr_pairs_info = (((family, proto),\n (local_addr, remote_addr)), )\n else:\n # join address by (family, protocol)\n addr_infos = {} # Using order preserving dict\n for idx, addr in ((0, local_addr), (1, remote_addr)):\n if addr is not None:\n if not (isinstance(addr, tuple) and len(addr) == 2):\n raise TypeError('2-tuple is expected')\n\n infos = await self._ensure_resolved(\n addr, family=family, type=socket.SOCK_DGRAM,\n proto=proto, flags=flags, loop=self)\n if not infos:\n raise OSError('getaddrinfo() returned empty list')\n\n for fam, _, pro, _, address in infos:\n key = (fam, pro)\n if key not in addr_infos:\n addr_infos[key] = [None, None]\n addr_infos[key][idx] = address\n\n # each addr has to have info for each (family, proto) pair\n addr_pairs_info = [\n (key, addr_pair) for key, addr_pair in addr_infos.items()\n if not ((local_addr and addr_pair[0] is None) or\n (remote_addr and addr_pair[1] is None))]\n\n if not addr_pairs_info:\n raise ValueError('can not get address information')\n\n exceptions = []\n\n for ((family, proto),\n (local_address, remote_address)) in addr_pairs_info:\n sock = None\n r_addr = None\n try:\n sock = socket.socket(\n family=family, type=socket.SOCK_DGRAM, proto=proto)\n if reuse_port:\n _set_reuseport(sock)\n if allow_broadcast:\n sock.setsockopt(\n socket.SOL_SOCKET, socket.SO_BROADCAST, 1)\n sock.setblocking(False)\n\n if local_addr:\n sock.bind(local_address)\n if remote_addr:\n if not allow_broadcast:\n await self.sock_connect(sock, remote_address)\n r_addr = remote_address\n except OSError as exc:\n if sock is not None:\n sock.close()\n exceptions.append(exc)\n except:\n if sock is not None:\n sock.close()\n raise\n else:\n break\n else:\n raise exceptions[0]\n\n protocol = protocol_factory()\n waiter = self.create_future()\n transport = self._make_datagram_transport(\n sock, protocol, r_addr, waiter)\n if self._debug:\n if local_addr:\n logger.info(\"Datagram endpoint local_addr=%r remote_addr=%r \"\n \"created: (%r, %r)\",\n local_addr, remote_addr, transport, protocol)\n else:\n logger.debug(\"Datagram endpoint remote_addr=%r created: \"\n \"(%r, %r)\",\n remote_addr, transport, protocol)\n\n try:\n await waiter\n except:\n transport.close()\n raise\n\n return transport, protocol\n\n async def _ensure_resolved(self, address, *,\n family=0, type=socket.SOCK_STREAM,\n proto=0, flags=0, loop):\n host, port = address[:2]\n info = _ipaddr_info(host, port, family, type, proto, *address[2:])\n if info is not None:\n # \"host\" is already a resolved IP.\n return [info]\n else:\n return await loop.getaddrinfo(host, port, family=family, type=type,\n proto=proto, flags=flags)\n\n async def _create_server_getaddrinfo(self, host, port, family, flags):\n infos = await self._ensure_resolved((host, port), family=family,\n type=socket.SOCK_STREAM,\n flags=flags, loop=self)\n if not infos:\n raise OSError(f'getaddrinfo({host!r}) returned empty list')\n return infos\n\n async def create_server(\n self, protocol_factory, host=None, port=None,\n *,\n family=socket.AF_UNSPEC,\n flags=socket.AI_PASSIVE,\n sock=None,\n backlog=100,\n ssl=None,\n reuse_address=None,\n reuse_port=None,\n keep_alive=None,\n ssl_handshake_timeout=None,\n ssl_shutdown_timeout=None,\n start_serving=True):\n \"\"\"Create a TCP server.\n\n The host parameter can be a string, in that case the TCP server is\n bound to host and port.\n\n The host parameter can also be a sequence of strings and in that case\n the TCP server is bound to all hosts of the sequence. If a host\n appears multiple times (possibly indirectly e.g. when hostnames\n resolve to the same IP address), the server is only bound once to that\n host.\n\n Return a Server object which can be used to stop the service.\n\n This method is a coroutine.\n \"\"\"\n if isinstance(ssl, bool):\n raise TypeError('ssl argument must be an SSLContext or None')\n\n if ssl_handshake_timeout is not None and ssl is None:\n raise ValueError(\n 'ssl_handshake_timeout is only meaningful with ssl')\n\n if ssl_shutdown_timeout is not None and ssl is None:\n raise ValueError(\n 'ssl_shutdown_timeout is only meaningful with ssl')\n\n if sock is not None:\n _check_ssl_socket(sock)\n\n if host is not None or port is not None:\n if sock is not None:\n raise ValueError(\n 'host/port and sock can not be specified at the same time')\n\n if reuse_address is None:\n reuse_address = os.name == \"posix\" and sys.platform != \"cygwin\"\n sockets = []\n if host == '':\n hosts = [None]\n elif (isinstance(host, str) or\n not isinstance(host, collections.abc.Iterable)):\n hosts = [host]\n else:\n hosts = host\n\n fs = [self._create_server_getaddrinfo(host, port, family=family,\n flags=flags)\n for host in hosts]\n infos = await tasks.gather(*fs)\n infos = set(itertools.chain.from_iterable(infos))\n\n completed = False\n try:\n for res in infos:\n af, socktype, proto, canonname, sa = res\n try:\n sock = socket.socket(af, socktype, proto)\n except socket.error:\n # Assume it's a bad family/type/protocol combination.\n if self._debug:\n logger.warning('create_server() failed to create '\n 'socket.socket(%r, %r, %r)',\n af, socktype, proto, exc_info=True)\n continue\n sockets.append(sock)\n if reuse_address:\n sock.setsockopt(\n socket.SOL_SOCKET, socket.SO_REUSEADDR, True)\n if reuse_port:\n _set_reuseport(sock)\n if keep_alive:\n sock.setsockopt(\n socket.SOL_SOCKET, socket.SO_KEEPALIVE, True)\n # Disable IPv4/IPv6 dual stack support (enabled by\n # default on Linux) which makes a single socket\n # listen on both address families.\n if (_HAS_IPv6 and\n af == socket.AF_INET6 and\n hasattr(socket, 'IPPROTO_IPV6')):\n sock.setsockopt(socket.IPPROTO_IPV6,\n socket.IPV6_V6ONLY,\n True)\n try:\n sock.bind(sa)\n except OSError as err:\n msg = ('error while attempting '\n 'to bind on address %r: %s'\n % (sa, str(err).lower()))\n if err.errno == errno.EADDRNOTAVAIL:\n # Assume the family is not enabled (bpo-30945)\n sockets.pop()\n sock.close()\n if self._debug:\n logger.warning(msg)\n continue\n raise OSError(err.errno, msg) from None\n\n if not sockets:\n raise OSError('could not bind on any address out of %r'\n % ([info[4] for info in infos],))\n\n completed = True\n finally:\n if not completed:\n for sock in sockets:\n sock.close()\n else:\n if sock is None:\n raise ValueError('Neither host/port nor sock were specified')\n if sock.type != socket.SOCK_STREAM:\n raise ValueError(f'A Stream Socket was expected, got {sock!r}')\n sockets = [sock]\n\n for sock in sockets:\n sock.setblocking(False)\n\n server = Server(self, sockets, protocol_factory,\n ssl, backlog, ssl_handshake_timeout,\n ssl_shutdown_timeout)\n if start_serving:\n server._start_serving()\n # Skip one loop iteration so that all 'loop.add_reader'\n # go through.\n await tasks.sleep(0)\n\n if self._debug:\n logger.info(\"%r is serving\", server)\n return server\n\n async def connect_accepted_socket(\n self, protocol_factory, sock,\n *, ssl=None,\n ssl_handshake_timeout=None,\n ssl_shutdown_timeout=None):\n if sock.type != socket.SOCK_STREAM:\n raise ValueError(f'A Stream Socket was expected, got {sock!r}')\n\n if ssl_handshake_timeout is not None and not ssl:\n raise ValueError(\n 'ssl_handshake_timeout is only meaningful with ssl')\n\n if ssl_shutdown_timeout is not None and not ssl:\n raise ValueError(\n 'ssl_shutdown_timeout is only meaningful with ssl')\n\n if sock is not None:\n _check_ssl_socket(sock)\n\n transport, protocol = await self._create_connection_transport(\n sock, protocol_factory, ssl, '', server_side=True,\n ssl_handshake_timeout=ssl_handshake_timeout,\n ssl_shutdown_timeout=ssl_shutdown_timeout)\n if self._debug:\n # Get the socket from the transport because SSL transport closes\n # the old socket and creates a new SSL socket\n sock = transport.get_extra_info('socket')\n logger.debug(\"%r handled: (%r, %r)\", sock, transport, protocol)\n return transport, protocol\n\n async def connect_read_pipe(self, protocol_factory, pipe):\n protocol = protocol_factory()\n waiter = self.create_future()\n transport = self._make_read_pipe_transport(pipe, protocol, waiter)\n\n try:\n await waiter\n except:\n transport.close()\n raise\n\n if self._debug:\n logger.debug('Read pipe %r connected: (%r, %r)',\n pipe.fileno(), transport, protocol)\n return transport, protocol\n\n async def connect_write_pipe(self, protocol_factory, pipe):\n protocol = protocol_factory()\n waiter = self.create_future()\n transport = self._make_write_pipe_transport(pipe, protocol, waiter)\n\n try:\n await waiter\n except:\n transport.close()\n raise\n\n if self._debug:\n logger.debug('Write pipe %r connected: (%r, %r)',\n pipe.fileno(), transport, protocol)\n return transport, protocol\n\n def _log_subprocess(self, msg, stdin, stdout, stderr):\n info = [msg]\n if stdin is not None:\n info.append(f'stdin={_format_pipe(stdin)}')\n if stdout is not None and stderr == subprocess.STDOUT:\n info.append(f'stdout=stderr={_format_pipe(stdout)}')\n else:\n if stdout is not None:\n info.append(f'stdout={_format_pipe(stdout)}')\n if stderr is not None:\n info.append(f'stderr={_format_pipe(stderr)}')\n logger.debug(' '.join(info))\n\n async def subprocess_shell(self, protocol_factory, cmd, *,\n stdin=subprocess.PIPE,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n universal_newlines=False,\n shell=True, bufsize=0,\n encoding=None, errors=None, text=None,\n **kwargs):\n if not isinstance(cmd, (bytes, str)):\n raise ValueError(\"cmd must be a string\")\n if universal_newlines:\n raise ValueError(\"universal_newlines must be False\")\n if not shell:\n raise ValueError(\"shell must be True\")\n if bufsize != 0:\n raise ValueError(\"bufsize must be 0\")\n if text:\n raise ValueError(\"text must be False\")\n if encoding is not None:\n raise ValueError(\"encoding must be None\")\n if errors is not None:\n raise ValueError(\"errors must be None\")\n\n protocol = protocol_factory()\n debug_log = None\n if self._debug:\n # don't log parameters: they may contain sensitive information\n # (password) and may be too long\n debug_log = 'run shell command %r' % cmd\n self._log_subprocess(debug_log, stdin, stdout, stderr)\n transport = await self._make_subprocess_transport(\n protocol, cmd, True, stdin, stdout, stderr, bufsize, **kwargs)\n if self._debug and debug_log is not None:\n logger.info('%s: %r', debug_log, transport)\n return transport, protocol\n\n async def subprocess_exec(self, protocol_factory, program, *args,\n stdin=subprocess.PIPE, stdout=subprocess.PIPE,\n stderr=subprocess.PIPE, universal_newlines=False,\n shell=False, bufsize=0,\n encoding=None, errors=None, text=None,\n **kwargs):\n if universal_newlines:\n raise ValueError(\"universal_newlines must be False\")\n if shell:\n raise ValueError(\"shell must be False\")\n if bufsize != 0:\n raise ValueError(\"bufsize must be 0\")\n if text:\n raise ValueError(\"text must be False\")\n if encoding is not None:\n raise ValueError(\"encoding must be None\")\n if errors is not None:\n raise ValueError(\"errors must be None\")\n\n popen_args = (program,) + args\n protocol = protocol_factory()\n debug_log = None\n if self._debug:\n # don't log parameters: they may contain sensitive information\n # (password) and may be too long\n debug_log = f'execute program {program!r}'\n self._log_subprocess(debug_log, stdin, stdout, stderr)\n transport = await self._make_subprocess_transport(\n protocol, popen_args, False, stdin, stdout, stderr,\n bufsize, **kwargs)\n if self._debug and debug_log is not None:\n logger.info('%s: %r', debug_log, transport)\n return transport, protocol\n\n def get_exception_handler(self):\n \"\"\"Return an exception handler, or None if the default one is in use.\n \"\"\"\n return self._exception_handler\n\n def set_exception_handler(self, handler):\n \"\"\"Set handler as the new event loop exception handler.\n\n If handler is None, the default exception handler will\n be set.\n\n If handler is a callable object, it should have a\n signature matching '(loop, context)', where 'loop'\n will be a reference to the active event loop, 'context'\n will be a dict object (see `call_exception_handler()`\n documentation for details about context).\n \"\"\"\n if handler is not None and not callable(handler):\n raise TypeError(f'A callable object or None is expected, '\n f'got {handler!r}')\n self._exception_handler = handler\n\n def default_exception_handler(self, context):\n \"\"\"Default exception handler.\n\n This is called when an exception occurs and no exception\n handler is set, and can be called by a custom exception\n handler that wants to defer to the default behavior.\n\n This default handler logs the error message and other\n context-dependent information. In debug mode, a truncated\n stack trace is also appended showing where the given object\n (e.g. a handle or future or task) was created, if any.\n\n The context parameter has the same meaning as in\n `call_exception_handler()`.\n \"\"\"\n message = context.get('message')\n if not message:\n message = 'Unhandled exception in event loop'\n\n exception = context.get('exception')\n if exception is not None:\n exc_info = (type(exception), exception, exception.__traceback__)\n else:\n exc_info = False\n\n if ('source_traceback' not in context and\n self._current_handle is not None and\n self._current_handle._source_traceback):\n context['handle_traceback'] = \\\n self._current_handle._source_traceback\n\n log_lines = [message]\n for key in sorted(context):\n if key in {'message', 'exception'}:\n continue\n value = context[key]\n if key == 'source_traceback':\n tb = ''.join(traceback.format_list(value))\n value = 'Object created at (most recent call last):\\n'\n value += tb.rstrip()\n elif key == 'handle_traceback':\n tb = ''.join(traceback.format_list(value))\n value = 'Handle created at (most recent call last):\\n'\n value += tb.rstrip()\n else:\n value = repr(value)\n log_lines.append(f'{key}: {value}')\n\n logger.error('\\n'.join(log_lines), exc_info=exc_info)\n\n def call_exception_handler(self, context):\n \"\"\"Call the current event loop's exception handler.\n\n The context argument is a dict containing the following keys:\n\n - 'message': Error message;\n - 'exception' (optional): Exception object;\n - 'future' (optional): Future instance;\n - 'task' (optional): Task instance;\n - 'handle' (optional): Handle instance;\n - 'protocol' (optional): Protocol instance;\n - 'transport' (optional): Transport instance;\n - 'socket' (optional): Socket instance;\n - 'asyncgen' (optional): Asynchronous generator that caused\n the exception.\n\n New keys maybe introduced in the future.\n\n Note: do not overload this method in an event loop subclass.\n For custom exception handling, use the\n `set_exception_handler()` method.\n \"\"\"\n if self._exception_handler is None:\n try:\n self.default_exception_handler(context)\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException:\n # Second protection layer for unexpected errors\n # in the default implementation, as well as for subclassed\n # event loops with overloaded \"default_exception_handler\".\n logger.error('Exception in default exception handler',\n exc_info=True)\n else:\n try:\n ctx = None\n thing = context.get(\"task\")\n if thing is None:\n # Even though Futures don't have a context,\n # Task is a subclass of Future,\n # and sometimes the 'future' key holds a Task.\n thing = context.get(\"future\")\n if thing is None:\n # Handles also have a context.\n thing = context.get(\"handle\")\n if thing is not None and hasattr(thing, \"get_context\"):\n ctx = thing.get_context()\n if ctx is not None and hasattr(ctx, \"run\"):\n ctx.run(self._exception_handler, self, context)\n else:\n self._exception_handler(self, context)\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n # Exception in the user set custom exception handler.\n try:\n # Let's try default handler.\n self.default_exception_handler({\n 'message': 'Unhandled error in exception handler',\n 'exception': exc,\n 'context': context,\n })\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException:\n # Guard 'default_exception_handler' in case it is\n # overloaded.\n logger.error('Exception in default exception handler '\n 'while handling an unexpected error '\n 'in custom exception handler',\n exc_info=True)\n\n def _add_callback(self, handle):\n \"\"\"Add a Handle to _ready.\"\"\"\n if not handle._cancelled:\n self._ready.append(handle)\n\n def _add_callback_signalsafe(self, handle):\n \"\"\"Like _add_callback() but called from a signal handler.\"\"\"\n self._add_callback(handle)\n self._write_to_self()\n\n def _timer_handle_cancelled(self, handle):\n \"\"\"Notification that a TimerHandle has been cancelled.\"\"\"\n if handle._scheduled:\n self._timer_cancelled_count += 1\n\n def _run_once(self):\n \"\"\"Run one full iteration of the event loop.\n\n This calls all currently ready callbacks, polls for I/O,\n schedules the resulting callbacks, and finally schedules\n 'call_later' callbacks.\n \"\"\"\n\n sched_count = len(self._scheduled)\n if (sched_count > _MIN_SCHEDULED_TIMER_HANDLES and\n self._timer_cancelled_count / sched_count >\n _MIN_CANCELLED_TIMER_HANDLES_FRACTION):\n # Remove delayed calls that were cancelled if their number\n # is too high\n new_scheduled = []\n for handle in self._scheduled:\n if handle._cancelled:\n handle._scheduled = False\n else:\n new_scheduled.append(handle)\n\n heapq.heapify(new_scheduled)\n self._scheduled = new_scheduled\n self._timer_cancelled_count = 0\n else:\n # Remove delayed calls that were cancelled from head of queue.\n while self._scheduled and self._scheduled[0]._cancelled:\n self._timer_cancelled_count -= 1\n handle = heapq.heappop(self._scheduled)\n handle._scheduled = False\n\n timeout = None\n if self._ready or self._stopping:\n timeout = 0\n elif self._scheduled:\n # Compute the desired timeout.\n timeout = self._scheduled[0]._when - self.time()\n if timeout > MAXIMUM_SELECT_TIMEOUT:\n timeout = MAXIMUM_SELECT_TIMEOUT\n elif timeout < 0:\n timeout = 0\n\n event_list = self._selector.select(timeout)\n self._process_events(event_list)\n # Needed to break cycles when an exception occurs.\n event_list = None\n\n # Handle 'later' callbacks that are ready.\n end_time = self.time() + self._clock_resolution\n while self._scheduled:\n handle = self._scheduled[0]\n if handle._when >= end_time:\n break\n handle = heapq.heappop(self._scheduled)\n handle._scheduled = False\n self._ready.append(handle)\n\n # This is the only place where callbacks are actually *called*.\n # All other places just add them to ready.\n # Note: We run all currently scheduled callbacks, but not any\n # callbacks scheduled by callbacks run this time around --\n # they will be run the next time (after another I/O poll).\n # Use an idiom that is thread-safe without using locks.\n ntodo = len(self._ready)\n for i in range(ntodo):\n handle = self._ready.popleft()\n if handle._cancelled:\n continue\n if self._debug:\n try:\n self._current_handle = handle\n t0 = self.time()\n handle._run()\n dt = self.time() - t0\n if dt >= self.slow_callback_duration:\n logger.warning('Executing %s took %.3f seconds',\n _format_handle(handle), dt)\n finally:\n self._current_handle = None\n else:\n handle._run()\n handle = None # Needed to break cycles when an exception occurs.\n\n def _set_coroutine_origin_tracking(self, enabled):\n if bool(enabled) == bool(self._coroutine_origin_tracking_enabled):\n return\n\n if enabled:\n self._coroutine_origin_tracking_saved_depth = (\n sys.get_coroutine_origin_tracking_depth())\n sys.set_coroutine_origin_tracking_depth(\n constants.DEBUG_STACK_DEPTH)\n else:\n sys.set_coroutine_origin_tracking_depth(\n self._coroutine_origin_tracking_saved_depth)\n\n self._coroutine_origin_tracking_enabled = enabled\n\n def get_debug(self):\n return self._debug\n\n def set_debug(self, enabled):\n self._debug = enabled\n\n if self.is_running():\n self.call_soon_threadsafe(self._set_coroutine_origin_tracking, enabled)\n", 2052], "/usr/lib/python3.13/_weakrefset.py": ["# Access WeakSet through the weakref module.\n# This code is separated-out because it is needed\n# by abc.py to load everything else at startup.\n\nfrom _weakref import ref\nfrom types import GenericAlias\n\n__all__ = ['WeakSet']\n\n\nclass _IterationGuard:\n # This context manager registers itself in the current iterators of the\n # weak container, such as to delay all removals until the context manager\n # exits.\n # This technique should be relatively thread-safe (since sets are).\n\n def __init__(self, weakcontainer):\n # Don't create cycles\n self.weakcontainer = ref(weakcontainer)\n\n def __enter__(self):\n w = self.weakcontainer()\n if w is not None:\n w._iterating.add(self)\n return self\n\n def __exit__(self, e, t, b):\n w = self.weakcontainer()\n if w is not None:\n s = w._iterating\n s.remove(self)\n if not s:\n w._commit_removals()\n\n\nclass WeakSet:\n def __init__(self, data=None):\n self.data = set()\n def _remove(item, selfref=ref(self)):\n self = selfref()\n if self is not None:\n if self._iterating:\n self._pending_removals.append(item)\n else:\n self.data.discard(item)\n self._remove = _remove\n # A list of keys to be removed\n self._pending_removals = []\n self._iterating = set()\n if data is not None:\n self.update(data)\n\n def _commit_removals(self):\n pop = self._pending_removals.pop\n discard = self.data.discard\n while True:\n try:\n item = pop()\n except IndexError:\n return\n discard(item)\n\n def __iter__(self):\n with _IterationGuard(self):\n for itemref in self.data:\n item = itemref()\n if item is not None:\n # Caveat: the iterator will keep a strong reference to\n # `item` until it is resumed or closed.\n yield item\n\n def __len__(self):\n return len(self.data) - len(self._pending_removals)\n\n def __contains__(self, item):\n try:\n wr = ref(item)\n except TypeError:\n return False\n return wr in self.data\n\n def __reduce__(self):\n return self.__class__, (list(self),), self.__getstate__()\n\n def add(self, item):\n if self._pending_removals:\n self._commit_removals()\n self.data.add(ref(item, self._remove))\n\n def clear(self):\n if self._pending_removals:\n self._commit_removals()\n self.data.clear()\n\n def copy(self):\n return self.__class__(self)\n\n def pop(self):\n if self._pending_removals:\n self._commit_removals()\n while True:\n try:\n itemref = self.data.pop()\n except KeyError:\n raise KeyError('pop from empty WeakSet') from None\n item = itemref()\n if item is not None:\n return item\n\n def remove(self, item):\n if self._pending_removals:\n self._commit_removals()\n self.data.remove(ref(item))\n\n def discard(self, item):\n if self._pending_removals:\n self._commit_removals()\n self.data.discard(ref(item))\n\n def update(self, other):\n if self._pending_removals:\n self._commit_removals()\n for element in other:\n self.add(element)\n\n def __ior__(self, other):\n self.update(other)\n return self\n\n def difference(self, other):\n newset = self.copy()\n newset.difference_update(other)\n return newset\n __sub__ = difference\n\n def difference_update(self, other):\n self.__isub__(other)\n def __isub__(self, other):\n if self._pending_removals:\n self._commit_removals()\n if self is other:\n self.data.clear()\n else:\n self.data.difference_update(ref(item) for item in other)\n return self\n\n def intersection(self, other):\n return self.__class__(item for item in other if item in self)\n __and__ = intersection\n\n def intersection_update(self, other):\n self.__iand__(other)\n def __iand__(self, other):\n if self._pending_removals:\n self._commit_removals()\n self.data.intersection_update(ref(item) for item in other)\n return self\n\n def issubset(self, other):\n return self.data.issubset(ref(item) for item in other)\n __le__ = issubset\n\n def __lt__(self, other):\n return self.data < set(map(ref, other))\n\n def issuperset(self, other):\n return self.data.issuperset(ref(item) for item in other)\n __ge__ = issuperset\n\n def __gt__(self, other):\n return self.data > set(map(ref, other))\n\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return NotImplemented\n return self.data == set(map(ref, other))\n\n def symmetric_difference(self, other):\n newset = self.copy()\n newset.symmetric_difference_update(other)\n return newset\n __xor__ = symmetric_difference\n\n def symmetric_difference_update(self, other):\n self.__ixor__(other)\n def __ixor__(self, other):\n if self._pending_removals:\n self._commit_removals()\n if self is other:\n self.data.clear()\n else:\n self.data.symmetric_difference_update(ref(item, self._remove) for item in other)\n return self\n\n def union(self, other):\n return self.__class__(e for s in (self, other) for e in s)\n __or__ = union\n\n def isdisjoint(self, other):\n return len(self.intersection(other)) == 0\n\n def __repr__(self):\n return repr(self.data)\n\n __class_getitem__ = classmethod(GenericAlias)\n", 205], "/usr/lib/python3.13/selectors.py": ["\"\"\"Selectors module.\n\nThis module allows high-level and efficient I/O multiplexing, built upon the\n`select` module primitives.\n\"\"\"\n\n\nfrom abc import ABCMeta, abstractmethod\nfrom collections import namedtuple\nfrom collections.abc import Mapping\nimport math\nimport select\nimport sys\n\n\n# generic events, that must be mapped to implementation-specific ones\nEVENT_READ = (1 << 0)\nEVENT_WRITE = (1 << 1)\n\n\ndef _fileobj_to_fd(fileobj):\n \"\"\"Return a file descriptor from a file object.\n\n Parameters:\n fileobj -- file object or file descriptor\n\n Returns:\n corresponding file descriptor\n\n Raises:\n ValueError if the object is invalid\n \"\"\"\n if isinstance(fileobj, int):\n fd = fileobj\n else:\n try:\n fd = int(fileobj.fileno())\n except (AttributeError, TypeError, ValueError):\n raise ValueError(\"Invalid file object: \"\n \"{!r}\".format(fileobj)) from None\n if fd < 0:\n raise ValueError(\"Invalid file descriptor: {}\".format(fd))\n return fd\n\n\nSelectorKey = namedtuple('SelectorKey', ['fileobj', 'fd', 'events', 'data'])\n\nSelectorKey.__doc__ = \"\"\"SelectorKey(fileobj, fd, events, data)\n\n Object used to associate a file object to its backing\n file descriptor, selected event mask, and attached data.\n\"\"\"\nSelectorKey.fileobj.__doc__ = 'File object registered.'\nSelectorKey.fd.__doc__ = 'Underlying file descriptor.'\nSelectorKey.events.__doc__ = 'Events that must be waited for on this file object.'\nSelectorKey.data.__doc__ = ('''Optional opaque data associated to this file object.\nFor example, this could be used to store a per-client session ID.''')\n\n\nclass _SelectorMapping(Mapping):\n \"\"\"Mapping of file objects to selector keys.\"\"\"\n\n def __init__(self, selector):\n self._selector = selector\n\n def __len__(self):\n return len(self._selector._fd_to_key)\n\n def get(self, fileobj, default=None):\n fd = self._selector._fileobj_lookup(fileobj)\n return self._selector._fd_to_key.get(fd, default)\n\n def __getitem__(self, fileobj):\n fd = self._selector._fileobj_lookup(fileobj)\n key = self._selector._fd_to_key.get(fd)\n if key is None:\n raise KeyError(\"{!r} is not registered\".format(fileobj))\n return key\n\n def __iter__(self):\n return iter(self._selector._fd_to_key)\n\n\nclass BaseSelector(metaclass=ABCMeta):\n \"\"\"Selector abstract base class.\n\n A selector supports registering file objects to be monitored for specific\n I/O events.\n\n A file object is a file descriptor or any object with a `fileno()` method.\n An arbitrary object can be attached to the file object, which can be used\n for example to store context information, a callback, etc.\n\n A selector can use various implementations (select(), poll(), epoll()...)\n depending on the platform. The default `Selector` class uses the most\n efficient implementation on the current platform.\n \"\"\"\n\n @abstractmethod\n def register(self, fileobj, events, data=None):\n \"\"\"Register a file object.\n\n Parameters:\n fileobj -- file object or file descriptor\n events -- events to monitor (bitwise mask of EVENT_READ|EVENT_WRITE)\n data -- attached data\n\n Returns:\n SelectorKey instance\n\n Raises:\n ValueError if events is invalid\n KeyError if fileobj is already registered\n OSError if fileobj is closed or otherwise is unacceptable to\n the underlying system call (if a system call is made)\n\n Note:\n OSError may or may not be raised\n \"\"\"\n raise NotImplementedError\n\n @abstractmethod\n def unregister(self, fileobj):\n \"\"\"Unregister a file object.\n\n Parameters:\n fileobj -- file object or file descriptor\n\n Returns:\n SelectorKey instance\n\n Raises:\n KeyError if fileobj is not registered\n\n Note:\n If fileobj is registered but has since been closed this does\n *not* raise OSError (even if the wrapped syscall does)\n \"\"\"\n raise NotImplementedError\n\n def modify(self, fileobj, events, data=None):\n \"\"\"Change a registered file object monitored events or attached data.\n\n Parameters:\n fileobj -- file object or file descriptor\n events -- events to monitor (bitwise mask of EVENT_READ|EVENT_WRITE)\n data -- attached data\n\n Returns:\n SelectorKey instance\n\n Raises:\n Anything that unregister() or register() raises\n \"\"\"\n self.unregister(fileobj)\n return self.register(fileobj, events, data)\n\n @abstractmethod\n def select(self, timeout=None):\n \"\"\"Perform the actual selection, until some monitored file objects are\n ready or a timeout expires.\n\n Parameters:\n timeout -- if timeout > 0, this specifies the maximum wait time, in\n seconds\n if timeout <= 0, the select() call won't block, and will\n report the currently ready file objects\n if timeout is None, select() will block until a monitored\n file object becomes ready\n\n Returns:\n list of (key, events) for ready file objects\n `events` is a bitwise mask of EVENT_READ|EVENT_WRITE\n \"\"\"\n raise NotImplementedError\n\n def close(self):\n \"\"\"Close the selector.\n\n This must be called to make sure that any underlying resource is freed.\n \"\"\"\n pass\n\n def get_key(self, fileobj):\n \"\"\"Return the key associated to a registered file object.\n\n Returns:\n SelectorKey for this file object\n \"\"\"\n mapping = self.get_map()\n if mapping is None:\n raise RuntimeError('Selector is closed')\n try:\n return mapping[fileobj]\n except KeyError:\n raise KeyError(\"{!r} is not registered\".format(fileobj)) from None\n\n @abstractmethod\n def get_map(self):\n \"\"\"Return a mapping of file objects to selector keys.\"\"\"\n raise NotImplementedError\n\n def __enter__(self):\n return self\n\n def __exit__(self, *args):\n self.close()\n\n\nclass _BaseSelectorImpl(BaseSelector):\n \"\"\"Base selector implementation.\"\"\"\n\n def __init__(self):\n # this maps file descriptors to keys\n self._fd_to_key = {}\n # read-only mapping returned by get_map()\n self._map = _SelectorMapping(self)\n\n def _fileobj_lookup(self, fileobj):\n \"\"\"Return a file descriptor from a file object.\n\n This wraps _fileobj_to_fd() to do an exhaustive search in case\n the object is invalid but we still have it in our map. This\n is used by unregister() so we can unregister an object that\n was previously registered even if it is closed. It is also\n used by _SelectorMapping.\n \"\"\"\n try:\n return _fileobj_to_fd(fileobj)\n except ValueError:\n # Do an exhaustive search.\n for key in self._fd_to_key.values():\n if key.fileobj is fileobj:\n return key.fd\n # Raise ValueError after all.\n raise\n\n def register(self, fileobj, events, data=None):\n if (not events) or (events & ~(EVENT_READ | EVENT_WRITE)):\n raise ValueError(\"Invalid events: {!r}\".format(events))\n\n key = SelectorKey(fileobj, self._fileobj_lookup(fileobj), events, data)\n\n if key.fd in self._fd_to_key:\n raise KeyError(\"{!r} (FD {}) is already registered\"\n .format(fileobj, key.fd))\n\n self._fd_to_key[key.fd] = key\n return key\n\n def unregister(self, fileobj):\n try:\n key = self._fd_to_key.pop(self._fileobj_lookup(fileobj))\n except KeyError:\n raise KeyError(\"{!r} is not registered\".format(fileobj)) from None\n return key\n\n def modify(self, fileobj, events, data=None):\n try:\n key = self._fd_to_key[self._fileobj_lookup(fileobj)]\n except KeyError:\n raise KeyError(\"{!r} is not registered\".format(fileobj)) from None\n if events != key.events:\n self.unregister(fileobj)\n key = self.register(fileobj, events, data)\n elif data != key.data:\n # Use a shortcut to update the data.\n key = key._replace(data=data)\n self._fd_to_key[key.fd] = key\n return key\n\n def close(self):\n self._fd_to_key.clear()\n self._map = None\n\n def get_map(self):\n return self._map\n\n\n\nclass SelectSelector(_BaseSelectorImpl):\n \"\"\"Select-based selector.\"\"\"\n\n def __init__(self):\n super().__init__()\n self._readers = set()\n self._writers = set()\n\n def register(self, fileobj, events, data=None):\n key = super().register(fileobj, events, data)\n if events & EVENT_READ:\n self._readers.add(key.fd)\n if events & EVENT_WRITE:\n self._writers.add(key.fd)\n return key\n\n def unregister(self, fileobj):\n key = super().unregister(fileobj)\n self._readers.discard(key.fd)\n self._writers.discard(key.fd)\n return key\n\n if sys.platform == 'win32':\n def _select(self, r, w, _, timeout=None):\n r, w, x = select.select(r, w, w, timeout)\n return r, w + x, []\n else:\n _select = select.select\n\n def select(self, timeout=None):\n timeout = None if timeout is None else max(timeout, 0)\n ready = []\n try:\n r, w, _ = self._select(self._readers, self._writers, [], timeout)\n except InterruptedError:\n return ready\n r = frozenset(r)\n w = frozenset(w)\n rw = r | w\n fd_to_key_get = self._fd_to_key.get\n for fd in rw:\n key = fd_to_key_get(fd)\n if key:\n events = ((fd in r and EVENT_READ)\n | (fd in w and EVENT_WRITE))\n ready.append((key, events & key.events))\n return ready\n\n\nclass _PollLikeSelector(_BaseSelectorImpl):\n \"\"\"Base class shared between poll, epoll and devpoll selectors.\"\"\"\n _selector_cls = None\n _EVENT_READ = None\n _EVENT_WRITE = None\n\n def __init__(self):\n super().__init__()\n self._selector = self._selector_cls()\n\n def register(self, fileobj, events, data=None):\n key = super().register(fileobj, events, data)\n poller_events = ((events & EVENT_READ and self._EVENT_READ)\n | (events & EVENT_WRITE and self._EVENT_WRITE) )\n try:\n self._selector.register(key.fd, poller_events)\n except:\n super().unregister(fileobj)\n raise\n return key\n\n def unregister(self, fileobj):\n key = super().unregister(fileobj)\n try:\n self._selector.unregister(key.fd)\n except OSError:\n # This can happen if the FD was closed since it\n # was registered.\n pass\n return key\n\n def modify(self, fileobj, events, data=None):\n try:\n key = self._fd_to_key[self._fileobj_lookup(fileobj)]\n except KeyError:\n raise KeyError(f\"{fileobj!r} is not registered\") from None\n\n changed = False\n if events != key.events:\n selector_events = ((events & EVENT_READ and self._EVENT_READ)\n | (events & EVENT_WRITE and self._EVENT_WRITE))\n try:\n self._selector.modify(key.fd, selector_events)\n except:\n super().unregister(fileobj)\n raise\n changed = True\n if data != key.data:\n changed = True\n\n if changed:\n key = key._replace(events=events, data=data)\n self._fd_to_key[key.fd] = key\n return key\n\n def select(self, timeout=None):\n # This is shared between poll() and epoll().\n # epoll() has a different signature and handling of timeout parameter.\n if timeout is None:\n timeout = None\n elif timeout <= 0:\n timeout = 0\n else:\n # poll() has a resolution of 1 millisecond, round away from\n # zero to wait *at least* timeout seconds.\n timeout = math.ceil(timeout * 1e3)\n ready = []\n try:\n fd_event_list = self._selector.poll(timeout)\n except InterruptedError:\n return ready\n\n fd_to_key_get = self._fd_to_key.get\n for fd, event in fd_event_list:\n key = fd_to_key_get(fd)\n if key:\n events = ((event & ~self._EVENT_READ and EVENT_WRITE)\n | (event & ~self._EVENT_WRITE and EVENT_READ))\n ready.append((key, events & key.events))\n return ready\n\n\nif hasattr(select, 'poll'):\n\n class PollSelector(_PollLikeSelector):\n \"\"\"Poll-based selector.\"\"\"\n _selector_cls = select.poll\n _EVENT_READ = select.POLLIN\n _EVENT_WRITE = select.POLLOUT\n\n\nif hasattr(select, 'epoll'):\n\n _NOT_EPOLLIN = ~select.EPOLLIN\n _NOT_EPOLLOUT = ~select.EPOLLOUT\n\n class EpollSelector(_PollLikeSelector):\n \"\"\"Epoll-based selector.\"\"\"\n _selector_cls = select.epoll\n _EVENT_READ = select.EPOLLIN\n _EVENT_WRITE = select.EPOLLOUT\n\n def fileno(self):\n return self._selector.fileno()\n\n def select(self, timeout=None):\n if timeout is None:\n timeout = -1\n elif timeout <= 0:\n timeout = 0\n else:\n # epoll_wait() has a resolution of 1 millisecond, round away\n # from zero to wait *at least* timeout seconds.\n timeout = math.ceil(timeout * 1e3) * 1e-3\n\n # epoll_wait() expects `maxevents` to be greater than zero;\n # we want to make sure that `select()` can be called when no\n # FD is registered.\n max_ev = len(self._fd_to_key) or 1\n\n ready = []\n try:\n fd_event_list = self._selector.poll(timeout, max_ev)\n except InterruptedError:\n return ready\n\n fd_to_key = self._fd_to_key\n for fd, event in fd_event_list:\n key = fd_to_key.get(fd)\n if key:\n events = ((event & _NOT_EPOLLIN and EVENT_WRITE)\n | (event & _NOT_EPOLLOUT and EVENT_READ))\n ready.append((key, events & key.events))\n return ready\n\n def close(self):\n self._selector.close()\n super().close()\n\n\nif hasattr(select, 'devpoll'):\n\n class DevpollSelector(_PollLikeSelector):\n \"\"\"Solaris /dev/poll selector.\"\"\"\n _selector_cls = select.devpoll\n _EVENT_READ = select.POLLIN\n _EVENT_WRITE = select.POLLOUT\n\n def fileno(self):\n return self._selector.fileno()\n\n def close(self):\n self._selector.close()\n super().close()\n\n\nif hasattr(select, 'kqueue'):\n\n class KqueueSelector(_BaseSelectorImpl):\n \"\"\"Kqueue-based selector.\"\"\"\n\n def __init__(self):\n super().__init__()\n self._selector = select.kqueue()\n self._max_events = 0\n\n def fileno(self):\n return self._selector.fileno()\n\n def register(self, fileobj, events, data=None):\n key = super().register(fileobj, events, data)\n try:\n if events & EVENT_READ:\n kev = select.kevent(key.fd, select.KQ_FILTER_READ,\n select.KQ_EV_ADD)\n self._selector.control([kev], 0, 0)\n self._max_events += 1\n if events & EVENT_WRITE:\n kev = select.kevent(key.fd, select.KQ_FILTER_WRITE,\n select.KQ_EV_ADD)\n self._selector.control([kev], 0, 0)\n self._max_events += 1\n except:\n super().unregister(fileobj)\n raise\n return key\n\n def unregister(self, fileobj):\n key = super().unregister(fileobj)\n if key.events & EVENT_READ:\n kev = select.kevent(key.fd, select.KQ_FILTER_READ,\n select.KQ_EV_DELETE)\n self._max_events -= 1\n try:\n self._selector.control([kev], 0, 0)\n except OSError:\n # This can happen if the FD was closed since it\n # was registered.\n pass\n if key.events & EVENT_WRITE:\n kev = select.kevent(key.fd, select.KQ_FILTER_WRITE,\n select.KQ_EV_DELETE)\n self._max_events -= 1\n try:\n self._selector.control([kev], 0, 0)\n except OSError:\n # See comment above.\n pass\n return key\n\n def select(self, timeout=None):\n timeout = None if timeout is None else max(timeout, 0)\n # If max_ev is 0, kqueue will ignore the timeout. For consistent\n # behavior with the other selector classes, we prevent that here\n # (using max). See https://bugs.python.org/issue29255\n max_ev = self._max_events or 1\n ready = []\n try:\n kev_list = self._selector.control(None, max_ev, timeout)\n except InterruptedError:\n return ready\n\n fd_to_key_get = self._fd_to_key.get\n for kev in kev_list:\n fd = kev.ident\n flag = kev.filter\n key = fd_to_key_get(fd)\n if key:\n events = ((flag == select.KQ_FILTER_READ and EVENT_READ)\n | (flag == select.KQ_FILTER_WRITE and EVENT_WRITE))\n ready.append((key, events & key.events))\n return ready\n\n def close(self):\n self._selector.close()\n super().close()\n\n\ndef _can_use(method):\n \"\"\"Check if we can use the selector depending upon the\n operating system. \"\"\"\n # Implementation based upon https://github.com/sethmlarson/selectors2/blob/master/selectors2.py\n selector = getattr(select, method, None)\n if selector is None:\n # select module does not implement method\n return False\n # check if the OS and Kernel actually support the method. Call may fail with\n # OSError: [Errno 38] Function not implemented\n try:\n selector_obj = selector()\n if method == 'poll':\n # check that poll actually works\n selector_obj.poll(0)\n else:\n # close epoll, kqueue, and devpoll fd\n selector_obj.close()\n return True\n except OSError:\n return False\n\n\n# Choose the best implementation, roughly:\n# epoll|kqueue|devpoll > poll > select.\n# select() also can't accept a FD > FD_SETSIZE (usually around 1024)\nif _can_use('kqueue'):\n DefaultSelector = KqueueSelector\nelif _can_use('epoll'):\n DefaultSelector = EpollSelector\nelif _can_use('devpoll'):\n DefaultSelector = DevpollSelector\nelif _can_use('poll'):\n DefaultSelector = PollSelector\nelse:\n DefaultSelector = SelectSelector\n", 603], "/usr/lib/python3.13/logging/__init__.py": ["# Copyright 2001-2022 by Vinay Sajip. All Rights Reserved.\n#\n# Permission to use, copy, modify, and distribute this software and its\n# documentation for any purpose and without fee is hereby granted,\n# provided that the above copyright notice appear in all copies and that\n# both that copyright notice and this permission notice appear in\n# supporting documentation, and that the name of Vinay Sajip\n# not be used in advertising or publicity pertaining to distribution\n# of the software without specific, written prior permission.\n# VINAY SAJIP DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING\n# ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL\n# VINAY SAJIP BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR\n# ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER\n# IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT\n# OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.\n\n\"\"\"\nLogging package for Python. Based on PEP 282 and comments thereto in\ncomp.lang.python.\n\nCopyright (C) 2001-2022 Vinay Sajip. All Rights Reserved.\n\nTo use, simply 'import logging' and log away!\n\"\"\"\n\nimport sys, os, time, io, re, traceback, warnings, weakref, collections.abc\n\nfrom types import GenericAlias\nfrom string import Template\nfrom string import Formatter as StrFormatter\n\n\n__all__ = ['BASIC_FORMAT', 'BufferingFormatter', 'CRITICAL', 'DEBUG', 'ERROR',\n 'FATAL', 'FileHandler', 'Filter', 'Formatter', 'Handler', 'INFO',\n 'LogRecord', 'Logger', 'LoggerAdapter', 'NOTSET', 'NullHandler',\n 'StreamHandler', 'WARN', 'WARNING', 'addLevelName', 'basicConfig',\n 'captureWarnings', 'critical', 'debug', 'disable', 'error',\n 'exception', 'fatal', 'getLevelName', 'getLogger', 'getLoggerClass',\n 'info', 'log', 'makeLogRecord', 'setLoggerClass', 'shutdown',\n 'warn', 'warning', 'getLogRecordFactory', 'setLogRecordFactory',\n 'lastResort', 'raiseExceptions', 'getLevelNamesMapping',\n 'getHandlerByName', 'getHandlerNames']\n\nimport threading\n\n__author__ = \"Vinay Sajip \"\n__status__ = \"production\"\n# The following module attributes are no longer updated.\n__version__ = \"0.5.1.2\"\n__date__ = \"07 February 2010\"\n\n#---------------------------------------------------------------------------\n# Miscellaneous module data\n#---------------------------------------------------------------------------\n\n#\n#_startTime is used as the base when calculating the relative time of events\n#\n_startTime = time.time_ns()\n\n#\n#raiseExceptions is used to see if exceptions during handling should be\n#propagated\n#\nraiseExceptions = True\n\n#\n# If you don't want threading information in the log, set this to False\n#\nlogThreads = True\n\n#\n# If you don't want multiprocessing information in the log, set this to False\n#\nlogMultiprocessing = True\n\n#\n# If you don't want process information in the log, set this to False\n#\nlogProcesses = True\n\n#\n# If you don't want asyncio task information in the log, set this to False\n#\nlogAsyncioTasks = True\n\n#---------------------------------------------------------------------------\n# Level related stuff\n#---------------------------------------------------------------------------\n#\n# Default levels and level names, these can be replaced with any positive set\n# of values having corresponding names. There is a pseudo-level, NOTSET, which\n# is only really there as a lower limit for user-defined levels. Handlers and\n# loggers are initialized with NOTSET so that they will log all messages, even\n# at user-defined levels.\n#\n\nCRITICAL = 50\nFATAL = CRITICAL\nERROR = 40\nWARNING = 30\nWARN = WARNING\nINFO = 20\nDEBUG = 10\nNOTSET = 0\n\n_levelToName = {\n CRITICAL: 'CRITICAL',\n ERROR: 'ERROR',\n WARNING: 'WARNING',\n INFO: 'INFO',\n DEBUG: 'DEBUG',\n NOTSET: 'NOTSET',\n}\n_nameToLevel = {\n 'CRITICAL': CRITICAL,\n 'FATAL': FATAL,\n 'ERROR': ERROR,\n 'WARN': WARNING,\n 'WARNING': WARNING,\n 'INFO': INFO,\n 'DEBUG': DEBUG,\n 'NOTSET': NOTSET,\n}\n\ndef getLevelNamesMapping():\n return _nameToLevel.copy()\n\ndef getLevelName(level):\n \"\"\"\n Return the textual or numeric representation of logging level 'level'.\n\n If the level is one of the predefined levels (CRITICAL, ERROR, WARNING,\n INFO, DEBUG) then you get the corresponding string. If you have\n associated levels with names using addLevelName then the name you have\n associated with 'level' is returned.\n\n If a numeric value corresponding to one of the defined levels is passed\n in, the corresponding string representation is returned.\n\n If a string representation of the level is passed in, the corresponding\n numeric value is returned.\n\n If no matching numeric or string value is passed in, the string\n 'Level %s' % level is returned.\n \"\"\"\n # See Issues #22386, #27937 and #29220 for why it's this way\n result = _levelToName.get(level)\n if result is not None:\n return result\n result = _nameToLevel.get(level)\n if result is not None:\n return result\n return \"Level %s\" % level\n\ndef addLevelName(level, levelName):\n \"\"\"\n Associate 'levelName' with 'level'.\n\n This is used when converting levels to text during message formatting.\n \"\"\"\n with _lock:\n _levelToName[level] = levelName\n _nameToLevel[levelName] = level\n\nif hasattr(sys, \"_getframe\"):\n currentframe = lambda: sys._getframe(1)\nelse: #pragma: no cover\n def currentframe():\n \"\"\"Return the frame object for the caller's stack frame.\"\"\"\n try:\n raise Exception\n except Exception as exc:\n return exc.__traceback__.tb_frame.f_back\n\n#\n# _srcfile is used when walking the stack to check when we've got the first\n# caller stack frame, by skipping frames whose filename is that of this\n# module's source. It therefore should contain the filename of this module's\n# source file.\n#\n# Ordinarily we would use __file__ for this, but frozen modules don't always\n# have __file__ set, for some reason (see Issue #21736). Thus, we get the\n# filename from a handy code object from a function defined in this module.\n# (There's no particular reason for picking addLevelName.)\n#\n\n_srcfile = os.path.normcase(addLevelName.__code__.co_filename)\n\n# _srcfile is only used in conjunction with sys._getframe().\n# Setting _srcfile to None will prevent findCaller() from being called. This\n# way, you can avoid the overhead of fetching caller information.\n\n# The following is based on warnings._is_internal_frame. It makes sure that\n# frames of the import mechanism are skipped when logging at module level and\n# using a stacklevel value greater than one.\ndef _is_internal_frame(frame):\n \"\"\"Signal whether the frame is a CPython or logging module internal.\"\"\"\n filename = os.path.normcase(frame.f_code.co_filename)\n return filename == _srcfile or (\n \"importlib\" in filename and \"_bootstrap\" in filename\n )\n\n\ndef _checkLevel(level):\n if isinstance(level, int):\n rv = level\n elif str(level) == level:\n if level not in _nameToLevel:\n raise ValueError(\"Unknown level: %r\" % level)\n rv = _nameToLevel[level]\n else:\n raise TypeError(\"Level not an integer or a valid string: %r\"\n % (level,))\n return rv\n\n#---------------------------------------------------------------------------\n# Thread-related stuff\n#---------------------------------------------------------------------------\n\n#\n#_lock is used to serialize access to shared data structures in this module.\n#This needs to be an RLock because fileConfig() creates and configures\n#Handlers, and so might arbitrary user threads. Since Handler code updates the\n#shared dictionary _handlers, it needs to acquire the lock. But if configuring,\n#the lock would already have been acquired - so we need an RLock.\n#The same argument applies to Loggers and Manager.loggerDict.\n#\n_lock = threading.RLock()\n\ndef _prepareFork():\n \"\"\"\n Prepare to fork a new child process by acquiring the module-level lock.\n\n This should be used in conjunction with _afterFork().\n \"\"\"\n # Wrap the lock acquisition in a try-except to prevent the lock from being\n # abandoned in the event of an asynchronous exception. See gh-106238.\n try:\n _lock.acquire()\n except BaseException:\n _lock.release()\n raise\n\ndef _afterFork():\n \"\"\"\n After a new child process has been forked, release the module-level lock.\n\n This should be used in conjunction with _prepareFork().\n \"\"\"\n _lock.release()\n\n\n# Prevent a held logging lock from blocking a child from logging.\n\nif not hasattr(os, 'register_at_fork'): # Windows and friends.\n def _register_at_fork_reinit_lock(instance):\n pass # no-op when os.register_at_fork does not exist.\nelse:\n # A collection of instances with a _at_fork_reinit method (logging.Handler)\n # to be called in the child after forking. The weakref avoids us keeping\n # discarded Handler instances alive.\n _at_fork_reinit_lock_weakset = weakref.WeakSet()\n\n def _register_at_fork_reinit_lock(instance):\n with _lock:\n _at_fork_reinit_lock_weakset.add(instance)\n\n def _after_at_fork_child_reinit_locks():\n for handler in _at_fork_reinit_lock_weakset:\n handler._at_fork_reinit()\n\n # _prepareFork() was called in the parent before forking.\n # The lock is reinitialized to unlocked state.\n _lock._at_fork_reinit()\n\n os.register_at_fork(before=_prepareFork,\n after_in_child=_after_at_fork_child_reinit_locks,\n after_in_parent=_afterFork)\n\n\n#---------------------------------------------------------------------------\n# The logging record\n#---------------------------------------------------------------------------\n\nclass LogRecord(object):\n \"\"\"\n A LogRecord instance represents an event being logged.\n\n LogRecord instances are created every time something is logged. They\n contain all the information pertinent to the event being logged. The\n main information passed in is in msg and args, which are combined\n using str(msg) % args to create the message field of the record. The\n record also includes information such as when the record was created,\n the source line where the logging call was made, and any exception\n information to be logged.\n \"\"\"\n def __init__(self, name, level, pathname, lineno,\n msg, args, exc_info, func=None, sinfo=None, **kwargs):\n \"\"\"\n Initialize a logging record with interesting information.\n \"\"\"\n ct = time.time_ns()\n self.name = name\n self.msg = msg\n #\n # The following statement allows passing of a dictionary as a sole\n # argument, so that you can do something like\n # logging.debug(\"a %(a)d b %(b)s\", {'a':1, 'b':2})\n # Suggested by Stefan Behnel.\n # Note that without the test for args[0], we get a problem because\n # during formatting, we test to see if the arg is present using\n # 'if self.args:'. If the event being logged is e.g. 'Value is %d'\n # and if the passed arg fails 'if self.args:' then no formatting\n # is done. For example, logger.warning('Value is %d', 0) would log\n # 'Value is %d' instead of 'Value is 0'.\n # For the use case of passing a dictionary, this should not be a\n # problem.\n # Issue #21172: a request was made to relax the isinstance check\n # to hasattr(args[0], '__getitem__'). However, the docs on string\n # formatting still seem to suggest a mapping object is required.\n # Thus, while not removing the isinstance check, it does now look\n # for collections.abc.Mapping rather than, as before, dict.\n if (args and len(args) == 1 and isinstance(args[0], collections.abc.Mapping)\n and args[0]):\n args = args[0]\n self.args = args\n self.levelname = getLevelName(level)\n self.levelno = level\n self.pathname = pathname\n try:\n self.filename = os.path.basename(pathname)\n self.module = os.path.splitext(self.filename)[0]\n except (TypeError, ValueError, AttributeError):\n self.filename = pathname\n self.module = \"Unknown module\"\n self.exc_info = exc_info\n self.exc_text = None # used to cache the traceback text\n self.stack_info = sinfo\n self.lineno = lineno\n self.funcName = func\n self.created = ct / 1e9 # ns to float seconds\n # Get the number of whole milliseconds (0-999) in the fractional part of seconds.\n # Eg: 1_677_903_920_999_998_503 ns --> 999_998_503 ns--> 999 ms\n # Convert to float by adding 0.0 for historical reasons. See gh-89047\n self.msecs = (ct % 1_000_000_000) // 1_000_000 + 0.0\n if self.msecs == 999.0 and int(self.created) != ct // 1_000_000_000:\n # ns -> sec conversion can round up, e.g:\n # 1_677_903_920_999_999_900 ns --> 1_677_903_921.0 sec\n self.msecs = 0.0\n\n self.relativeCreated = (ct - _startTime) / 1e6\n if logThreads:\n self.thread = threading.get_ident()\n self.threadName = threading.current_thread().name\n else: # pragma: no cover\n self.thread = None\n self.threadName = None\n if not logMultiprocessing: # pragma: no cover\n self.processName = None\n else:\n self.processName = 'MainProcess'\n mp = sys.modules.get('multiprocessing')\n if mp is not None:\n # Errors may occur if multiprocessing has not finished loading\n # yet - e.g. if a custom import hook causes third-party code\n # to run when multiprocessing calls import. See issue 8200\n # for an example\n try:\n self.processName = mp.current_process().name\n except Exception: #pragma: no cover\n pass\n if logProcesses and hasattr(os, 'getpid'):\n self.process = os.getpid()\n else:\n self.process = None\n\n self.taskName = None\n if logAsyncioTasks:\n asyncio = sys.modules.get('asyncio')\n if asyncio:\n try:\n self.taskName = asyncio.current_task().get_name()\n except Exception:\n pass\n\n def __repr__(self):\n return ''%(self.name, self.levelno,\n self.pathname, self.lineno, self.msg)\n\n def getMessage(self):\n \"\"\"\n Return the message for this LogRecord.\n\n Return the message for this LogRecord after merging any user-supplied\n arguments with the message.\n \"\"\"\n msg = str(self.msg)\n if self.args:\n msg = msg % self.args\n return msg\n\n#\n# Determine which class to use when instantiating log records.\n#\n_logRecordFactory = LogRecord\n\ndef setLogRecordFactory(factory):\n \"\"\"\n Set the factory to be used when instantiating a log record.\n\n :param factory: A callable which will be called to instantiate\n a log record.\n \"\"\"\n global _logRecordFactory\n _logRecordFactory = factory\n\ndef getLogRecordFactory():\n \"\"\"\n Return the factory to be used when instantiating a log record.\n \"\"\"\n\n return _logRecordFactory\n\ndef makeLogRecord(dict):\n \"\"\"\n Make a LogRecord whose attributes are defined by the specified dictionary,\n This function is useful for converting a logging event received over\n a socket connection (which is sent as a dictionary) into a LogRecord\n instance.\n \"\"\"\n rv = _logRecordFactory(None, None, \"\", 0, \"\", (), None, None)\n rv.__dict__.update(dict)\n return rv\n\n\n#---------------------------------------------------------------------------\n# Formatter classes and functions\n#---------------------------------------------------------------------------\n_str_formatter = StrFormatter()\ndel StrFormatter\n\n\nclass PercentStyle(object):\n\n default_format = '%(message)s'\n asctime_format = '%(asctime)s'\n asctime_search = '%(asctime)'\n validation_pattern = re.compile(r'%\\(\\w+\\)[#0+ -]*(\\*|\\d+)?(\\.(\\*|\\d+))?[diouxefgcrsa%]', re.I)\n\n def __init__(self, fmt, *, defaults=None):\n self._fmt = fmt or self.default_format\n self._defaults = defaults\n\n def usesTime(self):\n return self._fmt.find(self.asctime_search) >= 0\n\n def validate(self):\n \"\"\"Validate the input format, ensure it matches the correct style\"\"\"\n if not self.validation_pattern.search(self._fmt):\n raise ValueError(\"Invalid format '%s' for '%s' style\" % (self._fmt, self.default_format[0]))\n\n def _format(self, record):\n if defaults := self._defaults:\n values = defaults | record.__dict__\n else:\n values = record.__dict__\n return self._fmt % values\n\n def format(self, record):\n try:\n return self._format(record)\n except KeyError as e:\n raise ValueError('Formatting field not found in record: %s' % e)\n\n\nclass StrFormatStyle(PercentStyle):\n default_format = '{message}'\n asctime_format = '{asctime}'\n asctime_search = '{asctime'\n\n fmt_spec = re.compile(r'^(.?[<>=^])?[+ -]?#?0?(\\d+|{\\w+})?[,_]?(\\.(\\d+|{\\w+}))?[bcdefgnosx%]?$', re.I)\n field_spec = re.compile(r'^(\\d+|\\w+)(\\.\\w+|\\[[^]]+\\])*$')\n\n def _format(self, record):\n if defaults := self._defaults:\n values = defaults | record.__dict__\n else:\n values = record.__dict__\n return self._fmt.format(**values)\n\n def validate(self):\n \"\"\"Validate the input format, ensure it is the correct string formatting style\"\"\"\n fields = set()\n try:\n for _, fieldname, spec, conversion in _str_formatter.parse(self._fmt):\n if fieldname:\n if not self.field_spec.match(fieldname):\n raise ValueError('invalid field name/expression: %r' % fieldname)\n fields.add(fieldname)\n if conversion and conversion not in 'rsa':\n raise ValueError('invalid conversion: %r' % conversion)\n if spec and not self.fmt_spec.match(spec):\n raise ValueError('bad specifier: %r' % spec)\n except ValueError as e:\n raise ValueError('invalid format: %s' % e)\n if not fields:\n raise ValueError('invalid format: no fields')\n\n\nclass StringTemplateStyle(PercentStyle):\n default_format = '${message}'\n asctime_format = '${asctime}'\n asctime_search = '${asctime}'\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self._tpl = Template(self._fmt)\n\n def usesTime(self):\n fmt = self._fmt\n return fmt.find('$asctime') >= 0 or fmt.find(self.asctime_search) >= 0\n\n def validate(self):\n pattern = Template.pattern\n fields = set()\n for m in pattern.finditer(self._fmt):\n d = m.groupdict()\n if d['named']:\n fields.add(d['named'])\n elif d['braced']:\n fields.add(d['braced'])\n elif m.group(0) == '$':\n raise ValueError('invalid format: bare \\'$\\' not allowed')\n if not fields:\n raise ValueError('invalid format: no fields')\n\n def _format(self, record):\n if defaults := self._defaults:\n values = defaults | record.__dict__\n else:\n values = record.__dict__\n return self._tpl.substitute(**values)\n\n\nBASIC_FORMAT = \"%(levelname)s:%(name)s:%(message)s\"\n\n_STYLES = {\n '%': (PercentStyle, BASIC_FORMAT),\n '{': (StrFormatStyle, '{levelname}:{name}:{message}'),\n '$': (StringTemplateStyle, '${levelname}:${name}:${message}'),\n}\n\nclass Formatter(object):\n \"\"\"\n Formatter instances are used to convert a LogRecord to text.\n\n Formatters need to know how a LogRecord is constructed. They are\n responsible for converting a LogRecord to (usually) a string which can\n be interpreted by either a human or an external system. The base Formatter\n allows a formatting string to be specified. If none is supplied, the\n style-dependent default value, \"%(message)s\", \"{message}\", or\n \"${message}\", is used.\n\n The Formatter can be initialized with a format string which makes use of\n knowledge of the LogRecord attributes - e.g. the default value mentioned\n above makes use of the fact that the user's message and arguments are pre-\n formatted into a LogRecord's message attribute. Currently, the useful\n attributes in a LogRecord are described by:\n\n %(name)s Name of the logger (logging channel)\n %(levelno)s Numeric logging level for the message (DEBUG, INFO,\n WARNING, ERROR, CRITICAL)\n %(levelname)s Text logging level for the message (\"DEBUG\", \"INFO\",\n \"WARNING\", \"ERROR\", \"CRITICAL\")\n %(pathname)s Full pathname of the source file where the logging\n call was issued (if available)\n %(filename)s Filename portion of pathname\n %(module)s Module (name portion of filename)\n %(lineno)d Source line number where the logging call was issued\n (if available)\n %(funcName)s Function name\n %(created)f Time when the LogRecord was created (time.time_ns() / 1e9\n return value)\n %(asctime)s Textual time when the LogRecord was created\n %(msecs)d Millisecond portion of the creation time\n %(relativeCreated)d Time in milliseconds when the LogRecord was created,\n relative to the time the logging module was loaded\n (typically at application startup time)\n %(thread)d Thread ID (if available)\n %(threadName)s Thread name (if available)\n %(taskName)s Task name (if available)\n %(process)d Process ID (if available)\n %(message)s The result of record.getMessage(), computed just as\n the record is emitted\n \"\"\"\n\n converter = time.localtime\n\n def __init__(self, fmt=None, datefmt=None, style='%', validate=True, *,\n defaults=None):\n \"\"\"\n Initialize the formatter with specified format strings.\n\n Initialize the formatter either with the specified format string, or a\n default as described above. Allow for specialized date formatting with\n the optional datefmt argument. If datefmt is omitted, you get an\n ISO8601-like (or RFC 3339-like) format.\n\n Use a style parameter of '%', '{' or '$' to specify that you want to\n use one of %-formatting, :meth:`str.format` (``{}``) formatting or\n :class:`string.Template` formatting in your format string.\n\n .. versionchanged:: 3.2\n Added the ``style`` parameter.\n \"\"\"\n if style not in _STYLES:\n raise ValueError('Style must be one of: %s' % ','.join(\n _STYLES.keys()))\n self._style = _STYLES[style][0](fmt, defaults=defaults)\n if validate:\n self._style.validate()\n\n self._fmt = self._style._fmt\n self.datefmt = datefmt\n\n default_time_format = '%Y-%m-%d %H:%M:%S'\n default_msec_format = '%s,%03d'\n\n def formatTime(self, record, datefmt=None):\n \"\"\"\n Return the creation time of the specified LogRecord as formatted text.\n\n This method should be called from format() by a formatter which\n wants to make use of a formatted time. This method can be overridden\n in formatters to provide for any specific requirement, but the\n basic behaviour is as follows: if datefmt (a string) is specified,\n it is used with time.strftime() to format the creation time of the\n record. Otherwise, an ISO8601-like (or RFC 3339-like) format is used.\n The resulting string is returned. This function uses a user-configurable\n function to convert the creation time to a tuple. By default,\n time.localtime() is used; to change this for a particular formatter\n instance, set the 'converter' attribute to a function with the same\n signature as time.localtime() or time.gmtime(). To change it for all\n formatters, for example if you want all logging times to be shown in GMT,\n set the 'converter' attribute in the Formatter class.\n \"\"\"\n ct = self.converter(record.created)\n if datefmt:\n s = time.strftime(datefmt, ct)\n else:\n s = time.strftime(self.default_time_format, ct)\n if self.default_msec_format:\n s = self.default_msec_format % (s, record.msecs)\n return s\n\n def formatException(self, ei):\n \"\"\"\n Format and return the specified exception information as a string.\n\n This default implementation just uses\n traceback.print_exception()\n \"\"\"\n sio = io.StringIO()\n tb = ei[2]\n # See issues #9427, #1553375. Commented out for now.\n #if getattr(self, 'fullstack', False):\n # traceback.print_stack(tb.tb_frame.f_back, file=sio)\n traceback.print_exception(ei[0], ei[1], tb, limit=None, file=sio)\n s = sio.getvalue()\n sio.close()\n if s[-1:] == \"\\n\":\n s = s[:-1]\n return s\n\n def usesTime(self):\n \"\"\"\n Check if the format uses the creation time of the record.\n \"\"\"\n return self._style.usesTime()\n\n def formatMessage(self, record):\n return self._style.format(record)\n\n def formatStack(self, stack_info):\n \"\"\"\n This method is provided as an extension point for specialized\n formatting of stack information.\n\n The input data is a string as returned from a call to\n :func:`traceback.print_stack`, but with the last trailing newline\n removed.\n\n The base implementation just returns the value passed in.\n \"\"\"\n return stack_info\n\n def format(self, record):\n \"\"\"\n Format the specified record as text.\n\n The record's attribute dictionary is used as the operand to a\n string formatting operation which yields the returned string.\n Before formatting the dictionary, a couple of preparatory steps\n are carried out. The message attribute of the record is computed\n using LogRecord.getMessage(). If the formatting string uses the\n time (as determined by a call to usesTime(), formatTime() is\n called to format the event time. If there is exception information,\n it is formatted using formatException() and appended to the message.\n \"\"\"\n record.message = record.getMessage()\n if self.usesTime():\n record.asctime = self.formatTime(record, self.datefmt)\n s = self.formatMessage(record)\n if record.exc_info:\n # Cache the traceback text to avoid converting it multiple times\n # (it's constant anyway)\n if not record.exc_text:\n record.exc_text = self.formatException(record.exc_info)\n if record.exc_text:\n if s[-1:] != \"\\n\":\n s = s + \"\\n\"\n s = s + record.exc_text\n if record.stack_info:\n if s[-1:] != \"\\n\":\n s = s + \"\\n\"\n s = s + self.formatStack(record.stack_info)\n return s\n\n#\n# The default formatter to use when no other is specified\n#\n_defaultFormatter = Formatter()\n\nclass BufferingFormatter(object):\n \"\"\"\n A formatter suitable for formatting a number of records.\n \"\"\"\n def __init__(self, linefmt=None):\n \"\"\"\n Optionally specify a formatter which will be used to format each\n individual record.\n \"\"\"\n if linefmt:\n self.linefmt = linefmt\n else:\n self.linefmt = _defaultFormatter\n\n def formatHeader(self, records):\n \"\"\"\n Return the header string for the specified records.\n \"\"\"\n return \"\"\n\n def formatFooter(self, records):\n \"\"\"\n Return the footer string for the specified records.\n \"\"\"\n return \"\"\n\n def format(self, records):\n \"\"\"\n Format the specified records and return the result as a string.\n \"\"\"\n rv = \"\"\n if len(records) > 0:\n rv = rv + self.formatHeader(records)\n for record in records:\n rv = rv + self.linefmt.format(record)\n rv = rv + self.formatFooter(records)\n return rv\n\n#---------------------------------------------------------------------------\n# Filter classes and functions\n#---------------------------------------------------------------------------\n\nclass Filter(object):\n \"\"\"\n Filter instances are used to perform arbitrary filtering of LogRecords.\n\n Loggers and Handlers can optionally use Filter instances to filter\n records as desired. The base filter class only allows events which are\n below a certain point in the logger hierarchy. For example, a filter\n initialized with \"A.B\" will allow events logged by loggers \"A.B\",\n \"A.B.C\", \"A.B.C.D\", \"A.B.D\" etc. but not \"A.BB\", \"B.A.B\" etc. If\n initialized with the empty string, all events are passed.\n \"\"\"\n def __init__(self, name=''):\n \"\"\"\n Initialize a filter.\n\n Initialize with the name of the logger which, together with its\n children, will have its events allowed through the filter. If no\n name is specified, allow every event.\n \"\"\"\n self.name = name\n self.nlen = len(name)\n\n def filter(self, record):\n \"\"\"\n Determine if the specified record is to be logged.\n\n Returns True if the record should be logged, or False otherwise.\n If deemed appropriate, the record may be modified in-place.\n \"\"\"\n if self.nlen == 0:\n return True\n elif self.name == record.name:\n return True\n elif record.name.find(self.name, 0, self.nlen) != 0:\n return False\n return (record.name[self.nlen] == \".\")\n\nclass Filterer(object):\n \"\"\"\n A base class for loggers and handlers which allows them to share\n common code.\n \"\"\"\n def __init__(self):\n \"\"\"\n Initialize the list of filters to be an empty list.\n \"\"\"\n self.filters = []\n\n def addFilter(self, filter):\n \"\"\"\n Add the specified filter to this handler.\n \"\"\"\n if not (filter in self.filters):\n self.filters.append(filter)\n\n def removeFilter(self, filter):\n \"\"\"\n Remove the specified filter from this handler.\n \"\"\"\n if filter in self.filters:\n self.filters.remove(filter)\n\n def filter(self, record):\n \"\"\"\n Determine if a record is loggable by consulting all the filters.\n\n The default is to allow the record to be logged; any filter can veto\n this by returning a false value.\n If a filter attached to a handler returns a log record instance,\n then that instance is used in place of the original log record in\n any further processing of the event by that handler.\n If a filter returns any other true value, the original log record\n is used in any further processing of the event by that handler.\n\n If none of the filters return false values, this method returns\n a log record.\n If any of the filters return a false value, this method returns\n a false value.\n\n .. versionchanged:: 3.2\n\n Allow filters to be just callables.\n\n .. versionchanged:: 3.12\n Allow filters to return a LogRecord instead of\n modifying it in place.\n \"\"\"\n for f in self.filters:\n if hasattr(f, 'filter'):\n result = f.filter(record)\n else:\n result = f(record) # assume callable - will raise if not\n if not result:\n return False\n if isinstance(result, LogRecord):\n record = result\n return record\n\n#---------------------------------------------------------------------------\n# Handler classes and functions\n#---------------------------------------------------------------------------\n\n_handlers = weakref.WeakValueDictionary() #map of handler names to handlers\n_handlerList = [] # added to allow handlers to be removed in reverse of order initialized\n\ndef _removeHandlerRef(wr):\n \"\"\"\n Remove a handler reference from the internal cleanup list.\n \"\"\"\n # This function can be called during module teardown, when globals are\n # set to None. It can also be called from another thread. So we need to\n # pre-emptively grab the necessary globals and check if they're None,\n # to prevent race conditions and failures during interpreter shutdown.\n handlers, lock = _handlerList, _lock\n if lock and handlers:\n with lock:\n try:\n handlers.remove(wr)\n except ValueError:\n pass\n\ndef _addHandlerRef(handler):\n \"\"\"\n Add a handler to the internal cleanup list using a weak reference.\n \"\"\"\n with _lock:\n _handlerList.append(weakref.ref(handler, _removeHandlerRef))\n\n\ndef getHandlerByName(name):\n \"\"\"\n Get a handler with the specified *name*, or None if there isn't one with\n that name.\n \"\"\"\n return _handlers.get(name)\n\n\ndef getHandlerNames():\n \"\"\"\n Return all known handler names as an immutable set.\n \"\"\"\n return frozenset(_handlers)\n\n\nclass Handler(Filterer):\n \"\"\"\n Handler instances dispatch logging events to specific destinations.\n\n The base handler class. Acts as a placeholder which defines the Handler\n interface. Handlers can optionally use Formatter instances to format\n records as desired. By default, no formatter is specified; in this case,\n the 'raw' message as determined by record.message is logged.\n \"\"\"\n def __init__(self, level=NOTSET):\n \"\"\"\n Initializes the instance - basically setting the formatter to None\n and the filter list to empty.\n \"\"\"\n Filterer.__init__(self)\n self._name = None\n self.level = _checkLevel(level)\n self.formatter = None\n self._closed = False\n # Add the handler to the global _handlerList (for cleanup on shutdown)\n _addHandlerRef(self)\n self.createLock()\n\n def get_name(self):\n return self._name\n\n def set_name(self, name):\n with _lock:\n if self._name in _handlers:\n del _handlers[self._name]\n self._name = name\n if name:\n _handlers[name] = self\n\n name = property(get_name, set_name)\n\n def createLock(self):\n \"\"\"\n Acquire a thread lock for serializing access to the underlying I/O.\n \"\"\"\n self.lock = threading.RLock()\n _register_at_fork_reinit_lock(self)\n\n def _at_fork_reinit(self):\n self.lock._at_fork_reinit()\n\n def acquire(self):\n \"\"\"\n Acquire the I/O thread lock.\n \"\"\"\n if self.lock:\n self.lock.acquire()\n\n def release(self):\n \"\"\"\n Release the I/O thread lock.\n \"\"\"\n if self.lock:\n self.lock.release()\n\n def setLevel(self, level):\n \"\"\"\n Set the logging level of this handler. level must be an int or a str.\n \"\"\"\n self.level = _checkLevel(level)\n\n def format(self, record):\n \"\"\"\n Format the specified record.\n\n If a formatter is set, use it. Otherwise, use the default formatter\n for the module.\n \"\"\"\n if self.formatter:\n fmt = self.formatter\n else:\n fmt = _defaultFormatter\n return fmt.format(record)\n\n def emit(self, record):\n \"\"\"\n Do whatever it takes to actually log the specified logging record.\n\n This version is intended to be implemented by subclasses and so\n raises a NotImplementedError.\n \"\"\"\n raise NotImplementedError('emit must be implemented '\n 'by Handler subclasses')\n\n def handle(self, record):\n \"\"\"\n Conditionally emit the specified logging record.\n\n Emission depends on filters which may have been added to the handler.\n Wrap the actual emission of the record with acquisition/release of\n the I/O thread lock.\n\n Returns an instance of the log record that was emitted\n if it passed all filters, otherwise a false value is returned.\n \"\"\"\n rv = self.filter(record)\n if isinstance(rv, LogRecord):\n record = rv\n if rv:\n with self.lock:\n self.emit(record)\n return rv\n\n def setFormatter(self, fmt):\n \"\"\"\n Set the formatter for this handler.\n \"\"\"\n self.formatter = fmt\n\n def flush(self):\n \"\"\"\n Ensure all logging output has been flushed.\n\n This version does nothing and is intended to be implemented by\n subclasses.\n \"\"\"\n pass\n\n def close(self):\n \"\"\"\n Tidy up any resources used by the handler.\n\n This version removes the handler from an internal map of handlers,\n _handlers, which is used for handler lookup by name. Subclasses\n should ensure that this gets called from overridden close()\n methods.\n \"\"\"\n #get the module data lock, as we're updating a shared structure.\n with _lock:\n self._closed = True\n if self._name and self._name in _handlers:\n del _handlers[self._name]\n\n def handleError(self, record):\n \"\"\"\n Handle errors which occur during an emit() call.\n\n This method should be called from handlers when an exception is\n encountered during an emit() call. If raiseExceptions is false,\n exceptions get silently ignored. This is what is mostly wanted\n for a logging system - most users will not care about errors in\n the logging system, they are more interested in application errors.\n You could, however, replace this with a custom handler if you wish.\n The record which was being processed is passed in to this method.\n \"\"\"\n if raiseExceptions and sys.stderr: # see issue 13807\n exc = sys.exception()\n try:\n sys.stderr.write('--- Logging error ---\\n')\n traceback.print_exception(exc, limit=None, file=sys.stderr)\n sys.stderr.write('Call stack:\\n')\n # Walk the stack frame up until we're out of logging,\n # so as to print the calling context.\n frame = exc.__traceback__.tb_frame\n while (frame and os.path.dirname(frame.f_code.co_filename) ==\n __path__[0]):\n frame = frame.f_back\n if frame:\n traceback.print_stack(frame, file=sys.stderr)\n else:\n # couldn't find the right stack frame, for some reason\n sys.stderr.write('Logged from file %s, line %s\\n' % (\n record.filename, record.lineno))\n # Issue 18671: output logging message and arguments\n try:\n sys.stderr.write('Message: %r\\n'\n 'Arguments: %s\\n' % (record.msg,\n record.args))\n except RecursionError: # See issue 36272\n raise\n except Exception:\n sys.stderr.write('Unable to print the message and arguments'\n ' - possible formatting error.\\nUse the'\n ' traceback above to help find the error.\\n'\n )\n except OSError: #pragma: no cover\n pass # see issue 5971\n finally:\n del exc\n\n def __repr__(self):\n level = getLevelName(self.level)\n return '<%s (%s)>' % (self.__class__.__name__, level)\n\nclass StreamHandler(Handler):\n \"\"\"\n A handler class which writes logging records, appropriately formatted,\n to a stream. Note that this class does not close the stream, as\n sys.stdout or sys.stderr may be used.\n \"\"\"\n\n terminator = '\\n'\n\n def __init__(self, stream=None):\n \"\"\"\n Initialize the handler.\n\n If stream is not specified, sys.stderr is used.\n \"\"\"\n Handler.__init__(self)\n if stream is None:\n stream = sys.stderr\n self.stream = stream\n\n def flush(self):\n \"\"\"\n Flushes the stream.\n \"\"\"\n with self.lock:\n if self.stream and hasattr(self.stream, \"flush\"):\n self.stream.flush()\n\n def emit(self, record):\n \"\"\"\n Emit a record.\n\n If a formatter is specified, it is used to format the record.\n The record is then written to the stream with a trailing newline. If\n exception information is present, it is formatted using\n traceback.print_exception and appended to the stream. If the stream\n has an 'encoding' attribute, it is used to determine how to do the\n output to the stream.\n \"\"\"\n try:\n msg = self.format(record)\n stream = self.stream\n # issue 35046: merged two stream.writes into one.\n stream.write(msg + self.terminator)\n self.flush()\n except RecursionError: # See issue 36272\n raise\n except Exception:\n self.handleError(record)\n\n def setStream(self, stream):\n \"\"\"\n Sets the StreamHandler's stream to the specified value,\n if it is different.\n\n Returns the old stream, if the stream was changed, or None\n if it wasn't.\n \"\"\"\n if stream is self.stream:\n result = None\n else:\n result = self.stream\n with self.lock:\n self.flush()\n self.stream = stream\n return result\n\n def __repr__(self):\n level = getLevelName(self.level)\n name = getattr(self.stream, 'name', '')\n # bpo-36015: name can be an int\n name = str(name)\n if name:\n name += ' '\n return '<%s %s(%s)>' % (self.__class__.__name__, name, level)\n\n __class_getitem__ = classmethod(GenericAlias)\n\n\nclass FileHandler(StreamHandler):\n \"\"\"\n A handler class which writes formatted logging records to disk files.\n \"\"\"\n def __init__(self, filename, mode='a', encoding=None, delay=False, errors=None):\n \"\"\"\n Open the specified file and use it as the stream for logging.\n \"\"\"\n # Issue #27493: add support for Path objects to be passed in\n filename = os.fspath(filename)\n #keep the absolute path, otherwise derived classes which use this\n #may come a cropper when the current directory changes\n self.baseFilename = os.path.abspath(filename)\n self.mode = mode\n self.encoding = encoding\n if \"b\" not in mode:\n self.encoding = io.text_encoding(encoding)\n self.errors = errors\n self.delay = delay\n # bpo-26789: FileHandler keeps a reference to the builtin open()\n # function to be able to open or reopen the file during Python\n # finalization.\n self._builtin_open = open\n if delay:\n #We don't open the stream, but we still need to call the\n #Handler constructor to set level, formatter, lock etc.\n Handler.__init__(self)\n self.stream = None\n else:\n StreamHandler.__init__(self, self._open())\n\n def close(self):\n \"\"\"\n Closes the stream.\n \"\"\"\n with self.lock:\n try:\n if self.stream:\n try:\n self.flush()\n finally:\n stream = self.stream\n self.stream = None\n if hasattr(stream, \"close\"):\n stream.close()\n finally:\n # Issue #19523: call unconditionally to\n # prevent a handler leak when delay is set\n # Also see Issue #42378: we also rely on\n # self._closed being set to True there\n StreamHandler.close(self)\n\n def _open(self):\n \"\"\"\n Open the current base file with the (original) mode and encoding.\n Return the resulting stream.\n \"\"\"\n open_func = self._builtin_open\n return open_func(self.baseFilename, self.mode,\n encoding=self.encoding, errors=self.errors)\n\n def emit(self, record):\n \"\"\"\n Emit a record.\n\n If the stream was not opened because 'delay' was specified in the\n constructor, open it before calling the superclass's emit.\n\n If stream is not open, current mode is 'w' and `_closed=True`, record\n will not be emitted (see Issue #42378).\n \"\"\"\n if self.stream is None:\n if self.mode != 'w' or not self._closed:\n self.stream = self._open()\n if self.stream:\n StreamHandler.emit(self, record)\n\n def __repr__(self):\n level = getLevelName(self.level)\n return '<%s %s (%s)>' % (self.__class__.__name__, self.baseFilename, level)\n\n\nclass _StderrHandler(StreamHandler):\n \"\"\"\n This class is like a StreamHandler using sys.stderr, but always uses\n whatever sys.stderr is currently set to rather than the value of\n sys.stderr at handler construction time.\n \"\"\"\n def __init__(self, level=NOTSET):\n \"\"\"\n Initialize the handler.\n \"\"\"\n Handler.__init__(self, level)\n\n @property\n def stream(self):\n return sys.stderr\n\n\n_defaultLastResort = _StderrHandler(WARNING)\nlastResort = _defaultLastResort\n\n#---------------------------------------------------------------------------\n# Manager classes and functions\n#---------------------------------------------------------------------------\n\nclass PlaceHolder(object):\n \"\"\"\n PlaceHolder instances are used in the Manager logger hierarchy to take\n the place of nodes for which no loggers have been defined. This class is\n intended for internal use only and not as part of the public API.\n \"\"\"\n def __init__(self, alogger):\n \"\"\"\n Initialize with the specified logger being a child of this placeholder.\n \"\"\"\n self.loggerMap = { alogger : None }\n\n def append(self, alogger):\n \"\"\"\n Add the specified logger as a child of this placeholder.\n \"\"\"\n if alogger not in self.loggerMap:\n self.loggerMap[alogger] = None\n\n#\n# Determine which class to use when instantiating loggers.\n#\n\ndef setLoggerClass(klass):\n \"\"\"\n Set the class to be used when instantiating a logger. The class should\n define __init__() such that only a name argument is required, and the\n __init__() should call Logger.__init__()\n \"\"\"\n if klass != Logger:\n if not issubclass(klass, Logger):\n raise TypeError(\"logger not derived from logging.Logger: \"\n + klass.__name__)\n global _loggerClass\n _loggerClass = klass\n\ndef getLoggerClass():\n \"\"\"\n Return the class to be used when instantiating a logger.\n \"\"\"\n return _loggerClass\n\nclass Manager(object):\n \"\"\"\n There is [under normal circumstances] just one Manager instance, which\n holds the hierarchy of loggers.\n \"\"\"\n def __init__(self, rootnode):\n \"\"\"\n Initialize the manager with the root node of the logger hierarchy.\n \"\"\"\n self.root = rootnode\n self.disable = 0\n self.emittedNoHandlerWarning = False\n self.loggerDict = {}\n self.loggerClass = None\n self.logRecordFactory = None\n\n @property\n def disable(self):\n return self._disable\n\n @disable.setter\n def disable(self, value):\n self._disable = _checkLevel(value)\n\n def getLogger(self, name):\n \"\"\"\n Get a logger with the specified name (channel name), creating it\n if it doesn't yet exist. This name is a dot-separated hierarchical\n name, such as \"a\", \"a.b\", \"a.b.c\" or similar.\n\n If a PlaceHolder existed for the specified name [i.e. the logger\n didn't exist but a child of it did], replace it with the created\n logger and fix up the parent/child references which pointed to the\n placeholder to now point to the logger.\n \"\"\"\n rv = None\n if not isinstance(name, str):\n raise TypeError('A logger name must be a string')\n with _lock:\n if name in self.loggerDict:\n rv = self.loggerDict[name]\n if isinstance(rv, PlaceHolder):\n ph = rv\n rv = (self.loggerClass or _loggerClass)(name)\n rv.manager = self\n self.loggerDict[name] = rv\n self._fixupChildren(ph, rv)\n self._fixupParents(rv)\n else:\n rv = (self.loggerClass or _loggerClass)(name)\n rv.manager = self\n self.loggerDict[name] = rv\n self._fixupParents(rv)\n return rv\n\n def setLoggerClass(self, klass):\n \"\"\"\n Set the class to be used when instantiating a logger with this Manager.\n \"\"\"\n if klass != Logger:\n if not issubclass(klass, Logger):\n raise TypeError(\"logger not derived from logging.Logger: \"\n + klass.__name__)\n self.loggerClass = klass\n\n def setLogRecordFactory(self, factory):\n \"\"\"\n Set the factory to be used when instantiating a log record with this\n Manager.\n \"\"\"\n self.logRecordFactory = factory\n\n def _fixupParents(self, alogger):\n \"\"\"\n Ensure that there are either loggers or placeholders all the way\n from the specified logger to the root of the logger hierarchy.\n \"\"\"\n name = alogger.name\n i = name.rfind(\".\")\n rv = None\n while (i > 0) and not rv:\n substr = name[:i]\n if substr not in self.loggerDict:\n self.loggerDict[substr] = PlaceHolder(alogger)\n else:\n obj = self.loggerDict[substr]\n if isinstance(obj, Logger):\n rv = obj\n else:\n assert isinstance(obj, PlaceHolder)\n obj.append(alogger)\n i = name.rfind(\".\", 0, i - 1)\n if not rv:\n rv = self.root\n alogger.parent = rv\n\n def _fixupChildren(self, ph, alogger):\n \"\"\"\n Ensure that children of the placeholder ph are connected to the\n specified logger.\n \"\"\"\n name = alogger.name\n namelen = len(name)\n for c in ph.loggerMap.keys():\n #The if means ... if not c.parent.name.startswith(nm)\n if c.parent.name[:namelen] != name:\n alogger.parent = c.parent\n c.parent = alogger\n\n def _clear_cache(self):\n \"\"\"\n Clear the cache for all loggers in loggerDict\n Called when level changes are made\n \"\"\"\n\n with _lock:\n for logger in self.loggerDict.values():\n if isinstance(logger, Logger):\n logger._cache.clear()\n self.root._cache.clear()\n\n#---------------------------------------------------------------------------\n# Logger classes and functions\n#---------------------------------------------------------------------------\n\nclass Logger(Filterer):\n \"\"\"\n Instances of the Logger class represent a single logging channel. A\n \"logging channel\" indicates an area of an application. Exactly how an\n \"area\" is defined is up to the application developer. Since an\n application can have any number of areas, logging channels are identified\n by a unique string. Application areas can be nested (e.g. an area\n of \"input processing\" might include sub-areas \"read CSV files\", \"read\n XLS files\" and \"read Gnumeric files\"). To cater for this natural nesting,\n channel names are organized into a namespace hierarchy where levels are\n separated by periods, much like the Java or Python package namespace. So\n in the instance given above, channel names might be \"input\" for the upper\n level, and \"input.csv\", \"input.xls\" and \"input.gnu\" for the sub-levels.\n There is no arbitrary limit to the depth of nesting.\n \"\"\"\n def __init__(self, name, level=NOTSET):\n \"\"\"\n Initialize the logger with a name and an optional level.\n \"\"\"\n Filterer.__init__(self)\n self.name = name\n self.level = _checkLevel(level)\n self.parent = None\n self.propagate = True\n self.handlers = []\n self.disabled = False\n self._cache = {}\n\n def setLevel(self, level):\n \"\"\"\n Set the logging level of this logger. level must be an int or a str.\n \"\"\"\n self.level = _checkLevel(level)\n self.manager._clear_cache()\n\n def debug(self, msg, *args, **kwargs):\n \"\"\"\n Log 'msg % args' with severity 'DEBUG'.\n\n To pass exception information, use the keyword argument exc_info with\n a true value, e.g.\n\n logger.debug(\"Houston, we have a %s\", \"thorny problem\", exc_info=True)\n \"\"\"\n if self.isEnabledFor(DEBUG):\n self._log(DEBUG, msg, args, **kwargs)\n\n def info(self, msg, *args, **kwargs):\n \"\"\"\n Log 'msg % args' with severity 'INFO'.\n\n To pass exception information, use the keyword argument exc_info with\n a true value, e.g.\n\n logger.info(\"Houston, we have a %s\", \"notable problem\", exc_info=True)\n \"\"\"\n if self.isEnabledFor(INFO):\n self._log(INFO, msg, args, **kwargs)\n\n def warning(self, msg, *args, **kwargs):\n \"\"\"\n Log 'msg % args' with severity 'WARNING'.\n\n To pass exception information, use the keyword argument exc_info with\n a true value, e.g.\n\n logger.warning(\"Houston, we have a %s\", \"bit of a problem\", exc_info=True)\n \"\"\"\n if self.isEnabledFor(WARNING):\n self._log(WARNING, msg, args, **kwargs)\n\n def warn(self, msg, *args, **kwargs):\n warnings.warn(\"The 'warn' method is deprecated, \"\n \"use 'warning' instead\", DeprecationWarning, 2)\n self.warning(msg, *args, **kwargs)\n\n def error(self, msg, *args, **kwargs):\n \"\"\"\n Log 'msg % args' with severity 'ERROR'.\n\n To pass exception information, use the keyword argument exc_info with\n a true value, e.g.\n\n logger.error(\"Houston, we have a %s\", \"major problem\", exc_info=True)\n \"\"\"\n if self.isEnabledFor(ERROR):\n self._log(ERROR, msg, args, **kwargs)\n\n def exception(self, msg, *args, exc_info=True, **kwargs):\n \"\"\"\n Convenience method for logging an ERROR with exception information.\n \"\"\"\n self.error(msg, *args, exc_info=exc_info, **kwargs)\n\n def critical(self, msg, *args, **kwargs):\n \"\"\"\n Log 'msg % args' with severity 'CRITICAL'.\n\n To pass exception information, use the keyword argument exc_info with\n a true value, e.g.\n\n logger.critical(\"Houston, we have a %s\", \"major disaster\", exc_info=True)\n \"\"\"\n if self.isEnabledFor(CRITICAL):\n self._log(CRITICAL, msg, args, **kwargs)\n\n def fatal(self, msg, *args, **kwargs):\n \"\"\"\n Don't use this method, use critical() instead.\n \"\"\"\n self.critical(msg, *args, **kwargs)\n\n def log(self, level, msg, *args, **kwargs):\n \"\"\"\n Log 'msg % args' with the integer severity 'level'.\n\n To pass exception information, use the keyword argument exc_info with\n a true value, e.g.\n\n logger.log(level, \"We have a %s\", \"mysterious problem\", exc_info=True)\n \"\"\"\n if not isinstance(level, int):\n if raiseExceptions:\n raise TypeError(\"level must be an integer\")\n else:\n return\n if self.isEnabledFor(level):\n self._log(level, msg, args, **kwargs)\n\n def findCaller(self, stack_info=False, stacklevel=1):\n \"\"\"\n Find the stack frame of the caller so that we can note the source\n file name, line number and function name.\n \"\"\"\n f = currentframe()\n #On some versions of IronPython, currentframe() returns None if\n #IronPython isn't run with -X:Frames.\n if f is None:\n return \"(unknown file)\", 0, \"(unknown function)\", None\n while stacklevel > 0:\n next_f = f.f_back\n if next_f is None:\n ## We've got options here.\n ## If we want to use the last (deepest) frame:\n break\n ## If we want to mimic the warnings module:\n #return (\"sys\", 1, \"(unknown function)\", None)\n ## If we want to be pedantic:\n #raise ValueError(\"call stack is not deep enough\")\n f = next_f\n if not _is_internal_frame(f):\n stacklevel -= 1\n co = f.f_code\n sinfo = None\n if stack_info:\n with io.StringIO() as sio:\n sio.write(\"Stack (most recent call last):\\n\")\n traceback.print_stack(f, file=sio)\n sinfo = sio.getvalue()\n if sinfo[-1] == '\\n':\n sinfo = sinfo[:-1]\n return co.co_filename, f.f_lineno, co.co_name, sinfo\n\n def makeRecord(self, name, level, fn, lno, msg, args, exc_info,\n func=None, extra=None, sinfo=None):\n \"\"\"\n A factory method which can be overridden in subclasses to create\n specialized LogRecords.\n \"\"\"\n rv = _logRecordFactory(name, level, fn, lno, msg, args, exc_info, func,\n sinfo)\n if extra is not None:\n for key in extra:\n if (key in [\"message\", \"asctime\"]) or (key in rv.__dict__):\n raise KeyError(\"Attempt to overwrite %r in LogRecord\" % key)\n rv.__dict__[key] = extra[key]\n return rv\n\n def _log(self, level, msg, args, exc_info=None, extra=None, stack_info=False,\n stacklevel=1):\n \"\"\"\n Low-level logging routine which creates a LogRecord and then calls\n all the handlers of this logger to handle the record.\n \"\"\"\n sinfo = None\n if _srcfile:\n #IronPython doesn't track Python frames, so findCaller raises an\n #exception on some versions of IronPython. We trap it here so that\n #IronPython can use logging.\n try:\n fn, lno, func, sinfo = self.findCaller(stack_info, stacklevel)\n except ValueError: # pragma: no cover\n fn, lno, func = \"(unknown file)\", 0, \"(unknown function)\"\n else: # pragma: no cover\n fn, lno, func = \"(unknown file)\", 0, \"(unknown function)\"\n if exc_info:\n if isinstance(exc_info, BaseException):\n exc_info = (type(exc_info), exc_info, exc_info.__traceback__)\n elif not isinstance(exc_info, tuple):\n exc_info = sys.exc_info()\n record = self.makeRecord(self.name, level, fn, lno, msg, args,\n exc_info, func, extra, sinfo)\n self.handle(record)\n\n def handle(self, record):\n \"\"\"\n Call the handlers for the specified record.\n\n This method is used for unpickled records received from a socket, as\n well as those created locally. Logger-level filtering is applied.\n \"\"\"\n if self.disabled:\n return\n maybe_record = self.filter(record)\n if not maybe_record:\n return\n if isinstance(maybe_record, LogRecord):\n record = maybe_record\n self.callHandlers(record)\n\n def addHandler(self, hdlr):\n \"\"\"\n Add the specified handler to this logger.\n \"\"\"\n with _lock:\n if not (hdlr in self.handlers):\n self.handlers.append(hdlr)\n\n def removeHandler(self, hdlr):\n \"\"\"\n Remove the specified handler from this logger.\n \"\"\"\n with _lock:\n if hdlr in self.handlers:\n self.handlers.remove(hdlr)\n\n def hasHandlers(self):\n \"\"\"\n See if this logger has any handlers configured.\n\n Loop through all handlers for this logger and its parents in the\n logger hierarchy. Return True if a handler was found, else False.\n Stop searching up the hierarchy whenever a logger with the \"propagate\"\n attribute set to zero is found - that will be the last logger which\n is checked for the existence of handlers.\n \"\"\"\n c = self\n rv = False\n while c:\n if c.handlers:\n rv = True\n break\n if not c.propagate:\n break\n else:\n c = c.parent\n return rv\n\n def callHandlers(self, record):\n \"\"\"\n Pass a record to all relevant handlers.\n\n Loop through all handlers for this logger and its parents in the\n logger hierarchy. If no handler was found, output a one-off error\n message to sys.stderr. Stop searching up the hierarchy whenever a\n logger with the \"propagate\" attribute set to zero is found - that\n will be the last logger whose handlers are called.\n \"\"\"\n c = self\n found = 0\n while c:\n for hdlr in c.handlers:\n found = found + 1\n if record.levelno >= hdlr.level:\n hdlr.handle(record)\n if not c.propagate:\n c = None #break out\n else:\n c = c.parent\n if (found == 0):\n if lastResort:\n if record.levelno >= lastResort.level:\n lastResort.handle(record)\n elif raiseExceptions and not self.manager.emittedNoHandlerWarning:\n sys.stderr.write(\"No handlers could be found for logger\"\n \" \\\"%s\\\"\\n\" % self.name)\n self.manager.emittedNoHandlerWarning = True\n\n def getEffectiveLevel(self):\n \"\"\"\n Get the effective level for this logger.\n\n Loop through this logger and its parents in the logger hierarchy,\n looking for a non-zero logging level. Return the first one found.\n \"\"\"\n logger = self\n while logger:\n if logger.level:\n return logger.level\n logger = logger.parent\n return NOTSET\n\n def isEnabledFor(self, level):\n \"\"\"\n Is this logger enabled for level 'level'?\n \"\"\"\n if self.disabled:\n return False\n\n try:\n return self._cache[level]\n except KeyError:\n with _lock:\n if self.manager.disable >= level:\n is_enabled = self._cache[level] = False\n else:\n is_enabled = self._cache[level] = (\n level >= self.getEffectiveLevel()\n )\n return is_enabled\n\n def getChild(self, suffix):\n \"\"\"\n Get a logger which is a descendant to this one.\n\n This is a convenience method, such that\n\n logging.getLogger('abc').getChild('def.ghi')\n\n is the same as\n\n logging.getLogger('abc.def.ghi')\n\n It's useful, for example, when the parent logger is named using\n __name__ rather than a literal string.\n \"\"\"\n if self.root is not self:\n suffix = '.'.join((self.name, suffix))\n return self.manager.getLogger(suffix)\n\n def getChildren(self):\n\n def _hierlevel(logger):\n if logger is logger.manager.root:\n return 0\n return 1 + logger.name.count('.')\n\n d = self.manager.loggerDict\n with _lock:\n # exclude PlaceHolders - the last check is to ensure that lower-level\n # descendants aren't returned - if there are placeholders, a logger's\n # parent field might point to a grandparent or ancestor thereof.\n return set(item for item in d.values()\n if isinstance(item, Logger) and item.parent is self and\n _hierlevel(item) == 1 + _hierlevel(item.parent))\n\n def __repr__(self):\n level = getLevelName(self.getEffectiveLevel())\n return '<%s %s (%s)>' % (self.__class__.__name__, self.name, level)\n\n def __reduce__(self):\n if getLogger(self.name) is not self:\n import pickle\n raise pickle.PicklingError('logger cannot be pickled')\n return getLogger, (self.name,)\n\n\nclass RootLogger(Logger):\n \"\"\"\n A root logger is not that different to any other logger, except that\n it must have a logging level and there is only one instance of it in\n the hierarchy.\n \"\"\"\n def __init__(self, level):\n \"\"\"\n Initialize the logger with the name \"root\".\n \"\"\"\n Logger.__init__(self, \"root\", level)\n\n def __reduce__(self):\n return getLogger, ()\n\n_loggerClass = Logger\n\nclass LoggerAdapter(object):\n \"\"\"\n An adapter for loggers which makes it easier to specify contextual\n information in logging output.\n \"\"\"\n\n def __init__(self, logger, extra=None, merge_extra=False):\n \"\"\"\n Initialize the adapter with a logger and a dict-like object which\n provides contextual information. This constructor signature allows\n easy stacking of LoggerAdapters, if so desired.\n\n You can effectively pass keyword arguments as shown in the\n following example:\n\n adapter = LoggerAdapter(someLogger, dict(p1=v1, p2=\"v2\"))\n\n By default, LoggerAdapter objects will drop the \"extra\" argument\n passed on the individual log calls to use its own instead.\n\n Initializing it with merge_extra=True will instead merge both\n maps when logging, the individual call extra taking precedence\n over the LoggerAdapter instance extra\n\n .. versionchanged:: 3.13\n The *merge_extra* argument was added.\n \"\"\"\n self.logger = logger\n self.extra = extra\n self.merge_extra = merge_extra\n\n def process(self, msg, kwargs):\n \"\"\"\n Process the logging message and keyword arguments passed in to\n a logging call to insert contextual information. You can either\n manipulate the message itself, the keyword args or both. Return\n the message and kwargs modified (or not) to suit your needs.\n\n Normally, you'll only need to override this one method in a\n LoggerAdapter subclass for your specific needs.\n \"\"\"\n if self.merge_extra and \"extra\" in kwargs:\n kwargs[\"extra\"] = {**self.extra, **kwargs[\"extra\"]}\n else:\n kwargs[\"extra\"] = self.extra\n return msg, kwargs\n\n #\n # Boilerplate convenience methods\n #\n def debug(self, msg, *args, **kwargs):\n \"\"\"\n Delegate a debug call to the underlying logger.\n \"\"\"\n self.log(DEBUG, msg, *args, **kwargs)\n\n def info(self, msg, *args, **kwargs):\n \"\"\"\n Delegate an info call to the underlying logger.\n \"\"\"\n self.log(INFO, msg, *args, **kwargs)\n\n def warning(self, msg, *args, **kwargs):\n \"\"\"\n Delegate a warning call to the underlying logger.\n \"\"\"\n self.log(WARNING, msg, *args, **kwargs)\n\n def warn(self, msg, *args, **kwargs):\n warnings.warn(\"The 'warn' method is deprecated, \"\n \"use 'warning' instead\", DeprecationWarning, 2)\n self.warning(msg, *args, **kwargs)\n\n def error(self, msg, *args, **kwargs):\n \"\"\"\n Delegate an error call to the underlying logger.\n \"\"\"\n self.log(ERROR, msg, *args, **kwargs)\n\n def exception(self, msg, *args, exc_info=True, **kwargs):\n \"\"\"\n Delegate an exception call to the underlying logger.\n \"\"\"\n self.log(ERROR, msg, *args, exc_info=exc_info, **kwargs)\n\n def critical(self, msg, *args, **kwargs):\n \"\"\"\n Delegate a critical call to the underlying logger.\n \"\"\"\n self.log(CRITICAL, msg, *args, **kwargs)\n\n def log(self, level, msg, *args, **kwargs):\n \"\"\"\n Delegate a log call to the underlying logger, after adding\n contextual information from this adapter instance.\n \"\"\"\n if self.isEnabledFor(level):\n msg, kwargs = self.process(msg, kwargs)\n self.logger.log(level, msg, *args, **kwargs)\n\n def isEnabledFor(self, level):\n \"\"\"\n Is this logger enabled for level 'level'?\n \"\"\"\n return self.logger.isEnabledFor(level)\n\n def setLevel(self, level):\n \"\"\"\n Set the specified level on the underlying logger.\n \"\"\"\n self.logger.setLevel(level)\n\n def getEffectiveLevel(self):\n \"\"\"\n Get the effective level for the underlying logger.\n \"\"\"\n return self.logger.getEffectiveLevel()\n\n def hasHandlers(self):\n \"\"\"\n See if the underlying logger has any handlers.\n \"\"\"\n return self.logger.hasHandlers()\n\n def _log(self, level, msg, args, **kwargs):\n \"\"\"\n Low-level log implementation, proxied to allow nested logger adapters.\n \"\"\"\n return self.logger._log(level, msg, args, **kwargs)\n\n @property\n def manager(self):\n return self.logger.manager\n\n @manager.setter\n def manager(self, value):\n self.logger.manager = value\n\n @property\n def name(self):\n return self.logger.name\n\n def __repr__(self):\n logger = self.logger\n level = getLevelName(logger.getEffectiveLevel())\n return '<%s %s (%s)>' % (self.__class__.__name__, logger.name, level)\n\n __class_getitem__ = classmethod(GenericAlias)\n\nroot = RootLogger(WARNING)\nLogger.root = root\nLogger.manager = Manager(Logger.root)\n\n#---------------------------------------------------------------------------\n# Configuration classes and functions\n#---------------------------------------------------------------------------\n\ndef basicConfig(**kwargs):\n \"\"\"\n Do basic configuration for the logging system.\n\n This function does nothing if the root logger already has handlers\n configured, unless the keyword argument *force* is set to ``True``.\n It is a convenience method intended for use by simple scripts\n to do one-shot configuration of the logging package.\n\n The default behaviour is to create a StreamHandler which writes to\n sys.stderr, set a formatter using the BASIC_FORMAT format string, and\n add the handler to the root logger.\n\n A number of optional keyword arguments may be specified, which can alter\n the default behaviour.\n\n filename Specifies that a FileHandler be created, using the specified\n filename, rather than a StreamHandler.\n filemode Specifies the mode to open the file, if filename is specified\n (if filemode is unspecified, it defaults to 'a').\n format Use the specified format string for the handler.\n datefmt Use the specified date/time format.\n style If a format string is specified, use this to specify the\n type of format string (possible values '%', '{', '$', for\n %-formatting, :meth:`str.format` and :class:`string.Template`\n - defaults to '%').\n level Set the root logger level to the specified level.\n stream Use the specified stream to initialize the StreamHandler. Note\n that this argument is incompatible with 'filename' - if both\n are present, 'stream' is ignored.\n handlers If specified, this should be an iterable of already created\n handlers, which will be added to the root logger. Any handler\n in the list which does not have a formatter assigned will be\n assigned the formatter created in this function.\n force If this keyword is specified as true, any existing handlers\n attached to the root logger are removed and closed, before\n carrying out the configuration as specified by the other\n arguments.\n encoding If specified together with a filename, this encoding is passed to\n the created FileHandler, causing it to be used when the file is\n opened.\n errors If specified together with a filename, this value is passed to the\n created FileHandler, causing it to be used when the file is\n opened in text mode. If not specified, the default value is\n `backslashreplace`.\n\n Note that you could specify a stream created using open(filename, mode)\n rather than passing the filename and mode in. However, it should be\n remembered that StreamHandler does not close its stream (since it may be\n using sys.stdout or sys.stderr), whereas FileHandler closes its stream\n when the handler is closed.\n\n .. versionchanged:: 3.2\n Added the ``style`` parameter.\n\n .. versionchanged:: 3.3\n Added the ``handlers`` parameter. A ``ValueError`` is now thrown for\n incompatible arguments (e.g. ``handlers`` specified together with\n ``filename``/``filemode``, or ``filename``/``filemode`` specified\n together with ``stream``, or ``handlers`` specified together with\n ``stream``.\n\n .. versionchanged:: 3.8\n Added the ``force`` parameter.\n\n .. versionchanged:: 3.9\n Added the ``encoding`` and ``errors`` parameters.\n \"\"\"\n # Add thread safety in case someone mistakenly calls\n # basicConfig() from multiple threads\n with _lock:\n force = kwargs.pop('force', False)\n encoding = kwargs.pop('encoding', None)\n errors = kwargs.pop('errors', 'backslashreplace')\n if force:\n for h in root.handlers[:]:\n root.removeHandler(h)\n h.close()\n if len(root.handlers) == 0:\n handlers = kwargs.pop(\"handlers\", None)\n if handlers is None:\n if \"stream\" in kwargs and \"filename\" in kwargs:\n raise ValueError(\"'stream' and 'filename' should not be \"\n \"specified together\")\n else:\n if \"stream\" in kwargs or \"filename\" in kwargs:\n raise ValueError(\"'stream' or 'filename' should not be \"\n \"specified together with 'handlers'\")\n if handlers is None:\n filename = kwargs.pop(\"filename\", None)\n mode = kwargs.pop(\"filemode\", 'a')\n if filename:\n if 'b' in mode:\n errors = None\n else:\n encoding = io.text_encoding(encoding)\n h = FileHandler(filename, mode,\n encoding=encoding, errors=errors)\n else:\n stream = kwargs.pop(\"stream\", None)\n h = StreamHandler(stream)\n handlers = [h]\n dfs = kwargs.pop(\"datefmt\", None)\n style = kwargs.pop(\"style\", '%')\n if style not in _STYLES:\n raise ValueError('Style must be one of: %s' % ','.join(\n _STYLES.keys()))\n fs = kwargs.pop(\"format\", _STYLES[style][1])\n fmt = Formatter(fs, dfs, style)\n for h in handlers:\n if h.formatter is None:\n h.setFormatter(fmt)\n root.addHandler(h)\n level = kwargs.pop(\"level\", None)\n if level is not None:\n root.setLevel(level)\n if kwargs:\n keys = ', '.join(kwargs.keys())\n raise ValueError('Unrecognised argument(s): %s' % keys)\n\n#---------------------------------------------------------------------------\n# Utility functions at module level.\n# Basically delegate everything to the root logger.\n#---------------------------------------------------------------------------\n\ndef getLogger(name=None):\n \"\"\"\n Return a logger with the specified name, creating it if necessary.\n\n If no name is specified, return the root logger.\n \"\"\"\n if not name or isinstance(name, str) and name == root.name:\n return root\n return Logger.manager.getLogger(name)\n\ndef critical(msg, *args, **kwargs):\n \"\"\"\n Log a message with severity 'CRITICAL' on the root logger. If the logger\n has no handlers, call basicConfig() to add a console handler with a\n pre-defined format.\n \"\"\"\n if len(root.handlers) == 0:\n basicConfig()\n root.critical(msg, *args, **kwargs)\n\ndef fatal(msg, *args, **kwargs):\n \"\"\"\n Don't use this function, use critical() instead.\n \"\"\"\n critical(msg, *args, **kwargs)\n\ndef error(msg, *args, **kwargs):\n \"\"\"\n Log a message with severity 'ERROR' on the root logger. If the logger has\n no handlers, call basicConfig() to add a console handler with a pre-defined\n format.\n \"\"\"\n if len(root.handlers) == 0:\n basicConfig()\n root.error(msg, *args, **kwargs)\n\ndef exception(msg, *args, exc_info=True, **kwargs):\n \"\"\"\n Log a message with severity 'ERROR' on the root logger, with exception\n information. If the logger has no handlers, basicConfig() is called to add\n a console handler with a pre-defined format.\n \"\"\"\n error(msg, *args, exc_info=exc_info, **kwargs)\n\ndef warning(msg, *args, **kwargs):\n \"\"\"\n Log a message with severity 'WARNING' on the root logger. If the logger has\n no handlers, call basicConfig() to add a console handler with a pre-defined\n format.\n \"\"\"\n if len(root.handlers) == 0:\n basicConfig()\n root.warning(msg, *args, **kwargs)\n\ndef warn(msg, *args, **kwargs):\n warnings.warn(\"The 'warn' function is deprecated, \"\n \"use 'warning' instead\", DeprecationWarning, 2)\n warning(msg, *args, **kwargs)\n\ndef info(msg, *args, **kwargs):\n \"\"\"\n Log a message with severity 'INFO' on the root logger. If the logger has\n no handlers, call basicConfig() to add a console handler with a pre-defined\n format.\n \"\"\"\n if len(root.handlers) == 0:\n basicConfig()\n root.info(msg, *args, **kwargs)\n\ndef debug(msg, *args, **kwargs):\n \"\"\"\n Log a message with severity 'DEBUG' on the root logger. If the logger has\n no handlers, call basicConfig() to add a console handler with a pre-defined\n format.\n \"\"\"\n if len(root.handlers) == 0:\n basicConfig()\n root.debug(msg, *args, **kwargs)\n\ndef log(level, msg, *args, **kwargs):\n \"\"\"\n Log 'msg % args' with the integer severity 'level' on the root logger. If\n the logger has no handlers, call basicConfig() to add a console handler\n with a pre-defined format.\n \"\"\"\n if len(root.handlers) == 0:\n basicConfig()\n root.log(level, msg, *args, **kwargs)\n\ndef disable(level=CRITICAL):\n \"\"\"\n Disable all logging calls of severity 'level' and below.\n \"\"\"\n root.manager.disable = level\n root.manager._clear_cache()\n\ndef shutdown(handlerList=_handlerList):\n \"\"\"\n Perform any cleanup actions in the logging system (e.g. flushing\n buffers).\n\n Should be called at application exit.\n \"\"\"\n for wr in reversed(handlerList[:]):\n #errors might occur, for example, if files are locked\n #we just ignore them if raiseExceptions is not set\n try:\n h = wr()\n if h:\n try:\n h.acquire()\n # MemoryHandlers might not want to be flushed on close,\n # but circular imports prevent us scoping this to just\n # those handlers. hence the default to True.\n if getattr(h, 'flushOnClose', True):\n h.flush()\n h.close()\n except (OSError, ValueError):\n # Ignore errors which might be caused\n # because handlers have been closed but\n # references to them are still around at\n # application exit.\n pass\n finally:\n h.release()\n except: # ignore everything, as we're shutting down\n if raiseExceptions:\n raise\n #else, swallow\n\n#Let's try and shutdown automatically on application exit...\nimport atexit\natexit.register(shutdown)\n\n# Null handler\n\nclass NullHandler(Handler):\n \"\"\"\n This handler does nothing. It's intended to be used to avoid the\n \"No handlers could be found for logger XXX\" one-off warning. This is\n important for library code, which may contain code to log events. If a user\n of the library does not configure logging, the one-off warning might be\n produced; to avoid this, the library developer simply needs to instantiate\n a NullHandler and add it to the top-level logger of the library module or\n package.\n \"\"\"\n def handle(self, record):\n \"\"\"Stub.\"\"\"\n\n def emit(self, record):\n \"\"\"Stub.\"\"\"\n\n def createLock(self):\n self.lock = None\n\n def _at_fork_reinit(self):\n pass\n\n# Warnings integration\n\n_warnings_showwarning = None\n\ndef _showwarning(message, category, filename, lineno, file=None, line=None):\n \"\"\"\n Implementation of showwarnings which redirects to logging, which will first\n check to see if the file parameter is None. If a file is specified, it will\n delegate to the original warnings implementation of showwarning. Otherwise,\n it will call warnings.formatwarning and will log the resulting string to a\n warnings logger named \"py.warnings\" with level logging.WARNING.\n \"\"\"\n if file is not None:\n if _warnings_showwarning is not None:\n _warnings_showwarning(message, category, filename, lineno, file, line)\n else:\n s = warnings.formatwarning(message, category, filename, lineno, line)\n logger = getLogger(\"py.warnings\")\n if not logger.handlers:\n logger.addHandler(NullHandler())\n # bpo-46557: Log str(s) as msg instead of logger.warning(\"%s\", s)\n # since some log aggregation tools group logs by the msg arg\n logger.warning(str(s))\n\ndef captureWarnings(capture):\n \"\"\"\n If capture is true, redirect all warnings to the logging package.\n If capture is False, ensure that warnings are not redirected to logging\n but to their original destinations.\n \"\"\"\n global _warnings_showwarning\n if capture:\n if _warnings_showwarning is None:\n _warnings_showwarning = warnings.showwarning\n warnings.showwarning = _showwarning\n else:\n if _warnings_showwarning is not None:\n warnings.showwarning = _warnings_showwarning\n _warnings_showwarning = None\n", 2324], "/usr/lib/python3.13/socket.py": ["# Wrapper module for _socket, providing some additional facilities\n# implemented in Python.\n\n\"\"\"\\\nThis module provides socket operations and some related functions.\nOn Unix, it supports IP (Internet Protocol) and Unix domain sockets.\nOn other systems, it only supports IP. Functions specific for a\nsocket are available as methods of the socket object.\n\nFunctions:\n\nsocket() -- create a new socket object\nsocketpair() -- create a pair of new socket objects [*]\nfromfd() -- create a socket object from an open file descriptor [*]\nsend_fds() -- Send file descriptor to the socket.\nrecv_fds() -- Receive file descriptors from the socket.\nfromshare() -- create a socket object from data received from socket.share() [*]\ngethostname() -- return the current hostname\ngethostbyname() -- map a hostname to its IP number\ngethostbyaddr() -- map an IP number or hostname to DNS info\ngetservbyname() -- map a service name and a protocol name to a port number\ngetprotobyname() -- map a protocol name (e.g. 'tcp') to a number\nntohs(), ntohl() -- convert 16, 32 bit int from network to host byte order\nhtons(), htonl() -- convert 16, 32 bit int from host to network byte order\ninet_aton() -- convert IP addr string (123.45.67.89) to 32-bit packed format\ninet_ntoa() -- convert 32-bit packed format IP to string (123.45.67.89)\nsocket.getdefaulttimeout() -- get the default timeout value\nsocket.setdefaulttimeout() -- set the default timeout value\ncreate_connection() -- connects to an address, with an optional timeout and\n optional source address.\ncreate_server() -- create a TCP socket and bind it to a specified address.\n\n [*] not available on all platforms!\n\nSpecial objects:\n\nSocketType -- type object for socket objects\nerror -- exception raised for I/O errors\nhas_ipv6 -- boolean value indicating if IPv6 is supported\n\nIntEnum constants:\n\nAF_INET, AF_UNIX -- socket domains (first argument to socket() call)\nSOCK_STREAM, SOCK_DGRAM, SOCK_RAW -- socket types (second argument)\n\nInteger constants:\n\nMany other constants may be defined; these may be used in calls to\nthe setsockopt() and getsockopt() methods.\n\"\"\"\n\nimport _socket\nfrom _socket import *\n\nimport os, sys, io, selectors\nfrom enum import IntEnum, IntFlag\n\ntry:\n import errno\nexcept ImportError:\n errno = None\nEBADF = getattr(errno, 'EBADF', 9)\nEAGAIN = getattr(errno, 'EAGAIN', 11)\nEWOULDBLOCK = getattr(errno, 'EWOULDBLOCK', 11)\n\n__all__ = [\"fromfd\", \"getfqdn\", \"create_connection\", \"create_server\",\n \"has_dualstack_ipv6\", \"AddressFamily\", \"SocketKind\"]\n__all__.extend(os._get_exports_list(_socket))\n\n# Set up the socket.AF_* socket.SOCK_* constants as members of IntEnums for\n# nicer string representations.\n# Note that _socket only knows about the integer values. The public interface\n# in this module understands the enums and translates them back from integers\n# where needed (e.g. .family property of a socket object).\n\nIntEnum._convert_(\n 'AddressFamily',\n __name__,\n lambda C: C.isupper() and C.startswith('AF_'))\n\nIntEnum._convert_(\n 'SocketKind',\n __name__,\n lambda C: C.isupper() and C.startswith('SOCK_'))\n\nIntFlag._convert_(\n 'MsgFlag',\n __name__,\n lambda C: C.isupper() and C.startswith('MSG_'))\n\nIntFlag._convert_(\n 'AddressInfo',\n __name__,\n lambda C: C.isupper() and C.startswith('AI_'))\n\n_LOCALHOST = '127.0.0.1'\n_LOCALHOST_V6 = '::1'\n\n\ndef _intenum_converter(value, enum_klass):\n \"\"\"Convert a numeric family value to an IntEnum member.\n\n If it's not a known member, return the numeric value itself.\n \"\"\"\n try:\n return enum_klass(value)\n except ValueError:\n return value\n\n\n# WSA error codes\nif sys.platform.lower().startswith(\"win\"):\n errorTab = {}\n errorTab[6] = \"Specified event object handle is invalid.\"\n errorTab[8] = \"Insufficient memory available.\"\n errorTab[87] = \"One or more parameters are invalid.\"\n errorTab[995] = \"Overlapped operation aborted.\"\n errorTab[996] = \"Overlapped I/O event object not in signaled state.\"\n errorTab[997] = \"Overlapped operation will complete later.\"\n errorTab[10004] = \"The operation was interrupted.\"\n errorTab[10009] = \"A bad file handle was passed.\"\n errorTab[10013] = \"Permission denied.\"\n errorTab[10014] = \"A fault occurred on the network??\" # WSAEFAULT\n errorTab[10022] = \"An invalid operation was attempted.\"\n errorTab[10024] = \"Too many open files.\"\n errorTab[10035] = \"The socket operation would block.\"\n errorTab[10036] = \"A blocking operation is already in progress.\"\n errorTab[10037] = \"Operation already in progress.\"\n errorTab[10038] = \"Socket operation on nonsocket.\"\n errorTab[10039] = \"Destination address required.\"\n errorTab[10040] = \"Message too long.\"\n errorTab[10041] = \"Protocol wrong type for socket.\"\n errorTab[10042] = \"Bad protocol option.\"\n errorTab[10043] = \"Protocol not supported.\"\n errorTab[10044] = \"Socket type not supported.\"\n errorTab[10045] = \"Operation not supported.\"\n errorTab[10046] = \"Protocol family not supported.\"\n errorTab[10047] = \"Address family not supported by protocol family.\"\n errorTab[10048] = \"The network address is in use.\"\n errorTab[10049] = \"Cannot assign requested address.\"\n errorTab[10050] = \"Network is down.\"\n errorTab[10051] = \"Network is unreachable.\"\n errorTab[10052] = \"Network dropped connection on reset.\"\n errorTab[10053] = \"Software caused connection abort.\"\n errorTab[10054] = \"The connection has been reset.\"\n errorTab[10055] = \"No buffer space available.\"\n errorTab[10056] = \"Socket is already connected.\"\n errorTab[10057] = \"Socket is not connected.\"\n errorTab[10058] = \"The network has been shut down.\"\n errorTab[10059] = \"Too many references.\"\n errorTab[10060] = \"The operation timed out.\"\n errorTab[10061] = \"Connection refused.\"\n errorTab[10062] = \"Cannot translate name.\"\n errorTab[10063] = \"The name is too long.\"\n errorTab[10064] = \"The host is down.\"\n errorTab[10065] = \"The host is unreachable.\"\n errorTab[10066] = \"Directory not empty.\"\n errorTab[10067] = \"Too many processes.\"\n errorTab[10068] = \"User quota exceeded.\"\n errorTab[10069] = \"Disk quota exceeded.\"\n errorTab[10070] = \"Stale file handle reference.\"\n errorTab[10071] = \"Item is remote.\"\n errorTab[10091] = \"Network subsystem is unavailable.\"\n errorTab[10092] = \"Winsock.dll version out of range.\"\n errorTab[10093] = \"Successful WSAStartup not yet performed.\"\n errorTab[10101] = \"Graceful shutdown in progress.\"\n errorTab[10102] = \"No more results from WSALookupServiceNext.\"\n errorTab[10103] = \"Call has been canceled.\"\n errorTab[10104] = \"Procedure call table is invalid.\"\n errorTab[10105] = \"Service provider is invalid.\"\n errorTab[10106] = \"Service provider failed to initialize.\"\n errorTab[10107] = \"System call failure.\"\n errorTab[10108] = \"Service not found.\"\n errorTab[10109] = \"Class type not found.\"\n errorTab[10110] = \"No more results from WSALookupServiceNext.\"\n errorTab[10111] = \"Call was canceled.\"\n errorTab[10112] = \"Database query was refused.\"\n errorTab[11001] = \"Host not found.\"\n errorTab[11002] = \"Nonauthoritative host not found.\"\n errorTab[11003] = \"This is a nonrecoverable error.\"\n errorTab[11004] = \"Valid name, no data record requested type.\"\n errorTab[11005] = \"QoS receivers.\"\n errorTab[11006] = \"QoS senders.\"\n errorTab[11007] = \"No QoS senders.\"\n errorTab[11008] = \"QoS no receivers.\"\n errorTab[11009] = \"QoS request confirmed.\"\n errorTab[11010] = \"QoS admission error.\"\n errorTab[11011] = \"QoS policy failure.\"\n errorTab[11012] = \"QoS bad style.\"\n errorTab[11013] = \"QoS bad object.\"\n errorTab[11014] = \"QoS traffic control error.\"\n errorTab[11015] = \"QoS generic error.\"\n errorTab[11016] = \"QoS service type error.\"\n errorTab[11017] = \"QoS flowspec error.\"\n errorTab[11018] = \"Invalid QoS provider buffer.\"\n errorTab[11019] = \"Invalid QoS filter style.\"\n errorTab[11020] = \"Invalid QoS filter style.\"\n errorTab[11021] = \"Incorrect QoS filter count.\"\n errorTab[11022] = \"Invalid QoS object length.\"\n errorTab[11023] = \"Incorrect QoS flow count.\"\n errorTab[11024] = \"Unrecognized QoS object.\"\n errorTab[11025] = \"Invalid QoS policy object.\"\n errorTab[11026] = \"Invalid QoS flow descriptor.\"\n errorTab[11027] = \"Invalid QoS provider-specific flowspec.\"\n errorTab[11028] = \"Invalid QoS provider-specific filterspec.\"\n errorTab[11029] = \"Invalid QoS shape discard mode object.\"\n errorTab[11030] = \"Invalid QoS shaping rate object.\"\n errorTab[11031] = \"Reserved policy QoS element type.\"\n __all__.append(\"errorTab\")\n\n\nclass _GiveupOnSendfile(Exception): pass\n\n\nclass socket(_socket.socket):\n\n \"\"\"A subclass of _socket.socket adding the makefile() method.\"\"\"\n\n __slots__ = [\"__weakref__\", \"_io_refs\", \"_closed\"]\n\n def __init__(self, family=-1, type=-1, proto=-1, fileno=None):\n # For user code address family and type values are IntEnum members, but\n # for the underlying _socket.socket they're just integers. The\n # constructor of _socket.socket converts the given argument to an\n # integer automatically.\n if fileno is None:\n if family == -1:\n family = AF_INET\n if type == -1:\n type = SOCK_STREAM\n if proto == -1:\n proto = 0\n _socket.socket.__init__(self, family, type, proto, fileno)\n self._io_refs = 0\n self._closed = False\n\n def __enter__(self):\n return self\n\n def __exit__(self, *args):\n if not self._closed:\n self.close()\n\n def __repr__(self):\n \"\"\"Wrap __repr__() to reveal the real class name and socket\n address(es).\n \"\"\"\n closed = getattr(self, '_closed', False)\n s = \"<%s.%s%s fd=%i, family=%s, type=%s, proto=%i\" \\\n % (self.__class__.__module__,\n self.__class__.__qualname__,\n \" [closed]\" if closed else \"\",\n self.fileno(),\n self.family,\n self.type,\n self.proto)\n if not closed:\n # getsockname and getpeername may not be available on WASI.\n try:\n laddr = self.getsockname()\n if laddr:\n s += \", laddr=%s\" % str(laddr)\n except (error, AttributeError):\n pass\n try:\n raddr = self.getpeername()\n if raddr:\n s += \", raddr=%s\" % str(raddr)\n except (error, AttributeError):\n pass\n s += '>'\n return s\n\n def __getstate__(self):\n raise TypeError(f\"cannot pickle {self.__class__.__name__!r} object\")\n\n def dup(self):\n \"\"\"dup() -> socket object\n\n Duplicate the socket. Return a new socket object connected to the same\n system resource. The new socket is non-inheritable.\n \"\"\"\n fd = dup(self.fileno())\n sock = self.__class__(self.family, self.type, self.proto, fileno=fd)\n sock.settimeout(self.gettimeout())\n return sock\n\n def accept(self):\n \"\"\"accept() -> (socket object, address info)\n\n Wait for an incoming connection. Return a new socket\n representing the connection, and the address of the client.\n For IP sockets, the address info is a pair (hostaddr, port).\n \"\"\"\n fd, addr = self._accept()\n sock = socket(self.family, self.type, self.proto, fileno=fd)\n # Issue #7995: if no default timeout is set and the listening\n # socket had a (non-zero) timeout, force the new socket in blocking\n # mode to override platform-specific socket flags inheritance.\n if getdefaulttimeout() is None and self.gettimeout():\n sock.setblocking(True)\n return sock, addr\n\n def makefile(self, mode=\"r\", buffering=None, *,\n encoding=None, errors=None, newline=None):\n \"\"\"makefile(...) -> an I/O stream connected to the socket\n\n The arguments are as for io.open() after the filename, except the only\n supported mode values are 'r' (default), 'w', 'b', or a combination of\n those.\n \"\"\"\n # XXX refactor to share code?\n if not set(mode) <= {\"r\", \"w\", \"b\"}:\n raise ValueError(\"invalid mode %r (only r, w, b allowed)\" % (mode,))\n writing = \"w\" in mode\n reading = \"r\" in mode or not writing\n assert reading or writing\n binary = \"b\" in mode\n rawmode = \"\"\n if reading:\n rawmode += \"r\"\n if writing:\n rawmode += \"w\"\n raw = SocketIO(self, rawmode)\n self._io_refs += 1\n if buffering is None:\n buffering = -1\n if buffering < 0:\n buffering = io.DEFAULT_BUFFER_SIZE\n if buffering == 0:\n if not binary:\n raise ValueError(\"unbuffered streams must be binary\")\n return raw\n if reading and writing:\n buffer = io.BufferedRWPair(raw, raw, buffering)\n elif reading:\n buffer = io.BufferedReader(raw, buffering)\n else:\n assert writing\n buffer = io.BufferedWriter(raw, buffering)\n if binary:\n return buffer\n encoding = io.text_encoding(encoding)\n text = io.TextIOWrapper(buffer, encoding, errors, newline)\n text.mode = mode\n return text\n\n if hasattr(os, 'sendfile'):\n\n def _sendfile_use_sendfile(self, file, offset=0, count=None):\n self._check_sendfile_params(file, offset, count)\n sockno = self.fileno()\n try:\n fileno = file.fileno()\n except (AttributeError, io.UnsupportedOperation) as err:\n raise _GiveupOnSendfile(err) # not a regular file\n try:\n fsize = os.fstat(fileno).st_size\n except OSError as err:\n raise _GiveupOnSendfile(err) # not a regular file\n if not fsize:\n return 0 # empty file\n # Truncate to 1GiB to avoid OverflowError, see bpo-38319.\n blocksize = min(count or fsize, 2 ** 30)\n timeout = self.gettimeout()\n if timeout == 0:\n raise ValueError(\"non-blocking sockets are not supported\")\n # poll/select have the advantage of not requiring any\n # extra file descriptor, contrarily to epoll/kqueue\n # (also, they require a single syscall).\n if hasattr(selectors, 'PollSelector'):\n selector = selectors.PollSelector()\n else:\n selector = selectors.SelectSelector()\n selector.register(sockno, selectors.EVENT_WRITE)\n\n total_sent = 0\n # localize variable access to minimize overhead\n selector_select = selector.select\n os_sendfile = os.sendfile\n try:\n while True:\n if timeout and not selector_select(timeout):\n raise TimeoutError('timed out')\n if count:\n blocksize = min(count - total_sent, blocksize)\n if blocksize <= 0:\n break\n try:\n sent = os_sendfile(sockno, fileno, offset, blocksize)\n except BlockingIOError:\n if not timeout:\n # Block until the socket is ready to send some\n # data; avoids hogging CPU resources.\n selector_select()\n continue\n except OSError as err:\n if total_sent == 0:\n # We can get here for different reasons, the main\n # one being 'file' is not a regular mmap(2)-like\n # file, in which case we'll fall back on using\n # plain send().\n raise _GiveupOnSendfile(err)\n raise err from None\n else:\n if sent == 0:\n break # EOF\n offset += sent\n total_sent += sent\n return total_sent\n finally:\n if total_sent > 0 and hasattr(file, 'seek'):\n file.seek(offset)\n else:\n def _sendfile_use_sendfile(self, file, offset=0, count=None):\n raise _GiveupOnSendfile(\n \"os.sendfile() not available on this platform\")\n\n def _sendfile_use_send(self, file, offset=0, count=None):\n self._check_sendfile_params(file, offset, count)\n if self.gettimeout() == 0:\n raise ValueError(\"non-blocking sockets are not supported\")\n if offset:\n file.seek(offset)\n blocksize = min(count, 8192) if count else 8192\n total_sent = 0\n # localize variable access to minimize overhead\n file_read = file.read\n sock_send = self.send\n try:\n while True:\n if count:\n blocksize = min(count - total_sent, blocksize)\n if blocksize <= 0:\n break\n data = memoryview(file_read(blocksize))\n if not data:\n break # EOF\n while True:\n try:\n sent = sock_send(data)\n except BlockingIOError:\n continue\n else:\n total_sent += sent\n if sent < len(data):\n data = data[sent:]\n else:\n break\n return total_sent\n finally:\n if total_sent > 0 and hasattr(file, 'seek'):\n file.seek(offset + total_sent)\n\n def _check_sendfile_params(self, file, offset, count):\n if 'b' not in getattr(file, 'mode', 'b'):\n raise ValueError(\"file should be opened in binary mode\")\n if not self.type & SOCK_STREAM:\n raise ValueError(\"only SOCK_STREAM type sockets are supported\")\n if count is not None:\n if not isinstance(count, int):\n raise TypeError(\n \"count must be a positive integer (got {!r})\".format(count))\n if count <= 0:\n raise ValueError(\n \"count must be a positive integer (got {!r})\".format(count))\n\n def sendfile(self, file, offset=0, count=None):\n \"\"\"sendfile(file[, offset[, count]]) -> sent\n\n Send a file until EOF is reached by using high-performance\n os.sendfile() and return the total number of bytes which\n were sent.\n *file* must be a regular file object opened in binary mode.\n If os.sendfile() is not available (e.g. Windows) or file is\n not a regular file socket.send() will be used instead.\n *offset* tells from where to start reading the file.\n If specified, *count* is the total number of bytes to transmit\n as opposed to sending the file until EOF is reached.\n File position is updated on return or also in case of error in\n which case file.tell() can be used to figure out the number of\n bytes which were sent.\n The socket must be of SOCK_STREAM type.\n Non-blocking sockets are not supported.\n \"\"\"\n try:\n return self._sendfile_use_sendfile(file, offset, count)\n except _GiveupOnSendfile:\n return self._sendfile_use_send(file, offset, count)\n\n def _decref_socketios(self):\n if self._io_refs > 0:\n self._io_refs -= 1\n if self._closed:\n self.close()\n\n def _real_close(self, _ss=_socket.socket):\n # This function should not reference any globals. See issue #808164.\n _ss.close(self)\n\n def close(self):\n # This function should not reference any globals. See issue #808164.\n self._closed = True\n if self._io_refs <= 0:\n self._real_close()\n\n def detach(self):\n \"\"\"detach() -> file descriptor\n\n Close the socket object without closing the underlying file descriptor.\n The object cannot be used after this call, but the file descriptor\n can be reused for other purposes. The file descriptor is returned.\n \"\"\"\n self._closed = True\n return super().detach()\n\n @property\n def family(self):\n \"\"\"Read-only access to the address family for this socket.\n \"\"\"\n return _intenum_converter(super().family, AddressFamily)\n\n @property\n def type(self):\n \"\"\"Read-only access to the socket type.\n \"\"\"\n return _intenum_converter(super().type, SocketKind)\n\n if os.name == 'nt':\n def get_inheritable(self):\n return os.get_handle_inheritable(self.fileno())\n def set_inheritable(self, inheritable):\n os.set_handle_inheritable(self.fileno(), inheritable)\n else:\n def get_inheritable(self):\n return os.get_inheritable(self.fileno())\n def set_inheritable(self, inheritable):\n os.set_inheritable(self.fileno(), inheritable)\n get_inheritable.__doc__ = \"Get the inheritable flag of the socket\"\n set_inheritable.__doc__ = \"Set the inheritable flag of the socket\"\n\ndef fromfd(fd, family, type, proto=0):\n \"\"\" fromfd(fd, family, type[, proto]) -> socket object\n\n Create a socket object from a duplicate of the given file\n descriptor. The remaining arguments are the same as for socket().\n \"\"\"\n nfd = dup(fd)\n return socket(family, type, proto, nfd)\n\nif hasattr(_socket.socket, \"sendmsg\"):\n import array\n\n def send_fds(sock, buffers, fds, flags=0, address=None):\n \"\"\" send_fds(sock, buffers, fds[, flags[, address]]) -> integer\n\n Send the list of file descriptors fds over an AF_UNIX socket.\n \"\"\"\n return sock.sendmsg(buffers, [(_socket.SOL_SOCKET,\n _socket.SCM_RIGHTS, array.array(\"i\", fds))])\n __all__.append(\"send_fds\")\n\nif hasattr(_socket.socket, \"recvmsg\"):\n import array\n\n def recv_fds(sock, bufsize, maxfds, flags=0):\n \"\"\" recv_fds(sock, bufsize, maxfds[, flags]) -> (data, list of file\n descriptors, msg_flags, address)\n\n Receive up to maxfds file descriptors returning the message\n data and a list containing the descriptors.\n \"\"\"\n # Array of ints\n fds = array.array(\"i\")\n msg, ancdata, flags, addr = sock.recvmsg(bufsize,\n _socket.CMSG_LEN(maxfds * fds.itemsize))\n for cmsg_level, cmsg_type, cmsg_data in ancdata:\n if (cmsg_level == _socket.SOL_SOCKET and cmsg_type == _socket.SCM_RIGHTS):\n fds.frombytes(cmsg_data[:\n len(cmsg_data) - (len(cmsg_data) % fds.itemsize)])\n\n return msg, list(fds), flags, addr\n __all__.append(\"recv_fds\")\n\nif hasattr(_socket.socket, \"share\"):\n def fromshare(info):\n \"\"\" fromshare(info) -> socket object\n\n Create a socket object from the bytes object returned by\n socket.share(pid).\n \"\"\"\n return socket(0, 0, 0, info)\n __all__.append(\"fromshare\")\n\n# Origin: https://gist.github.com/4325783, by Geert Jansen. Public domain.\n# This is used if _socket doesn't natively provide socketpair. It's\n# always defined so that it can be patched in for testing purposes.\ndef _fallback_socketpair(family=AF_INET, type=SOCK_STREAM, proto=0):\n if family == AF_INET:\n host = _LOCALHOST\n elif family == AF_INET6:\n host = _LOCALHOST_V6\n else:\n raise ValueError(\"Only AF_INET and AF_INET6 socket address families \"\n \"are supported\")\n if type != SOCK_STREAM:\n raise ValueError(\"Only SOCK_STREAM socket type is supported\")\n if proto != 0:\n raise ValueError(\"Only protocol zero is supported\")\n\n # We create a connected TCP socket. Note the trick with\n # setblocking(False) that prevents us from having to create a thread.\n lsock = socket(family, type, proto)\n try:\n lsock.bind((host, 0))\n lsock.listen()\n # On IPv6, ignore flow_info and scope_id\n addr, port = lsock.getsockname()[:2]\n csock = socket(family, type, proto)\n try:\n csock.setblocking(False)\n try:\n csock.connect((addr, port))\n except (BlockingIOError, InterruptedError):\n pass\n csock.setblocking(True)\n ssock, _ = lsock.accept()\n except:\n csock.close()\n raise\n finally:\n lsock.close()\n\n # Authenticating avoids using a connection from something else\n # able to connect to {host}:{port} instead of us.\n # We expect only AF_INET and AF_INET6 families.\n try:\n if (\n ssock.getsockname() != csock.getpeername()\n or csock.getsockname() != ssock.getpeername()\n ):\n raise ConnectionError(\"Unexpected peer connection\")\n except:\n # getsockname() and getpeername() can fail\n # if either socket isn't connected.\n ssock.close()\n csock.close()\n raise\n\n return (ssock, csock)\n\nif hasattr(_socket, \"socketpair\"):\n def socketpair(family=None, type=SOCK_STREAM, proto=0):\n if family is None:\n try:\n family = AF_UNIX\n except NameError:\n family = AF_INET\n a, b = _socket.socketpair(family, type, proto)\n a = socket(family, type, proto, a.detach())\n b = socket(family, type, proto, b.detach())\n return a, b\n\nelse:\n socketpair = _fallback_socketpair\n __all__.append(\"socketpair\")\n\nsocketpair.__doc__ = \"\"\"socketpair([family[, type[, proto]]]) -> (socket object, socket object)\nCreate a pair of socket objects from the sockets returned by the platform\nsocketpair() function.\nThe arguments are the same as for socket() except the default family is AF_UNIX\nif defined on the platform; otherwise, the default is AF_INET.\n\"\"\"\n\n_blocking_errnos = { EAGAIN, EWOULDBLOCK }\n\nclass SocketIO(io.RawIOBase):\n\n \"\"\"Raw I/O implementation for stream sockets.\n\n This class supports the makefile() method on sockets. It provides\n the raw I/O interface on top of a socket object.\n \"\"\"\n\n # One might wonder why not let FileIO do the job instead. There are two\n # main reasons why FileIO is not adapted:\n # - it wouldn't work under Windows (where you can't used read() and\n # write() on a socket handle)\n # - it wouldn't work with socket timeouts (FileIO would ignore the\n # timeout and consider the socket non-blocking)\n\n # XXX More docs\n\n def __init__(self, sock, mode):\n if mode not in (\"r\", \"w\", \"rw\", \"rb\", \"wb\", \"rwb\"):\n raise ValueError(\"invalid mode: %r\" % mode)\n io.RawIOBase.__init__(self)\n self._sock = sock\n if \"b\" not in mode:\n mode += \"b\"\n self._mode = mode\n self._reading = \"r\" in mode\n self._writing = \"w\" in mode\n self._timeout_occurred = False\n\n def readinto(self, b):\n \"\"\"Read up to len(b) bytes into the writable buffer *b* and return\n the number of bytes read. If the socket is non-blocking and no bytes\n are available, None is returned.\n\n If *b* is non-empty, a 0 return value indicates that the connection\n was shutdown at the other end.\n \"\"\"\n self._checkClosed()\n self._checkReadable()\n if self._timeout_occurred:\n raise OSError(\"cannot read from timed out object\")\n try:\n return self._sock.recv_into(b)\n except timeout:\n self._timeout_occurred = True\n raise\n except error as e:\n if e.errno in _blocking_errnos:\n return None\n raise\n\n def write(self, b):\n \"\"\"Write the given bytes or bytearray object *b* to the socket\n and return the number of bytes written. This can be less than\n len(b) if not all data could be written. If the socket is\n non-blocking and no bytes could be written None is returned.\n \"\"\"\n self._checkClosed()\n self._checkWritable()\n try:\n return self._sock.send(b)\n except error as e:\n # XXX what about EINTR?\n if e.errno in _blocking_errnos:\n return None\n raise\n\n def readable(self):\n \"\"\"True if the SocketIO is open for reading.\n \"\"\"\n if self.closed:\n raise ValueError(\"I/O operation on closed socket.\")\n return self._reading\n\n def writable(self):\n \"\"\"True if the SocketIO is open for writing.\n \"\"\"\n if self.closed:\n raise ValueError(\"I/O operation on closed socket.\")\n return self._writing\n\n def seekable(self):\n \"\"\"True if the SocketIO is open for seeking.\n \"\"\"\n if self.closed:\n raise ValueError(\"I/O operation on closed socket.\")\n return super().seekable()\n\n def fileno(self):\n \"\"\"Return the file descriptor of the underlying socket.\n \"\"\"\n self._checkClosed()\n return self._sock.fileno()\n\n @property\n def name(self):\n if not self.closed:\n return self.fileno()\n else:\n return -1\n\n @property\n def mode(self):\n return self._mode\n\n def close(self):\n \"\"\"Close the SocketIO object. This doesn't close the underlying\n socket, except if all references to it have disappeared.\n \"\"\"\n if self.closed:\n return\n io.RawIOBase.close(self)\n self._sock._decref_socketios()\n self._sock = None\n\n\ndef getfqdn(name=''):\n \"\"\"Get fully qualified domain name from name.\n\n An empty argument is interpreted as meaning the local host.\n\n First the hostname returned by gethostbyaddr() is checked, then\n possibly existing aliases. In case no FQDN is available and `name`\n was given, it is returned unchanged. If `name` was empty, '0.0.0.0' or '::',\n hostname from gethostname() is returned.\n \"\"\"\n name = name.strip()\n if not name or name in ('0.0.0.0', '::'):\n name = gethostname()\n try:\n hostname, aliases, ipaddrs = gethostbyaddr(name)\n except error:\n pass\n else:\n aliases.insert(0, hostname)\n for name in aliases:\n if '.' in name:\n break\n else:\n name = hostname\n return name\n\n\n_GLOBAL_DEFAULT_TIMEOUT = object()\n\ndef create_connection(address, timeout=_GLOBAL_DEFAULT_TIMEOUT,\n source_address=None, *, all_errors=False):\n \"\"\"Connect to *address* and return the socket object.\n\n Convenience function. Connect to *address* (a 2-tuple ``(host,\n port)``) and return the socket object. Passing the optional\n *timeout* parameter will set the timeout on the socket instance\n before attempting to connect. If no *timeout* is supplied, the\n global default timeout setting returned by :func:`getdefaulttimeout`\n is used. If *source_address* is set it must be a tuple of (host, port)\n for the socket to bind as a source address before making the connection.\n A host of '' or port 0 tells the OS to use the default. When a connection\n cannot be created, raises the last error if *all_errors* is False,\n and an ExceptionGroup of all errors if *all_errors* is True.\n \"\"\"\n\n host, port = address\n exceptions = []\n for res in getaddrinfo(host, port, 0, SOCK_STREAM):\n af, socktype, proto, canonname, sa = res\n sock = None\n try:\n sock = socket(af, socktype, proto)\n if timeout is not _GLOBAL_DEFAULT_TIMEOUT:\n sock.settimeout(timeout)\n if source_address:\n sock.bind(source_address)\n sock.connect(sa)\n # Break explicitly a reference cycle\n exceptions.clear()\n return sock\n\n except error as exc:\n if not all_errors:\n exceptions.clear() # raise only the last error\n exceptions.append(exc)\n if sock is not None:\n sock.close()\n\n if len(exceptions):\n try:\n if not all_errors:\n raise exceptions[0]\n raise ExceptionGroup(\"create_connection failed\", exceptions)\n finally:\n # Break explicitly a reference cycle\n exceptions.clear()\n else:\n raise error(\"getaddrinfo returns an empty list\")\n\n\ndef has_dualstack_ipv6():\n \"\"\"Return True if the platform supports creating a SOCK_STREAM socket\n which can handle both AF_INET and AF_INET6 (IPv4 / IPv6) connections.\n \"\"\"\n if not has_ipv6 \\\n or not hasattr(_socket, 'IPPROTO_IPV6') \\\n or not hasattr(_socket, 'IPV6_V6ONLY'):\n return False\n try:\n with socket(AF_INET6, SOCK_STREAM) as sock:\n sock.setsockopt(IPPROTO_IPV6, IPV6_V6ONLY, 0)\n return True\n except error:\n return False\n\n\ndef create_server(address, *, family=AF_INET, backlog=None, reuse_port=False,\n dualstack_ipv6=False):\n \"\"\"Convenience function which creates a SOCK_STREAM type socket\n bound to *address* (a 2-tuple (host, port)) and return the socket\n object.\n\n *family* should be either AF_INET or AF_INET6.\n *backlog* is the queue size passed to socket.listen().\n *reuse_port* dictates whether to use the SO_REUSEPORT socket option.\n *dualstack_ipv6*: if true and the platform supports it, it will\n create an AF_INET6 socket able to accept both IPv4 or IPv6\n connections. When false it will explicitly disable this option on\n platforms that enable it by default (e.g. Linux).\n\n >>> with create_server(('', 8000)) as server:\n ... while True:\n ... conn, addr = server.accept()\n ... # handle new connection\n \"\"\"\n if reuse_port and not hasattr(_socket, \"SO_REUSEPORT\"):\n raise ValueError(\"SO_REUSEPORT not supported on this platform\")\n if dualstack_ipv6:\n if not has_dualstack_ipv6():\n raise ValueError(\"dualstack_ipv6 not supported on this platform\")\n if family != AF_INET6:\n raise ValueError(\"dualstack_ipv6 requires AF_INET6 family\")\n sock = socket(family, SOCK_STREAM)\n try:\n # Note about Windows. We don't set SO_REUSEADDR because:\n # 1) It's unnecessary: bind() will succeed even in case of a\n # previous closed socket on the same address and still in\n # TIME_WAIT state.\n # 2) If set, another socket is free to bind() on the same\n # address, effectively preventing this one from accepting\n # connections. Also, it may set the process in a state where\n # it'll no longer respond to any signals or graceful kills.\n # See: https://learn.microsoft.com/windows/win32/winsock/using-so-reuseaddr-and-so-exclusiveaddruse\n if os.name not in ('nt', 'cygwin') and \\\n hasattr(_socket, 'SO_REUSEADDR'):\n try:\n sock.setsockopt(SOL_SOCKET, SO_REUSEADDR, 1)\n except error:\n # Fail later on bind(), for platforms which may not\n # support this option.\n pass\n if reuse_port:\n sock.setsockopt(SOL_SOCKET, SO_REUSEPORT, 1)\n if has_ipv6 and family == AF_INET6:\n if dualstack_ipv6:\n sock.setsockopt(IPPROTO_IPV6, IPV6_V6ONLY, 0)\n elif hasattr(_socket, \"IPV6_V6ONLY\") and \\\n hasattr(_socket, \"IPPROTO_IPV6\"):\n sock.setsockopt(IPPROTO_IPV6, IPV6_V6ONLY, 1)\n try:\n sock.bind(address)\n except error as err:\n msg = '%s (while attempting to bind on address %r)' % \\\n (err.strerror, address)\n raise error(err.errno, msg) from None\n if backlog is None:\n sock.listen()\n else:\n sock.listen(backlog)\n return sock\n except error:\n sock.close()\n raise\n\n\ndef getaddrinfo(host, port, family=0, type=0, proto=0, flags=0):\n \"\"\"Resolve host and port into list of address info entries.\n\n Translate the host/port argument into a sequence of 5-tuples that contain\n all the necessary arguments for creating a socket connected to that service.\n host is a domain name, a string representation of an IPv4/v6 address or\n None. port is a string service name such as 'http', a numeric port number or\n None. By passing None as the value of host and port, you can pass NULL to\n the underlying C API.\n\n The family, type and proto arguments can be optionally specified in order to\n narrow the list of addresses returned. Passing zero as a value for each of\n these arguments selects the full range of results.\n \"\"\"\n # We override this function since we want to translate the numeric family\n # and socket type values to enum constants.\n addrlist = []\n for res in _socket.getaddrinfo(host, port, family, type, proto, flags):\n af, socktype, proto, canonname, sa = res\n addrlist.append((_intenum_converter(af, AddressFamily),\n _intenum_converter(socktype, SocketKind),\n proto, canonname, sa))\n return addrlist\n", 980], "/usr/lib/python3.13/asyncio/selector_events.py": ["\"\"\"Event loop using a selector and related classes.\n\nA selector is a \"notify-when-ready\" multiplexer. For a subclass which\nalso includes support for signal handling, see the unix_events sub-module.\n\"\"\"\n\n__all__ = 'BaseSelectorEventLoop',\n\nimport collections\nimport errno\nimport functools\nimport itertools\nimport os\nimport selectors\nimport socket\nimport warnings\nimport weakref\ntry:\n import ssl\nexcept ImportError: # pragma: no cover\n ssl = None\n\nfrom . import base_events\nfrom . import constants\nfrom . import events\nfrom . import futures\nfrom . import protocols\nfrom . import sslproto\nfrom . import transports\nfrom . import trsock\nfrom .log import logger\n\n_HAS_SENDMSG = hasattr(socket.socket, 'sendmsg')\n\nif _HAS_SENDMSG:\n try:\n SC_IOV_MAX = os.sysconf('SC_IOV_MAX')\n except OSError:\n # Fallback to send\n _HAS_SENDMSG = False\n\ndef _test_selector_event(selector, fd, event):\n # Test if the selector is monitoring 'event' events\n # for the file descriptor 'fd'.\n try:\n key = selector.get_key(fd)\n except KeyError:\n return False\n else:\n return bool(key.events & event)\n\n\nclass BaseSelectorEventLoop(base_events.BaseEventLoop):\n \"\"\"Selector event loop.\n\n See events.EventLoop for API specification.\n \"\"\"\n\n def __init__(self, selector=None):\n super().__init__()\n\n if selector is None:\n selector = selectors.DefaultSelector()\n logger.debug('Using selector: %s', selector.__class__.__name__)\n self._selector = selector\n self._make_self_pipe()\n self._transports = weakref.WeakValueDictionary()\n\n def _make_socket_transport(self, sock, protocol, waiter=None, *,\n extra=None, server=None):\n self._ensure_fd_no_transport(sock)\n return _SelectorSocketTransport(self, sock, protocol, waiter,\n extra, server)\n\n def _make_ssl_transport(\n self, rawsock, protocol, sslcontext, waiter=None,\n *, server_side=False, server_hostname=None,\n extra=None, server=None,\n ssl_handshake_timeout=constants.SSL_HANDSHAKE_TIMEOUT,\n ssl_shutdown_timeout=constants.SSL_SHUTDOWN_TIMEOUT,\n ):\n self._ensure_fd_no_transport(rawsock)\n ssl_protocol = sslproto.SSLProtocol(\n self, protocol, sslcontext, waiter,\n server_side, server_hostname,\n ssl_handshake_timeout=ssl_handshake_timeout,\n ssl_shutdown_timeout=ssl_shutdown_timeout\n )\n _SelectorSocketTransport(self, rawsock, ssl_protocol,\n extra=extra, server=server)\n return ssl_protocol._app_transport\n\n def _make_datagram_transport(self, sock, protocol,\n address=None, waiter=None, extra=None):\n self._ensure_fd_no_transport(sock)\n return _SelectorDatagramTransport(self, sock, protocol,\n address, waiter, extra)\n\n def close(self):\n if self.is_running():\n raise RuntimeError(\"Cannot close a running event loop\")\n if self.is_closed():\n return\n self._close_self_pipe()\n super().close()\n if self._selector is not None:\n self._selector.close()\n self._selector = None\n\n def _close_self_pipe(self):\n self._remove_reader(self._ssock.fileno())\n self._ssock.close()\n self._ssock = None\n self._csock.close()\n self._csock = None\n self._internal_fds -= 1\n\n def _make_self_pipe(self):\n # A self-socket, really. :-)\n self._ssock, self._csock = socket.socketpair()\n self._ssock.setblocking(False)\n self._csock.setblocking(False)\n self._internal_fds += 1\n self._add_reader(self._ssock.fileno(), self._read_from_self)\n\n def _process_self_data(self, data):\n pass\n\n def _read_from_self(self):\n while True:\n try:\n data = self._ssock.recv(4096)\n if not data:\n break\n self._process_self_data(data)\n except InterruptedError:\n continue\n except BlockingIOError:\n break\n\n def _write_to_self(self):\n # This may be called from a different thread, possibly after\n # _close_self_pipe() has been called or even while it is\n # running. Guard for self._csock being None or closed. When\n # a socket is closed, send() raises OSError (with errno set to\n # EBADF, but let's not rely on the exact error code).\n csock = self._csock\n if csock is None:\n return\n\n try:\n csock.send(b'\\0')\n except OSError:\n if self._debug:\n logger.debug(\"Fail to write a null byte into the \"\n \"self-pipe socket\",\n exc_info=True)\n\n def _start_serving(self, protocol_factory, sock,\n sslcontext=None, server=None, backlog=100,\n ssl_handshake_timeout=constants.SSL_HANDSHAKE_TIMEOUT,\n ssl_shutdown_timeout=constants.SSL_SHUTDOWN_TIMEOUT):\n self._add_reader(sock.fileno(), self._accept_connection,\n protocol_factory, sock, sslcontext, server, backlog,\n ssl_handshake_timeout, ssl_shutdown_timeout)\n\n def _accept_connection(\n self, protocol_factory, sock,\n sslcontext=None, server=None, backlog=100,\n ssl_handshake_timeout=constants.SSL_HANDSHAKE_TIMEOUT,\n ssl_shutdown_timeout=constants.SSL_SHUTDOWN_TIMEOUT):\n # This method is only called once for each event loop tick where the\n # listening socket has triggered an EVENT_READ. There may be multiple\n # connections waiting for an .accept() so it is called in a loop.\n # See https://bugs.python.org/issue27906 for more details.\n for _ in range(backlog):\n try:\n conn, addr = sock.accept()\n if self._debug:\n logger.debug(\"%r got a new connection from %r: %r\",\n server, addr, conn)\n conn.setblocking(False)\n except (BlockingIOError, InterruptedError, ConnectionAbortedError):\n # Early exit because the socket accept buffer is empty.\n return None\n except OSError as exc:\n # There's nowhere to send the error, so just log it.\n if exc.errno in (errno.EMFILE, errno.ENFILE,\n errno.ENOBUFS, errno.ENOMEM):\n # Some platforms (e.g. Linux keep reporting the FD as\n # ready, so we remove the read handler temporarily.\n # We'll try again in a while.\n self.call_exception_handler({\n 'message': 'socket.accept() out of system resource',\n 'exception': exc,\n 'socket': trsock.TransportSocket(sock),\n })\n self._remove_reader(sock.fileno())\n self.call_later(constants.ACCEPT_RETRY_DELAY,\n self._start_serving,\n protocol_factory, sock, sslcontext, server,\n backlog, ssl_handshake_timeout,\n ssl_shutdown_timeout)\n else:\n raise # The event loop will catch, log and ignore it.\n else:\n extra = {'peername': addr}\n accept = self._accept_connection2(\n protocol_factory, conn, extra, sslcontext, server,\n ssl_handshake_timeout, ssl_shutdown_timeout)\n self.create_task(accept)\n\n async def _accept_connection2(\n self, protocol_factory, conn, extra,\n sslcontext=None, server=None,\n ssl_handshake_timeout=constants.SSL_HANDSHAKE_TIMEOUT,\n ssl_shutdown_timeout=constants.SSL_SHUTDOWN_TIMEOUT):\n protocol = None\n transport = None\n try:\n protocol = protocol_factory()\n waiter = self.create_future()\n if sslcontext:\n transport = self._make_ssl_transport(\n conn, protocol, sslcontext, waiter=waiter,\n server_side=True, extra=extra, server=server,\n ssl_handshake_timeout=ssl_handshake_timeout,\n ssl_shutdown_timeout=ssl_shutdown_timeout)\n else:\n transport = self._make_socket_transport(\n conn, protocol, waiter=waiter, extra=extra,\n server=server)\n\n try:\n await waiter\n except BaseException:\n transport.close()\n # gh-109534: When an exception is raised by the SSLProtocol object the\n # exception set in this future can keep the protocol object alive and\n # cause a reference cycle.\n waiter = None\n raise\n # It's now up to the protocol to handle the connection.\n\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n if self._debug:\n context = {\n 'message':\n 'Error on transport creation for incoming connection',\n 'exception': exc,\n }\n if protocol is not None:\n context['protocol'] = protocol\n if transport is not None:\n context['transport'] = transport\n self.call_exception_handler(context)\n\n def _ensure_fd_no_transport(self, fd):\n fileno = fd\n if not isinstance(fileno, int):\n try:\n fileno = int(fileno.fileno())\n except (AttributeError, TypeError, ValueError):\n # This code matches selectors._fileobj_to_fd function.\n raise ValueError(f\"Invalid file object: {fd!r}\") from None\n transport = self._transports.get(fileno)\n if transport and not transport.is_closing():\n raise RuntimeError(\n f'File descriptor {fd!r} is used by transport '\n f'{transport!r}')\n\n def _add_reader(self, fd, callback, *args):\n self._check_closed()\n handle = events.Handle(callback, args, self, None)\n key = self._selector.get_map().get(fd)\n if key is None:\n self._selector.register(fd, selectors.EVENT_READ,\n (handle, None))\n else:\n mask, (reader, writer) = key.events, key.data\n self._selector.modify(fd, mask | selectors.EVENT_READ,\n (handle, writer))\n if reader is not None:\n reader.cancel()\n return handle\n\n def _remove_reader(self, fd):\n if self.is_closed():\n return False\n key = self._selector.get_map().get(fd)\n if key is None:\n return False\n mask, (reader, writer) = key.events, key.data\n mask &= ~selectors.EVENT_READ\n if not mask:\n self._selector.unregister(fd)\n else:\n self._selector.modify(fd, mask, (None, writer))\n\n if reader is not None:\n reader.cancel()\n return True\n else:\n return False\n\n def _add_writer(self, fd, callback, *args):\n self._check_closed()\n handle = events.Handle(callback, args, self, None)\n key = self._selector.get_map().get(fd)\n if key is None:\n self._selector.register(fd, selectors.EVENT_WRITE,\n (None, handle))\n else:\n mask, (reader, writer) = key.events, key.data\n self._selector.modify(fd, mask | selectors.EVENT_WRITE,\n (reader, handle))\n if writer is not None:\n writer.cancel()\n return handle\n\n def _remove_writer(self, fd):\n \"\"\"Remove a writer callback.\"\"\"\n if self.is_closed():\n return False\n key = self._selector.get_map().get(fd)\n if key is None:\n return False\n mask, (reader, writer) = key.events, key.data\n # Remove both writer and connector.\n mask &= ~selectors.EVENT_WRITE\n if not mask:\n self._selector.unregister(fd)\n else:\n self._selector.modify(fd, mask, (reader, None))\n\n if writer is not None:\n writer.cancel()\n return True\n else:\n return False\n\n def add_reader(self, fd, callback, *args):\n \"\"\"Add a reader callback.\"\"\"\n self._ensure_fd_no_transport(fd)\n self._add_reader(fd, callback, *args)\n\n def remove_reader(self, fd):\n \"\"\"Remove a reader callback.\"\"\"\n self._ensure_fd_no_transport(fd)\n return self._remove_reader(fd)\n\n def add_writer(self, fd, callback, *args):\n \"\"\"Add a writer callback..\"\"\"\n self._ensure_fd_no_transport(fd)\n self._add_writer(fd, callback, *args)\n\n def remove_writer(self, fd):\n \"\"\"Remove a writer callback.\"\"\"\n self._ensure_fd_no_transport(fd)\n return self._remove_writer(fd)\n\n async def sock_recv(self, sock, n):\n \"\"\"Receive data from the socket.\n\n The return value is a bytes object representing the data received.\n The maximum amount of data to be received at once is specified by\n nbytes.\n \"\"\"\n base_events._check_ssl_socket(sock)\n if self._debug and sock.gettimeout() != 0:\n raise ValueError(\"the socket must be non-blocking\")\n try:\n return sock.recv(n)\n except (BlockingIOError, InterruptedError):\n pass\n fut = self.create_future()\n fd = sock.fileno()\n self._ensure_fd_no_transport(fd)\n handle = self._add_reader(fd, self._sock_recv, fut, sock, n)\n fut.add_done_callback(\n functools.partial(self._sock_read_done, fd, handle=handle))\n return await fut\n\n def _sock_read_done(self, fd, fut, handle=None):\n if handle is None or not handle.cancelled():\n self.remove_reader(fd)\n\n def _sock_recv(self, fut, sock, n):\n # _sock_recv() can add itself as an I/O callback if the operation can't\n # be done immediately. Don't use it directly, call sock_recv().\n if fut.done():\n return\n try:\n data = sock.recv(n)\n except (BlockingIOError, InterruptedError):\n return # try again next time\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n fut.set_exception(exc)\n else:\n fut.set_result(data)\n\n async def sock_recv_into(self, sock, buf):\n \"\"\"Receive data from the socket.\n\n The received data is written into *buf* (a writable buffer).\n The return value is the number of bytes written.\n \"\"\"\n base_events._check_ssl_socket(sock)\n if self._debug and sock.gettimeout() != 0:\n raise ValueError(\"the socket must be non-blocking\")\n try:\n return sock.recv_into(buf)\n except (BlockingIOError, InterruptedError):\n pass\n fut = self.create_future()\n fd = sock.fileno()\n self._ensure_fd_no_transport(fd)\n handle = self._add_reader(fd, self._sock_recv_into, fut, sock, buf)\n fut.add_done_callback(\n functools.partial(self._sock_read_done, fd, handle=handle))\n return await fut\n\n def _sock_recv_into(self, fut, sock, buf):\n # _sock_recv_into() can add itself as an I/O callback if the operation\n # can't be done immediately. Don't use it directly, call\n # sock_recv_into().\n if fut.done():\n return\n try:\n nbytes = sock.recv_into(buf)\n except (BlockingIOError, InterruptedError):\n return # try again next time\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n fut.set_exception(exc)\n else:\n fut.set_result(nbytes)\n\n async def sock_recvfrom(self, sock, bufsize):\n \"\"\"Receive a datagram from a datagram socket.\n\n The return value is a tuple of (bytes, address) representing the\n datagram received and the address it came from.\n The maximum amount of data to be received at once is specified by\n nbytes.\n \"\"\"\n base_events._check_ssl_socket(sock)\n if self._debug and sock.gettimeout() != 0:\n raise ValueError(\"the socket must be non-blocking\")\n try:\n return sock.recvfrom(bufsize)\n except (BlockingIOError, InterruptedError):\n pass\n fut = self.create_future()\n fd = sock.fileno()\n self._ensure_fd_no_transport(fd)\n handle = self._add_reader(fd, self._sock_recvfrom, fut, sock, bufsize)\n fut.add_done_callback(\n functools.partial(self._sock_read_done, fd, handle=handle))\n return await fut\n\n def _sock_recvfrom(self, fut, sock, bufsize):\n # _sock_recvfrom() can add itself as an I/O callback if the operation\n # can't be done immediately. Don't use it directly, call\n # sock_recvfrom().\n if fut.done():\n return\n try:\n result = sock.recvfrom(bufsize)\n except (BlockingIOError, InterruptedError):\n return # try again next time\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n fut.set_exception(exc)\n else:\n fut.set_result(result)\n\n async def sock_recvfrom_into(self, sock, buf, nbytes=0):\n \"\"\"Receive data from the socket.\n\n The received data is written into *buf* (a writable buffer).\n The return value is a tuple of (number of bytes written, address).\n \"\"\"\n base_events._check_ssl_socket(sock)\n if self._debug and sock.gettimeout() != 0:\n raise ValueError(\"the socket must be non-blocking\")\n if not nbytes:\n nbytes = len(buf)\n\n try:\n return sock.recvfrom_into(buf, nbytes)\n except (BlockingIOError, InterruptedError):\n pass\n fut = self.create_future()\n fd = sock.fileno()\n self._ensure_fd_no_transport(fd)\n handle = self._add_reader(fd, self._sock_recvfrom_into, fut, sock, buf,\n nbytes)\n fut.add_done_callback(\n functools.partial(self._sock_read_done, fd, handle=handle))\n return await fut\n\n def _sock_recvfrom_into(self, fut, sock, buf, bufsize):\n # _sock_recv_into() can add itself as an I/O callback if the operation\n # can't be done immediately. Don't use it directly, call\n # sock_recv_into().\n if fut.done():\n return\n try:\n result = sock.recvfrom_into(buf, bufsize)\n except (BlockingIOError, InterruptedError):\n return # try again next time\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n fut.set_exception(exc)\n else:\n fut.set_result(result)\n\n async def sock_sendall(self, sock, data):\n \"\"\"Send data to the socket.\n\n The socket must be connected to a remote socket. This method continues\n to send data from data until either all data has been sent or an\n error occurs. None is returned on success. On error, an exception is\n raised, and there is no way to determine how much data, if any, was\n successfully processed by the receiving end of the connection.\n \"\"\"\n base_events._check_ssl_socket(sock)\n if self._debug and sock.gettimeout() != 0:\n raise ValueError(\"the socket must be non-blocking\")\n try:\n n = sock.send(data)\n except (BlockingIOError, InterruptedError):\n n = 0\n\n if n == len(data):\n # all data sent\n return\n\n fut = self.create_future()\n fd = sock.fileno()\n self._ensure_fd_no_transport(fd)\n # use a trick with a list in closure to store a mutable state\n handle = self._add_writer(fd, self._sock_sendall, fut, sock,\n memoryview(data), [n])\n fut.add_done_callback(\n functools.partial(self._sock_write_done, fd, handle=handle))\n return await fut\n\n def _sock_sendall(self, fut, sock, view, pos):\n if fut.done():\n # Future cancellation can be scheduled on previous loop iteration\n return\n start = pos[0]\n try:\n n = sock.send(view[start:])\n except (BlockingIOError, InterruptedError):\n return\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n fut.set_exception(exc)\n return\n\n start += n\n\n if start == len(view):\n fut.set_result(None)\n else:\n pos[0] = start\n\n async def sock_sendto(self, sock, data, address):\n \"\"\"Send data to the socket.\n\n The socket must be connected to a remote socket. This method continues\n to send data from data until either all data has been sent or an\n error occurs. None is returned on success. On error, an exception is\n raised, and there is no way to determine how much data, if any, was\n successfully processed by the receiving end of the connection.\n \"\"\"\n base_events._check_ssl_socket(sock)\n if self._debug and sock.gettimeout() != 0:\n raise ValueError(\"the socket must be non-blocking\")\n try:\n return sock.sendto(data, address)\n except (BlockingIOError, InterruptedError):\n pass\n\n fut = self.create_future()\n fd = sock.fileno()\n self._ensure_fd_no_transport(fd)\n # use a trick with a list in closure to store a mutable state\n handle = self._add_writer(fd, self._sock_sendto, fut, sock, data,\n address)\n fut.add_done_callback(\n functools.partial(self._sock_write_done, fd, handle=handle))\n return await fut\n\n def _sock_sendto(self, fut, sock, data, address):\n if fut.done():\n # Future cancellation can be scheduled on previous loop iteration\n return\n try:\n n = sock.sendto(data, 0, address)\n except (BlockingIOError, InterruptedError):\n return\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n fut.set_exception(exc)\n else:\n fut.set_result(n)\n\n async def sock_connect(self, sock, address):\n \"\"\"Connect to a remote socket at address.\n\n This method is a coroutine.\n \"\"\"\n base_events._check_ssl_socket(sock)\n if self._debug and sock.gettimeout() != 0:\n raise ValueError(\"the socket must be non-blocking\")\n\n if sock.family == socket.AF_INET or (\n base_events._HAS_IPv6 and sock.family == socket.AF_INET6):\n resolved = await self._ensure_resolved(\n address, family=sock.family, type=sock.type, proto=sock.proto,\n loop=self,\n )\n _, _, _, _, address = resolved[0]\n\n fut = self.create_future()\n self._sock_connect(fut, sock, address)\n try:\n return await fut\n finally:\n # Needed to break cycles when an exception occurs.\n fut = None\n\n def _sock_connect(self, fut, sock, address):\n fd = sock.fileno()\n try:\n sock.connect(address)\n except (BlockingIOError, InterruptedError):\n # Issue #23618: When the C function connect() fails with EINTR, the\n # connection runs in background. We have to wait until the socket\n # becomes writable to be notified when the connection succeed or\n # fails.\n self._ensure_fd_no_transport(fd)\n handle = self._add_writer(\n fd, self._sock_connect_cb, fut, sock, address)\n fut.add_done_callback(\n functools.partial(self._sock_write_done, fd, handle=handle))\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n fut.set_exception(exc)\n else:\n fut.set_result(None)\n finally:\n fut = None\n\n def _sock_write_done(self, fd, fut, handle=None):\n if handle is None or not handle.cancelled():\n self.remove_writer(fd)\n\n def _sock_connect_cb(self, fut, sock, address):\n if fut.done():\n return\n\n try:\n err = sock.getsockopt(socket.SOL_SOCKET, socket.SO_ERROR)\n if err != 0:\n # Jump to any except clause below.\n raise OSError(err, f'Connect call failed {address}')\n except (BlockingIOError, InterruptedError):\n # socket is still registered, the callback will be retried later\n pass\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n fut.set_exception(exc)\n else:\n fut.set_result(None)\n finally:\n fut = None\n\n async def sock_accept(self, sock):\n \"\"\"Accept a connection.\n\n The socket must be bound to an address and listening for connections.\n The return value is a pair (conn, address) where conn is a new socket\n object usable to send and receive data on the connection, and address\n is the address bound to the socket on the other end of the connection.\n \"\"\"\n base_events._check_ssl_socket(sock)\n if self._debug and sock.gettimeout() != 0:\n raise ValueError(\"the socket must be non-blocking\")\n fut = self.create_future()\n self._sock_accept(fut, sock)\n return await fut\n\n def _sock_accept(self, fut, sock):\n fd = sock.fileno()\n try:\n conn, address = sock.accept()\n conn.setblocking(False)\n except (BlockingIOError, InterruptedError):\n self._ensure_fd_no_transport(fd)\n handle = self._add_reader(fd, self._sock_accept, fut, sock)\n fut.add_done_callback(\n functools.partial(self._sock_read_done, fd, handle=handle))\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n fut.set_exception(exc)\n else:\n fut.set_result((conn, address))\n\n async def _sendfile_native(self, transp, file, offset, count):\n del self._transports[transp._sock_fd]\n resume_reading = transp.is_reading()\n transp.pause_reading()\n await transp._make_empty_waiter()\n try:\n return await self.sock_sendfile(transp._sock, file, offset, count,\n fallback=False)\n finally:\n transp._reset_empty_waiter()\n if resume_reading:\n transp.resume_reading()\n self._transports[transp._sock_fd] = transp\n\n def _process_events(self, event_list):\n for key, mask in event_list:\n fileobj, (reader, writer) = key.fileobj, key.data\n if mask & selectors.EVENT_READ and reader is not None:\n if reader._cancelled:\n self._remove_reader(fileobj)\n else:\n self._add_callback(reader)\n if mask & selectors.EVENT_WRITE and writer is not None:\n if writer._cancelled:\n self._remove_writer(fileobj)\n else:\n self._add_callback(writer)\n\n def _stop_serving(self, sock):\n self._remove_reader(sock.fileno())\n sock.close()\n\n\nclass _SelectorTransport(transports._FlowControlMixin,\n transports.Transport):\n\n max_size = 256 * 1024 # Buffer size passed to recv().\n\n # Attribute used in the destructor: it must be set even if the constructor\n # is not called (see _SelectorSslTransport which may start by raising an\n # exception)\n _sock = None\n\n def __init__(self, loop, sock, protocol, extra=None, server=None):\n super().__init__(extra, loop)\n self._extra['socket'] = trsock.TransportSocket(sock)\n try:\n self._extra['sockname'] = sock.getsockname()\n except OSError:\n self._extra['sockname'] = None\n if 'peername' not in self._extra:\n try:\n self._extra['peername'] = sock.getpeername()\n except socket.error:\n self._extra['peername'] = None\n self._sock = sock\n self._sock_fd = sock.fileno()\n\n self._protocol_connected = False\n self.set_protocol(protocol)\n\n self._server = server\n self._buffer = collections.deque()\n self._conn_lost = 0 # Set when call to connection_lost scheduled.\n self._closing = False # Set when close() called.\n self._paused = False # Set when pause_reading() called\n\n if self._server is not None:\n self._server._attach(self)\n loop._transports[self._sock_fd] = self\n\n def __repr__(self):\n info = [self.__class__.__name__]\n if self._sock is None:\n info.append('closed')\n elif self._closing:\n info.append('closing')\n info.append(f'fd={self._sock_fd}')\n # test if the transport was closed\n if self._loop is not None and not self._loop.is_closed():\n polling = _test_selector_event(self._loop._selector,\n self._sock_fd, selectors.EVENT_READ)\n if polling:\n info.append('read=polling')\n else:\n info.append('read=idle')\n\n polling = _test_selector_event(self._loop._selector,\n self._sock_fd,\n selectors.EVENT_WRITE)\n if polling:\n state = 'polling'\n else:\n state = 'idle'\n\n bufsize = self.get_write_buffer_size()\n info.append(f'write=<{state}, bufsize={bufsize}>')\n return '<{}>'.format(' '.join(info))\n\n def abort(self):\n self._force_close(None)\n\n def set_protocol(self, protocol):\n self._protocol = protocol\n self._protocol_connected = True\n\n def get_protocol(self):\n return self._protocol\n\n def is_closing(self):\n return self._closing\n\n def is_reading(self):\n return not self.is_closing() and not self._paused\n\n def pause_reading(self):\n if not self.is_reading():\n return\n self._paused = True\n self._loop._remove_reader(self._sock_fd)\n if self._loop.get_debug():\n logger.debug(\"%r pauses reading\", self)\n\n def resume_reading(self):\n if self._closing or not self._paused:\n return\n self._paused = False\n self._add_reader(self._sock_fd, self._read_ready)\n if self._loop.get_debug():\n logger.debug(\"%r resumes reading\", self)\n\n def close(self):\n if self._closing:\n return\n self._closing = True\n self._loop._remove_reader(self._sock_fd)\n if not self._buffer:\n self._conn_lost += 1\n self._loop._remove_writer(self._sock_fd)\n self._loop.call_soon(self._call_connection_lost, None)\n\n def __del__(self, _warn=warnings.warn):\n if self._sock is not None:\n _warn(f\"unclosed transport {self!r}\", ResourceWarning, source=self)\n self._sock.close()\n if self._server is not None:\n self._server._detach(self)\n\n def _fatal_error(self, exc, message='Fatal error on transport'):\n # Should be called from exception handler only.\n if isinstance(exc, OSError):\n if self._loop.get_debug():\n logger.debug(\"%r: %s\", self, message, exc_info=True)\n else:\n self._loop.call_exception_handler({\n 'message': message,\n 'exception': exc,\n 'transport': self,\n 'protocol': self._protocol,\n })\n self._force_close(exc)\n\n def _force_close(self, exc):\n if self._conn_lost:\n return\n if self._buffer:\n self._buffer.clear()\n self._loop._remove_writer(self._sock_fd)\n if not self._closing:\n self._closing = True\n self._loop._remove_reader(self._sock_fd)\n self._conn_lost += 1\n self._loop.call_soon(self._call_connection_lost, exc)\n\n def _call_connection_lost(self, exc):\n try:\n if self._protocol_connected:\n self._protocol.connection_lost(exc)\n finally:\n self._sock.close()\n self._sock = None\n self._protocol = None\n self._loop = None\n server = self._server\n if server is not None:\n server._detach(self)\n self._server = None\n\n def get_write_buffer_size(self):\n return sum(map(len, self._buffer))\n\n def _add_reader(self, fd, callback, *args):\n if not self.is_reading():\n return\n self._loop._add_reader(fd, callback, *args)\n\n\nclass _SelectorSocketTransport(_SelectorTransport):\n\n _start_tls_compatible = True\n _sendfile_compatible = constants._SendfileMode.TRY_NATIVE\n\n def __init__(self, loop, sock, protocol, waiter=None,\n extra=None, server=None):\n\n self._read_ready_cb = None\n super().__init__(loop, sock, protocol, extra, server)\n self._eof = False\n self._empty_waiter = None\n if _HAS_SENDMSG:\n self._write_ready = self._write_sendmsg\n else:\n self._write_ready = self._write_send\n # Disable the Nagle algorithm -- small writes will be\n # sent without waiting for the TCP ACK. This generally\n # decreases the latency (in some cases significantly.)\n base_events._set_nodelay(self._sock)\n\n self._loop.call_soon(self._protocol.connection_made, self)\n # only start reading when connection_made() has been called\n self._loop.call_soon(self._add_reader,\n self._sock_fd, self._read_ready)\n if waiter is not None:\n # only wake up the waiter when connection_made() has been called\n self._loop.call_soon(futures._set_result_unless_cancelled,\n waiter, None)\n\n def set_protocol(self, protocol):\n if isinstance(protocol, protocols.BufferedProtocol):\n self._read_ready_cb = self._read_ready__get_buffer\n else:\n self._read_ready_cb = self._read_ready__data_received\n\n super().set_protocol(protocol)\n\n def _read_ready(self):\n self._read_ready_cb()\n\n def _read_ready__get_buffer(self):\n if self._conn_lost:\n return\n\n try:\n buf = self._protocol.get_buffer(-1)\n if not len(buf):\n raise RuntimeError('get_buffer() returned an empty buffer')\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n self._fatal_error(\n exc, 'Fatal error: protocol.get_buffer() call failed.')\n return\n\n try:\n nbytes = self._sock.recv_into(buf)\n except (BlockingIOError, InterruptedError):\n return\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n self._fatal_error(exc, 'Fatal read error on socket transport')\n return\n\n if not nbytes:\n self._read_ready__on_eof()\n return\n\n try:\n self._protocol.buffer_updated(nbytes)\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n self._fatal_error(\n exc, 'Fatal error: protocol.buffer_updated() call failed.')\n\n def _read_ready__data_received(self):\n if self._conn_lost:\n return\n try:\n data = self._sock.recv(self.max_size)\n except (BlockingIOError, InterruptedError):\n return\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n self._fatal_error(exc, 'Fatal read error on socket transport')\n return\n\n if not data:\n self._read_ready__on_eof()\n return\n\n try:\n self._protocol.data_received(data)\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n self._fatal_error(\n exc, 'Fatal error: protocol.data_received() call failed.')\n\n def _read_ready__on_eof(self):\n if self._loop.get_debug():\n logger.debug(\"%r received EOF\", self)\n\n try:\n keep_open = self._protocol.eof_received()\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n self._fatal_error(\n exc, 'Fatal error: protocol.eof_received() call failed.')\n return\n\n if keep_open:\n # We're keeping the connection open so the\n # protocol can write more, but we still can't\n # receive more, so remove the reader callback.\n self._loop._remove_reader(self._sock_fd)\n else:\n self.close()\n\n def write(self, data):\n if not isinstance(data, (bytes, bytearray, memoryview)):\n raise TypeError(f'data argument must be a bytes-like object, '\n f'not {type(data).__name__!r}')\n if self._eof:\n raise RuntimeError('Cannot call write() after write_eof()')\n if self._empty_waiter is not None:\n raise RuntimeError('unable to write; sendfile is in progress')\n if not data:\n return\n\n if self._conn_lost:\n if self._conn_lost >= constants.LOG_THRESHOLD_FOR_CONNLOST_WRITES:\n logger.warning('socket.send() raised exception.')\n self._conn_lost += 1\n return\n\n if not self._buffer:\n # Optimization: try to send now.\n try:\n n = self._sock.send(data)\n except (BlockingIOError, InterruptedError):\n pass\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n self._fatal_error(exc, 'Fatal write error on socket transport')\n return\n else:\n data = memoryview(data)[n:]\n if not data:\n return\n # Not all was written; register write handler.\n self._loop._add_writer(self._sock_fd, self._write_ready)\n\n # Add it to the buffer.\n self._buffer.append(data)\n self._maybe_pause_protocol()\n\n def _get_sendmsg_buffer(self):\n return itertools.islice(self._buffer, SC_IOV_MAX)\n\n def _write_sendmsg(self):\n assert self._buffer, 'Data should not be empty'\n if self._conn_lost:\n return\n try:\n nbytes = self._sock.sendmsg(self._get_sendmsg_buffer())\n self._adjust_leftover_buffer(nbytes)\n except (BlockingIOError, InterruptedError):\n pass\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n self._loop._remove_writer(self._sock_fd)\n self._buffer.clear()\n self._fatal_error(exc, 'Fatal write error on socket transport')\n if self._empty_waiter is not None:\n self._empty_waiter.set_exception(exc)\n else:\n self._maybe_resume_protocol() # May append to buffer.\n if not self._buffer:\n self._loop._remove_writer(self._sock_fd)\n if self._empty_waiter is not None:\n self._empty_waiter.set_result(None)\n if self._closing:\n self._call_connection_lost(None)\n elif self._eof:\n self._sock.shutdown(socket.SHUT_WR)\n\n def _adjust_leftover_buffer(self, nbytes: int) -> None:\n buffer = self._buffer\n while nbytes:\n b = buffer.popleft()\n b_len = len(b)\n if b_len <= nbytes:\n nbytes -= b_len\n else:\n buffer.appendleft(b[nbytes:])\n break\n\n def _write_send(self):\n assert self._buffer, 'Data should not be empty'\n if self._conn_lost:\n return\n try:\n buffer = self._buffer.popleft()\n n = self._sock.send(buffer)\n if n != len(buffer):\n # Not all data was written\n self._buffer.appendleft(buffer[n:])\n except (BlockingIOError, InterruptedError):\n pass\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n self._loop._remove_writer(self._sock_fd)\n self._buffer.clear()\n self._fatal_error(exc, 'Fatal write error on socket transport')\n if self._empty_waiter is not None:\n self._empty_waiter.set_exception(exc)\n else:\n self._maybe_resume_protocol() # May append to buffer.\n if not self._buffer:\n self._loop._remove_writer(self._sock_fd)\n if self._empty_waiter is not None:\n self._empty_waiter.set_result(None)\n if self._closing:\n self._call_connection_lost(None)\n elif self._eof:\n self._sock.shutdown(socket.SHUT_WR)\n\n def write_eof(self):\n if self._closing or self._eof:\n return\n self._eof = True\n if not self._buffer:\n self._sock.shutdown(socket.SHUT_WR)\n\n def writelines(self, list_of_data):\n if self._eof:\n raise RuntimeError('Cannot call writelines() after write_eof()')\n if self._empty_waiter is not None:\n raise RuntimeError('unable to writelines; sendfile is in progress')\n if not list_of_data:\n return\n self._buffer.extend([memoryview(data) for data in list_of_data])\n self._write_ready()\n # If the entire buffer couldn't be written, register a write handler\n if self._buffer:\n self._loop._add_writer(self._sock_fd, self._write_ready)\n\n def can_write_eof(self):\n return True\n\n def _call_connection_lost(self, exc):\n super()._call_connection_lost(exc)\n if self._empty_waiter is not None:\n self._empty_waiter.set_exception(\n ConnectionError(\"Connection is closed by peer\"))\n\n def _make_empty_waiter(self):\n if self._empty_waiter is not None:\n raise RuntimeError(\"Empty waiter is already set\")\n self._empty_waiter = self._loop.create_future()\n if not self._buffer:\n self._empty_waiter.set_result(None)\n return self._empty_waiter\n\n def _reset_empty_waiter(self):\n self._empty_waiter = None\n\n def close(self):\n self._read_ready_cb = None\n self._write_ready = None\n super().close()\n\n\nclass _SelectorDatagramTransport(_SelectorTransport, transports.DatagramTransport):\n\n _buffer_factory = collections.deque\n\n def __init__(self, loop, sock, protocol, address=None,\n waiter=None, extra=None):\n super().__init__(loop, sock, protocol, extra)\n self._address = address\n self._buffer_size = 0\n self._loop.call_soon(self._protocol.connection_made, self)\n # only start reading when connection_made() has been called\n self._loop.call_soon(self._add_reader,\n self._sock_fd, self._read_ready)\n if waiter is not None:\n # only wake up the waiter when connection_made() has been called\n self._loop.call_soon(futures._set_result_unless_cancelled,\n waiter, None)\n\n def get_write_buffer_size(self):\n return self._buffer_size\n\n def _read_ready(self):\n if self._conn_lost:\n return\n try:\n data, addr = self._sock.recvfrom(self.max_size)\n except (BlockingIOError, InterruptedError):\n pass\n except OSError as exc:\n self._protocol.error_received(exc)\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n self._fatal_error(exc, 'Fatal read error on datagram transport')\n else:\n self._protocol.datagram_received(data, addr)\n\n def sendto(self, data, addr=None):\n if not isinstance(data, (bytes, bytearray, memoryview)):\n raise TypeError(f'data argument must be a bytes-like object, '\n f'not {type(data).__name__!r}')\n\n if self._address:\n if addr not in (None, self._address):\n raise ValueError(\n f'Invalid address: must be None or {self._address}')\n addr = self._address\n\n if self._conn_lost and self._address:\n if self._conn_lost >= constants.LOG_THRESHOLD_FOR_CONNLOST_WRITES:\n logger.warning('socket.send() raised exception.')\n self._conn_lost += 1\n return\n\n if not self._buffer:\n # Attempt to send it right away first.\n try:\n if self._extra['peername']:\n self._sock.send(data)\n else:\n self._sock.sendto(data, addr)\n return\n except (BlockingIOError, InterruptedError):\n self._loop._add_writer(self._sock_fd, self._sendto_ready)\n except OSError as exc:\n self._protocol.error_received(exc)\n return\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n self._fatal_error(\n exc, 'Fatal write error on datagram transport')\n return\n\n # Ensure that what we buffer is immutable.\n self._buffer.append((bytes(data), addr))\n self._buffer_size += len(data) + 8 # include header bytes\n self._maybe_pause_protocol()\n\n def _sendto_ready(self):\n while self._buffer:\n data, addr = self._buffer.popleft()\n self._buffer_size -= len(data)\n try:\n if self._extra['peername']:\n self._sock.send(data)\n else:\n self._sock.sendto(data, addr)\n except (BlockingIOError, InterruptedError):\n self._buffer.appendleft((data, addr)) # Try again later.\n self._buffer_size += len(data)\n break\n except OSError as exc:\n self._protocol.error_received(exc)\n return\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n self._fatal_error(\n exc, 'Fatal write error on datagram transport')\n return\n\n self._maybe_resume_protocol() # May append to buffer.\n if not self._buffer:\n self._loop._remove_writer(self._sock_fd)\n if self._closing:\n self._call_connection_lost(None)\n", 1311], "/usr/lib/python3.13/weakref.py": ["\"\"\"Weak reference support for Python.\n\nThis module is an implementation of PEP 205:\n\nhttps://peps.python.org/pep-0205/\n\"\"\"\n\n# Naming convention: Variables named \"wr\" are weak reference objects;\n# they are called this instead of \"ref\" to avoid name collisions with\n# the module-global ref() function imported from _weakref.\n\nfrom _weakref import (\n getweakrefcount,\n getweakrefs,\n ref,\n proxy,\n CallableProxyType,\n ProxyType,\n ReferenceType,\n _remove_dead_weakref)\n\nfrom _weakrefset import WeakSet, _IterationGuard\n\nimport _collections_abc # Import after _weakref to avoid circular import.\nimport sys\nimport itertools\n\nProxyTypes = (ProxyType, CallableProxyType)\n\n__all__ = [\"ref\", \"proxy\", \"getweakrefcount\", \"getweakrefs\",\n \"WeakKeyDictionary\", \"ReferenceType\", \"ProxyType\",\n \"CallableProxyType\", \"ProxyTypes\", \"WeakValueDictionary\",\n \"WeakSet\", \"WeakMethod\", \"finalize\"]\n\n\n_collections_abc.MutableSet.register(WeakSet)\n\nclass WeakMethod(ref):\n \"\"\"\n A custom `weakref.ref` subclass which simulates a weak reference to\n a bound method, working around the lifetime problem of bound methods.\n \"\"\"\n\n __slots__ = \"_func_ref\", \"_meth_type\", \"_alive\", \"__weakref__\"\n\n def __new__(cls, meth, callback=None):\n try:\n obj = meth.__self__\n func = meth.__func__\n except AttributeError:\n raise TypeError(\"argument should be a bound method, not {}\"\n .format(type(meth))) from None\n def _cb(arg):\n # The self-weakref trick is needed to avoid creating a reference\n # cycle.\n self = self_wr()\n if self._alive:\n self._alive = False\n if callback is not None:\n callback(self)\n self = ref.__new__(cls, obj, _cb)\n self._func_ref = ref(func, _cb)\n self._meth_type = type(meth)\n self._alive = True\n self_wr = ref(self)\n return self\n\n def __call__(self):\n obj = super().__call__()\n func = self._func_ref()\n if obj is None or func is None:\n return None\n return self._meth_type(func, obj)\n\n def __eq__(self, other):\n if isinstance(other, WeakMethod):\n if not self._alive or not other._alive:\n return self is other\n return ref.__eq__(self, other) and self._func_ref == other._func_ref\n return NotImplemented\n\n def __ne__(self, other):\n if isinstance(other, WeakMethod):\n if not self._alive or not other._alive:\n return self is not other\n return ref.__ne__(self, other) or self._func_ref != other._func_ref\n return NotImplemented\n\n __hash__ = ref.__hash__\n\n\nclass WeakValueDictionary(_collections_abc.MutableMapping):\n \"\"\"Mapping class that references values weakly.\n\n Entries in the dictionary will be discarded when no strong\n reference to the value exists anymore\n \"\"\"\n # We inherit the constructor without worrying about the input\n # dictionary; since it uses our .update() method, we get the right\n # checks (if the other dictionary is a WeakValueDictionary,\n # objects are unwrapped on the way out, and we always wrap on the\n # way in).\n\n def __init__(self, other=(), /, **kw):\n def remove(wr, selfref=ref(self), _atomic_removal=_remove_dead_weakref):\n self = selfref()\n if self is not None:\n if self._iterating:\n self._pending_removals.append(wr.key)\n else:\n # Atomic removal is necessary since this function\n # can be called asynchronously by the GC\n _atomic_removal(self.data, wr.key)\n self._remove = remove\n # A list of keys to be removed\n self._pending_removals = []\n self._iterating = set()\n self.data = {}\n self.update(other, **kw)\n\n def _commit_removals(self, _atomic_removal=_remove_dead_weakref):\n pop = self._pending_removals.pop\n d = self.data\n # We shouldn't encounter any KeyError, because this method should\n # always be called *before* mutating the dict.\n while True:\n try:\n key = pop()\n except IndexError:\n return\n _atomic_removal(d, key)\n\n def __getitem__(self, key):\n if self._pending_removals:\n self._commit_removals()\n o = self.data[key]()\n if o is None:\n raise KeyError(key)\n else:\n return o\n\n def __delitem__(self, key):\n if self._pending_removals:\n self._commit_removals()\n del self.data[key]\n\n def __len__(self):\n if self._pending_removals:\n self._commit_removals()\n return len(self.data)\n\n def __contains__(self, key):\n if self._pending_removals:\n self._commit_removals()\n try:\n o = self.data[key]()\n except KeyError:\n return False\n return o is not None\n\n def __repr__(self):\n return \"<%s at %#x>\" % (self.__class__.__name__, id(self))\n\n def __setitem__(self, key, value):\n if self._pending_removals:\n self._commit_removals()\n self.data[key] = KeyedRef(value, self._remove, key)\n\n def copy(self):\n if self._pending_removals:\n self._commit_removals()\n new = WeakValueDictionary()\n with _IterationGuard(self):\n for key, wr in self.data.items():\n o = wr()\n if o is not None:\n new[key] = o\n return new\n\n __copy__ = copy\n\n def __deepcopy__(self, memo):\n from copy import deepcopy\n if self._pending_removals:\n self._commit_removals()\n new = self.__class__()\n with _IterationGuard(self):\n for key, wr in self.data.items():\n o = wr()\n if o is not None:\n new[deepcopy(key, memo)] = o\n return new\n\n def get(self, key, default=None):\n if self._pending_removals:\n self._commit_removals()\n try:\n wr = self.data[key]\n except KeyError:\n return default\n else:\n o = wr()\n if o is None:\n # This should only happen\n return default\n else:\n return o\n\n def items(self):\n if self._pending_removals:\n self._commit_removals()\n with _IterationGuard(self):\n for k, wr in self.data.items():\n v = wr()\n if v is not None:\n yield k, v\n\n def keys(self):\n if self._pending_removals:\n self._commit_removals()\n with _IterationGuard(self):\n for k, wr in self.data.items():\n if wr() is not None:\n yield k\n\n __iter__ = keys\n\n def itervaluerefs(self):\n \"\"\"Return an iterator that yields the weak references to the values.\n\n The references are not guaranteed to be 'live' at the time\n they are used, so the result of calling the references needs\n to be checked before being used. This can be used to avoid\n creating references that will cause the garbage collector to\n keep the values around longer than needed.\n\n \"\"\"\n if self._pending_removals:\n self._commit_removals()\n with _IterationGuard(self):\n yield from self.data.values()\n\n def values(self):\n if self._pending_removals:\n self._commit_removals()\n with _IterationGuard(self):\n for wr in self.data.values():\n obj = wr()\n if obj is not None:\n yield obj\n\n def popitem(self):\n if self._pending_removals:\n self._commit_removals()\n while True:\n key, wr = self.data.popitem()\n o = wr()\n if o is not None:\n return key, o\n\n def pop(self, key, *args):\n if self._pending_removals:\n self._commit_removals()\n try:\n o = self.data.pop(key)()\n except KeyError:\n o = None\n if o is None:\n if args:\n return args[0]\n else:\n raise KeyError(key)\n else:\n return o\n\n def setdefault(self, key, default=None):\n try:\n o = self.data[key]()\n except KeyError:\n o = None\n if o is None:\n if self._pending_removals:\n self._commit_removals()\n self.data[key] = KeyedRef(default, self._remove, key)\n return default\n else:\n return o\n\n def update(self, other=None, /, **kwargs):\n if self._pending_removals:\n self._commit_removals()\n d = self.data\n if other is not None:\n if not hasattr(other, \"items\"):\n other = dict(other)\n for key, o in other.items():\n d[key] = KeyedRef(o, self._remove, key)\n for key, o in kwargs.items():\n d[key] = KeyedRef(o, self._remove, key)\n\n def valuerefs(self):\n \"\"\"Return a list of weak references to the values.\n\n The references are not guaranteed to be 'live' at the time\n they are used, so the result of calling the references needs\n to be checked before being used. This can be used to avoid\n creating references that will cause the garbage collector to\n keep the values around longer than needed.\n\n \"\"\"\n if self._pending_removals:\n self._commit_removals()\n return list(self.data.values())\n\n def __ior__(self, other):\n self.update(other)\n return self\n\n def __or__(self, other):\n if isinstance(other, _collections_abc.Mapping):\n c = self.copy()\n c.update(other)\n return c\n return NotImplemented\n\n def __ror__(self, other):\n if isinstance(other, _collections_abc.Mapping):\n c = self.__class__()\n c.update(other)\n c.update(self)\n return c\n return NotImplemented\n\n\nclass KeyedRef(ref):\n \"\"\"Specialized reference that includes a key corresponding to the value.\n\n This is used in the WeakValueDictionary to avoid having to create\n a function object for each key stored in the mapping. A shared\n callback object can use the 'key' attribute of a KeyedRef instead\n of getting a reference to the key from an enclosing scope.\n\n \"\"\"\n\n __slots__ = \"key\",\n\n def __new__(type, ob, callback, key):\n self = ref.__new__(type, ob, callback)\n self.key = key\n return self\n\n def __init__(self, ob, callback, key):\n super().__init__(ob, callback)\n\n\nclass WeakKeyDictionary(_collections_abc.MutableMapping):\n \"\"\" Mapping class that references keys weakly.\n\n Entries in the dictionary will be discarded when there is no\n longer a strong reference to the key. This can be used to\n associate additional data with an object owned by other parts of\n an application without adding attributes to those objects. This\n can be especially useful with objects that override attribute\n accesses.\n \"\"\"\n\n def __init__(self, dict=None):\n self.data = {}\n def remove(k, selfref=ref(self)):\n self = selfref()\n if self is not None:\n if self._iterating:\n self._pending_removals.append(k)\n else:\n try:\n del self.data[k]\n except KeyError:\n pass\n self._remove = remove\n # A list of dead weakrefs (keys to be removed)\n self._pending_removals = []\n self._iterating = set()\n self._dirty_len = False\n if dict is not None:\n self.update(dict)\n\n def _commit_removals(self):\n # NOTE: We don't need to call this method before mutating the dict,\n # because a dead weakref never compares equal to a live weakref,\n # even if they happened to refer to equal objects.\n # However, it means keys may already have been removed.\n pop = self._pending_removals.pop\n d = self.data\n while True:\n try:\n key = pop()\n except IndexError:\n return\n\n try:\n del d[key]\n except KeyError:\n pass\n\n def _scrub_removals(self):\n d = self.data\n self._pending_removals = [k for k in self._pending_removals if k in d]\n self._dirty_len = False\n\n def __delitem__(self, key):\n self._dirty_len = True\n del self.data[ref(key)]\n\n def __getitem__(self, key):\n return self.data[ref(key)]\n\n def __len__(self):\n if self._dirty_len and self._pending_removals:\n # self._pending_removals may still contain keys which were\n # explicitly removed, we have to scrub them (see issue #21173).\n self._scrub_removals()\n return len(self.data) - len(self._pending_removals)\n\n def __repr__(self):\n return \"<%s at %#x>\" % (self.__class__.__name__, id(self))\n\n def __setitem__(self, key, value):\n self.data[ref(key, self._remove)] = value\n\n def copy(self):\n new = WeakKeyDictionary()\n with _IterationGuard(self):\n for key, value in self.data.items():\n o = key()\n if o is not None:\n new[o] = value\n return new\n\n __copy__ = copy\n\n def __deepcopy__(self, memo):\n from copy import deepcopy\n new = self.__class__()\n with _IterationGuard(self):\n for key, value in self.data.items():\n o = key()\n if o is not None:\n new[o] = deepcopy(value, memo)\n return new\n\n def get(self, key, default=None):\n return self.data.get(ref(key),default)\n\n def __contains__(self, key):\n try:\n wr = ref(key)\n except TypeError:\n return False\n return wr in self.data\n\n def items(self):\n with _IterationGuard(self):\n for wr, value in self.data.items():\n key = wr()\n if key is not None:\n yield key, value\n\n def keys(self):\n with _IterationGuard(self):\n for wr in self.data:\n obj = wr()\n if obj is not None:\n yield obj\n\n __iter__ = keys\n\n def values(self):\n with _IterationGuard(self):\n for wr, value in self.data.items():\n if wr() is not None:\n yield value\n\n def keyrefs(self):\n \"\"\"Return a list of weak references to the keys.\n\n The references are not guaranteed to be 'live' at the time\n they are used, so the result of calling the references needs\n to be checked before being used. This can be used to avoid\n creating references that will cause the garbage collector to\n keep the keys around longer than needed.\n\n \"\"\"\n return list(self.data)\n\n def popitem(self):\n self._dirty_len = True\n while True:\n key, value = self.data.popitem()\n o = key()\n if o is not None:\n return o, value\n\n def pop(self, key, *args):\n self._dirty_len = True\n return self.data.pop(ref(key), *args)\n\n def setdefault(self, key, default=None):\n return self.data.setdefault(ref(key, self._remove),default)\n\n def update(self, dict=None, /, **kwargs):\n d = self.data\n if dict is not None:\n if not hasattr(dict, \"items\"):\n dict = type({})(dict)\n for key, value in dict.items():\n d[ref(key, self._remove)] = value\n if len(kwargs):\n self.update(kwargs)\n\n def __ior__(self, other):\n self.update(other)\n return self\n\n def __or__(self, other):\n if isinstance(other, _collections_abc.Mapping):\n c = self.copy()\n c.update(other)\n return c\n return NotImplemented\n\n def __ror__(self, other):\n if isinstance(other, _collections_abc.Mapping):\n c = self.__class__()\n c.update(other)\n c.update(self)\n return c\n return NotImplemented\n\n\nclass finalize:\n \"\"\"Class for finalization of weakrefable objects\n\n finalize(obj, func, *args, **kwargs) returns a callable finalizer\n object which will be called when obj is garbage collected. The\n first time the finalizer is called it evaluates func(*arg, **kwargs)\n and returns the result. After this the finalizer is dead, and\n calling it just returns None.\n\n When the program exits any remaining finalizers for which the\n atexit attribute is true will be run in reverse order of creation.\n By default atexit is true.\n \"\"\"\n\n # Finalizer objects don't have any state of their own. They are\n # just used as keys to lookup _Info objects in the registry. This\n # ensures that they cannot be part of a ref-cycle.\n\n __slots__ = ()\n _registry = {}\n _shutdown = False\n _index_iter = itertools.count()\n _dirty = False\n _registered_with_atexit = False\n\n class _Info:\n __slots__ = (\"weakref\", \"func\", \"args\", \"kwargs\", \"atexit\", \"index\")\n\n def __init__(self, obj, func, /, *args, **kwargs):\n if not self._registered_with_atexit:\n # We may register the exit function more than once because\n # of a thread race, but that is harmless\n import atexit\n atexit.register(self._exitfunc)\n finalize._registered_with_atexit = True\n info = self._Info()\n info.weakref = ref(obj, self)\n info.func = func\n info.args = args\n info.kwargs = kwargs or None\n info.atexit = True\n info.index = next(self._index_iter)\n self._registry[self] = info\n finalize._dirty = True\n\n def __call__(self, _=None):\n \"\"\"If alive then mark as dead and return func(*args, **kwargs);\n otherwise return None\"\"\"\n info = self._registry.pop(self, None)\n if info and not self._shutdown:\n return info.func(*info.args, **(info.kwargs or {}))\n\n def detach(self):\n \"\"\"If alive then mark as dead and return (obj, func, args, kwargs);\n otherwise return None\"\"\"\n info = self._registry.get(self)\n obj = info and info.weakref()\n if obj is not None and self._registry.pop(self, None):\n return (obj, info.func, info.args, info.kwargs or {})\n\n def peek(self):\n \"\"\"If alive then return (obj, func, args, kwargs);\n otherwise return None\"\"\"\n info = self._registry.get(self)\n obj = info and info.weakref()\n if obj is not None:\n return (obj, info.func, info.args, info.kwargs or {})\n\n @property\n def alive(self):\n \"\"\"Whether finalizer is alive\"\"\"\n return self in self._registry\n\n @property\n def atexit(self):\n \"\"\"Whether finalizer should be called at exit\"\"\"\n info = self._registry.get(self)\n return bool(info) and info.atexit\n\n @atexit.setter\n def atexit(self, value):\n info = self._registry.get(self)\n if info:\n info.atexit = bool(value)\n\n def __repr__(self):\n info = self._registry.get(self)\n obj = info and info.weakref()\n if obj is None:\n return '<%s object at %#x; dead>' % (type(self).__name__, id(self))\n else:\n return '<%s object at %#x; for %r at %#x>' % \\\n (type(self).__name__, id(self), type(obj).__name__, id(obj))\n\n @classmethod\n def _select_for_exit(cls):\n # Return live finalizers marked for exit, oldest first\n L = [(f,i) for (f,i) in cls._registry.items() if i.atexit]\n L.sort(key=lambda item:item[1].index)\n return [f for (f,i) in L]\n\n @classmethod\n def _exitfunc(cls):\n # At shutdown invoke finalizers for which atexit is true.\n # This is called once all other non-daemonic threads have been\n # joined.\n reenable_gc = False\n try:\n if cls._registry:\n import gc\n if gc.isenabled():\n reenable_gc = True\n gc.disable()\n pending = None\n while True:\n if pending is None or finalize._dirty:\n pending = cls._select_for_exit()\n finalize._dirty = False\n if not pending:\n break\n f = pending.pop()\n try:\n # gc is disabled, so (assuming no daemonic\n # threads) the following is the only line in\n # this function which might trigger creation\n # of a new finalizer\n f()\n except Exception:\n sys.excepthook(*sys.exc_info())\n assert f not in cls._registry\n finally:\n # prevent any more finalizers from executing during shutdown\n finalize._shutdown = True\n if reenable_gc:\n gc.enable()\n", 674], "/usr/lib/python3.13/threading.py": ["\"\"\"Thread module emulating a subset of Java's threading model.\"\"\"\n\nimport os as _os\nimport sys as _sys\nimport _thread\nimport warnings\n\nfrom time import monotonic as _time\nfrom _weakrefset import WeakSet\nfrom itertools import count as _count\ntry:\n from _collections import deque as _deque\nexcept ImportError:\n from collections import deque as _deque\n\n# Note regarding PEP 8 compliant names\n# This threading model was originally inspired by Java, and inherited\n# the convention of camelCase function and method names from that\n# language. Those original names are not in any imminent danger of\n# being deprecated (even for Py3k),so this module provides them as an\n# alias for the PEP 8 compliant names\n# Note that using the new PEP 8 compliant names facilitates substitution\n# with the multiprocessing module, which doesn't provide the old\n# Java inspired names.\n\n__all__ = ['get_ident', 'active_count', 'Condition', 'current_thread',\n 'enumerate', 'main_thread', 'TIMEOUT_MAX',\n 'Event', 'Lock', 'RLock', 'Semaphore', 'BoundedSemaphore', 'Thread',\n 'Barrier', 'BrokenBarrierError', 'Timer', 'ThreadError',\n 'setprofile', 'settrace', 'local', 'stack_size',\n 'excepthook', 'ExceptHookArgs', 'gettrace', 'getprofile',\n 'setprofile_all_threads','settrace_all_threads']\n\n# Rename some stuff so \"from threading import *\" is safe\n_start_joinable_thread = _thread.start_joinable_thread\n_daemon_threads_allowed = _thread.daemon_threads_allowed\n_allocate_lock = _thread.allocate_lock\n_LockType = _thread.LockType\n_thread_shutdown = _thread._shutdown\n_make_thread_handle = _thread._make_thread_handle\n_ThreadHandle = _thread._ThreadHandle\nget_ident = _thread.get_ident\n_get_main_thread_ident = _thread._get_main_thread_ident\n_is_main_interpreter = _thread._is_main_interpreter\ntry:\n get_native_id = _thread.get_native_id\n _HAVE_THREAD_NATIVE_ID = True\n __all__.append('get_native_id')\nexcept AttributeError:\n _HAVE_THREAD_NATIVE_ID = False\nThreadError = _thread.error\ntry:\n _CRLock = _thread.RLock\nexcept AttributeError:\n _CRLock = None\nTIMEOUT_MAX = _thread.TIMEOUT_MAX\ndel _thread\n\n# get thread-local implementation, either from the thread\n# module, or from the python fallback\n\ntry:\n from _thread import _local as local\nexcept ImportError:\n from _threading_local import local\n\n# Support for profile and trace hooks\n\n_profile_hook = None\n_trace_hook = None\n\ndef setprofile(func):\n \"\"\"Set a profile function for all threads started from the threading module.\n\n The func will be passed to sys.setprofile() for each thread, before its\n run() method is called.\n \"\"\"\n global _profile_hook\n _profile_hook = func\n\ndef setprofile_all_threads(func):\n \"\"\"Set a profile function for all threads started from the threading module\n and all Python threads that are currently executing.\n\n The func will be passed to sys.setprofile() for each thread, before its\n run() method is called.\n \"\"\"\n setprofile(func)\n _sys._setprofileallthreads(func)\n\ndef getprofile():\n \"\"\"Get the profiler function as set by threading.setprofile().\"\"\"\n return _profile_hook\n\ndef settrace(func):\n \"\"\"Set a trace function for all threads started from the threading module.\n\n The func will be passed to sys.settrace() for each thread, before its run()\n method is called.\n \"\"\"\n global _trace_hook\n _trace_hook = func\n\ndef settrace_all_threads(func):\n \"\"\"Set a trace function for all threads started from the threading module\n and all Python threads that are currently executing.\n\n The func will be passed to sys.settrace() for each thread, before its run()\n method is called.\n \"\"\"\n settrace(func)\n _sys._settraceallthreads(func)\n\ndef gettrace():\n \"\"\"Get the trace function as set by threading.settrace().\"\"\"\n return _trace_hook\n\n# Synchronization classes\n\nLock = _LockType\n\ndef RLock(*args, **kwargs):\n \"\"\"Factory function that returns a new reentrant lock.\n\n A reentrant lock must be released by the thread that acquired it. Once a\n thread has acquired a reentrant lock, the same thread may acquire it again\n without blocking; the thread must release it once for each time it has\n acquired it.\n\n \"\"\"\n if args or kwargs:\n warnings.warn(\n 'Passing arguments to RLock is deprecated and will be removed in 3.15',\n DeprecationWarning,\n stacklevel=2,\n )\n if _CRLock is None:\n return _PyRLock(*args, **kwargs)\n return _CRLock(*args, **kwargs)\n\nclass _RLock:\n \"\"\"This class implements reentrant lock objects.\n\n A reentrant lock must be released by the thread that acquired it. Once a\n thread has acquired a reentrant lock, the same thread may acquire it\n again without blocking; the thread must release it once for each time it\n has acquired it.\n\n \"\"\"\n\n def __init__(self):\n self._block = _allocate_lock()\n self._owner = None\n self._count = 0\n\n def __repr__(self):\n owner = self._owner\n try:\n owner = _active[owner].name\n except KeyError:\n pass\n return \"<%s %s.%s object owner=%r count=%d at %s>\" % (\n \"locked\" if self._block.locked() else \"unlocked\",\n self.__class__.__module__,\n self.__class__.__qualname__,\n owner,\n self._count,\n hex(id(self))\n )\n\n def _at_fork_reinit(self):\n self._block._at_fork_reinit()\n self._owner = None\n self._count = 0\n\n def acquire(self, blocking=True, timeout=-1):\n \"\"\"Acquire a lock, blocking or non-blocking.\n\n When invoked without arguments: if this thread already owns the lock,\n increment the recursion level by one, and return immediately. Otherwise,\n if another thread owns the lock, block until the lock is unlocked. Once\n the lock is unlocked (not owned by any thread), then grab ownership, set\n the recursion level to one, and return. If more than one thread is\n blocked waiting until the lock is unlocked, only one at a time will be\n able to grab ownership of the lock. There is no return value in this\n case.\n\n When invoked with the blocking argument set to true, do the same thing\n as when called without arguments, and return true.\n\n When invoked with the blocking argument set to false, do not block. If a\n call without an argument would block, return false immediately;\n otherwise, do the same thing as when called without arguments, and\n return true.\n\n When invoked with the floating-point timeout argument set to a positive\n value, block for at most the number of seconds specified by timeout\n and as long as the lock cannot be acquired. Return true if the lock has\n been acquired, false if the timeout has elapsed.\n\n \"\"\"\n me = get_ident()\n if self._owner == me:\n self._count += 1\n return 1\n rc = self._block.acquire(blocking, timeout)\n if rc:\n self._owner = me\n self._count = 1\n return rc\n\n __enter__ = acquire\n\n def release(self):\n \"\"\"Release a lock, decrementing the recursion level.\n\n If after the decrement it is zero, reset the lock to unlocked (not owned\n by any thread), and if any other threads are blocked waiting for the\n lock to become unlocked, allow exactly one of them to proceed. If after\n the decrement the recursion level is still nonzero, the lock remains\n locked and owned by the calling thread.\n\n Only call this method when the calling thread owns the lock. A\n RuntimeError is raised if this method is called when the lock is\n unlocked.\n\n There is no return value.\n\n \"\"\"\n if self._owner != get_ident():\n raise RuntimeError(\"cannot release un-acquired lock\")\n self._count = count = self._count - 1\n if not count:\n self._owner = None\n self._block.release()\n\n def __exit__(self, t, v, tb):\n self.release()\n\n # Internal methods used by condition variables\n\n def _acquire_restore(self, state):\n self._block.acquire()\n self._count, self._owner = state\n\n def _release_save(self):\n if self._count == 0:\n raise RuntimeError(\"cannot release un-acquired lock\")\n count = self._count\n self._count = 0\n owner = self._owner\n self._owner = None\n self._block.release()\n return (count, owner)\n\n def _is_owned(self):\n return self._owner == get_ident()\n\n # Internal method used for reentrancy checks\n\n def _recursion_count(self):\n if self._owner != get_ident():\n return 0\n return self._count\n\n_PyRLock = _RLock\n\n\nclass Condition:\n \"\"\"Class that implements a condition variable.\n\n A condition variable allows one or more threads to wait until they are\n notified by another thread.\n\n If the lock argument is given and not None, it must be a Lock or RLock\n object, and it is used as the underlying lock. Otherwise, a new RLock object\n is created and used as the underlying lock.\n\n \"\"\"\n\n def __init__(self, lock=None):\n if lock is None:\n lock = RLock()\n self._lock = lock\n # Export the lock's acquire() and release() methods\n self.acquire = lock.acquire\n self.release = lock.release\n # If the lock defines _release_save() and/or _acquire_restore(),\n # these override the default implementations (which just call\n # release() and acquire() on the lock). Ditto for _is_owned().\n if hasattr(lock, '_release_save'):\n self._release_save = lock._release_save\n if hasattr(lock, '_acquire_restore'):\n self._acquire_restore = lock._acquire_restore\n if hasattr(lock, '_is_owned'):\n self._is_owned = lock._is_owned\n self._waiters = _deque()\n\n def _at_fork_reinit(self):\n self._lock._at_fork_reinit()\n self._waiters.clear()\n\n def __enter__(self):\n return self._lock.__enter__()\n\n def __exit__(self, *args):\n return self._lock.__exit__(*args)\n\n def __repr__(self):\n return \"\" % (self._lock, len(self._waiters))\n\n def _release_save(self):\n self._lock.release() # No state to save\n\n def _acquire_restore(self, x):\n self._lock.acquire() # Ignore saved state\n\n def _is_owned(self):\n # Return True if lock is owned by current_thread.\n # This method is called only if _lock doesn't have _is_owned().\n if self._lock.acquire(False):\n self._lock.release()\n return False\n else:\n return True\n\n def wait(self, timeout=None):\n \"\"\"Wait until notified or until a timeout occurs.\n\n If the calling thread has not acquired the lock when this method is\n called, a RuntimeError is raised.\n\n This method releases the underlying lock, and then blocks until it is\n awakened by a notify() or notify_all() call for the same condition\n variable in another thread, or until the optional timeout occurs. Once\n awakened or timed out, it re-acquires the lock and returns.\n\n When the timeout argument is present and not None, it should be a\n floating-point number specifying a timeout for the operation in seconds\n (or fractions thereof).\n\n When the underlying lock is an RLock, it is not released using its\n release() method, since this may not actually unlock the lock when it\n was acquired multiple times recursively. Instead, an internal interface\n of the RLock class is used, which really unlocks it even when it has\n been recursively acquired several times. Another internal interface is\n then used to restore the recursion level when the lock is reacquired.\n\n \"\"\"\n if not self._is_owned():\n raise RuntimeError(\"cannot wait on un-acquired lock\")\n waiter = _allocate_lock()\n waiter.acquire()\n self._waiters.append(waiter)\n saved_state = self._release_save()\n gotit = False\n try: # restore state no matter what (e.g., KeyboardInterrupt)\n if timeout is None:\n waiter.acquire()\n gotit = True\n else:\n if timeout > 0:\n gotit = waiter.acquire(True, timeout)\n else:\n gotit = waiter.acquire(False)\n return gotit\n finally:\n self._acquire_restore(saved_state)\n if not gotit:\n try:\n self._waiters.remove(waiter)\n except ValueError:\n pass\n\n def wait_for(self, predicate, timeout=None):\n \"\"\"Wait until a condition evaluates to True.\n\n predicate should be a callable which result will be interpreted as a\n boolean value. A timeout may be provided giving the maximum time to\n wait.\n\n \"\"\"\n endtime = None\n waittime = timeout\n result = predicate()\n while not result:\n if waittime is not None:\n if endtime is None:\n endtime = _time() + waittime\n else:\n waittime = endtime - _time()\n if waittime <= 0:\n break\n self.wait(waittime)\n result = predicate()\n return result\n\n def notify(self, n=1):\n \"\"\"Wake up one or more threads waiting on this condition, if any.\n\n If the calling thread has not acquired the lock when this method is\n called, a RuntimeError is raised.\n\n This method wakes up at most n of the threads waiting for the condition\n variable; it is a no-op if no threads are waiting.\n\n \"\"\"\n if not self._is_owned():\n raise RuntimeError(\"cannot notify on un-acquired lock\")\n waiters = self._waiters\n while waiters and n > 0:\n waiter = waiters[0]\n try:\n waiter.release()\n except RuntimeError:\n # gh-92530: The previous call of notify() released the lock,\n # but was interrupted before removing it from the queue.\n # It can happen if a signal handler raises an exception,\n # like CTRL+C which raises KeyboardInterrupt.\n pass\n else:\n n -= 1\n try:\n waiters.remove(waiter)\n except ValueError:\n pass\n\n def notify_all(self):\n \"\"\"Wake up all threads waiting on this condition.\n\n If the calling thread has not acquired the lock when this method\n is called, a RuntimeError is raised.\n\n \"\"\"\n self.notify(len(self._waiters))\n\n def notifyAll(self):\n \"\"\"Wake up all threads waiting on this condition.\n\n This method is deprecated, use notify_all() instead.\n\n \"\"\"\n import warnings\n warnings.warn('notifyAll() is deprecated, use notify_all() instead',\n DeprecationWarning, stacklevel=2)\n self.notify_all()\n\n\nclass Semaphore:\n \"\"\"This class implements semaphore objects.\n\n Semaphores manage a counter representing the number of release() calls minus\n the number of acquire() calls, plus an initial value. The acquire() method\n blocks if necessary until it can return without making the counter\n negative. If not given, value defaults to 1.\n\n \"\"\"\n\n # After Tim Peters' semaphore class, but not quite the same (no maximum)\n\n def __init__(self, value=1):\n if value < 0:\n raise ValueError(\"semaphore initial value must be >= 0\")\n self._cond = Condition(Lock())\n self._value = value\n\n def __repr__(self):\n cls = self.__class__\n return (f\"<{cls.__module__}.{cls.__qualname__} at {id(self):#x}:\"\n f\" value={self._value}>\")\n\n def acquire(self, blocking=True, timeout=None):\n \"\"\"Acquire a semaphore, decrementing the internal counter by one.\n\n When invoked without arguments: if the internal counter is larger than\n zero on entry, decrement it by one and return immediately. If it is zero\n on entry, block, waiting until some other thread has called release() to\n make it larger than zero. This is done with proper interlocking so that\n if multiple acquire() calls are blocked, release() will wake exactly one\n of them up. The implementation may pick one at random, so the order in\n which blocked threads are awakened should not be relied on. There is no\n return value in this case.\n\n When invoked with blocking set to true, do the same thing as when called\n without arguments, and return true.\n\n When invoked with blocking set to false, do not block. If a call without\n an argument would block, return false immediately; otherwise, do the\n same thing as when called without arguments, and return true.\n\n When invoked with a timeout other than None, it will block for at\n most timeout seconds. If acquire does not complete successfully in\n that interval, return false. Return true otherwise.\n\n \"\"\"\n if not blocking and timeout is not None:\n raise ValueError(\"can't specify timeout for non-blocking acquire\")\n rc = False\n endtime = None\n with self._cond:\n while self._value == 0:\n if not blocking:\n break\n if timeout is not None:\n if endtime is None:\n endtime = _time() + timeout\n else:\n timeout = endtime - _time()\n if timeout <= 0:\n break\n self._cond.wait(timeout)\n else:\n self._value -= 1\n rc = True\n return rc\n\n __enter__ = acquire\n\n def release(self, n=1):\n \"\"\"Release a semaphore, incrementing the internal counter by one or more.\n\n When the counter is zero on entry and another thread is waiting for it\n to become larger than zero again, wake up that thread.\n\n \"\"\"\n if n < 1:\n raise ValueError('n must be one or more')\n with self._cond:\n self._value += n\n self._cond.notify(n)\n\n def __exit__(self, t, v, tb):\n self.release()\n\n\nclass BoundedSemaphore(Semaphore):\n \"\"\"Implements a bounded semaphore.\n\n A bounded semaphore checks to make sure its current value doesn't exceed its\n initial value. If it does, ValueError is raised. In most situations\n semaphores are used to guard resources with limited capacity.\n\n If the semaphore is released too many times it's a sign of a bug. If not\n given, value defaults to 1.\n\n Like regular semaphores, bounded semaphores manage a counter representing\n the number of release() calls minus the number of acquire() calls, plus an\n initial value. The acquire() method blocks if necessary until it can return\n without making the counter negative. If not given, value defaults to 1.\n\n \"\"\"\n\n def __init__(self, value=1):\n super().__init__(value)\n self._initial_value = value\n\n def __repr__(self):\n cls = self.__class__\n return (f\"<{cls.__module__}.{cls.__qualname__} at {id(self):#x}:\"\n f\" value={self._value}/{self._initial_value}>\")\n\n def release(self, n=1):\n \"\"\"Release a semaphore, incrementing the internal counter by one or more.\n\n When the counter is zero on entry and another thread is waiting for it\n to become larger than zero again, wake up that thread.\n\n If the number of releases exceeds the number of acquires,\n raise a ValueError.\n\n \"\"\"\n if n < 1:\n raise ValueError('n must be one or more')\n with self._cond:\n if self._value + n > self._initial_value:\n raise ValueError(\"Semaphore released too many times\")\n self._value += n\n self._cond.notify(n)\n\n\nclass Event:\n \"\"\"Class implementing event objects.\n\n Events manage a flag that can be set to true with the set() method and reset\n to false with the clear() method. The wait() method blocks until the flag is\n true. The flag is initially false.\n\n \"\"\"\n\n # After Tim Peters' event class (without is_posted())\n\n def __init__(self):\n self._cond = Condition(Lock())\n self._flag = False\n\n def __repr__(self):\n cls = self.__class__\n status = 'set' if self._flag else 'unset'\n return f\"<{cls.__module__}.{cls.__qualname__} at {id(self):#x}: {status}>\"\n\n def _at_fork_reinit(self):\n # Private method called by Thread._after_fork()\n self._cond._at_fork_reinit()\n\n def is_set(self):\n \"\"\"Return true if and only if the internal flag is true.\"\"\"\n return self._flag\n\n def isSet(self):\n \"\"\"Return true if and only if the internal flag is true.\n\n This method is deprecated, use is_set() instead.\n\n \"\"\"\n import warnings\n warnings.warn('isSet() is deprecated, use is_set() instead',\n DeprecationWarning, stacklevel=2)\n return self.is_set()\n\n def set(self):\n \"\"\"Set the internal flag to true.\n\n All threads waiting for it to become true are awakened. Threads\n that call wait() once the flag is true will not block at all.\n\n \"\"\"\n with self._cond:\n self._flag = True\n self._cond.notify_all()\n\n def clear(self):\n \"\"\"Reset the internal flag to false.\n\n Subsequently, threads calling wait() will block until set() is called to\n set the internal flag to true again.\n\n \"\"\"\n with self._cond:\n self._flag = False\n\n def wait(self, timeout=None):\n \"\"\"Block until the internal flag is true.\n\n If the internal flag is true on entry, return immediately. Otherwise,\n block until another thread calls set() to set the flag to true, or until\n the optional timeout occurs.\n\n When the timeout argument is present and not None, it should be a\n floating-point number specifying a timeout for the operation in seconds\n (or fractions thereof).\n\n This method returns the internal flag on exit, so it will always return\n True except if a timeout is given and the operation times out.\n\n \"\"\"\n with self._cond:\n signaled = self._flag\n if not signaled:\n signaled = self._cond.wait(timeout)\n return signaled\n\n\n# A barrier class. Inspired in part by the pthread_barrier_* api and\n# the CyclicBarrier class from Java. See\n# http://sourceware.org/pthreads-win32/manual/pthread_barrier_init.html and\n# http://java.sun.com/j2se/1.5.0/docs/api/java/util/concurrent/\n# CyclicBarrier.html\n# for information.\n# We maintain two main states, 'filling' and 'draining' enabling the barrier\n# to be cyclic. Threads are not allowed into it until it has fully drained\n# since the previous cycle. In addition, a 'resetting' state exists which is\n# similar to 'draining' except that threads leave with a BrokenBarrierError,\n# and a 'broken' state in which all threads get the exception.\nclass Barrier:\n \"\"\"Implements a Barrier.\n\n Useful for synchronizing a fixed number of threads at known synchronization\n points. Threads block on 'wait()' and are simultaneously awoken once they\n have all made that call.\n\n \"\"\"\n\n def __init__(self, parties, action=None, timeout=None):\n \"\"\"Create a barrier, initialised to 'parties' threads.\n\n 'action' is a callable which, when supplied, will be called by one of\n the threads after they have all entered the barrier and just prior to\n releasing them all. If a 'timeout' is provided, it is used as the\n default for all subsequent 'wait()' calls.\n\n \"\"\"\n if parties < 1:\n raise ValueError(\"parties must be > 0\")\n self._cond = Condition(Lock())\n self._action = action\n self._timeout = timeout\n self._parties = parties\n self._state = 0 # 0 filling, 1 draining, -1 resetting, -2 broken\n self._count = 0\n\n def __repr__(self):\n cls = self.__class__\n if self.broken:\n return f\"<{cls.__module__}.{cls.__qualname__} at {id(self):#x}: broken>\"\n return (f\"<{cls.__module__}.{cls.__qualname__} at {id(self):#x}:\"\n f\" waiters={self.n_waiting}/{self.parties}>\")\n\n def wait(self, timeout=None):\n \"\"\"Wait for the barrier.\n\n When the specified number of threads have started waiting, they are all\n simultaneously awoken. If an 'action' was provided for the barrier, one\n of the threads will have executed that callback prior to returning.\n Returns an individual index number from 0 to 'parties-1'.\n\n \"\"\"\n if timeout is None:\n timeout = self._timeout\n with self._cond:\n self._enter() # Block while the barrier drains.\n index = self._count\n self._count += 1\n try:\n if index + 1 == self._parties:\n # We release the barrier\n self._release()\n else:\n # We wait until someone releases us\n self._wait(timeout)\n return index\n finally:\n self._count -= 1\n # Wake up any threads waiting for barrier to drain.\n self._exit()\n\n # Block until the barrier is ready for us, or raise an exception\n # if it is broken.\n def _enter(self):\n while self._state in (-1, 1):\n # It is draining or resetting, wait until done\n self._cond.wait()\n #see if the barrier is in a broken state\n if self._state < 0:\n raise BrokenBarrierError\n assert self._state == 0\n\n # Optionally run the 'action' and release the threads waiting\n # in the barrier.\n def _release(self):\n try:\n if self._action:\n self._action()\n # enter draining state\n self._state = 1\n self._cond.notify_all()\n except:\n #an exception during the _action handler. Break and reraise\n self._break()\n raise\n\n # Wait in the barrier until we are released. Raise an exception\n # if the barrier is reset or broken.\n def _wait(self, timeout):\n if not self._cond.wait_for(lambda : self._state != 0, timeout):\n #timed out. Break the barrier\n self._break()\n raise BrokenBarrierError\n if self._state < 0:\n raise BrokenBarrierError\n assert self._state == 1\n\n # If we are the last thread to exit the barrier, signal any threads\n # waiting for the barrier to drain.\n def _exit(self):\n if self._count == 0:\n if self._state in (-1, 1):\n #resetting or draining\n self._state = 0\n self._cond.notify_all()\n\n def reset(self):\n \"\"\"Reset the barrier to the initial state.\n\n Any threads currently waiting will get the BrokenBarrier exception\n raised.\n\n \"\"\"\n with self._cond:\n if self._count > 0:\n if self._state == 0:\n #reset the barrier, waking up threads\n self._state = -1\n elif self._state == -2:\n #was broken, set it to reset state\n #which clears when the last thread exits\n self._state = -1\n else:\n self._state = 0\n self._cond.notify_all()\n\n def abort(self):\n \"\"\"Place the barrier into a 'broken' state.\n\n Useful in case of error. Any currently waiting threads and threads\n attempting to 'wait()' will have BrokenBarrierError raised.\n\n \"\"\"\n with self._cond:\n self._break()\n\n def _break(self):\n # An internal error was detected. The barrier is set to\n # a broken state all parties awakened.\n self._state = -2\n self._cond.notify_all()\n\n @property\n def parties(self):\n \"\"\"Return the number of threads required to trip the barrier.\"\"\"\n return self._parties\n\n @property\n def n_waiting(self):\n \"\"\"Return the number of threads currently waiting at the barrier.\"\"\"\n # We don't need synchronization here since this is an ephemeral result\n # anyway. It returns the correct value in the steady state.\n if self._state == 0:\n return self._count\n return 0\n\n @property\n def broken(self):\n \"\"\"Return True if the barrier is in a broken state.\"\"\"\n return self._state == -2\n\n# exception raised by the Barrier class\nclass BrokenBarrierError(RuntimeError):\n pass\n\n\n# Helper to generate new thread names\n_counter = _count(1).__next__\ndef _newname(name_template):\n return name_template % _counter()\n\n# Active thread administration.\n#\n# bpo-44422: Use a reentrant lock to allow reentrant calls to functions like\n# threading.enumerate().\n_active_limbo_lock = RLock()\n_active = {} # maps thread id to Thread object\n_limbo = {}\n_dangling = WeakSet()\n\n\n# Main class for threads\n\nclass Thread:\n \"\"\"A class that represents a thread of control.\n\n This class can be safely subclassed in a limited fashion. There are two ways\n to specify the activity: by passing a callable object to the constructor, or\n by overriding the run() method in a subclass.\n\n \"\"\"\n\n _initialized = False\n\n def __init__(self, group=None, target=None, name=None,\n args=(), kwargs=None, *, daemon=None):\n \"\"\"This constructor should always be called with keyword arguments. Arguments are:\n\n *group* should be None; reserved for future extension when a ThreadGroup\n class is implemented.\n\n *target* is the callable object to be invoked by the run()\n method. Defaults to None, meaning nothing is called.\n\n *name* is the thread name. By default, a unique name is constructed of\n the form \"Thread-N\" where N is a small decimal number.\n\n *args* is a list or tuple of arguments for the target invocation. Defaults to ().\n\n *kwargs* is a dictionary of keyword arguments for the target\n invocation. Defaults to {}.\n\n If a subclass overrides the constructor, it must make sure to invoke\n the base class constructor (Thread.__init__()) before doing anything\n else to the thread.\n\n \"\"\"\n assert group is None, \"group argument must be None for now\"\n if kwargs is None:\n kwargs = {}\n if name:\n name = str(name)\n else:\n name = _newname(\"Thread-%d\")\n if target is not None:\n try:\n target_name = target.__name__\n name += f\" ({target_name})\"\n except AttributeError:\n pass\n\n self._target = target\n self._name = name\n self._args = args\n self._kwargs = kwargs\n if daemon is not None:\n if daemon and not _daemon_threads_allowed():\n raise RuntimeError('daemon threads are disabled in this (sub)interpreter')\n self._daemonic = daemon\n else:\n self._daemonic = current_thread().daemon\n self._ident = None\n if _HAVE_THREAD_NATIVE_ID:\n self._native_id = None\n self._handle = _ThreadHandle()\n self._started = Event()\n self._initialized = True\n # Copy of sys.stderr used by self._invoke_excepthook()\n self._stderr = _sys.stderr\n self._invoke_excepthook = _make_invoke_excepthook()\n # For debugging and _after_fork()\n _dangling.add(self)\n\n def _after_fork(self, new_ident=None):\n # Private! Called by threading._after_fork().\n self._started._at_fork_reinit()\n if new_ident is not None:\n # This thread is alive.\n self._ident = new_ident\n assert self._handle.ident == new_ident\n else:\n # Otherwise, the thread is dead, Jim. _PyThread_AfterFork()\n # already marked our handle done.\n pass\n\n def __repr__(self):\n assert self._initialized, \"Thread.__init__() was not called\"\n status = \"initial\"\n if self._started.is_set():\n status = \"started\"\n if self._handle.is_done():\n status = \"stopped\"\n if self._daemonic:\n status += \" daemon\"\n if self._ident is not None:\n status += \" %s\" % self._ident\n return \"<%s(%s, %s)>\" % (self.__class__.__name__, self._name, status)\n\n def start(self):\n \"\"\"Start the thread's activity.\n\n It must be called at most once per thread object. It arranges for the\n object's run() method to be invoked in a separate thread of control.\n\n This method will raise a RuntimeError if called more than once on the\n same thread object.\n\n \"\"\"\n if not self._initialized:\n raise RuntimeError(\"thread.__init__() not called\")\n\n if self._started.is_set():\n raise RuntimeError(\"threads can only be started once\")\n\n with _active_limbo_lock:\n _limbo[self] = self\n try:\n # Start joinable thread\n _start_joinable_thread(self._bootstrap, handle=self._handle,\n daemon=self.daemon)\n except Exception:\n with _active_limbo_lock:\n del _limbo[self]\n raise\n self._started.wait() # Will set ident and native_id\n\n def run(self):\n \"\"\"Method representing the thread's activity.\n\n You may override this method in a subclass. The standard run() method\n invokes the callable object passed to the object's constructor as the\n target argument, if any, with sequential and keyword arguments taken\n from the args and kwargs arguments, respectively.\n\n \"\"\"\n try:\n if self._target is not None:\n self._target(*self._args, **self._kwargs)\n finally:\n # Avoid a refcycle if the thread is running a function with\n # an argument that has a member that points to the thread.\n del self._target, self._args, self._kwargs\n\n def _bootstrap(self):\n # Wrapper around the real bootstrap code that ignores\n # exceptions during interpreter cleanup. Those typically\n # happen when a daemon thread wakes up at an unfortunate\n # moment, finds the world around it destroyed, and raises some\n # random exception *** while trying to report the exception in\n # _bootstrap_inner() below ***. Those random exceptions\n # don't help anybody, and they confuse users, so we suppress\n # them. We suppress them only when it appears that the world\n # indeed has already been destroyed, so that exceptions in\n # _bootstrap_inner() during normal business hours are properly\n # reported. Also, we only suppress them for daemonic threads;\n # if a non-daemonic encounters this, something else is wrong.\n try:\n self._bootstrap_inner()\n except:\n if self._daemonic and _sys is None:\n return\n raise\n\n def _set_ident(self):\n self._ident = get_ident()\n\n if _HAVE_THREAD_NATIVE_ID:\n def _set_native_id(self):\n self._native_id = get_native_id()\n\n def _bootstrap_inner(self):\n try:\n self._set_ident()\n if _HAVE_THREAD_NATIVE_ID:\n self._set_native_id()\n self._started.set()\n with _active_limbo_lock:\n _active[self._ident] = self\n del _limbo[self]\n\n if _trace_hook:\n _sys.settrace(_trace_hook)\n if _profile_hook:\n _sys.setprofile(_profile_hook)\n\n try:\n self.run()\n except:\n self._invoke_excepthook(self)\n finally:\n self._delete()\n\n def _delete(self):\n \"Remove current thread from the dict of currently running threads.\"\n with _active_limbo_lock:\n del _active[get_ident()]\n # There must not be any python code between the previous line\n # and after the lock is released. Otherwise a tracing function\n # could try to acquire the lock again in the same thread, (in\n # current_thread()), and would block.\n\n def join(self, timeout=None):\n \"\"\"Wait until the thread terminates.\n\n This blocks the calling thread until the thread whose join() method is\n called terminates -- either normally or through an unhandled exception\n or until the optional timeout occurs.\n\n When the timeout argument is present and not None, it should be a\n floating-point number specifying a timeout for the operation in seconds\n (or fractions thereof). As join() always returns None, you must call\n is_alive() after join() to decide whether a timeout happened -- if the\n thread is still alive, the join() call timed out.\n\n When the timeout argument is not present or None, the operation will\n block until the thread terminates.\n\n A thread can be join()ed many times.\n\n join() raises a RuntimeError if an attempt is made to join the current\n thread as that would cause a deadlock. It is also an error to join() a\n thread before it has been started and attempts to do so raises the same\n exception.\n\n \"\"\"\n if not self._initialized:\n raise RuntimeError(\"Thread.__init__() not called\")\n if not self._started.is_set():\n raise RuntimeError(\"cannot join thread before it is started\")\n if self is current_thread():\n raise RuntimeError(\"cannot join current thread\")\n\n # the behavior of a negative timeout isn't documented, but\n # historically .join(timeout=x) for x<0 has acted as if timeout=0\n if timeout is not None:\n timeout = max(timeout, 0)\n\n self._handle.join(timeout)\n\n @property\n def name(self):\n \"\"\"A string used for identification purposes only.\n\n It has no semantics. Multiple threads may be given the same name. The\n initial name is set by the constructor.\n\n \"\"\"\n assert self._initialized, \"Thread.__init__() not called\"\n return self._name\n\n @name.setter\n def name(self, name):\n assert self._initialized, \"Thread.__init__() not called\"\n self._name = str(name)\n\n @property\n def ident(self):\n \"\"\"Thread identifier of this thread or None if it has not been started.\n\n This is a nonzero integer. See the get_ident() function. Thread\n identifiers may be recycled when a thread exits and another thread is\n created. The identifier is available even after the thread has exited.\n\n \"\"\"\n assert self._initialized, \"Thread.__init__() not called\"\n return self._ident\n\n if _HAVE_THREAD_NATIVE_ID:\n @property\n def native_id(self):\n \"\"\"Native integral thread ID of this thread, or None if it has not been started.\n\n This is a non-negative integer. See the get_native_id() function.\n This represents the Thread ID as reported by the kernel.\n\n \"\"\"\n assert self._initialized, \"Thread.__init__() not called\"\n return self._native_id\n\n def is_alive(self):\n \"\"\"Return whether the thread is alive.\n\n This method returns True just before the run() method starts until just\n after the run() method terminates. See also the module function\n enumerate().\n\n \"\"\"\n assert self._initialized, \"Thread.__init__() not called\"\n return self._started.is_set() and not self._handle.is_done()\n\n @property\n def daemon(self):\n \"\"\"A boolean value indicating whether this thread is a daemon thread.\n\n This must be set before start() is called, otherwise RuntimeError is\n raised. Its initial value is inherited from the creating thread; the\n main thread is not a daemon thread and therefore all threads created in\n the main thread default to daemon = False.\n\n The entire Python program exits when only daemon threads are left.\n\n \"\"\"\n assert self._initialized, \"Thread.__init__() not called\"\n return self._daemonic\n\n @daemon.setter\n def daemon(self, daemonic):\n if not self._initialized:\n raise RuntimeError(\"Thread.__init__() not called\")\n if daemonic and not _daemon_threads_allowed():\n raise RuntimeError('daemon threads are disabled in this interpreter')\n if self._started.is_set():\n raise RuntimeError(\"cannot set daemon status of active thread\")\n self._daemonic = daemonic\n\n def isDaemon(self):\n \"\"\"Return whether this thread is a daemon.\n\n This method is deprecated, use the daemon attribute instead.\n\n \"\"\"\n import warnings\n warnings.warn('isDaemon() is deprecated, get the daemon attribute instead',\n DeprecationWarning, stacklevel=2)\n return self.daemon\n\n def setDaemon(self, daemonic):\n \"\"\"Set whether this thread is a daemon.\n\n This method is deprecated, use the .daemon property instead.\n\n \"\"\"\n import warnings\n warnings.warn('setDaemon() is deprecated, set the daemon attribute instead',\n DeprecationWarning, stacklevel=2)\n self.daemon = daemonic\n\n def getName(self):\n \"\"\"Return a string used for identification purposes only.\n\n This method is deprecated, use the name attribute instead.\n\n \"\"\"\n import warnings\n warnings.warn('getName() is deprecated, get the name attribute instead',\n DeprecationWarning, stacklevel=2)\n return self.name\n\n def setName(self, name):\n \"\"\"Set the name string for this thread.\n\n This method is deprecated, use the name attribute instead.\n\n \"\"\"\n import warnings\n warnings.warn('setName() is deprecated, set the name attribute instead',\n DeprecationWarning, stacklevel=2)\n self.name = name\n\n\ntry:\n from _thread import (_excepthook as excepthook,\n _ExceptHookArgs as ExceptHookArgs)\nexcept ImportError:\n # Simple Python implementation if _thread._excepthook() is not available\n from traceback import print_exception as _print_exception\n from collections import namedtuple\n\n _ExceptHookArgs = namedtuple(\n 'ExceptHookArgs',\n 'exc_type exc_value exc_traceback thread')\n\n def ExceptHookArgs(args):\n return _ExceptHookArgs(*args)\n\n def excepthook(args, /):\n \"\"\"\n Handle uncaught Thread.run() exception.\n \"\"\"\n if args.exc_type == SystemExit:\n # silently ignore SystemExit\n return\n\n if _sys is not None and _sys.stderr is not None:\n stderr = _sys.stderr\n elif args.thread is not None:\n stderr = args.thread._stderr\n if stderr is None:\n # do nothing if sys.stderr is None and sys.stderr was None\n # when the thread was created\n return\n else:\n # do nothing if sys.stderr is None and args.thread is None\n return\n\n if args.thread is not None:\n name = args.thread.name\n else:\n name = get_ident()\n print(f\"Exception in thread {name}:\",\n file=stderr, flush=True)\n _print_exception(args.exc_type, args.exc_value, args.exc_traceback,\n file=stderr)\n stderr.flush()\n\n\n# Original value of threading.excepthook\n__excepthook__ = excepthook\n\n\ndef _make_invoke_excepthook():\n # Create a local namespace to ensure that variables remain alive\n # when _invoke_excepthook() is called, even if it is called late during\n # Python shutdown. It is mostly needed for daemon threads.\n\n old_excepthook = excepthook\n old_sys_excepthook = _sys.excepthook\n if old_excepthook is None:\n raise RuntimeError(\"threading.excepthook is None\")\n if old_sys_excepthook is None:\n raise RuntimeError(\"sys.excepthook is None\")\n\n sys_exc_info = _sys.exc_info\n local_print = print\n local_sys = _sys\n\n def invoke_excepthook(thread):\n global excepthook\n try:\n hook = excepthook\n if hook is None:\n hook = old_excepthook\n\n args = ExceptHookArgs([*sys_exc_info(), thread])\n\n hook(args)\n except Exception as exc:\n exc.__suppress_context__ = True\n del exc\n\n if local_sys is not None and local_sys.stderr is not None:\n stderr = local_sys.stderr\n else:\n stderr = thread._stderr\n\n local_print(\"Exception in threading.excepthook:\",\n file=stderr, flush=True)\n\n if local_sys is not None and local_sys.excepthook is not None:\n sys_excepthook = local_sys.excepthook\n else:\n sys_excepthook = old_sys_excepthook\n\n sys_excepthook(*sys_exc_info())\n finally:\n # Break reference cycle (exception stored in a variable)\n args = None\n\n return invoke_excepthook\n\n\n# The timer class was contributed by Itamar Shtull-Trauring\n\nclass Timer(Thread):\n \"\"\"Call a function after a specified number of seconds:\n\n t = Timer(30.0, f, args=None, kwargs=None)\n t.start()\n t.cancel() # stop the timer's action if it's still waiting\n\n \"\"\"\n\n def __init__(self, interval, function, args=None, kwargs=None):\n Thread.__init__(self)\n self.interval = interval\n self.function = function\n self.args = args if args is not None else []\n self.kwargs = kwargs if kwargs is not None else {}\n self.finished = Event()\n\n def cancel(self):\n \"\"\"Stop the timer if it hasn't finished yet.\"\"\"\n self.finished.set()\n\n def run(self):\n self.finished.wait(self.interval)\n if not self.finished.is_set():\n self.function(*self.args, **self.kwargs)\n self.finished.set()\n\n\n# Special thread class to represent the main thread\n\nclass _MainThread(Thread):\n\n def __init__(self):\n Thread.__init__(self, name=\"MainThread\", daemon=False)\n self._started.set()\n self._ident = _get_main_thread_ident()\n self._handle = _make_thread_handle(self._ident)\n if _HAVE_THREAD_NATIVE_ID:\n self._set_native_id()\n with _active_limbo_lock:\n _active[self._ident] = self\n\n\n# Helper thread-local instance to detect when a _DummyThread\n# is collected. Not a part of the public API.\n_thread_local_info = local()\n\n\nclass _DeleteDummyThreadOnDel:\n '''\n Helper class to remove a dummy thread from threading._active on __del__.\n '''\n\n def __init__(self, dummy_thread):\n self._dummy_thread = dummy_thread\n self._tident = dummy_thread.ident\n # Put the thread on a thread local variable so that when\n # the related thread finishes this instance is collected.\n #\n # Note: no other references to this instance may be created.\n # If any client code creates a reference to this instance,\n # the related _DummyThread will be kept forever!\n _thread_local_info._track_dummy_thread_ref = self\n\n def __del__(self):\n with _active_limbo_lock:\n if _active.get(self._tident) is self._dummy_thread:\n _active.pop(self._tident, None)\n\n\n# Dummy thread class to represent threads not started here.\n# These should be added to `_active` and removed automatically\n# when they die, although they can't be waited for.\n# Their purpose is to return *something* from current_thread().\n# They are marked as daemon threads so we won't wait for them\n# when we exit (conform previous semantics).\n\nclass _DummyThread(Thread):\n\n def __init__(self):\n Thread.__init__(self, name=_newname(\"Dummy-%d\"),\n daemon=_daemon_threads_allowed())\n self._started.set()\n self._set_ident()\n self._handle = _make_thread_handle(self._ident)\n if _HAVE_THREAD_NATIVE_ID:\n self._set_native_id()\n with _active_limbo_lock:\n _active[self._ident] = self\n _DeleteDummyThreadOnDel(self)\n\n def is_alive(self):\n if not self._handle.is_done() and self._started.is_set():\n return True\n raise RuntimeError(\"thread is not alive\")\n\n def join(self, timeout=None):\n raise RuntimeError(\"cannot join a dummy thread\")\n\n def _after_fork(self, new_ident=None):\n if new_ident is not None:\n self.__class__ = _MainThread\n self._name = 'MainThread'\n self._daemonic = False\n Thread._after_fork(self, new_ident=new_ident)\n\n\n# Global API functions\n\ndef current_thread():\n \"\"\"Return the current Thread object, corresponding to the caller's thread of control.\n\n If the caller's thread of control was not created through the threading\n module, a dummy thread object with limited functionality is returned.\n\n \"\"\"\n try:\n return _active[get_ident()]\n except KeyError:\n return _DummyThread()\n\ndef currentThread():\n \"\"\"Return the current Thread object, corresponding to the caller's thread of control.\n\n This function is deprecated, use current_thread() instead.\n\n \"\"\"\n import warnings\n warnings.warn('currentThread() is deprecated, use current_thread() instead',\n DeprecationWarning, stacklevel=2)\n return current_thread()\n\ndef active_count():\n \"\"\"Return the number of Thread objects currently alive.\n\n The returned count is equal to the length of the list returned by\n enumerate().\n\n \"\"\"\n # NOTE: if the logic in here ever changes, update Modules/posixmodule.c\n # warn_about_fork_with_threads() to match.\n with _active_limbo_lock:\n return len(_active) + len(_limbo)\n\ndef activeCount():\n \"\"\"Return the number of Thread objects currently alive.\n\n This function is deprecated, use active_count() instead.\n\n \"\"\"\n import warnings\n warnings.warn('activeCount() is deprecated, use active_count() instead',\n DeprecationWarning, stacklevel=2)\n return active_count()\n\ndef _enumerate():\n # Same as enumerate(), but without the lock. Internal use only.\n return list(_active.values()) + list(_limbo.values())\n\ndef enumerate():\n \"\"\"Return a list of all Thread objects currently alive.\n\n The list includes daemonic threads, dummy thread objects created by\n current_thread(), and the main thread. It excludes terminated threads and\n threads that have not yet been started.\n\n \"\"\"\n with _active_limbo_lock:\n return list(_active.values()) + list(_limbo.values())\n\n\n_threading_atexits = []\n_SHUTTING_DOWN = False\n\ndef _register_atexit(func, *arg, **kwargs):\n \"\"\"CPython internal: register *func* to be called before joining threads.\n\n The registered *func* is called with its arguments just before all\n non-daemon threads are joined in `_shutdown()`. It provides a similar\n purpose to `atexit.register()`, but its functions are called prior to\n threading shutdown instead of interpreter shutdown.\n\n For similarity to atexit, the registered functions are called in reverse.\n \"\"\"\n if _SHUTTING_DOWN:\n raise RuntimeError(\"can't register atexit after shutdown\")\n\n _threading_atexits.append(lambda: func(*arg, **kwargs))\n\n\nfrom _thread import stack_size\n\n# Create the main thread object,\n# and make it available for the interpreter\n# (Py_Main) as threading._shutdown.\n\n_main_thread = _MainThread()\n\ndef _shutdown():\n \"\"\"\n Wait until the Python thread state of all non-daemon threads get deleted.\n \"\"\"\n # Obscure: other threads may be waiting to join _main_thread. That's\n # dubious, but some code does it. We can't wait for it to be marked as done\n # normally - that won't happen until the interpreter is nearly dead. So\n # mark it done here.\n if _main_thread._handle.is_done() and _is_main_interpreter():\n # _shutdown() was already called\n return\n\n global _SHUTTING_DOWN\n _SHUTTING_DOWN = True\n\n # Call registered threading atexit functions before threads are joined.\n # Order is reversed, similar to atexit.\n for atexit_call in reversed(_threading_atexits):\n atexit_call()\n\n if _is_main_interpreter():\n _main_thread._handle._set_done()\n\n # Wait for all non-daemon threads to exit.\n _thread_shutdown()\n\n\ndef main_thread():\n \"\"\"Return the main thread object.\n\n In normal conditions, the main thread is the thread from which the\n Python interpreter was started.\n \"\"\"\n # XXX Figure this out for subinterpreters. (See gh-75698.)\n return _main_thread\n\n\ndef _after_fork():\n \"\"\"\n Cleanup threading module state that should not exist after a fork.\n \"\"\"\n # Reset _active_limbo_lock, in case we forked while the lock was held\n # by another (non-forked) thread. http://bugs.python.org/issue874900\n global _active_limbo_lock, _main_thread\n _active_limbo_lock = RLock()\n\n # fork() only copied the current thread; clear references to others.\n new_active = {}\n\n try:\n current = _active[get_ident()]\n except KeyError:\n # fork() was called in a thread which was not spawned\n # by threading.Thread. For example, a thread spawned\n # by thread.start_new_thread().\n current = _MainThread()\n\n _main_thread = current\n\n with _active_limbo_lock:\n # Dangling thread instances must still have their locks reset,\n # because someone may join() them.\n threads = set(_enumerate())\n threads.update(_dangling)\n for thread in threads:\n # Any lock/condition variable may be currently locked or in an\n # invalid state, so we reinitialize them.\n if thread is current:\n # This is the one and only active thread.\n ident = get_ident()\n thread._after_fork(new_ident=ident)\n new_active[ident] = thread\n else:\n # All the others are already stopped.\n thread._after_fork()\n\n _limbo.clear()\n _active.clear()\n _active.update(new_active)\n assert len(_active) == 1\n\n\nif hasattr(_os, \"register_at_fork\"):\n _os.register_at_fork(after_in_child=_after_fork)\n", 1599], "/usr/lib/python3.13/signal.py": ["import _signal\nfrom _signal import *\nfrom enum import IntEnum as _IntEnum\n\n_globals = globals()\n\n_IntEnum._convert_(\n 'Signals', __name__,\n lambda name:\n name.isupper()\n and (name.startswith('SIG') and not name.startswith('SIG_'))\n or name.startswith('CTRL_'))\n\n_IntEnum._convert_(\n 'Handlers', __name__,\n lambda name: name in ('SIG_DFL', 'SIG_IGN'))\n\nif 'pthread_sigmask' in _globals:\n _IntEnum._convert_(\n 'Sigmasks', __name__,\n lambda name: name in ('SIG_BLOCK', 'SIG_UNBLOCK', 'SIG_SETMASK'))\n\n\ndef _int_to_enum(value, enum_klass):\n \"\"\"Convert a possible numeric value to an IntEnum member.\n If it's not a known member, return the value itself.\n \"\"\"\n if not isinstance(value, int):\n return value\n try:\n return enum_klass(value)\n except ValueError:\n return value\n\n\ndef _enum_to_int(value):\n \"\"\"Convert an IntEnum member to a numeric value.\n If it's not an IntEnum member return the value itself.\n \"\"\"\n try:\n return int(value)\n except (ValueError, TypeError):\n return value\n\n\n# Similar to functools.wraps(), but only assign __doc__.\n# __module__ should be preserved,\n# __name__ and __qualname__ are already fine,\n# __annotations__ is not set.\ndef _wraps(wrapped):\n def decorator(wrapper):\n wrapper.__doc__ = wrapped.__doc__\n return wrapper\n return decorator\n\n@_wraps(_signal.signal)\ndef signal(signalnum, handler):\n handler = _signal.signal(_enum_to_int(signalnum), _enum_to_int(handler))\n return _int_to_enum(handler, Handlers)\n\n\n@_wraps(_signal.getsignal)\ndef getsignal(signalnum):\n handler = _signal.getsignal(signalnum)\n return _int_to_enum(handler, Handlers)\n\n\nif 'pthread_sigmask' in _globals:\n @_wraps(_signal.pthread_sigmask)\n def pthread_sigmask(how, mask):\n sigs_set = _signal.pthread_sigmask(how, mask)\n return set(_int_to_enum(x, Signals) for x in sigs_set)\n\n\nif 'sigpending' in _globals:\n @_wraps(_signal.sigpending)\n def sigpending():\n return {_int_to_enum(x, Signals) for x in _signal.sigpending()}\n\n\nif 'sigwait' in _globals:\n @_wraps(_signal.sigwait)\n def sigwait(sigset):\n retsig = _signal.sigwait(sigset)\n return _int_to_enum(retsig, Signals)\n\n\nif 'valid_signals' in _globals:\n @_wraps(_signal.valid_signals)\n def valid_signals():\n return {_int_to_enum(x, Signals) for x in _signal.valid_signals()}\n\n\ndel _globals, _wraps\n", 94], "/usr/lib/python3.13/asyncio/base_futures.py": ["__all__ = ()\n\nimport reprlib\n\nfrom . import format_helpers\n\n# States for Future.\n_PENDING = 'PENDING'\n_CANCELLED = 'CANCELLED'\n_FINISHED = 'FINISHED'\n\n\ndef isfuture(obj):\n \"\"\"Check for a Future.\n\n This returns True when obj is a Future instance or is advertising\n itself as duck-type compatible by setting _asyncio_future_blocking.\n See comment in Future for more details.\n \"\"\"\n return (hasattr(obj.__class__, '_asyncio_future_blocking') and\n obj._asyncio_future_blocking is not None)\n\n\ndef _format_callbacks(cb):\n \"\"\"helper function for Future.__repr__\"\"\"\n size = len(cb)\n if not size:\n cb = ''\n\n def format_cb(callback):\n return format_helpers._format_callback_source(callback, ())\n\n if size == 1:\n cb = format_cb(cb[0][0])\n elif size == 2:\n cb = '{}, {}'.format(format_cb(cb[0][0]), format_cb(cb[1][0]))\n elif size > 2:\n cb = '{}, <{} more>, {}'.format(format_cb(cb[0][0]),\n size - 2,\n format_cb(cb[-1][0]))\n return f'cb=[{cb}]'\n\n\ndef _future_repr_info(future):\n # (Future) -> str\n \"\"\"helper function for Future.__repr__\"\"\"\n info = [future._state.lower()]\n if future._state == _FINISHED:\n if future._exception is not None:\n info.append(f'exception={future._exception!r}')\n else:\n # use reprlib to limit the length of the output, especially\n # for very long strings\n result = reprlib.repr(future._result)\n info.append(f'result={result}')\n if future._callbacks:\n info.append(_format_callbacks(future._callbacks))\n if future._source_traceback:\n frame = future._source_traceback[-1]\n info.append(f'created at {frame[0]}:{frame[1]}')\n return info\n\n\n@reprlib.recursive_repr()\ndef _future_repr(future):\n info = ' '.join(_future_repr_info(future))\n return f'<{future.__class__.__name__} {info}>'\n", 67], "/usr/lib/python3.13/asyncio/futures.py": ["\"\"\"A Future class similar to the one in PEP 3148.\"\"\"\n\n__all__ = (\n 'Future', 'wrap_future', 'isfuture',\n)\n\nimport concurrent.futures\nimport contextvars\nimport logging\nimport sys\nfrom types import GenericAlias\n\nfrom . import base_futures\nfrom . import events\nfrom . import exceptions\nfrom . import format_helpers\n\n\nisfuture = base_futures.isfuture\n\n\n_PENDING = base_futures._PENDING\n_CANCELLED = base_futures._CANCELLED\n_FINISHED = base_futures._FINISHED\n\n\nSTACK_DEBUG = logging.DEBUG - 1 # heavy-duty debugging\n\n\nclass Future:\n \"\"\"This class is *almost* compatible with concurrent.futures.Future.\n\n Differences:\n\n - This class is not thread-safe.\n\n - result() and exception() do not take a timeout argument and\n raise an exception when the future isn't done yet.\n\n - Callbacks registered with add_done_callback() are always called\n via the event loop's call_soon().\n\n - This class is not compatible with the wait() and as_completed()\n methods in the concurrent.futures package.\n\n (In Python 3.4 or later we may be able to unify the implementations.)\n \"\"\"\n\n # Class variables serving as defaults for instance variables.\n _state = _PENDING\n _result = None\n _exception = None\n _loop = None\n _source_traceback = None\n _cancel_message = None\n # A saved CancelledError for later chaining as an exception context.\n _cancelled_exc = None\n\n # This field is used for a dual purpose:\n # - Its presence is a marker to declare that a class implements\n # the Future protocol (i.e. is intended to be duck-type compatible).\n # The value must also be not-None, to enable a subclass to declare\n # that it is not compatible by setting this to None.\n # - It is set by __iter__() below so that Task._step() can tell\n # the difference between\n # `await Future()` or`yield from Future()` (correct) vs.\n # `yield Future()` (incorrect).\n _asyncio_future_blocking = False\n\n __log_traceback = False\n\n def __init__(self, *, loop=None):\n \"\"\"Initialize the future.\n\n The optional event_loop argument allows explicitly setting the event\n loop object used by the future. If it's not provided, the future uses\n the default event loop.\n \"\"\"\n if loop is None:\n self._loop = events.get_event_loop()\n else:\n self._loop = loop\n self._callbacks = []\n if self._loop.get_debug():\n self._source_traceback = format_helpers.extract_stack(\n sys._getframe(1))\n\n def __repr__(self):\n return base_futures._future_repr(self)\n\n def __del__(self):\n if not self.__log_traceback:\n # set_exception() was not called, or result() or exception()\n # has consumed the exception\n return\n exc = self._exception\n context = {\n 'message':\n f'{self.__class__.__name__} exception was never retrieved',\n 'exception': exc,\n 'future': self,\n }\n if self._source_traceback:\n context['source_traceback'] = self._source_traceback\n self._loop.call_exception_handler(context)\n\n __class_getitem__ = classmethod(GenericAlias)\n\n @property\n def _log_traceback(self):\n return self.__log_traceback\n\n @_log_traceback.setter\n def _log_traceback(self, val):\n if val:\n raise ValueError('_log_traceback can only be set to False')\n self.__log_traceback = False\n\n def get_loop(self):\n \"\"\"Return the event loop the Future is bound to.\"\"\"\n loop = self._loop\n if loop is None:\n raise RuntimeError(\"Future object is not initialized.\")\n return loop\n\n def _make_cancelled_error(self):\n \"\"\"Create the CancelledError to raise if the Future is cancelled.\n\n This should only be called once when handling a cancellation since\n it erases the saved context exception value.\n \"\"\"\n if self._cancelled_exc is not None:\n exc = self._cancelled_exc\n self._cancelled_exc = None\n return exc\n\n if self._cancel_message is None:\n exc = exceptions.CancelledError()\n else:\n exc = exceptions.CancelledError(self._cancel_message)\n return exc\n\n def cancel(self, msg=None):\n \"\"\"Cancel the future and schedule callbacks.\n\n If the future is already done or cancelled, return False. Otherwise,\n change the future's state to cancelled, schedule the callbacks and\n return True.\n \"\"\"\n self.__log_traceback = False\n if self._state != _PENDING:\n return False\n self._state = _CANCELLED\n self._cancel_message = msg\n self.__schedule_callbacks()\n return True\n\n def __schedule_callbacks(self):\n \"\"\"Internal: Ask the event loop to call all callbacks.\n\n The callbacks are scheduled to be called as soon as possible. Also\n clears the callback list.\n \"\"\"\n callbacks = self._callbacks[:]\n if not callbacks:\n return\n\n self._callbacks[:] = []\n for callback, ctx in callbacks:\n self._loop.call_soon(callback, self, context=ctx)\n\n def cancelled(self):\n \"\"\"Return True if the future was cancelled.\"\"\"\n return self._state == _CANCELLED\n\n # Don't implement running(); see http://bugs.python.org/issue18699\n\n def done(self):\n \"\"\"Return True if the future is done.\n\n Done means either that a result / exception are available, or that the\n future was cancelled.\n \"\"\"\n return self._state != _PENDING\n\n def result(self):\n \"\"\"Return the result this future represents.\n\n If the future has been cancelled, raises CancelledError. If the\n future's result isn't yet available, raises InvalidStateError. If\n the future is done and has an exception set, this exception is raised.\n \"\"\"\n if self._state == _CANCELLED:\n exc = self._make_cancelled_error()\n raise exc\n if self._state != _FINISHED:\n raise exceptions.InvalidStateError('Result is not ready.')\n self.__log_traceback = False\n if self._exception is not None:\n raise self._exception.with_traceback(self._exception_tb)\n return self._result\n\n def exception(self):\n \"\"\"Return the exception that was set on this future.\n\n The exception (or None if no exception was set) is returned only if\n the future is done. If the future has been cancelled, raises\n CancelledError. If the future isn't done yet, raises\n InvalidStateError.\n \"\"\"\n if self._state == _CANCELLED:\n exc = self._make_cancelled_error()\n raise exc\n if self._state != _FINISHED:\n raise exceptions.InvalidStateError('Exception is not set.')\n self.__log_traceback = False\n return self._exception\n\n def add_done_callback(self, fn, *, context=None):\n \"\"\"Add a callback to be run when the future becomes done.\n\n The callback is called with a single argument - the future object. If\n the future is already done when this is called, the callback is\n scheduled with call_soon.\n \"\"\"\n if self._state != _PENDING:\n self._loop.call_soon(fn, self, context=context)\n else:\n if context is None:\n context = contextvars.copy_context()\n self._callbacks.append((fn, context))\n\n # New method not in PEP 3148.\n\n def remove_done_callback(self, fn):\n \"\"\"Remove all instances of a callback from the \"call when done\" list.\n\n Returns the number of callbacks removed.\n \"\"\"\n filtered_callbacks = [(f, ctx)\n for (f, ctx) in self._callbacks\n if f != fn]\n removed_count = len(self._callbacks) - len(filtered_callbacks)\n if removed_count:\n self._callbacks[:] = filtered_callbacks\n return removed_count\n\n # So-called internal methods (note: no set_running_or_notify_cancel()).\n\n def set_result(self, result):\n \"\"\"Mark the future done and set its result.\n\n If the future is already done when this method is called, raises\n InvalidStateError.\n \"\"\"\n if self._state != _PENDING:\n raise exceptions.InvalidStateError(f'{self._state}: {self!r}')\n self._result = result\n self._state = _FINISHED\n self.__schedule_callbacks()\n\n def set_exception(self, exception):\n \"\"\"Mark the future done and set an exception.\n\n If the future is already done when this method is called, raises\n InvalidStateError.\n \"\"\"\n if self._state != _PENDING:\n raise exceptions.InvalidStateError(f'{self._state}: {self!r}')\n if isinstance(exception, type):\n exception = exception()\n if isinstance(exception, StopIteration):\n new_exc = RuntimeError(\"StopIteration interacts badly with \"\n \"generators and cannot be raised into a \"\n \"Future\")\n new_exc.__cause__ = exception\n new_exc.__context__ = exception\n exception = new_exc\n self._exception = exception\n self._exception_tb = exception.__traceback__\n self._state = _FINISHED\n self.__schedule_callbacks()\n self.__log_traceback = True\n\n def __await__(self):\n if not self.done():\n self._asyncio_future_blocking = True\n yield self # This tells Task to wait for completion.\n if not self.done():\n raise RuntimeError(\"await wasn't used with future\")\n return self.result() # May raise too.\n\n __iter__ = __await__ # make compatible with 'yield from'.\n\n\n# Needed for testing purposes.\n_PyFuture = Future\n\n\ndef _get_loop(fut):\n # Tries to call Future.get_loop() if it's available.\n # Otherwise fallbacks to using the old '_loop' property.\n try:\n get_loop = fut.get_loop\n except AttributeError:\n pass\n else:\n return get_loop()\n return fut._loop\n\n\ndef _set_result_unless_cancelled(fut, result):\n \"\"\"Helper setting the result only if the future was not cancelled.\"\"\"\n if fut.cancelled():\n return\n fut.set_result(result)\n\n\ndef _convert_future_exc(exc):\n exc_class = type(exc)\n if exc_class is concurrent.futures.CancelledError:\n return exceptions.CancelledError(*exc.args).with_traceback(exc.__traceback__)\n elif exc_class is concurrent.futures.InvalidStateError:\n return exceptions.InvalidStateError(*exc.args).with_traceback(exc.__traceback__)\n else:\n return exc\n\n\ndef _set_concurrent_future_state(concurrent, source):\n \"\"\"Copy state from a future to a concurrent.futures.Future.\"\"\"\n assert source.done()\n if source.cancelled():\n concurrent.cancel()\n if not concurrent.set_running_or_notify_cancel():\n return\n exception = source.exception()\n if exception is not None:\n concurrent.set_exception(_convert_future_exc(exception))\n else:\n result = source.result()\n concurrent.set_result(result)\n\n\ndef _copy_future_state(source, dest):\n \"\"\"Internal helper to copy state from another Future.\n\n The other Future may be a concurrent.futures.Future.\n \"\"\"\n assert source.done()\n if dest.cancelled():\n return\n assert not dest.done()\n if source.cancelled():\n dest.cancel()\n else:\n exception = source.exception()\n if exception is not None:\n dest.set_exception(_convert_future_exc(exception))\n else:\n result = source.result()\n dest.set_result(result)\n\n\ndef _chain_future(source, destination):\n \"\"\"Chain two futures so that when one completes, so does the other.\n\n The result (or exception) of source will be copied to destination.\n If destination is cancelled, source gets cancelled too.\n Compatible with both asyncio.Future and concurrent.futures.Future.\n \"\"\"\n if not isfuture(source) and not isinstance(source,\n concurrent.futures.Future):\n raise TypeError('A future is required for source argument')\n if not isfuture(destination) and not isinstance(destination,\n concurrent.futures.Future):\n raise TypeError('A future is required for destination argument')\n source_loop = _get_loop(source) if isfuture(source) else None\n dest_loop = _get_loop(destination) if isfuture(destination) else None\n\n def _set_state(future, other):\n if isfuture(future):\n _copy_future_state(other, future)\n else:\n _set_concurrent_future_state(future, other)\n\n def _call_check_cancel(destination):\n if destination.cancelled():\n if source_loop is None or source_loop is dest_loop:\n source.cancel()\n else:\n source_loop.call_soon_threadsafe(source.cancel)\n\n def _call_set_state(source):\n if (destination.cancelled() and\n dest_loop is not None and dest_loop.is_closed()):\n return\n if dest_loop is None or dest_loop is source_loop:\n _set_state(destination, source)\n else:\n if dest_loop.is_closed():\n return\n dest_loop.call_soon_threadsafe(_set_state, destination, source)\n\n destination.add_done_callback(_call_check_cancel)\n source.add_done_callback(_call_set_state)\n\n\ndef wrap_future(future, *, loop=None):\n \"\"\"Wrap concurrent.futures.Future object.\"\"\"\n if isfuture(future):\n return future\n assert isinstance(future, concurrent.futures.Future), \\\n f'concurrent.futures.Future is expected, got {future!r}'\n if loop is None:\n loop = events.get_event_loop()\n new_future = loop.create_future()\n _chain_future(future, new_future)\n return new_future\n\n\ntry:\n import _asyncio\nexcept ImportError:\n pass\nelse:\n # _CFuture is needed for tests.\n Future = _CFuture = _asyncio.Future\n", 427], "/usr/lib/python3.13/asyncio/tasks.py": ["\"\"\"Support for tasks, coroutines and the scheduler.\"\"\"\n\n__all__ = (\n 'Task', 'create_task',\n 'FIRST_COMPLETED', 'FIRST_EXCEPTION', 'ALL_COMPLETED',\n 'wait', 'wait_for', 'as_completed', 'sleep',\n 'gather', 'shield', 'ensure_future', 'run_coroutine_threadsafe',\n 'current_task', 'all_tasks',\n 'create_eager_task_factory', 'eager_task_factory',\n '_register_task', '_unregister_task', '_enter_task', '_leave_task',\n)\n\nimport concurrent.futures\nimport contextvars\nimport functools\nimport inspect\nimport itertools\nimport math\nimport types\nimport weakref\nfrom types import GenericAlias\n\nfrom . import base_tasks\nfrom . import coroutines\nfrom . import events\nfrom . import exceptions\nfrom . import futures\nfrom . import queues\nfrom . import timeouts\n\n# Helper to generate new task names\n# This uses itertools.count() instead of a \"+= 1\" operation because the latter\n# is not thread safe. See bpo-11866 for a longer explanation.\n_task_name_counter = itertools.count(1).__next__\n\n\ndef current_task(loop=None):\n \"\"\"Return a currently executed task.\"\"\"\n if loop is None:\n loop = events.get_running_loop()\n return _current_tasks.get(loop)\n\n\ndef all_tasks(loop=None):\n \"\"\"Return a set of all tasks for the loop.\"\"\"\n if loop is None:\n loop = events.get_running_loop()\n # capturing the set of eager tasks first, so if an eager task \"graduates\"\n # to a regular task in another thread, we don't risk missing it.\n eager_tasks = list(_eager_tasks)\n # Looping over the WeakSet isn't safe as it can be updated from another\n # thread, therefore we cast it to list prior to filtering. The list cast\n # itself requires iteration, so we repeat it several times ignoring\n # RuntimeErrors (which are not very likely to occur).\n # See issues 34970 and 36607 for details.\n scheduled_tasks = None\n i = 0\n while True:\n try:\n scheduled_tasks = list(_scheduled_tasks)\n except RuntimeError:\n i += 1\n if i >= 1000:\n raise\n else:\n break\n return {t for t in itertools.chain(scheduled_tasks, eager_tasks)\n if futures._get_loop(t) is loop and not t.done()}\n\n\nclass Task(futures._PyFuture): # Inherit Python Task implementation\n # from a Python Future implementation.\n\n \"\"\"A coroutine wrapped in a Future.\"\"\"\n\n # An important invariant maintained while a Task not done:\n # _fut_waiter is either None or a Future. The Future\n # can be either done() or not done().\n # The task can be in any of 3 states:\n #\n # - 1: _fut_waiter is not None and not _fut_waiter.done():\n # __step() is *not* scheduled and the Task is waiting for _fut_waiter.\n # - 2: (_fut_waiter is None or _fut_waiter.done()) and __step() is scheduled:\n # the Task is waiting for __step() to be executed.\n # - 3: _fut_waiter is None and __step() is *not* scheduled:\n # the Task is currently executing (in __step()).\n #\n # * In state 1, one of the callbacks of __fut_waiter must be __wakeup().\n # * The transition from 1 to 2 happens when _fut_waiter becomes done(),\n # as it schedules __wakeup() to be called (which calls __step() so\n # we way that __step() is scheduled).\n # * It transitions from 2 to 3 when __step() is executed, and it clears\n # _fut_waiter to None.\n\n # If False, don't log a message if the task is destroyed while its\n # status is still pending\n _log_destroy_pending = True\n\n def __init__(self, coro, *, loop=None, name=None, context=None,\n eager_start=False):\n super().__init__(loop=loop)\n if self._source_traceback:\n del self._source_traceback[-1]\n if not coroutines.iscoroutine(coro):\n # raise after Future.__init__(), attrs are required for __del__\n # prevent logging for pending task in __del__\n self._log_destroy_pending = False\n raise TypeError(f\"a coroutine was expected, got {coro!r}\")\n\n if name is None:\n self._name = f'Task-{_task_name_counter()}'\n else:\n self._name = str(name)\n\n self._num_cancels_requested = 0\n self._must_cancel = False\n self._fut_waiter = None\n self._coro = coro\n if context is None:\n self._context = contextvars.copy_context()\n else:\n self._context = context\n\n if eager_start and self._loop.is_running():\n self.__eager_start()\n else:\n self._loop.call_soon(self.__step, context=self._context)\n _register_task(self)\n\n def __del__(self):\n if self._state == futures._PENDING and self._log_destroy_pending:\n context = {\n 'task': self,\n 'message': 'Task was destroyed but it is pending!',\n }\n if self._source_traceback:\n context['source_traceback'] = self._source_traceback\n self._loop.call_exception_handler(context)\n super().__del__()\n\n __class_getitem__ = classmethod(GenericAlias)\n\n def __repr__(self):\n return base_tasks._task_repr(self)\n\n def get_coro(self):\n return self._coro\n\n def get_context(self):\n return self._context\n\n def get_name(self):\n return self._name\n\n def set_name(self, value):\n self._name = str(value)\n\n def set_result(self, result):\n raise RuntimeError('Task does not support set_result operation')\n\n def set_exception(self, exception):\n raise RuntimeError('Task does not support set_exception operation')\n\n def get_stack(self, *, limit=None):\n \"\"\"Return the list of stack frames for this task's coroutine.\n\n If the coroutine is not done, this returns the stack where it is\n suspended. If the coroutine has completed successfully or was\n cancelled, this returns an empty list. If the coroutine was\n terminated by an exception, this returns the list of traceback\n frames.\n\n The frames are always ordered from oldest to newest.\n\n The optional limit gives the maximum number of frames to\n return; by default all available frames are returned. Its\n meaning differs depending on whether a stack or a traceback is\n returned: the newest frames of a stack are returned, but the\n oldest frames of a traceback are returned. (This matches the\n behavior of the traceback module.)\n\n For reasons beyond our control, only one stack frame is\n returned for a suspended coroutine.\n \"\"\"\n return base_tasks._task_get_stack(self, limit)\n\n def print_stack(self, *, limit=None, file=None):\n \"\"\"Print the stack or traceback for this task's coroutine.\n\n This produces output similar to that of the traceback module,\n for the frames retrieved by get_stack(). The limit argument\n is passed to get_stack(). The file argument is an I/O stream\n to which the output is written; by default output is written\n to sys.stderr.\n \"\"\"\n return base_tasks._task_print_stack(self, limit, file)\n\n def cancel(self, msg=None):\n \"\"\"Request that this task cancel itself.\n\n This arranges for a CancelledError to be thrown into the\n wrapped coroutine on the next cycle through the event loop.\n The coroutine then has a chance to clean up or even deny\n the request using try/except/finally.\n\n Unlike Future.cancel, this does not guarantee that the\n task will be cancelled: the exception might be caught and\n acted upon, delaying cancellation of the task or preventing\n cancellation completely. The task may also return a value or\n raise a different exception.\n\n Immediately after this method is called, Task.cancelled() will\n not return True (unless the task was already cancelled). A\n task will be marked as cancelled when the wrapped coroutine\n terminates with a CancelledError exception (even if cancel()\n was not called).\n\n This also increases the task's count of cancellation requests.\n \"\"\"\n self._log_traceback = False\n if self.done():\n return False\n self._num_cancels_requested += 1\n # These two lines are controversial. See discussion starting at\n # https://github.com/python/cpython/pull/31394#issuecomment-1053545331\n # Also remember that this is duplicated in _asynciomodule.c.\n # if self._num_cancels_requested > 1:\n # return False\n if self._fut_waiter is not None:\n if self._fut_waiter.cancel(msg=msg):\n # Leave self._fut_waiter; it may be a Task that\n # catches and ignores the cancellation so we may have\n # to cancel it again later.\n return True\n # It must be the case that self.__step is already scheduled.\n self._must_cancel = True\n self._cancel_message = msg\n return True\n\n def cancelling(self):\n \"\"\"Return the count of the task's cancellation requests.\n\n This count is incremented when .cancel() is called\n and may be decremented using .uncancel().\n \"\"\"\n return self._num_cancels_requested\n\n def uncancel(self):\n \"\"\"Decrement the task's count of cancellation requests.\n\n This should be called by the party that called `cancel()` on the task\n beforehand.\n\n Returns the remaining number of cancellation requests.\n \"\"\"\n if self._num_cancels_requested > 0:\n self._num_cancels_requested -= 1\n if self._num_cancels_requested == 0:\n self._must_cancel = False\n return self._num_cancels_requested\n\n def __eager_start(self):\n prev_task = _swap_current_task(self._loop, self)\n try:\n _register_eager_task(self)\n try:\n self._context.run(self.__step_run_and_handle_result, None)\n finally:\n _unregister_eager_task(self)\n finally:\n try:\n curtask = _swap_current_task(self._loop, prev_task)\n assert curtask is self\n finally:\n if self.done():\n self._coro = None\n self = None # Needed to break cycles when an exception occurs.\n else:\n _register_task(self)\n\n def __step(self, exc=None):\n if self.done():\n raise exceptions.InvalidStateError(\n f'_step(): already done: {self!r}, {exc!r}')\n if self._must_cancel:\n if not isinstance(exc, exceptions.CancelledError):\n exc = self._make_cancelled_error()\n self._must_cancel = False\n self._fut_waiter = None\n\n _enter_task(self._loop, self)\n try:\n self.__step_run_and_handle_result(exc)\n finally:\n _leave_task(self._loop, self)\n self = None # Needed to break cycles when an exception occurs.\n\n def __step_run_and_handle_result(self, exc):\n coro = self._coro\n try:\n if exc is None:\n # We use the `send` method directly, because coroutines\n # don't have `__iter__` and `__next__` methods.\n result = coro.send(None)\n else:\n result = coro.throw(exc)\n except StopIteration as exc:\n if self._must_cancel:\n # Task is cancelled right before coro stops.\n self._must_cancel = False\n super().cancel(msg=self._cancel_message)\n else:\n super().set_result(exc.value)\n except exceptions.CancelledError as exc:\n # Save the original exception so we can chain it later.\n self._cancelled_exc = exc\n super().cancel() # I.e., Future.cancel(self).\n except (KeyboardInterrupt, SystemExit) as exc:\n super().set_exception(exc)\n raise\n except BaseException as exc:\n super().set_exception(exc)\n else:\n blocking = getattr(result, '_asyncio_future_blocking', None)\n if blocking is not None:\n # Yielded Future must come from Future.__iter__().\n if futures._get_loop(result) is not self._loop:\n new_exc = RuntimeError(\n f'Task {self!r} got Future '\n f'{result!r} attached to a different loop')\n self._loop.call_soon(\n self.__step, new_exc, context=self._context)\n elif blocking:\n if result is self:\n new_exc = RuntimeError(\n f'Task cannot await on itself: {self!r}')\n self._loop.call_soon(\n self.__step, new_exc, context=self._context)\n else:\n result._asyncio_future_blocking = False\n result.add_done_callback(\n self.__wakeup, context=self._context)\n self._fut_waiter = result\n if self._must_cancel:\n if self._fut_waiter.cancel(\n msg=self._cancel_message):\n self._must_cancel = False\n else:\n new_exc = RuntimeError(\n f'yield was used instead of yield from '\n f'in task {self!r} with {result!r}')\n self._loop.call_soon(\n self.__step, new_exc, context=self._context)\n\n elif result is None:\n # Bare yield relinquishes control for one event loop iteration.\n self._loop.call_soon(self.__step, context=self._context)\n elif inspect.isgenerator(result):\n # Yielding a generator is just wrong.\n new_exc = RuntimeError(\n f'yield was used instead of yield from for '\n f'generator in task {self!r} with {result!r}')\n self._loop.call_soon(\n self.__step, new_exc, context=self._context)\n else:\n # Yielding something else is an error.\n new_exc = RuntimeError(f'Task got bad yield: {result!r}')\n self._loop.call_soon(\n self.__step, new_exc, context=self._context)\n finally:\n self = None # Needed to break cycles when an exception occurs.\n\n def __wakeup(self, future):\n try:\n future.result()\n except BaseException as exc:\n # This may also be a cancellation.\n self.__step(exc)\n else:\n # Don't pass the value of `future.result()` explicitly,\n # as `Future.__iter__` and `Future.__await__` don't need it.\n # If we call `_step(value, None)` instead of `_step()`,\n # Python eval loop would use `.send(value)` method call,\n # instead of `__next__()`, which is slower for futures\n # that return non-generator iterators from their `__iter__`.\n self.__step()\n self = None # Needed to break cycles when an exception occurs.\n\n\n_PyTask = Task\n\n\ntry:\n import _asyncio\nexcept ImportError:\n pass\nelse:\n # _CTask is needed for tests.\n Task = _CTask = _asyncio.Task\n\n\ndef create_task(coro, *, name=None, context=None):\n \"\"\"Schedule the execution of a coroutine object in a spawn task.\n\n Return a Task object.\n \"\"\"\n loop = events.get_running_loop()\n if context is None:\n # Use legacy API if context is not needed\n task = loop.create_task(coro, name=name)\n else:\n task = loop.create_task(coro, name=name, context=context)\n\n return task\n\n\n# wait() and as_completed() similar to those in PEP 3148.\n\nFIRST_COMPLETED = concurrent.futures.FIRST_COMPLETED\nFIRST_EXCEPTION = concurrent.futures.FIRST_EXCEPTION\nALL_COMPLETED = concurrent.futures.ALL_COMPLETED\n\n\nasync def wait(fs, *, timeout=None, return_when=ALL_COMPLETED):\n \"\"\"Wait for the Futures or Tasks given by fs to complete.\n\n The fs iterable must not be empty.\n\n Returns two sets of Future: (done, pending).\n\n Usage:\n\n done, pending = await asyncio.wait(fs)\n\n Note: This does not raise TimeoutError! Futures that aren't done\n when the timeout occurs are returned in the second set.\n \"\"\"\n if futures.isfuture(fs) or coroutines.iscoroutine(fs):\n raise TypeError(f\"expect a list of futures, not {type(fs).__name__}\")\n if not fs:\n raise ValueError('Set of Tasks/Futures is empty.')\n if return_when not in (FIRST_COMPLETED, FIRST_EXCEPTION, ALL_COMPLETED):\n raise ValueError(f'Invalid return_when value: {return_when}')\n\n fs = set(fs)\n\n if any(coroutines.iscoroutine(f) for f in fs):\n raise TypeError(\"Passing coroutines is forbidden, use tasks explicitly.\")\n\n loop = events.get_running_loop()\n return await _wait(fs, timeout, return_when, loop)\n\n\ndef _release_waiter(waiter, *args):\n if not waiter.done():\n waiter.set_result(None)\n\n\nasync def wait_for(fut, timeout):\n \"\"\"Wait for the single Future or coroutine to complete, with timeout.\n\n Coroutine will be wrapped in Task.\n\n Returns result of the Future or coroutine. When a timeout occurs,\n it cancels the task and raises TimeoutError. To avoid the task\n cancellation, wrap it in shield().\n\n If the wait is cancelled, the task is also cancelled.\n\n If the task suppresses the cancellation and returns a value instead,\n that value is returned.\n\n This function is a coroutine.\n \"\"\"\n # The special case for timeout <= 0 is for the following case:\n #\n # async def test_waitfor():\n # func_started = False\n #\n # async def func():\n # nonlocal func_started\n # func_started = True\n #\n # try:\n # await asyncio.wait_for(func(), 0)\n # except asyncio.TimeoutError:\n # assert not func_started\n # else:\n # assert False\n #\n # asyncio.run(test_waitfor())\n\n\n if timeout is not None and timeout <= 0:\n fut = ensure_future(fut)\n\n if fut.done():\n return fut.result()\n\n await _cancel_and_wait(fut)\n try:\n return fut.result()\n except exceptions.CancelledError as exc:\n raise TimeoutError from exc\n\n async with timeouts.timeout(timeout):\n return await fut\n\nasync def _wait(fs, timeout, return_when, loop):\n \"\"\"Internal helper for wait().\n\n The fs argument must be a collection of Futures.\n \"\"\"\n assert fs, 'Set of Futures is empty.'\n waiter = loop.create_future()\n timeout_handle = None\n if timeout is not None:\n timeout_handle = loop.call_later(timeout, _release_waiter, waiter)\n counter = len(fs)\n\n def _on_completion(f):\n nonlocal counter\n counter -= 1\n if (counter <= 0 or\n return_when == FIRST_COMPLETED or\n return_when == FIRST_EXCEPTION and (not f.cancelled() and\n f.exception() is not None)):\n if timeout_handle is not None:\n timeout_handle.cancel()\n if not waiter.done():\n waiter.set_result(None)\n\n for f in fs:\n f.add_done_callback(_on_completion)\n\n try:\n await waiter\n finally:\n if timeout_handle is not None:\n timeout_handle.cancel()\n for f in fs:\n f.remove_done_callback(_on_completion)\n\n done, pending = set(), set()\n for f in fs:\n if f.done():\n done.add(f)\n else:\n pending.add(f)\n return done, pending\n\n\nasync def _cancel_and_wait(fut):\n \"\"\"Cancel the *fut* future or task and wait until it completes.\"\"\"\n\n loop = events.get_running_loop()\n waiter = loop.create_future()\n cb = functools.partial(_release_waiter, waiter)\n fut.add_done_callback(cb)\n\n try:\n fut.cancel()\n # We cannot wait on *fut* directly to make\n # sure _cancel_and_wait itself is reliably cancellable.\n await waiter\n finally:\n fut.remove_done_callback(cb)\n\n\nclass _AsCompletedIterator:\n \"\"\"Iterator of awaitables representing tasks of asyncio.as_completed.\n\n As an asynchronous iterator, iteration yields futures as they finish. As a\n plain iterator, new coroutines are yielded that will return or raise the\n result of the next underlying future to complete.\n \"\"\"\n def __init__(self, aws, timeout):\n self._done = queues.Queue()\n self._timeout_handle = None\n\n loop = events.get_event_loop()\n todo = {ensure_future(aw, loop=loop) for aw in set(aws)}\n for f in todo:\n f.add_done_callback(self._handle_completion)\n if todo and timeout is not None:\n self._timeout_handle = (\n loop.call_later(timeout, self._handle_timeout)\n )\n self._todo = todo\n self._todo_left = len(todo)\n\n def __aiter__(self):\n return self\n\n def __iter__(self):\n return self\n\n async def __anext__(self):\n if not self._todo_left:\n raise StopAsyncIteration\n assert self._todo_left > 0\n self._todo_left -= 1\n return await self._wait_for_one()\n\n def __next__(self):\n if not self._todo_left:\n raise StopIteration\n assert self._todo_left > 0\n self._todo_left -= 1\n return self._wait_for_one(resolve=True)\n\n def _handle_timeout(self):\n for f in self._todo:\n f.remove_done_callback(self._handle_completion)\n self._done.put_nowait(None) # Sentinel for _wait_for_one().\n self._todo.clear() # Can't do todo.remove(f) in the loop.\n\n def _handle_completion(self, f):\n if not self._todo:\n return # _handle_timeout() was here first.\n self._todo.remove(f)\n self._done.put_nowait(f)\n if not self._todo and self._timeout_handle is not None:\n self._timeout_handle.cancel()\n\n async def _wait_for_one(self, resolve=False):\n # Wait for the next future to be done and return it unless resolve is\n # set, in which case return either the result of the future or raise\n # an exception.\n f = await self._done.get()\n if f is None:\n # Dummy value from _handle_timeout().\n raise exceptions.TimeoutError\n return f.result() if resolve else f\n\n\ndef as_completed(fs, *, timeout=None):\n \"\"\"Create an iterator of awaitables or their results in completion order.\n\n Run the supplied awaitables concurrently. The returned object can be\n iterated to obtain the results of the awaitables as they finish.\n\n The object returned can be iterated as an asynchronous iterator or a plain\n iterator. When asynchronous iteration is used, the originally-supplied\n awaitables are yielded if they are tasks or futures. This makes it easy to\n correlate previously-scheduled tasks with their results:\n\n ipv4_connect = create_task(open_connection(\"127.0.0.1\", 80))\n ipv6_connect = create_task(open_connection(\"::1\", 80))\n tasks = [ipv4_connect, ipv6_connect]\n\n async for earliest_connect in as_completed(tasks):\n # earliest_connect is done. The result can be obtained by\n # awaiting it or calling earliest_connect.result()\n reader, writer = await earliest_connect\n\n if earliest_connect is ipv6_connect:\n print(\"IPv6 connection established.\")\n else:\n print(\"IPv4 connection established.\")\n\n During asynchronous iteration, implicitly-created tasks will be yielded for\n supplied awaitables that aren't tasks or futures.\n\n When used as a plain iterator, each iteration yields a new coroutine that\n returns the result or raises the exception of the next completed awaitable.\n This pattern is compatible with Python versions older than 3.13:\n\n ipv4_connect = create_task(open_connection(\"127.0.0.1\", 80))\n ipv6_connect = create_task(open_connection(\"::1\", 80))\n tasks = [ipv4_connect, ipv6_connect]\n\n for next_connect in as_completed(tasks):\n # next_connect is not one of the original task objects. It must be\n # awaited to obtain the result value or raise the exception of the\n # awaitable that finishes next.\n reader, writer = await next_connect\n\n A TimeoutError is raised if the timeout occurs before all awaitables are\n done. This is raised by the async for loop during asynchronous iteration or\n by the coroutines yielded during plain iteration.\n \"\"\"\n if inspect.isawaitable(fs):\n raise TypeError(\n f\"expects an iterable of awaitables, not {type(fs).__name__}\"\n )\n\n return _AsCompletedIterator(fs, timeout)\n\n\n@types.coroutine\ndef __sleep0():\n \"\"\"Skip one event loop run cycle.\n\n This is a private helper for 'asyncio.sleep()', used\n when the 'delay' is set to 0. It uses a bare 'yield'\n expression (which Task.__step knows how to handle)\n instead of creating a Future object.\n \"\"\"\n yield\n\n\nasync def sleep(delay, result=None):\n \"\"\"Coroutine that completes after a given time (in seconds).\"\"\"\n if delay <= 0:\n await __sleep0()\n return result\n\n if math.isnan(delay):\n raise ValueError(\"Invalid delay: NaN (not a number)\")\n\n loop = events.get_running_loop()\n future = loop.create_future()\n h = loop.call_later(delay,\n futures._set_result_unless_cancelled,\n future, result)\n try:\n return await future\n finally:\n h.cancel()\n\n\ndef ensure_future(coro_or_future, *, loop=None):\n \"\"\"Wrap a coroutine or an awaitable in a future.\n\n If the argument is a Future, it is returned directly.\n \"\"\"\n if futures.isfuture(coro_or_future):\n if loop is not None and loop is not futures._get_loop(coro_or_future):\n raise ValueError('The future belongs to a different loop than '\n 'the one specified as the loop argument')\n return coro_or_future\n should_close = True\n if not coroutines.iscoroutine(coro_or_future):\n if inspect.isawaitable(coro_or_future):\n async def _wrap_awaitable(awaitable):\n return await awaitable\n\n coro_or_future = _wrap_awaitable(coro_or_future)\n should_close = False\n else:\n raise TypeError('An asyncio.Future, a coroutine or an awaitable '\n 'is required')\n\n if loop is None:\n loop = events.get_event_loop()\n try:\n return loop.create_task(coro_or_future)\n except RuntimeError:\n if should_close:\n coro_or_future.close()\n raise\n\n\nclass _GatheringFuture(futures.Future):\n \"\"\"Helper for gather().\n\n This overrides cancel() to cancel all the children and act more\n like Task.cancel(), which doesn't immediately mark itself as\n cancelled.\n \"\"\"\n\n def __init__(self, children, *, loop):\n assert loop is not None\n super().__init__(loop=loop)\n self._children = children\n self._cancel_requested = False\n\n def cancel(self, msg=None):\n if self.done():\n return False\n ret = False\n for child in self._children:\n if child.cancel(msg=msg):\n ret = True\n if ret:\n # If any child tasks were actually cancelled, we should\n # propagate the cancellation request regardless of\n # *return_exceptions* argument. See issue 32684.\n self._cancel_requested = True\n return ret\n\n\ndef gather(*coros_or_futures, return_exceptions=False):\n \"\"\"Return a future aggregating results from the given coroutines/futures.\n\n Coroutines will be wrapped in a future and scheduled in the event\n loop. They will not necessarily be scheduled in the same order as\n passed in.\n\n All futures must share the same event loop. If all the tasks are\n done successfully, the returned future's result is the list of\n results (in the order of the original sequence, not necessarily\n the order of results arrival). If *return_exceptions* is True,\n exceptions in the tasks are treated the same as successful\n results, and gathered in the result list; otherwise, the first\n raised exception will be immediately propagated to the returned\n future.\n\n Cancellation: if the outer Future is cancelled, all children (that\n have not completed yet) are also cancelled. If any child is\n cancelled, this is treated as if it raised CancelledError --\n the outer Future is *not* cancelled in this case. (This is to\n prevent the cancellation of one child to cause other children to\n be cancelled.)\n\n If *return_exceptions* is False, cancelling gather() after it\n has been marked done won't cancel any submitted awaitables.\n For instance, gather can be marked done after propagating an\n exception to the caller, therefore, calling ``gather.cancel()``\n after catching an exception (raised by one of the awaitables) from\n gather won't cancel any other awaitables.\n \"\"\"\n if not coros_or_futures:\n loop = events.get_event_loop()\n outer = loop.create_future()\n outer.set_result([])\n return outer\n\n def _done_callback(fut):\n nonlocal nfinished\n nfinished += 1\n\n if outer is None or outer.done():\n if not fut.cancelled():\n # Mark exception retrieved.\n fut.exception()\n return\n\n if not return_exceptions:\n if fut.cancelled():\n # Check if 'fut' is cancelled first, as\n # 'fut.exception()' will *raise* a CancelledError\n # instead of returning it.\n exc = fut._make_cancelled_error()\n outer.set_exception(exc)\n return\n else:\n exc = fut.exception()\n if exc is not None:\n outer.set_exception(exc)\n return\n\n if nfinished == nfuts:\n # All futures are done; create a list of results\n # and set it to the 'outer' future.\n results = []\n\n for fut in children:\n if fut.cancelled():\n # Check if 'fut' is cancelled first, as 'fut.exception()'\n # will *raise* a CancelledError instead of returning it.\n # Also, since we're adding the exception return value\n # to 'results' instead of raising it, don't bother\n # setting __context__. This also lets us preserve\n # calling '_make_cancelled_error()' at most once.\n res = exceptions.CancelledError(\n '' if fut._cancel_message is None else\n fut._cancel_message)\n else:\n res = fut.exception()\n if res is None:\n res = fut.result()\n results.append(res)\n\n if outer._cancel_requested:\n # If gather is being cancelled we must propagate the\n # cancellation regardless of *return_exceptions* argument.\n # See issue 32684.\n exc = fut._make_cancelled_error()\n outer.set_exception(exc)\n else:\n outer.set_result(results)\n\n arg_to_fut = {}\n children = []\n nfuts = 0\n nfinished = 0\n done_futs = []\n loop = None\n outer = None # bpo-46672\n for arg in coros_or_futures:\n if arg not in arg_to_fut:\n fut = ensure_future(arg, loop=loop)\n if loop is None:\n loop = futures._get_loop(fut)\n if fut is not arg:\n # 'arg' was not a Future, therefore, 'fut' is a new\n # Future created specifically for 'arg'. Since the caller\n # can't control it, disable the \"destroy pending task\"\n # warning.\n fut._log_destroy_pending = False\n\n nfuts += 1\n arg_to_fut[arg] = fut\n if fut.done():\n done_futs.append(fut)\n else:\n fut.add_done_callback(_done_callback)\n\n else:\n # There's a duplicate Future object in coros_or_futures.\n fut = arg_to_fut[arg]\n\n children.append(fut)\n\n outer = _GatheringFuture(children, loop=loop)\n # Run done callbacks after GatheringFuture created so any post-processing\n # can be performed at this point\n # optimization: in the special case that *all* futures finished eagerly,\n # this will effectively complete the gather eagerly, with the last\n # callback setting the result (or exception) on outer before returning it\n for fut in done_futs:\n _done_callback(fut)\n return outer\n\n\ndef shield(arg):\n \"\"\"Wait for a future, shielding it from cancellation.\n\n The statement\n\n task = asyncio.create_task(something())\n res = await shield(task)\n\n is exactly equivalent to the statement\n\n res = await something()\n\n *except* that if the coroutine containing it is cancelled, the\n task running in something() is not cancelled. From the POV of\n something(), the cancellation did not happen. But its caller is\n still cancelled, so the yield-from expression still raises\n CancelledError. Note: If something() is cancelled by other means\n this will still cancel shield().\n\n If you want to completely ignore cancellation (not recommended)\n you can combine shield() with a try/except clause, as follows:\n\n task = asyncio.create_task(something())\n try:\n res = await shield(task)\n except CancelledError:\n res = None\n\n Save a reference to tasks passed to this function, to avoid\n a task disappearing mid-execution. The event loop only keeps\n weak references to tasks. A task that isn't referenced elsewhere\n may get garbage collected at any time, even before it's done.\n \"\"\"\n inner = ensure_future(arg)\n if inner.done():\n # Shortcut.\n return inner\n loop = futures._get_loop(inner)\n outer = loop.create_future()\n\n def _inner_done_callback(inner):\n if outer.cancelled():\n if not inner.cancelled():\n # Mark inner's result as retrieved.\n inner.exception()\n return\n\n if inner.cancelled():\n outer.cancel()\n else:\n exc = inner.exception()\n if exc is not None:\n outer.set_exception(exc)\n else:\n outer.set_result(inner.result())\n\n\n def _outer_done_callback(outer):\n if not inner.done():\n inner.remove_done_callback(_inner_done_callback)\n\n inner.add_done_callback(_inner_done_callback)\n outer.add_done_callback(_outer_done_callback)\n return outer\n\n\ndef run_coroutine_threadsafe(coro, loop):\n \"\"\"Submit a coroutine object to a given event loop.\n\n Return a concurrent.futures.Future to access the result.\n \"\"\"\n if not coroutines.iscoroutine(coro):\n raise TypeError('A coroutine object is required')\n future = concurrent.futures.Future()\n\n def callback():\n try:\n futures._chain_future(ensure_future(coro, loop=loop), future)\n except (SystemExit, KeyboardInterrupt):\n raise\n except BaseException as exc:\n if future.set_running_or_notify_cancel():\n future.set_exception(exc)\n raise\n\n loop.call_soon_threadsafe(callback)\n return future\n\n\ndef create_eager_task_factory(custom_task_constructor):\n \"\"\"Create a function suitable for use as a task factory on an event-loop.\n\n Example usage:\n\n loop.set_task_factory(\n asyncio.create_eager_task_factory(my_task_constructor))\n\n Now, tasks created will be started immediately (rather than being first\n scheduled to an event loop). The constructor argument can be any callable\n that returns a Task-compatible object and has a signature compatible\n with `Task.__init__`; it must have the `eager_start` keyword argument.\n\n Most applications will use `Task` for `custom_task_constructor` and in\n this case there's no need to call `create_eager_task_factory()`\n directly. Instead the global `eager_task_factory` instance can be\n used. E.g. `loop.set_task_factory(asyncio.eager_task_factory)`.\n \"\"\"\n\n def factory(loop, coro, *, name=None, context=None):\n return custom_task_constructor(\n coro, loop=loop, name=name, context=context, eager_start=True)\n\n return factory\n\n\neager_task_factory = create_eager_task_factory(Task)\n\n\n# Collectively these two sets hold references to the complete set of active\n# tasks. Eagerly executed tasks use a faster regular set as an optimization\n# but may graduate to a WeakSet if the task blocks on IO.\n_scheduled_tasks = weakref.WeakSet()\n_eager_tasks = set()\n\n# Dictionary containing tasks that are currently active in\n# all running event loops. {EventLoop: Task}\n_current_tasks = {}\n\n\ndef _register_task(task):\n \"\"\"Register an asyncio Task scheduled to run on an event loop.\"\"\"\n _scheduled_tasks.add(task)\n\n\ndef _register_eager_task(task):\n \"\"\"Register an asyncio Task about to be eagerly executed.\"\"\"\n _eager_tasks.add(task)\n\n\ndef _enter_task(loop, task):\n current_task = _current_tasks.get(loop)\n if current_task is not None:\n raise RuntimeError(f\"Cannot enter into task {task!r} while another \"\n f\"task {current_task!r} is being executed.\")\n _current_tasks[loop] = task\n\n\ndef _leave_task(loop, task):\n current_task = _current_tasks.get(loop)\n if current_task is not task:\n raise RuntimeError(f\"Leaving task {task!r} does not match \"\n f\"the current task {current_task!r}.\")\n del _current_tasks[loop]\n\n\ndef _swap_current_task(loop, task):\n prev_task = _current_tasks.get(loop)\n if task is None:\n del _current_tasks[loop]\n else:\n _current_tasks[loop] = task\n return prev_task\n\n\ndef _unregister_task(task):\n \"\"\"Unregister a completed, scheduled Task.\"\"\"\n _scheduled_tasks.discard(task)\n\n\ndef _unregister_eager_task(task):\n \"\"\"Unregister a task which finished its first eager step.\"\"\"\n _eager_tasks.discard(task)\n\n\n_py_current_task = current_task\n_py_register_task = _register_task\n_py_register_eager_task = _register_eager_task\n_py_unregister_task = _unregister_task\n_py_unregister_eager_task = _unregister_eager_task\n_py_enter_task = _enter_task\n_py_leave_task = _leave_task\n_py_swap_current_task = _swap_current_task\n\n\ntry:\n from _asyncio import (_register_task, _register_eager_task,\n _unregister_task, _unregister_eager_task,\n _enter_task, _leave_task, _swap_current_task,\n _scheduled_tasks, _eager_tasks, _current_tasks,\n current_task)\nexcept ImportError:\n pass\nelse:\n _c_current_task = current_task\n _c_register_task = _register_task\n _c_register_eager_task = _register_eager_task\n _c_unregister_task = _unregister_task\n _c_unregister_eager_task = _unregister_eager_task\n _c_enter_task = _enter_task\n _c_leave_task = _leave_task\n _c_swap_current_task = _swap_current_task\n", 1118], "/home/gaogaotiantian/programs/viztracer/example/src/async_simple.py": ["import asyncio\n\n\nasync def io_task():\n await asyncio.sleep(0.01)\n\n\nasync def main():\n t1 = asyncio.create_task(io_task())\n t2 = asyncio.create_task(io_task())\n t3 = asyncio.create_task(io_task())\n\n await t1\n await t2\n await t3\n\n\nif __name__ == \"__main__\":\n asyncio.run(main())\n", 19]}, "functions": {"Runner.__init__ (/usr/lib/python3.13/asyncio/runners.py:48)": ["/usr/lib/python3.13/asyncio/runners.py", 48], "BaseDefaultEventLoopPolicy.__init__ (/usr/lib/python3.13/asyncio/events.py:685)": ["/usr/lib/python3.13/asyncio/events.py", 685], "_UnixDefaultEventLoopPolicy.__init__ (/usr/lib/python3.13/asyncio/unix_events.py:1481)": ["/usr/lib/python3.13/asyncio/unix_events.py", 1481], "_init_event_loop_policy (/usr/lib/python3.13/asyncio/events.py:789)": ["/usr/lib/python3.13/asyncio/events.py", 789], "get_event_loop_policy (/usr/lib/python3.13/asyncio/events.py:797)": ["/usr/lib/python3.13/asyncio/events.py", 797], "_is_debug_mode (/usr/lib/python3.13/asyncio/coroutines.py:10)": ["/usr/lib/python3.13/asyncio/coroutines.py", 10], "BaseEventLoop.is_running (/usr/lib/python3.13/asyncio/base_events.py:764)": ["/usr/lib/python3.13/asyncio/base_events.py", 764], "BaseEventLoop.set_debug (/usr/lib/python3.13/asyncio/base_events.py:2048)": ["/usr/lib/python3.13/asyncio/base_events.py", 2048], "WeakSet.__init__ (/usr/lib/python3.13/_weakrefset.py:37)": ["/usr/lib/python3.13/_weakrefset.py", 37], "BaseEventLoop.__init__ (/usr/lib/python3.13/asyncio/base_events.py:420)": ["/usr/lib/python3.13/asyncio/base_events.py", 420], "_SelectorMapping.__init__ (/usr/lib/python3.13/selectors.py:63)": ["/usr/lib/python3.13/selectors.py", 63], "_BaseSelectorImpl.__init__ (/usr/lib/python3.13/selectors.py:213)": ["/usr/lib/python3.13/selectors.py", 213], "_PollLikeSelector.__init__ (/usr/lib/python3.13/selectors.py:336)": ["/usr/lib/python3.13/selectors.py", 336], "Manager.disable (/usr/lib/python3.13/logging/__init__.py:1353)": ["/usr/lib/python3.13/logging/__init__.py", 1353], "Logger.getEffectiveLevel (/usr/lib/python3.13/logging/__init__.py:1750)": ["/usr/lib/python3.13/logging/__init__.py", 1750], "Logger.isEnabledFor (/usr/lib/python3.13/logging/__init__.py:1764)": ["/usr/lib/python3.13/logging/__init__.py", 1764], "Logger.debug (/usr/lib/python3.13/logging/__init__.py:1497)": ["/usr/lib/python3.13/logging/__init__.py", 1497], "socket.__init__ (/usr/lib/python3.13/socket.py:221)": 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comparable items inside\n :return: the same collection ordered by ascending\n\n Examples:\n >>> merge_sort([0, 5, 3, 2, 2])\n [0, 2, 2, 3, 5]\n\n >>> merge_sort([])\n []\n\n >>> merge_sort([-2, -5, -45])\n [-45, -5, -2]\n \"\"\"\n\n def merge(left, right):\n \"\"\"merge left and right\n :param left: left collection\n :param right: right collection\n :return: merge result\n \"\"\"\n result = []\n while left and right:\n result.append((left if left[0] <= right[0] else right).pop(0))\n return result + left + right\n\n if len(collection) <= 1:\n return collection\n mid = len(collection) // 2\n return merge(merge_sort(collection[:mid]), merge_sort(collection[mid:]))\n\n\ndef quick_sort(collection):\n \"\"\"Pure implementation of quick sort algorithm in Python\n\n :param collection: some mutable ordered collection with heterogeneous\n comparable items inside\n :return: the same collection ordered by ascending\n\n Examples:\n >>> quick_sort([0, 5, 3, 2, 2])\n [0, 2, 2, 3, 5]\n\n >>> quick_sort([])\n []\n\n >>> quick_sort([-2, -5, -45])\n [-45, -5, -2]\n \"\"\"\n length = len(collection)\n if length <= 1:\n return collection\n else:\n # Use the last element as the first pivot\n pivot = collection.pop()\n # Put elements greater than pivot in greater list\n # Put elements lesser than pivot in lesser list\n greater, lesser = [], []\n for element in collection:\n if element > pivot:\n greater.append(element)\n else:\n lesser.append(element)\n return quick_sort(lesser) + [pivot] + quick_sort(greater)\n\n\ndef heapify(unsorted, index, heap_size):\n largest = index\n left_index = 2 * index + 1\n right_index = 2 * index + 2\n if left_index < heap_size and unsorted[left_index] > unsorted[largest]:\n largest = left_index\n\n if right_index < heap_size and unsorted[right_index] > unsorted[largest]:\n largest = right_index\n\n if largest != index:\n unsorted[largest], unsorted[index] = unsorted[index], unsorted[largest]\n heapify(unsorted, largest, heap_size)\n\n\ndef heap_sort(unsorted):\n \"\"\"\n Pure implementation of the heap sort algorithm in Python\n :param collection: some mutable ordered collection with heterogeneous\n comparable items inside\n :return: the same collection ordered by ascending\n\n Examples:\n >>> heap_sort([0, 5, 3, 2, 2])\n [0, 2, 2, 3, 5]\n\n >>> heap_sort([])\n []\n\n >>> heap_sort([-2, -5, -45])\n [-45, -5, -2]\n \"\"\"\n n = len(unsorted)\n for i in range(n // 2 - 1, -1, -1):\n heapify(unsorted, i, n)\n for i in range(n - 1, 0, -1):\n unsorted[0], unsorted[i] = unsorted[i], unsorted[0]\n heapify(unsorted, 0, i)\n return unsorted\n\n\narr1 = [random.randrange(100000) for _ in range(500)]\narr2 = [random.randrange(100000) for _ in range(500)]\narr3 = [random.randrange(100000) for _ in range(500)]\n\n\nwith VizTracer(output_file=os.path.join(os.path.dirname(__file__), \"../\", \"json/different_sorts.json\"), file_info=True) as _:\n merge_sort(arr1)\n quick_sort(arr2)\n heap_sort(arr3)\n",127]},"functions":{"merge_sort 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"cat": "FEE"}], "viztracer_metadata": {"version": "0.17.1", "overflow": false, "baseTimeNanoseconds": 1731943920418642200}, "file_info": {"files": {"/home/gaogaotiantian/programs/viztracer/example/src/different_sorts.py": ["# https://github.com/TheAlgorithms/Python\n\n\nimport os\nimport random\n\nfrom viztracer import VizTracer\n\n\ndef merge_sort(collection):\n \"\"\"Pure implementation of the merge sort algorithm in Python\n\n :param collection: some mutable ordered collection with heterogeneous\n comparable items inside\n :return: the same collection ordered by ascending\n\n Examples:\n >>> merge_sort([0, 5, 3, 2, 2])\n [0, 2, 2, 3, 5]\n\n >>> merge_sort([])\n []\n\n >>> merge_sort([-2, -5, -45])\n [-45, -5, -2]\n \"\"\"\n\n def merge(left, right):\n \"\"\"merge left and right\n :param left: left collection\n :param right: right collection\n :return: merge result\n \"\"\"\n result = []\n while left and right:\n result.append((left if left[0] <= right[0] else right).pop(0))\n return result + left + right\n\n if len(collection) <= 1:\n return collection\n mid = len(collection) // 2\n return merge(merge_sort(collection[:mid]), merge_sort(collection[mid:]))\n\n\ndef quick_sort(collection):\n \"\"\"Pure implementation of quick sort algorithm in Python\n\n :param collection: some mutable ordered collection with heterogeneous\n comparable items inside\n :return: the same collection ordered by ascending\n\n Examples:\n >>> quick_sort([0, 5, 3, 2, 2])\n [0, 2, 2, 3, 5]\n\n >>> quick_sort([])\n []\n\n >>> quick_sort([-2, -5, -45])\n [-45, -5, -2]\n \"\"\"\n length = len(collection)\n if length <= 1:\n return collection\n else:\n # Use the last element as the first pivot\n pivot = collection.pop()\n # Put elements greater than pivot in greater list\n # Put elements lesser than pivot in lesser list\n greater, lesser = [], []\n for element in collection:\n if element > pivot:\n greater.append(element)\n else:\n lesser.append(element)\n return quick_sort(lesser) + [pivot] + quick_sort(greater)\n\n\ndef heapify(unsorted, index, heap_size):\n largest = index\n left_index = 2 * index + 1\n right_index = 2 * index + 2\n if left_index < heap_size and unsorted[left_index] > unsorted[largest]:\n largest = left_index\n\n if right_index < heap_size and unsorted[right_index] > unsorted[largest]:\n largest = right_index\n\n if largest != index:\n unsorted[largest], unsorted[index] = unsorted[index], unsorted[largest]\n heapify(unsorted, largest, heap_size)\n\n\ndef heap_sort(unsorted):\n \"\"\"\n Pure implementation of the heap sort algorithm in Python\n :param collection: some mutable ordered collection with heterogeneous\n comparable items inside\n :return: the same collection ordered by ascending\n\n Examples:\n >>> heap_sort([0, 5, 3, 2, 2])\n [0, 2, 2, 3, 5]\n\n >>> heap_sort([])\n []\n\n >>> heap_sort([-2, -5, -45])\n [-45, -5, -2]\n \"\"\"\n n = len(unsorted)\n for i in range(n // 2 - 1, -1, -1):\n heapify(unsorted, i, n)\n for i in range(n - 1, 0, -1):\n unsorted[0], unsorted[i] = unsorted[i], unsorted[0]\n heapify(unsorted, 0, i)\n return unsorted\n\n\narr1 = [random.randrange(100000) for _ in range(500)]\narr2 = [random.randrange(100000) for _ in range(500)]\narr3 = [random.randrange(100000) for _ in range(500)]\n\n\nwith VizTracer(output_file=os.path.join(os.path.dirname(__file__), \"../\", \"json/different_sorts.json\"), file_info=True) as _:\n merge_sort(arr1)\n quick_sort(arr2)\n heap_sort(arr3)\n", 128]}, "functions": {"merge_sort (/home/gaogaotiantian/programs/viztracer/example/src/different_sorts.py:10)": ["/home/gaogaotiantian/programs/viztracer/example/src/different_sorts.py", 10], "merge_sort..merge (/home/gaogaotiantian/programs/viztracer/example/src/different_sorts.py:28)": ["/home/gaogaotiantian/programs/viztracer/example/src/different_sorts.py", 28], "quick_sort (/home/gaogaotiantian/programs/viztracer/example/src/different_sorts.py:45)": ["/home/gaogaotiantian/programs/viztracer/example/src/different_sorts.py", 45], "heapify (/home/gaogaotiantian/programs/viztracer/example/src/different_sorts.py:79)": ["/home/gaogaotiantian/programs/viztracer/example/src/different_sorts.py", 79], "heap_sort (/home/gaogaotiantian/programs/viztracer/example/src/different_sorts.py:94)": ["/home/gaogaotiantian/programs/viztracer/example/src/different_sorts.py", 94]}}} \ No newline at end of file diff --git a/example/json/function_args_return.json b/example/json/function_args_return.json index a7af66b0..aca13cc9 100644 --- a/example/json/function_args_return.json +++ b/example/json/function_args_return.json @@ -1 +1 @@ -{"traceEvents":[{"ph":"M","pid":15841,"tid":15841,"name":"process_name","args":{"name":"MainProcess"}},{"ph":"M","pid":15841,"tid":15841,"name":"thread_name","args":{"name":"MainThread"}},{"pid":15841,"tid":15841,"ts":14606199172.889,"dur":1.2,"name":"fib 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test data\n :param example_no: example number whose error has to be checked\n :return: error in example pointed by example number.\n \"\"\"\n return calculate_hypothesis_value(example_no, data_set) - output(\n example_no, data_set\n )\n\n\ndef _hypothesis_value(data_input_tuple):\n \"\"\"\n Calculates hypothesis function value for a given input\n :param data_input_tuple: Input tuple of a particular example\n :return: Value of hypothesis function at that point.\n Note that there is an 'biased input' whose value is fixed as 1.\n It is not explicitly mentioned in input data.. But, ML hypothesis functions use it.\n So, we have to take care of it separately. Line 36 takes care of it.\n \"\"\"\n hyp_val = 0\n for i in range(len(parameter_vector) - 1):\n hyp_val += data_input_tuple[i] * parameter_vector[i + 1]\n hyp_val += parameter_vector[0]\n return hyp_val\n\n\ndef output(example_no, data_set):\n \"\"\"\n :param data_set: test data or train data\n :param example_no: example whose output is to be fetched\n :return: output for that example\n \"\"\"\n if data_set == \"train\":\n return train_data[example_no][1]\n elif data_set == \"test\":\n return test_data[example_no][1]\n\n\ndef calculate_hypothesis_value(example_no, data_set):\n \"\"\"\n Calculates hypothesis value for a given example\n :param data_set: test data or train_data\n :param example_no: example whose hypothesis value is to be calculated\n :return: hypothesis value for that example\n \"\"\"\n if data_set == \"train\":\n return _hypothesis_value(train_data[example_no][0])\n elif data_set == \"test\":\n return _hypothesis_value(test_data[example_no][0])\n\n\ndef summation_of_cost_derivative(index, end=m):\n \"\"\"\n Calculates the sum of cost function derivative\n :param index: index wrt derivative is being calculated\n :param end: value where summation ends, default is m, number of examples\n :return: Returns the summation of cost derivative\n Note: If index is -1, this means we are calculating summation wrt to biased\n parameter.\n \"\"\"\n summation_value = 0\n for i in range(end):\n if index == -1:\n summation_value += _error(i)\n else:\n summation_value += _error(i) * train_data[i][0][index]\n return summation_value\n\n\ndef get_cost_derivative(index):\n \"\"\"\n :param index: index of the parameter vector wrt to derivative is to be calculated\n :return: derivative wrt to that index\n Note: If index is -1, this means we are calculating summation wrt to biased\n parameter.\n \"\"\"\n cost_derivative_value = summation_of_cost_derivative(index, m) / m\n return cost_derivative_value\n\n\ndef run_gradient_descent():\n global parameter_vector\n # Tune these values to set a tolerance value for predicted output\n absolute_error_limit = 0.004\n relative_error_limit = 0\n j = 0\n while True:\n j += 1\n temp_parameter_vector = [0, 0, 0, 0]\n err = 0\n for i in range(0, len(parameter_vector)):\n cost_derivative = get_cost_derivative(i - 1)\n err += abs(cost_derivative)\n temp_parameter_vector[i] = (\n parameter_vector[i] - LEARNING_RATE * cost_derivative\n )\n counter.cost = math.log(1 + err)\n if numpy.allclose(\n parameter_vector,\n temp_parameter_vector,\n atol=absolute_error_limit,\n rtol=relative_error_limit,\n ):\n break\n parameter_vector = temp_parameter_vector\n print((\"Number of iterations:\", j))\n\n\ndef test_gradient_descent():\n for i in range(len(test_data)):\n print((\"Actual output value:\", output(i, \"test\")))\n print((\"Hypothesis output:\", calculate_hypothesis_value(i, \"test\")))\n\n\nif __name__ == \"__main__\":\n with VizTracer(log_print=True,\n output_file=os.path.join(os.path.dirname(__file__), \"../\", \"json/gradient_descent.json\"),\n file_info=True) as tracer:\n counter = VizCounter(tracer, \"log(1 + cost)\")\n run_gradient_descent()\n test_gradient_descent()\n",144],"/home/gaogaotiantian/programs/codesnap/venv/lib/python3.6/site-packages/numpy/core/numeric.py":["import functools\nimport itertools\nimport operator\nimport sys\nimport warnings\nimport numbers\n\nimport numpy as np\nfrom . import multiarray\nfrom .multiarray import (\n _fastCopyAndTranspose as fastCopyAndTranspose, ALLOW_THREADS,\n BUFSIZE, CLIP, MAXDIMS, MAY_SHARE_BOUNDS, MAY_SHARE_EXACT, RAISE,\n WRAP, arange, array, broadcast, can_cast, compare_chararrays,\n concatenate, copyto, dot, dtype, empty,\n empty_like, flatiter, frombuffer, fromfile, fromiter, fromstring,\n inner, lexsort, matmul, may_share_memory,\n min_scalar_type, ndarray, nditer, nested_iters, promote_types,\n putmask, result_type, set_numeric_ops, shares_memory, vdot, where,\n zeros, normalize_axis_index)\n\nfrom . import overrides\nfrom . import umath\nfrom . import shape_base\nfrom .overrides import set_module\nfrom .umath import (multiply, invert, sin, PINF, NAN)\nfrom . import numerictypes\nfrom .numerictypes import longlong, intc, int_, float_, complex_, bool_\nfrom ._exceptions import TooHardError, AxisError\nfrom ._asarray import asarray, asanyarray\nfrom ._ufunc_config import errstate\n\nbitwise_not = invert\nufunc = type(sin)\nnewaxis = None\n\narray_function_dispatch = functools.partial(\n overrides.array_function_dispatch, module='numpy')\n\n\n__all__ = [\n 'newaxis', 'ndarray', 'flatiter', 'nditer', 'nested_iters', 'ufunc',\n 'arange', 'array', 'zeros', 'count_nonzero', 'empty', 'broadcast', 'dtype',\n 'fromstring', 'fromfile', 'frombuffer', 'where',\n 'argwhere', 'copyto', 'concatenate', 'fastCopyAndTranspose', 'lexsort',\n 'set_numeric_ops', 'can_cast', 'promote_types', 'min_scalar_type',\n 'result_type', 'isfortran', 'empty_like', 'zeros_like', 'ones_like',\n 'correlate', 'convolve', 'inner', 'dot', 'outer', 'vdot', 'roll',\n 'rollaxis', 'moveaxis', 'cross', 'tensordot', 'little_endian',\n 'fromiter', 'array_equal', 'array_equiv', 'indices', 'fromfunction',\n 'isclose', 'isscalar', 'binary_repr', 'base_repr', 'ones',\n 'identity', 'allclose', 'compare_chararrays', 'putmask',\n 'flatnonzero', 'Inf', 'inf', 'infty', 'Infinity', 'nan', 'NaN',\n 'False_', 'True_', 'bitwise_not', 'CLIP', 'RAISE', 'WRAP', 'MAXDIMS',\n 'BUFSIZE', 'ALLOW_THREADS', 'ComplexWarning', 'full', 'full_like',\n 'matmul', 'shares_memory', 'may_share_memory', 'MAY_SHARE_BOUNDS',\n 'MAY_SHARE_EXACT', 'TooHardError', 'AxisError']\n\n\n@set_module('numpy')\nclass ComplexWarning(RuntimeWarning):\n \"\"\"\n The warning raised when casting a complex dtype to a real dtype.\n\n As implemented, casting a complex number to a real discards its imaginary\n part, but this behavior may not be what the user actually wants.\n\n \"\"\"\n pass\n\n\ndef _zeros_like_dispatcher(a, dtype=None, order=None, subok=None, shape=None):\n return (a,)\n\n\n@array_function_dispatch(_zeros_like_dispatcher)\ndef zeros_like(a, dtype=None, order='K', subok=True, shape=None):\n \"\"\"\n Return an array of zeros with the same shape and type as a given array.\n\n Parameters\n ----------\n a : array_like\n The shape and data-type of `a` define these same attributes of\n the returned array.\n dtype : data-type, optional\n Overrides the data type of the result.\n\n .. versionadded:: 1.6.0\n order : {'C', 'F', 'A', or 'K'}, optional\n Overrides the memory layout of the result. 'C' means C-order,\n 'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,\n 'C' otherwise. 'K' means match the layout of `a` as closely\n as possible.\n\n .. versionadded:: 1.6.0\n subok : bool, optional.\n If True, then the newly created array will use the sub-class\n type of 'a', otherwise it will be a base-class array. Defaults\n to True.\n shape : int or sequence of ints, optional.\n Overrides the shape of the result. If order='K' and the number of\n dimensions is unchanged, will try to keep order, otherwise,\n order='C' is implied.\n\n .. versionadded:: 1.17.0\n\n Returns\n -------\n out : ndarray\n Array of zeros with the same shape and type as `a`.\n\n See Also\n --------\n empty_like : Return an empty array with shape and type of input.\n ones_like : Return an array of ones with shape and type of input.\n full_like : Return a new array with shape of input filled with value.\n zeros : Return a new array setting values to zero.\n\n Examples\n --------\n >>> x = np.arange(6)\n >>> x = x.reshape((2, 3))\n >>> x\n array([[0, 1, 2],\n [3, 4, 5]])\n >>> np.zeros_like(x)\n array([[0, 0, 0],\n [0, 0, 0]])\n\n >>> y = np.arange(3, dtype=float)\n >>> y\n array([0., 1., 2.])\n >>> np.zeros_like(y)\n array([0., 0., 0.])\n\n \"\"\"\n res = empty_like(a, dtype=dtype, order=order, subok=subok, shape=shape)\n # needed instead of a 0 to get same result as zeros for for string dtypes\n z = zeros(1, dtype=res.dtype)\n multiarray.copyto(res, z, casting='unsafe')\n return res\n\n\n@set_module('numpy')\ndef ones(shape, dtype=None, order='C'):\n \"\"\"\n Return a new array of given shape and type, filled with ones.\n\n Parameters\n ----------\n shape : int or sequence of ints\n Shape of the new array, e.g., ``(2, 3)`` or ``2``.\n dtype : data-type, optional\n The desired data-type for the array, e.g., `numpy.int8`. Default is\n `numpy.float64`.\n order : {'C', 'F'}, optional, default: C\n Whether to store multi-dimensional data in row-major\n (C-style) or column-major (Fortran-style) order in\n memory.\n\n Returns\n -------\n out : ndarray\n Array of ones with the given shape, dtype, and order.\n\n See Also\n --------\n ones_like : Return an array of ones with shape and type of input.\n empty : Return a new uninitialized array.\n zeros : Return a new array setting values to zero.\n full : Return a new array of given shape filled with value.\n\n\n Examples\n --------\n >>> np.ones(5)\n array([1., 1., 1., 1., 1.])\n\n >>> np.ones((5,), dtype=int)\n array([1, 1, 1, 1, 1])\n\n >>> np.ones((2, 1))\n array([[1.],\n [1.]])\n\n >>> s = (2,2)\n >>> np.ones(s)\n array([[1., 1.],\n [1., 1.]])\n\n \"\"\"\n a = empty(shape, dtype, order)\n multiarray.copyto(a, 1, casting='unsafe')\n return a\n\n\ndef _ones_like_dispatcher(a, dtype=None, order=None, subok=None, shape=None):\n return (a,)\n\n\n@array_function_dispatch(_ones_like_dispatcher)\ndef ones_like(a, dtype=None, order='K', subok=True, shape=None):\n \"\"\"\n Return an array of ones with the same shape and type as a given array.\n\n Parameters\n ----------\n a : array_like\n The shape and data-type of `a` define these same attributes of\n the returned array.\n dtype : data-type, optional\n Overrides the data type of the result.\n\n .. versionadded:: 1.6.0\n order : {'C', 'F', 'A', or 'K'}, optional\n Overrides the memory layout of the result. 'C' means C-order,\n 'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,\n 'C' otherwise. 'K' means match the layout of `a` as closely\n as possible.\n\n .. versionadded:: 1.6.0\n subok : bool, optional.\n If True, then the newly created array will use the sub-class\n type of 'a', otherwise it will be a base-class array. Defaults\n to True.\n shape : int or sequence of ints, optional.\n Overrides the shape of the result. If order='K' and the number of\n dimensions is unchanged, will try to keep order, otherwise,\n order='C' is implied.\n\n .. versionadded:: 1.17.0\n\n Returns\n -------\n out : ndarray\n Array of ones with the same shape and type as `a`.\n\n See Also\n --------\n empty_like : Return an empty array with shape and type of input.\n zeros_like : Return an array of zeros with shape and type of input.\n full_like : Return a new array with shape of input filled with value.\n ones : Return a new array setting values to one.\n\n Examples\n --------\n >>> x = np.arange(6)\n >>> x = x.reshape((2, 3))\n >>> x\n array([[0, 1, 2],\n [3, 4, 5]])\n >>> np.ones_like(x)\n array([[1, 1, 1],\n [1, 1, 1]])\n\n >>> y = np.arange(3, dtype=float)\n >>> y\n array([0., 1., 2.])\n >>> np.ones_like(y)\n array([1., 1., 1.])\n\n \"\"\"\n res = empty_like(a, dtype=dtype, order=order, subok=subok, shape=shape)\n multiarray.copyto(res, 1, casting='unsafe')\n return res\n\n\n@set_module('numpy')\ndef full(shape, fill_value, dtype=None, order='C'):\n \"\"\"\n Return a new array of given shape and type, filled with `fill_value`.\n\n Parameters\n ----------\n shape : int or sequence of ints\n Shape of the new array, e.g., ``(2, 3)`` or ``2``.\n fill_value : scalar or array_like\n Fill value.\n dtype : data-type, optional\n The desired data-type for the array The default, None, means\n `np.array(fill_value).dtype`.\n order : {'C', 'F'}, optional\n Whether to store multidimensional data in C- or Fortran-contiguous\n (row- or column-wise) order in memory.\n\n Returns\n -------\n out : ndarray\n Array of `fill_value` with the given shape, dtype, and order.\n\n See Also\n --------\n full_like : Return a new array with shape of input filled with value.\n empty : Return a new uninitialized array.\n ones : Return a new array setting values to one.\n zeros : Return a new array setting values to zero.\n\n Examples\n --------\n >>> np.full((2, 2), np.inf)\n array([[inf, inf],\n [inf, inf]])\n >>> np.full((2, 2), 10)\n array([[10, 10],\n [10, 10]])\n\n >>> np.full((2, 2), [1, 2])\n array([[1, 2],\n [1, 2]])\n\n \"\"\"\n if dtype is None:\n dtype = array(fill_value).dtype\n a = empty(shape, dtype, order)\n multiarray.copyto(a, fill_value, casting='unsafe')\n return a\n\n\ndef _full_like_dispatcher(a, fill_value, dtype=None, order=None, subok=None, shape=None):\n return (a,)\n\n\n@array_function_dispatch(_full_like_dispatcher)\ndef full_like(a, fill_value, dtype=None, order='K', subok=True, shape=None):\n \"\"\"\n Return a full array with the same shape and type as a given array.\n\n Parameters\n ----------\n a : array_like\n The shape and data-type of `a` define these same attributes of\n the returned array.\n fill_value : scalar\n Fill value.\n dtype : data-type, optional\n Overrides the data type of the result.\n order : {'C', 'F', 'A', or 'K'}, optional\n Overrides the memory layout of the result. 'C' means C-order,\n 'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,\n 'C' otherwise. 'K' means match the layout of `a` as closely\n as possible.\n subok : bool, optional.\n If True, then the newly created array will use the sub-class\n type of 'a', otherwise it will be a base-class array. Defaults\n to True.\n shape : int or sequence of ints, optional.\n Overrides the shape of the result. If order='K' and the number of\n dimensions is unchanged, will try to keep order, otherwise,\n order='C' is implied.\n\n .. versionadded:: 1.17.0\n\n Returns\n -------\n out : ndarray\n Array of `fill_value` with the same shape and type as `a`.\n\n See Also\n --------\n empty_like : Return an empty array with shape and type of input.\n ones_like : Return an array of ones with shape and type of input.\n zeros_like : Return an array of zeros with shape and type of input.\n full : Return a new array of given shape filled with value.\n\n Examples\n --------\n >>> x = np.arange(6, dtype=int)\n >>> np.full_like(x, 1)\n array([1, 1, 1, 1, 1, 1])\n >>> np.full_like(x, 0.1)\n array([0, 0, 0, 0, 0, 0])\n >>> np.full_like(x, 0.1, dtype=np.double)\n array([0.1, 0.1, 0.1, 0.1, 0.1, 0.1])\n >>> np.full_like(x, np.nan, dtype=np.double)\n array([nan, nan, nan, nan, nan, nan])\n\n >>> y = np.arange(6, dtype=np.double)\n >>> np.full_like(y, 0.1)\n array([0.1, 0.1, 0.1, 0.1, 0.1, 0.1])\n\n \"\"\"\n res = empty_like(a, dtype=dtype, order=order, subok=subok, shape=shape)\n multiarray.copyto(res, fill_value, casting='unsafe')\n return res\n\n\ndef _count_nonzero_dispatcher(a, axis=None, *, keepdims=None):\n return (a,)\n\n\n@array_function_dispatch(_count_nonzero_dispatcher)\ndef count_nonzero(a, axis=None, *, keepdims=False):\n \"\"\"\n Counts the number of non-zero values in the array ``a``.\n\n The word \"non-zero\" is in reference to the Python 2.x\n built-in method ``__nonzero__()`` (renamed ``__bool__()``\n in Python 3.x) of Python objects that tests an object's\n \"truthfulness\". For example, any number is considered\n truthful if it is nonzero, whereas any string is considered\n truthful if it is not the empty string. Thus, this function\n (recursively) counts how many elements in ``a`` (and in\n sub-arrays thereof) have their ``__nonzero__()`` or ``__bool__()``\n method evaluated to ``True``.\n\n Parameters\n ----------\n a : array_like\n The array for which to count non-zeros.\n axis : int or tuple, optional\n Axis or tuple of axes along which to count non-zeros.\n Default is None, meaning that non-zeros will be counted\n along a flattened version of ``a``.\n\n .. versionadded:: 1.12.0\n\n keepdims : bool, optional\n If this is set to True, the axes that are counted are left\n in the result as dimensions with size one. With this option,\n the result will broadcast correctly against the input array.\n\n .. versionadded:: 1.19.0\n\n Returns\n -------\n count : int or array of int\n Number of non-zero values in the array along a given axis.\n Otherwise, the total number of non-zero values in the array\n is returned.\n\n See Also\n --------\n nonzero : Return the coordinates of all the non-zero values.\n\n Examples\n --------\n >>> np.count_nonzero(np.eye(4))\n 4\n >>> a = np.array([[0, 1, 7, 0],\n ... [3, 0, 2, 19]])\n >>> np.count_nonzero(a)\n 5\n >>> np.count_nonzero(a, axis=0)\n array([1, 1, 2, 1])\n >>> np.count_nonzero(a, axis=1)\n array([2, 3])\n >>> np.count_nonzero(a, axis=1, keepdims=True)\n array([[2],\n [3]])\n \"\"\"\n if axis is None and not keepdims:\n return multiarray.count_nonzero(a)\n\n a = asanyarray(a)\n\n # TODO: this works around .astype(bool) not working properly (gh-9847)\n if np.issubdtype(a.dtype, np.character):\n a_bool = a != a.dtype.type()\n else:\n a_bool = a.astype(np.bool_, copy=False)\n\n return a_bool.sum(axis=axis, dtype=np.intp, keepdims=keepdims)\n\n\n@set_module('numpy')\ndef isfortran(a):\n \"\"\"\n Check if the array is Fortran contiguous but *not* C contiguous.\n\n This function is obsolete and, because of changes due to relaxed stride\n checking, its return value for the same array may differ for versions\n of NumPy >= 1.10.0 and previous versions. If you only want to check if an\n array is Fortran contiguous use ``a.flags.f_contiguous`` instead.\n\n Parameters\n ----------\n a : ndarray\n Input array.\n\n Returns\n -------\n isfortran : bool\n Returns True if the array is Fortran contiguous but *not* C contiguous.\n\n\n Examples\n --------\n\n np.array allows to specify whether the array is written in C-contiguous\n order (last index varies the fastest), or FORTRAN-contiguous order in\n memory (first index varies the fastest).\n\n >>> a = np.array([[1, 2, 3], [4, 5, 6]], order='C')\n >>> a\n array([[1, 2, 3],\n [4, 5, 6]])\n >>> np.isfortran(a)\n False\n\n >>> b = np.array([[1, 2, 3], [4, 5, 6]], order='F')\n >>> b\n array([[1, 2, 3],\n [4, 5, 6]])\n >>> np.isfortran(b)\n True\n\n\n The transpose of a C-ordered array is a FORTRAN-ordered array.\n\n >>> a = np.array([[1, 2, 3], [4, 5, 6]], order='C')\n >>> a\n array([[1, 2, 3],\n [4, 5, 6]])\n >>> np.isfortran(a)\n False\n >>> b = a.T\n >>> b\n array([[1, 4],\n [2, 5],\n [3, 6]])\n >>> np.isfortran(b)\n True\n\n C-ordered arrays evaluate as False even if they are also FORTRAN-ordered.\n\n >>> np.isfortran(np.array([1, 2], order='F'))\n False\n\n \"\"\"\n return a.flags.fnc\n\n\ndef _argwhere_dispatcher(a):\n return (a,)\n\n\n@array_function_dispatch(_argwhere_dispatcher)\ndef argwhere(a):\n \"\"\"\n Find the indices of array elements that are non-zero, grouped by element.\n\n Parameters\n ----------\n a : array_like\n Input data.\n\n Returns\n -------\n index_array : (N, a.ndim) ndarray\n Indices of elements that are non-zero. Indices are grouped by element.\n This array will have shape ``(N, a.ndim)`` where ``N`` is the number of\n non-zero items.\n\n See Also\n --------\n where, nonzero\n\n Notes\n -----\n ``np.argwhere(a)`` is almost the same as ``np.transpose(np.nonzero(a))``,\n but produces a result of the correct shape for a 0D array.\n\n The output of ``argwhere`` is not suitable for indexing arrays.\n For this purpose use ``nonzero(a)`` instead.\n\n Examples\n --------\n >>> x = np.arange(6).reshape(2,3)\n >>> x\n array([[0, 1, 2],\n [3, 4, 5]])\n >>> np.argwhere(x>1)\n array([[0, 2],\n [1, 0],\n [1, 1],\n [1, 2]])\n\n \"\"\"\n # nonzero does not behave well on 0d, so promote to 1d\n if np.ndim(a) == 0:\n a = shape_base.atleast_1d(a)\n # then remove the added dimension\n return argwhere(a)[:,:0]\n return transpose(nonzero(a))\n\n\ndef _flatnonzero_dispatcher(a):\n return (a,)\n\n\n@array_function_dispatch(_flatnonzero_dispatcher)\ndef flatnonzero(a):\n \"\"\"\n Return indices that are non-zero in the flattened version of a.\n\n This is equivalent to np.nonzero(np.ravel(a))[0].\n\n Parameters\n ----------\n a : array_like\n Input data.\n\n Returns\n -------\n res : ndarray\n Output array, containing the indices of the elements of `a.ravel()`\n that are non-zero.\n\n See Also\n --------\n nonzero : Return the indices of the non-zero elements of the input array.\n ravel : Return a 1-D array containing the elements of the input array.\n\n Examples\n --------\n >>> x = np.arange(-2, 3)\n >>> x\n array([-2, -1, 0, 1, 2])\n >>> np.flatnonzero(x)\n array([0, 1, 3, 4])\n\n Use the indices of the non-zero elements as an index array to extract\n these elements:\n\n >>> x.ravel()[np.flatnonzero(x)]\n array([-2, -1, 1, 2])\n\n \"\"\"\n return np.nonzero(np.ravel(a))[0]\n\n\n_mode_from_name_dict = {'v': 0,\n 's': 1,\n 'f': 2}\n\n\ndef _mode_from_name(mode):\n if isinstance(mode, str):\n return _mode_from_name_dict[mode.lower()[0]]\n return mode\n\n\ndef _correlate_dispatcher(a, v, mode=None):\n return (a, v)\n\n\n@array_function_dispatch(_correlate_dispatcher)\ndef correlate(a, v, mode='valid'):\n \"\"\"\n Cross-correlation of two 1-dimensional sequences.\n\n This function computes the correlation as generally defined in signal\n processing texts::\n\n c_{av}[k] = sum_n a[n+k] * conj(v[n])\n\n with a and v sequences being zero-padded where necessary and conj being\n the conjugate.\n\n Parameters\n ----------\n a, v : array_like\n Input sequences.\n mode : {'valid', 'same', 'full'}, optional\n Refer to the `convolve` docstring. Note that the default\n is 'valid', unlike `convolve`, which uses 'full'.\n old_behavior : bool\n `old_behavior` was removed in NumPy 1.10. If you need the old\n behavior, use `multiarray.correlate`.\n\n Returns\n -------\n out : ndarray\n Discrete cross-correlation of `a` and `v`.\n\n See Also\n --------\n convolve : Discrete, linear convolution of two one-dimensional sequences.\n multiarray.correlate : Old, no conjugate, version of correlate.\n\n Notes\n -----\n The definition of correlation above is not unique and sometimes correlation\n may be defined differently. Another common definition is::\n\n c'_{av}[k] = sum_n a[n] conj(v[n+k])\n\n which is related to ``c_{av}[k]`` by ``c'_{av}[k] = c_{av}[-k]``.\n\n Examples\n --------\n >>> np.correlate([1, 2, 3], [0, 1, 0.5])\n array([3.5])\n >>> np.correlate([1, 2, 3], [0, 1, 0.5], \"same\")\n array([2. , 3.5, 3. ])\n >>> np.correlate([1, 2, 3], [0, 1, 0.5], \"full\")\n array([0.5, 2. , 3.5, 3. , 0. ])\n\n Using complex sequences:\n\n >>> np.correlate([1+1j, 2, 3-1j], [0, 1, 0.5j], 'full')\n array([ 0.5-0.5j, 1.0+0.j , 1.5-1.5j, 3.0-1.j , 0.0+0.j ])\n\n Note that you get the time reversed, complex conjugated result\n when the two input sequences change places, i.e.,\n ``c_{va}[k] = c^{*}_{av}[-k]``:\n\n >>> np.correlate([0, 1, 0.5j], [1+1j, 2, 3-1j], 'full')\n array([ 0.0+0.j , 3.0+1.j , 1.5+1.5j, 1.0+0.j , 0.5+0.5j])\n\n \"\"\"\n mode = _mode_from_name(mode)\n return multiarray.correlate2(a, v, mode)\n\n\ndef _convolve_dispatcher(a, v, mode=None):\n return (a, v)\n\n\n@array_function_dispatch(_convolve_dispatcher)\ndef convolve(a, v, mode='full'):\n \"\"\"\n Returns the discrete, linear convolution of two one-dimensional sequences.\n\n The convolution operator is often seen in signal processing, where it\n models the effect of a linear time-invariant system on a signal [1]_. In\n probability theory, the sum of two independent random variables is\n distributed according to the convolution of their individual\n distributions.\n\n If `v` is longer than `a`, the arrays are swapped before computation.\n\n Parameters\n ----------\n a : (N,) array_like\n First one-dimensional input array.\n v : (M,) array_like\n Second one-dimensional input array.\n mode : {'full', 'valid', 'same'}, optional\n 'full':\n By default, mode is 'full'. This returns the convolution\n at each point of overlap, with an output shape of (N+M-1,). At\n the end-points of the convolution, the signals do not overlap\n completely, and boundary effects may be seen.\n\n 'same':\n Mode 'same' returns output of length ``max(M, N)``. Boundary\n effects are still visible.\n\n 'valid':\n Mode 'valid' returns output of length\n ``max(M, N) - min(M, N) + 1``. The convolution product is only given\n for points where the signals overlap completely. Values outside\n the signal boundary have no effect.\n\n Returns\n -------\n out : ndarray\n Discrete, linear convolution of `a` and `v`.\n\n See Also\n --------\n scipy.signal.fftconvolve : Convolve two arrays using the Fast Fourier\n Transform.\n scipy.linalg.toeplitz : Used to construct the convolution operator.\n polymul : Polynomial multiplication. Same output as convolve, but also\n accepts poly1d objects as input.\n\n Notes\n -----\n The discrete convolution operation is defined as\n\n .. math:: (a * v)[n] = \\\\sum_{m = -\\\\infty}^{\\\\infty} a[m] v[n - m]\n\n It can be shown that a convolution :math:`x(t) * y(t)` in time/space\n is equivalent to the multiplication :math:`X(f) Y(f)` in the Fourier\n domain, after appropriate padding (padding is necessary to prevent\n circular convolution). Since multiplication is more efficient (faster)\n than convolution, the function `scipy.signal.fftconvolve` exploits the\n FFT to calculate the convolution of large data-sets.\n\n References\n ----------\n .. [1] Wikipedia, \"Convolution\",\n https://en.wikipedia.org/wiki/Convolution\n\n Examples\n --------\n Note how the convolution operator flips the second array\n before \"sliding\" the two across one another:\n\n >>> np.convolve([1, 2, 3], [0, 1, 0.5])\n array([0. , 1. , 2.5, 4. , 1.5])\n\n Only return the middle values of the convolution.\n Contains boundary effects, where zeros are taken\n into account:\n\n >>> np.convolve([1,2,3],[0,1,0.5], 'same')\n array([1. , 2.5, 4. ])\n\n The two arrays are of the same length, so there\n is only one position where they completely overlap:\n\n >>> np.convolve([1,2,3],[0,1,0.5], 'valid')\n array([2.5])\n\n \"\"\"\n a, v = array(a, copy=False, ndmin=1), array(v, copy=False, ndmin=1)\n if (len(v) > len(a)):\n a, v = v, a\n if len(a) == 0:\n raise ValueError('a cannot be empty')\n if len(v) == 0:\n raise ValueError('v cannot be empty')\n mode = _mode_from_name(mode)\n return multiarray.correlate(a, v[::-1], mode)\n\n\ndef _outer_dispatcher(a, b, out=None):\n return (a, b, out)\n\n\n@array_function_dispatch(_outer_dispatcher)\ndef outer(a, b, out=None):\n \"\"\"\n Compute the outer product of two vectors.\n\n Given two vectors, ``a = [a0, a1, ..., aM]`` and\n ``b = [b0, b1, ..., bN]``,\n the outer product [1]_ is::\n\n [[a0*b0 a0*b1 ... a0*bN ]\n [a1*b0 .\n [ ... .\n [aM*b0 aM*bN ]]\n\n Parameters\n ----------\n a : (M,) array_like\n First input vector. Input is flattened if\n not already 1-dimensional.\n b : (N,) array_like\n Second input vector. Input is flattened if\n not already 1-dimensional.\n out : (M, N) ndarray, optional\n A location where the result is stored\n\n .. versionadded:: 1.9.0\n\n Returns\n -------\n out : (M, N) ndarray\n ``out[i, j] = a[i] * b[j]``\n\n See also\n --------\n inner\n einsum : ``einsum('i,j->ij', a.ravel(), b.ravel())`` is the equivalent.\n ufunc.outer : A generalization to dimensions other than 1D and other\n operations. ``np.multiply.outer(a.ravel(), b.ravel())``\n is the equivalent.\n tensordot : ``np.tensordot(a.ravel(), b.ravel(), axes=((), ()))``\n is the equivalent.\n\n References\n ----------\n .. [1] : G. H. Golub and C. F. Van Loan, *Matrix Computations*, 3rd\n ed., Baltimore, MD, Johns Hopkins University Press, 1996,\n pg. 8.\n\n Examples\n --------\n Make a (*very* coarse) grid for computing a Mandelbrot set:\n\n >>> rl = np.outer(np.ones((5,)), np.linspace(-2, 2, 5))\n >>> rl\n array([[-2., -1., 0., 1., 2.],\n [-2., -1., 0., 1., 2.],\n [-2., -1., 0., 1., 2.],\n [-2., -1., 0., 1., 2.],\n [-2., -1., 0., 1., 2.]])\n >>> im = np.outer(1j*np.linspace(2, -2, 5), np.ones((5,)))\n >>> im\n array([[0.+2.j, 0.+2.j, 0.+2.j, 0.+2.j, 0.+2.j],\n [0.+1.j, 0.+1.j, 0.+1.j, 0.+1.j, 0.+1.j],\n [0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j],\n [0.-1.j, 0.-1.j, 0.-1.j, 0.-1.j, 0.-1.j],\n [0.-2.j, 0.-2.j, 0.-2.j, 0.-2.j, 0.-2.j]])\n >>> grid = rl + im\n >>> grid\n array([[-2.+2.j, -1.+2.j, 0.+2.j, 1.+2.j, 2.+2.j],\n [-2.+1.j, -1.+1.j, 0.+1.j, 1.+1.j, 2.+1.j],\n [-2.+0.j, -1.+0.j, 0.+0.j, 1.+0.j, 2.+0.j],\n [-2.-1.j, -1.-1.j, 0.-1.j, 1.-1.j, 2.-1.j],\n [-2.-2.j, -1.-2.j, 0.-2.j, 1.-2.j, 2.-2.j]])\n\n An example using a \"vector\" of letters:\n\n >>> x = np.array(['a', 'b', 'c'], dtype=object)\n >>> np.outer(x, [1, 2, 3])\n array([['a', 'aa', 'aaa'],\n ['b', 'bb', 'bbb'],\n ['c', 'cc', 'ccc']], dtype=object)\n\n \"\"\"\n a = asarray(a)\n b = asarray(b)\n return multiply(a.ravel()[:, newaxis], b.ravel()[newaxis, :], out)\n\n\ndef _tensordot_dispatcher(a, b, axes=None):\n return (a, b)\n\n\n@array_function_dispatch(_tensordot_dispatcher)\ndef tensordot(a, b, axes=2):\n \"\"\"\n Compute tensor dot product along specified axes.\n\n Given two tensors, `a` and `b`, and an array_like object containing\n two array_like objects, ``(a_axes, b_axes)``, sum the products of\n `a`'s and `b`'s elements (components) over the axes specified by\n ``a_axes`` and ``b_axes``. The third argument can be a single non-negative\n integer_like scalar, ``N``; if it is such, then the last ``N`` dimensions\n of `a` and the first ``N`` dimensions of `b` are summed over.\n\n Parameters\n ----------\n a, b : array_like\n Tensors to \"dot\".\n\n axes : int or (2,) array_like\n * integer_like\n If an int N, sum over the last N axes of `a` and the first N axes\n of `b` in order. The sizes of the corresponding axes must match.\n * (2,) array_like\n Or, a list of axes to be summed over, first sequence applying to `a`,\n second to `b`. Both elements array_like must be of the same length.\n\n Returns\n -------\n output : ndarray\n The tensor dot product of the input.\n\n See Also\n --------\n dot, einsum\n\n Notes\n -----\n Three common use cases are:\n * ``axes = 0`` : tensor product :math:`a\\\\otimes b`\n * ``axes = 1`` : tensor dot product :math:`a\\\\cdot b`\n * ``axes = 2`` : (default) tensor double contraction :math:`a:b`\n\n When `axes` is integer_like, the sequence for evaluation will be: first\n the -Nth axis in `a` and 0th axis in `b`, and the -1th axis in `a` and\n Nth axis in `b` last.\n\n When there is more than one axis to sum over - and they are not the last\n (first) axes of `a` (`b`) - the argument `axes` should consist of\n two sequences of the same length, with the first axis to sum over given\n first in both sequences, the second axis second, and so forth.\n\n The shape of the result consists of the non-contracted axes of the\n first tensor, followed by the non-contracted axes of the second.\n\n Examples\n --------\n A \"traditional\" example:\n\n >>> a = np.arange(60.).reshape(3,4,5)\n >>> b = np.arange(24.).reshape(4,3,2)\n >>> c = np.tensordot(a,b, axes=([1,0],[0,1]))\n >>> c.shape\n (5, 2)\n >>> c\n array([[4400., 4730.],\n [4532., 4874.],\n [4664., 5018.],\n [4796., 5162.],\n [4928., 5306.]])\n >>> # A slower but equivalent way of computing the same...\n >>> d = np.zeros((5,2))\n >>> for i in range(5):\n ... for j in range(2):\n ... for k in range(3):\n ... for n in range(4):\n ... d[i,j] += a[k,n,i] * b[n,k,j]\n >>> c == d\n array([[ True, True],\n [ True, True],\n [ True, True],\n [ True, True],\n [ True, True]])\n\n An extended example taking advantage of the overloading of + and \\\\*:\n\n >>> a = np.array(range(1, 9))\n >>> a.shape = (2, 2, 2)\n >>> A = np.array(('a', 'b', 'c', 'd'), dtype=object)\n >>> A.shape = (2, 2)\n >>> a; A\n array([[[1, 2],\n [3, 4]],\n [[5, 6],\n [7, 8]]])\n array([['a', 'b'],\n ['c', 'd']], dtype=object)\n\n >>> np.tensordot(a, A) # third argument default is 2 for double-contraction\n array(['abbcccdddd', 'aaaaabbbbbbcccccccdddddddd'], dtype=object)\n\n >>> np.tensordot(a, A, 1)\n array([[['acc', 'bdd'],\n ['aaacccc', 'bbbdddd']],\n [['aaaaacccccc', 'bbbbbdddddd'],\n ['aaaaaaacccccccc', 'bbbbbbbdddddddd']]], dtype=object)\n\n >>> np.tensordot(a, A, 0) # tensor product (result too long to incl.)\n array([[[[['a', 'b'],\n ['c', 'd']],\n ...\n\n >>> np.tensordot(a, A, (0, 1))\n array([[['abbbbb', 'cddddd'],\n ['aabbbbbb', 'ccdddddd']],\n [['aaabbbbbbb', 'cccddddddd'],\n ['aaaabbbbbbbb', 'ccccdddddddd']]], dtype=object)\n\n >>> np.tensordot(a, A, (2, 1))\n array([[['abb', 'cdd'],\n ['aaabbbb', 'cccdddd']],\n [['aaaaabbbbbb', 'cccccdddddd'],\n ['aaaaaaabbbbbbbb', 'cccccccdddddddd']]], dtype=object)\n\n >>> np.tensordot(a, A, ((0, 1), (0, 1)))\n array(['abbbcccccddddddd', 'aabbbbccccccdddddddd'], dtype=object)\n\n >>> np.tensordot(a, A, ((2, 1), (1, 0)))\n array(['acccbbdddd', 'aaaaacccccccbbbbbbdddddddd'], dtype=object)\n\n \"\"\"\n try:\n iter(axes)\n except Exception:\n axes_a = list(range(-axes, 0))\n axes_b = list(range(0, axes))\n else:\n axes_a, axes_b = axes\n try:\n na = len(axes_a)\n axes_a = list(axes_a)\n except TypeError:\n axes_a = [axes_a]\n na = 1\n try:\n nb = len(axes_b)\n axes_b = list(axes_b)\n except TypeError:\n axes_b = [axes_b]\n nb = 1\n\n a, b = asarray(a), asarray(b)\n as_ = a.shape\n nda = a.ndim\n bs = b.shape\n ndb = b.ndim\n equal = True\n if na != nb:\n equal = False\n else:\n for k in range(na):\n if as_[axes_a[k]] != bs[axes_b[k]]:\n equal = False\n break\n if axes_a[k] < 0:\n axes_a[k] += nda\n if axes_b[k] < 0:\n axes_b[k] += ndb\n if not equal:\n raise ValueError(\"shape-mismatch for sum\")\n\n # Move the axes to sum over to the end of \"a\"\n # and to the front of \"b\"\n notin = [k for k in range(nda) if k not in axes_a]\n newaxes_a = notin + axes_a\n N2 = 1\n for axis in axes_a:\n N2 *= as_[axis]\n newshape_a = (int(multiply.reduce([as_[ax] for ax in notin])), N2)\n olda = [as_[axis] for axis in notin]\n\n notin = [k for k in range(ndb) if k not in axes_b]\n newaxes_b = axes_b + notin\n N2 = 1\n for axis in axes_b:\n N2 *= bs[axis]\n newshape_b = (N2, int(multiply.reduce([bs[ax] for ax in notin])))\n oldb = [bs[axis] for axis in notin]\n\n at = a.transpose(newaxes_a).reshape(newshape_a)\n bt = b.transpose(newaxes_b).reshape(newshape_b)\n res = dot(at, bt)\n return res.reshape(olda + oldb)\n\n\ndef _roll_dispatcher(a, shift, axis=None):\n return (a,)\n\n\n@array_function_dispatch(_roll_dispatcher)\ndef roll(a, shift, axis=None):\n \"\"\"\n Roll array elements along a given axis.\n\n Elements that roll beyond the last position are re-introduced at\n the first.\n\n Parameters\n ----------\n a : array_like\n Input array.\n shift : int or tuple of ints\n The number of places by which elements are shifted. If a tuple,\n then `axis` must be a tuple of the same size, and each of the\n given axes is shifted by the corresponding number. If an int\n while `axis` is a tuple of ints, then the same value is used for\n all given axes.\n axis : int or tuple of ints, optional\n Axis or axes along which elements are shifted. By default, the\n array is flattened before shifting, after which the original\n shape is restored.\n\n Returns\n -------\n res : ndarray\n Output array, with the same shape as `a`.\n\n See Also\n --------\n rollaxis : Roll the specified axis backwards, until it lies in a\n given position.\n\n Notes\n -----\n .. versionadded:: 1.12.0\n\n Supports rolling over multiple dimensions simultaneously.\n\n Examples\n --------\n >>> x = np.arange(10)\n >>> np.roll(x, 2)\n array([8, 9, 0, 1, 2, 3, 4, 5, 6, 7])\n >>> np.roll(x, -2)\n array([2, 3, 4, 5, 6, 7, 8, 9, 0, 1])\n\n >>> x2 = np.reshape(x, (2,5))\n >>> x2\n array([[0, 1, 2, 3, 4],\n [5, 6, 7, 8, 9]])\n >>> np.roll(x2, 1)\n array([[9, 0, 1, 2, 3],\n [4, 5, 6, 7, 8]])\n >>> np.roll(x2, -1)\n array([[1, 2, 3, 4, 5],\n [6, 7, 8, 9, 0]])\n >>> np.roll(x2, 1, axis=0)\n array([[5, 6, 7, 8, 9],\n [0, 1, 2, 3, 4]])\n >>> np.roll(x2, -1, axis=0)\n array([[5, 6, 7, 8, 9],\n [0, 1, 2, 3, 4]])\n >>> np.roll(x2, 1, axis=1)\n array([[4, 0, 1, 2, 3],\n [9, 5, 6, 7, 8]])\n >>> np.roll(x2, -1, axis=1)\n array([[1, 2, 3, 4, 0],\n [6, 7, 8, 9, 5]])\n\n \"\"\"\n a = asanyarray(a)\n if axis is None:\n return roll(a.ravel(), shift, 0).reshape(a.shape)\n\n else:\n axis = normalize_axis_tuple(axis, a.ndim, allow_duplicate=True)\n broadcasted = broadcast(shift, axis)\n if broadcasted.ndim > 1:\n raise ValueError(\n \"'shift' and 'axis' should be scalars or 1D sequences\")\n shifts = {ax: 0 for ax in range(a.ndim)}\n for sh, ax in broadcasted:\n shifts[ax] += sh\n\n rolls = [((slice(None), slice(None)),)] * a.ndim\n for ax, offset in shifts.items():\n offset %= a.shape[ax] or 1 # If `a` is empty, nothing matters.\n if offset:\n # (original, result), (original, result)\n rolls[ax] = ((slice(None, -offset), slice(offset, None)),\n (slice(-offset, None), slice(None, offset)))\n\n result = empty_like(a)\n for indices in itertools.product(*rolls):\n arr_index, res_index = zip(*indices)\n result[res_index] = a[arr_index]\n\n return result\n\n\ndef _rollaxis_dispatcher(a, axis, start=None):\n return (a,)\n\n\n@array_function_dispatch(_rollaxis_dispatcher)\ndef rollaxis(a, axis, start=0):\n \"\"\"\n Roll the specified axis backwards, until it lies in a given position.\n\n This function continues to be supported for backward compatibility, but you\n should prefer `moveaxis`. The `moveaxis` function was added in NumPy\n 1.11.\n\n Parameters\n ----------\n a : ndarray\n Input array.\n axis : int\n The axis to be rolled. The positions of the other axes do not\n change relative to one another.\n start : int, optional\n When ``start <= axis``, the axis is rolled back until it lies in\n this position. When ``start > axis``, the axis is rolled until it\n lies before this position. The default, 0, results in a \"complete\"\n roll. The following table describes how negative values of ``start``\n are interpreted:\n\n .. table::\n :align: left\n\n +-------------------+----------------------+\n | ``start`` | Normalized ``start`` |\n +===================+======================+\n | ``-(arr.ndim+1)`` | raise ``AxisError`` |\n +-------------------+----------------------+\n | ``-arr.ndim`` | 0 |\n +-------------------+----------------------+\n | |vdots| | |vdots| |\n +-------------------+----------------------+\n | ``-1`` | ``arr.ndim-1`` |\n +-------------------+----------------------+\n | ``0`` | ``0`` |\n +-------------------+----------------------+\n | |vdots| | |vdots| |\n +-------------------+----------------------+\n | ``arr.ndim`` | ``arr.ndim`` |\n +-------------------+----------------------+\n | ``arr.ndim + 1`` | raise ``AxisError`` |\n +-------------------+----------------------+\n \n .. |vdots| unicode:: U+22EE .. Vertical Ellipsis\n\n Returns\n -------\n res : ndarray\n For NumPy >= 1.10.0 a view of `a` is always returned. For earlier\n NumPy versions a view of `a` is returned only if the order of the\n axes is changed, otherwise the input array is returned.\n\n See Also\n --------\n moveaxis : Move array axes to new positions.\n roll : Roll the elements of an array by a number of positions along a\n given axis.\n\n Examples\n --------\n >>> a = np.ones((3,4,5,6))\n >>> np.rollaxis(a, 3, 1).shape\n (3, 6, 4, 5)\n >>> np.rollaxis(a, 2).shape\n (5, 3, 4, 6)\n >>> np.rollaxis(a, 1, 4).shape\n (3, 5, 6, 4)\n\n \"\"\"\n n = a.ndim\n axis = normalize_axis_index(axis, n)\n if start < 0:\n start += n\n msg = \"'%s' arg requires %d <= %s < %d, but %d was passed in\"\n if not (0 <= start < n + 1):\n raise AxisError(msg % ('start', -n, 'start', n + 1, start))\n if axis < start:\n # it's been removed\n start -= 1\n if axis == start:\n return a[...]\n axes = list(range(0, n))\n axes.remove(axis)\n axes.insert(start, axis)\n return a.transpose(axes)\n\n\ndef normalize_axis_tuple(axis, ndim, argname=None, allow_duplicate=False):\n \"\"\"\n Normalizes an axis argument into a tuple of non-negative integer axes.\n\n This handles shorthands such as ``1`` and converts them to ``(1,)``,\n as well as performing the handling of negative indices covered by\n `normalize_axis_index`.\n\n By default, this forbids axes from being specified multiple times.\n\n Used internally by multi-axis-checking logic.\n\n .. versionadded:: 1.13.0\n\n Parameters\n ----------\n axis : int, iterable of int\n The un-normalized index or indices of the axis.\n ndim : int\n The number of dimensions of the array that `axis` should be normalized\n against.\n argname : str, optional\n A prefix to put before the error message, typically the name of the\n argument.\n allow_duplicate : bool, optional\n If False, the default, disallow an axis from being specified twice.\n\n Returns\n -------\n normalized_axes : tuple of int\n The normalized axis index, such that `0 <= normalized_axis < ndim`\n\n Raises\n ------\n AxisError\n If any axis provided is out of range\n ValueError\n If an axis is repeated\n\n See also\n --------\n normalize_axis_index : normalizing a single scalar axis\n \"\"\"\n # Optimization to speed-up the most common cases.\n if type(axis) not in (tuple, list):\n try:\n axis = [operator.index(axis)]\n except TypeError:\n pass\n # Going via an iterator directly is slower than via list comprehension.\n axis = tuple([normalize_axis_index(ax, ndim, argname) for ax in axis])\n if not allow_duplicate and len(set(axis)) != len(axis):\n if argname:\n raise ValueError('repeated axis in `{}` argument'.format(argname))\n else:\n raise ValueError('repeated axis')\n return axis\n\n\ndef _moveaxis_dispatcher(a, source, destination):\n return (a,)\n\n\n@array_function_dispatch(_moveaxis_dispatcher)\ndef moveaxis(a, source, destination):\n \"\"\"\n Move axes of an array to new positions.\n\n Other axes remain in their original order.\n\n .. versionadded:: 1.11.0\n\n Parameters\n ----------\n a : np.ndarray\n The array whose axes should be reordered.\n source : int or sequence of int\n Original positions of the axes to move. These must be unique.\n destination : int or sequence of int\n Destination positions for each of the original axes. These must also be\n unique.\n\n Returns\n -------\n result : np.ndarray\n Array with moved axes. This array is a view of the input array.\n\n See Also\n --------\n transpose: Permute the dimensions of an array.\n swapaxes: Interchange two axes of an array.\n\n Examples\n --------\n\n >>> x = np.zeros((3, 4, 5))\n >>> np.moveaxis(x, 0, -1).shape\n (4, 5, 3)\n >>> np.moveaxis(x, -1, 0).shape\n (5, 3, 4)\n\n These all achieve the same result:\n\n >>> np.transpose(x).shape\n (5, 4, 3)\n >>> np.swapaxes(x, 0, -1).shape\n (5, 4, 3)\n >>> np.moveaxis(x, [0, 1], [-1, -2]).shape\n (5, 4, 3)\n >>> np.moveaxis(x, [0, 1, 2], [-1, -2, -3]).shape\n (5, 4, 3)\n\n \"\"\"\n try:\n # allow duck-array types if they define transpose\n transpose = a.transpose\n except AttributeError:\n a = asarray(a)\n transpose = a.transpose\n\n source = normalize_axis_tuple(source, a.ndim, 'source')\n destination = normalize_axis_tuple(destination, a.ndim, 'destination')\n if len(source) != len(destination):\n raise ValueError('`source` and `destination` arguments must have '\n 'the same number of elements')\n\n order = [n for n in range(a.ndim) if n not in source]\n\n for dest, src in sorted(zip(destination, source)):\n order.insert(dest, src)\n\n result = transpose(order)\n return result\n\n\n# fix hack in scipy which imports this function\ndef _move_axis_to_0(a, axis):\n return moveaxis(a, axis, 0)\n\n\ndef _cross_dispatcher(a, b, axisa=None, axisb=None, axisc=None, axis=None):\n return (a, b)\n\n\n@array_function_dispatch(_cross_dispatcher)\ndef cross(a, b, axisa=-1, axisb=-1, axisc=-1, axis=None):\n \"\"\"\n Return the cross product of two (arrays of) vectors.\n\n The cross product of `a` and `b` in :math:`R^3` is a vector perpendicular\n to both `a` and `b`. If `a` and `b` are arrays of vectors, the vectors\n are defined by the last axis of `a` and `b` by default, and these axes\n can have dimensions 2 or 3. Where the dimension of either `a` or `b` is\n 2, the third component of the input vector is assumed to be zero and the\n cross product calculated accordingly. In cases where both input vectors\n have dimension 2, the z-component of the cross product is returned.\n\n Parameters\n ----------\n a : array_like\n Components of the first vector(s).\n b : array_like\n Components of the second vector(s).\n axisa : int, optional\n Axis of `a` that defines the vector(s). By default, the last axis.\n axisb : int, optional\n Axis of `b` that defines the vector(s). By default, the last axis.\n axisc : int, optional\n Axis of `c` containing the cross product vector(s). Ignored if\n both input vectors have dimension 2, as the return is scalar.\n By default, the last axis.\n axis : int, optional\n If defined, the axis of `a`, `b` and `c` that defines the vector(s)\n and cross product(s). Overrides `axisa`, `axisb` and `axisc`.\n\n Returns\n -------\n c : ndarray\n Vector cross product(s).\n\n Raises\n ------\n ValueError\n When the dimension of the vector(s) in `a` and/or `b` does not\n equal 2 or 3.\n\n See Also\n --------\n inner : Inner product\n outer : Outer product.\n ix_ : Construct index arrays.\n\n Notes\n -----\n .. versionadded:: 1.9.0\n\n Supports full broadcasting of the inputs.\n\n Examples\n --------\n Vector cross-product.\n\n >>> x = [1, 2, 3]\n >>> y = [4, 5, 6]\n >>> np.cross(x, y)\n array([-3, 6, -3])\n\n One vector with dimension 2.\n\n >>> x = [1, 2]\n >>> y = [4, 5, 6]\n >>> np.cross(x, y)\n array([12, -6, -3])\n\n Equivalently:\n\n >>> x = [1, 2, 0]\n >>> y = [4, 5, 6]\n >>> np.cross(x, y)\n array([12, -6, -3])\n\n Both vectors with dimension 2.\n\n >>> x = [1,2]\n >>> y = [4,5]\n >>> np.cross(x, y)\n array(-3)\n\n Multiple vector cross-products. Note that the direction of the cross\n product vector is defined by the `right-hand rule`.\n\n >>> x = np.array([[1,2,3], [4,5,6]])\n >>> y = np.array([[4,5,6], [1,2,3]])\n >>> np.cross(x, y)\n array([[-3, 6, -3],\n [ 3, -6, 3]])\n\n The orientation of `c` can be changed using the `axisc` keyword.\n\n >>> np.cross(x, y, axisc=0)\n array([[-3, 3],\n [ 6, -6],\n [-3, 3]])\n\n Change the vector definition of `x` and `y` using `axisa` and `axisb`.\n\n >>> x = np.array([[1,2,3], [4,5,6], [7, 8, 9]])\n >>> y = np.array([[7, 8, 9], [4,5,6], [1,2,3]])\n >>> np.cross(x, y)\n array([[ -6, 12, -6],\n [ 0, 0, 0],\n [ 6, -12, 6]])\n >>> np.cross(x, y, axisa=0, axisb=0)\n array([[-24, 48, -24],\n [-30, 60, -30],\n [-36, 72, -36]])\n\n \"\"\"\n if axis is not None:\n axisa, axisb, axisc = (axis,) * 3\n a = asarray(a)\n b = asarray(b)\n # Check axisa and axisb are within bounds\n axisa = normalize_axis_index(axisa, a.ndim, msg_prefix='axisa')\n axisb = normalize_axis_index(axisb, b.ndim, msg_prefix='axisb')\n\n # Move working axis to the end of the shape\n a = moveaxis(a, axisa, -1)\n b = moveaxis(b, axisb, -1)\n msg = (\"incompatible dimensions for cross product\\n\"\n \"(dimension must be 2 or 3)\")\n if a.shape[-1] not in (2, 3) or b.shape[-1] not in (2, 3):\n raise ValueError(msg)\n\n # Create the output array\n shape = broadcast(a[..., 0], b[..., 0]).shape\n if a.shape[-1] == 3 or b.shape[-1] == 3:\n shape += (3,)\n # Check axisc is within bounds\n axisc = normalize_axis_index(axisc, len(shape), msg_prefix='axisc')\n dtype = promote_types(a.dtype, b.dtype)\n cp = empty(shape, dtype)\n\n # create local aliases for readability\n a0 = a[..., 0]\n a1 = a[..., 1]\n if a.shape[-1] == 3:\n a2 = a[..., 2]\n b0 = b[..., 0]\n b1 = b[..., 1]\n if b.shape[-1] == 3:\n b2 = b[..., 2]\n if cp.ndim != 0 and cp.shape[-1] == 3:\n cp0 = cp[..., 0]\n cp1 = cp[..., 1]\n cp2 = cp[..., 2]\n\n if a.shape[-1] == 2:\n if b.shape[-1] == 2:\n # a0 * b1 - a1 * b0\n multiply(a0, b1, out=cp)\n cp -= a1 * b0\n return cp\n else:\n assert b.shape[-1] == 3\n # cp0 = a1 * b2 - 0 (a2 = 0)\n # cp1 = 0 - a0 * b2 (a2 = 0)\n # cp2 = a0 * b1 - a1 * b0\n multiply(a1, b2, out=cp0)\n multiply(a0, b2, out=cp1)\n negative(cp1, out=cp1)\n multiply(a0, b1, out=cp2)\n cp2 -= a1 * b0\n else:\n assert a.shape[-1] == 3\n if b.shape[-1] == 3:\n # cp0 = a1 * b2 - a2 * b1\n # cp1 = a2 * b0 - a0 * b2\n # cp2 = a0 * b1 - a1 * b0\n multiply(a1, b2, out=cp0)\n tmp = array(a2 * b1)\n cp0 -= tmp\n multiply(a2, b0, out=cp1)\n multiply(a0, b2, out=tmp)\n cp1 -= tmp\n multiply(a0, b1, out=cp2)\n multiply(a1, b0, out=tmp)\n cp2 -= tmp\n else:\n assert b.shape[-1] == 2\n # cp0 = 0 - a2 * b1 (b2 = 0)\n # cp1 = a2 * b0 - 0 (b2 = 0)\n # cp2 = a0 * b1 - a1 * b0\n multiply(a2, b1, out=cp0)\n negative(cp0, out=cp0)\n multiply(a2, b0, out=cp1)\n multiply(a0, b1, out=cp2)\n cp2 -= a1 * b0\n\n return moveaxis(cp, -1, axisc)\n\n\nlittle_endian = (sys.byteorder == 'little')\n\n\n@set_module('numpy')\ndef indices(dimensions, dtype=int, sparse=False):\n \"\"\"\n Return an array representing the indices of a grid.\n\n Compute an array where the subarrays contain index values 0, 1, ...\n varying only along the corresponding axis.\n\n Parameters\n ----------\n dimensions : sequence of ints\n The shape of the grid.\n dtype : dtype, optional\n Data type of the result.\n sparse : boolean, optional\n Return a sparse representation of the grid instead of a dense\n representation. Default is False.\n\n .. versionadded:: 1.17\n\n Returns\n -------\n grid : one ndarray or tuple of ndarrays\n If sparse is False:\n Returns one array of grid indices,\n ``grid.shape = (len(dimensions),) + tuple(dimensions)``.\n If sparse is True:\n Returns a tuple of arrays, with\n ``grid[i].shape = (1, ..., 1, dimensions[i], 1, ..., 1)`` with\n dimensions[i] in the ith place\n\n See Also\n --------\n mgrid, ogrid, meshgrid\n\n Notes\n -----\n The output shape in the dense case is obtained by prepending the number\n of dimensions in front of the tuple of dimensions, i.e. if `dimensions`\n is a tuple ``(r0, ..., rN-1)`` of length ``N``, the output shape is\n ``(N, r0, ..., rN-1)``.\n\n The subarrays ``grid[k]`` contains the N-D array of indices along the\n ``k-th`` axis. Explicitly::\n\n grid[k, i0, i1, ..., iN-1] = ik\n\n Examples\n --------\n >>> grid = np.indices((2, 3))\n >>> grid.shape\n (2, 2, 3)\n >>> grid[0] # row indices\n array([[0, 0, 0],\n [1, 1, 1]])\n >>> grid[1] # column indices\n array([[0, 1, 2],\n [0, 1, 2]])\n\n The indices can be used as an index into an array.\n\n >>> x = np.arange(20).reshape(5, 4)\n >>> row, col = np.indices((2, 3))\n >>> x[row, col]\n array([[0, 1, 2],\n [4, 5, 6]])\n\n Note that it would be more straightforward in the above example to\n extract the required elements directly with ``x[:2, :3]``.\n\n If sparse is set to true, the grid will be returned in a sparse\n representation.\n\n >>> i, j = np.indices((2, 3), sparse=True)\n >>> i.shape\n (2, 1)\n >>> j.shape\n (1, 3)\n >>> i # row indices\n array([[0],\n [1]])\n >>> j # column indices\n array([[0, 1, 2]])\n\n \"\"\"\n dimensions = tuple(dimensions)\n N = len(dimensions)\n shape = (1,)*N\n if sparse:\n res = tuple()\n else:\n res = empty((N,)+dimensions, dtype=dtype)\n for i, dim in enumerate(dimensions):\n idx = arange(dim, dtype=dtype).reshape(\n shape[:i] + (dim,) + shape[i+1:]\n )\n if sparse:\n res = res + (idx,)\n else:\n res[i] = idx\n return res\n\n\n@set_module('numpy')\ndef fromfunction(function, shape, *, dtype=float, **kwargs):\n \"\"\"\n Construct an array by executing a function over each coordinate.\n\n The resulting array therefore has a value ``fn(x, y, z)`` at\n coordinate ``(x, y, z)``.\n\n Parameters\n ----------\n function : callable\n The function is called with N parameters, where N is the rank of\n `shape`. Each parameter represents the coordinates of the array\n varying along a specific axis. For example, if `shape`\n were ``(2, 2)``, then the parameters would be\n ``array([[0, 0], [1, 1]])`` and ``array([[0, 1], [0, 1]])``\n shape : (N,) tuple of ints\n Shape of the output array, which also determines the shape of\n the coordinate arrays passed to `function`.\n dtype : data-type, optional\n Data-type of the coordinate arrays passed to `function`.\n By default, `dtype` is float.\n\n Returns\n -------\n fromfunction : any\n The result of the call to `function` is passed back directly.\n Therefore the shape of `fromfunction` is completely determined by\n `function`. If `function` returns a scalar value, the shape of\n `fromfunction` would not match the `shape` parameter.\n\n See Also\n --------\n indices, meshgrid\n\n Notes\n -----\n Keywords other than `dtype` are passed to `function`.\n\n Examples\n --------\n >>> np.fromfunction(lambda i, j: i == j, (3, 3), dtype=int)\n array([[ True, False, False],\n [False, True, False],\n [False, False, True]])\n\n >>> np.fromfunction(lambda i, j: i + j, (3, 3), dtype=int)\n array([[0, 1, 2],\n [1, 2, 3],\n [2, 3, 4]])\n\n \"\"\"\n args = indices(shape, dtype=dtype)\n return function(*args, **kwargs)\n\n\ndef _frombuffer(buf, dtype, shape, order):\n return frombuffer(buf, dtype=dtype).reshape(shape, order=order)\n\n\n@set_module('numpy')\ndef isscalar(element):\n \"\"\"\n Returns True if the type of `element` is a scalar type.\n\n Parameters\n ----------\n element : any\n Input argument, can be of any type and shape.\n\n Returns\n -------\n val : bool\n True if `element` is a scalar type, False if it is not.\n\n See Also\n --------\n ndim : Get the number of dimensions of an array\n\n Notes\n -----\n If you need a stricter way to identify a *numerical* scalar, use\n ``isinstance(x, numbers.Number)``, as that returns ``False`` for most\n non-numerical elements such as strings.\n\n In most cases ``np.ndim(x) == 0`` should be used instead of this function,\n as that will also return true for 0d arrays. This is how numpy overloads\n functions in the style of the ``dx`` arguments to `gradient` and the ``bins``\n argument to `histogram`. Some key differences:\n\n +--------------------------------------+---------------+-------------------+\n | x |``isscalar(x)``|``np.ndim(x) == 0``|\n +======================================+===============+===================+\n | PEP 3141 numeric objects (including | ``True`` | ``True`` |\n | builtins) | | |\n +--------------------------------------+---------------+-------------------+\n | builtin string and buffer objects | ``True`` | ``True`` |\n +--------------------------------------+---------------+-------------------+\n | other builtin objects, like | ``False`` | ``True`` |\n | `pathlib.Path`, `Exception`, | | |\n | the result of `re.compile` | | |\n +--------------------------------------+---------------+-------------------+\n | third-party objects like | ``False`` | ``True`` |\n | `matplotlib.figure.Figure` | | |\n +--------------------------------------+---------------+-------------------+\n | zero-dimensional numpy arrays | ``False`` | ``True`` |\n +--------------------------------------+---------------+-------------------+\n | other numpy arrays | ``False`` | ``False`` |\n +--------------------------------------+---------------+-------------------+\n | `list`, `tuple`, and other sequence | ``False`` | ``False`` |\n | objects | | |\n +--------------------------------------+---------------+-------------------+\n\n Examples\n --------\n >>> np.isscalar(3.1)\n True\n >>> np.isscalar(np.array(3.1))\n False\n >>> np.isscalar([3.1])\n False\n >>> np.isscalar(False)\n True\n >>> np.isscalar('numpy')\n True\n\n NumPy supports PEP 3141 numbers:\n\n >>> from fractions import Fraction\n >>> np.isscalar(Fraction(5, 17))\n True\n >>> from numbers import Number\n >>> np.isscalar(Number())\n True\n\n \"\"\"\n return (isinstance(element, generic)\n or type(element) in ScalarType\n or isinstance(element, numbers.Number))\n\n\n@set_module('numpy')\ndef binary_repr(num, width=None):\n \"\"\"\n Return the binary representation of the input number as a string.\n\n For negative numbers, if width is not given, a minus sign is added to the\n front. If width is given, the two's complement of the number is\n returned, with respect to that width.\n\n In a two's-complement system negative numbers are represented by the two's\n complement of the absolute value. This is the most common method of\n representing signed integers on computers [1]_. A N-bit two's-complement\n system can represent every integer in the range\n :math:`-2^{N-1}` to :math:`+2^{N-1}-1`.\n\n Parameters\n ----------\n num : int\n Only an integer decimal number can be used.\n width : int, optional\n The length of the returned string if `num` is positive, or the length\n of the two's complement if `num` is negative, provided that `width` is\n at least a sufficient number of bits for `num` to be represented in the\n designated form.\n\n If the `width` value is insufficient, it will be ignored, and `num` will\n be returned in binary (`num` > 0) or two's complement (`num` < 0) form\n with its width equal to the minimum number of bits needed to represent\n the number in the designated form. This behavior is deprecated and will\n later raise an error.\n\n .. deprecated:: 1.12.0\n\n Returns\n -------\n bin : str\n Binary representation of `num` or two's complement of `num`.\n\n See Also\n --------\n base_repr: Return a string representation of a number in the given base\n system.\n bin: Python's built-in binary representation generator of an integer.\n\n Notes\n -----\n `binary_repr` is equivalent to using `base_repr` with base 2, but about 25x\n faster.\n\n References\n ----------\n .. [1] Wikipedia, \"Two's complement\",\n https://en.wikipedia.org/wiki/Two's_complement\n\n Examples\n --------\n >>> np.binary_repr(3)\n '11'\n >>> np.binary_repr(-3)\n '-11'\n >>> np.binary_repr(3, width=4)\n '0011'\n\n The two's complement is returned when the input number is negative and\n width is specified:\n\n >>> np.binary_repr(-3, width=3)\n '101'\n >>> np.binary_repr(-3, width=5)\n '11101'\n\n \"\"\"\n def warn_if_insufficient(width, binwidth):\n if width is not None and width < binwidth:\n warnings.warn(\n \"Insufficient bit width provided. This behavior \"\n \"will raise an error in the future.\", DeprecationWarning,\n stacklevel=3)\n\n # Ensure that num is a Python integer to avoid overflow or unwanted\n # casts to floating point.\n num = operator.index(num)\n\n if num == 0:\n return '0' * (width or 1)\n\n elif num > 0:\n binary = bin(num)[2:]\n binwidth = len(binary)\n outwidth = (binwidth if width is None\n else max(binwidth, width))\n warn_if_insufficient(width, binwidth)\n return binary.zfill(outwidth)\n\n else:\n if width is None:\n return '-' + bin(-num)[2:]\n\n else:\n poswidth = len(bin(-num)[2:])\n\n # See gh-8679: remove extra digit\n # for numbers at boundaries.\n if 2**(poswidth - 1) == -num:\n poswidth -= 1\n\n twocomp = 2**(poswidth + 1) + num\n binary = bin(twocomp)[2:]\n binwidth = len(binary)\n\n outwidth = max(binwidth, width)\n warn_if_insufficient(width, binwidth)\n return '1' * (outwidth - binwidth) + binary\n\n\n@set_module('numpy')\ndef base_repr(number, base=2, padding=0):\n \"\"\"\n Return a string representation of a number in the given base system.\n\n Parameters\n ----------\n number : int\n The value to convert. Positive and negative values are handled.\n base : int, optional\n Convert `number` to the `base` number system. The valid range is 2-36,\n the default value is 2.\n padding : int, optional\n Number of zeros padded on the left. Default is 0 (no padding).\n\n Returns\n -------\n out : str\n String representation of `number` in `base` system.\n\n See Also\n --------\n binary_repr : Faster version of `base_repr` for base 2.\n\n Examples\n --------\n >>> np.base_repr(5)\n '101'\n >>> np.base_repr(6, 5)\n '11'\n >>> np.base_repr(7, base=5, padding=3)\n '00012'\n\n >>> np.base_repr(10, base=16)\n 'A'\n >>> np.base_repr(32, base=16)\n '20'\n\n \"\"\"\n digits = '0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ'\n if base > len(digits):\n raise ValueError(\"Bases greater than 36 not handled in base_repr.\")\n elif base < 2:\n raise ValueError(\"Bases less than 2 not handled in base_repr.\")\n\n num = abs(number)\n res = []\n while num:\n res.append(digits[num % base])\n num //= base\n if padding:\n res.append('0' * padding)\n if number < 0:\n res.append('-')\n return ''.join(reversed(res or '0'))\n\n\n# These are all essentially abbreviations\n# These might wind up in a special abbreviations module\n\n\ndef _maketup(descr, val):\n dt = dtype(descr)\n # Place val in all scalar tuples:\n fields = dt.fields\n if fields is None:\n return val\n else:\n res = [_maketup(fields[name][0], val) for name in dt.names]\n return tuple(res)\n\n\n@set_module('numpy')\ndef identity(n, dtype=None):\n \"\"\"\n Return the identity array.\n\n The identity array is a square array with ones on\n the main diagonal.\n\n Parameters\n ----------\n n : int\n Number of rows (and columns) in `n` x `n` output.\n dtype : data-type, optional\n Data-type of the output. Defaults to ``float``.\n\n Returns\n -------\n out : ndarray\n `n` x `n` array with its main diagonal set to one,\n and all other elements 0.\n\n Examples\n --------\n >>> np.identity(3)\n array([[1., 0., 0.],\n [0., 1., 0.],\n [0., 0., 1.]])\n\n \"\"\"\n from numpy import eye\n return eye(n, dtype=dtype)\n\n\ndef _allclose_dispatcher(a, b, rtol=None, atol=None, equal_nan=None):\n return (a, b)\n\n\n@array_function_dispatch(_allclose_dispatcher)\ndef allclose(a, b, rtol=1.e-5, atol=1.e-8, equal_nan=False):\n \"\"\"\n Returns True if two arrays are element-wise equal within a tolerance.\n\n The tolerance values are positive, typically very small numbers. The\n relative difference (`rtol` * abs(`b`)) and the absolute difference\n `atol` are added together to compare against the absolute difference\n between `a` and `b`.\n\n NaNs are treated as equal if they are in the same place and if\n ``equal_nan=True``. Infs are treated as equal if they are in the same\n place and of the same sign in both arrays.\n\n Parameters\n ----------\n a, b : array_like\n Input arrays to compare.\n rtol : float\n The relative tolerance parameter (see Notes).\n atol : float\n The absolute tolerance parameter (see Notes).\n equal_nan : bool\n Whether to compare NaN's as equal. If True, NaN's in `a` will be\n considered equal to NaN's in `b` in the output array.\n\n .. versionadded:: 1.10.0\n\n Returns\n -------\n allclose : bool\n Returns True if the two arrays are equal within the given\n tolerance; False otherwise.\n\n See Also\n --------\n isclose, all, any, equal\n\n Notes\n -----\n If the following equation is element-wise True, then allclose returns\n True.\n\n absolute(`a` - `b`) <= (`atol` + `rtol` * absolute(`b`))\n\n The above equation is not symmetric in `a` and `b`, so that\n ``allclose(a, b)`` might be different from ``allclose(b, a)`` in\n some rare cases.\n\n The comparison of `a` and `b` uses standard broadcasting, which\n means that `a` and `b` need not have the same shape in order for\n ``allclose(a, b)`` to evaluate to True. The same is true for\n `equal` but not `array_equal`.\n\n Examples\n --------\n >>> np.allclose([1e10,1e-7], [1.00001e10,1e-8])\n False\n >>> np.allclose([1e10,1e-8], [1.00001e10,1e-9])\n True\n >>> np.allclose([1e10,1e-8], [1.0001e10,1e-9])\n False\n >>> np.allclose([1.0, np.nan], [1.0, np.nan])\n False\n >>> np.allclose([1.0, np.nan], [1.0, np.nan], equal_nan=True)\n True\n\n \"\"\"\n res = all(isclose(a, b, rtol=rtol, atol=atol, equal_nan=equal_nan))\n return bool(res)\n\n\ndef _isclose_dispatcher(a, b, rtol=None, atol=None, equal_nan=None):\n return (a, b)\n\n\n@array_function_dispatch(_isclose_dispatcher)\ndef isclose(a, b, rtol=1.e-5, atol=1.e-8, equal_nan=False):\n \"\"\"\n Returns a boolean array where two arrays are element-wise equal within a\n tolerance.\n\n The tolerance values are positive, typically very small numbers. The\n relative difference (`rtol` * abs(`b`)) and the absolute difference\n `atol` are added together to compare against the absolute difference\n between `a` and `b`.\n\n .. warning:: The default `atol` is not appropriate for comparing numbers\n that are much smaller than one (see Notes).\n\n Parameters\n ----------\n a, b : array_like\n Input arrays to compare.\n rtol : float\n The relative tolerance parameter (see Notes).\n atol : float\n The absolute tolerance parameter (see Notes).\n equal_nan : bool\n Whether to compare NaN's as equal. If True, NaN's in `a` will be\n considered equal to NaN's in `b` in the output array.\n\n Returns\n -------\n y : array_like\n Returns a boolean array of where `a` and `b` are equal within the\n given tolerance. If both `a` and `b` are scalars, returns a single\n boolean value.\n\n See Also\n --------\n allclose\n\n Notes\n -----\n .. versionadded:: 1.7.0\n\n For finite values, isclose uses the following equation to test whether\n two floating point values are equivalent.\n\n absolute(`a` - `b`) <= (`atol` + `rtol` * absolute(`b`))\n\n Unlike the built-in `math.isclose`, the above equation is not symmetric\n in `a` and `b` -- it assumes `b` is the reference value -- so that\n `isclose(a, b)` might be different from `isclose(b, a)`. Furthermore,\n the default value of atol is not zero, and is used to determine what\n small values should be considered close to zero. The default value is\n appropriate for expected values of order unity: if the expected values\n are significantly smaller than one, it can result in false positives.\n `atol` should be carefully selected for the use case at hand. A zero value\n for `atol` will result in `False` if either `a` or `b` is zero.\n\n Examples\n --------\n >>> np.isclose([1e10,1e-7], [1.00001e10,1e-8])\n array([ True, False])\n >>> np.isclose([1e10,1e-8], [1.00001e10,1e-9])\n array([ True, True])\n >>> np.isclose([1e10,1e-8], [1.0001e10,1e-9])\n array([False, True])\n >>> np.isclose([1.0, np.nan], [1.0, np.nan])\n array([ True, False])\n >>> np.isclose([1.0, np.nan], [1.0, np.nan], equal_nan=True)\n array([ True, True])\n >>> np.isclose([1e-8, 1e-7], [0.0, 0.0])\n array([ True, False])\n >>> np.isclose([1e-100, 1e-7], [0.0, 0.0], atol=0.0)\n array([False, False])\n >>> np.isclose([1e-10, 1e-10], [1e-20, 0.0])\n array([ True, True])\n >>> np.isclose([1e-10, 1e-10], [1e-20, 0.999999e-10], atol=0.0)\n array([False, True])\n \"\"\"\n def within_tol(x, y, atol, rtol):\n with errstate(invalid='ignore'):\n return less_equal(abs(x-y), atol + rtol * abs(y))\n\n x = asanyarray(a)\n y = asanyarray(b)\n\n # Make sure y is an inexact type to avoid bad behavior on abs(MIN_INT).\n # This will cause casting of x later. Also, make sure to allow subclasses\n # (e.g., for numpy.ma).\n dt = multiarray.result_type(y, 1.)\n y = array(y, dtype=dt, copy=False, subok=True)\n\n xfin = isfinite(x)\n yfin = isfinite(y)\n if all(xfin) and all(yfin):\n return within_tol(x, y, atol, rtol)\n else:\n finite = xfin & yfin\n cond = zeros_like(finite, subok=True)\n # Because we're using boolean indexing, x & y must be the same shape.\n # Ideally, we'd just do x, y = broadcast_arrays(x, y). It's in\n # lib.stride_tricks, though, so we can't import it here.\n x = x * ones_like(cond)\n y = y * ones_like(cond)\n # Avoid subtraction with infinite/nan values...\n cond[finite] = within_tol(x[finite], y[finite], atol, rtol)\n # Check for equality of infinite values...\n cond[~finite] = (x[~finite] == y[~finite])\n if equal_nan:\n # Make NaN == NaN\n both_nan = isnan(x) & isnan(y)\n\n # Needed to treat masked arrays correctly. = True would not work.\n cond[both_nan] = both_nan[both_nan]\n\n return cond[()] # Flatten 0d arrays to scalars\n\n\ndef _array_equal_dispatcher(a1, a2, equal_nan=None):\n return (a1, a2)\n\n\n@array_function_dispatch(_array_equal_dispatcher)\ndef array_equal(a1, a2, equal_nan=False):\n \"\"\"\n True if two arrays have the same shape and elements, False otherwise.\n\n Parameters\n ----------\n a1, a2 : array_like\n Input arrays.\n equal_nan : bool\n Whether to compare NaN's as equal. If the dtype of a1 and a2 is\n complex, values will be considered equal if either the real or the\n imaginary component of a given value is ``nan``.\n\n .. versionadded:: 1.19.0\n\n Returns\n -------\n b : bool\n Returns True if the arrays are equal.\n\n See Also\n --------\n allclose: Returns True if two arrays are element-wise equal within a\n tolerance.\n array_equiv: Returns True if input arrays are shape consistent and all\n elements equal.\n\n Examples\n --------\n >>> np.array_equal([1, 2], [1, 2])\n True\n >>> np.array_equal(np.array([1, 2]), np.array([1, 2]))\n True\n >>> np.array_equal([1, 2], [1, 2, 3])\n False\n >>> np.array_equal([1, 2], [1, 4])\n False\n >>> a = np.array([1, np.nan])\n >>> np.array_equal(a, a)\n False\n >>> np.array_equal(a, a, equal_nan=True)\n True\n\n When ``equal_nan`` is True, complex values with nan components are\n considered equal if either the real *or* the imaginary components are nan.\n\n >>> a = np.array([1 + 1j])\n >>> b = a.copy()\n >>> a.real = np.nan\n >>> b.imag = np.nan\n >>> np.array_equal(a, b, equal_nan=True)\n True\n \"\"\"\n try:\n a1, a2 = asarray(a1), asarray(a2)\n except Exception:\n return False\n if a1.shape != a2.shape:\n return False\n if not equal_nan:\n return bool(asarray(a1 == a2).all())\n # Handling NaN values if equal_nan is True\n a1nan, a2nan = isnan(a1), isnan(a2)\n # NaN's occur at different locations\n if not (a1nan == a2nan).all():\n return False\n # Shapes of a1, a2 and masks are guaranteed to be consistent by this point\n return bool(asarray(a1[~a1nan] == a2[~a1nan]).all())\n\n\ndef _array_equiv_dispatcher(a1, a2):\n return (a1, a2)\n\n\n@array_function_dispatch(_array_equiv_dispatcher)\ndef array_equiv(a1, a2):\n \"\"\"\n Returns True if input arrays are shape consistent and all elements equal.\n\n Shape consistent means they are either the same shape, or one input array\n can be broadcasted to create the same shape as the other one.\n\n Parameters\n ----------\n a1, a2 : array_like\n Input arrays.\n\n Returns\n -------\n out : bool\n True if equivalent, False otherwise.\n\n Examples\n --------\n >>> np.array_equiv([1, 2], [1, 2])\n True\n >>> np.array_equiv([1, 2], [1, 3])\n False\n\n Showing the shape equivalence:\n\n >>> np.array_equiv([1, 2], [[1, 2], [1, 2]])\n True\n >>> np.array_equiv([1, 2], [[1, 2, 1, 2], [1, 2, 1, 2]])\n False\n\n >>> np.array_equiv([1, 2], [[1, 2], [1, 3]])\n False\n\n \"\"\"\n try:\n a1, a2 = asarray(a1), asarray(a2)\n except Exception:\n return False\n try:\n multiarray.broadcast(a1, a2)\n except Exception:\n return False\n\n return bool(asarray(a1 == a2).all())\n\n\nInf = inf = infty = Infinity = PINF\nnan = NaN = NAN\nFalse_ = bool_(False)\nTrue_ = bool_(True)\n\n\ndef extend_all(module):\n existing = set(__all__)\n mall = getattr(module, '__all__')\n for a in mall:\n if a not in existing:\n __all__.append(a)\n\n\nfrom .umath import *\nfrom .numerictypes import *\nfrom . import fromnumeric\nfrom .fromnumeric import *\nfrom . import arrayprint\nfrom .arrayprint import *\nfrom . import _asarray\nfrom ._asarray import *\nfrom . import _ufunc_config\nfrom ._ufunc_config import *\nextend_all(fromnumeric)\nextend_all(umath)\nextend_all(numerictypes)\nextend_all(arrayprint)\nextend_all(_asarray)\nextend_all(_ufunc_config)\n",2469],"/home/gaogaotiantian/programs/codesnap/venv/lib/python3.6/site-packages/numpy/core/_asarray.py":["\"\"\"\nFunctions in the ``as*array`` family that promote array-likes into arrays.\n\n`require` fits this category despite its name not matching this pattern.\n\"\"\"\nfrom .overrides import set_module\nfrom .multiarray import array\n\n\n__all__ = [\n \"asarray\", \"asanyarray\", \"ascontiguousarray\", \"asfortranarray\", \"require\",\n]\n\n@set_module('numpy')\ndef asarray(a, dtype=None, order=None):\n \"\"\"Convert the input to an array.\n\n Parameters\n ----------\n a : array_like\n Input data, in any form that can be converted to an array. This\n includes lists, lists of tuples, tuples, tuples of tuples, tuples\n of lists and ndarrays.\n dtype : data-type, optional\n By default, the data-type is inferred from the input data.\n order : {'C', 'F'}, optional\n Whether to use row-major (C-style) or\n column-major (Fortran-style) memory representation.\n Defaults to 'C'.\n\n Returns\n -------\n out : ndarray\n Array interpretation of `a`. No copy is performed if the input\n is already an ndarray with matching dtype and order. If `a` is a\n subclass of ndarray, a base class ndarray is returned.\n\n See Also\n --------\n asanyarray : Similar function which passes through subclasses.\n ascontiguousarray : Convert input to a contiguous array.\n asfarray : Convert input to a floating point ndarray.\n asfortranarray : Convert input to an ndarray with column-major\n memory order.\n asarray_chkfinite : Similar function which checks input for NaNs and Infs.\n fromiter : Create an array from an iterator.\n fromfunction : Construct an array by executing a function on grid\n positions.\n\n Examples\n --------\n Convert a list into an array:\n\n >>> a = [1, 2]\n >>> np.asarray(a)\n array([1, 2])\n\n Existing arrays are not copied:\n\n >>> a = np.array([1, 2])\n >>> np.asarray(a) is a\n True\n\n If `dtype` is set, array is copied only if dtype does not match:\n\n >>> a = np.array([1, 2], dtype=np.float32)\n >>> np.asarray(a, dtype=np.float32) is a\n True\n >>> np.asarray(a, dtype=np.float64) is a\n False\n\n Contrary to `asanyarray`, ndarray subclasses are not passed through:\n\n >>> issubclass(np.recarray, np.ndarray)\n True\n >>> a = np.array([(1.0, 2), (3.0, 4)], dtype='f4,i4').view(np.recarray)\n >>> np.asarray(a) is a\n False\n >>> np.asanyarray(a) is a\n True\n\n \"\"\"\n return array(a, dtype, copy=False, order=order)\n\n\n@set_module('numpy')\ndef asanyarray(a, dtype=None, order=None):\n \"\"\"Convert the input to an ndarray, but pass ndarray subclasses through.\n\n Parameters\n ----------\n a : array_like\n Input data, in any form that can be converted to an array. This\n includes scalars, lists, lists of tuples, tuples, tuples of tuples,\n tuples of lists, and ndarrays.\n dtype : data-type, optional\n By default, the data-type is inferred from the input data.\n order : {'C', 'F'}, optional\n Whether to use row-major (C-style) or column-major\n (Fortran-style) memory representation. Defaults to 'C'.\n\n Returns\n -------\n out : ndarray or an ndarray subclass\n Array interpretation of `a`. If `a` is an ndarray or a subclass\n of ndarray, it is returned as-is and no copy is performed.\n\n See Also\n --------\n asarray : Similar function which always returns ndarrays.\n ascontiguousarray : Convert input to a contiguous array.\n asfarray : Convert input to a floating point ndarray.\n asfortranarray : Convert input to an ndarray with column-major\n memory order.\n asarray_chkfinite : Similar function which checks input for NaNs and\n Infs.\n fromiter : Create an array from an iterator.\n fromfunction : Construct an array by executing a function on grid\n positions.\n\n Examples\n --------\n Convert a list into an array:\n\n >>> a = [1, 2]\n >>> np.asanyarray(a)\n array([1, 2])\n\n Instances of `ndarray` subclasses are passed through as-is:\n\n >>> a = np.array([(1.0, 2), (3.0, 4)], dtype='f4,i4').view(np.recarray)\n >>> np.asanyarray(a) is a\n True\n\n \"\"\"\n return array(a, dtype, copy=False, order=order, subok=True)\n\n\n@set_module('numpy')\ndef ascontiguousarray(a, dtype=None):\n \"\"\"\n Return a contiguous array (ndim >= 1) in memory (C order).\n\n Parameters\n ----------\n a : array_like\n Input array.\n dtype : str or dtype object, optional\n Data-type of returned array.\n\n Returns\n -------\n out : ndarray\n Contiguous array of same shape and content as `a`, with type `dtype`\n if specified.\n\n See Also\n --------\n asfortranarray : Convert input to an ndarray with column-major\n memory order.\n require : Return an ndarray that satisfies requirements.\n ndarray.flags : Information about the memory layout of the array.\n\n Examples\n --------\n >>> x = np.arange(6).reshape(2,3)\n >>> np.ascontiguousarray(x, dtype=np.float32)\n array([[0., 1., 2.],\n [3., 4., 5.]], dtype=float32)\n >>> x.flags['C_CONTIGUOUS']\n True\n\n Note: This function returns an array with at least one-dimension (1-d) \n so it will not preserve 0-d arrays. \n\n \"\"\"\n return array(a, dtype, copy=False, order='C', ndmin=1)\n\n\n@set_module('numpy')\ndef asfortranarray(a, dtype=None):\n \"\"\"\n Return an array (ndim >= 1) laid out in Fortran order in memory.\n\n Parameters\n ----------\n a : array_like\n Input array.\n dtype : str or dtype object, optional\n By default, the data-type is inferred from the input data.\n\n Returns\n -------\n out : ndarray\n The input `a` in Fortran, or column-major, order.\n\n See Also\n --------\n ascontiguousarray : Convert input to a contiguous (C order) array.\n asanyarray : Convert input to an ndarray with either row or\n column-major memory order.\n require : Return an ndarray that satisfies requirements.\n ndarray.flags : Information about the memory layout of the array.\n\n Examples\n --------\n >>> x = np.arange(6).reshape(2,3)\n >>> y = np.asfortranarray(x)\n >>> x.flags['F_CONTIGUOUS']\n False\n >>> y.flags['F_CONTIGUOUS']\n True\n\n Note: This function returns an array with at least one-dimension (1-d) \n so it will not preserve 0-d arrays. \n\n \"\"\"\n return array(a, dtype, copy=False, order='F', ndmin=1)\n\n\n@set_module('numpy')\ndef require(a, dtype=None, requirements=None):\n \"\"\"\n Return an ndarray of the provided type that satisfies requirements.\n\n This function is useful to be sure that an array with the correct flags\n is returned for passing to compiled code (perhaps through ctypes).\n\n Parameters\n ----------\n a : array_like\n The object to be converted to a type-and-requirement-satisfying array.\n dtype : data-type\n The required data-type. If None preserve the current dtype. If your\n application requires the data to be in native byteorder, include\n a byteorder specification as a part of the dtype specification.\n requirements : str or list of str\n The requirements list can be any of the following\n\n * 'F_CONTIGUOUS' ('F') - ensure a Fortran-contiguous array\n * 'C_CONTIGUOUS' ('C') - ensure a C-contiguous array\n * 'ALIGNED' ('A') - ensure a data-type aligned array\n * 'WRITEABLE' ('W') - ensure a writable array\n * 'OWNDATA' ('O') - ensure an array that owns its own data\n * 'ENSUREARRAY', ('E') - ensure a base array, instead of a subclass\n\n Returns\n -------\n out : ndarray\n Array with specified requirements and type if given.\n\n See Also\n --------\n asarray : Convert input to an ndarray.\n asanyarray : Convert to an ndarray, but pass through ndarray subclasses.\n ascontiguousarray : Convert input to a contiguous array.\n asfortranarray : Convert input to an ndarray with column-major\n memory order.\n ndarray.flags : Information about the memory layout of the array.\n\n Notes\n -----\n The returned array will be guaranteed to have the listed requirements\n by making a copy if needed.\n\n Examples\n --------\n >>> x = np.arange(6).reshape(2,3)\n >>> x.flags\n C_CONTIGUOUS : True\n F_CONTIGUOUS : False\n OWNDATA : False\n WRITEABLE : True\n ALIGNED : True\n WRITEBACKIFCOPY : False\n UPDATEIFCOPY : False\n\n >>> y = np.require(x, dtype=np.float32, requirements=['A', 'O', 'W', 'F'])\n >>> y.flags\n C_CONTIGUOUS : False\n F_CONTIGUOUS : True\n OWNDATA : True\n WRITEABLE : True\n ALIGNED : True\n WRITEBACKIFCOPY : False\n UPDATEIFCOPY : False\n\n \"\"\"\n possible_flags = {'C': 'C', 'C_CONTIGUOUS': 'C', 'CONTIGUOUS': 'C',\n 'F': 'F', 'F_CONTIGUOUS': 'F', 'FORTRAN': 'F',\n 'A': 'A', 'ALIGNED': 'A',\n 'W': 'W', 'WRITEABLE': 'W',\n 'O': 'O', 'OWNDATA': 'O',\n 'E': 'E', 'ENSUREARRAY': 'E'}\n if not requirements:\n return asanyarray(a, dtype=dtype)\n else:\n requirements = {possible_flags[x.upper()] for x in requirements}\n\n if 'E' in requirements:\n requirements.remove('E')\n subok = False\n else:\n subok = True\n\n order = 'A'\n if requirements >= {'C', 'F'}:\n raise ValueError('Cannot specify both \"C\" and \"F\" order')\n elif 'F' in requirements:\n order = 'F'\n requirements.remove('F')\n elif 'C' in requirements:\n order = 'C'\n requirements.remove('C')\n\n arr = array(a, dtype=dtype, order=order, copy=False, subok=subok)\n\n for prop in requirements:\n if not arr.flags[prop]:\n arr = arr.copy(order)\n break\n return arr\n",322],"/home/gaogaotiantian/programs/codesnap/venv/lib/python3.6/site-packages/numpy/core/multiarray.py":["\"\"\"\nCreate the numpy.core.multiarray namespace for backward compatibility. In v1.16\nthe multiarray and umath c-extension modules were merged into a single\n_multiarray_umath extension module. So we replicate the old namespace\nby importing from the extension module.\n\n\"\"\"\n\nimport functools\nimport warnings\n\nfrom . import overrides\nfrom . import _multiarray_umath\nfrom ._multiarray_umath import * # noqa: F403\n# These imports are needed for backward compatibility,\n# do not change them. issue gh-15518\n# _get_ndarray_c_version is semi-public, on purpose not added to __all__\nfrom ._multiarray_umath import (\n _fastCopyAndTranspose, _flagdict, _insert, _reconstruct, _vec_string,\n _ARRAY_API, _monotonicity, _get_ndarray_c_version, _set_madvise_hugepage,\n )\n\n__all__ = [\n '_ARRAY_API', 'ALLOW_THREADS', 'BUFSIZE', 'CLIP', 'DATETIMEUNITS',\n 'ITEM_HASOBJECT', 'ITEM_IS_POINTER', 'LIST_PICKLE', 'MAXDIMS',\n 'MAY_SHARE_BOUNDS', 'MAY_SHARE_EXACT', 'NEEDS_INIT', 'NEEDS_PYAPI',\n 'RAISE', 'USE_GETITEM', 'USE_SETITEM', 'WRAP', '_fastCopyAndTranspose',\n '_flagdict', '_insert', '_reconstruct', '_vec_string', '_monotonicity',\n 'add_docstring', 'arange', 'array', 'bincount', 'broadcast',\n 'busday_count', 'busday_offset', 'busdaycalendar', 'can_cast',\n 'compare_chararrays', 'concatenate', 'copyto', 'correlate', 'correlate2',\n 'count_nonzero', 'c_einsum', 'datetime_as_string', 'datetime_data',\n 'digitize', 'dot', 'dragon4_positional', 'dragon4_scientific', 'dtype',\n 'empty', 'empty_like', 'error', 'flagsobj', 'flatiter', 'format_longfloat',\n 'frombuffer', 'fromfile', 'fromiter', 'fromstring', 'inner',\n 'interp', 'interp_complex', 'is_busday', 'lexsort',\n 'matmul', 'may_share_memory', 'min_scalar_type', 'ndarray', 'nditer',\n 'nested_iters', 'normalize_axis_index', 'packbits',\n 'promote_types', 'putmask', 'ravel_multi_index', 'result_type', 'scalar',\n 'set_datetimeparse_function', 'set_legacy_print_mode', 'set_numeric_ops',\n 'set_string_function', 'set_typeDict', 'shares_memory',\n 'tracemalloc_domain', 'typeinfo', 'unpackbits', 'unravel_index', 'vdot',\n 'where', 'zeros']\n\n# For backward compatibility, make sure pickle imports these functions from here\n_reconstruct.__module__ = 'numpy.core.multiarray'\nscalar.__module__ = 'numpy.core.multiarray'\n\n\narange.__module__ = 'numpy'\narray.__module__ = 'numpy'\ndatetime_data.__module__ = 'numpy'\nempty.__module__ = 'numpy'\nfrombuffer.__module__ = 'numpy'\nfromfile.__module__ = 'numpy'\nfromiter.__module__ = 'numpy'\nfrompyfunc.__module__ = 'numpy'\nfromstring.__module__ = 'numpy'\ngeterrobj.__module__ = 'numpy'\nmay_share_memory.__module__ = 'numpy'\nnested_iters.__module__ = 'numpy'\npromote_types.__module__ = 'numpy'\nset_numeric_ops.__module__ = 'numpy'\nseterrobj.__module__ = 'numpy'\nzeros.__module__ = 'numpy'\n\n\n# We can't verify dispatcher signatures because NumPy's C functions don't\n# support introspection.\narray_function_from_c_func_and_dispatcher = functools.partial(\n overrides.array_function_from_dispatcher,\n module='numpy', docs_from_dispatcher=True, verify=False)\n\n\n@array_function_from_c_func_and_dispatcher(_multiarray_umath.empty_like)\ndef empty_like(prototype, dtype=None, order=None, subok=None, shape=None):\n \"\"\"\n empty_like(prototype, dtype=None, order='K', subok=True, shape=None)\n\n Return a new array with the same shape and type as a given array.\n\n Parameters\n ----------\n prototype : array_like\n The shape and data-type of `prototype` define these same attributes\n of the returned array.\n dtype : data-type, optional\n Overrides the data type of the result.\n\n .. versionadded:: 1.6.0\n order : {'C', 'F', 'A', or 'K'}, optional\n Overrides the memory layout of the result. 'C' means C-order,\n 'F' means F-order, 'A' means 'F' if ``prototype`` is Fortran\n contiguous, 'C' otherwise. 'K' means match the layout of ``prototype``\n as closely as possible.\n\n .. versionadded:: 1.6.0\n subok : bool, optional.\n If True, then the newly created array will use the sub-class\n type of 'a', otherwise it will be a base-class array. Defaults\n to True.\n shape : int or sequence of ints, optional.\n Overrides the shape of the result. If order='K' and the number of\n dimensions is unchanged, will try to keep order, otherwise,\n order='C' is implied.\n\n .. versionadded:: 1.17.0\n\n Returns\n -------\n out : ndarray\n Array of uninitialized (arbitrary) data with the same\n shape and type as `prototype`.\n\n See Also\n --------\n ones_like : Return an array of ones with shape and type of input.\n zeros_like : Return an array of zeros with shape and type of input.\n full_like : Return a new array with shape of input filled with value.\n empty : Return a new uninitialized array.\n\n Notes\n -----\n This function does *not* initialize the returned array; to do that use\n `zeros_like` or `ones_like` instead. It may be marginally faster than\n the functions that do set the array values.\n\n Examples\n --------\n >>> a = ([1,2,3], [4,5,6]) # a is array-like\n >>> np.empty_like(a)\n array([[-1073741821, -1073741821, 3], # uninitialized\n [ 0, 0, -1073741821]])\n >>> a = np.array([[1., 2., 3.],[4.,5.,6.]])\n >>> np.empty_like(a)\n array([[ -2.00000715e+000, 1.48219694e-323, -2.00000572e+000], # uninitialized\n [ 4.38791518e-305, -2.00000715e+000, 4.17269252e-309]])\n\n \"\"\"\n return (prototype,)\n\n\n@array_function_from_c_func_and_dispatcher(_multiarray_umath.concatenate)\ndef concatenate(arrays, axis=None, out=None):\n \"\"\"\n concatenate((a1, a2, ...), axis=0, out=None)\n\n Join a sequence of arrays along an existing axis.\n\n Parameters\n ----------\n a1, a2, ... : sequence of array_like\n The arrays must have the same shape, except in the dimension\n corresponding to `axis` (the first, by default).\n axis : int, optional\n The axis along which the arrays will be joined. If axis is None,\n arrays are flattened before use. Default is 0.\n out : ndarray, optional\n If provided, the destination to place the result. The shape must be\n correct, matching that of what concatenate would have returned if no\n out argument were specified.\n\n Returns\n -------\n res : ndarray\n The concatenated array.\n\n See Also\n --------\n ma.concatenate : Concatenate function that preserves input masks.\n array_split : Split an array into multiple sub-arrays of equal or\n near-equal size.\n split : Split array into a list of multiple sub-arrays of equal size.\n hsplit : Split array into multiple sub-arrays horizontally (column wise).\n vsplit : Split array into multiple sub-arrays vertically (row wise).\n dsplit : Split array into multiple sub-arrays along the 3rd axis (depth).\n stack : Stack a sequence of arrays along a new axis.\n block : Assemble arrays from blocks.\n hstack : Stack arrays in sequence horizontally (column wise).\n vstack : Stack arrays in sequence vertically (row wise).\n dstack : Stack arrays in sequence depth wise (along third dimension).\n column_stack : Stack 1-D arrays as columns into a 2-D array.\n\n Notes\n -----\n When one or more of the arrays to be concatenated is a MaskedArray,\n this function will return a MaskedArray object instead of an ndarray,\n but the input masks are *not* preserved. In cases where a MaskedArray\n is expected as input, use the ma.concatenate function from the masked\n array module instead.\n\n Examples\n --------\n >>> a = np.array([[1, 2], [3, 4]])\n >>> b = np.array([[5, 6]])\n >>> np.concatenate((a, b), axis=0)\n array([[1, 2],\n [3, 4],\n [5, 6]])\n >>> np.concatenate((a, b.T), axis=1)\n array([[1, 2, 5],\n [3, 4, 6]])\n >>> np.concatenate((a, b), axis=None)\n array([1, 2, 3, 4, 5, 6])\n\n This function will not preserve masking of MaskedArray inputs.\n\n >>> a = np.ma.arange(3)\n >>> a[1] = np.ma.masked\n >>> b = np.arange(2, 5)\n >>> a\n masked_array(data=[0, --, 2],\n mask=[False, True, False],\n fill_value=999999)\n >>> b\n array([2, 3, 4])\n >>> np.concatenate([a, b])\n masked_array(data=[0, 1, 2, 2, 3, 4],\n mask=False,\n fill_value=999999)\n >>> np.ma.concatenate([a, b])\n masked_array(data=[0, --, 2, 2, 3, 4],\n mask=[False, True, False, False, False, False],\n fill_value=999999)\n\n \"\"\"\n if out is not None:\n # optimize for the typical case where only arrays is provided\n arrays = list(arrays)\n arrays.append(out)\n return arrays\n\n\n@array_function_from_c_func_and_dispatcher(_multiarray_umath.inner)\ndef inner(a, b):\n \"\"\"\n inner(a, b)\n\n Inner product of two arrays.\n\n Ordinary inner product of vectors for 1-D arrays (without complex\n conjugation), in higher dimensions a sum product over the last axes.\n\n Parameters\n ----------\n a, b : array_like\n If `a` and `b` are nonscalar, their last dimensions must match.\n\n Returns\n -------\n out : ndarray\n `out.shape = a.shape[:-1] + b.shape[:-1]`\n\n Raises\n ------\n ValueError\n If the last dimension of `a` and `b` has different size.\n\n See Also\n --------\n tensordot : Sum products over arbitrary axes.\n dot : Generalised matrix product, using second last dimension of `b`.\n einsum : Einstein summation convention.\n\n Notes\n -----\n For vectors (1-D arrays) it computes the ordinary inner-product::\n\n np.inner(a, b) = sum(a[:]*b[:])\n\n More generally, if `ndim(a) = r > 0` and `ndim(b) = s > 0`::\n\n np.inner(a, b) = np.tensordot(a, b, axes=(-1,-1))\n\n or explicitly::\n\n np.inner(a, b)[i0,...,ir-1,j0,...,js-1]\n = sum(a[i0,...,ir-1,:]*b[j0,...,js-1,:])\n\n In addition `a` or `b` may be scalars, in which case::\n\n np.inner(a,b) = a*b\n\n Examples\n --------\n Ordinary inner product for vectors:\n\n >>> a = np.array([1,2,3])\n >>> b = np.array([0,1,0])\n >>> np.inner(a, b)\n 2\n\n A multidimensional example:\n\n >>> a = np.arange(24).reshape((2,3,4))\n >>> b = np.arange(4)\n >>> np.inner(a, b)\n array([[ 14, 38, 62],\n [ 86, 110, 134]])\n\n An example where `b` is a scalar:\n\n >>> np.inner(np.eye(2), 7)\n array([[7., 0.],\n [0., 7.]])\n\n \"\"\"\n return (a, b)\n\n\n@array_function_from_c_func_and_dispatcher(_multiarray_umath.where)\ndef where(condition, x=None, y=None):\n \"\"\"\n where(condition, [x, y])\n\n Return elements chosen from `x` or `y` depending on `condition`.\n\n .. note::\n When only `condition` is provided, this function is a shorthand for\n ``np.asarray(condition).nonzero()``. Using `nonzero` directly should be\n preferred, as it behaves correctly for subclasses. The rest of this\n documentation covers only the case where all three arguments are\n provided.\n\n Parameters\n ----------\n condition : array_like, bool\n Where True, yield `x`, otherwise yield `y`.\n x, y : array_like\n Values from which to choose. `x`, `y` and `condition` need to be\n broadcastable to some shape.\n\n Returns\n -------\n out : ndarray\n An array with elements from `x` where `condition` is True, and elements\n from `y` elsewhere.\n\n See Also\n --------\n choose\n nonzero : The function that is called when x and y are omitted\n\n Notes\n -----\n If all the arrays are 1-D, `where` is equivalent to::\n\n [xv if c else yv\n for c, xv, yv in zip(condition, x, y)]\n\n Examples\n --------\n >>> a = np.arange(10)\n >>> a\n array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n >>> np.where(a < 5, a, 10*a)\n array([ 0, 1, 2, 3, 4, 50, 60, 70, 80, 90])\n\n This can be used on multidimensional arrays too:\n\n >>> np.where([[True, False], [True, True]],\n ... [[1, 2], [3, 4]],\n ... [[9, 8], [7, 6]])\n array([[1, 8],\n [3, 4]])\n\n The shapes of x, y, and the condition are broadcast together:\n\n >>> x, y = np.ogrid[:3, :4]\n >>> np.where(x < y, x, 10 + y) # both x and 10+y are broadcast\n array([[10, 0, 0, 0],\n [10, 11, 1, 1],\n [10, 11, 12, 2]])\n\n >>> a = np.array([[0, 1, 2],\n ... [0, 2, 4],\n ... [0, 3, 6]])\n >>> np.where(a < 4, a, -1) # -1 is broadcast\n array([[ 0, 1, 2],\n [ 0, 2, -1],\n [ 0, 3, -1]])\n \"\"\"\n return (condition, x, y)\n\n\n@array_function_from_c_func_and_dispatcher(_multiarray_umath.lexsort)\ndef lexsort(keys, axis=None):\n \"\"\"\n lexsort(keys, axis=-1)\n\n Perform an indirect stable sort using a sequence of keys.\n\n Given multiple sorting keys, which can be interpreted as columns in a\n spreadsheet, lexsort returns an array of integer indices that describes\n the sort order by multiple columns. The last key in the sequence is used\n for the primary sort order, the second-to-last key for the secondary sort\n order, and so on. The keys argument must be a sequence of objects that\n can be converted to arrays of the same shape. If a 2D array is provided\n for the keys argument, it's rows are interpreted as the sorting keys and\n sorting is according to the last row, second last row etc.\n\n Parameters\n ----------\n keys : (k, N) array or tuple containing k (N,)-shaped sequences\n The `k` different \"columns\" to be sorted. The last column (or row if\n `keys` is a 2D array) is the primary sort key.\n axis : int, optional\n Axis to be indirectly sorted. By default, sort over the last axis.\n\n Returns\n -------\n indices : (N,) ndarray of ints\n Array of indices that sort the keys along the specified axis.\n\n See Also\n --------\n argsort : Indirect sort.\n ndarray.sort : In-place sort.\n sort : Return a sorted copy of an array.\n\n Examples\n --------\n Sort names: first by surname, then by name.\n\n >>> surnames = ('Hertz', 'Galilei', 'Hertz')\n >>> first_names = ('Heinrich', 'Galileo', 'Gustav')\n >>> ind = np.lexsort((first_names, surnames))\n >>> ind\n array([1, 2, 0])\n\n >>> [surnames[i] + \", \" + first_names[i] for i in ind]\n ['Galilei, Galileo', 'Hertz, Gustav', 'Hertz, Heinrich']\n\n Sort two columns of numbers:\n\n >>> a = [1,5,1,4,3,4,4] # First column\n >>> b = [9,4,0,4,0,2,1] # Second column\n >>> ind = np.lexsort((b,a)) # Sort by a, then by b\n >>> ind\n array([2, 0, 4, 6, 5, 3, 1])\n\n >>> [(a[i],b[i]) for i in ind]\n [(1, 0), (1, 9), (3, 0), (4, 1), (4, 2), (4, 4), (5, 4)]\n\n Note that sorting is first according to the elements of ``a``.\n Secondary sorting is according to the elements of ``b``.\n\n A normal ``argsort`` would have yielded:\n\n >>> [(a[i],b[i]) for i in np.argsort(a)]\n [(1, 9), (1, 0), (3, 0), (4, 4), (4, 2), (4, 1), (5, 4)]\n\n Structured arrays are sorted lexically by ``argsort``:\n\n >>> x = np.array([(1,9), (5,4), (1,0), (4,4), (3,0), (4,2), (4,1)],\n ... dtype=np.dtype([('x', int), ('y', int)]))\n\n >>> np.argsort(x) # or np.argsort(x, order=('x', 'y'))\n array([2, 0, 4, 6, 5, 3, 1])\n\n \"\"\"\n if isinstance(keys, tuple):\n return keys\n else:\n return (keys,)\n\n\n@array_function_from_c_func_and_dispatcher(_multiarray_umath.can_cast)\ndef can_cast(from_, to, casting=None):\n \"\"\"\n can_cast(from_, to, casting='safe')\n\n Returns True if cast between data types can occur according to the\n casting rule. If from is a scalar or array scalar, also returns\n True if the scalar value can be cast without overflow or truncation\n to an integer.\n\n Parameters\n ----------\n from_ : dtype, dtype specifier, scalar, or array\n Data type, scalar, or array to cast from.\n to : dtype or dtype specifier\n Data type to cast to.\n casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional\n Controls what kind of data casting may occur.\n\n * 'no' means the data types should not be cast at all.\n * 'equiv' means only byte-order changes are allowed.\n * 'safe' means only casts which can preserve values are allowed.\n * 'same_kind' means only safe casts or casts within a kind,\n like float64 to float32, are allowed.\n * 'unsafe' means any data conversions may be done.\n\n Returns\n -------\n out : bool\n True if cast can occur according to the casting rule.\n\n Notes\n -----\n .. versionchanged:: 1.17.0\n Casting between a simple data type and a structured one is possible only\n for \"unsafe\" casting. Casting to multiple fields is allowed, but\n casting from multiple fields is not.\n\n .. versionchanged:: 1.9.0\n Casting from numeric to string types in 'safe' casting mode requires\n that the string dtype length is long enough to store the maximum\n integer/float value converted.\n\n See also\n --------\n dtype, result_type\n\n Examples\n --------\n Basic examples\n\n >>> np.can_cast(np.int32, np.int64)\n True\n >>> np.can_cast(np.float64, complex)\n True\n >>> np.can_cast(complex, float)\n False\n\n >>> np.can_cast('i8', 'f8')\n True\n >>> np.can_cast('i8', 'f4')\n False\n >>> np.can_cast('i4', 'S4')\n False\n\n Casting scalars\n\n >>> np.can_cast(100, 'i1')\n True\n >>> np.can_cast(150, 'i1')\n False\n >>> np.can_cast(150, 'u1')\n True\n\n >>> np.can_cast(3.5e100, np.float32)\n False\n >>> np.can_cast(1000.0, np.float32)\n True\n\n Array scalar checks the value, array does not\n\n >>> np.can_cast(np.array(1000.0), np.float32)\n True\n >>> np.can_cast(np.array([1000.0]), np.float32)\n False\n\n Using the casting rules\n\n >>> np.can_cast('i8', 'i8', 'no')\n True\n >>> np.can_cast('i8', 'no')\n False\n\n >>> np.can_cast('i8', 'equiv')\n True\n >>> np.can_cast('i8', 'equiv')\n False\n\n >>> np.can_cast('i8', 'safe')\n True\n >>> np.can_cast('i4', 'safe')\n False\n\n >>> np.can_cast('i4', 'same_kind')\n True\n >>> np.can_cast('u4', 'same_kind')\n False\n\n >>> np.can_cast('u4', 'unsafe')\n True\n\n \"\"\"\n return (from_,)\n\n\n@array_function_from_c_func_and_dispatcher(_multiarray_umath.min_scalar_type)\ndef min_scalar_type(a):\n \"\"\"\n min_scalar_type(a)\n\n For scalar ``a``, returns the data type with the smallest size\n and smallest scalar kind which can hold its value. For non-scalar\n array ``a``, returns the vector's dtype unmodified.\n\n Floating point values are not demoted to integers,\n and complex values are not demoted to floats.\n\n Parameters\n ----------\n a : scalar or array_like\n The value whose minimal data type is to be found.\n\n Returns\n -------\n out : dtype\n The minimal data type.\n\n Notes\n -----\n .. versionadded:: 1.6.0\n\n See Also\n --------\n result_type, promote_types, dtype, can_cast\n\n Examples\n --------\n >>> np.min_scalar_type(10)\n dtype('uint8')\n\n >>> np.min_scalar_type(-260)\n dtype('int16')\n\n >>> np.min_scalar_type(3.1)\n dtype('float16')\n\n >>> np.min_scalar_type(1e50)\n dtype('float64')\n\n >>> np.min_scalar_type(np.arange(4,dtype='f8'))\n dtype('float64')\n\n \"\"\"\n return (a,)\n\n\n@array_function_from_c_func_and_dispatcher(_multiarray_umath.result_type)\ndef result_type(*arrays_and_dtypes):\n \"\"\"\n result_type(*arrays_and_dtypes)\n\n Returns the type that results from applying the NumPy\n type promotion rules to the arguments.\n\n Type promotion in NumPy works similarly to the rules in languages\n like C++, with some slight differences. When both scalars and\n arrays are used, the array's type takes precedence and the actual value\n of the scalar is taken into account.\n\n For example, calculating 3*a, where a is an array of 32-bit floats,\n intuitively should result in a 32-bit float output. If the 3 is a\n 32-bit integer, the NumPy rules indicate it can't convert losslessly\n into a 32-bit float, so a 64-bit float should be the result type.\n By examining the value of the constant, '3', we see that it fits in\n an 8-bit integer, which can be cast losslessly into the 32-bit float.\n\n Parameters\n ----------\n arrays_and_dtypes : list of arrays and dtypes\n The operands of some operation whose result type is needed.\n\n Returns\n -------\n out : dtype\n The result type.\n\n See also\n --------\n dtype, promote_types, min_scalar_type, can_cast\n\n Notes\n -----\n .. versionadded:: 1.6.0\n\n The specific algorithm used is as follows.\n\n Categories are determined by first checking which of boolean,\n integer (int/uint), or floating point (float/complex) the maximum\n kind of all the arrays and the scalars are.\n\n If there are only scalars or the maximum category of the scalars\n is higher than the maximum category of the arrays,\n the data types are combined with :func:`promote_types`\n to produce the return value.\n\n Otherwise, `min_scalar_type` is called on each array, and\n the resulting data types are all combined with :func:`promote_types`\n to produce the return value.\n\n The set of int values is not a subset of the uint values for types\n with the same number of bits, something not reflected in\n :func:`min_scalar_type`, but handled as a special case in `result_type`.\n\n Examples\n --------\n >>> np.result_type(3, np.arange(7, dtype='i1'))\n dtype('int8')\n\n >>> np.result_type('i4', 'c8')\n dtype('complex128')\n\n >>> np.result_type(3.0, -2)\n dtype('float64')\n\n \"\"\"\n return arrays_and_dtypes\n\n\n@array_function_from_c_func_and_dispatcher(_multiarray_umath.dot)\ndef dot(a, b, out=None):\n \"\"\"\n dot(a, b, out=None)\n\n Dot product of two arrays. Specifically,\n\n - If both `a` and `b` are 1-D arrays, it is inner product of vectors\n (without complex conjugation).\n\n - If both `a` and `b` are 2-D arrays, it is matrix multiplication,\n but using :func:`matmul` or ``a @ b`` is preferred.\n\n - If either `a` or `b` is 0-D (scalar), it is equivalent to :func:`multiply`\n and using ``numpy.multiply(a, b)`` or ``a * b`` is preferred.\n\n - If `a` is an N-D array and `b` is a 1-D array, it is a sum product over\n the last axis of `a` and `b`.\n\n - If `a` is an N-D array and `b` is an M-D array (where ``M>=2``), it is a\n sum product over the last axis of `a` and the second-to-last axis of `b`::\n\n dot(a, b)[i,j,k,m] = sum(a[i,j,:] * b[k,:,m])\n\n Parameters\n ----------\n a : array_like\n First argument.\n b : array_like\n Second argument.\n out : ndarray, optional\n Output argument. This must have the exact kind that would be returned\n if it was not used. In particular, it must have the right type, must be\n C-contiguous, and its dtype must be the dtype that would be returned\n for `dot(a,b)`. This is a performance feature. Therefore, if these\n conditions are not met, an exception is raised, instead of attempting\n to be flexible.\n\n Returns\n -------\n output : ndarray\n Returns the dot product of `a` and `b`. If `a` and `b` are both\n scalars or both 1-D arrays then a scalar is returned; otherwise\n an array is returned.\n If `out` is given, then it is returned.\n\n Raises\n ------\n ValueError\n If the last dimension of `a` is not the same size as\n the second-to-last dimension of `b`.\n\n See Also\n --------\n vdot : Complex-conjugating dot product.\n tensordot : Sum products over arbitrary axes.\n einsum : Einstein summation convention.\n matmul : '@' operator as method with out parameter.\n\n Examples\n --------\n >>> np.dot(3, 4)\n 12\n\n Neither argument is complex-conjugated:\n\n >>> np.dot([2j, 3j], [2j, 3j])\n (-13+0j)\n\n For 2-D arrays it is the matrix product:\n\n >>> a = [[1, 0], [0, 1]]\n >>> b = [[4, 1], [2, 2]]\n >>> np.dot(a, b)\n array([[4, 1],\n [2, 2]])\n\n >>> a = np.arange(3*4*5*6).reshape((3,4,5,6))\n >>> b = np.arange(3*4*5*6)[::-1].reshape((5,4,6,3))\n >>> np.dot(a, b)[2,3,2,1,2,2]\n 499128\n >>> sum(a[2,3,2,:] * b[1,2,:,2])\n 499128\n\n \"\"\"\n return (a, b, out)\n\n\n@array_function_from_c_func_and_dispatcher(_multiarray_umath.vdot)\ndef vdot(a, b):\n \"\"\"\n vdot(a, b)\n\n Return the dot product of two vectors.\n\n The vdot(`a`, `b`) function handles complex numbers differently than\n dot(`a`, `b`). If the first argument is complex the complex conjugate\n of the first argument is used for the calculation of the dot product.\n\n Note that `vdot` handles multidimensional arrays differently than `dot`:\n it does *not* perform a matrix product, but flattens input arguments\n to 1-D vectors first. Consequently, it should only be used for vectors.\n\n Parameters\n ----------\n a : array_like\n If `a` is complex the complex conjugate is taken before calculation\n of the dot product.\n b : array_like\n Second argument to the dot product.\n\n Returns\n -------\n output : ndarray\n Dot product of `a` and `b`. Can be an int, float, or\n complex depending on the types of `a` and `b`.\n\n See Also\n --------\n dot : Return the dot product without using the complex conjugate of the\n first argument.\n\n Examples\n --------\n >>> a = np.array([1+2j,3+4j])\n >>> b = np.array([5+6j,7+8j])\n >>> np.vdot(a, b)\n (70-8j)\n >>> np.vdot(b, a)\n (70+8j)\n\n Note that higher-dimensional arrays are flattened!\n\n >>> a = np.array([[1, 4], [5, 6]])\n >>> b = np.array([[4, 1], [2, 2]])\n >>> np.vdot(a, b)\n 30\n >>> np.vdot(b, a)\n 30\n >>> 1*4 + 4*1 + 5*2 + 6*2\n 30\n\n \"\"\"\n return (a, b)\n\n\n@array_function_from_c_func_and_dispatcher(_multiarray_umath.bincount)\ndef bincount(x, weights=None, minlength=None):\n \"\"\"\n bincount(x, weights=None, minlength=0)\n\n Count number of occurrences of each value in array of non-negative ints.\n\n The number of bins (of size 1) is one larger than the largest value in\n `x`. If `minlength` is specified, there will be at least this number\n of bins in the output array (though it will be longer if necessary,\n depending on the contents of `x`).\n Each bin gives the number of occurrences of its index value in `x`.\n If `weights` is specified the input array is weighted by it, i.e. if a\n value ``n`` is found at position ``i``, ``out[n] += weight[i]`` instead\n of ``out[n] += 1``.\n\n Parameters\n ----------\n x : array_like, 1 dimension, nonnegative ints\n Input array.\n weights : array_like, optional\n Weights, array of the same shape as `x`.\n minlength : int, optional\n A minimum number of bins for the output array.\n\n .. versionadded:: 1.6.0\n\n Returns\n -------\n out : ndarray of ints\n The result of binning the input array.\n The length of `out` is equal to ``np.amax(x)+1``.\n\n Raises\n ------\n ValueError\n If the input is not 1-dimensional, or contains elements with negative\n values, or if `minlength` is negative.\n TypeError\n If the type of the input is float or complex.\n\n See Also\n --------\n histogram, digitize, unique\n\n Examples\n --------\n >>> np.bincount(np.arange(5))\n array([1, 1, 1, 1, 1])\n >>> np.bincount(np.array([0, 1, 1, 3, 2, 1, 7]))\n array([1, 3, 1, 1, 0, 0, 0, 1])\n\n >>> x = np.array([0, 1, 1, 3, 2, 1, 7, 23])\n >>> np.bincount(x).size == np.amax(x)+1\n True\n\n The input array needs to be of integer dtype, otherwise a\n TypeError is raised:\n\n >>> np.bincount(np.arange(5, dtype=float))\n Traceback (most recent call last):\n ...\n TypeError: Cannot cast array data from dtype('float64') to dtype('int64')\n according to the rule 'safe'\n\n A possible use of ``bincount`` is to perform sums over\n variable-size chunks of an array, using the ``weights`` keyword.\n\n >>> w = np.array([0.3, 0.5, 0.2, 0.7, 1., -0.6]) # weights\n >>> x = np.array([0, 1, 1, 2, 2, 2])\n >>> np.bincount(x, weights=w)\n array([ 0.3, 0.7, 1.1])\n\n \"\"\"\n return (x, weights)\n\n\n@array_function_from_c_func_and_dispatcher(_multiarray_umath.ravel_multi_index)\ndef ravel_multi_index(multi_index, dims, mode=None, order=None):\n \"\"\"\n ravel_multi_index(multi_index, dims, mode='raise', order='C')\n\n Converts a tuple of index arrays into an array of flat\n indices, applying boundary modes to the multi-index.\n\n Parameters\n ----------\n multi_index : tuple of array_like\n A tuple of integer arrays, one array for each dimension.\n dims : tuple of ints\n The shape of array into which the indices from ``multi_index`` apply.\n mode : {'raise', 'wrap', 'clip'}, optional\n Specifies how out-of-bounds indices are handled. Can specify\n either one mode or a tuple of modes, one mode per index.\n\n * 'raise' -- raise an error (default)\n * 'wrap' -- wrap around\n * 'clip' -- clip to the range\n\n In 'clip' mode, a negative index which would normally\n wrap will clip to 0 instead.\n order : {'C', 'F'}, optional\n Determines whether the multi-index should be viewed as\n indexing in row-major (C-style) or column-major\n (Fortran-style) order.\n\n Returns\n -------\n raveled_indices : ndarray\n An array of indices into the flattened version of an array\n of dimensions ``dims``.\n\n See Also\n --------\n unravel_index\n\n Notes\n -----\n .. versionadded:: 1.6.0\n\n Examples\n --------\n >>> arr = np.array([[3,6,6],[4,5,1]])\n >>> np.ravel_multi_index(arr, (7,6))\n array([22, 41, 37])\n >>> np.ravel_multi_index(arr, (7,6), order='F')\n array([31, 41, 13])\n >>> np.ravel_multi_index(arr, (4,6), mode='clip')\n array([22, 23, 19])\n >>> np.ravel_multi_index(arr, (4,4), mode=('clip','wrap'))\n array([12, 13, 13])\n\n >>> np.ravel_multi_index((3,1,4,1), (6,7,8,9))\n 1621\n \"\"\"\n return multi_index\n\n\n@array_function_from_c_func_and_dispatcher(_multiarray_umath.unravel_index)\ndef unravel_index(indices, shape=None, order=None, dims=None):\n \"\"\"\n unravel_index(indices, shape, order='C')\n\n Converts a flat index or array of flat indices into a tuple\n of coordinate arrays.\n\n Parameters\n ----------\n indices : array_like\n An integer array whose elements are indices into the flattened\n version of an array of dimensions ``shape``. Before version 1.6.0,\n this function accepted just one index value.\n shape : tuple of ints\n The shape of the array to use for unraveling ``indices``.\n\n .. versionchanged:: 1.16.0\n Renamed from ``dims`` to ``shape``.\n\n order : {'C', 'F'}, optional\n Determines whether the indices should be viewed as indexing in\n row-major (C-style) or column-major (Fortran-style) order.\n\n .. versionadded:: 1.6.0\n\n Returns\n -------\n unraveled_coords : tuple of ndarray\n Each array in the tuple has the same shape as the ``indices``\n array.\n\n See Also\n --------\n ravel_multi_index\n\n Examples\n --------\n >>> np.unravel_index([22, 41, 37], (7,6))\n (array([3, 6, 6]), array([4, 5, 1]))\n >>> np.unravel_index([31, 41, 13], (7,6), order='F')\n (array([3, 6, 6]), array([4, 5, 1]))\n\n >>> np.unravel_index(1621, (6,7,8,9))\n (3, 1, 4, 1)\n\n \"\"\"\n if dims is not None:\n warnings.warn(\"'shape' argument should be used instead of 'dims'\",\n DeprecationWarning, stacklevel=3)\n return (indices,)\n\n\n@array_function_from_c_func_and_dispatcher(_multiarray_umath.copyto)\ndef copyto(dst, src, casting=None, where=None):\n \"\"\"\n copyto(dst, src, casting='same_kind', where=True)\n\n Copies values from one array to another, broadcasting as necessary.\n\n Raises a TypeError if the `casting` rule is violated, and if\n `where` is provided, it selects which elements to copy.\n\n .. versionadded:: 1.7.0\n\n Parameters\n ----------\n dst : ndarray\n The array into which values are copied.\n src : array_like\n The array from which values are copied.\n casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional\n Controls what kind of data casting may occur when copying.\n\n * 'no' means the data types should not be cast at all.\n * 'equiv' means only byte-order changes are allowed.\n * 'safe' means only casts which can preserve values are allowed.\n * 'same_kind' means only safe casts or casts within a kind,\n like float64 to float32, are allowed.\n * 'unsafe' means any data conversions may be done.\n where : array_like of bool, optional\n A boolean array which is broadcasted to match the dimensions\n of `dst`, and selects elements to copy from `src` to `dst`\n wherever it contains the value True.\n \"\"\"\n return (dst, src, where)\n\n\n@array_function_from_c_func_and_dispatcher(_multiarray_umath.putmask)\ndef putmask(a, mask, values):\n \"\"\"\n putmask(a, mask, values)\n\n Changes elements of an array based on conditional and input values.\n\n Sets ``a.flat[n] = values[n]`` for each n where ``mask.flat[n]==True``.\n\n If `values` is not the same size as `a` and `mask` then it will repeat.\n This gives behavior different from ``a[mask] = values``.\n\n Parameters\n ----------\n a : array_like\n Target array.\n mask : array_like\n Boolean mask array. It has to be the same shape as `a`.\n values : array_like\n Values to put into `a` where `mask` is True. If `values` is smaller\n than `a` it will be repeated.\n\n See Also\n --------\n place, put, take, copyto\n\n Examples\n --------\n >>> x = np.arange(6).reshape(2, 3)\n >>> np.putmask(x, x>2, x**2)\n >>> x\n array([[ 0, 1, 2],\n [ 9, 16, 25]])\n\n If `values` is smaller than `a` it is repeated:\n\n >>> x = np.arange(5)\n >>> np.putmask(x, x>1, [-33, -44])\n >>> x\n array([ 0, 1, -33, -44, -33])\n\n \"\"\"\n return (a, mask, values)\n\n\n@array_function_from_c_func_and_dispatcher(_multiarray_umath.packbits)\ndef packbits(a, axis=None, bitorder='big'):\n \"\"\"\n packbits(a, axis=None, bitorder='big')\n\n Packs the elements of a binary-valued array into bits in a uint8 array.\n\n The result is padded to full bytes by inserting zero bits at the end.\n\n Parameters\n ----------\n a : array_like\n An array of integers or booleans whose elements should be packed to\n bits.\n axis : int, optional\n The dimension over which bit-packing is done.\n ``None`` implies packing the flattened array.\n bitorder : {'big', 'little'}, optional\n The order of the input bits. 'big' will mimic bin(val),\n ``[0, 0, 0, 0, 0, 0, 1, 1] => 3 = 0b00000011``, 'little' will\n reverse the order so ``[1, 1, 0, 0, 0, 0, 0, 0] => 3``.\n Defaults to 'big'.\n\n .. versionadded:: 1.17.0\n\n Returns\n -------\n packed : ndarray\n Array of type uint8 whose elements represent bits corresponding to the\n logical (0 or nonzero) value of the input elements. The shape of\n `packed` has the same number of dimensions as the input (unless `axis`\n is None, in which case the output is 1-D).\n\n See Also\n --------\n unpackbits: Unpacks elements of a uint8 array into a binary-valued output\n array.\n\n Examples\n --------\n >>> a = np.array([[[1,0,1],\n ... [0,1,0]],\n ... [[1,1,0],\n ... [0,0,1]]])\n >>> b = np.packbits(a, axis=-1)\n >>> b\n array([[[160],\n [ 64]],\n [[192],\n [ 32]]], dtype=uint8)\n\n Note that in binary 160 = 1010 0000, 64 = 0100 0000, 192 = 1100 0000,\n and 32 = 0010 0000.\n\n \"\"\"\n return (a,)\n\n\n@array_function_from_c_func_and_dispatcher(_multiarray_umath.unpackbits)\ndef unpackbits(a, axis=None, count=None, bitorder='big'):\n \"\"\"\n unpackbits(a, axis=None, count=None, bitorder='big')\n\n Unpacks elements of a uint8 array into a binary-valued output array.\n\n Each element of `a` represents a bit-field that should be unpacked\n into a binary-valued output array. The shape of the output array is\n either 1-D (if `axis` is ``None``) or the same shape as the input\n array with unpacking done along the axis specified.\n\n Parameters\n ----------\n a : ndarray, uint8 type\n Input array.\n axis : int, optional\n The dimension over which bit-unpacking is done.\n ``None`` implies unpacking the flattened array.\n count : int or None, optional\n The number of elements to unpack along `axis`, provided as a way\n of undoing the effect of packing a size that is not a multiple\n of eight. A non-negative number means to only unpack `count`\n bits. A negative number means to trim off that many bits from\n the end. ``None`` means to unpack the entire array (the\n default). Counts larger than the available number of bits will\n add zero padding to the output. Negative counts must not\n exceed the available number of bits.\n\n .. versionadded:: 1.17.0\n\n bitorder : {'big', 'little'}, optional\n The order of the returned bits. 'big' will mimic bin(val),\n ``3 = 0b00000011 => [0, 0, 0, 0, 0, 0, 1, 1]``, 'little' will reverse\n the order to ``[1, 1, 0, 0, 0, 0, 0, 0]``.\n Defaults to 'big'.\n\n .. versionadded:: 1.17.0\n\n Returns\n -------\n unpacked : ndarray, uint8 type\n The elements are binary-valued (0 or 1).\n\n See Also\n --------\n packbits : Packs the elements of a binary-valued array into bits in\n a uint8 array.\n\n Examples\n --------\n >>> a = np.array([[2], [7], [23]], dtype=np.uint8)\n >>> a\n array([[ 2],\n [ 7],\n [23]], dtype=uint8)\n >>> b = np.unpackbits(a, axis=1)\n >>> b\n array([[0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 1, 1, 1],\n [0, 0, 0, 1, 0, 1, 1, 1]], dtype=uint8)\n >>> c = np.unpackbits(a, axis=1, count=-3)\n >>> c\n array([[0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0]], dtype=uint8)\n\n >>> p = np.packbits(b, axis=0)\n >>> np.unpackbits(p, axis=0)\n array([[0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 1, 1, 1],\n [0, 0, 0, 1, 0, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)\n >>> np.array_equal(b, np.unpackbits(p, axis=0, count=b.shape[0]))\n True\n\n \"\"\"\n return (a,)\n\n\n@array_function_from_c_func_and_dispatcher(_multiarray_umath.shares_memory)\ndef shares_memory(a, b, max_work=None):\n \"\"\"\n shares_memory(a, b, max_work=None)\n\n Determine if two arrays share memory.\n\n .. warning::\n\n This function can be exponentially slow for some inputs, unless\n `max_work` is set to a finite number or ``MAY_SHARE_BOUNDS``.\n If in doubt, use `numpy.may_share_memory` instead.\n\n Parameters\n ----------\n a, b : ndarray\n Input arrays\n max_work : int, optional\n Effort to spend on solving the overlap problem (maximum number\n of candidate solutions to consider). The following special\n values are recognized:\n\n max_work=MAY_SHARE_EXACT (default)\n The problem is solved exactly. In this case, the function returns\n True only if there is an element shared between the arrays. Finding\n the exact solution may take extremely long in some cases.\n max_work=MAY_SHARE_BOUNDS\n Only the memory bounds of a and b are checked.\n\n Raises\n ------\n numpy.TooHardError\n Exceeded max_work.\n\n Returns\n -------\n out : bool\n\n See Also\n --------\n may_share_memory\n\n Examples\n --------\n >>> x = np.array([1, 2, 3, 4])\n >>> np.shares_memory(x, np.array([5, 6, 7]))\n False\n >>> np.shares_memory(x[::2], x)\n True\n >>> np.shares_memory(x[::2], x[1::2])\n False\n\n Checking whether two arrays share memory is NP-complete, and\n runtime may increase exponentially in the number of\n dimensions. Hence, `max_work` should generally be set to a finite\n number, as it is possible to construct examples that take\n extremely long to run:\n\n >>> from numpy.lib.stride_tricks import as_strided\n >>> x = np.zeros([192163377], dtype=np.int8)\n >>> x1 = as_strided(x, strides=(36674, 61119, 85569), shape=(1049, 1049, 1049))\n >>> x2 = as_strided(x[64023025:], strides=(12223, 12224, 1), shape=(1049, 1049, 1))\n >>> np.shares_memory(x1, x2, max_work=1000)\n Traceback (most recent call last):\n ...\n numpy.TooHardError: Exceeded max_work\n\n Running ``np.shares_memory(x1, x2)`` without `max_work` set takes\n around 1 minute for this case. It is possible to find problems\n that take still significantly longer.\n\n \"\"\"\n return (a, b)\n\n\n@array_function_from_c_func_and_dispatcher(_multiarray_umath.may_share_memory)\ndef may_share_memory(a, b, max_work=None):\n \"\"\"\n may_share_memory(a, b, max_work=None)\n\n Determine if two arrays might share memory\n\n A return of True does not necessarily mean that the two arrays\n share any element. It just means that they *might*.\n\n Only the memory bounds of a and b are checked by default.\n\n Parameters\n ----------\n a, b : ndarray\n Input arrays\n max_work : int, optional\n Effort to spend on solving the overlap problem. See\n `shares_memory` for details. Default for ``may_share_memory``\n is to do a bounds check.\n\n Returns\n -------\n out : bool\n\n See Also\n --------\n shares_memory\n\n Examples\n --------\n >>> np.may_share_memory(np.array([1,2]), np.array([5,8,9]))\n False\n >>> x = np.zeros([3, 4])\n >>> np.may_share_memory(x[:,0], x[:,1])\n True\n\n \"\"\"\n return (a, b)\n\n\n@array_function_from_c_func_and_dispatcher(_multiarray_umath.is_busday)\ndef is_busday(dates, weekmask=None, holidays=None, busdaycal=None, out=None):\n \"\"\"\n is_busday(dates, weekmask='1111100', holidays=None, busdaycal=None, out=None)\n\n Calculates which of the given dates are valid days, and which are not.\n\n .. versionadded:: 1.7.0\n\n Parameters\n ----------\n dates : array_like of datetime64[D]\n The array of dates to process.\n weekmask : str or array_like of bool, optional\n A seven-element array indicating which of Monday through Sunday are\n valid days. May be specified as a length-seven list or array, like\n [1,1,1,1,1,0,0]; a length-seven string, like '1111100'; or a string\n like \"Mon Tue Wed Thu Fri\", made up of 3-character abbreviations for\n weekdays, optionally separated by white space. Valid abbreviations\n are: Mon Tue Wed Thu Fri Sat Sun\n holidays : array_like of datetime64[D], optional\n An array of dates to consider as invalid dates. They may be\n specified in any order, and NaT (not-a-time) dates are ignored.\n This list is saved in a normalized form that is suited for\n fast calculations of valid days.\n busdaycal : busdaycalendar, optional\n A `busdaycalendar` object which specifies the valid days. If this\n parameter is provided, neither weekmask nor holidays may be\n provided.\n out : array of bool, optional\n If provided, this array is filled with the result.\n\n Returns\n -------\n out : array of bool\n An array with the same shape as ``dates``, containing True for\n each valid day, and False for each invalid day.\n\n See Also\n --------\n busdaycalendar: An object that specifies a custom set of valid days.\n busday_offset : Applies an offset counted in valid days.\n busday_count : Counts how many valid days are in a half-open date range.\n\n Examples\n --------\n >>> # The weekdays are Friday, Saturday, and Monday\n ... np.is_busday(['2011-07-01', '2011-07-02', '2011-07-18'],\n ... holidays=['2011-07-01', '2011-07-04', '2011-07-17'])\n array([False, False, True])\n \"\"\"\n return (dates, weekmask, holidays, out)\n\n\n@array_function_from_c_func_and_dispatcher(_multiarray_umath.busday_offset)\ndef busday_offset(dates, offsets, roll=None, weekmask=None, holidays=None,\n busdaycal=None, out=None):\n \"\"\"\n busday_offset(dates, offsets, roll='raise', weekmask='1111100', holidays=None, busdaycal=None, out=None)\n\n First adjusts the date to fall on a valid day according to\n the ``roll`` rule, then applies offsets to the given dates\n counted in valid days.\n\n .. versionadded:: 1.7.0\n\n Parameters\n ----------\n dates : array_like of datetime64[D]\n The array of dates to process.\n offsets : array_like of int\n The array of offsets, which is broadcast with ``dates``.\n roll : {'raise', 'nat', 'forward', 'following', 'backward', 'preceding', 'modifiedfollowing', 'modifiedpreceding'}, optional\n How to treat dates that do not fall on a valid day. The default\n is 'raise'.\n\n * 'raise' means to raise an exception for an invalid day.\n * 'nat' means to return a NaT (not-a-time) for an invalid day.\n * 'forward' and 'following' mean to take the first valid day\n later in time.\n * 'backward' and 'preceding' mean to take the first valid day\n earlier in time.\n * 'modifiedfollowing' means to take the first valid day\n later in time unless it is across a Month boundary, in which\n case to take the first valid day earlier in time.\n * 'modifiedpreceding' means to take the first valid day\n earlier in time unless it is across a Month boundary, in which\n case to take the first valid day later in time.\n weekmask : str or array_like of bool, optional\n A seven-element array indicating which of Monday through Sunday are\n valid days. May be specified as a length-seven list or array, like\n [1,1,1,1,1,0,0]; a length-seven string, like '1111100'; or a string\n like \"Mon Tue Wed Thu Fri\", made up of 3-character abbreviations for\n weekdays, optionally separated by white space. Valid abbreviations\n are: Mon Tue Wed Thu Fri Sat Sun\n holidays : array_like of datetime64[D], optional\n An array of dates to consider as invalid dates. They may be\n specified in any order, and NaT (not-a-time) dates are ignored.\n This list is saved in a normalized form that is suited for\n fast calculations of valid days.\n busdaycal : busdaycalendar, optional\n A `busdaycalendar` object which specifies the valid days. If this\n parameter is provided, neither weekmask nor holidays may be\n provided.\n out : array of datetime64[D], optional\n If provided, this array is filled with the result.\n\n Returns\n -------\n out : array of datetime64[D]\n An array with a shape from broadcasting ``dates`` and ``offsets``\n together, containing the dates with offsets applied.\n\n See Also\n --------\n busdaycalendar: An object that specifies a custom set of valid days.\n is_busday : Returns a boolean array indicating valid days.\n busday_count : Counts how many valid days are in a half-open date range.\n\n Examples\n --------\n >>> # First business day in October 2011 (not accounting for holidays)\n ... np.busday_offset('2011-10', 0, roll='forward')\n numpy.datetime64('2011-10-03')\n >>> # Last business day in February 2012 (not accounting for holidays)\n ... np.busday_offset('2012-03', -1, roll='forward')\n numpy.datetime64('2012-02-29')\n >>> # Third Wednesday in January 2011\n ... np.busday_offset('2011-01', 2, roll='forward', weekmask='Wed')\n numpy.datetime64('2011-01-19')\n >>> # 2012 Mother's Day in Canada and the U.S.\n ... np.busday_offset('2012-05', 1, roll='forward', weekmask='Sun')\n numpy.datetime64('2012-05-13')\n\n >>> # First business day on or after a date\n ... np.busday_offset('2011-03-20', 0, roll='forward')\n numpy.datetime64('2011-03-21')\n >>> np.busday_offset('2011-03-22', 0, roll='forward')\n numpy.datetime64('2011-03-22')\n >>> # First business day after a date\n ... np.busday_offset('2011-03-20', 1, roll='backward')\n numpy.datetime64('2011-03-21')\n >>> np.busday_offset('2011-03-22', 1, roll='backward')\n numpy.datetime64('2011-03-23')\n \"\"\"\n return (dates, offsets, weekmask, holidays, out)\n\n\n@array_function_from_c_func_and_dispatcher(_multiarray_umath.busday_count)\ndef busday_count(begindates, enddates, weekmask=None, holidays=None,\n busdaycal=None, out=None):\n \"\"\"\n busday_count(begindates, enddates, weekmask='1111100', holidays=[], busdaycal=None, out=None)\n\n Counts the number of valid days between `begindates` and\n `enddates`, not including the day of `enddates`.\n\n If ``enddates`` specifies a date value that is earlier than the\n corresponding ``begindates`` date value, the count will be negative.\n\n .. versionadded:: 1.7.0\n\n Parameters\n ----------\n begindates : array_like of datetime64[D]\n The array of the first dates for counting.\n enddates : array_like of datetime64[D]\n The array of the end dates for counting, which are excluded\n from the count themselves.\n weekmask : str or array_like of bool, optional\n A seven-element array indicating which of Monday through Sunday are\n valid days. May be specified as a length-seven list or array, like\n [1,1,1,1,1,0,0]; a length-seven string, like '1111100'; or a string\n like \"Mon Tue Wed Thu Fri\", made up of 3-character abbreviations for\n weekdays, optionally separated by white space. Valid abbreviations\n are: Mon Tue Wed Thu Fri Sat Sun\n holidays : array_like of datetime64[D], optional\n An array of dates to consider as invalid dates. They may be\n specified in any order, and NaT (not-a-time) dates are ignored.\n This list is saved in a normalized form that is suited for\n fast calculations of valid days.\n busdaycal : busdaycalendar, optional\n A `busdaycalendar` object which specifies the valid days. If this\n parameter is provided, neither weekmask nor holidays may be\n provided.\n out : array of int, optional\n If provided, this array is filled with the result.\n\n Returns\n -------\n out : array of int\n An array with a shape from broadcasting ``begindates`` and ``enddates``\n together, containing the number of valid days between\n the begin and end dates.\n\n See Also\n --------\n busdaycalendar: An object that specifies a custom set of valid days.\n is_busday : Returns a boolean array indicating valid days.\n busday_offset : Applies an offset counted in valid days.\n\n Examples\n --------\n >>> # Number of weekdays in January 2011\n ... np.busday_count('2011-01', '2011-02')\n 21\n >>> # Number of weekdays in 2011\n >>> np.busday_count('2011', '2012')\n 260\n >>> # Number of Saturdays in 2011\n ... np.busday_count('2011', '2012', weekmask='Sat')\n 53\n \"\"\"\n return (begindates, enddates, weekmask, holidays, out)\n\n\n@array_function_from_c_func_and_dispatcher(\n _multiarray_umath.datetime_as_string)\ndef datetime_as_string(arr, unit=None, timezone=None, casting=None):\n \"\"\"\n datetime_as_string(arr, unit=None, timezone='naive', casting='same_kind')\n\n Convert an array of datetimes into an array of strings.\n\n Parameters\n ----------\n arr : array_like of datetime64\n The array of UTC timestamps to format.\n unit : str\n One of None, 'auto', or a :ref:`datetime unit `.\n timezone : {'naive', 'UTC', 'local'} or tzinfo\n Timezone information to use when displaying the datetime. If 'UTC', end\n with a Z to indicate UTC time. If 'local', convert to the local timezone\n first, and suffix with a +-#### timezone offset. If a tzinfo object,\n then do as with 'local', but use the specified timezone.\n casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}\n Casting to allow when changing between datetime units.\n\n Returns\n -------\n str_arr : ndarray\n An array of strings the same shape as `arr`.\n\n Examples\n --------\n >>> import pytz\n >>> d = np.arange('2002-10-27T04:30', 4*60, 60, dtype='M8[m]')\n >>> d\n array(['2002-10-27T04:30', '2002-10-27T05:30', '2002-10-27T06:30',\n '2002-10-27T07:30'], dtype='datetime64[m]')\n\n Setting the timezone to UTC shows the same information, but with a Z suffix\n\n >>> np.datetime_as_string(d, timezone='UTC')\n array(['2002-10-27T04:30Z', '2002-10-27T05:30Z', '2002-10-27T06:30Z',\n '2002-10-27T07:30Z'], dtype='>> np.datetime_as_string(d, timezone=pytz.timezone('US/Eastern'))\n array(['2002-10-27T00:30-0400', '2002-10-27T01:30-0400',\n '2002-10-27T01:30-0500', '2002-10-27T02:30-0500'], dtype='>> np.datetime_as_string(d, unit='h')\n array(['2002-10-27T04', '2002-10-27T05', '2002-10-27T06', '2002-10-27T07'],\n dtype='>> np.datetime_as_string(d, unit='s')\n array(['2002-10-27T04:30:00', '2002-10-27T05:30:00', '2002-10-27T06:30:00',\n '2002-10-27T07:30:00'], dtype='>> np.datetime_as_string(d, unit='h', casting='safe')\n Traceback (most recent call last):\n ...\n TypeError: Cannot create a datetime string as units 'h' from a NumPy\n datetime with units 'm' according to the rule 'safe'\n \"\"\"\n return (arr,)\n",1662],"/home/gaogaotiantian/programs/codesnap/venv/lib/python3.6/site-packages/numpy/core/fromnumeric.py":["\"\"\"Module containing non-deprecated functions borrowed from Numeric.\n\n\"\"\"\nimport functools\nimport types\nimport warnings\n\nimport numpy as np\nfrom . import multiarray as mu\nfrom . import overrides\nfrom . import umath as um\nfrom . import numerictypes as nt\nfrom ._asarray import asarray, array, asanyarray\nfrom .multiarray import concatenate\nfrom . import _methods\n\n_dt_ = nt.sctype2char\n\n# functions that are methods\n__all__ = [\n 'alen', 'all', 'alltrue', 'amax', 'amin', 'any', 'argmax',\n 'argmin', 'argpartition', 'argsort', 'around', 'choose', 'clip',\n 'compress', 'cumprod', 'cumproduct', 'cumsum', 'diagonal', 'mean',\n 'ndim', 'nonzero', 'partition', 'prod', 'product', 'ptp', 'put',\n 'ravel', 'repeat', 'reshape', 'resize', 'round_',\n 'searchsorted', 'shape', 'size', 'sometrue', 'sort', 'squeeze',\n 'std', 'sum', 'swapaxes', 'take', 'trace', 'transpose', 'var',\n]\n\n_gentype = types.GeneratorType\n# save away Python sum\n_sum_ = sum\n\narray_function_dispatch = functools.partial(\n overrides.array_function_dispatch, module='numpy')\n\n\n# functions that are now methods\ndef _wrapit(obj, method, *args, **kwds):\n try:\n wrap = obj.__array_wrap__\n except AttributeError:\n wrap = None\n result = getattr(asarray(obj), method)(*args, **kwds)\n if wrap:\n if not isinstance(result, mu.ndarray):\n result = asarray(result)\n result = wrap(result)\n return result\n\n\ndef _wrapfunc(obj, method, *args, **kwds):\n bound = getattr(obj, method, None)\n if bound is None:\n return _wrapit(obj, method, *args, **kwds)\n\n try:\n return bound(*args, **kwds)\n except TypeError:\n # A TypeError occurs if the object does have such a method in its\n # class, but its signature is not identical to that of NumPy's. This\n # situation has occurred in the case of a downstream library like\n # 'pandas'.\n #\n # Call _wrapit from within the except clause to ensure a potential\n # exception has a traceback chain.\n return _wrapit(obj, method, *args, **kwds)\n\n\ndef _wrapreduction(obj, ufunc, method, axis, dtype, out, **kwargs):\n passkwargs = {k: v for k, v in kwargs.items()\n if v is not np._NoValue}\n\n if type(obj) is not mu.ndarray:\n try:\n reduction = getattr(obj, method)\n except AttributeError:\n pass\n else:\n # This branch is needed for reductions like any which don't\n # support a dtype.\n if dtype is not None:\n return reduction(axis=axis, dtype=dtype, out=out, **passkwargs)\n else:\n return reduction(axis=axis, out=out, **passkwargs)\n\n return ufunc.reduce(obj, axis, dtype, out, **passkwargs)\n\n\ndef _take_dispatcher(a, indices, axis=None, out=None, mode=None):\n return (a, out)\n\n\n@array_function_dispatch(_take_dispatcher)\ndef take(a, indices, axis=None, out=None, mode='raise'):\n \"\"\"\n Take elements from an array along an axis.\n\n When axis is not None, this function does the same thing as \"fancy\"\n indexing (indexing arrays using arrays); however, it can be easier to use\n if you need elements along a given axis. A call such as\n ``np.take(arr, indices, axis=3)`` is equivalent to\n ``arr[:,:,:,indices,...]``.\n\n Explained without fancy indexing, this is equivalent to the following use\n of `ndindex`, which sets each of ``ii``, ``jj``, and ``kk`` to a tuple of\n indices::\n\n Ni, Nk = a.shape[:axis], a.shape[axis+1:]\n Nj = indices.shape\n for ii in ndindex(Ni):\n for jj in ndindex(Nj):\n for kk in ndindex(Nk):\n out[ii + jj + kk] = a[ii + (indices[jj],) + kk]\n\n Parameters\n ----------\n a : array_like (Ni..., M, Nk...)\n The source array.\n indices : array_like (Nj...)\n The indices of the values to extract.\n\n .. versionadded:: 1.8.0\n\n Also allow scalars for indices.\n axis : int, optional\n The axis over which to select values. By default, the flattened\n input array is used.\n out : ndarray, optional (Ni..., Nj..., Nk...)\n If provided, the result will be placed in this array. It should\n be of the appropriate shape and dtype. Note that `out` is always\n buffered if `mode='raise'`; use other modes for better performance.\n mode : {'raise', 'wrap', 'clip'}, optional\n Specifies how out-of-bounds indices will behave.\n\n * 'raise' -- raise an error (default)\n * 'wrap' -- wrap around\n * 'clip' -- clip to the range\n\n 'clip' mode means that all indices that are too large are replaced\n by the index that addresses the last element along that axis. Note\n that this disables indexing with negative numbers.\n\n Returns\n -------\n out : ndarray (Ni..., Nj..., Nk...)\n The returned array has the same type as `a`.\n\n See Also\n --------\n compress : Take elements using a boolean mask\n ndarray.take : equivalent method\n take_along_axis : Take elements by matching the array and the index arrays\n\n Notes\n -----\n\n By eliminating the inner loop in the description above, and using `s_` to\n build simple slice objects, `take` can be expressed in terms of applying\n fancy indexing to each 1-d slice::\n\n Ni, Nk = a.shape[:axis], a.shape[axis+1:]\n for ii in ndindex(Ni):\n for kk in ndindex(Nj):\n out[ii + s_[...,] + kk] = a[ii + s_[:,] + kk][indices]\n\n For this reason, it is equivalent to (but faster than) the following use\n of `apply_along_axis`::\n\n out = np.apply_along_axis(lambda a_1d: a_1d[indices], axis, a)\n\n Examples\n --------\n >>> a = [4, 3, 5, 7, 6, 8]\n >>> indices = [0, 1, 4]\n >>> np.take(a, indices)\n array([4, 3, 6])\n\n In this example if `a` is an ndarray, \"fancy\" indexing can be used.\n\n >>> a = np.array(a)\n >>> a[indices]\n array([4, 3, 6])\n\n If `indices` is not one dimensional, the output also has these dimensions.\n\n >>> np.take(a, [[0, 1], [2, 3]])\n array([[4, 3],\n [5, 7]])\n \"\"\"\n return _wrapfunc(a, 'take', indices, axis=axis, out=out, mode=mode)\n\n\ndef _reshape_dispatcher(a, newshape, order=None):\n return (a,)\n\n\n# not deprecated --- copy if necessary, view otherwise\n@array_function_dispatch(_reshape_dispatcher)\ndef reshape(a, newshape, order='C'):\n \"\"\"\n Gives a new shape to an array without changing its data.\n\n Parameters\n ----------\n a : array_like\n Array to be reshaped.\n newshape : int or tuple of ints\n The new shape should be compatible with the original shape. If\n an integer, then the result will be a 1-D array of that length.\n One shape dimension can be -1. In this case, the value is\n inferred from the length of the array and remaining dimensions.\n order : {'C', 'F', 'A'}, optional\n Read the elements of `a` using this index order, and place the\n elements into the reshaped array using this index order. 'C'\n means to read / write the elements using C-like index order,\n with the last axis index changing fastest, back to the first\n axis index changing slowest. 'F' means to read / write the\n elements using Fortran-like index order, with the first index\n changing fastest, and the last index changing slowest. Note that\n the 'C' and 'F' options take no account of the memory layout of\n the underlying array, and only refer to the order of indexing.\n 'A' means to read / write the elements in Fortran-like index\n order if `a` is Fortran *contiguous* in memory, C-like order\n otherwise.\n\n Returns\n -------\n reshaped_array : ndarray\n This will be a new view object if possible; otherwise, it will\n be a copy. Note there is no guarantee of the *memory layout* (C- or\n Fortran- contiguous) of the returned array.\n\n See Also\n --------\n ndarray.reshape : Equivalent method.\n\n Notes\n -----\n It is not always possible to change the shape of an array without\n copying the data. If you want an error to be raised when the data is copied,\n you should assign the new shape to the shape attribute of the array::\n\n >>> a = np.zeros((10, 2))\n\n # A transpose makes the array non-contiguous\n >>> b = a.T\n\n # Taking a view makes it possible to modify the shape without modifying\n # the initial object.\n >>> c = b.view()\n >>> c.shape = (20)\n Traceback (most recent call last):\n ...\n AttributeError: Incompatible shape for in-place modification. Use\n `.reshape()` to make a copy with the desired shape.\n\n The `order` keyword gives the index ordering both for *fetching* the values\n from `a`, and then *placing* the values into the output array.\n For example, let's say you have an array:\n\n >>> a = np.arange(6).reshape((3, 2))\n >>> a\n array([[0, 1],\n [2, 3],\n [4, 5]])\n\n You can think of reshaping as first raveling the array (using the given\n index order), then inserting the elements from the raveled array into the\n new array using the same kind of index ordering as was used for the\n raveling.\n\n >>> np.reshape(a, (2, 3)) # C-like index ordering\n array([[0, 1, 2],\n [3, 4, 5]])\n >>> np.reshape(np.ravel(a), (2, 3)) # equivalent to C ravel then C reshape\n array([[0, 1, 2],\n [3, 4, 5]])\n >>> np.reshape(a, (2, 3), order='F') # Fortran-like index ordering\n array([[0, 4, 3],\n [2, 1, 5]])\n >>> np.reshape(np.ravel(a, order='F'), (2, 3), order='F')\n array([[0, 4, 3],\n [2, 1, 5]])\n\n Examples\n --------\n >>> a = np.array([[1,2,3], [4,5,6]])\n >>> np.reshape(a, 6)\n array([1, 2, 3, 4, 5, 6])\n >>> np.reshape(a, 6, order='F')\n array([1, 4, 2, 5, 3, 6])\n\n >>> np.reshape(a, (3,-1)) # the unspecified value is inferred to be 2\n array([[1, 2],\n [3, 4],\n [5, 6]])\n \"\"\"\n return _wrapfunc(a, 'reshape', newshape, order=order)\n\n\ndef _choose_dispatcher(a, choices, out=None, mode=None):\n yield a\n yield from choices\n yield out\n\n\n@array_function_dispatch(_choose_dispatcher)\ndef choose(a, choices, out=None, mode='raise'):\n \"\"\"\n Construct an array from an index array and a set of arrays to choose from.\n\n First of all, if confused or uncertain, definitely look at the Examples -\n in its full generality, this function is less simple than it might\n seem from the following code description (below ndi =\n `numpy.lib.index_tricks`):\n\n ``np.choose(a,c) == np.array([c[a[I]][I] for I in ndi.ndindex(a.shape)])``.\n\n But this omits some subtleties. Here is a fully general summary:\n\n Given an \"index\" array (`a`) of integers and a sequence of `n` arrays\n (`choices`), `a` and each choice array are first broadcast, as necessary,\n to arrays of a common shape; calling these *Ba* and *Bchoices[i], i =\n 0,...,n-1* we have that, necessarily, ``Ba.shape == Bchoices[i].shape``\n for each `i`. Then, a new array with shape ``Ba.shape`` is created as\n follows:\n\n * if ``mode=raise`` (the default), then, first of all, each element of\n `a` (and thus `Ba`) must be in the range `[0, n-1]`; now, suppose that\n `i` (in that range) is the value at the `(j0, j1, ..., jm)` position\n in `Ba` - then the value at the same position in the new array is the\n value in `Bchoices[i]` at that same position;\n\n * if ``mode=wrap``, values in `a` (and thus `Ba`) may be any (signed)\n integer; modular arithmetic is used to map integers outside the range\n `[0, n-1]` back into that range; and then the new array is constructed\n as above;\n\n * if ``mode=clip``, values in `a` (and thus `Ba`) may be any (signed)\n integer; negative integers are mapped to 0; values greater than `n-1`\n are mapped to `n-1`; and then the new array is constructed as above.\n\n Parameters\n ----------\n a : int array\n This array must contain integers in `[0, n-1]`, where `n` is the number\n of choices, unless ``mode=wrap`` or ``mode=clip``, in which cases any\n integers are permissible.\n choices : sequence of arrays\n Choice arrays. `a` and all of the choices must be broadcastable to the\n same shape. If `choices` is itself an array (not recommended), then\n its outermost dimension (i.e., the one corresponding to\n ``choices.shape[0]``) is taken as defining the \"sequence\".\n out : array, optional\n If provided, the result will be inserted into this array. It should\n be of the appropriate shape and dtype. Note that `out` is always\n buffered if `mode='raise'`; use other modes for better performance.\n mode : {'raise' (default), 'wrap', 'clip'}, optional\n Specifies how indices outside `[0, n-1]` will be treated:\n\n * 'raise' : an exception is raised\n * 'wrap' : value becomes value mod `n`\n * 'clip' : values < 0 are mapped to 0, values > n-1 are mapped to n-1\n\n Returns\n -------\n merged_array : array\n The merged result.\n\n Raises\n ------\n ValueError: shape mismatch\n If `a` and each choice array are not all broadcastable to the same\n shape.\n\n See Also\n --------\n ndarray.choose : equivalent method\n numpy.take_along_axis : Preferable if `choices` is an array\n\n Notes\n -----\n To reduce the chance of misinterpretation, even though the following\n \"abuse\" is nominally supported, `choices` should neither be, nor be\n thought of as, a single array, i.e., the outermost sequence-like container\n should be either a list or a tuple.\n\n Examples\n --------\n\n >>> choices = [[0, 1, 2, 3], [10, 11, 12, 13],\n ... [20, 21, 22, 23], [30, 31, 32, 33]]\n >>> np.choose([2, 3, 1, 0], choices\n ... # the first element of the result will be the first element of the\n ... # third (2+1) \"array\" in choices, namely, 20; the second element\n ... # will be the second element of the fourth (3+1) choice array, i.e.,\n ... # 31, etc.\n ... )\n array([20, 31, 12, 3])\n >>> np.choose([2, 4, 1, 0], choices, mode='clip') # 4 goes to 3 (4-1)\n array([20, 31, 12, 3])\n >>> # because there are 4 choice arrays\n >>> np.choose([2, 4, 1, 0], choices, mode='wrap') # 4 goes to (4 mod 4)\n array([20, 1, 12, 3])\n >>> # i.e., 0\n\n A couple examples illustrating how choose broadcasts:\n\n >>> a = [[1, 0, 1], [0, 1, 0], [1, 0, 1]]\n >>> choices = [-10, 10]\n >>> np.choose(a, choices)\n array([[ 10, -10, 10],\n [-10, 10, -10],\n [ 10, -10, 10]])\n\n >>> # With thanks to Anne Archibald\n >>> a = np.array([0, 1]).reshape((2,1,1))\n >>> c1 = np.array([1, 2, 3]).reshape((1,3,1))\n >>> c2 = np.array([-1, -2, -3, -4, -5]).reshape((1,1,5))\n >>> np.choose(a, (c1, c2)) # result is 2x3x5, res[0,:,:]=c1, res[1,:,:]=c2\n array([[[ 1, 1, 1, 1, 1],\n [ 2, 2, 2, 2, 2],\n [ 3, 3, 3, 3, 3]],\n [[-1, -2, -3, -4, -5],\n [-1, -2, -3, -4, -5],\n [-1, -2, -3, -4, -5]]])\n\n \"\"\"\n return _wrapfunc(a, 'choose', choices, out=out, mode=mode)\n\n\ndef _repeat_dispatcher(a, repeats, axis=None):\n return (a,)\n\n\n@array_function_dispatch(_repeat_dispatcher)\ndef repeat(a, repeats, axis=None):\n \"\"\"\n Repeat elements of an array.\n\n Parameters\n ----------\n a : array_like\n Input array.\n repeats : int or array of ints\n The number of repetitions for each element. `repeats` is broadcasted\n to fit the shape of the given axis.\n axis : int, optional\n The axis along which to repeat values. By default, use the\n flattened input array, and return a flat output array.\n\n Returns\n -------\n repeated_array : ndarray\n Output array which has the same shape as `a`, except along\n the given axis.\n\n See Also\n --------\n tile : Tile an array.\n\n Examples\n --------\n >>> np.repeat(3, 4)\n array([3, 3, 3, 3])\n >>> x = np.array([[1,2],[3,4]])\n >>> np.repeat(x, 2)\n array([1, 1, 2, 2, 3, 3, 4, 4])\n >>> np.repeat(x, 3, axis=1)\n array([[1, 1, 1, 2, 2, 2],\n [3, 3, 3, 4, 4, 4]])\n >>> np.repeat(x, [1, 2], axis=0)\n array([[1, 2],\n [3, 4],\n [3, 4]])\n\n \"\"\"\n return _wrapfunc(a, 'repeat', repeats, axis=axis)\n\n\ndef _put_dispatcher(a, ind, v, mode=None):\n return (a, ind, v)\n\n\n@array_function_dispatch(_put_dispatcher)\ndef put(a, ind, v, mode='raise'):\n \"\"\"\n Replaces specified elements of an array with given values.\n\n The indexing works on the flattened target array. `put` is roughly\n equivalent to:\n\n ::\n\n a.flat[ind] = v\n\n Parameters\n ----------\n a : ndarray\n Target array.\n ind : array_like\n Target indices, interpreted as integers.\n v : array_like\n Values to place in `a` at target indices. If `v` is shorter than\n `ind` it will be repeated as necessary.\n mode : {'raise', 'wrap', 'clip'}, optional\n Specifies how out-of-bounds indices will behave.\n\n * 'raise' -- raise an error (default)\n * 'wrap' -- wrap around\n * 'clip' -- clip to the range\n\n 'clip' mode means that all indices that are too large are replaced\n by the index that addresses the last element along that axis. Note\n that this disables indexing with negative numbers. In 'raise' mode,\n if an exception occurs the target array may still be modified.\n\n See Also\n --------\n putmask, place\n put_along_axis : Put elements by matching the array and the index arrays\n\n Examples\n --------\n >>> a = np.arange(5)\n >>> np.put(a, [0, 2], [-44, -55])\n >>> a\n array([-44, 1, -55, 3, 4])\n\n >>> a = np.arange(5)\n >>> np.put(a, 22, -5, mode='clip')\n >>> a\n array([ 0, 1, 2, 3, -5])\n\n \"\"\"\n try:\n put = a.put\n except AttributeError:\n raise TypeError(\"argument 1 must be numpy.ndarray, \"\n \"not {name}\".format(name=type(a).__name__))\n\n return put(ind, v, mode=mode)\n\n\ndef _swapaxes_dispatcher(a, axis1, axis2):\n return (a,)\n\n\n@array_function_dispatch(_swapaxes_dispatcher)\ndef swapaxes(a, axis1, axis2):\n \"\"\"\n Interchange two axes of an array.\n\n Parameters\n ----------\n a : array_like\n Input array.\n axis1 : int\n First axis.\n axis2 : int\n Second axis.\n\n Returns\n -------\n a_swapped : ndarray\n For NumPy >= 1.10.0, if `a` is an ndarray, then a view of `a` is\n returned; otherwise a new array is created. For earlier NumPy\n versions a view of `a` is returned only if the order of the\n axes is changed, otherwise the input array is returned.\n\n Examples\n --------\n >>> x = np.array([[1,2,3]])\n >>> np.swapaxes(x,0,1)\n array([[1],\n [2],\n [3]])\n\n >>> x = np.array([[[0,1],[2,3]],[[4,5],[6,7]]])\n >>> x\n array([[[0, 1],\n [2, 3]],\n [[4, 5],\n [6, 7]]])\n\n >>> np.swapaxes(x,0,2)\n array([[[0, 4],\n [2, 6]],\n [[1, 5],\n [3, 7]]])\n\n \"\"\"\n return _wrapfunc(a, 'swapaxes', axis1, axis2)\n\n\ndef _transpose_dispatcher(a, axes=None):\n return (a,)\n\n\n@array_function_dispatch(_transpose_dispatcher)\ndef transpose(a, axes=None):\n \"\"\"\n Reverse or permute the axes of an array; returns the modified array.\n\n For an array a with two axes, transpose(a) gives the matrix transpose.\n\n Parameters\n ----------\n a : array_like\n Input array.\n axes : tuple or list of ints, optional\n If specified, it must be a tuple or list which contains a permutation of\n [0,1,..,N-1] where N is the number of axes of a. The i'th axis of the\n returned array will correspond to the axis numbered ``axes[i]`` of the\n input. If not specified, defaults to ``range(a.ndim)[::-1]``, which\n reverses the order of the axes.\n\n Returns\n -------\n p : ndarray\n `a` with its axes permuted. A view is returned whenever\n possible.\n\n See Also\n --------\n moveaxis\n argsort\n\n Notes\n -----\n Use `transpose(a, argsort(axes))` to invert the transposition of tensors\n when using the `axes` keyword argument.\n\n Transposing a 1-D array returns an unchanged view of the original array.\n\n Examples\n --------\n >>> x = np.arange(4).reshape((2,2))\n >>> x\n array([[0, 1],\n [2, 3]])\n\n >>> np.transpose(x)\n array([[0, 2],\n [1, 3]])\n\n >>> x = np.ones((1, 2, 3))\n >>> np.transpose(x, (1, 0, 2)).shape\n (2, 1, 3)\n\n \"\"\"\n return _wrapfunc(a, 'transpose', axes)\n\n\ndef _partition_dispatcher(a, kth, axis=None, kind=None, order=None):\n return (a,)\n\n\n@array_function_dispatch(_partition_dispatcher)\ndef partition(a, kth, axis=-1, kind='introselect', order=None):\n \"\"\"\n Return a partitioned copy of an array.\n\n Creates a copy of the array with its elements rearranged in such a\n way that the value of the element in k-th position is in the\n position it would be in a sorted array. All elements smaller than\n the k-th element are moved before this element and all equal or\n greater are moved behind it. The ordering of the elements in the two\n partitions is undefined.\n\n .. versionadded:: 1.8.0\n\n Parameters\n ----------\n a : array_like\n Array to be sorted.\n kth : int or sequence of ints\n Element index to partition by. The k-th value of the element\n will be in its final sorted position and all smaller elements\n will be moved before it and all equal or greater elements behind\n it. The order of all elements in the partitions is undefined. If\n provided with a sequence of k-th it will partition all elements\n indexed by k-th of them into their sorted position at once.\n axis : int or None, optional\n Axis along which to sort. If None, the array is flattened before\n sorting. The default is -1, which sorts along the last axis.\n kind : {'introselect'}, optional\n Selection algorithm. Default is 'introselect'.\n order : str or list of str, optional\n When `a` is an array with fields defined, this argument\n specifies which fields to compare first, second, etc. A single\n field can be specified as a string. Not all fields need be\n specified, but unspecified fields will still be used, in the\n order in which they come up in the dtype, to break ties.\n\n Returns\n -------\n partitioned_array : ndarray\n Array of the same type and shape as `a`.\n\n See Also\n --------\n ndarray.partition : Method to sort an array in-place.\n argpartition : Indirect partition.\n sort : Full sorting\n\n Notes\n -----\n The various selection algorithms are characterized by their average\n speed, worst case performance, work space size, and whether they are\n stable. A stable sort keeps items with the same key in the same\n relative order. The available algorithms have the following\n properties:\n\n ================= ======= ============= ============ =======\n kind speed worst case work space stable\n ================= ======= ============= ============ =======\n 'introselect' 1 O(n) 0 no\n ================= ======= ============= ============ =======\n\n All the partition algorithms make temporary copies of the data when\n partitioning along any but the last axis. Consequently,\n partitioning along the last axis is faster and uses less space than\n partitioning along any other axis.\n\n The sort order for complex numbers is lexicographic. If both the\n real and imaginary parts are non-nan then the order is determined by\n the real parts except when they are equal, in which case the order\n is determined by the imaginary parts.\n\n Examples\n --------\n >>> a = np.array([3, 4, 2, 1])\n >>> np.partition(a, 3)\n array([2, 1, 3, 4])\n\n >>> np.partition(a, (1, 3))\n array([1, 2, 3, 4])\n\n \"\"\"\n if axis is None:\n # flatten returns (1, N) for np.matrix, so always use the last axis\n a = asanyarray(a).flatten()\n axis = -1\n else:\n a = asanyarray(a).copy(order=\"K\")\n a.partition(kth, axis=axis, kind=kind, order=order)\n return a\n\n\ndef _argpartition_dispatcher(a, kth, axis=None, kind=None, order=None):\n return (a,)\n\n\n@array_function_dispatch(_argpartition_dispatcher)\ndef argpartition(a, kth, axis=-1, kind='introselect', order=None):\n \"\"\"\n Perform an indirect partition along the given axis using the\n algorithm specified by the `kind` keyword. It returns an array of\n indices of the same shape as `a` that index data along the given\n axis in partitioned order.\n\n .. versionadded:: 1.8.0\n\n Parameters\n ----------\n a : array_like\n Array to sort.\n kth : int or sequence of ints\n Element index to partition by. The k-th element will be in its\n final sorted position and all smaller elements will be moved\n before it and all larger elements behind it. The order all\n elements in the partitions is undefined. If provided with a\n sequence of k-th it will partition all of them into their sorted\n position at once.\n axis : int or None, optional\n Axis along which to sort. The default is -1 (the last axis). If\n None, the flattened array is used.\n kind : {'introselect'}, optional\n Selection algorithm. Default is 'introselect'\n order : str or list of str, optional\n When `a` is an array with fields defined, this argument\n specifies which fields to compare first, second, etc. A single\n field can be specified as a string, and not all fields need be\n specified, but unspecified fields will still be used, in the\n order in which they come up in the dtype, to break ties.\n\n Returns\n -------\n index_array : ndarray, int\n Array of indices that partition `a` along the specified axis.\n If `a` is one-dimensional, ``a[index_array]`` yields a partitioned `a`.\n More generally, ``np.take_along_axis(a, index_array, axis=a)`` always\n yields the partitioned `a`, irrespective of dimensionality.\n\n See Also\n --------\n partition : Describes partition algorithms used.\n ndarray.partition : Inplace partition.\n argsort : Full indirect sort.\n take_along_axis : Apply ``index_array`` from argpartition\n to an array as if by calling partition.\n\n Notes\n -----\n See `partition` for notes on the different selection algorithms.\n\n Examples\n --------\n One dimensional array:\n\n >>> x = np.array([3, 4, 2, 1])\n >>> x[np.argpartition(x, 3)]\n array([2, 1, 3, 4])\n >>> x[np.argpartition(x, (1, 3))]\n array([1, 2, 3, 4])\n\n >>> x = [3, 4, 2, 1]\n >>> np.array(x)[np.argpartition(x, 3)]\n array([2, 1, 3, 4])\n\n Multi-dimensional array:\n\n >>> x = np.array([[3, 4, 2], [1, 3, 1]])\n >>> index_array = np.argpartition(x, kth=1, axis=-1)\n >>> np.take_along_axis(x, index_array, axis=-1) # same as np.partition(x, kth=1)\n array([[2, 3, 4],\n [1, 1, 3]])\n\n \"\"\"\n return _wrapfunc(a, 'argpartition', kth, axis=axis, kind=kind, order=order)\n\n\ndef _sort_dispatcher(a, axis=None, kind=None, order=None):\n return (a,)\n\n\n@array_function_dispatch(_sort_dispatcher)\ndef sort(a, axis=-1, kind=None, order=None):\n \"\"\"\n Return a sorted copy of an array.\n\n Parameters\n ----------\n a : array_like\n Array to be sorted.\n axis : int or None, optional\n Axis along which to sort. If None, the array is flattened before\n sorting. The default is -1, which sorts along the last axis.\n kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, optional\n Sorting algorithm. The default is 'quicksort'. Note that both 'stable'\n and 'mergesort' use timsort or radix sort under the covers and, in general,\n the actual implementation will vary with data type. The 'mergesort' option\n is retained for backwards compatibility.\n\n .. versionchanged:: 1.15.0.\n The 'stable' option was added.\n\n order : str or list of str, optional\n When `a` is an array with fields defined, this argument specifies\n which fields to compare first, second, etc. A single field can\n be specified as a string, and not all fields need be specified,\n but unspecified fields will still be used, in the order in which\n they come up in the dtype, to break ties.\n\n Returns\n -------\n sorted_array : ndarray\n Array of the same type and shape as `a`.\n\n See Also\n --------\n ndarray.sort : Method to sort an array in-place.\n argsort : Indirect sort.\n lexsort : Indirect stable sort on multiple keys.\n searchsorted : Find elements in a sorted array.\n partition : Partial sort.\n\n Notes\n -----\n The various sorting algorithms are characterized by their average speed,\n worst case performance, work space size, and whether they are stable. A\n stable sort keeps items with the same key in the same relative\n order. The four algorithms implemented in NumPy have the following\n properties:\n\n =========== ======= ============= ============ ========\n kind speed worst case work space stable\n =========== ======= ============= ============ ========\n 'quicksort' 1 O(n^2) 0 no\n 'heapsort' 3 O(n*log(n)) 0 no\n 'mergesort' 2 O(n*log(n)) ~n/2 yes\n 'timsort' 2 O(n*log(n)) ~n/2 yes\n =========== ======= ============= ============ ========\n\n .. note:: The datatype determines which of 'mergesort' or 'timsort'\n is actually used, even if 'mergesort' is specified. User selection\n at a finer scale is not currently available.\n\n All the sort algorithms make temporary copies of the data when\n sorting along any but the last axis. Consequently, sorting along\n the last axis is faster and uses less space than sorting along\n any other axis.\n\n The sort order for complex numbers is lexicographic. If both the real\n and imaginary parts are non-nan then the order is determined by the\n real parts except when they are equal, in which case the order is\n determined by the imaginary parts.\n\n Previous to numpy 1.4.0 sorting real and complex arrays containing nan\n values led to undefined behaviour. In numpy versions >= 1.4.0 nan\n values are sorted to the end. The extended sort order is:\n\n * Real: [R, nan]\n * Complex: [R + Rj, R + nanj, nan + Rj, nan + nanj]\n\n where R is a non-nan real value. Complex values with the same nan\n placements are sorted according to the non-nan part if it exists.\n Non-nan values are sorted as before.\n\n .. versionadded:: 1.12.0\n\n quicksort has been changed to `introsort `_.\n When sorting does not make enough progress it switches to\n `heapsort `_.\n This implementation makes quicksort O(n*log(n)) in the worst case.\n\n 'stable' automatically chooses the best stable sorting algorithm\n for the data type being sorted.\n It, along with 'mergesort' is currently mapped to\n `timsort `_\n or `radix sort `_\n depending on the data type.\n API forward compatibility currently limits the\n ability to select the implementation and it is hardwired for the different\n data types.\n\n .. versionadded:: 1.17.0\n\n Timsort is added for better performance on already or nearly\n sorted data. On random data timsort is almost identical to\n mergesort. It is now used for stable sort while quicksort is still the\n default sort if none is chosen. For timsort details, refer to\n `CPython listsort.txt `_.\n 'mergesort' and 'stable' are mapped to radix sort for integer data types. Radix sort is an\n O(n) sort instead of O(n log n).\n\n .. versionchanged:: 1.18.0\n\n NaT now sorts to the end of arrays for consistency with NaN.\n\n Examples\n --------\n >>> a = np.array([[1,4],[3,1]])\n >>> np.sort(a) # sort along the last axis\n array([[1, 4],\n [1, 3]])\n >>> np.sort(a, axis=None) # sort the flattened array\n array([1, 1, 3, 4])\n >>> np.sort(a, axis=0) # sort along the first axis\n array([[1, 1],\n [3, 4]])\n\n Use the `order` keyword to specify a field to use when sorting a\n structured array:\n\n >>> dtype = [('name', 'S10'), ('height', float), ('age', int)]\n >>> values = [('Arthur', 1.8, 41), ('Lancelot', 1.9, 38),\n ... ('Galahad', 1.7, 38)]\n >>> a = np.array(values, dtype=dtype) # create a structured array\n >>> np.sort(a, order='height') # doctest: +SKIP\n array([('Galahad', 1.7, 38), ('Arthur', 1.8, 41),\n ('Lancelot', 1.8999999999999999, 38)],\n dtype=[('name', '|S10'), ('height', '>> np.sort(a, order=['age', 'height']) # doctest: +SKIP\n array([('Galahad', 1.7, 38), ('Lancelot', 1.8999999999999999, 38),\n ('Arthur', 1.8, 41)],\n dtype=[('name', '|S10'), ('height', '>> x = np.array([3, 1, 2])\n >>> np.argsort(x)\n array([1, 2, 0])\n\n Two-dimensional array:\n\n >>> x = np.array([[0, 3], [2, 2]])\n >>> x\n array([[0, 3],\n [2, 2]])\n\n >>> ind = np.argsort(x, axis=0) # sorts along first axis (down)\n >>> ind\n array([[0, 1],\n [1, 0]])\n >>> np.take_along_axis(x, ind, axis=0) # same as np.sort(x, axis=0)\n array([[0, 2],\n [2, 3]])\n\n >>> ind = np.argsort(x, axis=1) # sorts along last axis (across)\n >>> ind\n array([[0, 1],\n [0, 1]])\n >>> np.take_along_axis(x, ind, axis=1) # same as np.sort(x, axis=1)\n array([[0, 3],\n [2, 2]])\n\n Indices of the sorted elements of a N-dimensional array:\n\n >>> ind = np.unravel_index(np.argsort(x, axis=None), x.shape)\n >>> ind\n (array([0, 1, 1, 0]), array([0, 0, 1, 1]))\n >>> x[ind] # same as np.sort(x, axis=None)\n array([0, 2, 2, 3])\n\n Sorting with keys:\n\n >>> x = np.array([(1, 0), (0, 1)], dtype=[('x', '>> x\n array([(1, 0), (0, 1)],\n dtype=[('x', '>> np.argsort(x, order=('x','y'))\n array([1, 0])\n\n >>> np.argsort(x, order=('y','x'))\n array([0, 1])\n\n \"\"\"\n return _wrapfunc(a, 'argsort', axis=axis, kind=kind, order=order)\n\n\ndef _argmax_dispatcher(a, axis=None, out=None):\n return (a, out)\n\n\n@array_function_dispatch(_argmax_dispatcher)\ndef argmax(a, axis=None, out=None):\n \"\"\"\n Returns the indices of the maximum values along an axis.\n\n Parameters\n ----------\n a : array_like\n Input array.\n axis : int, optional\n By default, the index is into the flattened array, otherwise\n along the specified axis.\n out : array, optional\n If provided, the result will be inserted into this array. It should\n be of the appropriate shape and dtype.\n\n Returns\n -------\n index_array : ndarray of ints\n Array of indices into the array. It has the same shape as `a.shape`\n with the dimension along `axis` removed.\n\n See Also\n --------\n ndarray.argmax, argmin\n amax : The maximum value along a given axis.\n unravel_index : Convert a flat index into an index tuple.\n take_along_axis : Apply ``np.expand_dims(index_array, axis)``\n from argmax to an array as if by calling max.\n\n Notes\n -----\n In case of multiple occurrences of the maximum values, the indices\n corresponding to the first occurrence are returned.\n\n Examples\n --------\n >>> a = np.arange(6).reshape(2,3) + 10\n >>> a\n array([[10, 11, 12],\n [13, 14, 15]])\n >>> np.argmax(a)\n 5\n >>> np.argmax(a, axis=0)\n array([1, 1, 1])\n >>> np.argmax(a, axis=1)\n array([2, 2])\n\n Indexes of the maximal elements of a N-dimensional array:\n\n >>> ind = np.unravel_index(np.argmax(a, axis=None), a.shape)\n >>> ind\n (1, 2)\n >>> a[ind]\n 15\n\n >>> b = np.arange(6)\n >>> b[1] = 5\n >>> b\n array([0, 5, 2, 3, 4, 5])\n >>> np.argmax(b) # Only the first occurrence is returned.\n 1\n\n >>> x = np.array([[4,2,3], [1,0,3]])\n >>> index_array = np.argmax(x, axis=-1)\n >>> # Same as np.max(x, axis=-1, keepdims=True)\n >>> np.take_along_axis(x, np.expand_dims(index_array, axis=-1), axis=-1)\n array([[4],\n [3]])\n >>> # Same as np.max(x, axis=-1)\n >>> np.take_along_axis(x, np.expand_dims(index_array, axis=-1), axis=-1).squeeze(axis=-1)\n array([4, 3])\n\n \"\"\"\n return _wrapfunc(a, 'argmax', axis=axis, out=out)\n\n\ndef _argmin_dispatcher(a, axis=None, out=None):\n return (a, out)\n\n\n@array_function_dispatch(_argmin_dispatcher)\ndef argmin(a, axis=None, out=None):\n \"\"\"\n Returns the indices of the minimum values along an axis.\n\n Parameters\n ----------\n a : array_like\n Input array.\n axis : int, optional\n By default, the index is into the flattened array, otherwise\n along the specified axis.\n out : array, optional\n If provided, the result will be inserted into this array. It should\n be of the appropriate shape and dtype.\n\n Returns\n -------\n index_array : ndarray of ints\n Array of indices into the array. It has the same shape as `a.shape`\n with the dimension along `axis` removed.\n\n See Also\n --------\n ndarray.argmin, argmax\n amin : The minimum value along a given axis.\n unravel_index : Convert a flat index into an index tuple.\n take_along_axis : Apply ``np.expand_dims(index_array, axis)``\n from argmin to an array as if by calling min.\n\n Notes\n -----\n In case of multiple occurrences of the minimum values, the indices\n corresponding to the first occurrence are returned.\n\n Examples\n --------\n >>> a = np.arange(6).reshape(2,3) + 10\n >>> a\n array([[10, 11, 12],\n [13, 14, 15]])\n >>> np.argmin(a)\n 0\n >>> np.argmin(a, axis=0)\n array([0, 0, 0])\n >>> np.argmin(a, axis=1)\n array([0, 0])\n\n Indices of the minimum elements of a N-dimensional array:\n\n >>> ind = np.unravel_index(np.argmin(a, axis=None), a.shape)\n >>> ind\n (0, 0)\n >>> a[ind]\n 10\n\n >>> b = np.arange(6) + 10\n >>> b[4] = 10\n >>> b\n array([10, 11, 12, 13, 10, 15])\n >>> np.argmin(b) # Only the first occurrence is returned.\n 0\n\n >>> x = np.array([[4,2,3], [1,0,3]])\n >>> index_array = np.argmin(x, axis=-1)\n >>> # Same as np.min(x, axis=-1, keepdims=True)\n >>> np.take_along_axis(x, np.expand_dims(index_array, axis=-1), axis=-1)\n array([[2],\n [0]])\n >>> # Same as np.max(x, axis=-1)\n >>> np.take_along_axis(x, np.expand_dims(index_array, axis=-1), axis=-1).squeeze(axis=-1)\n array([2, 0])\n\n \"\"\"\n return _wrapfunc(a, 'argmin', axis=axis, out=out)\n\n\ndef _searchsorted_dispatcher(a, v, side=None, sorter=None):\n return (a, v, sorter)\n\n\n@array_function_dispatch(_searchsorted_dispatcher)\ndef searchsorted(a, v, side='left', sorter=None):\n \"\"\"\n Find indices where elements should be inserted to maintain order.\n\n Find the indices into a sorted array `a` such that, if the\n corresponding elements in `v` were inserted before the indices, the\n order of `a` would be preserved.\n\n Assuming that `a` is sorted:\n\n ====== ============================\n `side` returned index `i` satisfies\n ====== ============================\n left ``a[i-1] < v <= a[i]``\n right ``a[i-1] <= v < a[i]``\n ====== ============================\n\n Parameters\n ----------\n a : 1-D array_like\n Input array. If `sorter` is None, then it must be sorted in\n ascending order, otherwise `sorter` must be an array of indices\n that sort it.\n v : array_like\n Values to insert into `a`.\n side : {'left', 'right'}, optional\n If 'left', the index of the first suitable location found is given.\n If 'right', return the last such index. If there is no suitable\n index, return either 0 or N (where N is the length of `a`).\n sorter : 1-D array_like, optional\n Optional array of integer indices that sort array a into ascending\n order. They are typically the result of argsort.\n\n .. versionadded:: 1.7.0\n\n Returns\n -------\n indices : array of ints\n Array of insertion points with the same shape as `v`.\n\n See Also\n --------\n sort : Return a sorted copy of an array.\n histogram : Produce histogram from 1-D data.\n\n Notes\n -----\n Binary search is used to find the required insertion points.\n\n As of NumPy 1.4.0 `searchsorted` works with real/complex arrays containing\n `nan` values. The enhanced sort order is documented in `sort`.\n\n This function uses the same algorithm as the builtin python `bisect.bisect_left`\n (``side='left'``) and `bisect.bisect_right` (``side='right'``) functions,\n which is also vectorized in the `v` argument.\n\n Examples\n --------\n >>> np.searchsorted([1,2,3,4,5], 3)\n 2\n >>> np.searchsorted([1,2,3,4,5], 3, side='right')\n 3\n >>> np.searchsorted([1,2,3,4,5], [-10, 10, 2, 3])\n array([0, 5, 1, 2])\n\n \"\"\"\n return _wrapfunc(a, 'searchsorted', v, side=side, sorter=sorter)\n\n\ndef _resize_dispatcher(a, new_shape):\n return (a,)\n\n\n@array_function_dispatch(_resize_dispatcher)\ndef resize(a, new_shape):\n \"\"\"\n Return a new array with the specified shape.\n\n If the new array is larger than the original array, then the new\n array is filled with repeated copies of `a`. Note that this behavior\n is different from a.resize(new_shape) which fills with zeros instead\n of repeated copies of `a`.\n\n Parameters\n ----------\n a : array_like\n Array to be resized.\n\n new_shape : int or tuple of int\n Shape of resized array.\n\n Returns\n -------\n reshaped_array : ndarray\n The new array is formed from the data in the old array, repeated\n if necessary to fill out the required number of elements. The\n data are repeated in the order that they are stored in memory.\n\n See Also\n --------\n ndarray.resize : resize an array in-place.\n\n Notes\n -----\n Warning: This functionality does **not** consider axes separately,\n i.e. it does not apply interpolation/extrapolation.\n It fills the return array with the required number of elements, taken\n from `a` as they are laid out in memory, disregarding strides and axes.\n (This is in case the new shape is smaller. For larger, see above.)\n This functionality is therefore not suitable to resize images,\n or data where each axis represents a separate and distinct entity.\n\n Examples\n --------\n >>> a=np.array([[0,1],[2,3]])\n >>> np.resize(a,(2,3))\n array([[0, 1, 2],\n [3, 0, 1]])\n >>> np.resize(a,(1,4))\n array([[0, 1, 2, 3]])\n >>> np.resize(a,(2,4))\n array([[0, 1, 2, 3],\n [0, 1, 2, 3]])\n\n \"\"\"\n if isinstance(new_shape, (int, nt.integer)):\n new_shape = (new_shape,)\n a = ravel(a)\n Na = len(a)\n total_size = um.multiply.reduce(new_shape)\n if Na == 0 or total_size == 0:\n return mu.zeros(new_shape, a.dtype)\n\n n_copies = int(total_size / Na)\n extra = total_size % Na\n\n if extra != 0:\n n_copies = n_copies + 1\n extra = Na - extra\n\n a = concatenate((a,) * n_copies)\n if extra > 0:\n a = a[:-extra]\n\n return reshape(a, new_shape)\n\n\ndef _squeeze_dispatcher(a, axis=None):\n return (a,)\n\n\n@array_function_dispatch(_squeeze_dispatcher)\ndef squeeze(a, axis=None):\n \"\"\"\n Remove single-dimensional entries from the shape of an array.\n\n Parameters\n ----------\n a : array_like\n Input data.\n axis : None or int or tuple of ints, optional\n .. versionadded:: 1.7.0\n\n Selects a subset of the single-dimensional entries in the\n shape. If an axis is selected with shape entry greater than\n one, an error is raised.\n\n Returns\n -------\n squeezed : ndarray\n The input array, but with all or a subset of the\n dimensions of length 1 removed. This is always `a` itself\n or a view into `a`. Note that if all axes are squeezed,\n the result is a 0d array and not a scalar.\n\n Raises\n ------\n ValueError\n If `axis` is not None, and an axis being squeezed is not of length 1\n\n See Also\n --------\n expand_dims : The inverse operation, adding singleton dimensions\n reshape : Insert, remove, and combine dimensions, and resize existing ones\n\n Examples\n --------\n >>> x = np.array([[[0], [1], [2]]])\n >>> x.shape\n (1, 3, 1)\n >>> np.squeeze(x).shape\n (3,)\n >>> np.squeeze(x, axis=0).shape\n (3, 1)\n >>> np.squeeze(x, axis=1).shape\n Traceback (most recent call last):\n ...\n ValueError: cannot select an axis to squeeze out which has size not equal to one\n >>> np.squeeze(x, axis=2).shape\n (1, 3)\n >>> x = np.array([[1234]])\n >>> x.shape\n (1, 1)\n >>> np.squeeze(x)\n array(1234) # 0d array\n >>> np.squeeze(x).shape\n ()\n >>> np.squeeze(x)[()]\n 1234\n\n \"\"\"\n try:\n squeeze = a.squeeze\n except AttributeError:\n return _wrapit(a, 'squeeze', axis=axis)\n if axis is None:\n return squeeze()\n else:\n return squeeze(axis=axis)\n\n\ndef _diagonal_dispatcher(a, offset=None, axis1=None, axis2=None):\n return (a,)\n\n\n@array_function_dispatch(_diagonal_dispatcher)\ndef diagonal(a, offset=0, axis1=0, axis2=1):\n \"\"\"\n Return specified diagonals.\n\n If `a` is 2-D, returns the diagonal of `a` with the given offset,\n i.e., the collection of elements of the form ``a[i, i+offset]``. If\n `a` has more than two dimensions, then the axes specified by `axis1`\n and `axis2` are used to determine the 2-D sub-array whose diagonal is\n returned. The shape of the resulting array can be determined by\n removing `axis1` and `axis2` and appending an index to the right equal\n to the size of the resulting diagonals.\n\n In versions of NumPy prior to 1.7, this function always returned a new,\n independent array containing a copy of the values in the diagonal.\n\n In NumPy 1.7 and 1.8, it continues to return a copy of the diagonal,\n but depending on this fact is deprecated. Writing to the resulting\n array continues to work as it used to, but a FutureWarning is issued.\n\n Starting in NumPy 1.9 it returns a read-only view on the original array.\n Attempting to write to the resulting array will produce an error.\n\n In some future release, it will return a read/write view and writing to\n the returned array will alter your original array. The returned array\n will have the same type as the input array.\n\n If you don't write to the array returned by this function, then you can\n just ignore all of the above.\n\n If you depend on the current behavior, then we suggest copying the\n returned array explicitly, i.e., use ``np.diagonal(a).copy()`` instead\n of just ``np.diagonal(a)``. This will work with both past and future\n versions of NumPy.\n\n Parameters\n ----------\n a : array_like\n Array from which the diagonals are taken.\n offset : int, optional\n Offset of the diagonal from the main diagonal. Can be positive or\n negative. Defaults to main diagonal (0).\n axis1 : int, optional\n Axis to be used as the first axis of the 2-D sub-arrays from which\n the diagonals should be taken. Defaults to first axis (0).\n axis2 : int, optional\n Axis to be used as the second axis of the 2-D sub-arrays from\n which the diagonals should be taken. Defaults to second axis (1).\n\n Returns\n -------\n array_of_diagonals : ndarray\n If `a` is 2-D, then a 1-D array containing the diagonal and of the\n same type as `a` is returned unless `a` is a `matrix`, in which case\n a 1-D array rather than a (2-D) `matrix` is returned in order to\n maintain backward compatibility.\n\n If ``a.ndim > 2``, then the dimensions specified by `axis1` and `axis2`\n are removed, and a new axis inserted at the end corresponding to the\n diagonal.\n\n Raises\n ------\n ValueError\n If the dimension of `a` is less than 2.\n\n See Also\n --------\n diag : MATLAB work-a-like for 1-D and 2-D arrays.\n diagflat : Create diagonal arrays.\n trace : Sum along diagonals.\n\n Examples\n --------\n >>> a = np.arange(4).reshape(2,2)\n >>> a\n array([[0, 1],\n [2, 3]])\n >>> a.diagonal()\n array([0, 3])\n >>> a.diagonal(1)\n array([1])\n\n A 3-D example:\n\n >>> a = np.arange(8).reshape(2,2,2); a\n array([[[0, 1],\n [2, 3]],\n [[4, 5],\n [6, 7]]])\n >>> a.diagonal(0, # Main diagonals of two arrays created by skipping\n ... 0, # across the outer(left)-most axis last and\n ... 1) # the \"middle\" (row) axis first.\n array([[0, 6],\n [1, 7]])\n\n The sub-arrays whose main diagonals we just obtained; note that each\n corresponds to fixing the right-most (column) axis, and that the\n diagonals are \"packed\" in rows.\n\n >>> a[:,:,0] # main diagonal is [0 6]\n array([[0, 2],\n [4, 6]])\n >>> a[:,:,1] # main diagonal is [1 7]\n array([[1, 3],\n [5, 7]])\n\n The anti-diagonal can be obtained by reversing the order of elements\n using either `numpy.flipud` or `numpy.fliplr`.\n\n >>> a = np.arange(9).reshape(3, 3)\n >>> a\n array([[0, 1, 2],\n [3, 4, 5],\n [6, 7, 8]])\n >>> np.fliplr(a).diagonal() # Horizontal flip\n array([2, 4, 6])\n >>> np.flipud(a).diagonal() # Vertical flip\n array([6, 4, 2])\n\n Note that the order in which the diagonal is retrieved varies depending\n on the flip function.\n \"\"\"\n if isinstance(a, np.matrix):\n # Make diagonal of matrix 1-D to preserve backward compatibility.\n return asarray(a).diagonal(offset=offset, axis1=axis1, axis2=axis2)\n else:\n return asanyarray(a).diagonal(offset=offset, axis1=axis1, axis2=axis2)\n\n\ndef _trace_dispatcher(\n a, offset=None, axis1=None, axis2=None, dtype=None, out=None):\n return (a, out)\n\n\n@array_function_dispatch(_trace_dispatcher)\ndef trace(a, offset=0, axis1=0, axis2=1, dtype=None, out=None):\n \"\"\"\n Return the sum along diagonals of the array.\n\n If `a` is 2-D, the sum along its diagonal with the given offset\n is returned, i.e., the sum of elements ``a[i,i+offset]`` for all i.\n\n If `a` has more than two dimensions, then the axes specified by axis1 and\n axis2 are used to determine the 2-D sub-arrays whose traces are returned.\n The shape of the resulting array is the same as that of `a` with `axis1`\n and `axis2` removed.\n\n Parameters\n ----------\n a : array_like\n Input array, from which the diagonals are taken.\n offset : int, optional\n Offset of the diagonal from the main diagonal. Can be both positive\n and negative. Defaults to 0.\n axis1, axis2 : int, optional\n Axes to be used as the first and second axis of the 2-D sub-arrays\n from which the diagonals should be taken. Defaults are the first two\n axes of `a`.\n dtype : dtype, optional\n Determines the data-type of the returned array and of the accumulator\n where the elements are summed. If dtype has the value None and `a` is\n of integer type of precision less than the default integer\n precision, then the default integer precision is used. Otherwise,\n the precision is the same as that of `a`.\n out : ndarray, optional\n Array into which the output is placed. Its type is preserved and\n it must be of the right shape to hold the output.\n\n Returns\n -------\n sum_along_diagonals : ndarray\n If `a` is 2-D, the sum along the diagonal is returned. If `a` has\n larger dimensions, then an array of sums along diagonals is returned.\n\n See Also\n --------\n diag, diagonal, diagflat\n\n Examples\n --------\n >>> np.trace(np.eye(3))\n 3.0\n >>> a = np.arange(8).reshape((2,2,2))\n >>> np.trace(a)\n array([6, 8])\n\n >>> a = np.arange(24).reshape((2,2,2,3))\n >>> np.trace(a).shape\n (2, 3)\n\n \"\"\"\n if isinstance(a, np.matrix):\n # Get trace of matrix via an array to preserve backward compatibility.\n return asarray(a).trace(offset=offset, axis1=axis1, axis2=axis2, dtype=dtype, out=out)\n else:\n return asanyarray(a).trace(offset=offset, axis1=axis1, axis2=axis2, dtype=dtype, out=out)\n\n\ndef _ravel_dispatcher(a, order=None):\n return (a,)\n\n\n@array_function_dispatch(_ravel_dispatcher)\ndef ravel(a, order='C'):\n \"\"\"Return a contiguous flattened array.\n\n A 1-D array, containing the elements of the input, is returned. A copy is\n made only if needed.\n\n As of NumPy 1.10, the returned array will have the same type as the input\n array. (for example, a masked array will be returned for a masked array\n input)\n\n Parameters\n ----------\n a : array_like\n Input array. The elements in `a` are read in the order specified by\n `order`, and packed as a 1-D array.\n order : {'C','F', 'A', 'K'}, optional\n\n The elements of `a` are read using this index order. 'C' means\n to index the elements in row-major, C-style order,\n with the last axis index changing fastest, back to the first\n axis index changing slowest. 'F' means to index the elements\n in column-major, Fortran-style order, with the\n first index changing fastest, and the last index changing\n slowest. Note that the 'C' and 'F' options take no account of\n the memory layout of the underlying array, and only refer to\n the order of axis indexing. 'A' means to read the elements in\n Fortran-like index order if `a` is Fortran *contiguous* in\n memory, C-like order otherwise. 'K' means to read the\n elements in the order they occur in memory, except for\n reversing the data when strides are negative. By default, 'C'\n index order is used.\n\n Returns\n -------\n y : array_like\n y is an array of the same subtype as `a`, with shape ``(a.size,)``.\n Note that matrices are special cased for backward compatibility, if `a`\n is a matrix, then y is a 1-D ndarray.\n\n See Also\n --------\n ndarray.flat : 1-D iterator over an array.\n ndarray.flatten : 1-D array copy of the elements of an array\n in row-major order.\n ndarray.reshape : Change the shape of an array without changing its data.\n\n Notes\n -----\n In row-major, C-style order, in two dimensions, the row index\n varies the slowest, and the column index the quickest. This can\n be generalized to multiple dimensions, where row-major order\n implies that the index along the first axis varies slowest, and\n the index along the last quickest. The opposite holds for\n column-major, Fortran-style index ordering.\n\n When a view is desired in as many cases as possible, ``arr.reshape(-1)``\n may be preferable.\n\n Examples\n --------\n It is equivalent to ``reshape(-1, order=order)``.\n\n >>> x = np.array([[1, 2, 3], [4, 5, 6]])\n >>> np.ravel(x)\n array([1, 2, 3, 4, 5, 6])\n\n >>> x.reshape(-1)\n array([1, 2, 3, 4, 5, 6])\n\n >>> np.ravel(x, order='F')\n array([1, 4, 2, 5, 3, 6])\n\n When ``order`` is 'A', it will preserve the array's 'C' or 'F' ordering:\n\n >>> np.ravel(x.T)\n array([1, 4, 2, 5, 3, 6])\n >>> np.ravel(x.T, order='A')\n array([1, 2, 3, 4, 5, 6])\n\n When ``order`` is 'K', it will preserve orderings that are neither 'C'\n nor 'F', but won't reverse axes:\n\n >>> a = np.arange(3)[::-1]; a\n array([2, 1, 0])\n >>> a.ravel(order='C')\n array([2, 1, 0])\n >>> a.ravel(order='K')\n array([2, 1, 0])\n\n >>> a = np.arange(12).reshape(2,3,2).swapaxes(1,2); a\n array([[[ 0, 2, 4],\n [ 1, 3, 5]],\n [[ 6, 8, 10],\n [ 7, 9, 11]]])\n >>> a.ravel(order='C')\n array([ 0, 2, 4, 1, 3, 5, 6, 8, 10, 7, 9, 11])\n >>> a.ravel(order='K')\n array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])\n\n \"\"\"\n if isinstance(a, np.matrix):\n return asarray(a).ravel(order=order)\n else:\n return asanyarray(a).ravel(order=order)\n\n\ndef _nonzero_dispatcher(a):\n return (a,)\n\n\n@array_function_dispatch(_nonzero_dispatcher)\ndef nonzero(a):\n \"\"\"\n Return the indices of the elements that are non-zero.\n\n Returns a tuple of arrays, one for each dimension of `a`,\n containing the indices of the non-zero elements in that\n dimension. The values in `a` are always tested and returned in\n row-major, C-style order.\n\n To group the indices by element, rather than dimension, use `argwhere`,\n which returns a row for each non-zero element.\n\n .. note::\n\n When called on a zero-d array or scalar, ``nonzero(a)`` is treated\n as ``nonzero(atleast1d(a))``.\n\n .. deprecated:: 1.17.0\n\n Use `atleast1d` explicitly if this behavior is deliberate.\n\n Parameters\n ----------\n a : array_like\n Input array.\n\n Returns\n -------\n tuple_of_arrays : tuple\n Indices of elements that are non-zero.\n\n See Also\n --------\n flatnonzero :\n Return indices that are non-zero in the flattened version of the input\n array.\n ndarray.nonzero :\n Equivalent ndarray method.\n count_nonzero :\n Counts the number of non-zero elements in the input array.\n\n Notes\n -----\n While the nonzero values can be obtained with ``a[nonzero(a)]``, it is\n recommended to use ``x[x.astype(bool)]`` or ``x[x != 0]`` instead, which\n will correctly handle 0-d arrays.\n\n Examples\n --------\n >>> x = np.array([[3, 0, 0], [0, 4, 0], [5, 6, 0]])\n >>> x\n array([[3, 0, 0],\n [0, 4, 0],\n [5, 6, 0]])\n >>> np.nonzero(x)\n (array([0, 1, 2, 2]), array([0, 1, 0, 1]))\n\n >>> x[np.nonzero(x)]\n array([3, 4, 5, 6])\n >>> np.transpose(np.nonzero(x))\n array([[0, 0],\n [1, 1],\n [2, 0],\n [2, 1]])\n\n A common use for ``nonzero`` is to find the indices of an array, where\n a condition is True. Given an array `a`, the condition `a` > 3 is a\n boolean array and since False is interpreted as 0, np.nonzero(a > 3)\n yields the indices of the `a` where the condition is true.\n\n >>> a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n >>> a > 3\n array([[False, False, False],\n [ True, True, True],\n [ True, True, True]])\n >>> np.nonzero(a > 3)\n (array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))\n\n Using this result to index `a` is equivalent to using the mask directly:\n\n >>> a[np.nonzero(a > 3)]\n array([4, 5, 6, 7, 8, 9])\n >>> a[a > 3] # prefer this spelling\n array([4, 5, 6, 7, 8, 9])\n\n ``nonzero`` can also be called as a method of the array.\n\n >>> (a > 3).nonzero()\n (array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))\n\n \"\"\"\n return _wrapfunc(a, 'nonzero')\n\n\ndef _shape_dispatcher(a):\n return (a,)\n\n\n@array_function_dispatch(_shape_dispatcher)\ndef shape(a):\n \"\"\"\n Return the shape of an array.\n\n Parameters\n ----------\n a : array_like\n Input array.\n\n Returns\n -------\n shape : tuple of ints\n The elements of the shape tuple give the lengths of the\n corresponding array dimensions.\n\n See Also\n --------\n alen\n ndarray.shape : Equivalent array method.\n\n Examples\n --------\n >>> np.shape(np.eye(3))\n (3, 3)\n >>> np.shape([[1, 2]])\n (1, 2)\n >>> np.shape([0])\n (1,)\n >>> np.shape(0)\n ()\n\n >>> a = np.array([(1, 2), (3, 4)], dtype=[('x', 'i4'), ('y', 'i4')])\n >>> np.shape(a)\n (2,)\n >>> a.shape\n (2,)\n\n \"\"\"\n try:\n result = a.shape\n except AttributeError:\n result = asarray(a).shape\n return result\n\n\ndef _compress_dispatcher(condition, a, axis=None, out=None):\n return (condition, a, out)\n\n\n@array_function_dispatch(_compress_dispatcher)\ndef compress(condition, a, axis=None, out=None):\n \"\"\"\n Return selected slices of an array along given axis.\n\n When working along a given axis, a slice along that axis is returned in\n `output` for each index where `condition` evaluates to True. When\n working on a 1-D array, `compress` is equivalent to `extract`.\n\n Parameters\n ----------\n condition : 1-D array of bools\n Array that selects which entries to return. If len(condition)\n is less than the size of `a` along the given axis, then output is\n truncated to the length of the condition array.\n a : array_like\n Array from which to extract a part.\n axis : int, optional\n Axis along which to take slices. If None (default), work on the\n flattened array.\n out : ndarray, optional\n Output array. Its type is preserved and it must be of the right\n shape to hold the output.\n\n Returns\n -------\n compressed_array : ndarray\n A copy of `a` without the slices along axis for which `condition`\n is false.\n\n See Also\n --------\n take, choose, diag, diagonal, select\n ndarray.compress : Equivalent method in ndarray\n np.extract: Equivalent method when working on 1-D arrays\n ufuncs-output-type\n\n Examples\n --------\n >>> a = np.array([[1, 2], [3, 4], [5, 6]])\n >>> a\n array([[1, 2],\n [3, 4],\n [5, 6]])\n >>> np.compress([0, 1], a, axis=0)\n array([[3, 4]])\n >>> np.compress([False, True, True], a, axis=0)\n array([[3, 4],\n [5, 6]])\n >>> np.compress([False, True], a, axis=1)\n array([[2],\n [4],\n [6]])\n\n Working on the flattened array does not return slices along an axis but\n selects elements.\n\n >>> np.compress([False, True], a)\n array([2])\n\n \"\"\"\n return _wrapfunc(a, 'compress', condition, axis=axis, out=out)\n\n\ndef _clip_dispatcher(a, a_min, a_max, out=None, **kwargs):\n return (a, a_min, a_max)\n\n\n@array_function_dispatch(_clip_dispatcher)\ndef clip(a, a_min, a_max, out=None, **kwargs):\n \"\"\"\n Clip (limit) the values in an array.\n\n Given an interval, values outside the interval are clipped to\n the interval edges. For example, if an interval of ``[0, 1]``\n is specified, values smaller than 0 become 0, and values larger\n than 1 become 1.\n\n Equivalent to but faster than ``np.minimum(a_max, np.maximum(a, a_min))``.\n\n No check is performed to ensure ``a_min < a_max``.\n\n Parameters\n ----------\n a : array_like\n Array containing elements to clip.\n a_min : scalar or array_like or None\n Minimum value. If None, clipping is not performed on lower\n interval edge. Not more than one of `a_min` and `a_max` may be\n None.\n a_max : scalar or array_like or None\n Maximum value. If None, clipping is not performed on upper\n interval edge. Not more than one of `a_min` and `a_max` may be\n None. If `a_min` or `a_max` are array_like, then the three\n arrays will be broadcasted to match their shapes.\n out : ndarray, optional\n The results will be placed in this array. It may be the input\n array for in-place clipping. `out` must be of the right shape\n to hold the output. Its type is preserved.\n **kwargs\n For other keyword-only arguments, see the\n :ref:`ufunc docs `.\n\n .. versionadded:: 1.17.0\n\n Returns\n -------\n clipped_array : ndarray\n An array with the elements of `a`, but where values\n < `a_min` are replaced with `a_min`, and those > `a_max`\n with `a_max`.\n\n See Also\n --------\n ufuncs-output-type\n\n Examples\n --------\n >>> a = np.arange(10)\n >>> np.clip(a, 1, 8)\n array([1, 1, 2, 3, 4, 5, 6, 7, 8, 8])\n >>> a\n array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n >>> np.clip(a, 3, 6, out=a)\n array([3, 3, 3, 3, 4, 5, 6, 6, 6, 6])\n >>> a = np.arange(10)\n >>> a\n array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n >>> np.clip(a, [3, 4, 1, 1, 1, 4, 4, 4, 4, 4], 8)\n array([3, 4, 2, 3, 4, 5, 6, 7, 8, 8])\n\n \"\"\"\n return _wrapfunc(a, 'clip', a_min, a_max, out=out, **kwargs)\n\n\ndef _sum_dispatcher(a, axis=None, dtype=None, out=None, keepdims=None,\n initial=None, where=None):\n return (a, out)\n\n\n@array_function_dispatch(_sum_dispatcher)\ndef sum(a, axis=None, dtype=None, out=None, keepdims=np._NoValue,\n initial=np._NoValue, where=np._NoValue):\n \"\"\"\n Sum of array elements over a given axis.\n\n Parameters\n ----------\n a : array_like\n Elements to sum.\n axis : None or int or tuple of ints, optional\n Axis or axes along which a sum is performed. The default,\n axis=None, will sum all of the elements of the input array. If\n axis is negative it counts from the last to the first axis.\n\n .. versionadded:: 1.7.0\n\n If axis is a tuple of ints, a sum is performed on all of the axes\n specified in the tuple instead of a single axis or all the axes as\n before.\n dtype : dtype, optional\n The type of the returned array and of the accumulator in which the\n elements are summed. The dtype of `a` is used by default unless `a`\n has an integer dtype of less precision than the default platform\n integer. In that case, if `a` is signed then the platform integer\n is used while if `a` is unsigned then an unsigned integer of the\n same precision as the platform integer is used.\n out : ndarray, optional\n Alternative output array in which to place the result. It must have\n the same shape as the expected output, but the type of the output\n values will be cast if necessary.\n keepdims : bool, optional\n If this is set to True, the axes which are reduced are left\n in the result as dimensions with size one. With this option,\n the result will broadcast correctly against the input array.\n\n If the default value is passed, then `keepdims` will not be\n passed through to the `sum` method of sub-classes of\n `ndarray`, however any non-default value will be. If the\n sub-class' method does not implement `keepdims` any\n exceptions will be raised.\n initial : scalar, optional\n Starting value for the sum. See `~numpy.ufunc.reduce` for details.\n\n .. versionadded:: 1.15.0\n\n where : array_like of bool, optional\n Elements to include in the sum. See `~numpy.ufunc.reduce` for details.\n\n .. versionadded:: 1.17.0\n\n Returns\n -------\n sum_along_axis : ndarray\n An array with the same shape as `a`, with the specified\n axis removed. If `a` is a 0-d array, or if `axis` is None, a scalar\n is returned. If an output array is specified, a reference to\n `out` is returned.\n\n See Also\n --------\n ndarray.sum : Equivalent method.\n\n add.reduce : Equivalent functionality of `add`.\n\n cumsum : Cumulative sum of array elements.\n\n trapz : Integration of array values using the composite trapezoidal rule.\n\n mean, average\n\n Notes\n -----\n Arithmetic is modular when using integer types, and no error is\n raised on overflow.\n\n The sum of an empty array is the neutral element 0:\n\n >>> np.sum([])\n 0.0\n\n For floating point numbers the numerical precision of sum (and\n ``np.add.reduce``) is in general limited by directly adding each number\n individually to the result causing rounding errors in every step.\n However, often numpy will use a numerically better approach (partial\n pairwise summation) leading to improved precision in many use-cases.\n This improved precision is always provided when no ``axis`` is given.\n When ``axis`` is given, it will depend on which axis is summed.\n Technically, to provide the best speed possible, the improved precision\n is only used when the summation is along the fast axis in memory.\n Note that the exact precision may vary depending on other parameters.\n In contrast to NumPy, Python's ``math.fsum`` function uses a slower but\n more precise approach to summation.\n Especially when summing a large number of lower precision floating point\n numbers, such as ``float32``, numerical errors can become significant.\n In such cases it can be advisable to use `dtype=\"float64\"` to use a higher\n precision for the output.\n\n Examples\n --------\n >>> np.sum([0.5, 1.5])\n 2.0\n >>> np.sum([0.5, 0.7, 0.2, 1.5], dtype=np.int32)\n 1\n >>> np.sum([[0, 1], [0, 5]])\n 6\n >>> np.sum([[0, 1], [0, 5]], axis=0)\n array([0, 6])\n >>> np.sum([[0, 1], [0, 5]], axis=1)\n array([1, 5])\n >>> np.sum([[0, 1], [np.nan, 5]], where=[False, True], axis=1)\n array([1., 5.])\n\n If the accumulator is too small, overflow occurs:\n\n >>> np.ones(128, dtype=np.int8).sum(dtype=np.int8)\n -128\n\n You can also start the sum with a value other than zero:\n\n >>> np.sum([10], initial=5)\n 15\n \"\"\"\n if isinstance(a, _gentype):\n # 2018-02-25, 1.15.0\n warnings.warn(\n \"Calling np.sum(generator) is deprecated, and in the future will give a different result. \"\n \"Use np.sum(np.fromiter(generator)) or the python sum builtin instead.\",\n DeprecationWarning, stacklevel=3)\n\n res = _sum_(a)\n if out is not None:\n out[...] = res\n return out\n return res\n\n return _wrapreduction(a, np.add, 'sum', axis, dtype, out, keepdims=keepdims,\n initial=initial, where=where)\n\n\ndef _any_dispatcher(a, axis=None, out=None, keepdims=None):\n return (a, out)\n\n\n@array_function_dispatch(_any_dispatcher)\ndef any(a, axis=None, out=None, keepdims=np._NoValue):\n \"\"\"\n Test whether any array element along a given axis evaluates to True.\n\n Returns single boolean unless `axis` is not ``None``\n\n Parameters\n ----------\n a : array_like\n Input array or object that can be converted to an array.\n axis : None or int or tuple of ints, optional\n Axis or axes along which a logical OR reduction is performed.\n The default (``axis=None``) is to perform a logical OR over all\n the dimensions of the input array. `axis` may be negative, in\n which case it counts from the last to the first axis.\n\n .. versionadded:: 1.7.0\n\n If this is a tuple of ints, a reduction is performed on multiple\n axes, instead of a single axis or all the axes as before.\n out : ndarray, optional\n Alternate output array in which to place the result. It must have\n the same shape as the expected output and its type is preserved\n (e.g., if it is of type float, then it will remain so, returning\n 1.0 for True and 0.0 for False, regardless of the type of `a`).\n See `ufuncs-output-type` for more details.\n\n keepdims : bool, optional\n If this is set to True, the axes which are reduced are left\n in the result as dimensions with size one. With this option,\n the result will broadcast correctly against the input array.\n\n If the default value is passed, then `keepdims` will not be\n passed through to the `any` method of sub-classes of\n `ndarray`, however any non-default value will be. If the\n sub-class' method does not implement `keepdims` any\n exceptions will be raised.\n\n Returns\n -------\n any : bool or ndarray\n A new boolean or `ndarray` is returned unless `out` is specified,\n in which case a reference to `out` is returned.\n\n See Also\n --------\n ndarray.any : equivalent method\n\n all : Test whether all elements along a given axis evaluate to True.\n\n Notes\n -----\n Not a Number (NaN), positive infinity and negative infinity evaluate\n to `True` because these are not equal to zero.\n\n Examples\n --------\n >>> np.any([[True, False], [True, True]])\n True\n\n >>> np.any([[True, False], [False, False]], axis=0)\n array([ True, False])\n\n >>> np.any([-1, 0, 5])\n True\n\n >>> np.any(np.nan)\n True\n\n >>> o=np.array(False)\n >>> z=np.any([-1, 4, 5], out=o)\n >>> z, o\n (array(True), array(True))\n >>> # Check now that z is a reference to o\n >>> z is o\n True\n >>> id(z), id(o) # identity of z and o # doctest: +SKIP\n (191614240, 191614240)\n\n \"\"\"\n return _wrapreduction(a, np.logical_or, 'any', axis, None, out, keepdims=keepdims)\n\n\ndef _all_dispatcher(a, axis=None, out=None, keepdims=None):\n return (a, out)\n\n\n@array_function_dispatch(_all_dispatcher)\ndef all(a, axis=None, out=None, keepdims=np._NoValue):\n \"\"\"\n Test whether all array elements along a given axis evaluate to True.\n\n Parameters\n ----------\n a : array_like\n Input array or object that can be converted to an array.\n axis : None or int or tuple of ints, optional\n Axis or axes along which a logical AND reduction is performed.\n The default (``axis=None``) is to perform a logical AND over all\n the dimensions of the input array. `axis` may be negative, in\n which case it counts from the last to the first axis.\n\n .. versionadded:: 1.7.0\n\n If this is a tuple of ints, a reduction is performed on multiple\n axes, instead of a single axis or all the axes as before.\n out : ndarray, optional\n Alternate output array in which to place the result.\n It must have the same shape as the expected output and its\n type is preserved (e.g., if ``dtype(out)`` is float, the result\n will consist of 0.0's and 1.0's). See `ufuncs-output-type` for more\n details.\n\n keepdims : bool, optional\n If this is set to True, the axes which are reduced are left\n in the result as dimensions with size one. With this option,\n the result will broadcast correctly against the input array.\n\n If the default value is passed, then `keepdims` will not be\n passed through to the `all` method of sub-classes of\n `ndarray`, however any non-default value will be. If the\n sub-class' method does not implement `keepdims` any\n exceptions will be raised.\n\n Returns\n -------\n all : ndarray, bool\n A new boolean or array is returned unless `out` is specified,\n in which case a reference to `out` is returned.\n\n See Also\n --------\n ndarray.all : equivalent method\n\n any : Test whether any element along a given axis evaluates to True.\n\n Notes\n -----\n Not a Number (NaN), positive infinity and negative infinity\n evaluate to `True` because these are not equal to zero.\n\n Examples\n --------\n >>> np.all([[True,False],[True,True]])\n False\n\n >>> np.all([[True,False],[True,True]], axis=0)\n array([ True, False])\n\n >>> np.all([-1, 4, 5])\n True\n\n >>> np.all([1.0, np.nan])\n True\n\n >>> o=np.array(False)\n >>> z=np.all([-1, 4, 5], out=o)\n >>> id(z), id(o), z\n (28293632, 28293632, array(True)) # may vary\n\n \"\"\"\n return _wrapreduction(a, np.logical_and, 'all', axis, None, out, keepdims=keepdims)\n\n\ndef _cumsum_dispatcher(a, axis=None, dtype=None, out=None):\n return (a, out)\n\n\n@array_function_dispatch(_cumsum_dispatcher)\ndef cumsum(a, axis=None, dtype=None, out=None):\n \"\"\"\n Return the cumulative sum of the elements along a given axis.\n\n Parameters\n ----------\n a : array_like\n Input array.\n axis : int, optional\n Axis along which the cumulative sum is computed. The default\n (None) is to compute the cumsum over the flattened array.\n dtype : dtype, optional\n Type of the returned array and of the accumulator in which the\n elements are summed. If `dtype` is not specified, it defaults\n to the dtype of `a`, unless `a` has an integer dtype with a\n precision less than that of the default platform integer. In\n that case, the default platform integer is used.\n out : ndarray, optional\n Alternative output array in which to place the result. It must\n have the same shape and buffer length as the expected output\n but the type will be cast if necessary. See `ufuncs-output-type` for\n more details.\n\n Returns\n -------\n cumsum_along_axis : ndarray.\n A new array holding the result is returned unless `out` is\n specified, in which case a reference to `out` is returned. The\n result has the same size as `a`, and the same shape as `a` if\n `axis` is not None or `a` is a 1-d array.\n\n\n See Also\n --------\n sum : Sum array elements.\n\n trapz : Integration of array values using the composite trapezoidal rule.\n\n diff : Calculate the n-th discrete difference along given axis.\n\n Notes\n -----\n Arithmetic is modular when using integer types, and no error is\n raised on overflow.\n\n Examples\n --------\n >>> a = np.array([[1,2,3], [4,5,6]])\n >>> a\n array([[1, 2, 3],\n [4, 5, 6]])\n >>> np.cumsum(a)\n array([ 1, 3, 6, 10, 15, 21])\n >>> np.cumsum(a, dtype=float) # specifies type of output value(s)\n array([ 1., 3., 6., 10., 15., 21.])\n\n >>> np.cumsum(a,axis=0) # sum over rows for each of the 3 columns\n array([[1, 2, 3],\n [5, 7, 9]])\n >>> np.cumsum(a,axis=1) # sum over columns for each of the 2 rows\n array([[ 1, 3, 6],\n [ 4, 9, 15]])\n\n \"\"\"\n return _wrapfunc(a, 'cumsum', axis=axis, dtype=dtype, out=out)\n\n\ndef _ptp_dispatcher(a, axis=None, out=None, keepdims=None):\n return (a, out)\n\n\n@array_function_dispatch(_ptp_dispatcher)\ndef ptp(a, axis=None, out=None, keepdims=np._NoValue):\n \"\"\"\n Range of values (maximum - minimum) along an axis.\n\n The name of the function comes from the acronym for 'peak to peak'.\n\n .. warning::\n `ptp` preserves the data type of the array. This means the\n return value for an input of signed integers with n bits\n (e.g. `np.int8`, `np.int16`, etc) is also a signed integer\n with n bits. In that case, peak-to-peak values greater than\n ``2**(n-1)-1`` will be returned as negative values. An example\n with a work-around is shown below.\n\n Parameters\n ----------\n a : array_like\n Input values.\n axis : None or int or tuple of ints, optional\n Axis along which to find the peaks. By default, flatten the\n array. `axis` may be negative, in\n which case it counts from the last to the first axis.\n\n .. versionadded:: 1.15.0\n\n If this is a tuple of ints, a reduction is performed on multiple\n axes, instead of a single axis or all the axes as before.\n out : array_like\n Alternative output array in which to place the result. It must\n have the same shape and buffer length as the expected output,\n but the type of the output values will be cast if necessary.\n\n keepdims : bool, optional\n If this is set to True, the axes which are reduced are left\n in the result as dimensions with size one. With this option,\n the result will broadcast correctly against the input array.\n\n If the default value is passed, then `keepdims` will not be\n passed through to the `ptp` method of sub-classes of\n `ndarray`, however any non-default value will be. If the\n sub-class' method does not implement `keepdims` any\n exceptions will be raised.\n\n Returns\n -------\n ptp : ndarray\n A new array holding the result, unless `out` was\n specified, in which case a reference to `out` is returned.\n\n Examples\n --------\n >>> x = np.array([[4, 9, 2, 10],\n ... [6, 9, 7, 12]])\n\n >>> np.ptp(x, axis=1)\n array([8, 6])\n\n >>> np.ptp(x, axis=0)\n array([2, 0, 5, 2])\n\n >>> np.ptp(x)\n 10\n\n This example shows that a negative value can be returned when\n the input is an array of signed integers.\n\n >>> y = np.array([[1, 127],\n ... [0, 127],\n ... [-1, 127],\n ... [-2, 127]], dtype=np.int8)\n >>> np.ptp(y, axis=1)\n array([ 126, 127, -128, -127], dtype=int8)\n\n A work-around is to use the `view()` method to view the result as\n unsigned integers with the same bit width:\n\n >>> np.ptp(y, axis=1).view(np.uint8)\n array([126, 127, 128, 129], dtype=uint8)\n\n \"\"\"\n kwargs = {}\n if keepdims is not np._NoValue:\n kwargs['keepdims'] = keepdims\n if type(a) is not mu.ndarray:\n try:\n ptp = a.ptp\n except AttributeError:\n pass\n else:\n return ptp(axis=axis, out=out, **kwargs)\n return _methods._ptp(a, axis=axis, out=out, **kwargs)\n\n\ndef _amax_dispatcher(a, axis=None, out=None, keepdims=None, initial=None,\n where=None):\n return (a, out)\n\n\n@array_function_dispatch(_amax_dispatcher)\ndef amax(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue,\n where=np._NoValue):\n \"\"\"\n Return the maximum of an array or maximum along an axis.\n\n Parameters\n ----------\n a : array_like\n Input data.\n axis : None or int or tuple of ints, optional\n Axis or axes along which to operate. By default, flattened input is\n used.\n\n .. versionadded:: 1.7.0\n\n If this is a tuple of ints, the maximum is selected over multiple axes,\n instead of a single axis or all the axes as before.\n out : ndarray, optional\n Alternative output array in which to place the result. Must\n be of the same shape and buffer length as the expected output.\n See `ufuncs-output-type` for more details.\n\n keepdims : bool, optional\n If this is set to True, the axes which are reduced are left\n in the result as dimensions with size one. With this option,\n the result will broadcast correctly against the input array.\n\n If the default value is passed, then `keepdims` will not be\n passed through to the `amax` method of sub-classes of\n `ndarray`, however any non-default value will be. If the\n sub-class' method does not implement `keepdims` any\n exceptions will be raised.\n\n initial : scalar, optional\n The minimum value of an output element. Must be present to allow\n computation on empty slice. See `~numpy.ufunc.reduce` for details.\n\n .. versionadded:: 1.15.0\n\n where : array_like of bool, optional\n Elements to compare for the maximum. See `~numpy.ufunc.reduce`\n for details.\n\n .. versionadded:: 1.17.0\n\n Returns\n -------\n amax : ndarray or scalar\n Maximum of `a`. If `axis` is None, the result is a scalar value.\n If `axis` is given, the result is an array of dimension\n ``a.ndim - 1``.\n\n See Also\n --------\n amin :\n The minimum value of an array along a given axis, propagating any NaNs.\n nanmax :\n The maximum value of an array along a given axis, ignoring any NaNs.\n maximum :\n Element-wise maximum of two arrays, propagating any NaNs.\n fmax :\n Element-wise maximum of two arrays, ignoring any NaNs.\n argmax :\n Return the indices of the maximum values.\n\n nanmin, minimum, fmin\n\n Notes\n -----\n NaN values are propagated, that is if at least one item is NaN, the\n corresponding max value will be NaN as well. To ignore NaN values\n (MATLAB behavior), please use nanmax.\n\n Don't use `amax` for element-wise comparison of 2 arrays; when\n ``a.shape[0]`` is 2, ``maximum(a[0], a[1])`` is faster than\n ``amax(a, axis=0)``.\n\n Examples\n --------\n >>> a = np.arange(4).reshape((2,2))\n >>> a\n array([[0, 1],\n [2, 3]])\n >>> np.amax(a) # Maximum of the flattened array\n 3\n >>> np.amax(a, axis=0) # Maxima along the first axis\n array([2, 3])\n >>> np.amax(a, axis=1) # Maxima along the second axis\n array([1, 3])\n >>> np.amax(a, where=[False, True], initial=-1, axis=0)\n array([-1, 3])\n >>> b = np.arange(5, dtype=float)\n >>> b[2] = np.NaN\n >>> np.amax(b)\n nan\n >>> np.amax(b, where=~np.isnan(b), initial=-1)\n 4.0\n >>> np.nanmax(b)\n 4.0\n\n You can use an initial value to compute the maximum of an empty slice, or\n to initialize it to a different value:\n\n >>> np.max([[-50], [10]], axis=-1, initial=0)\n array([ 0, 10])\n\n Notice that the initial value is used as one of the elements for which the\n maximum is determined, unlike for the default argument Python's max\n function, which is only used for empty iterables.\n\n >>> np.max([5], initial=6)\n 6\n >>> max([5], default=6)\n 5\n \"\"\"\n return _wrapreduction(a, np.maximum, 'max', axis, None, out,\n keepdims=keepdims, initial=initial, where=where)\n\n\ndef _amin_dispatcher(a, axis=None, out=None, keepdims=None, initial=None,\n where=None):\n return (a, out)\n\n\n@array_function_dispatch(_amin_dispatcher)\ndef amin(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue,\n where=np._NoValue):\n \"\"\"\n Return the minimum of an array or minimum along an axis.\n\n Parameters\n ----------\n a : array_like\n Input data.\n axis : None or int or tuple of ints, optional\n Axis or axes along which to operate. By default, flattened input is\n used.\n\n .. versionadded:: 1.7.0\n\n If this is a tuple of ints, the minimum is selected over multiple axes,\n instead of a single axis or all the axes as before.\n out : ndarray, optional\n Alternative output array in which to place the result. Must\n be of the same shape and buffer length as the expected output.\n See `ufuncs-output-type` for more details.\n\n keepdims : bool, optional\n If this is set to True, the axes which are reduced are left\n in the result as dimensions with size one. With this option,\n the result will broadcast correctly against the input array.\n\n If the default value is passed, then `keepdims` will not be\n passed through to the `amin` method of sub-classes of\n `ndarray`, however any non-default value will be. If the\n sub-class' method does not implement `keepdims` any\n exceptions will be raised.\n\n initial : scalar, optional\n The maximum value of an output element. Must be present to allow\n computation on empty slice. See `~numpy.ufunc.reduce` for details.\n\n .. versionadded:: 1.15.0\n\n where : array_like of bool, optional\n Elements to compare for the minimum. See `~numpy.ufunc.reduce`\n for details.\n\n .. versionadded:: 1.17.0\n\n Returns\n -------\n amin : ndarray or scalar\n Minimum of `a`. If `axis` is None, the result is a scalar value.\n If `axis` is given, the result is an array of dimension\n ``a.ndim - 1``.\n\n See Also\n --------\n amax :\n The maximum value of an array along a given axis, propagating any NaNs.\n nanmin :\n The minimum value of an array along a given axis, ignoring any NaNs.\n minimum :\n Element-wise minimum of two arrays, propagating any NaNs.\n fmin :\n Element-wise minimum of two arrays, ignoring any NaNs.\n argmin :\n Return the indices of the minimum values.\n\n nanmax, maximum, fmax\n\n Notes\n -----\n NaN values are propagated, that is if at least one item is NaN, the\n corresponding min value will be NaN as well. To ignore NaN values\n (MATLAB behavior), please use nanmin.\n\n Don't use `amin` for element-wise comparison of 2 arrays; when\n ``a.shape[0]`` is 2, ``minimum(a[0], a[1])`` is faster than\n ``amin(a, axis=0)``.\n\n Examples\n --------\n >>> a = np.arange(4).reshape((2,2))\n >>> a\n array([[0, 1],\n [2, 3]])\n >>> np.amin(a) # Minimum of the flattened array\n 0\n >>> np.amin(a, axis=0) # Minima along the first axis\n array([0, 1])\n >>> np.amin(a, axis=1) # Minima along the second axis\n array([0, 2])\n >>> np.amin(a, where=[False, True], initial=10, axis=0)\n array([10, 1])\n\n >>> b = np.arange(5, dtype=float)\n >>> b[2] = np.NaN\n >>> np.amin(b)\n nan\n >>> np.amin(b, where=~np.isnan(b), initial=10)\n 0.0\n >>> np.nanmin(b)\n 0.0\n\n >>> np.min([[-50], [10]], axis=-1, initial=0)\n array([-50, 0])\n\n Notice that the initial value is used as one of the elements for which the\n minimum is determined, unlike for the default argument Python's max\n function, which is only used for empty iterables.\n\n Notice that this isn't the same as Python's ``default`` argument.\n\n >>> np.min([6], initial=5)\n 5\n >>> min([6], default=5)\n 6\n \"\"\"\n return _wrapreduction(a, np.minimum, 'min', axis, None, out,\n keepdims=keepdims, initial=initial, where=where)\n\n\ndef _alen_dispathcer(a):\n return (a,)\n\n\n@array_function_dispatch(_alen_dispathcer)\ndef alen(a):\n \"\"\"\n Return the length of the first dimension of the input array.\n\n Parameters\n ----------\n a : array_like\n Input array.\n\n Returns\n -------\n alen : int\n Length of the first dimension of `a`.\n\n See Also\n --------\n shape, size\n\n Examples\n --------\n >>> a = np.zeros((7,4,5))\n >>> a.shape[0]\n 7\n >>> np.alen(a)\n 7\n\n \"\"\"\n # NumPy 1.18.0, 2019-08-02\n warnings.warn(\n \"`np.alen` is deprecated, use `len` instead\",\n DeprecationWarning, stacklevel=2)\n try:\n return len(a)\n except TypeError:\n return len(array(a, ndmin=1))\n\n\ndef _prod_dispatcher(a, axis=None, dtype=None, out=None, keepdims=None,\n initial=None, where=None):\n return (a, out)\n\n\n@array_function_dispatch(_prod_dispatcher)\ndef prod(a, axis=None, dtype=None, out=None, keepdims=np._NoValue,\n initial=np._NoValue, where=np._NoValue):\n \"\"\"\n Return the product of array elements over a given axis.\n\n Parameters\n ----------\n a : array_like\n Input data.\n axis : None or int or tuple of ints, optional\n Axis or axes along which a product is performed. The default,\n axis=None, will calculate the product of all the elements in the\n input array. If axis is negative it counts from the last to the\n first axis.\n\n .. versionadded:: 1.7.0\n\n If axis is a tuple of ints, a product is performed on all of the\n axes specified in the tuple instead of a single axis or all the\n axes as before.\n dtype : dtype, optional\n The type of the returned array, as well as of the accumulator in\n which the elements are multiplied. The dtype of `a` is used by\n default unless `a` has an integer dtype of less precision than the\n default platform integer. In that case, if `a` is signed then the\n platform integer is used while if `a` is unsigned then an unsigned\n integer of the same precision as the platform integer is used.\n out : ndarray, optional\n Alternative output array in which to place the result. It must have\n the same shape as the expected output, but the type of the output\n values will be cast if necessary.\n keepdims : bool, optional\n If this is set to True, the axes which are reduced are left in the\n result as dimensions with size one. With this option, the result\n will broadcast correctly against the input array.\n\n If the default value is passed, then `keepdims` will not be\n passed through to the `prod` method of sub-classes of\n `ndarray`, however any non-default value will be. If the\n sub-class' method does not implement `keepdims` any\n exceptions will be raised.\n initial : scalar, optional\n The starting value for this product. See `~numpy.ufunc.reduce` for details.\n\n .. versionadded:: 1.15.0\n\n where : array_like of bool, optional\n Elements to include in the product. See `~numpy.ufunc.reduce` for details.\n\n .. versionadded:: 1.17.0\n\n Returns\n -------\n product_along_axis : ndarray, see `dtype` parameter above.\n An array shaped as `a` but with the specified axis removed.\n Returns a reference to `out` if specified.\n\n See Also\n --------\n ndarray.prod : equivalent method\n ufuncs-output-type\n\n Notes\n -----\n Arithmetic is modular when using integer types, and no error is\n raised on overflow. That means that, on a 32-bit platform:\n\n >>> x = np.array([536870910, 536870910, 536870910, 536870910])\n >>> np.prod(x)\n 16 # may vary\n\n The product of an empty array is the neutral element 1:\n\n >>> np.prod([])\n 1.0\n\n Examples\n --------\n By default, calculate the product of all elements:\n\n >>> np.prod([1.,2.])\n 2.0\n\n Even when the input array is two-dimensional:\n\n >>> np.prod([[1.,2.],[3.,4.]])\n 24.0\n\n But we can also specify the axis over which to multiply:\n\n >>> np.prod([[1.,2.],[3.,4.]], axis=1)\n array([ 2., 12.])\n\n Or select specific elements to include:\n\n >>> np.prod([1., np.nan, 3.], where=[True, False, True])\n 3.0\n\n If the type of `x` is unsigned, then the output type is\n the unsigned platform integer:\n\n >>> x = np.array([1, 2, 3], dtype=np.uint8)\n >>> np.prod(x).dtype == np.uint\n True\n\n If `x` is of a signed integer type, then the output type\n is the default platform integer:\n\n >>> x = np.array([1, 2, 3], dtype=np.int8)\n >>> np.prod(x).dtype == int\n True\n\n You can also start the product with a value other than one:\n\n >>> np.prod([1, 2], initial=5)\n 10\n \"\"\"\n return _wrapreduction(a, np.multiply, 'prod', axis, dtype, out,\n keepdims=keepdims, initial=initial, where=where)\n\n\ndef _cumprod_dispatcher(a, axis=None, dtype=None, out=None):\n return (a, out)\n\n\n@array_function_dispatch(_cumprod_dispatcher)\ndef cumprod(a, axis=None, dtype=None, out=None):\n \"\"\"\n Return the cumulative product of elements along a given axis.\n\n Parameters\n ----------\n a : array_like\n Input array.\n axis : int, optional\n Axis along which the cumulative product is computed. By default\n the input is flattened.\n dtype : dtype, optional\n Type of the returned array, as well as of the accumulator in which\n the elements are multiplied. If *dtype* is not specified, it\n defaults to the dtype of `a`, unless `a` has an integer dtype with\n a precision less than that of the default platform integer. In\n that case, the default platform integer is used instead.\n out : ndarray, optional\n Alternative output array in which to place the result. It must\n have the same shape and buffer length as the expected output\n but the type of the resulting values will be cast if necessary.\n\n Returns\n -------\n cumprod : ndarray\n A new array holding the result is returned unless `out` is\n specified, in which case a reference to out is returned.\n\n See Also\n --------\n ufuncs-output-type\n\n Notes\n -----\n Arithmetic is modular when using integer types, and no error is\n raised on overflow.\n\n Examples\n --------\n >>> a = np.array([1,2,3])\n >>> np.cumprod(a) # intermediate results 1, 1*2\n ... # total product 1*2*3 = 6\n array([1, 2, 6])\n >>> a = np.array([[1, 2, 3], [4, 5, 6]])\n >>> np.cumprod(a, dtype=float) # specify type of output\n array([ 1., 2., 6., 24., 120., 720.])\n\n The cumulative product for each column (i.e., over the rows) of `a`:\n\n >>> np.cumprod(a, axis=0)\n array([[ 1, 2, 3],\n [ 4, 10, 18]])\n\n The cumulative product for each row (i.e. over the columns) of `a`:\n\n >>> np.cumprod(a,axis=1)\n array([[ 1, 2, 6],\n [ 4, 20, 120]])\n\n \"\"\"\n return _wrapfunc(a, 'cumprod', axis=axis, dtype=dtype, out=out)\n\n\ndef _ndim_dispatcher(a):\n return (a,)\n\n\n@array_function_dispatch(_ndim_dispatcher)\ndef ndim(a):\n \"\"\"\n Return the number of dimensions of an array.\n\n Parameters\n ----------\n a : array_like\n Input array. If it is not already an ndarray, a conversion is\n attempted.\n\n Returns\n -------\n number_of_dimensions : int\n The number of dimensions in `a`. Scalars are zero-dimensional.\n\n See Also\n --------\n ndarray.ndim : equivalent method\n shape : dimensions of array\n ndarray.shape : dimensions of array\n\n Examples\n --------\n >>> np.ndim([[1,2,3],[4,5,6]])\n 2\n >>> np.ndim(np.array([[1,2,3],[4,5,6]]))\n 2\n >>> np.ndim(1)\n 0\n\n \"\"\"\n try:\n return a.ndim\n except AttributeError:\n return asarray(a).ndim\n\n\ndef _size_dispatcher(a, axis=None):\n return (a,)\n\n\n@array_function_dispatch(_size_dispatcher)\ndef size(a, axis=None):\n \"\"\"\n Return the number of elements along a given axis.\n\n Parameters\n ----------\n a : array_like\n Input data.\n axis : int, optional\n Axis along which the elements are counted. By default, give\n the total number of elements.\n\n Returns\n -------\n element_count : int\n Number of elements along the specified axis.\n\n See Also\n --------\n shape : dimensions of array\n ndarray.shape : dimensions of array\n ndarray.size : number of elements in array\n\n Examples\n --------\n >>> a = np.array([[1,2,3],[4,5,6]])\n >>> np.size(a)\n 6\n >>> np.size(a,1)\n 3\n >>> np.size(a,0)\n 2\n\n \"\"\"\n if axis is None:\n try:\n return a.size\n except AttributeError:\n return asarray(a).size\n else:\n try:\n return a.shape[axis]\n except AttributeError:\n return asarray(a).shape[axis]\n\n\ndef _around_dispatcher(a, decimals=None, out=None):\n return (a, out)\n\n\n@array_function_dispatch(_around_dispatcher)\ndef around(a, decimals=0, out=None):\n \"\"\"\n Evenly round to the given number of decimals.\n\n Parameters\n ----------\n a : array_like\n Input data.\n decimals : int, optional\n Number of decimal places to round to (default: 0). If\n decimals is negative, it specifies the number of positions to\n the left of the decimal point.\n out : ndarray, optional\n Alternative output array in which to place the result. It must have\n the same shape as the expected output, but the type of the output\n values will be cast if necessary. See `ufuncs-output-type` for more\n details.\n\n Returns\n -------\n rounded_array : ndarray\n An array of the same type as `a`, containing the rounded values.\n Unless `out` was specified, a new array is created. A reference to\n the result is returned.\n\n The real and imaginary parts of complex numbers are rounded\n separately. The result of rounding a float is a float.\n\n See Also\n --------\n ndarray.round : equivalent method\n\n ceil, fix, floor, rint, trunc\n\n\n Notes\n -----\n For values exactly halfway between rounded decimal values, NumPy\n rounds to the nearest even value. Thus 1.5 and 2.5 round to 2.0,\n -0.5 and 0.5 round to 0.0, etc.\n\n ``np.around`` uses a fast but sometimes inexact algorithm to round\n floating-point datatypes. For positive `decimals` it is equivalent to\n ``np.true_divide(np.rint(a * 10**decimals), 10**decimals)``, which has\n error due to the inexact representation of decimal fractions in the IEEE\n floating point standard [1]_ and errors introduced when scaling by powers\n of ten. For instance, note the extra \"1\" in the following:\n\n >>> np.round(56294995342131.5, 3)\n 56294995342131.51\n\n If your goal is to print such values with a fixed number of decimals, it is\n preferable to use numpy's float printing routines to limit the number of\n printed decimals:\n\n >>> np.format_float_positional(56294995342131.5, precision=3)\n '56294995342131.5'\n\n The float printing routines use an accurate but much more computationally\n demanding algorithm to compute the number of digits after the decimal\n point.\n\n Alternatively, Python's builtin `round` function uses a more accurate\n but slower algorithm for 64-bit floating point values:\n\n >>> round(56294995342131.5, 3)\n 56294995342131.5\n >>> np.round(16.055, 2), round(16.055, 2) # equals 16.0549999999999997\n (16.06, 16.05)\n\n\n References\n ----------\n .. [1] \"Lecture Notes on the Status of IEEE 754\", William Kahan,\n https://people.eecs.berkeley.edu/~wkahan/ieee754status/IEEE754.PDF\n .. [2] \"How Futile are Mindless Assessments of\n Roundoff in Floating-Point Computation?\", William Kahan,\n https://people.eecs.berkeley.edu/~wkahan/Mindless.pdf\n\n Examples\n --------\n >>> np.around([0.37, 1.64])\n array([0., 2.])\n >>> np.around([0.37, 1.64], decimals=1)\n array([0.4, 1.6])\n >>> np.around([.5, 1.5, 2.5, 3.5, 4.5]) # rounds to nearest even value\n array([0., 2., 2., 4., 4.])\n >>> np.around([1,2,3,11], decimals=1) # ndarray of ints is returned\n array([ 1, 2, 3, 11])\n >>> np.around([1,2,3,11], decimals=-1)\n array([ 0, 0, 0, 10])\n\n \"\"\"\n return _wrapfunc(a, 'round', decimals=decimals, out=out)\n\n\ndef _mean_dispatcher(a, axis=None, dtype=None, out=None, keepdims=None):\n return (a, out)\n\n\n@array_function_dispatch(_mean_dispatcher)\ndef mean(a, axis=None, dtype=None, out=None, keepdims=np._NoValue):\n \"\"\"\n Compute the arithmetic mean along the specified axis.\n\n Returns the average of the array elements. The average is taken over\n the flattened array by default, otherwise over the specified axis.\n `float64` intermediate and return values are used for integer inputs.\n\n Parameters\n ----------\n a : array_like\n Array containing numbers whose mean is desired. If `a` is not an\n array, a conversion is attempted.\n axis : None or int or tuple of ints, optional\n Axis or axes along which the means are computed. The default is to\n compute the mean of the flattened array.\n\n .. versionadded:: 1.7.0\n\n If this is a tuple of ints, a mean is performed over multiple axes,\n instead of a single axis or all the axes as before.\n dtype : data-type, optional\n Type to use in computing the mean. For integer inputs, the default\n is `float64`; for floating point inputs, it is the same as the\n input dtype.\n out : ndarray, optional\n Alternate output array in which to place the result. The default\n is ``None``; if provided, it must have the same shape as the\n expected output, but the type will be cast if necessary.\n See `ufuncs-output-type` for more details.\n\n keepdims : bool, optional\n If this is set to True, the axes which are reduced are left\n in the result as dimensions with size one. With this option,\n the result will broadcast correctly against the input array.\n\n If the default value is passed, then `keepdims` will not be\n passed through to the `mean` method of sub-classes of\n `ndarray`, however any non-default value will be. If the\n sub-class' method does not implement `keepdims` any\n exceptions will be raised.\n\n Returns\n -------\n m : ndarray, see dtype parameter above\n If `out=None`, returns a new array containing the mean values,\n otherwise a reference to the output array is returned.\n\n See Also\n --------\n average : Weighted average\n std, var, nanmean, nanstd, nanvar\n\n Notes\n -----\n The arithmetic mean is the sum of the elements along the axis divided\n by the number of elements.\n\n Note that for floating-point input, the mean is computed using the\n same precision the input has. Depending on the input data, this can\n cause the results to be inaccurate, especially for `float32` (see\n example below). Specifying a higher-precision accumulator using the\n `dtype` keyword can alleviate this issue.\n\n By default, `float16` results are computed using `float32` intermediates\n for extra precision.\n\n Examples\n --------\n >>> a = np.array([[1, 2], [3, 4]])\n >>> np.mean(a)\n 2.5\n >>> np.mean(a, axis=0)\n array([2., 3.])\n >>> np.mean(a, axis=1)\n array([1.5, 3.5])\n\n In single precision, `mean` can be inaccurate:\n\n >>> a = np.zeros((2, 512*512), dtype=np.float32)\n >>> a[0, :] = 1.0\n >>> a[1, :] = 0.1\n >>> np.mean(a)\n 0.54999924\n\n Computing the mean in float64 is more accurate:\n\n >>> np.mean(a, dtype=np.float64)\n 0.55000000074505806 # may vary\n\n \"\"\"\n kwargs = {}\n if keepdims is not np._NoValue:\n kwargs['keepdims'] = keepdims\n if type(a) is not mu.ndarray:\n try:\n mean = a.mean\n except AttributeError:\n pass\n else:\n return mean(axis=axis, dtype=dtype, out=out, **kwargs)\n\n return _methods._mean(a, axis=axis, dtype=dtype,\n out=out, **kwargs)\n\n\ndef _std_dispatcher(\n a, axis=None, dtype=None, out=None, ddof=None, keepdims=None):\n return (a, out)\n\n\n@array_function_dispatch(_std_dispatcher)\ndef std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue):\n \"\"\"\n Compute the standard deviation along the specified axis.\n\n Returns the standard deviation, a measure of the spread of a distribution,\n of the array elements. The standard deviation is computed for the\n flattened array by default, otherwise over the specified axis.\n\n Parameters\n ----------\n a : array_like\n Calculate the standard deviation of these values.\n axis : None or int or tuple of ints, optional\n Axis or axes along which the standard deviation is computed. The\n default is to compute the standard deviation of the flattened array.\n\n .. versionadded:: 1.7.0\n\n If this is a tuple of ints, a standard deviation is performed over\n multiple axes, instead of a single axis or all the axes as before.\n dtype : dtype, optional\n Type to use in computing the standard deviation. For arrays of\n integer type the default is float64, for arrays of float types it is\n the same as the array type.\n out : ndarray, optional\n Alternative output array in which to place the result. It must have\n the same shape as the expected output but the type (of the calculated\n values) will be cast if necessary.\n ddof : int, optional\n Means Delta Degrees of Freedom. The divisor used in calculations\n is ``N - ddof``, where ``N`` represents the number of elements.\n By default `ddof` is zero.\n keepdims : bool, optional\n If this is set to True, the axes which are reduced are left\n in the result as dimensions with size one. With this option,\n the result will broadcast correctly against the input array.\n\n If the default value is passed, then `keepdims` will not be\n passed through to the `std` method of sub-classes of\n `ndarray`, however any non-default value will be. If the\n sub-class' method does not implement `keepdims` any\n exceptions will be raised.\n\n Returns\n -------\n standard_deviation : ndarray, see dtype parameter above.\n If `out` is None, return a new array containing the standard deviation,\n otherwise return a reference to the output array.\n\n See Also\n --------\n var, mean, nanmean, nanstd, nanvar\n ufuncs-output-type\n\n Notes\n -----\n The standard deviation is the square root of the average of the squared\n deviations from the mean, i.e., ``std = sqrt(mean(abs(x - x.mean())**2))``.\n\n The average squared deviation is normally calculated as\n ``x.sum() / N``, where ``N = len(x)``. If, however, `ddof` is specified,\n the divisor ``N - ddof`` is used instead. In standard statistical\n practice, ``ddof=1`` provides an unbiased estimator of the variance\n of the infinite population. ``ddof=0`` provides a maximum likelihood\n estimate of the variance for normally distributed variables. The\n standard deviation computed in this function is the square root of\n the estimated variance, so even with ``ddof=1``, it will not be an\n unbiased estimate of the standard deviation per se.\n\n Note that, for complex numbers, `std` takes the absolute\n value before squaring, so that the result is always real and nonnegative.\n\n For floating-point input, the *std* is computed using the same\n precision the input has. Depending on the input data, this can cause\n the results to be inaccurate, especially for float32 (see example below).\n Specifying a higher-accuracy accumulator using the `dtype` keyword can\n alleviate this issue.\n\n Examples\n --------\n >>> a = np.array([[1, 2], [3, 4]])\n >>> np.std(a)\n 1.1180339887498949 # may vary\n >>> np.std(a, axis=0)\n array([1., 1.])\n >>> np.std(a, axis=1)\n array([0.5, 0.5])\n\n In single precision, std() can be inaccurate:\n\n >>> a = np.zeros((2, 512*512), dtype=np.float32)\n >>> a[0, :] = 1.0\n >>> a[1, :] = 0.1\n >>> np.std(a)\n 0.45000005\n\n Computing the standard deviation in float64 is more accurate:\n\n >>> np.std(a, dtype=np.float64)\n 0.44999999925494177 # may vary\n\n \"\"\"\n kwargs = {}\n if keepdims is not np._NoValue:\n kwargs['keepdims'] = keepdims\n\n if type(a) is not mu.ndarray:\n try:\n std = a.std\n except AttributeError:\n pass\n else:\n return std(axis=axis, dtype=dtype, out=out, ddof=ddof, **kwargs)\n\n return _methods._std(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n **kwargs)\n\n\ndef _var_dispatcher(\n a, axis=None, dtype=None, out=None, ddof=None, keepdims=None):\n return (a, out)\n\n\n@array_function_dispatch(_var_dispatcher)\ndef var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue):\n \"\"\"\n Compute the variance along the specified axis.\n\n Returns the variance of the array elements, a measure of the spread of a\n distribution. The variance is computed for the flattened array by\n default, otherwise over the specified axis.\n\n Parameters\n ----------\n a : array_like\n Array containing numbers whose variance is desired. If `a` is not an\n array, a conversion is attempted.\n axis : None or int or tuple of ints, optional\n Axis or axes along which the variance is computed. The default is to\n compute the variance of the flattened array.\n\n .. versionadded:: 1.7.0\n\n If this is a tuple of ints, a variance is performed over multiple axes,\n instead of a single axis or all the axes as before.\n dtype : data-type, optional\n Type to use in computing the variance. For arrays of integer type\n the default is `float64`; for arrays of float types it is the same as\n the array type.\n out : ndarray, optional\n Alternate output array in which to place the result. It must have\n the same shape as the expected output, but the type is cast if\n necessary.\n ddof : int, optional\n \"Delta Degrees of Freedom\": the divisor used in the calculation is\n ``N - ddof``, where ``N`` represents the number of elements. By\n default `ddof` is zero.\n keepdims : bool, optional\n If this is set to True, the axes which are reduced are left\n in the result as dimensions with size one. With this option,\n the result will broadcast correctly against the input array.\n\n If the default value is passed, then `keepdims` will not be\n passed through to the `var` method of sub-classes of\n `ndarray`, however any non-default value will be. If the\n sub-class' method does not implement `keepdims` any\n exceptions will be raised.\n\n Returns\n -------\n variance : ndarray, see dtype parameter above\n If ``out=None``, returns a new array containing the variance;\n otherwise, a reference to the output array is returned.\n\n See Also\n --------\n std, mean, nanmean, nanstd, nanvar\n ufuncs-output-type\n\n Notes\n -----\n The variance is the average of the squared deviations from the mean,\n i.e., ``var = mean(abs(x - x.mean())**2)``.\n\n The mean is normally calculated as ``x.sum() / N``, where ``N = len(x)``.\n If, however, `ddof` is specified, the divisor ``N - ddof`` is used\n instead. In standard statistical practice, ``ddof=1`` provides an\n unbiased estimator of the variance of a hypothetical infinite population.\n ``ddof=0`` provides a maximum likelihood estimate of the variance for\n normally distributed variables.\n\n Note that for complex numbers, the absolute value is taken before\n squaring, so that the result is always real and nonnegative.\n\n For floating-point input, the variance is computed using the same\n precision the input has. Depending on the input data, this can cause\n the results to be inaccurate, especially for `float32` (see example\n below). Specifying a higher-accuracy accumulator using the ``dtype``\n keyword can alleviate this issue.\n\n Examples\n --------\n >>> a = np.array([[1, 2], [3, 4]])\n >>> np.var(a)\n 1.25\n >>> np.var(a, axis=0)\n array([1., 1.])\n >>> np.var(a, axis=1)\n array([0.25, 0.25])\n\n In single precision, var() can be inaccurate:\n\n >>> a = np.zeros((2, 512*512), dtype=np.float32)\n >>> a[0, :] = 1.0\n >>> a[1, :] = 0.1\n >>> np.var(a)\n 0.20250003\n\n Computing the variance in float64 is more accurate:\n\n >>> np.var(a, dtype=np.float64)\n 0.20249999932944759 # may vary\n >>> ((1-0.55)**2 + (0.1-0.55)**2)/2\n 0.2025\n\n \"\"\"\n kwargs = {}\n if keepdims is not np._NoValue:\n kwargs['keepdims'] = keepdims\n\n if type(a) is not mu.ndarray:\n try:\n var = a.var\n\n except AttributeError:\n pass\n else:\n return var(axis=axis, dtype=dtype, out=out, ddof=ddof, **kwargs)\n\n return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n **kwargs)\n\n\n# Aliases of other functions. These have their own definitions only so that\n# they can have unique docstrings.\n\n@array_function_dispatch(_around_dispatcher)\ndef round_(a, decimals=0, out=None):\n \"\"\"\n Round an array to the given number of decimals.\n\n See Also\n --------\n around : equivalent function; see for details.\n \"\"\"\n return around(a, decimals=decimals, out=out)\n\n\n@array_function_dispatch(_prod_dispatcher, verify=False)\ndef product(*args, **kwargs):\n \"\"\"\n Return the product of array elements over a given axis.\n\n See Also\n --------\n prod : equivalent function; see for details.\n \"\"\"\n return prod(*args, **kwargs)\n\n\n@array_function_dispatch(_cumprod_dispatcher, verify=False)\ndef cumproduct(*args, **kwargs):\n \"\"\"\n Return the cumulative product over the given axis.\n\n See Also\n --------\n cumprod : equivalent function; see for details.\n \"\"\"\n return cumprod(*args, **kwargs)\n\n\n@array_function_dispatch(_any_dispatcher, verify=False)\ndef sometrue(*args, **kwargs):\n \"\"\"\n Check whether some values are true.\n\n Refer to `any` for full documentation.\n\n See Also\n --------\n any : equivalent function; see for details.\n \"\"\"\n return any(*args, **kwargs)\n\n\n@array_function_dispatch(_all_dispatcher, verify=False)\ndef alltrue(*args, **kwargs):\n \"\"\"\n Check if all elements of input array are true.\n\n See Also\n --------\n numpy.all : Equivalent function; see for details.\n \"\"\"\n return all(*args, **kwargs)\n",3687],"/home/gaogaotiantian/programs/codesnap/venv/lib/python3.6/site-packages/numpy/core/_ufunc_config.py":["\"\"\"\nFunctions for changing global ufunc configuration\n\nThis provides helpers which wrap `umath.geterrobj` and `umath.seterrobj`\n\"\"\"\nimport collections.abc\nimport contextlib\n\nfrom .overrides import set_module\nfrom .umath import (\n UFUNC_BUFSIZE_DEFAULT,\n ERR_IGNORE, ERR_WARN, ERR_RAISE, ERR_CALL, ERR_PRINT, ERR_LOG, ERR_DEFAULT,\n SHIFT_DIVIDEBYZERO, SHIFT_OVERFLOW, SHIFT_UNDERFLOW, SHIFT_INVALID,\n)\nfrom . import umath\n\n__all__ = [\n \"seterr\", \"geterr\", \"setbufsize\", \"getbufsize\", \"seterrcall\", \"geterrcall\",\n \"errstate\",\n]\n\n_errdict = {\"ignore\": ERR_IGNORE,\n \"warn\": ERR_WARN,\n \"raise\": ERR_RAISE,\n \"call\": ERR_CALL,\n \"print\": ERR_PRINT,\n \"log\": ERR_LOG}\n\n_errdict_rev = {value: key for key, value in _errdict.items()}\n\n\n@set_module('numpy')\ndef seterr(all=None, divide=None, over=None, under=None, invalid=None):\n \"\"\"\n Set how floating-point errors are handled.\n\n Note that operations on integer scalar types (such as `int16`) are\n handled like floating point, and are affected by these settings.\n\n Parameters\n ----------\n all : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional\n Set treatment for all types of floating-point errors at once:\n\n - ignore: Take no action when the exception occurs.\n - warn: Print a `RuntimeWarning` (via the Python `warnings` module).\n - raise: Raise a `FloatingPointError`.\n - call: Call a function specified using the `seterrcall` function.\n - print: Print a warning directly to ``stdout``.\n - log: Record error in a Log object specified by `seterrcall`.\n\n The default is not to change the current behavior.\n divide : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional\n Treatment for division by zero.\n over : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional\n Treatment for floating-point overflow.\n under : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional\n Treatment for floating-point underflow.\n invalid : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional\n Treatment for invalid floating-point operation.\n\n Returns\n -------\n old_settings : dict\n Dictionary containing the old settings.\n\n See also\n --------\n seterrcall : Set a callback function for the 'call' mode.\n geterr, geterrcall, errstate\n\n Notes\n -----\n The floating-point exceptions are defined in the IEEE 754 standard [1]_:\n\n - Division by zero: infinite result obtained from finite numbers.\n - Overflow: result too large to be expressed.\n - Underflow: result so close to zero that some precision\n was lost.\n - Invalid operation: result is not an expressible number, typically\n indicates that a NaN was produced.\n\n .. [1] https://en.wikipedia.org/wiki/IEEE_754\n\n Examples\n --------\n >>> old_settings = np.seterr(all='ignore') #seterr to known value\n >>> np.seterr(over='raise')\n {'divide': 'ignore', 'over': 'ignore', 'under': 'ignore', 'invalid': 'ignore'}\n >>> np.seterr(**old_settings) # reset to default\n {'divide': 'ignore', 'over': 'raise', 'under': 'ignore', 'invalid': 'ignore'}\n\n >>> np.int16(32000) * np.int16(3)\n 30464\n >>> old_settings = np.seterr(all='warn', over='raise')\n >>> np.int16(32000) * np.int16(3)\n Traceback (most recent call last):\n File \"\", line 1, in \n FloatingPointError: overflow encountered in short_scalars\n\n >>> from collections import OrderedDict\n >>> old_settings = np.seterr(all='print')\n >>> OrderedDict(np.geterr())\n OrderedDict([('divide', 'print'), ('over', 'print'), ('under', 'print'), ('invalid', 'print')])\n >>> np.int16(32000) * np.int16(3)\n 30464\n\n \"\"\"\n\n pyvals = umath.geterrobj()\n old = geterr()\n\n if divide is None:\n divide = all or old['divide']\n if over is None:\n over = all or old['over']\n if under is None:\n under = all or old['under']\n if invalid is None:\n invalid = all or old['invalid']\n\n maskvalue = ((_errdict[divide] << SHIFT_DIVIDEBYZERO) +\n (_errdict[over] << SHIFT_OVERFLOW) +\n (_errdict[under] << SHIFT_UNDERFLOW) +\n (_errdict[invalid] << SHIFT_INVALID))\n\n pyvals[1] = maskvalue\n umath.seterrobj(pyvals)\n return old\n\n\n@set_module('numpy')\ndef geterr():\n \"\"\"\n Get the current way of handling floating-point errors.\n\n Returns\n -------\n res : dict\n A dictionary with keys \"divide\", \"over\", \"under\", and \"invalid\",\n whose values are from the strings \"ignore\", \"print\", \"log\", \"warn\",\n \"raise\", and \"call\". The keys represent possible floating-point\n exceptions, and the values define how these exceptions are handled.\n\n See Also\n --------\n geterrcall, seterr, seterrcall\n\n Notes\n -----\n For complete documentation of the types of floating-point exceptions and\n treatment options, see `seterr`.\n\n Examples\n --------\n >>> from collections import OrderedDict\n >>> sorted(np.geterr().items())\n [('divide', 'warn'), ('invalid', 'warn'), ('over', 'warn'), ('under', 'ignore')]\n >>> np.arange(3.) / np.arange(3.)\n array([nan, 1., 1.])\n\n >>> oldsettings = np.seterr(all='warn', over='raise')\n >>> OrderedDict(sorted(np.geterr().items()))\n OrderedDict([('divide', 'warn'), ('invalid', 'warn'), ('over', 'raise'), ('under', 'warn')])\n >>> np.arange(3.) / np.arange(3.)\n array([nan, 1., 1.])\n\n \"\"\"\n maskvalue = umath.geterrobj()[1]\n mask = 7\n res = {}\n val = (maskvalue >> SHIFT_DIVIDEBYZERO) & mask\n res['divide'] = _errdict_rev[val]\n val = (maskvalue >> SHIFT_OVERFLOW) & mask\n res['over'] = _errdict_rev[val]\n val = (maskvalue >> SHIFT_UNDERFLOW) & mask\n res['under'] = _errdict_rev[val]\n val = (maskvalue >> SHIFT_INVALID) & mask\n res['invalid'] = _errdict_rev[val]\n return res\n\n\n@set_module('numpy')\ndef setbufsize(size):\n \"\"\"\n Set the size of the buffer used in ufuncs.\n\n Parameters\n ----------\n size : int\n Size of buffer.\n\n \"\"\"\n if size > 10e6:\n raise ValueError(\"Buffer size, %s, is too big.\" % size)\n if size < 5:\n raise ValueError(\"Buffer size, %s, is too small.\" % size)\n if size % 16 != 0:\n raise ValueError(\"Buffer size, %s, is not a multiple of 16.\" % size)\n\n pyvals = umath.geterrobj()\n old = getbufsize()\n pyvals[0] = size\n umath.seterrobj(pyvals)\n return old\n\n\n@set_module('numpy')\ndef getbufsize():\n \"\"\"\n Return the size of the buffer used in ufuncs.\n\n Returns\n -------\n getbufsize : int\n Size of ufunc buffer in bytes.\n\n \"\"\"\n return umath.geterrobj()[0]\n\n\n@set_module('numpy')\ndef seterrcall(func):\n \"\"\"\n Set the floating-point error callback function or log object.\n\n There are two ways to capture floating-point error messages. The first\n is to set the error-handler to 'call', using `seterr`. Then, set\n the function to call using this function.\n\n The second is to set the error-handler to 'log', using `seterr`.\n Floating-point errors then trigger a call to the 'write' method of\n the provided object.\n\n Parameters\n ----------\n func : callable f(err, flag) or object with write method\n Function to call upon floating-point errors ('call'-mode) or\n object whose 'write' method is used to log such message ('log'-mode).\n\n The call function takes two arguments. The first is a string describing\n the type of error (such as \"divide by zero\", \"overflow\", \"underflow\",\n or \"invalid value\"), and the second is the status flag. The flag is a\n byte, whose four least-significant bits indicate the type of error, one\n of \"divide\", \"over\", \"under\", \"invalid\"::\n\n [0 0 0 0 divide over under invalid]\n\n In other words, ``flags = divide + 2*over + 4*under + 8*invalid``.\n\n If an object is provided, its write method should take one argument,\n a string.\n\n Returns\n -------\n h : callable, log instance or None\n The old error handler.\n\n See Also\n --------\n seterr, geterr, geterrcall\n\n Examples\n --------\n Callback upon error:\n\n >>> def err_handler(type, flag):\n ... print(\"Floating point error (%s), with flag %s\" % (type, flag))\n ...\n\n >>> saved_handler = np.seterrcall(err_handler)\n >>> save_err = np.seterr(all='call')\n >>> from collections import OrderedDict\n\n >>> np.array([1, 2, 3]) / 0.0\n Floating point error (divide by zero), with flag 1\n array([inf, inf, inf])\n\n >>> np.seterrcall(saved_handler)\n \n >>> OrderedDict(sorted(np.seterr(**save_err).items()))\n OrderedDict([('divide', 'call'), ('invalid', 'call'), ('over', 'call'), ('under', 'call')])\n\n Log error message:\n\n >>> class Log:\n ... def write(self, msg):\n ... print(\"LOG: %s\" % msg)\n ...\n\n >>> log = Log()\n >>> saved_handler = np.seterrcall(log)\n >>> save_err = np.seterr(all='log')\n\n >>> np.array([1, 2, 3]) / 0.0\n LOG: Warning: divide by zero encountered in true_divide\n array([inf, inf, inf])\n\n >>> np.seterrcall(saved_handler)\n \n >>> OrderedDict(sorted(np.seterr(**save_err).items()))\n OrderedDict([('divide', 'log'), ('invalid', 'log'), ('over', 'log'), ('under', 'log')])\n\n \"\"\"\n if func is not None and not isinstance(func, collections.abc.Callable):\n if (not hasattr(func, 'write') or\n not isinstance(func.write, collections.abc.Callable)):\n raise ValueError(\"Only callable can be used as callback\")\n pyvals = umath.geterrobj()\n old = geterrcall()\n pyvals[2] = func\n umath.seterrobj(pyvals)\n return old\n\n\n@set_module('numpy')\ndef geterrcall():\n \"\"\"\n Return the current callback function used on floating-point errors.\n\n When the error handling for a floating-point error (one of \"divide\",\n \"over\", \"under\", or \"invalid\") is set to 'call' or 'log', the function\n that is called or the log instance that is written to is returned by\n `geterrcall`. This function or log instance has been set with\n `seterrcall`.\n\n Returns\n -------\n errobj : callable, log instance or None\n The current error handler. If no handler was set through `seterrcall`,\n ``None`` is returned.\n\n See Also\n --------\n seterrcall, seterr, geterr\n\n Notes\n -----\n For complete documentation of the types of floating-point exceptions and\n treatment options, see `seterr`.\n\n Examples\n --------\n >>> np.geterrcall() # we did not yet set a handler, returns None\n\n >>> oldsettings = np.seterr(all='call')\n >>> def err_handler(type, flag):\n ... print(\"Floating point error (%s), with flag %s\" % (type, flag))\n >>> oldhandler = np.seterrcall(err_handler)\n >>> np.array([1, 2, 3]) / 0.0\n Floating point error (divide by zero), with flag 1\n array([inf, inf, inf])\n\n >>> cur_handler = np.geterrcall()\n >>> cur_handler is err_handler\n True\n\n \"\"\"\n return umath.geterrobj()[2]\n\n\nclass _unspecified:\n pass\n\n\n_Unspecified = _unspecified()\n\n\n@set_module('numpy')\nclass errstate(contextlib.ContextDecorator):\n \"\"\"\n errstate(**kwargs)\n\n Context manager for floating-point error handling.\n\n Using an instance of `errstate` as a context manager allows statements in\n that context to execute with a known error handling behavior. Upon entering\n the context the error handling is set with `seterr` and `seterrcall`, and\n upon exiting it is reset to what it was before.\n\n .. versionchanged:: 1.17.0\n `errstate` is also usable as a function decorator, saving\n a level of indentation if an entire function is wrapped.\n See :py:class:`contextlib.ContextDecorator` for more information.\n\n Parameters\n ----------\n kwargs : {divide, over, under, invalid}\n Keyword arguments. The valid keywords are the possible floating-point\n exceptions. Each keyword should have a string value that defines the\n treatment for the particular error. Possible values are\n {'ignore', 'warn', 'raise', 'call', 'print', 'log'}.\n\n See Also\n --------\n seterr, geterr, seterrcall, geterrcall\n\n Notes\n -----\n For complete documentation of the types of floating-point exceptions and\n treatment options, see `seterr`.\n\n Examples\n --------\n >>> from collections import OrderedDict\n >>> olderr = np.seterr(all='ignore') # Set error handling to known state.\n\n >>> np.arange(3) / 0.\n array([nan, inf, inf])\n >>> with np.errstate(divide='warn'):\n ... np.arange(3) / 0.\n array([nan, inf, inf])\n\n >>> np.sqrt(-1)\n nan\n >>> with np.errstate(invalid='raise'):\n ... np.sqrt(-1)\n Traceback (most recent call last):\n File \"\", line 2, in \n FloatingPointError: invalid value encountered in sqrt\n\n Outside the context the error handling behavior has not changed:\n\n >>> OrderedDict(sorted(np.geterr().items()))\n OrderedDict([('divide', 'ignore'), ('invalid', 'ignore'), ('over', 'ignore'), ('under', 'ignore')])\n\n \"\"\"\n\n def __init__(self, *, call=_Unspecified, **kwargs):\n self.call = call\n self.kwargs = kwargs\n\n def __enter__(self):\n self.oldstate = seterr(**self.kwargs)\n if self.call is not _Unspecified:\n self.oldcall = seterrcall(self.call)\n\n def __exit__(self, *exc_info):\n seterr(**self.oldstate)\n if self.call is not _Unspecified:\n seterrcall(self.oldcall)\n\n\ndef _setdef():\n defval = [UFUNC_BUFSIZE_DEFAULT, ERR_DEFAULT, None]\n umath.seterrobj(defval)\n\n\n# set the default values\n_setdef()\n",450]},"functions":{"_hypothesis_value (example/src/gradient_descent.py:34)":["example/src/gradient_descent.py",34],"calculate_hypothesis_value (example/src/gradient_descent.py:62)":["example/src/gradient_descent.py",62],"output (example/src/gradient_descent.py:50)":["example/src/gradient_descent.py",50],"_error (example/src/gradient_descent.py:23)":["example/src/gradient_descent.py",23],"summation_of_cost_derivative 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1), 8),\n ((11, 12, 13), 41),\n)\ntest_data = (((515, 22, 13), 555), ((61, 35, 49), 150))\nparameter_vector = [2, 4, 1, 5]\nm = len(train_data)\nLEARNING_RATE = 0.009\n\n\ndef _error(example_no, data_set=\"train\"):\n \"\"\"\n :param data_set: train data or test data\n :param example_no: example number whose error has to be checked\n :return: error in example pointed by example number.\n \"\"\"\n return calculate_hypothesis_value(example_no, data_set) - \\\n output(example_no, data_set)\n\n\ndef _hypothesis_value(data_input_tuple):\n \"\"\"\n Calculates hypothesis function value for a given input\n :param data_input_tuple: Input tuple of a particular example\n :return: Value of hypothesis function at that point.\n Note that there is an 'biased input' whose value is fixed as 1.\n It is not explicitly mentioned in input data.. But, ML hypothesis functions use it.\n So, we have to take care of it separately. Line 36 takes care of it.\n \"\"\"\n hyp_val = 0\n for i in range(len(parameter_vector) - 1):\n hyp_val += data_input_tuple[i] * parameter_vector[i + 1]\n hyp_val += parameter_vector[0]\n return hyp_val\n\n\ndef output(example_no, data_set):\n \"\"\"\n :param data_set: test data or train data\n :param example_no: example whose output is to be fetched\n :return: output for that example\n \"\"\"\n if data_set == \"train\":\n return train_data[example_no][1]\n elif data_set == \"test\":\n return test_data[example_no][1]\n\n\ndef calculate_hypothesis_value(example_no, data_set):\n \"\"\"\n Calculates hypothesis value for a given example\n :param data_set: test data or train_data\n :param example_no: example whose hypothesis value is to be calculated\n :return: hypothesis value for that example\n \"\"\"\n if data_set == \"train\":\n return _hypothesis_value(train_data[example_no][0])\n elif data_set == \"test\":\n return _hypothesis_value(test_data[example_no][0])\n\n\ndef summation_of_cost_derivative(index, end=m):\n \"\"\"\n Calculates the sum of cost function derivative\n :param index: index wrt derivative is being calculated\n :param end: value where summation ends, default is m, number of examples\n :return: Returns the summation of cost derivative\n Note: If index is -1, this means we are calculating summation wrt to biased\n parameter.\n \"\"\"\n summation_value = 0\n for i in range(end):\n if index == -1:\n summation_value += _error(i)\n else:\n summation_value += _error(i) * train_data[i][0][index]\n return summation_value\n\n\ndef get_cost_derivative(index):\n \"\"\"\n :param index: index of the parameter vector wrt to derivative is to be calculated\n :return: derivative wrt to that index\n Note: If index is -1, this means we are calculating summation wrt to biased\n parameter.\n \"\"\"\n cost_derivative_value = summation_of_cost_derivative(index, m) / m\n return cost_derivative_value\n\n\ndef run_gradient_descent():\n global parameter_vector\n # Tune these values to set a tolerance value for predicted output\n absolute_error_limit = 0.004\n relative_error_limit = 0\n j = 0\n while True:\n j += 1\n temp_parameter_vector = [0, 0, 0, 0]\n err = 0\n for i in range(0, len(parameter_vector)):\n cost_derivative = get_cost_derivative(i - 1)\n err += abs(cost_derivative)\n temp_parameter_vector[i] = (\n parameter_vector[i] - LEARNING_RATE * cost_derivative\n )\n counter.cost = math.log(1 + err)\n if numpy.allclose(\n parameter_vector,\n temp_parameter_vector,\n atol=absolute_error_limit,\n rtol=relative_error_limit,\n ):\n break\n parameter_vector = temp_parameter_vector\n print((\"Number of iterations:\", j))\n\n\ndef test_gradient_descent():\n for i in range(len(test_data)):\n print((\"Actual output value:\", output(i, \"test\")))\n print((\"Hypothesis output:\", calculate_hypothesis_value(i, \"test\")))\n\n\nif __name__ == \"__main__\":\n with VizTracer(log_print=True,\n output_file=os.path.join(os.path.dirname(__file__), \"../\", \"json/gradient_descent.json\"),\n file_info=True) as tracer:\n counter = VizCounter(tracer, \"log(1 + cost)\")\n run_gradient_descent()\n test_gradient_descent()\n", 145], "/home/gaogaotiantian/programs/viztracer/venv3.13/lib/python3.13/site-packages/numpy/_core/numeric.py": ["import functools\nimport itertools\nimport operator\nimport sys\nimport warnings\nimport numbers\nimport builtins\nimport math\n\nimport numpy as np\nfrom . import multiarray\nfrom . import numerictypes as nt\nfrom .multiarray import (\n ALLOW_THREADS, BUFSIZE, CLIP, MAXDIMS, MAY_SHARE_BOUNDS, MAY_SHARE_EXACT,\n RAISE, WRAP, arange, array, asarray, asanyarray, ascontiguousarray,\n asfortranarray, broadcast, can_cast, concatenate, copyto, dot, dtype,\n empty, empty_like, flatiter, frombuffer, from_dlpack, fromfile, fromiter,\n fromstring, inner, lexsort, matmul, may_share_memory, min_scalar_type,\n ndarray, nditer, nested_iters, promote_types, putmask, result_type,\n shares_memory, vdot, where, zeros, normalize_axis_index,\n _get_promotion_state, _set_promotion_state, vecdot\n)\n\nfrom . import overrides\nfrom . import umath\nfrom . import shape_base\nfrom .overrides import set_array_function_like_doc, set_module\nfrom .umath import (multiply, invert, sin, PINF, NAN)\nfrom . import numerictypes\nfrom ..exceptions import AxisError\nfrom ._ufunc_config import errstate, _no_nep50_warning\n\nbitwise_not = invert\nufunc = type(sin)\nnewaxis = None\n\narray_function_dispatch = functools.partial(\n overrides.array_function_dispatch, module='numpy')\n\n\n__all__ = [\n 'newaxis', 'ndarray', 'flatiter', 'nditer', 'nested_iters', 'ufunc',\n 'arange', 'array', 'asarray', 'asanyarray', 'ascontiguousarray',\n 'asfortranarray', 'zeros', 'count_nonzero', 'empty', 'broadcast', 'dtype',\n 'fromstring', 'fromfile', 'frombuffer', 'from_dlpack', 'where',\n 'argwhere', 'copyto', 'concatenate', 'lexsort', 'astype',\n 'can_cast', 'promote_types', 'min_scalar_type',\n 'result_type', 'isfortran', 'empty_like', 'zeros_like', 'ones_like',\n 'correlate', 'convolve', 'inner', 'dot', 'outer', 'vdot', 'roll',\n 'rollaxis', 'moveaxis', 'cross', 'tensordot', 'little_endian',\n 'fromiter', 'array_equal', 'array_equiv', 'indices', 'fromfunction',\n 'isclose', 'isscalar', 'binary_repr', 'base_repr', 'ones',\n 'identity', 'allclose', 'putmask',\n 'flatnonzero', 'inf', 'nan', 'False_', 'True_', 'bitwise_not',\n 'full', 'full_like', 'matmul', 'vecdot', 'shares_memory',\n 'may_share_memory', '_get_promotion_state', '_set_promotion_state']\n\n\ndef _zeros_like_dispatcher(\n a, dtype=None, order=None, subok=None, shape=None, *, device=None\n):\n return (a,)\n\n\n@array_function_dispatch(_zeros_like_dispatcher)\ndef zeros_like(\n a, dtype=None, order='K', subok=True, shape=None, *, device=None\n):\n \"\"\"\n Return an array of zeros with the same shape and type as a given array.\n\n Parameters\n ----------\n a : array_like\n The shape and data-type of `a` define these same attributes of\n the returned array.\n dtype : data-type, optional\n Overrides the data type of the result.\n\n .. versionadded:: 1.6.0\n order : {'C', 'F', 'A', or 'K'}, optional\n Overrides the memory layout of the result. 'C' means C-order,\n 'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,\n 'C' otherwise. 'K' means match the layout of `a` as closely\n as possible.\n\n .. versionadded:: 1.6.0\n subok : bool, optional.\n If True, then the newly created array will use the sub-class\n type of `a`, otherwise it will be a base-class array. Defaults\n to True.\n shape : int or sequence of ints, optional.\n Overrides the shape of the result. If order='K' and the number of\n dimensions is unchanged, will try to keep order, otherwise,\n order='C' is implied.\n\n .. versionadded:: 1.17.0\n device : str, optional\n The device on which to place the created array. Default: None.\n For Array-API interoperability only, so must be ``\"cpu\"`` if passed.\n\n .. versionadded:: 2.0.0\n\n Returns\n -------\n out : ndarray\n Array of zeros with the same shape and type as `a`.\n\n See Also\n --------\n empty_like : Return an empty array with shape and type of input.\n ones_like : Return an array of ones with shape and type of input.\n full_like : Return a new array with shape of input filled with value.\n zeros : Return a new array setting values to zero.\n\n Examples\n --------\n >>> import numpy as np\n >>> x = np.arange(6)\n >>> x = x.reshape((2, 3))\n >>> x\n array([[0, 1, 2],\n [3, 4, 5]])\n >>> np.zeros_like(x)\n array([[0, 0, 0],\n [0, 0, 0]])\n\n >>> y = np.arange(3, dtype=float)\n >>> y\n array([0., 1., 2.])\n >>> np.zeros_like(y)\n array([0., 0., 0.])\n\n \"\"\"\n res = empty_like(\n a, dtype=dtype, order=order, subok=subok, shape=shape, device=device\n )\n # needed instead of a 0 to get same result as zeros for string dtypes\n z = zeros(1, dtype=res.dtype)\n multiarray.copyto(res, z, casting='unsafe')\n return res\n\n\n@set_array_function_like_doc\n@set_module('numpy')\ndef ones(shape, dtype=None, order='C', *, device=None, like=None):\n \"\"\"\n Return a new array of given shape and type, filled with ones.\n\n Parameters\n ----------\n shape : int or sequence of ints\n Shape of the new array, e.g., ``(2, 3)`` or ``2``.\n dtype : data-type, optional\n The desired data-type for the array, e.g., `numpy.int8`. Default is\n `numpy.float64`.\n order : {'C', 'F'}, optional, default: C\n Whether to store multi-dimensional data in row-major\n (C-style) or column-major (Fortran-style) order in\n memory.\n device : str, optional\n The device on which to place the created array. Default: None.\n For Array-API interoperability only, so must be ``\"cpu\"`` if passed.\n\n .. versionadded:: 2.0.0\n ${ARRAY_FUNCTION_LIKE}\n\n .. versionadded:: 1.20.0\n\n Returns\n -------\n out : ndarray\n Array of ones with the given shape, dtype, and order.\n\n See Also\n --------\n ones_like : Return an array of ones with shape and type of input.\n empty : Return a new uninitialized array.\n zeros : Return a new array setting values to zero.\n full : Return a new array of given shape filled with value.\n\n Examples\n --------\n >>> import numpy as np\n >>> np.ones(5)\n array([1., 1., 1., 1., 1.])\n\n >>> np.ones((5,), dtype=int)\n array([1, 1, 1, 1, 1])\n\n >>> np.ones((2, 1))\n array([[1.],\n [1.]])\n\n >>> s = (2,2)\n >>> np.ones(s)\n array([[1., 1.],\n [1., 1.]])\n\n \"\"\"\n if like is not None:\n return _ones_with_like(\n like, shape, dtype=dtype, order=order, device=device\n )\n\n a = empty(shape, dtype, order, device=device)\n multiarray.copyto(a, 1, casting='unsafe')\n return a\n\n\n_ones_with_like = array_function_dispatch()(ones)\n\n\ndef _ones_like_dispatcher(\n a, dtype=None, order=None, subok=None, shape=None, *, device=None\n):\n return (a,)\n\n\n@array_function_dispatch(_ones_like_dispatcher)\ndef ones_like(\n a, dtype=None, order='K', subok=True, shape=None, *, device=None\n):\n \"\"\"\n Return an array of ones with the same shape and type as a given array.\n\n Parameters\n ----------\n a : array_like\n The shape and data-type of `a` define these same attributes of\n the returned array.\n dtype : data-type, optional\n Overrides the data type of the result.\n\n .. versionadded:: 1.6.0\n order : {'C', 'F', 'A', or 'K'}, optional\n Overrides the memory layout of the result. 'C' means C-order,\n 'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,\n 'C' otherwise. 'K' means match the layout of `a` as closely\n as possible.\n\n .. versionadded:: 1.6.0\n subok : bool, optional.\n If True, then the newly created array will use the sub-class\n type of `a`, otherwise it will be a base-class array. Defaults\n to True.\n shape : int or sequence of ints, optional.\n Overrides the shape of the result. If order='K' and the number of\n dimensions is unchanged, will try to keep order, otherwise,\n order='C' is implied.\n\n .. versionadded:: 1.17.0\n device : str, optional\n The device on which to place the created array. Default: None.\n For Array-API interoperability only, so must be ``\"cpu\"`` if passed.\n\n .. versionadded:: 2.0.0\n\n Returns\n -------\n out : ndarray\n Array of ones with the same shape and type as `a`.\n\n See Also\n --------\n empty_like : Return an empty array with shape and type of input.\n zeros_like : Return an array of zeros with shape and type of input.\n full_like : Return a new array with shape of input filled with value.\n ones : Return a new array setting values to one.\n\n Examples\n --------\n >>> import numpy as np\n >>> x = np.arange(6)\n >>> x = x.reshape((2, 3))\n >>> x\n array([[0, 1, 2],\n [3, 4, 5]])\n >>> np.ones_like(x)\n array([[1, 1, 1],\n [1, 1, 1]])\n\n >>> y = np.arange(3, dtype=float)\n >>> y\n array([0., 1., 2.])\n >>> np.ones_like(y)\n array([1., 1., 1.])\n\n \"\"\"\n res = empty_like(\n a, dtype=dtype, order=order, subok=subok, shape=shape, device=device\n )\n multiarray.copyto(res, 1, casting='unsafe')\n return res\n\n\ndef _full_dispatcher(\n shape, fill_value, dtype=None, order=None, *, device=None, like=None\n):\n return(like,)\n\n\n@set_array_function_like_doc\n@set_module('numpy')\ndef full(shape, fill_value, dtype=None, order='C', *, device=None, like=None):\n \"\"\"\n Return a new array of given shape and type, filled with `fill_value`.\n\n Parameters\n ----------\n shape : int or sequence of ints\n Shape of the new array, e.g., ``(2, 3)`` or ``2``.\n fill_value : scalar or array_like\n Fill value.\n dtype : data-type, optional\n The desired data-type for the array The default, None, means\n ``np.array(fill_value).dtype``.\n order : {'C', 'F'}, optional\n Whether to store multidimensional data in C- or Fortran-contiguous\n (row- or column-wise) order in memory.\n device : str, optional\n The device on which to place the created array. Default: None.\n For Array-API interoperability only, so must be ``\"cpu\"`` if passed.\n\n .. versionadded:: 2.0.0\n ${ARRAY_FUNCTION_LIKE}\n\n .. versionadded:: 1.20.0\n\n Returns\n -------\n out : ndarray\n Array of `fill_value` with the given shape, dtype, and order.\n\n See Also\n --------\n full_like : Return a new array with shape of input filled with value.\n empty : Return a new uninitialized array.\n ones : Return a new array setting values to one.\n zeros : Return a new array setting values to zero.\n\n Examples\n --------\n >>> import numpy as np\n >>> np.full((2, 2), np.inf)\n array([[inf, inf],\n [inf, inf]])\n >>> np.full((2, 2), 10)\n array([[10, 10],\n [10, 10]])\n\n >>> np.full((2, 2), [1, 2])\n array([[1, 2],\n [1, 2]])\n\n \"\"\"\n if like is not None:\n return _full_with_like(\n like, shape, fill_value, dtype=dtype, order=order, device=device\n )\n\n if dtype is None:\n fill_value = asarray(fill_value)\n dtype = fill_value.dtype\n a = empty(shape, dtype, order, device=device)\n multiarray.copyto(a, fill_value, casting='unsafe')\n return a\n\n\n_full_with_like = array_function_dispatch()(full)\n\n\ndef _full_like_dispatcher(\n a, fill_value, dtype=None, order=None, subok=None, shape=None,\n *, device=None\n):\n return (a,)\n\n\n@array_function_dispatch(_full_like_dispatcher)\ndef full_like(\n a, fill_value, dtype=None, order='K', subok=True, shape=None,\n *, device=None\n):\n \"\"\"\n Return a full array with the same shape and type as a given array.\n\n Parameters\n ----------\n a : array_like\n The shape and data-type of `a` define these same attributes of\n the returned array.\n fill_value : array_like\n Fill value.\n dtype : data-type, optional\n Overrides the data type of the result.\n order : {'C', 'F', 'A', or 'K'}, optional\n Overrides the memory layout of the result. 'C' means C-order,\n 'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,\n 'C' otherwise. 'K' means match the layout of `a` as closely\n as possible.\n subok : bool, optional.\n If True, then the newly created array will use the sub-class\n type of `a`, otherwise it will be a base-class array. Defaults\n to True.\n shape : int or sequence of ints, optional.\n Overrides the shape of the result. If order='K' and the number of\n dimensions is unchanged, will try to keep order, otherwise,\n order='C' is implied.\n\n .. versionadded:: 1.17.0\n device : str, optional\n The device on which to place the created array. Default: None.\n For Array-API interoperability only, so must be ``\"cpu\"`` if passed.\n\n .. versionadded:: 2.0.0\n\n Returns\n -------\n out : ndarray\n Array of `fill_value` with the same shape and type as `a`.\n\n See Also\n --------\n empty_like : Return an empty array with shape and type of input.\n ones_like : Return an array of ones with shape and type of input.\n zeros_like : Return an array of zeros with shape and type of input.\n full : Return a new array of given shape filled with value.\n\n Examples\n --------\n >>> import numpy as np\n >>> x = np.arange(6, dtype=int)\n >>> np.full_like(x, 1)\n array([1, 1, 1, 1, 1, 1])\n >>> np.full_like(x, 0.1)\n array([0, 0, 0, 0, 0, 0])\n >>> np.full_like(x, 0.1, dtype=np.double)\n array([0.1, 0.1, 0.1, 0.1, 0.1, 0.1])\n >>> np.full_like(x, np.nan, dtype=np.double)\n array([nan, nan, nan, nan, nan, nan])\n\n >>> y = np.arange(6, dtype=np.double)\n >>> np.full_like(y, 0.1)\n array([0.1, 0.1, 0.1, 0.1, 0.1, 0.1])\n\n >>> y = np.zeros([2, 2, 3], dtype=int)\n >>> np.full_like(y, [0, 0, 255])\n array([[[ 0, 0, 255],\n [ 0, 0, 255]],\n [[ 0, 0, 255],\n [ 0, 0, 255]]])\n \"\"\"\n res = empty_like(\n a, dtype=dtype, order=order, subok=subok, shape=shape, device=device\n )\n multiarray.copyto(res, fill_value, casting='unsafe')\n return res\n\n\ndef _count_nonzero_dispatcher(a, axis=None, *, keepdims=None):\n return (a,)\n\n\n@array_function_dispatch(_count_nonzero_dispatcher)\ndef count_nonzero(a, axis=None, *, keepdims=False):\n \"\"\"\n Counts the number of non-zero values in the array ``a``.\n\n The word \"non-zero\" is in reference to the Python 2.x\n built-in method ``__nonzero__()`` (renamed ``__bool__()``\n in Python 3.x) of Python objects that tests an object's\n \"truthfulness\". For example, any number is considered\n truthful if it is nonzero, whereas any string is considered\n truthful if it is not the empty string. Thus, this function\n (recursively) counts how many elements in ``a`` (and in\n sub-arrays thereof) have their ``__nonzero__()`` or ``__bool__()``\n method evaluated to ``True``.\n\n Parameters\n ----------\n a : array_like\n The array for which to count non-zeros.\n axis : int or tuple, optional\n Axis or tuple of axes along which to count non-zeros.\n Default is None, meaning that non-zeros will be counted\n along a flattened version of ``a``.\n\n .. versionadded:: 1.12.0\n\n keepdims : bool, optional\n If this is set to True, the axes that are counted are left\n in the result as dimensions with size one. With this option,\n the result will broadcast correctly against the input array.\n\n .. versionadded:: 1.19.0\n\n Returns\n -------\n count : int or array of int\n Number of non-zero values in the array along a given axis.\n Otherwise, the total number of non-zero values in the array\n is returned.\n\n See Also\n --------\n nonzero : Return the coordinates of all the non-zero values.\n\n Examples\n --------\n >>> import numpy as np\n >>> np.count_nonzero(np.eye(4))\n 4\n >>> a = np.array([[0, 1, 7, 0],\n ... [3, 0, 2, 19]])\n >>> np.count_nonzero(a)\n 5\n >>> np.count_nonzero(a, axis=0)\n array([1, 1, 2, 1])\n >>> np.count_nonzero(a, axis=1)\n array([2, 3])\n >>> np.count_nonzero(a, axis=1, keepdims=True)\n array([[2],\n [3]])\n \"\"\"\n if axis is None and not keepdims:\n return multiarray.count_nonzero(a)\n\n a = asanyarray(a)\n\n # TODO: this works around .astype(bool) not working properly (gh-9847)\n if np.issubdtype(a.dtype, np.character):\n a_bool = a != a.dtype.type()\n else:\n a_bool = a.astype(np.bool, copy=False)\n\n return a_bool.sum(axis=axis, dtype=np.intp, keepdims=keepdims)\n\n\n@set_module('numpy')\ndef isfortran(a):\n \"\"\"\n Check if the array is Fortran contiguous but *not* C contiguous.\n\n This function is obsolete. If you only want to check if an array is Fortran\n contiguous use ``a.flags.f_contiguous`` instead.\n\n Parameters\n ----------\n a : ndarray\n Input array.\n\n Returns\n -------\n isfortran : bool\n Returns True if the array is Fortran contiguous but *not* C contiguous.\n\n\n Examples\n --------\n\n np.array allows to specify whether the array is written in C-contiguous\n order (last index varies the fastest), or FORTRAN-contiguous order in\n memory (first index varies the fastest).\n\n >>> import numpy as np\n >>> a = np.array([[1, 2, 3], [4, 5, 6]], order='C')\n >>> a\n array([[1, 2, 3],\n [4, 5, 6]])\n >>> np.isfortran(a)\n False\n\n >>> b = np.array([[1, 2, 3], [4, 5, 6]], order='F')\n >>> b\n array([[1, 2, 3],\n [4, 5, 6]])\n >>> np.isfortran(b)\n True\n\n\n The transpose of a C-ordered array is a FORTRAN-ordered array.\n\n >>> a = np.array([[1, 2, 3], [4, 5, 6]], order='C')\n >>> a\n array([[1, 2, 3],\n [4, 5, 6]])\n >>> np.isfortran(a)\n False\n >>> b = a.T\n >>> b\n array([[1, 4],\n [2, 5],\n [3, 6]])\n >>> np.isfortran(b)\n True\n\n C-ordered arrays evaluate as False even if they are also FORTRAN-ordered.\n\n >>> np.isfortran(np.array([1, 2], order='F'))\n False\n\n \"\"\"\n return a.flags.fnc\n\n\ndef _argwhere_dispatcher(a):\n return (a,)\n\n\n@array_function_dispatch(_argwhere_dispatcher)\ndef argwhere(a):\n \"\"\"\n Find the indices of array elements that are non-zero, grouped by element.\n\n Parameters\n ----------\n a : array_like\n Input data.\n\n Returns\n -------\n index_array : (N, a.ndim) ndarray\n Indices of elements that are non-zero. Indices are grouped by element.\n This array will have shape ``(N, a.ndim)`` where ``N`` is the number of\n non-zero items.\n\n See Also\n --------\n where, nonzero\n\n Notes\n -----\n ``np.argwhere(a)`` is almost the same as ``np.transpose(np.nonzero(a))``,\n but produces a result of the correct shape for a 0D array.\n\n The output of ``argwhere`` is not suitable for indexing arrays.\n For this purpose use ``nonzero(a)`` instead.\n\n Examples\n --------\n >>> import numpy as np\n >>> x = np.arange(6).reshape(2,3)\n >>> x\n array([[0, 1, 2],\n [3, 4, 5]])\n >>> np.argwhere(x>1)\n array([[0, 2],\n [1, 0],\n [1, 1],\n [1, 2]])\n\n \"\"\"\n # nonzero does not behave well on 0d, so promote to 1d\n if np.ndim(a) == 0:\n a = shape_base.atleast_1d(a)\n # then remove the added dimension\n return argwhere(a)[:, :0]\n return transpose(nonzero(a))\n\n\ndef _flatnonzero_dispatcher(a):\n return (a,)\n\n\n@array_function_dispatch(_flatnonzero_dispatcher)\ndef flatnonzero(a):\n \"\"\"\n Return indices that are non-zero in the flattened version of a.\n\n This is equivalent to ``np.nonzero(np.ravel(a))[0]``.\n\n Parameters\n ----------\n a : array_like\n Input data.\n\n Returns\n -------\n res : ndarray\n Output array, containing the indices of the elements of ``a.ravel()``\n that are non-zero.\n\n See Also\n --------\n nonzero : Return the indices of the non-zero elements of the input array.\n ravel : Return a 1-D array containing the elements of the input array.\n\n Examples\n --------\n >>> import numpy as np\n >>> x = np.arange(-2, 3)\n >>> x\n array([-2, -1, 0, 1, 2])\n >>> np.flatnonzero(x)\n array([0, 1, 3, 4])\n\n Use the indices of the non-zero elements as an index array to extract\n these elements:\n\n >>> x.ravel()[np.flatnonzero(x)]\n array([-2, -1, 1, 2])\n\n \"\"\"\n return np.nonzero(np.ravel(a))[0]\n\n\ndef _correlate_dispatcher(a, v, mode=None):\n return (a, v)\n\n\n@array_function_dispatch(_correlate_dispatcher)\ndef correlate(a, v, mode='valid'):\n r\"\"\"\n Cross-correlation of two 1-dimensional sequences.\n\n This function computes the correlation as generally defined in signal\n processing texts [1]_:\n\n .. math:: c_k = \\sum_n a_{n+k} \\cdot \\overline{v}_n\n\n with a and v sequences being zero-padded where necessary and\n :math:`\\overline v` denoting complex conjugation.\n\n Parameters\n ----------\n a, v : array_like\n Input sequences.\n mode : {'valid', 'same', 'full'}, optional\n Refer to the `convolve` docstring. Note that the default\n is 'valid', unlike `convolve`, which uses 'full'.\n\n Returns\n -------\n out : ndarray\n Discrete cross-correlation of `a` and `v`.\n\n See Also\n --------\n convolve : Discrete, linear convolution of two one-dimensional sequences.\n scipy.signal.correlate : uses FFT which has superior performance\n on large arrays.\n\n Notes\n -----\n The definition of correlation above is not unique and sometimes\n correlation may be defined differently. Another common definition is [1]_:\n\n .. math:: c'_k = \\sum_n a_{n} \\cdot \\overline{v_{n+k}}\n\n which is related to :math:`c_k` by :math:`c'_k = c_{-k}`.\n\n `numpy.correlate` may perform slowly in large arrays (i.e. n = 1e5)\n because it does not use the FFT to compute the convolution; in that case,\n `scipy.signal.correlate` might be preferable.\n\n References\n ----------\n .. [1] Wikipedia, \"Cross-correlation\",\n https://en.wikipedia.org/wiki/Cross-correlation\n\n Examples\n --------\n >>> import numpy as np\n >>> np.correlate([1, 2, 3], [0, 1, 0.5])\n array([3.5])\n >>> np.correlate([1, 2, 3], [0, 1, 0.5], \"same\")\n array([2. , 3.5, 3. ])\n >>> np.correlate([1, 2, 3], [0, 1, 0.5], \"full\")\n array([0.5, 2. , 3.5, 3. , 0. ])\n\n Using complex sequences:\n\n >>> np.correlate([1+1j, 2, 3-1j], [0, 1, 0.5j], 'full')\n array([ 0.5-0.5j, 1.0+0.j , 1.5-1.5j, 3.0-1.j , 0.0+0.j ])\n\n Note that you get the time reversed, complex conjugated result\n (:math:`\\overline{c_{-k}}`) when the two input sequences a and v change\n places:\n\n >>> np.correlate([0, 1, 0.5j], [1+1j, 2, 3-1j], 'full')\n array([ 0.0+0.j , 3.0+1.j , 1.5+1.5j, 1.0+0.j , 0.5+0.5j])\n\n \"\"\"\n return multiarray.correlate2(a, v, mode)\n\n\ndef _convolve_dispatcher(a, v, mode=None):\n return (a, v)\n\n\n@array_function_dispatch(_convolve_dispatcher)\ndef convolve(a, v, mode='full'):\n \"\"\"\n Returns the discrete, linear convolution of two one-dimensional sequences.\n\n The convolution operator is often seen in signal processing, where it\n models the effect of a linear time-invariant system on a signal [1]_. In\n probability theory, the sum of two independent random variables is\n distributed according to the convolution of their individual\n distributions.\n\n If `v` is longer than `a`, the arrays are swapped before computation.\n\n Parameters\n ----------\n a : (N,) array_like\n First one-dimensional input array.\n v : (M,) array_like\n Second one-dimensional input array.\n mode : {'full', 'valid', 'same'}, optional\n 'full':\n By default, mode is 'full'. This returns the convolution\n at each point of overlap, with an output shape of (N+M-1,). At\n the end-points of the convolution, the signals do not overlap\n completely, and boundary effects may be seen.\n\n 'same':\n Mode 'same' returns output of length ``max(M, N)``. Boundary\n effects are still visible.\n\n 'valid':\n Mode 'valid' returns output of length\n ``max(M, N) - min(M, N) + 1``. The convolution product is only given\n for points where the signals overlap completely. Values outside\n the signal boundary have no effect.\n\n Returns\n -------\n out : ndarray\n Discrete, linear convolution of `a` and `v`.\n\n See Also\n --------\n scipy.signal.fftconvolve : Convolve two arrays using the Fast Fourier\n Transform.\n scipy.linalg.toeplitz : Used to construct the convolution operator.\n polymul : Polynomial multiplication. Same output as convolve, but also\n accepts poly1d objects as input.\n\n Notes\n -----\n The discrete convolution operation is defined as\n\n .. math:: (a * v)_n = \\\\sum_{m = -\\\\infty}^{\\\\infty} a_m v_{n - m}\n\n It can be shown that a convolution :math:`x(t) * y(t)` in time/space\n is equivalent to the multiplication :math:`X(f) Y(f)` in the Fourier\n domain, after appropriate padding (padding is necessary to prevent\n circular convolution). Since multiplication is more efficient (faster)\n than convolution, the function `scipy.signal.fftconvolve` exploits the\n FFT to calculate the convolution of large data-sets.\n\n References\n ----------\n .. [1] Wikipedia, \"Convolution\",\n https://en.wikipedia.org/wiki/Convolution\n\n Examples\n --------\n Note how the convolution operator flips the second array\n before \"sliding\" the two across one another:\n\n >>> import numpy as np\n >>> np.convolve([1, 2, 3], [0, 1, 0.5])\n array([0. , 1. , 2.5, 4. , 1.5])\n\n Only return the middle values of the convolution.\n Contains boundary effects, where zeros are taken\n into account:\n\n >>> np.convolve([1,2,3],[0,1,0.5], 'same')\n array([1. , 2.5, 4. ])\n\n The two arrays are of the same length, so there\n is only one position where they completely overlap:\n\n >>> np.convolve([1,2,3],[0,1,0.5], 'valid')\n array([2.5])\n\n \"\"\"\n a, v = array(a, copy=None, ndmin=1), array(v, copy=None, ndmin=1)\n if (len(v) > len(a)):\n a, v = v, a\n if len(a) == 0:\n raise ValueError('a cannot be empty')\n if len(v) == 0:\n raise ValueError('v cannot be empty')\n return multiarray.correlate(a, v[::-1], mode)\n\n\ndef _outer_dispatcher(a, b, out=None):\n return (a, b, out)\n\n\n@array_function_dispatch(_outer_dispatcher)\ndef outer(a, b, out=None):\n \"\"\"\n Compute the outer product of two vectors.\n\n Given two vectors `a` and `b` of length ``M`` and ``N``, respectively,\n the outer product [1]_ is::\n\n [[a_0*b_0 a_0*b_1 ... a_0*b_{N-1} ]\n [a_1*b_0 .\n [ ... .\n [a_{M-1}*b_0 a_{M-1}*b_{N-1} ]]\n\n Parameters\n ----------\n a : (M,) array_like\n First input vector. Input is flattened if\n not already 1-dimensional.\n b : (N,) array_like\n Second input vector. Input is flattened if\n not already 1-dimensional.\n out : (M, N) ndarray, optional\n A location where the result is stored\n\n .. versionadded:: 1.9.0\n\n Returns\n -------\n out : (M, N) ndarray\n ``out[i, j] = a[i] * b[j]``\n\n See also\n --------\n inner\n einsum : ``einsum('i,j->ij', a.ravel(), b.ravel())`` is the equivalent.\n ufunc.outer : A generalization to dimensions other than 1D and other\n operations. ``np.multiply.outer(a.ravel(), b.ravel())``\n is the equivalent.\n linalg.outer : An Array API compatible variation of ``np.outer``,\n which accepts 1-dimensional inputs only.\n tensordot : ``np.tensordot(a.ravel(), b.ravel(), axes=((), ()))``\n is the equivalent.\n\n References\n ----------\n .. [1] G. H. Golub and C. F. Van Loan, *Matrix Computations*, 3rd\n ed., Baltimore, MD, Johns Hopkins University Press, 1996,\n pg. 8.\n\n Examples\n --------\n Make a (*very* coarse) grid for computing a Mandelbrot set:\n\n >>> import numpy as np\n >>> rl = np.outer(np.ones((5,)), np.linspace(-2, 2, 5))\n >>> rl\n array([[-2., -1., 0., 1., 2.],\n [-2., -1., 0., 1., 2.],\n [-2., -1., 0., 1., 2.],\n [-2., -1., 0., 1., 2.],\n [-2., -1., 0., 1., 2.]])\n >>> im = np.outer(1j*np.linspace(2, -2, 5), np.ones((5,)))\n >>> im\n array([[0.+2.j, 0.+2.j, 0.+2.j, 0.+2.j, 0.+2.j],\n [0.+1.j, 0.+1.j, 0.+1.j, 0.+1.j, 0.+1.j],\n [0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j],\n [0.-1.j, 0.-1.j, 0.-1.j, 0.-1.j, 0.-1.j],\n [0.-2.j, 0.-2.j, 0.-2.j, 0.-2.j, 0.-2.j]])\n >>> grid = rl + im\n >>> grid\n array([[-2.+2.j, -1.+2.j, 0.+2.j, 1.+2.j, 2.+2.j],\n [-2.+1.j, -1.+1.j, 0.+1.j, 1.+1.j, 2.+1.j],\n [-2.+0.j, -1.+0.j, 0.+0.j, 1.+0.j, 2.+0.j],\n [-2.-1.j, -1.-1.j, 0.-1.j, 1.-1.j, 2.-1.j],\n [-2.-2.j, -1.-2.j, 0.-2.j, 1.-2.j, 2.-2.j]])\n\n An example using a \"vector\" of letters:\n\n >>> x = np.array(['a', 'b', 'c'], dtype=object)\n >>> np.outer(x, [1, 2, 3])\n array([['a', 'aa', 'aaa'],\n ['b', 'bb', 'bbb'],\n ['c', 'cc', 'ccc']], dtype=object)\n\n \"\"\"\n a = asarray(a)\n b = asarray(b)\n return multiply(a.ravel()[:, newaxis], b.ravel()[newaxis, :], out)\n\n\ndef _tensordot_dispatcher(a, b, axes=None):\n return (a, b)\n\n\n@array_function_dispatch(_tensordot_dispatcher)\ndef tensordot(a, b, axes=2):\n \"\"\"\n Compute tensor dot product along specified axes.\n\n Given two tensors, `a` and `b`, and an array_like object containing\n two array_like objects, ``(a_axes, b_axes)``, sum the products of\n `a`'s and `b`'s elements (components) over the axes specified by\n ``a_axes`` and ``b_axes``. The third argument can be a single non-negative\n integer_like scalar, ``N``; if it is such, then the last ``N`` dimensions\n of `a` and the first ``N`` dimensions of `b` are summed over.\n\n Parameters\n ----------\n a, b : array_like\n Tensors to \"dot\".\n\n axes : int or (2,) array_like\n * integer_like\n If an int N, sum over the last N axes of `a` and the first N axes\n of `b` in order. The sizes of the corresponding axes must match.\n * (2,) array_like\n Or, a list of axes to be summed over, first sequence applying to `a`,\n second to `b`. Both elements array_like must be of the same length.\n\n Returns\n -------\n output : ndarray\n The tensor dot product of the input.\n\n See Also\n --------\n dot, einsum\n\n Notes\n -----\n Three common use cases are:\n\n * ``axes = 0`` : tensor product :math:`a\\\\otimes b`\n * ``axes = 1`` : tensor dot product :math:`a\\\\cdot b`\n * ``axes = 2`` : (default) tensor double contraction :math:`a:b`\n\n When `axes` is a positive integer ``N``, the operation starts with\n axis ``-N`` of `a` and axis ``0`` of `b`, and it continues through\n axis ``-1`` of `a` and axis ``N-1`` of `b` (inclusive).\n\n When there is more than one axis to sum over - and they are not the last\n (first) axes of `a` (`b`) - the argument `axes` should consist of\n two sequences of the same length, with the first axis to sum over given\n first in both sequences, the second axis second, and so forth.\n\n The shape of the result consists of the non-contracted axes of the\n first tensor, followed by the non-contracted axes of the second.\n\n Examples\n --------\n A \"traditional\" example:\n\n >>> import numpy as np\n >>> a = np.arange(60.).reshape(3,4,5)\n >>> b = np.arange(24.).reshape(4,3,2)\n >>> c = np.tensordot(a,b, axes=([1,0],[0,1]))\n >>> c.shape\n (5, 2)\n >>> c\n array([[4400., 4730.],\n [4532., 4874.],\n [4664., 5018.],\n [4796., 5162.],\n [4928., 5306.]])\n >>> # A slower but equivalent way of computing the same...\n >>> d = np.zeros((5,2))\n >>> for i in range(5):\n ... for j in range(2):\n ... for k in range(3):\n ... for n in range(4):\n ... d[i,j] += a[k,n,i] * b[n,k,j]\n >>> c == d\n array([[ True, True],\n [ True, True],\n [ True, True],\n [ True, True],\n [ True, True]])\n\n An extended example taking advantage of the overloading of + and \\\\*:\n\n >>> a = np.array(range(1, 9))\n >>> a.shape = (2, 2, 2)\n >>> A = np.array(('a', 'b', 'c', 'd'), dtype=object)\n >>> A.shape = (2, 2)\n >>> a; A\n array([[[1, 2],\n [3, 4]],\n [[5, 6],\n [7, 8]]])\n array([['a', 'b'],\n ['c', 'd']], dtype=object)\n\n >>> np.tensordot(a, A) # third argument default is 2 for double-contraction\n array(['abbcccdddd', 'aaaaabbbbbbcccccccdddddddd'], dtype=object)\n\n >>> np.tensordot(a, A, 1)\n array([[['acc', 'bdd'],\n ['aaacccc', 'bbbdddd']],\n [['aaaaacccccc', 'bbbbbdddddd'],\n ['aaaaaaacccccccc', 'bbbbbbbdddddddd']]], dtype=object)\n\n >>> np.tensordot(a, A, 0) # tensor product (result too long to incl.)\n array([[[[['a', 'b'],\n ['c', 'd']],\n ...\n\n >>> np.tensordot(a, A, (0, 1))\n array([[['abbbbb', 'cddddd'],\n ['aabbbbbb', 'ccdddddd']],\n [['aaabbbbbbb', 'cccddddddd'],\n ['aaaabbbbbbbb', 'ccccdddddddd']]], dtype=object)\n\n >>> np.tensordot(a, A, (2, 1))\n array([[['abb', 'cdd'],\n ['aaabbbb', 'cccdddd']],\n [['aaaaabbbbbb', 'cccccdddddd'],\n ['aaaaaaabbbbbbbb', 'cccccccdddddddd']]], dtype=object)\n\n >>> np.tensordot(a, A, ((0, 1), (0, 1)))\n array(['abbbcccccddddddd', 'aabbbbccccccdddddddd'], dtype=object)\n\n >>> np.tensordot(a, A, ((2, 1), (1, 0)))\n array(['acccbbdddd', 'aaaaacccccccbbbbbbdddddddd'], dtype=object)\n\n \"\"\"\n try:\n iter(axes)\n except Exception:\n axes_a = list(range(-axes, 0))\n axes_b = list(range(0, axes))\n else:\n axes_a, axes_b = axes\n try:\n na = len(axes_a)\n axes_a = list(axes_a)\n except TypeError:\n axes_a = [axes_a]\n na = 1\n try:\n nb = len(axes_b)\n axes_b = list(axes_b)\n except TypeError:\n axes_b = [axes_b]\n nb = 1\n\n a, b = asarray(a), asarray(b)\n as_ = a.shape\n nda = a.ndim\n bs = b.shape\n ndb = b.ndim\n equal = True\n if na != nb:\n equal = False\n else:\n for k in range(na):\n if as_[axes_a[k]] != bs[axes_b[k]]:\n equal = False\n break\n if axes_a[k] < 0:\n axes_a[k] += nda\n if axes_b[k] < 0:\n axes_b[k] += ndb\n if not equal:\n raise ValueError(\"shape-mismatch for sum\")\n\n # Move the axes to sum over to the end of \"a\"\n # and to the front of \"b\"\n notin = [k for k in range(nda) if k not in axes_a]\n newaxes_a = notin + axes_a\n N2 = math.prod(as_[axis] for axis in axes_a)\n newshape_a = (math.prod([as_[ax] for ax in notin]), N2)\n olda = [as_[axis] for axis in notin]\n\n notin = [k for k in range(ndb) if k not in axes_b]\n newaxes_b = axes_b + notin\n N2 = math.prod(bs[axis] for axis in axes_b)\n newshape_b = (N2, math.prod([bs[ax] for ax in notin]))\n oldb = [bs[axis] for axis in notin]\n\n at = a.transpose(newaxes_a).reshape(newshape_a)\n bt = b.transpose(newaxes_b).reshape(newshape_b)\n res = dot(at, bt)\n return res.reshape(olda + oldb)\n\n\ndef _roll_dispatcher(a, shift, axis=None):\n return (a,)\n\n\n@array_function_dispatch(_roll_dispatcher)\ndef roll(a, shift, axis=None):\n \"\"\"\n Roll array elements along a given axis.\n\n Elements that roll beyond the last position are re-introduced at\n the first.\n\n Parameters\n ----------\n a : array_like\n Input array.\n shift : int or tuple of ints\n The number of places by which elements are shifted. If a tuple,\n then `axis` must be a tuple of the same size, and each of the\n given axes is shifted by the corresponding number. If an int\n while `axis` is a tuple of ints, then the same value is used for\n all given axes.\n axis : int or tuple of ints, optional\n Axis or axes along which elements are shifted. By default, the\n array is flattened before shifting, after which the original\n shape is restored.\n\n Returns\n -------\n res : ndarray\n Output array, with the same shape as `a`.\n\n See Also\n --------\n rollaxis : Roll the specified axis backwards, until it lies in a\n given position.\n\n Notes\n -----\n .. versionadded:: 1.12.0\n\n Supports rolling over multiple dimensions simultaneously.\n\n Examples\n --------\n >>> import numpy as np\n >>> x = np.arange(10)\n >>> np.roll(x, 2)\n array([8, 9, 0, 1, 2, 3, 4, 5, 6, 7])\n >>> np.roll(x, -2)\n array([2, 3, 4, 5, 6, 7, 8, 9, 0, 1])\n\n >>> x2 = np.reshape(x, (2, 5))\n >>> x2\n array([[0, 1, 2, 3, 4],\n [5, 6, 7, 8, 9]])\n >>> np.roll(x2, 1)\n array([[9, 0, 1, 2, 3],\n [4, 5, 6, 7, 8]])\n >>> np.roll(x2, -1)\n array([[1, 2, 3, 4, 5],\n [6, 7, 8, 9, 0]])\n >>> np.roll(x2, 1, axis=0)\n array([[5, 6, 7, 8, 9],\n [0, 1, 2, 3, 4]])\n >>> np.roll(x2, -1, axis=0)\n array([[5, 6, 7, 8, 9],\n [0, 1, 2, 3, 4]])\n >>> np.roll(x2, 1, axis=1)\n array([[4, 0, 1, 2, 3],\n [9, 5, 6, 7, 8]])\n >>> np.roll(x2, -1, axis=1)\n array([[1, 2, 3, 4, 0],\n [6, 7, 8, 9, 5]])\n >>> np.roll(x2, (1, 1), axis=(1, 0))\n array([[9, 5, 6, 7, 8],\n [4, 0, 1, 2, 3]])\n >>> np.roll(x2, (2, 1), axis=(1, 0))\n array([[8, 9, 5, 6, 7],\n [3, 4, 0, 1, 2]])\n\n \"\"\"\n a = asanyarray(a)\n if axis is None:\n return roll(a.ravel(), shift, 0).reshape(a.shape)\n\n else:\n axis = normalize_axis_tuple(axis, a.ndim, allow_duplicate=True)\n broadcasted = broadcast(shift, axis)\n if broadcasted.ndim > 1:\n raise ValueError(\n \"'shift' and 'axis' should be scalars or 1D sequences\")\n shifts = {ax: 0 for ax in range(a.ndim)}\n for sh, ax in broadcasted:\n shifts[ax] += int(sh)\n\n rolls = [((slice(None), slice(None)),)] * a.ndim\n for ax, offset in shifts.items():\n offset %= a.shape[ax] or 1 # If `a` is empty, nothing matters.\n if offset:\n # (original, result), (original, result)\n rolls[ax] = ((slice(None, -offset), slice(offset, None)),\n (slice(-offset, None), slice(None, offset)))\n\n result = empty_like(a)\n for indices in itertools.product(*rolls):\n arr_index, res_index = zip(*indices)\n result[res_index] = a[arr_index]\n\n return result\n\n\ndef _rollaxis_dispatcher(a, axis, start=None):\n return (a,)\n\n\n@array_function_dispatch(_rollaxis_dispatcher)\ndef rollaxis(a, axis, start=0):\n \"\"\"\n Roll the specified axis backwards, until it lies in a given position.\n\n This function continues to be supported for backward compatibility, but you\n should prefer `moveaxis`. The `moveaxis` function was added in NumPy\n 1.11.\n\n Parameters\n ----------\n a : ndarray\n Input array.\n axis : int\n The axis to be rolled. The positions of the other axes do not\n change relative to one another.\n start : int, optional\n When ``start <= axis``, the axis is rolled back until it lies in\n this position. When ``start > axis``, the axis is rolled until it\n lies before this position. The default, 0, results in a \"complete\"\n roll. The following table describes how negative values of ``start``\n are interpreted:\n\n .. table::\n :align: left\n\n +-------------------+----------------------+\n | ``start`` | Normalized ``start`` |\n +===================+======================+\n | ``-(arr.ndim+1)`` | raise ``AxisError`` |\n +-------------------+----------------------+\n | ``-arr.ndim`` | 0 |\n +-------------------+----------------------+\n | |vdots| | |vdots| |\n +-------------------+----------------------+\n | ``-1`` | ``arr.ndim-1`` |\n +-------------------+----------------------+\n | ``0`` | ``0`` |\n +-------------------+----------------------+\n | |vdots| | |vdots| |\n +-------------------+----------------------+\n | ``arr.ndim`` | ``arr.ndim`` |\n +-------------------+----------------------+\n | ``arr.ndim + 1`` | raise ``AxisError`` |\n +-------------------+----------------------+\n\n .. |vdots| unicode:: U+22EE .. Vertical Ellipsis\n\n Returns\n -------\n res : ndarray\n For NumPy >= 1.10.0 a view of `a` is always returned. For earlier\n NumPy versions a view of `a` is returned only if the order of the\n axes is changed, otherwise the input array is returned.\n\n See Also\n --------\n moveaxis : Move array axes to new positions.\n roll : Roll the elements of an array by a number of positions along a\n given axis.\n\n Examples\n --------\n >>> import numpy as np\n >>> a = np.ones((3,4,5,6))\n >>> np.rollaxis(a, 3, 1).shape\n (3, 6, 4, 5)\n >>> np.rollaxis(a, 2).shape\n (5, 3, 4, 6)\n >>> np.rollaxis(a, 1, 4).shape\n (3, 5, 6, 4)\n\n \"\"\"\n n = a.ndim\n axis = normalize_axis_index(axis, n)\n if start < 0:\n start += n\n msg = \"'%s' arg requires %d <= %s < %d, but %d was passed in\"\n if not (0 <= start < n + 1):\n raise AxisError(msg % ('start', -n, 'start', n + 1, start))\n if axis < start:\n # it's been removed\n start -= 1\n if axis == start:\n return a[...]\n axes = list(range(0, n))\n axes.remove(axis)\n axes.insert(start, axis)\n return a.transpose(axes)\n\n\n@set_module(\"numpy.lib.array_utils\")\ndef normalize_axis_tuple(axis, ndim, argname=None, allow_duplicate=False):\n \"\"\"\n Normalizes an axis argument into a tuple of non-negative integer axes.\n\n This handles shorthands such as ``1`` and converts them to ``(1,)``,\n as well as performing the handling of negative indices covered by\n `normalize_axis_index`.\n\n By default, this forbids axes from being specified multiple times.\n\n Used internally by multi-axis-checking logic.\n\n .. versionadded:: 1.13.0\n\n Parameters\n ----------\n axis : int, iterable of int\n The un-normalized index or indices of the axis.\n ndim : int\n The number of dimensions of the array that `axis` should be normalized\n against.\n argname : str, optional\n A prefix to put before the error message, typically the name of the\n argument.\n allow_duplicate : bool, optional\n If False, the default, disallow an axis from being specified twice.\n\n Returns\n -------\n normalized_axes : tuple of int\n The normalized axis index, such that `0 <= normalized_axis < ndim`\n\n Raises\n ------\n AxisError\n If any axis provided is out of range\n ValueError\n If an axis is repeated\n\n See also\n --------\n normalize_axis_index : normalizing a single scalar axis\n \"\"\"\n # Optimization to speed-up the most common cases.\n if type(axis) not in (tuple, list):\n try:\n axis = [operator.index(axis)]\n except TypeError:\n pass\n # Going via an iterator directly is slower than via list comprehension.\n axis = tuple([normalize_axis_index(ax, ndim, argname) for ax in axis])\n if not allow_duplicate and len(set(axis)) != len(axis):\n if argname:\n raise ValueError('repeated axis in `{}` argument'.format(argname))\n else:\n raise ValueError('repeated axis')\n return axis\n\n\ndef _moveaxis_dispatcher(a, source, destination):\n return (a,)\n\n\n@array_function_dispatch(_moveaxis_dispatcher)\ndef moveaxis(a, source, destination):\n \"\"\"\n Move axes of an array to new positions.\n\n Other axes remain in their original order.\n\n .. versionadded:: 1.11.0\n\n Parameters\n ----------\n a : np.ndarray\n The array whose axes should be reordered.\n source : int or sequence of int\n Original positions of the axes to move. These must be unique.\n destination : int or sequence of int\n Destination positions for each of the original axes. These must also be\n unique.\n\n Returns\n -------\n result : np.ndarray\n Array with moved axes. This array is a view of the input array.\n\n See Also\n --------\n transpose : Permute the dimensions of an array.\n swapaxes : Interchange two axes of an array.\n\n Examples\n --------\n >>> import numpy as np\n >>> x = np.zeros((3, 4, 5))\n >>> np.moveaxis(x, 0, -1).shape\n (4, 5, 3)\n >>> np.moveaxis(x, -1, 0).shape\n (5, 3, 4)\n\n These all achieve the same result:\n\n >>> np.transpose(x).shape\n (5, 4, 3)\n >>> np.swapaxes(x, 0, -1).shape\n (5, 4, 3)\n >>> np.moveaxis(x, [0, 1], [-1, -2]).shape\n (5, 4, 3)\n >>> np.moveaxis(x, [0, 1, 2], [-1, -2, -3]).shape\n (5, 4, 3)\n\n \"\"\"\n try:\n # allow duck-array types if they define transpose\n transpose = a.transpose\n except AttributeError:\n a = asarray(a)\n transpose = a.transpose\n\n source = normalize_axis_tuple(source, a.ndim, 'source')\n destination = normalize_axis_tuple(destination, a.ndim, 'destination')\n if len(source) != len(destination):\n raise ValueError('`source` and `destination` arguments must have '\n 'the same number of elements')\n\n order = [n for n in range(a.ndim) if n not in source]\n\n for dest, src in sorted(zip(destination, source)):\n order.insert(dest, src)\n\n result = transpose(order)\n return result\n\n\ndef _cross_dispatcher(a, b, axisa=None, axisb=None, axisc=None, axis=None):\n return (a, b)\n\n\n@array_function_dispatch(_cross_dispatcher)\ndef cross(a, b, axisa=-1, axisb=-1, axisc=-1, axis=None):\n \"\"\"\n Return the cross product of two (arrays of) vectors.\n\n The cross product of `a` and `b` in :math:`R^3` is a vector perpendicular\n to both `a` and `b`. If `a` and `b` are arrays of vectors, the vectors\n are defined by the last axis of `a` and `b` by default, and these axes\n can have dimensions 2 or 3. Where the dimension of either `a` or `b` is\n 2, the third component of the input vector is assumed to be zero and the\n cross product calculated accordingly. In cases where both input vectors\n have dimension 2, the z-component of the cross product is returned.\n\n Parameters\n ----------\n a : array_like\n Components of the first vector(s).\n b : array_like\n Components of the second vector(s).\n axisa : int, optional\n Axis of `a` that defines the vector(s). By default, the last axis.\n axisb : int, optional\n Axis of `b` that defines the vector(s). By default, the last axis.\n axisc : int, optional\n Axis of `c` containing the cross product vector(s). Ignored if\n both input vectors have dimension 2, as the return is scalar.\n By default, the last axis.\n axis : int, optional\n If defined, the axis of `a`, `b` and `c` that defines the vector(s)\n and cross product(s). Overrides `axisa`, `axisb` and `axisc`.\n\n Returns\n -------\n c : ndarray\n Vector cross product(s).\n\n Raises\n ------\n ValueError\n When the dimension of the vector(s) in `a` and/or `b` does not\n equal 2 or 3.\n\n See Also\n --------\n inner : Inner product\n outer : Outer product.\n linalg.cross : An Array API compatible variation of ``np.cross``,\n which accepts (arrays of) 3-element vectors only.\n ix_ : Construct index arrays.\n\n Notes\n -----\n .. versionadded:: 1.9.0\n\n Supports full broadcasting of the inputs.\n\n Dimension-2 input arrays were deprecated in 2.0.0. If you do need this\n functionality, you can use::\n\n def cross2d(x, y):\n return x[..., 0] * y[..., 1] - x[..., 1] * y[..., 0]\n\n Examples\n --------\n Vector cross-product.\n\n >>> import numpy as np\n >>> x = [1, 2, 3]\n >>> y = [4, 5, 6]\n >>> np.cross(x, y)\n array([-3, 6, -3])\n\n One vector with dimension 2.\n\n >>> x = [1, 2]\n >>> y = [4, 5, 6]\n >>> np.cross(x, y)\n array([12, -6, -3])\n\n Equivalently:\n\n >>> x = [1, 2, 0]\n >>> y = [4, 5, 6]\n >>> np.cross(x, y)\n array([12, -6, -3])\n\n Both vectors with dimension 2.\n\n >>> x = [1,2]\n >>> y = [4,5]\n >>> np.cross(x, y)\n array(-3)\n\n Multiple vector cross-products. Note that the direction of the cross\n product vector is defined by the *right-hand rule*.\n\n >>> x = np.array([[1,2,3], [4,5,6]])\n >>> y = np.array([[4,5,6], [1,2,3]])\n >>> np.cross(x, y)\n array([[-3, 6, -3],\n [ 3, -6, 3]])\n\n The orientation of `c` can be changed using the `axisc` keyword.\n\n >>> np.cross(x, y, axisc=0)\n array([[-3, 3],\n [ 6, -6],\n [-3, 3]])\n\n Change the vector definition of `x` and `y` using `axisa` and `axisb`.\n\n >>> x = np.array([[1,2,3], [4,5,6], [7, 8, 9]])\n >>> y = np.array([[7, 8, 9], [4,5,6], [1,2,3]])\n >>> np.cross(x, y)\n array([[ -6, 12, -6],\n [ 0, 0, 0],\n [ 6, -12, 6]])\n >>> np.cross(x, y, axisa=0, axisb=0)\n array([[-24, 48, -24],\n [-30, 60, -30],\n [-36, 72, -36]])\n\n \"\"\"\n if axis is not None:\n axisa, axisb, axisc = (axis,) * 3\n a = asarray(a)\n b = asarray(b)\n\n if (a.ndim < 1) or (b.ndim < 1):\n raise ValueError(\"At least one array has zero dimension\")\n\n # Check axisa and axisb are within bounds\n axisa = normalize_axis_index(axisa, a.ndim, msg_prefix='axisa')\n axisb = normalize_axis_index(axisb, b.ndim, msg_prefix='axisb')\n\n # Move working axis to the end of the shape\n a = moveaxis(a, axisa, -1)\n b = moveaxis(b, axisb, -1)\n msg = (\"incompatible dimensions for cross product\\n\"\n \"(dimension must be 2 or 3)\")\n if a.shape[-1] not in (2, 3) or b.shape[-1] not in (2, 3):\n raise ValueError(msg)\n if a.shape[-1] == 2 or b.shape[-1] == 2:\n # Deprecated in NumPy 2.0, 2023-09-26\n warnings.warn(\n \"Arrays of 2-dimensional vectors are deprecated. Use arrays of \"\n \"3-dimensional vectors instead. (deprecated in NumPy 2.0)\",\n DeprecationWarning, stacklevel=2\n )\n\n # Create the output array\n shape = broadcast(a[..., 0], b[..., 0]).shape\n if a.shape[-1] == 3 or b.shape[-1] == 3:\n shape += (3,)\n # Check axisc is within bounds\n axisc = normalize_axis_index(axisc, len(shape), msg_prefix='axisc')\n dtype = promote_types(a.dtype, b.dtype)\n cp = empty(shape, dtype)\n\n # recast arrays as dtype\n a = a.astype(dtype)\n b = b.astype(dtype)\n\n # create local aliases for readability\n a0 = a[..., 0]\n a1 = a[..., 1]\n if a.shape[-1] == 3:\n a2 = a[..., 2]\n b0 = b[..., 0]\n b1 = b[..., 1]\n if b.shape[-1] == 3:\n b2 = b[..., 2]\n if cp.ndim != 0 and cp.shape[-1] == 3:\n cp0 = cp[..., 0]\n cp1 = cp[..., 1]\n cp2 = cp[..., 2]\n\n if a.shape[-1] == 2:\n if b.shape[-1] == 2:\n # a0 * b1 - a1 * b0\n multiply(a0, b1, out=cp)\n cp -= a1 * b0\n return cp\n else:\n assert b.shape[-1] == 3\n # cp0 = a1 * b2 - 0 (a2 = 0)\n # cp1 = 0 - a0 * b2 (a2 = 0)\n # cp2 = a0 * b1 - a1 * b0\n multiply(a1, b2, out=cp0)\n multiply(a0, b2, out=cp1)\n negative(cp1, out=cp1)\n multiply(a0, b1, out=cp2)\n cp2 -= a1 * b0\n else:\n assert a.shape[-1] == 3\n if b.shape[-1] == 3:\n # cp0 = a1 * b2 - a2 * b1\n # cp1 = a2 * b0 - a0 * b2\n # cp2 = a0 * b1 - a1 * b0\n multiply(a1, b2, out=cp0)\n tmp = array(a2 * b1)\n cp0 -= tmp\n multiply(a2, b0, out=cp1)\n multiply(a0, b2, out=tmp)\n cp1 -= tmp\n multiply(a0, b1, out=cp2)\n multiply(a1, b0, out=tmp)\n cp2 -= tmp\n else:\n assert b.shape[-1] == 2\n # cp0 = 0 - a2 * b1 (b2 = 0)\n # cp1 = a2 * b0 - 0 (b2 = 0)\n # cp2 = a0 * b1 - a1 * b0\n multiply(a2, b1, out=cp0)\n negative(cp0, out=cp0)\n multiply(a2, b0, out=cp1)\n multiply(a0, b1, out=cp2)\n cp2 -= a1 * b0\n\n return moveaxis(cp, -1, axisc)\n\n\nlittle_endian = (sys.byteorder == 'little')\n\n\n@set_module('numpy')\ndef indices(dimensions, dtype=int, sparse=False):\n \"\"\"\n Return an array representing the indices of a grid.\n\n Compute an array where the subarrays contain index values 0, 1, ...\n varying only along the corresponding axis.\n\n Parameters\n ----------\n dimensions : sequence of ints\n The shape of the grid.\n dtype : dtype, optional\n Data type of the result.\n sparse : boolean, optional\n Return a sparse representation of the grid instead of a dense\n representation. Default is False.\n\n .. versionadded:: 1.17\n\n Returns\n -------\n grid : one ndarray or tuple of ndarrays\n If sparse is False:\n Returns one array of grid indices,\n ``grid.shape = (len(dimensions),) + tuple(dimensions)``.\n If sparse is True:\n Returns a tuple of arrays, with\n ``grid[i].shape = (1, ..., 1, dimensions[i], 1, ..., 1)`` with\n dimensions[i] in the ith place\n\n See Also\n --------\n mgrid, ogrid, meshgrid\n\n Notes\n -----\n The output shape in the dense case is obtained by prepending the number\n of dimensions in front of the tuple of dimensions, i.e. if `dimensions`\n is a tuple ``(r0, ..., rN-1)`` of length ``N``, the output shape is\n ``(N, r0, ..., rN-1)``.\n\n The subarrays ``grid[k]`` contains the N-D array of indices along the\n ``k-th`` axis. Explicitly::\n\n grid[k, i0, i1, ..., iN-1] = ik\n\n Examples\n --------\n >>> import numpy as np\n >>> grid = np.indices((2, 3))\n >>> grid.shape\n (2, 2, 3)\n >>> grid[0] # row indices\n array([[0, 0, 0],\n [1, 1, 1]])\n >>> grid[1] # column indices\n array([[0, 1, 2],\n [0, 1, 2]])\n\n The indices can be used as an index into an array.\n\n >>> x = np.arange(20).reshape(5, 4)\n >>> row, col = np.indices((2, 3))\n >>> x[row, col]\n array([[0, 1, 2],\n [4, 5, 6]])\n\n Note that it would be more straightforward in the above example to\n extract the required elements directly with ``x[:2, :3]``.\n\n If sparse is set to true, the grid will be returned in a sparse\n representation.\n\n >>> i, j = np.indices((2, 3), sparse=True)\n >>> i.shape\n (2, 1)\n >>> j.shape\n (1, 3)\n >>> i # row indices\n array([[0],\n [1]])\n >>> j # column indices\n array([[0, 1, 2]])\n\n \"\"\"\n dimensions = tuple(dimensions)\n N = len(dimensions)\n shape = (1,)*N\n if sparse:\n res = tuple()\n else:\n res = empty((N,)+dimensions, dtype=dtype)\n for i, dim in enumerate(dimensions):\n idx = arange(dim, dtype=dtype).reshape(\n shape[:i] + (dim,) + shape[i+1:]\n )\n if sparse:\n res = res + (idx,)\n else:\n res[i] = idx\n return res\n\n\n@set_array_function_like_doc\n@set_module('numpy')\ndef fromfunction(function, shape, *, dtype=float, like=None, **kwargs):\n \"\"\"\n Construct an array by executing a function over each coordinate.\n\n The resulting array therefore has a value ``fn(x, y, z)`` at\n coordinate ``(x, y, z)``.\n\n Parameters\n ----------\n function : callable\n The function is called with N parameters, where N is the rank of\n `shape`. Each parameter represents the coordinates of the array\n varying along a specific axis. For example, if `shape`\n were ``(2, 2)``, then the parameters would be\n ``array([[0, 0], [1, 1]])`` and ``array([[0, 1], [0, 1]])``\n shape : (N,) tuple of ints\n Shape of the output array, which also determines the shape of\n the coordinate arrays passed to `function`.\n dtype : data-type, optional\n Data-type of the coordinate arrays passed to `function`.\n By default, `dtype` is float.\n ${ARRAY_FUNCTION_LIKE}\n\n .. versionadded:: 1.20.0\n\n Returns\n -------\n fromfunction : any\n The result of the call to `function` is passed back directly.\n Therefore the shape of `fromfunction` is completely determined by\n `function`. If `function` returns a scalar value, the shape of\n `fromfunction` would not match the `shape` parameter.\n\n See Also\n --------\n indices, meshgrid\n\n Notes\n -----\n Keywords other than `dtype` and `like` are passed to `function`.\n\n Examples\n --------\n >>> import numpy as np\n >>> np.fromfunction(lambda i, j: i, (2, 2), dtype=float)\n array([[0., 0.],\n [1., 1.]])\n\n >>> np.fromfunction(lambda i, j: j, (2, 2), dtype=float)\n array([[0., 1.],\n [0., 1.]])\n\n >>> np.fromfunction(lambda i, j: i == j, (3, 3), dtype=int)\n array([[ True, False, False],\n [False, True, False],\n [False, False, True]])\n\n >>> np.fromfunction(lambda i, j: i + j, (3, 3), dtype=int)\n array([[0, 1, 2],\n [1, 2, 3],\n [2, 3, 4]])\n\n \"\"\"\n if like is not None:\n return _fromfunction_with_like(\n like, function, shape, dtype=dtype, **kwargs)\n\n args = indices(shape, dtype=dtype)\n return function(*args, **kwargs)\n\n\n_fromfunction_with_like = array_function_dispatch()(fromfunction)\n\n\ndef _frombuffer(buf, dtype, shape, order):\n return frombuffer(buf, dtype=dtype).reshape(shape, order=order)\n\n\n@set_module('numpy')\ndef isscalar(element):\n \"\"\"\n Returns True if the type of `element` is a scalar type.\n\n Parameters\n ----------\n element : any\n Input argument, can be of any type and shape.\n\n Returns\n -------\n val : bool\n True if `element` is a scalar type, False if it is not.\n\n See Also\n --------\n ndim : Get the number of dimensions of an array\n\n Notes\n -----\n If you need a stricter way to identify a *numerical* scalar, use\n ``isinstance(x, numbers.Number)``, as that returns ``False`` for most\n non-numerical elements such as strings.\n\n In most cases ``np.ndim(x) == 0`` should be used instead of this function,\n as that will also return true for 0d arrays. This is how numpy overloads\n functions in the style of the ``dx`` arguments to `gradient` and\n the ``bins`` argument to `histogram`. Some key differences:\n\n +------------------------------------+---------------+-------------------+\n | x |``isscalar(x)``|``np.ndim(x) == 0``|\n +====================================+===============+===================+\n | PEP 3141 numeric objects | ``True`` | ``True`` |\n | (including builtins) | | |\n +------------------------------------+---------------+-------------------+\n | builtin string and buffer objects | ``True`` | ``True`` |\n +------------------------------------+---------------+-------------------+\n | other builtin objects, like | ``False`` | ``True`` |\n | `pathlib.Path`, `Exception`, | | |\n | the result of `re.compile` | | |\n +------------------------------------+---------------+-------------------+\n | third-party objects like | ``False`` | ``True`` |\n | `matplotlib.figure.Figure` | | |\n +------------------------------------+---------------+-------------------+\n | zero-dimensional numpy arrays | ``False`` | ``True`` |\n +------------------------------------+---------------+-------------------+\n | other numpy arrays | ``False`` | ``False`` |\n +------------------------------------+---------------+-------------------+\n | `list`, `tuple`, and other | ``False`` | ``False`` |\n | sequence objects | | |\n +------------------------------------+---------------+-------------------+\n\n Examples\n --------\n >>> import numpy as np\n\n >>> np.isscalar(3.1)\n True\n\n >>> np.isscalar(np.array(3.1))\n False\n\n >>> np.isscalar([3.1])\n False\n\n >>> np.isscalar(False)\n True\n\n >>> np.isscalar('numpy')\n True\n\n NumPy supports PEP 3141 numbers:\n\n >>> from fractions import Fraction\n >>> np.isscalar(Fraction(5, 17))\n True\n >>> from numbers import Number\n >>> np.isscalar(Number())\n True\n\n \"\"\"\n return (isinstance(element, generic)\n or type(element) in ScalarType\n or isinstance(element, numbers.Number))\n\n\n@set_module('numpy')\ndef binary_repr(num, width=None):\n \"\"\"\n Return the binary representation of the input number as a string.\n\n For negative numbers, if width is not given, a minus sign is added to the\n front. If width is given, the two's complement of the number is\n returned, with respect to that width.\n\n In a two's-complement system negative numbers are represented by the two's\n complement of the absolute value. This is the most common method of\n representing signed integers on computers [1]_. A N-bit two's-complement\n system can represent every integer in the range\n :math:`-2^{N-1}` to :math:`+2^{N-1}-1`.\n\n Parameters\n ----------\n num : int\n Only an integer decimal number can be used.\n width : int, optional\n The length of the returned string if `num` is positive, or the length\n of the two's complement if `num` is negative, provided that `width` is\n at least a sufficient number of bits for `num` to be represented in\n the designated form. If the `width` value is insufficient, an error is\n raised.\n\n Returns\n -------\n bin : str\n Binary representation of `num` or two's complement of `num`.\n\n See Also\n --------\n base_repr: Return a string representation of a number in the given base\n system.\n bin: Python's built-in binary representation generator of an integer.\n\n Notes\n -----\n `binary_repr` is equivalent to using `base_repr` with base 2, but about 25x\n faster.\n\n References\n ----------\n .. [1] Wikipedia, \"Two's complement\",\n https://en.wikipedia.org/wiki/Two's_complement\n\n Examples\n --------\n >>> import numpy as np\n >>> np.binary_repr(3)\n '11'\n >>> np.binary_repr(-3)\n '-11'\n >>> np.binary_repr(3, width=4)\n '0011'\n\n The two's complement is returned when the input number is negative and\n width is specified:\n\n >>> np.binary_repr(-3, width=3)\n '101'\n >>> np.binary_repr(-3, width=5)\n '11101'\n\n \"\"\"\n def err_if_insufficient(width, binwidth):\n if width is not None and width < binwidth:\n raise ValueError(\n f\"Insufficient bit {width=} provided for {binwidth=}\"\n )\n\n # Ensure that num is a Python integer to avoid overflow or unwanted\n # casts to floating point.\n num = operator.index(num)\n\n if num == 0:\n return '0' * (width or 1)\n\n elif num > 0:\n binary = bin(num)[2:]\n binwidth = len(binary)\n outwidth = (binwidth if width is None\n else builtins.max(binwidth, width))\n err_if_insufficient(width, binwidth)\n return binary.zfill(outwidth)\n\n else:\n if width is None:\n return '-' + bin(-num)[2:]\n\n else:\n poswidth = len(bin(-num)[2:])\n\n # See gh-8679: remove extra digit\n # for numbers at boundaries.\n if 2**(poswidth - 1) == -num:\n poswidth -= 1\n\n twocomp = 2**(poswidth + 1) + num\n binary = bin(twocomp)[2:]\n binwidth = len(binary)\n\n outwidth = builtins.max(binwidth, width)\n err_if_insufficient(width, binwidth)\n return '1' * (outwidth - binwidth) + binary\n\n\n@set_module('numpy')\ndef base_repr(number, base=2, padding=0):\n \"\"\"\n Return a string representation of a number in the given base system.\n\n Parameters\n ----------\n number : int\n The value to convert. Positive and negative values are handled.\n base : int, optional\n Convert `number` to the `base` number system. The valid range is 2-36,\n the default value is 2.\n padding : int, optional\n Number of zeros padded on the left. Default is 0 (no padding).\n\n Returns\n -------\n out : str\n String representation of `number` in `base` system.\n\n See Also\n --------\n binary_repr : Faster version of `base_repr` for base 2.\n\n Examples\n --------\n >>> import numpy as np\n >>> np.base_repr(5)\n '101'\n >>> np.base_repr(6, 5)\n '11'\n >>> np.base_repr(7, base=5, padding=3)\n '00012'\n\n >>> np.base_repr(10, base=16)\n 'A'\n >>> np.base_repr(32, base=16)\n '20'\n\n \"\"\"\n digits = '0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ'\n if base > len(digits):\n raise ValueError(\"Bases greater than 36 not handled in base_repr.\")\n elif base < 2:\n raise ValueError(\"Bases less than 2 not handled in base_repr.\")\n\n num = abs(int(number))\n res = []\n while num:\n res.append(digits[num % base])\n num //= base\n if padding:\n res.append('0' * padding)\n if number < 0:\n res.append('-')\n return ''.join(reversed(res or '0'))\n\n\n# These are all essentially abbreviations\n# These might wind up in a special abbreviations module\n\n\ndef _maketup(descr, val):\n dt = dtype(descr)\n # Place val in all scalar tuples:\n fields = dt.fields\n if fields is None:\n return val\n else:\n res = [_maketup(fields[name][0], val) for name in dt.names]\n return tuple(res)\n\n\n@set_array_function_like_doc\n@set_module('numpy')\ndef identity(n, dtype=None, *, like=None):\n \"\"\"\n Return the identity array.\n\n The identity array is a square array with ones on\n the main diagonal.\n\n Parameters\n ----------\n n : int\n Number of rows (and columns) in `n` x `n` output.\n dtype : data-type, optional\n Data-type of the output. Defaults to ``float``.\n ${ARRAY_FUNCTION_LIKE}\n\n .. versionadded:: 1.20.0\n\n Returns\n -------\n out : ndarray\n `n` x `n` array with its main diagonal set to one,\n and all other elements 0.\n\n Examples\n --------\n >>> import numpy as np\n >>> np.identity(3)\n array([[1., 0., 0.],\n [0., 1., 0.],\n [0., 0., 1.]])\n\n \"\"\"\n if like is not None:\n return _identity_with_like(like, n, dtype=dtype)\n\n from numpy import eye\n return eye(n, dtype=dtype, like=like)\n\n\n_identity_with_like = array_function_dispatch()(identity)\n\n\ndef _allclose_dispatcher(a, b, rtol=None, atol=None, equal_nan=None):\n return (a, b, rtol, atol)\n\n\n@array_function_dispatch(_allclose_dispatcher)\ndef allclose(a, b, rtol=1.e-5, atol=1.e-8, equal_nan=False):\n \"\"\"\n Returns True if two arrays are element-wise equal within a tolerance.\n\n The tolerance values are positive, typically very small numbers. The\n relative difference (`rtol` * abs(`b`)) and the absolute difference\n `atol` are added together to compare against the absolute difference\n between `a` and `b`.\n\n .. warning:: The default `atol` is not appropriate for comparing numbers\n with magnitudes much smaller than one (see Notes).\n\n NaNs are treated as equal if they are in the same place and if\n ``equal_nan=True``. Infs are treated as equal if they are in the same\n place and of the same sign in both arrays.\n\n Parameters\n ----------\n a, b : array_like\n Input arrays to compare.\n rtol : array_like\n The relative tolerance parameter (see Notes).\n atol : array_like\n The absolute tolerance parameter (see Notes).\n equal_nan : bool\n Whether to compare NaN's as equal. If True, NaN's in `a` will be\n considered equal to NaN's in `b` in the output array.\n\n .. versionadded:: 1.10.0\n\n Returns\n -------\n allclose : bool\n Returns True if the two arrays are equal within the given\n tolerance; False otherwise.\n\n See Also\n --------\n isclose, all, any, equal\n\n Notes\n -----\n If the following equation is element-wise True, then allclose returns\n True.::\n\n absolute(a - b) <= (atol + rtol * absolute(b))\n\n The above equation is not symmetric in `a` and `b`, so that\n ``allclose(a, b)`` might be different from ``allclose(b, a)`` in\n some rare cases.\n\n The default value of `atol` is not appropriate when the reference value\n `b` has magnitude smaller than one. For example, it is unlikely that\n ``a = 1e-9`` and ``b = 2e-9`` should be considered \"close\", yet\n ``allclose(1e-9, 2e-9)`` is ``True`` with default settings. Be sure\n to select `atol` for the use case at hand, especially for defining the\n threshold below which a non-zero value in `a` will be considered \"close\"\n to a very small or zero value in `b`.\n\n The comparison of `a` and `b` uses standard broadcasting, which\n means that `a` and `b` need not have the same shape in order for\n ``allclose(a, b)`` to evaluate to True. The same is true for\n `equal` but not `array_equal`.\n\n `allclose` is not defined for non-numeric data types.\n `bool` is considered a numeric data-type for this purpose.\n\n Examples\n --------\n >>> import numpy as np\n >>> np.allclose([1e10,1e-7], [1.00001e10,1e-8])\n False\n\n >>> np.allclose([1e10,1e-8], [1.00001e10,1e-9])\n True\n\n >>> np.allclose([1e10,1e-8], [1.0001e10,1e-9])\n False\n\n >>> np.allclose([1.0, np.nan], [1.0, np.nan])\n False\n\n >>> np.allclose([1.0, np.nan], [1.0, np.nan], equal_nan=True)\n True\n\n\n \"\"\"\n res = all(isclose(a, b, rtol=rtol, atol=atol, equal_nan=equal_nan))\n return builtins.bool(res)\n\n\ndef _isclose_dispatcher(a, b, rtol=None, atol=None, equal_nan=None):\n return (a, b, rtol, atol)\n\n\n@array_function_dispatch(_isclose_dispatcher)\ndef isclose(a, b, rtol=1.e-5, atol=1.e-8, equal_nan=False):\n \"\"\"\n Returns a boolean array where two arrays are element-wise equal within a\n tolerance.\n\n The tolerance values are positive, typically very small numbers. The\n relative difference (`rtol` * abs(`b`)) and the absolute difference\n `atol` are added together to compare against the absolute difference\n between `a` and `b`.\n\n .. warning:: The default `atol` is not appropriate for comparing numbers\n with magnitudes much smaller than one (see Notes).\n\n Parameters\n ----------\n a, b : array_like\n Input arrays to compare.\n rtol : array_like\n The relative tolerance parameter (see Notes).\n atol : array_like\n The absolute tolerance parameter (see Notes).\n equal_nan : bool\n Whether to compare NaN's as equal. If True, NaN's in `a` will be\n considered equal to NaN's in `b` in the output array.\n\n Returns\n -------\n y : array_like\n Returns a boolean array of where `a` and `b` are equal within the\n given tolerance. If both `a` and `b` are scalars, returns a single\n boolean value.\n\n See Also\n --------\n allclose\n math.isclose\n\n Notes\n -----\n .. versionadded:: 1.7.0\n\n For finite values, isclose uses the following equation to test whether\n two floating point values are equivalent.::\n\n absolute(a - b) <= (atol + rtol * absolute(b))\n\n Unlike the built-in `math.isclose`, the above equation is not symmetric\n in `a` and `b` -- it assumes `b` is the reference value -- so that\n `isclose(a, b)` might be different from `isclose(b, a)`.\n\n The default value of `atol` is not appropriate when the reference value\n `b` has magnitude smaller than one. For example, it is unlikely that\n ``a = 1e-9`` and ``b = 2e-9`` should be considered \"close\", yet\n ``isclose(1e-9, 2e-9)`` is ``True`` with default settings. Be sure\n to select `atol` for the use case at hand, especially for defining the\n threshold below which a non-zero value in `a` will be considered \"close\"\n to a very small or zero value in `b`.\n\n `isclose` is not defined for non-numeric data types.\n :class:`bool` is considered a numeric data-type for this purpose.\n\n Examples\n --------\n >>> import numpy as np\n >>> np.isclose([1e10,1e-7], [1.00001e10,1e-8])\n array([ True, False])\n\n >>> np.isclose([1e10,1e-8], [1.00001e10,1e-9])\n array([ True, True])\n\n >>> np.isclose([1e10,1e-8], [1.0001e10,1e-9])\n array([False, True])\n\n >>> np.isclose([1.0, np.nan], [1.0, np.nan])\n array([ True, False])\n\n >>> np.isclose([1.0, np.nan], [1.0, np.nan], equal_nan=True)\n array([ True, True])\n\n >>> np.isclose([1e-8, 1e-7], [0.0, 0.0])\n array([ True, False])\n\n >>> np.isclose([1e-100, 1e-7], [0.0, 0.0], atol=0.0)\n array([False, False])\n\n >>> np.isclose([1e-10, 1e-10], [1e-20, 0.0])\n array([ True, True])\n\n >>> np.isclose([1e-10, 1e-10], [1e-20, 0.999999e-10], atol=0.0)\n array([False, True])\n\n \"\"\"\n # Turn all but python scalars into arrays.\n x, y, atol, rtol = (\n a if isinstance(a, (int, float, complex)) else asanyarray(a)\n for a in (a, b, atol, rtol))\n\n # Make sure y is an inexact type to avoid bad behavior on abs(MIN_INT).\n # This will cause casting of x later. Also, make sure to allow subclasses\n # (e.g., for numpy.ma).\n # NOTE: We explicitly allow timedelta, which used to work. This could\n # possibly be deprecated. See also gh-18286.\n # timedelta works if `atol` is an integer or also a timedelta.\n # Although, the default tolerances are unlikely to be useful\n if (dtype := getattr(y, \"dtype\", None)) is not None and dtype.kind != \"m\":\n dt = multiarray.result_type(y, 1.)\n y = asanyarray(y, dtype=dt)\n elif isinstance(y, int):\n y = float(y)\n\n with errstate(invalid='ignore'), _no_nep50_warning():\n result = (less_equal(abs(x-y), atol + rtol * abs(y))\n & isfinite(y)\n | (x == y))\n if equal_nan:\n result |= isnan(x) & isnan(y)\n\n return result[()] # Flatten 0d arrays to scalars\n\n\ndef _array_equal_dispatcher(a1, a2, equal_nan=None):\n return (a1, a2)\n\n\n_no_nan_types = {\n # should use np.dtype.BoolDType, but as of writing\n # that fails the reloading test.\n type(dtype(nt.bool)),\n type(dtype(nt.int8)),\n type(dtype(nt.int16)),\n type(dtype(nt.int32)),\n type(dtype(nt.int64)),\n}\n\n\ndef _dtype_cannot_hold_nan(dtype):\n return type(dtype) in _no_nan_types\n\n\n@array_function_dispatch(_array_equal_dispatcher)\ndef array_equal(a1, a2, equal_nan=False):\n \"\"\"\n True if two arrays have the same shape and elements, False otherwise.\n\n Parameters\n ----------\n a1, a2 : array_like\n Input arrays.\n equal_nan : bool\n Whether to compare NaN's as equal. If the dtype of a1 and a2 is\n complex, values will be considered equal if either the real or the\n imaginary component of a given value is ``nan``.\n\n .. versionadded:: 1.19.0\n\n Returns\n -------\n b : bool\n Returns True if the arrays are equal.\n\n See Also\n --------\n allclose: Returns True if two arrays are element-wise equal within a\n tolerance.\n array_equiv: Returns True if input arrays are shape consistent and all\n elements equal.\n\n Examples\n --------\n >>> import numpy as np\n\n >>> np.array_equal([1, 2], [1, 2])\n True\n\n >>> np.array_equal(np.array([1, 2]), np.array([1, 2]))\n True\n\n >>> np.array_equal([1, 2], [1, 2, 3])\n False\n\n >>> np.array_equal([1, 2], [1, 4])\n False\n\n >>> a = np.array([1, np.nan])\n >>> np.array_equal(a, a)\n False\n\n >>> np.array_equal(a, a, equal_nan=True)\n True\n\n When ``equal_nan`` is True, complex values with nan components are\n considered equal if either the real *or* the imaginary components are nan.\n\n >>> a = np.array([1 + 1j])\n >>> b = a.copy()\n >>> a.real = np.nan\n >>> b.imag = np.nan\n >>> np.array_equal(a, b, equal_nan=True)\n True\n \"\"\"\n try:\n a1, a2 = asarray(a1), asarray(a2)\n except Exception:\n return False\n if a1.shape != a2.shape:\n return False\n if not equal_nan:\n return builtins.bool((asanyarray(a1 == a2)).all())\n\n if a1 is a2:\n # nan will compare equal so an array will compare equal to itself.\n return True\n\n cannot_have_nan = (_dtype_cannot_hold_nan(a1.dtype)\n and _dtype_cannot_hold_nan(a2.dtype))\n if cannot_have_nan:\n return builtins.bool(asarray(a1 == a2).all())\n\n # Handling NaN values if equal_nan is True\n a1nan, a2nan = isnan(a1), isnan(a2)\n # NaN's occur at different locations\n if not (a1nan == a2nan).all():\n return False\n # Shapes of a1, a2 and masks are guaranteed to be consistent by this point\n return builtins.bool((a1[~a1nan] == a2[~a1nan]).all())\n\n\ndef _array_equiv_dispatcher(a1, a2):\n return (a1, a2)\n\n\n@array_function_dispatch(_array_equiv_dispatcher)\ndef array_equiv(a1, a2):\n \"\"\"\n Returns True if input arrays are shape consistent and all elements equal.\n\n Shape consistent means they are either the same shape, or one input array\n can be broadcasted to create the same shape as the other one.\n\n Parameters\n ----------\n a1, a2 : array_like\n Input arrays.\n\n Returns\n -------\n out : bool\n True if equivalent, False otherwise.\n\n Examples\n --------\n >>> import numpy as np\n >>> np.array_equiv([1, 2], [1, 2])\n True\n >>> np.array_equiv([1, 2], [1, 3])\n False\n\n Showing the shape equivalence:\n\n >>> np.array_equiv([1, 2], [[1, 2], [1, 2]])\n True\n >>> np.array_equiv([1, 2], [[1, 2, 1, 2], [1, 2, 1, 2]])\n False\n\n >>> np.array_equiv([1, 2], [[1, 2], [1, 3]])\n False\n\n \"\"\"\n try:\n a1, a2 = asarray(a1), asarray(a2)\n except Exception:\n return False\n try:\n multiarray.broadcast(a1, a2)\n except Exception:\n return False\n\n return builtins.bool(asanyarray(a1 == a2).all())\n\n\ndef _astype_dispatcher(x, dtype, /, *, copy=None, device=None):\n return (x, dtype)\n\n\n@array_function_dispatch(_astype_dispatcher)\ndef astype(x, dtype, /, *, copy=True, device=None):\n \"\"\"\n Copies an array to a specified data type.\n\n This function is an Array API compatible alternative to\n `numpy.ndarray.astype`.\n\n Parameters\n ----------\n x : ndarray\n Input NumPy array to cast. ``array_likes`` are explicitly not\n supported here.\n dtype : dtype\n Data type of the result.\n copy : bool, optional\n Specifies whether to copy an array when the specified dtype matches\n the data type of the input array ``x``. If ``True``, a newly allocated\n array must always be returned. If ``False`` and the specified dtype\n matches the data type of the input array, the input array must be\n returned; otherwise, a newly allocated array must be returned.\n Defaults to ``True``.\n device : str, optional\n The device on which to place the returned array. Default: None.\n For Array-API interoperability only, so must be ``\"cpu\"`` if passed.\n\n .. versionadded:: 2.1.0\n\n Returns\n -------\n out : ndarray\n An array having the specified data type.\n\n See Also\n --------\n ndarray.astype\n\n Examples\n --------\n >>> import numpy as np\n >>> arr = np.array([1, 2, 3]); arr\n array([1, 2, 3])\n >>> np.astype(arr, np.float64)\n array([1., 2., 3.])\n\n Non-copy case:\n\n >>> arr = np.array([1, 2, 3])\n >>> arr_noncpy = np.astype(arr, arr.dtype, copy=False)\n >>> np.shares_memory(arr, arr_noncpy)\n True\n\n \"\"\"\n if not (isinstance(x, np.ndarray) or isscalar(x)):\n raise TypeError(\n \"Input should be a NumPy array or scalar. \"\n f\"It is a {type(x)} instead.\"\n )\n if device is not None and device != \"cpu\":\n raise ValueError(\n 'Device not understood. Only \"cpu\" is allowed, but received:'\n f' {device}'\n )\n return x.astype(dtype, copy=copy)\n\n\ninf = PINF\nnan = NAN\nFalse_ = nt.bool(False)\nTrue_ = nt.bool(True)\n\n\ndef extend_all(module):\n existing = set(__all__)\n mall = getattr(module, '__all__')\n for a in mall:\n if a not in existing:\n __all__.append(a)\n\n\nfrom .umath import *\nfrom .numerictypes import *\nfrom . import fromnumeric\nfrom .fromnumeric import *\nfrom . import arrayprint\nfrom .arrayprint import *\nfrom . import _asarray\nfrom ._asarray import *\nfrom . import _ufunc_config\nfrom ._ufunc_config import *\nextend_all(fromnumeric)\nextend_all(umath)\nextend_all(numerictypes)\nextend_all(arrayprint)\nextend_all(_asarray)\nextend_all(_ufunc_config)\n", 2729], "/home/gaogaotiantian/programs/viztracer/venv3.13/lib/python3.13/site-packages/numpy/_core/multiarray.py": ["\"\"\"\nCreate the numpy._core.multiarray namespace for backward compatibility. \nIn v1.16 the multiarray and umath c-extension modules were merged into \na single _multiarray_umath extension module. So we replicate the old \nnamespace by importing from the extension module.\n\n\"\"\"\n\nimport functools\nfrom . import overrides\nfrom . import _multiarray_umath\nfrom ._multiarray_umath import * # noqa: F403\n# These imports are needed for backward compatibility,\n# do not change them. issue gh-15518\n# _get_ndarray_c_version is semi-public, on purpose not added to __all__\nfrom ._multiarray_umath import (\n _flagdict, from_dlpack, _place, _reconstruct,\n _vec_string, _ARRAY_API, _monotonicity, _get_ndarray_c_version,\n _get_madvise_hugepage, _set_madvise_hugepage,\n _get_promotion_state, _set_promotion_state\n )\n\n__all__ = [\n '_ARRAY_API', 'ALLOW_THREADS', 'BUFSIZE', 'CLIP', 'DATETIMEUNITS',\n 'ITEM_HASOBJECT', 'ITEM_IS_POINTER', 'LIST_PICKLE', 'MAXDIMS',\n 'MAY_SHARE_BOUNDS', 'MAY_SHARE_EXACT', 'NEEDS_INIT', 'NEEDS_PYAPI',\n 'RAISE', 'USE_GETITEM', 'USE_SETITEM', 'WRAP',\n '_flagdict', 'from_dlpack', '_place', '_reconstruct', '_vec_string',\n '_monotonicity', 'add_docstring', 'arange', 'array', 'asarray',\n 'asanyarray', 'ascontiguousarray', 'asfortranarray', 'bincount',\n 'broadcast', 'busday_count', 'busday_offset', 'busdaycalendar', 'can_cast',\n 'compare_chararrays', 'concatenate', 'copyto', 'correlate', 'correlate2',\n 'count_nonzero', 'c_einsum', 'datetime_as_string', 'datetime_data',\n 'dot', 'dragon4_positional', 'dragon4_scientific', 'dtype',\n 'empty', 'empty_like', 'error', 'flagsobj', 'flatiter', 'format_longfloat',\n 'frombuffer', 'fromfile', 'fromiter', 'fromstring',\n 'get_handler_name', 'get_handler_version', 'inner', 'interp',\n 'interp_complex', 'is_busday', 'lexsort', 'matmul', 'vecdot',\n 'may_share_memory', 'min_scalar_type', 'ndarray', 'nditer', 'nested_iters',\n 'normalize_axis_index', 'packbits', 'promote_types', 'putmask',\n 'ravel_multi_index', 'result_type', 'scalar', 'set_datetimeparse_function',\n 'set_typeDict', 'shares_memory', 'typeinfo',\n 'unpackbits', 'unravel_index', 'vdot', 'where', 'zeros',\n '_get_promotion_state', '_set_promotion_state']\n\n# For backward compatibility, make sure pickle imports \n# these functions from here\n_reconstruct.__module__ = 'numpy._core.multiarray'\nscalar.__module__ = 'numpy._core.multiarray'\n\n\nfrom_dlpack.__module__ = 'numpy'\narange.__module__ = 'numpy'\narray.__module__ = 'numpy'\nasarray.__module__ = 'numpy'\nasanyarray.__module__ = 'numpy'\nascontiguousarray.__module__ = 'numpy'\nasfortranarray.__module__ = 'numpy'\ndatetime_data.__module__ = 'numpy'\nempty.__module__ = 'numpy'\nfrombuffer.__module__ = 'numpy'\nfromfile.__module__ = 'numpy'\nfromiter.__module__ = 'numpy'\nfrompyfunc.__module__ = 'numpy'\nfromstring.__module__ = 'numpy'\nmay_share_memory.__module__ = 'numpy'\nnested_iters.__module__ = 'numpy'\npromote_types.__module__ = 'numpy'\nzeros.__module__ = 'numpy'\n_get_promotion_state.__module__ = 'numpy'\n_set_promotion_state.__module__ = 'numpy'\nnormalize_axis_index.__module__ = 'numpy.lib.array_utils'\n\n\n# We can't verify dispatcher signatures because NumPy's C functions don't\n# support introspection.\narray_function_from_c_func_and_dispatcher = functools.partial(\n overrides.array_function_from_dispatcher,\n module='numpy', docs_from_dispatcher=True, verify=False)\n\n\n@array_function_from_c_func_and_dispatcher(_multiarray_umath.empty_like)\ndef empty_like(\n prototype, dtype=None, order=None, subok=None, shape=None, *, device=None\n):\n \"\"\"\n empty_like(prototype, dtype=None, order='K', subok=True, shape=None, *,\n device=None)\n\n Return a new array with the same shape and type as a given array.\n\n Parameters\n ----------\n prototype : array_like\n The shape and data-type of `prototype` define these same attributes\n of the returned array.\n dtype : data-type, optional\n Overrides the data type of the result.\n\n .. versionadded:: 1.6.0\n order : {'C', 'F', 'A', or 'K'}, optional\n Overrides the memory layout of the result. 'C' means C-order,\n 'F' means F-order, 'A' means 'F' if `prototype` is Fortran\n contiguous, 'C' otherwise. 'K' means match the layout of `prototype`\n as closely as possible.\n\n .. versionadded:: 1.6.0\n subok : bool, optional.\n If True, then the newly created array will use the sub-class\n type of `prototype`, otherwise it will be a base-class array. Defaults\n to True.\n shape : int or sequence of ints, optional.\n Overrides the shape of the result. If order='K' and the number of\n dimensions is unchanged, will try to keep order, otherwise,\n order='C' is implied.\n\n .. versionadded:: 1.17.0\n device : str, optional\n The device on which to place the created array. Default: None.\n For Array-API interoperability only, so must be ``\"cpu\"`` if passed.\n\n .. versionadded:: 2.0.0\n\n Returns\n -------\n out : ndarray\n Array of uninitialized (arbitrary) data with the same\n shape and type as `prototype`.\n\n See Also\n --------\n ones_like : Return an array of ones with shape and type of input.\n zeros_like : Return an array of zeros with shape and type of input.\n full_like : Return a new array with shape of input filled with value.\n empty : Return a new uninitialized array.\n\n Notes\n -----\n Unlike other array creation functions (e.g. `zeros_like`, `ones_like`,\n `full_like`), `empty_like` does not initialize the values of the array,\n and may therefore be marginally faster. However, the values stored in the\n newly allocated array are arbitrary. For reproducible behavior, be sure\n to set each element of the array before reading.\n\n Examples\n --------\n >>> import numpy as np\n >>> a = ([1,2,3], [4,5,6]) # a is array-like\n >>> np.empty_like(a)\n array([[-1073741821, -1073741821, 3], # uninitialized\n [ 0, 0, -1073741821]])\n >>> a = np.array([[1., 2., 3.],[4.,5.,6.]])\n >>> np.empty_like(a)\n array([[ -2.00000715e+000, 1.48219694e-323, -2.00000572e+000], # uninitialized\n [ 4.38791518e-305, -2.00000715e+000, 4.17269252e-309]])\n\n \"\"\" # NOQA\n return (prototype,)\n\n\n@array_function_from_c_func_and_dispatcher(_multiarray_umath.concatenate)\ndef concatenate(arrays, axis=None, out=None, *, dtype=None, casting=None):\n \"\"\"\n concatenate(\n (a1, a2, ...), \n axis=0, \n out=None, \n dtype=None, \n casting=\"same_kind\"\n )\n\n Join a sequence of arrays along an existing axis.\n\n Parameters\n ----------\n a1, a2, ... : sequence of array_like\n The arrays must have the same shape, except in the dimension\n corresponding to `axis` (the first, by default).\n axis : int, optional\n The axis along which the arrays will be joined. If axis is None,\n arrays are flattened before use. Default is 0.\n out : ndarray, optional\n If provided, the destination to place the result. The shape must be\n correct, matching that of what concatenate would have returned if no\n out argument were specified.\n dtype : str or dtype\n If provided, the destination array will have this dtype. Cannot be\n provided together with `out`.\n\n .. versionadded:: 1.20.0\n\n casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional\n Controls what kind of data casting may occur. Defaults to 'same_kind'.\n For a description of the options, please see :term:`casting`.\n \n .. versionadded:: 1.20.0\n\n Returns\n -------\n res : ndarray\n The concatenated array.\n\n See Also\n --------\n ma.concatenate : Concatenate function that preserves input masks.\n array_split : Split an array into multiple sub-arrays of equal or\n near-equal size.\n split : Split array into a list of multiple sub-arrays of equal size.\n hsplit : Split array into multiple sub-arrays horizontally (column wise).\n vsplit : Split array into multiple sub-arrays vertically (row wise).\n dsplit : Split array into multiple sub-arrays along the 3rd axis (depth).\n stack : Stack a sequence of arrays along a new axis.\n block : Assemble arrays from blocks.\n hstack : Stack arrays in sequence horizontally (column wise).\n vstack : Stack arrays in sequence vertically (row wise).\n dstack : Stack arrays in sequence depth wise (along third dimension).\n column_stack : Stack 1-D arrays as columns into a 2-D array.\n\n Notes\n -----\n When one or more of the arrays to be concatenated is a MaskedArray,\n this function will return a MaskedArray object instead of an ndarray,\n but the input masks are *not* preserved. In cases where a MaskedArray\n is expected as input, use the ma.concatenate function from the masked\n array module instead.\n\n Examples\n --------\n >>> import numpy as np\n >>> a = np.array([[1, 2], [3, 4]])\n >>> b = np.array([[5, 6]])\n >>> np.concatenate((a, b), axis=0)\n array([[1, 2],\n [3, 4],\n [5, 6]])\n >>> np.concatenate((a, b.T), axis=1)\n array([[1, 2, 5],\n [3, 4, 6]])\n >>> np.concatenate((a, b), axis=None)\n array([1, 2, 3, 4, 5, 6])\n\n This function will not preserve masking of MaskedArray inputs.\n\n >>> a = np.ma.arange(3)\n >>> a[1] = np.ma.masked\n >>> b = np.arange(2, 5)\n >>> a\n masked_array(data=[0, --, 2],\n mask=[False, True, False],\n fill_value=999999)\n >>> b\n array([2, 3, 4])\n >>> np.concatenate([a, b])\n masked_array(data=[0, 1, 2, 2, 3, 4],\n mask=False,\n fill_value=999999)\n >>> np.ma.concatenate([a, b])\n masked_array(data=[0, --, 2, 2, 3, 4],\n mask=[False, True, False, False, False, False],\n fill_value=999999)\n\n \"\"\"\n if out is not None:\n # optimize for the typical case where only arrays is provided\n arrays = list(arrays)\n arrays.append(out)\n return arrays\n\n\n@array_function_from_c_func_and_dispatcher(_multiarray_umath.inner)\ndef inner(a, b):\n \"\"\"\n inner(a, b, /)\n\n Inner product of two arrays.\n\n Ordinary inner product of vectors for 1-D arrays (without complex\n conjugation), in higher dimensions a sum product over the last axes.\n\n Parameters\n ----------\n a, b : array_like\n If `a` and `b` are nonscalar, their last dimensions must match.\n\n Returns\n -------\n out : ndarray\n If `a` and `b` are both\n scalars or both 1-D arrays then a scalar is returned; otherwise\n an array is returned.\n ``out.shape = (*a.shape[:-1], *b.shape[:-1])``\n\n Raises\n ------\n ValueError\n If both `a` and `b` are nonscalar and their last dimensions have\n different sizes.\n\n See Also\n --------\n tensordot : Sum products over arbitrary axes.\n dot : Generalised matrix product, using second last dimension of `b`.\n einsum : Einstein summation convention.\n\n Notes\n -----\n For vectors (1-D arrays) it computes the ordinary inner-product::\n\n np.inner(a, b) = sum(a[:]*b[:])\n\n More generally, if ``ndim(a) = r > 0`` and ``ndim(b) = s > 0``::\n\n np.inner(a, b) = np.tensordot(a, b, axes=(-1,-1))\n\n or explicitly::\n\n np.inner(a, b)[i0,...,ir-2,j0,...,js-2]\n = sum(a[i0,...,ir-2,:]*b[j0,...,js-2,:])\n\n In addition `a` or `b` may be scalars, in which case::\n\n np.inner(a,b) = a*b\n\n Examples\n --------\n Ordinary inner product for vectors:\n\n >>> import numpy as np\n >>> a = np.array([1,2,3])\n >>> b = np.array([0,1,0])\n >>> np.inner(a, b)\n 2\n\n Some multidimensional examples:\n\n >>> a = np.arange(24).reshape((2,3,4))\n >>> b = np.arange(4)\n >>> c = np.inner(a, b)\n >>> c.shape\n (2, 3)\n >>> c\n array([[ 14, 38, 62],\n [ 86, 110, 134]])\n\n >>> a = np.arange(2).reshape((1,1,2))\n >>> b = np.arange(6).reshape((3,2))\n >>> c = np.inner(a, b)\n >>> c.shape\n (1, 1, 3)\n >>> c\n array([[[1, 3, 5]]])\n\n An example where `b` is a scalar:\n\n >>> np.inner(np.eye(2), 7)\n array([[7., 0.],\n [0., 7.]])\n\n \"\"\"\n return (a, b)\n\n\n@array_function_from_c_func_and_dispatcher(_multiarray_umath.where)\ndef where(condition, x=None, y=None):\n \"\"\"\n where(condition, [x, y], /)\n\n Return elements chosen from `x` or `y` depending on `condition`.\n\n .. note::\n When only `condition` is provided, this function is a shorthand for\n ``np.asarray(condition).nonzero()``. Using `nonzero` directly should be\n preferred, as it behaves correctly for subclasses. The rest of this\n documentation covers only the case where all three arguments are\n provided.\n\n Parameters\n ----------\n condition : array_like, bool\n Where True, yield `x`, otherwise yield `y`.\n x, y : array_like\n Values from which to choose. `x`, `y` and `condition` need to be\n broadcastable to some shape.\n\n Returns\n -------\n out : ndarray\n An array with elements from `x` where `condition` is True, and elements\n from `y` elsewhere.\n\n See Also\n --------\n choose\n nonzero : The function that is called when x and y are omitted\n\n Notes\n -----\n If all the arrays are 1-D, `where` is equivalent to::\n\n [xv if c else yv\n for c, xv, yv in zip(condition, x, y)]\n\n Examples\n --------\n >>> import numpy as np\n >>> a = np.arange(10)\n >>> a\n array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n >>> np.where(a < 5, a, 10*a)\n array([ 0, 1, 2, 3, 4, 50, 60, 70, 80, 90])\n\n This can be used on multidimensional arrays too:\n\n >>> np.where([[True, False], [True, True]],\n ... [[1, 2], [3, 4]],\n ... [[9, 8], [7, 6]])\n array([[1, 8],\n [3, 4]])\n\n The shapes of x, y, and the condition are broadcast together:\n\n >>> x, y = np.ogrid[:3, :4]\n >>> np.where(x < y, x, 10 + y) # both x and 10+y are broadcast\n array([[10, 0, 0, 0],\n [10, 11, 1, 1],\n [10, 11, 12, 2]])\n\n >>> a = np.array([[0, 1, 2],\n ... [0, 2, 4],\n ... [0, 3, 6]])\n >>> np.where(a < 4, a, -1) # -1 is broadcast\n array([[ 0, 1, 2],\n [ 0, 2, -1],\n [ 0, 3, -1]])\n \"\"\"\n return (condition, x, y)\n\n\n@array_function_from_c_func_and_dispatcher(_multiarray_umath.lexsort)\ndef lexsort(keys, axis=None):\n \"\"\"\n lexsort(keys, axis=-1)\n\n Perform an indirect stable sort using a sequence of keys.\n\n Given multiple sorting keys, lexsort returns an array of integer indices\n that describes the sort order by multiple keys. The last key in the\n sequence is used for the primary sort order, ties are broken by the\n second-to-last key, and so on.\n\n Parameters\n ----------\n keys : (k, m, n, ...) array-like\n The `k` keys to be sorted. The *last* key (e.g, the last\n row if `keys` is a 2D array) is the primary sort key.\n Each element of `keys` along the zeroth axis must be\n an array-like object of the same shape.\n axis : int, optional\n Axis to be indirectly sorted. By default, sort over the last axis\n of each sequence. Separate slices along `axis` sorted over\n independently; see last example.\n\n Returns\n -------\n indices : (m, n, ...) ndarray of ints\n Array of indices that sort the keys along the specified axis.\n\n See Also\n --------\n argsort : Indirect sort.\n ndarray.sort : In-place sort.\n sort : Return a sorted copy of an array.\n\n Examples\n --------\n Sort names: first by surname, then by name.\n\n >>> import numpy as np\n >>> surnames = ('Hertz', 'Galilei', 'Hertz')\n >>> first_names = ('Heinrich', 'Galileo', 'Gustav')\n >>> ind = np.lexsort((first_names, surnames))\n >>> ind\n array([1, 2, 0])\n\n >>> [surnames[i] + \", \" + first_names[i] for i in ind]\n ['Galilei, Galileo', 'Hertz, Gustav', 'Hertz, Heinrich']\n\n Sort according to two numerical keys, first by elements\n of ``a``, then breaking ties according to elements of ``b``:\n\n >>> a = [1, 5, 1, 4, 3, 4, 4] # First sequence\n >>> b = [9, 4, 0, 4, 0, 2, 1] # Second sequence\n >>> ind = np.lexsort((b, a)) # Sort by `a`, then by `b`\n >>> ind\n array([2, 0, 4, 6, 5, 3, 1])\n >>> [(a[i], b[i]) for i in ind]\n [(1, 0), (1, 9), (3, 0), (4, 1), (4, 2), (4, 4), (5, 4)]\n\n Compare against `argsort`, which would sort each key independently.\n\n >>> np.argsort((b, a), kind='stable')\n array([[2, 4, 6, 5, 1, 3, 0],\n [0, 2, 4, 3, 5, 6, 1]])\n\n To sort lexicographically with `argsort`, we would need to provide a\n structured array.\n\n >>> x = np.array([(ai, bi) for ai, bi in zip(a, b)],\n ... dtype = np.dtype([('x', int), ('y', int)]))\n >>> np.argsort(x) # or np.argsort(x, order=('x', 'y'))\n array([2, 0, 4, 6, 5, 3, 1])\n\n The zeroth axis of `keys` always corresponds with the sequence of keys,\n so 2D arrays are treated just like other sequences of keys.\n\n >>> arr = np.asarray([b, a])\n >>> ind2 = np.lexsort(arr)\n >>> np.testing.assert_equal(ind2, ind)\n\n Accordingly, the `axis` parameter refers to an axis of *each* key, not of\n the `keys` argument itself. For instance, the array ``arr`` is treated as\n a sequence of two 1-D keys, so specifying ``axis=0`` is equivalent to\n using the default axis, ``axis=-1``.\n\n >>> np.testing.assert_equal(np.lexsort(arr, axis=0),\n ... np.lexsort(arr, axis=-1))\n\n For higher-dimensional arrays, the axis parameter begins to matter. The\n resulting array has the same shape as each key, and the values are what\n we would expect if `lexsort` were performed on corresponding slices\n of the keys independently. For instance,\n\n >>> x = [[1, 2, 3, 4],\n ... [4, 3, 2, 1],\n ... [2, 1, 4, 3]]\n >>> y = [[2, 2, 1, 1],\n ... [1, 2, 1, 2],\n ... [1, 1, 2, 1]]\n >>> np.lexsort((x, y), axis=1)\n array([[2, 3, 0, 1],\n [2, 0, 3, 1],\n [1, 0, 3, 2]])\n\n Each row of the result is what we would expect if we were to perform\n `lexsort` on the corresponding row of the keys:\n\n >>> for i in range(3):\n ... print(np.lexsort((x[i], y[i])))\n [2 3 0 1]\n [2 0 3 1]\n [1 0 3 2]\n\n \"\"\"\n if isinstance(keys, tuple):\n return keys\n else:\n return (keys,)\n\n\n@array_function_from_c_func_and_dispatcher(_multiarray_umath.can_cast)\ndef can_cast(from_, to, casting=None):\n \"\"\"\n can_cast(from_, to, casting='safe')\n\n Returns True if cast between data types can occur according to the\n casting rule.\n\n Parameters\n ----------\n from_ : dtype, dtype specifier, NumPy scalar, or array\n Data type, NumPy scalar, or array to cast from.\n to : dtype or dtype specifier\n Data type to cast to.\n casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional\n Controls what kind of data casting may occur.\n\n * 'no' means the data types should not be cast at all.\n * 'equiv' means only byte-order changes are allowed.\n * 'safe' means only casts which can preserve values are allowed.\n * 'same_kind' means only safe casts or casts within a kind,\n like float64 to float32, are allowed.\n * 'unsafe' means any data conversions may be done.\n\n Returns\n -------\n out : bool\n True if cast can occur according to the casting rule.\n\n Notes\n -----\n .. versionchanged:: 1.17.0\n Casting between a simple data type and a structured one is possible only\n for \"unsafe\" casting. Casting to multiple fields is allowed, but\n casting from multiple fields is not.\n\n .. versionchanged:: 1.9.0\n Casting from numeric to string types in 'safe' casting mode requires\n that the string dtype length is long enough to store the maximum\n integer/float value converted.\n\n .. versionchanged:: 2.0\n This function does not support Python scalars anymore and does not\n apply any value-based logic for 0-D arrays and NumPy scalars.\n\n See also\n --------\n dtype, result_type\n\n Examples\n --------\n Basic examples\n\n >>> import numpy as np\n >>> np.can_cast(np.int32, np.int64)\n True\n >>> np.can_cast(np.float64, complex)\n True\n >>> np.can_cast(complex, float)\n False\n\n >>> np.can_cast('i8', 'f8')\n True\n >>> np.can_cast('i8', 'f4')\n False\n >>> np.can_cast('i4', 'S4')\n False\n\n \"\"\"\n return (from_,)\n\n\n@array_function_from_c_func_and_dispatcher(_multiarray_umath.min_scalar_type)\ndef min_scalar_type(a):\n \"\"\"\n min_scalar_type(a, /)\n\n For scalar ``a``, returns the data type with the smallest size\n and smallest scalar kind which can hold its value. For non-scalar\n array ``a``, returns the vector's dtype unmodified.\n\n Floating point values are not demoted to integers,\n and complex values are not demoted to floats.\n\n Parameters\n ----------\n a : scalar or array_like\n The value whose minimal data type is to be found.\n\n Returns\n -------\n out : dtype\n The minimal data type.\n\n Notes\n -----\n .. versionadded:: 1.6.0\n\n See Also\n --------\n result_type, promote_types, dtype, can_cast\n\n Examples\n --------\n >>> import numpy as np\n >>> np.min_scalar_type(10)\n dtype('uint8')\n\n >>> np.min_scalar_type(-260)\n dtype('int16')\n\n >>> np.min_scalar_type(3.1)\n dtype('float16')\n\n >>> np.min_scalar_type(1e50)\n dtype('float64')\n\n >>> np.min_scalar_type(np.arange(4,dtype='f8'))\n dtype('float64')\n\n \"\"\"\n return (a,)\n\n\n@array_function_from_c_func_and_dispatcher(_multiarray_umath.result_type)\ndef result_type(*arrays_and_dtypes):\n \"\"\"\n result_type(*arrays_and_dtypes)\n\n Returns the type that results from applying the NumPy\n type promotion rules to the arguments.\n\n Type promotion in NumPy works similarly to the rules in languages\n like C++, with some slight differences. When both scalars and\n arrays are used, the array's type takes precedence and the actual value\n of the scalar is taken into account.\n\n For example, calculating 3*a, where a is an array of 32-bit floats,\n intuitively should result in a 32-bit float output. If the 3 is a\n 32-bit integer, the NumPy rules indicate it can't convert losslessly\n into a 32-bit float, so a 64-bit float should be the result type.\n By examining the value of the constant, '3', we see that it fits in\n an 8-bit integer, which can be cast losslessly into the 32-bit float.\n\n Parameters\n ----------\n arrays_and_dtypes : list of arrays and dtypes\n The operands of some operation whose result type is needed.\n\n Returns\n -------\n out : dtype\n The result type.\n\n See also\n --------\n dtype, promote_types, min_scalar_type, can_cast\n\n Notes\n -----\n .. versionadded:: 1.6.0\n\n The specific algorithm used is as follows.\n\n Categories are determined by first checking which of boolean,\n integer (int/uint), or floating point (float/complex) the maximum\n kind of all the arrays and the scalars are.\n\n If there are only scalars or the maximum category of the scalars\n is higher than the maximum category of the arrays,\n the data types are combined with :func:`promote_types`\n to produce the return value.\n\n Otherwise, `min_scalar_type` is called on each scalar, and\n the resulting data types are all combined with :func:`promote_types`\n to produce the return value.\n\n The set of int values is not a subset of the uint values for types\n with the same number of bits, something not reflected in\n :func:`min_scalar_type`, but handled as a special case in `result_type`.\n\n Examples\n --------\n >>> import numpy as np\n >>> np.result_type(3, np.arange(7, dtype='i1'))\n dtype('int8')\n\n >>> np.result_type('i4', 'c8')\n dtype('complex128')\n\n >>> np.result_type(3.0, -2)\n dtype('float64')\n\n \"\"\"\n return arrays_and_dtypes\n\n\n@array_function_from_c_func_and_dispatcher(_multiarray_umath.dot)\ndef dot(a, b, out=None):\n \"\"\"\n dot(a, b, out=None)\n\n Dot product of two arrays. Specifically,\n\n - If both `a` and `b` are 1-D arrays, it is inner product of vectors\n (without complex conjugation).\n\n - If both `a` and `b` are 2-D arrays, it is matrix multiplication,\n but using :func:`matmul` or ``a @ b`` is preferred.\n\n - If either `a` or `b` is 0-D (scalar), it is equivalent to\n :func:`multiply` and using ``numpy.multiply(a, b)`` or ``a * b`` is\n preferred.\n\n - If `a` is an N-D array and `b` is a 1-D array, it is a sum product over\n the last axis of `a` and `b`.\n\n - If `a` is an N-D array and `b` is an M-D array (where ``M>=2``), it is a\n sum product over the last axis of `a` and the second-to-last axis of\n `b`::\n\n dot(a, b)[i,j,k,m] = sum(a[i,j,:] * b[k,:,m])\n\n It uses an optimized BLAS library when possible (see `numpy.linalg`).\n\n Parameters\n ----------\n a : array_like\n First argument.\n b : array_like\n Second argument.\n out : ndarray, optional\n Output argument. This must have the exact kind that would be returned\n if it was not used. In particular, it must have the right type, must be\n C-contiguous, and its dtype must be the dtype that would be returned\n for `dot(a,b)`. This is a performance feature. Therefore, if these\n conditions are not met, an exception is raised, instead of attempting\n to be flexible.\n\n Returns\n -------\n output : ndarray\n Returns the dot product of `a` and `b`. If `a` and `b` are both\n scalars or both 1-D arrays then a scalar is returned; otherwise\n an array is returned.\n If `out` is given, then it is returned.\n\n Raises\n ------\n ValueError\n If the last dimension of `a` is not the same size as\n the second-to-last dimension of `b`.\n\n See Also\n --------\n vdot : Complex-conjugating dot product.\n tensordot : Sum products over arbitrary axes.\n einsum : Einstein summation convention.\n matmul : '@' operator as method with out parameter.\n linalg.multi_dot : Chained dot product.\n\n Examples\n --------\n >>> import numpy as np\n >>> np.dot(3, 4)\n 12\n\n Neither argument is complex-conjugated:\n\n >>> np.dot([2j, 3j], [2j, 3j])\n (-13+0j)\n\n For 2-D arrays it is the matrix product:\n\n >>> a = [[1, 0], [0, 1]]\n >>> b = [[4, 1], [2, 2]]\n >>> np.dot(a, b)\n array([[4, 1],\n [2, 2]])\n\n >>> a = np.arange(3*4*5*6).reshape((3,4,5,6))\n >>> b = np.arange(3*4*5*6)[::-1].reshape((5,4,6,3))\n >>> np.dot(a, b)[2,3,2,1,2,2]\n 499128\n >>> sum(a[2,3,2,:] * b[1,2,:,2])\n 499128\n\n \"\"\"\n return (a, b, out)\n\n\n@array_function_from_c_func_and_dispatcher(_multiarray_umath.vdot)\ndef vdot(a, b):\n \"\"\"\n vdot(a, b, /)\n\n Return the dot product of two vectors.\n\n The vdot(`a`, `b`) function handles complex numbers differently than\n dot(`a`, `b`). If the first argument is complex the complex conjugate\n of the first argument is used for the calculation of the dot product.\n\n Note that `vdot` handles multidimensional arrays differently than `dot`:\n it does *not* perform a matrix product, but flattens input arguments\n to 1-D vectors first. Consequently, it should only be used for vectors.\n\n Parameters\n ----------\n a : array_like\n If `a` is complex the complex conjugate is taken before calculation\n of the dot product.\n b : array_like\n Second argument to the dot product.\n\n Returns\n -------\n output : ndarray\n Dot product of `a` and `b`. Can be an int, float, or\n complex depending on the types of `a` and `b`.\n\n See Also\n --------\n dot : Return the dot product without using the complex conjugate of the\n first argument.\n\n Examples\n --------\n >>> import numpy as np\n >>> a = np.array([1+2j,3+4j])\n >>> b = np.array([5+6j,7+8j])\n >>> np.vdot(a, b)\n (70-8j)\n >>> np.vdot(b, a)\n (70+8j)\n\n Note that higher-dimensional arrays are flattened!\n\n >>> a = np.array([[1, 4], [5, 6]])\n >>> b = np.array([[4, 1], [2, 2]])\n >>> np.vdot(a, b)\n 30\n >>> np.vdot(b, a)\n 30\n >>> 1*4 + 4*1 + 5*2 + 6*2\n 30\n\n \"\"\"\n return (a, b)\n\n\n@array_function_from_c_func_and_dispatcher(_multiarray_umath.bincount)\ndef bincount(x, weights=None, minlength=None):\n \"\"\"\n bincount(x, /, weights=None, minlength=0)\n\n Count number of occurrences of each value in array of non-negative ints.\n\n The number of bins (of size 1) is one larger than the largest value in\n `x`. If `minlength` is specified, there will be at least this number\n of bins in the output array (though it will be longer if necessary,\n depending on the contents of `x`).\n Each bin gives the number of occurrences of its index value in `x`.\n If `weights` is specified the input array is weighted by it, i.e. if a\n value ``n`` is found at position ``i``, ``out[n] += weight[i]`` instead\n of ``out[n] += 1``.\n\n Parameters\n ----------\n x : array_like, 1 dimension, nonnegative ints\n Input array.\n weights : array_like, optional\n Weights, array of the same shape as `x`.\n minlength : int, optional\n A minimum number of bins for the output array.\n\n .. versionadded:: 1.6.0\n\n Returns\n -------\n out : ndarray of ints\n The result of binning the input array.\n The length of `out` is equal to ``np.amax(x)+1``.\n\n Raises\n ------\n ValueError\n If the input is not 1-dimensional, or contains elements with negative\n values, or if `minlength` is negative.\n TypeError\n If the type of the input is float or complex.\n\n See Also\n --------\n histogram, digitize, unique\n\n Examples\n --------\n >>> import numpy as np\n >>> np.bincount(np.arange(5))\n array([1, 1, 1, 1, 1])\n >>> np.bincount(np.array([0, 1, 1, 3, 2, 1, 7]))\n array([1, 3, 1, 1, 0, 0, 0, 1])\n\n >>> x = np.array([0, 1, 1, 3, 2, 1, 7, 23])\n >>> np.bincount(x).size == np.amax(x)+1\n True\n\n The input array needs to be of integer dtype, otherwise a\n TypeError is raised:\n\n >>> np.bincount(np.arange(5, dtype=float))\n Traceback (most recent call last):\n ...\n TypeError: Cannot cast array data from dtype('float64') to dtype('int64')\n according to the rule 'safe'\n\n A possible use of ``bincount`` is to perform sums over\n variable-size chunks of an array, using the ``weights`` keyword.\n\n >>> w = np.array([0.3, 0.5, 0.2, 0.7, 1., -0.6]) # weights\n >>> x = np.array([0, 1, 1, 2, 2, 2])\n >>> np.bincount(x, weights=w)\n array([ 0.3, 0.7, 1.1])\n\n \"\"\"\n return (x, weights)\n\n\n@array_function_from_c_func_and_dispatcher(_multiarray_umath.ravel_multi_index)\ndef ravel_multi_index(multi_index, dims, mode=None, order=None):\n \"\"\"\n ravel_multi_index(multi_index, dims, mode='raise', order='C')\n\n Converts a tuple of index arrays into an array of flat\n indices, applying boundary modes to the multi-index.\n\n Parameters\n ----------\n multi_index : tuple of array_like\n A tuple of integer arrays, one array for each dimension.\n dims : tuple of ints\n The shape of array into which the indices from ``multi_index`` apply.\n mode : {'raise', 'wrap', 'clip'}, optional\n Specifies how out-of-bounds indices are handled. Can specify\n either one mode or a tuple of modes, one mode per index.\n\n * 'raise' -- raise an error (default)\n * 'wrap' -- wrap around\n * 'clip' -- clip to the range\n\n In 'clip' mode, a negative index which would normally\n wrap will clip to 0 instead.\n order : {'C', 'F'}, optional\n Determines whether the multi-index should be viewed as\n indexing in row-major (C-style) or column-major\n (Fortran-style) order.\n\n Returns\n -------\n raveled_indices : ndarray\n An array of indices into the flattened version of an array\n of dimensions ``dims``.\n\n See Also\n --------\n unravel_index\n\n Notes\n -----\n .. versionadded:: 1.6.0\n\n Examples\n --------\n >>> import numpy as np\n >>> arr = np.array([[3,6,6],[4,5,1]])\n >>> np.ravel_multi_index(arr, (7,6))\n array([22, 41, 37])\n >>> np.ravel_multi_index(arr, (7,6), order='F')\n array([31, 41, 13])\n >>> np.ravel_multi_index(arr, (4,6), mode='clip')\n array([22, 23, 19])\n >>> np.ravel_multi_index(arr, (4,4), mode=('clip','wrap'))\n array([12, 13, 13])\n\n >>> np.ravel_multi_index((3,1,4,1), (6,7,8,9))\n 1621\n \"\"\"\n return multi_index\n\n\n@array_function_from_c_func_and_dispatcher(_multiarray_umath.unravel_index)\ndef unravel_index(indices, shape=None, order=None):\n \"\"\"\n unravel_index(indices, shape, order='C')\n\n Converts a flat index or array of flat indices into a tuple\n of coordinate arrays.\n\n Parameters\n ----------\n indices : array_like\n An integer array whose elements are indices into the flattened\n version of an array of dimensions ``shape``. Before version 1.6.0,\n this function accepted just one index value.\n shape : tuple of ints\n The shape of the array to use for unraveling ``indices``.\n\n .. versionchanged:: 1.16.0\n Renamed from ``dims`` to ``shape``.\n\n order : {'C', 'F'}, optional\n Determines whether the indices should be viewed as indexing in\n row-major (C-style) or column-major (Fortran-style) order.\n\n .. versionadded:: 1.6.0\n\n Returns\n -------\n unraveled_coords : tuple of ndarray\n Each array in the tuple has the same shape as the ``indices``\n array.\n\n See Also\n --------\n ravel_multi_index\n\n Examples\n --------\n >>> import numpy as np\n >>> np.unravel_index([22, 41, 37], (7,6))\n (array([3, 6, 6]), array([4, 5, 1]))\n >>> np.unravel_index([31, 41, 13], (7,6), order='F')\n (array([3, 6, 6]), array([4, 5, 1]))\n\n >>> np.unravel_index(1621, (6,7,8,9))\n (3, 1, 4, 1)\n\n \"\"\"\n return (indices,)\n\n\n@array_function_from_c_func_and_dispatcher(_multiarray_umath.copyto)\ndef copyto(dst, src, casting=None, where=None):\n \"\"\"\n copyto(dst, src, casting='same_kind', where=True)\n\n Copies values from one array to another, broadcasting as necessary.\n\n Raises a TypeError if the `casting` rule is violated, and if\n `where` is provided, it selects which elements to copy.\n\n .. versionadded:: 1.7.0\n\n Parameters\n ----------\n dst : ndarray\n The array into which values are copied.\n src : array_like\n The array from which values are copied.\n casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional\n Controls what kind of data casting may occur when copying.\n\n * 'no' means the data types should not be cast at all.\n * 'equiv' means only byte-order changes are allowed.\n * 'safe' means only casts which can preserve values are allowed.\n * 'same_kind' means only safe casts or casts within a kind,\n like float64 to float32, are allowed.\n * 'unsafe' means any data conversions may be done.\n where : array_like of bool, optional\n A boolean array which is broadcasted to match the dimensions\n of `dst`, and selects elements to copy from `src` to `dst`\n wherever it contains the value True.\n\n Examples\n --------\n >>> import numpy as np\n >>> A = np.array([4, 5, 6])\n >>> B = [1, 2, 3]\n >>> np.copyto(A, B)\n >>> A\n array([1, 2, 3])\n\n >>> A = np.array([[1, 2, 3], [4, 5, 6]])\n >>> B = [[4, 5, 6], [7, 8, 9]]\n >>> np.copyto(A, B)\n >>> A\n array([[4, 5, 6],\n [7, 8, 9]])\n\n \"\"\"\n return (dst, src, where)\n\n\n@array_function_from_c_func_and_dispatcher(_multiarray_umath.putmask)\ndef putmask(a, /, mask, values):\n \"\"\"\n putmask(a, mask, values)\n\n Changes elements of an array based on conditional and input values.\n\n Sets ``a.flat[n] = values[n]`` for each n where ``mask.flat[n]==True``.\n\n If `values` is not the same size as `a` and `mask` then it will repeat.\n This gives behavior different from ``a[mask] = values``.\n\n Parameters\n ----------\n a : ndarray\n Target array.\n mask : array_like\n Boolean mask array. It has to be the same shape as `a`.\n values : array_like\n Values to put into `a` where `mask` is True. If `values` is smaller\n than `a` it will be repeated.\n\n See Also\n --------\n place, put, take, copyto\n\n Examples\n --------\n >>> import numpy as np\n >>> x = np.arange(6).reshape(2, 3)\n >>> np.putmask(x, x>2, x**2)\n >>> x\n array([[ 0, 1, 2],\n [ 9, 16, 25]])\n\n If `values` is smaller than `a` it is repeated:\n\n >>> x = np.arange(5)\n >>> np.putmask(x, x>1, [-33, -44])\n >>> x\n array([ 0, 1, -33, -44, -33])\n\n \"\"\"\n return (a, mask, values)\n\n\n@array_function_from_c_func_and_dispatcher(_multiarray_umath.packbits)\ndef packbits(a, axis=None, bitorder='big'):\n \"\"\"\n packbits(a, /, axis=None, bitorder='big')\n\n Packs the elements of a binary-valued array into bits in a uint8 array.\n\n The result is padded to full bytes by inserting zero bits at the end.\n\n Parameters\n ----------\n a : array_like\n An array of integers or booleans whose elements should be packed to\n bits.\n axis : int, optional\n The dimension over which bit-packing is done.\n ``None`` implies packing the flattened array.\n bitorder : {'big', 'little'}, optional\n The order of the input bits. 'big' will mimic bin(val),\n ``[0, 0, 0, 0, 0, 0, 1, 1] => 3 = 0b00000011``, 'little' will\n reverse the order so ``[1, 1, 0, 0, 0, 0, 0, 0] => 3``.\n Defaults to 'big'.\n\n .. versionadded:: 1.17.0\n\n Returns\n -------\n packed : ndarray\n Array of type uint8 whose elements represent bits corresponding to the\n logical (0 or nonzero) value of the input elements. The shape of\n `packed` has the same number of dimensions as the input (unless `axis`\n is None, in which case the output is 1-D).\n\n See Also\n --------\n unpackbits: Unpacks elements of a uint8 array into a binary-valued output\n array.\n\n Examples\n --------\n >>> import numpy as np\n >>> a = np.array([[[1,0,1],\n ... [0,1,0]],\n ... [[1,1,0],\n ... [0,0,1]]])\n >>> b = np.packbits(a, axis=-1)\n >>> b\n array([[[160],\n [ 64]],\n [[192],\n [ 32]]], dtype=uint8)\n\n Note that in binary 160 = 1010 0000, 64 = 0100 0000, 192 = 1100 0000,\n and 32 = 0010 0000.\n\n \"\"\"\n return (a,)\n\n\n@array_function_from_c_func_and_dispatcher(_multiarray_umath.unpackbits)\ndef unpackbits(a, axis=None, count=None, bitorder='big'):\n \"\"\"\n unpackbits(a, /, axis=None, count=None, bitorder='big')\n\n Unpacks elements of a uint8 array into a binary-valued output array.\n\n Each element of `a` represents a bit-field that should be unpacked\n into a binary-valued output array. The shape of the output array is\n either 1-D (if `axis` is ``None``) or the same shape as the input\n array with unpacking done along the axis specified.\n\n Parameters\n ----------\n a : ndarray, uint8 type\n Input array.\n axis : int, optional\n The dimension over which bit-unpacking is done.\n ``None`` implies unpacking the flattened array.\n count : int or None, optional\n The number of elements to unpack along `axis`, provided as a way\n of undoing the effect of packing a size that is not a multiple\n of eight. A non-negative number means to only unpack `count`\n bits. A negative number means to trim off that many bits from\n the end. ``None`` means to unpack the entire array (the\n default). Counts larger than the available number of bits will\n add zero padding to the output. Negative counts must not\n exceed the available number of bits.\n\n .. versionadded:: 1.17.0\n\n bitorder : {'big', 'little'}, optional\n The order of the returned bits. 'big' will mimic bin(val),\n ``3 = 0b00000011 => [0, 0, 0, 0, 0, 0, 1, 1]``, 'little' will reverse\n the order to ``[1, 1, 0, 0, 0, 0, 0, 0]``.\n Defaults to 'big'.\n\n .. versionadded:: 1.17.0\n\n Returns\n -------\n unpacked : ndarray, uint8 type\n The elements are binary-valued (0 or 1).\n\n See Also\n --------\n packbits : Packs the elements of a binary-valued array into bits in\n a uint8 array.\n\n Examples\n --------\n >>> import numpy as np\n >>> a = np.array([[2], [7], [23]], dtype=np.uint8)\n >>> a\n array([[ 2],\n [ 7],\n [23]], dtype=uint8)\n >>> b = np.unpackbits(a, axis=1)\n >>> b\n array([[0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 1, 1, 1],\n [0, 0, 0, 1, 0, 1, 1, 1]], dtype=uint8)\n >>> c = np.unpackbits(a, axis=1, count=-3)\n >>> c\n array([[0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0]], dtype=uint8)\n\n >>> p = np.packbits(b, axis=0)\n >>> np.unpackbits(p, axis=0)\n array([[0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 1, 1, 1],\n [0, 0, 0, 1, 0, 1, 1, 1],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)\n >>> np.array_equal(b, np.unpackbits(p, axis=0, count=b.shape[0]))\n True\n\n \"\"\"\n return (a,)\n\n\n@array_function_from_c_func_and_dispatcher(_multiarray_umath.shares_memory)\ndef shares_memory(a, b, max_work=None):\n \"\"\"\n shares_memory(a, b, /, max_work=None)\n\n Determine if two arrays share memory.\n\n .. warning::\n\n This function can be exponentially slow for some inputs, unless\n `max_work` is set to zero or a positive integer.\n If in doubt, use `numpy.may_share_memory` instead.\n\n Parameters\n ----------\n a, b : ndarray\n Input arrays\n max_work : int, optional\n Effort to spend on solving the overlap problem (maximum number\n of candidate solutions to consider). The following special\n values are recognized:\n\n max_work=-1 (default)\n The problem is solved exactly. In this case, the function returns\n True only if there is an element shared between the arrays. Finding\n the exact solution may take extremely long in some cases.\n max_work=0\n Only the memory bounds of a and b are checked.\n This is equivalent to using ``may_share_memory()``.\n\n Raises\n ------\n numpy.exceptions.TooHardError\n Exceeded max_work.\n\n Returns\n -------\n out : bool\n\n See Also\n --------\n may_share_memory\n\n Examples\n --------\n >>> import numpy as np\n >>> x = np.array([1, 2, 3, 4])\n >>> np.shares_memory(x, np.array([5, 6, 7]))\n False\n >>> np.shares_memory(x[::2], x)\n True\n >>> np.shares_memory(x[::2], x[1::2])\n False\n\n Checking whether two arrays share memory is NP-complete, and\n runtime may increase exponentially in the number of\n dimensions. Hence, `max_work` should generally be set to a finite\n number, as it is possible to construct examples that take\n extremely long to run:\n\n >>> from numpy.lib.stride_tricks import as_strided\n >>> x = np.zeros([192163377], dtype=np.int8)\n >>> x1 = as_strided(\n ... x, strides=(36674, 61119, 85569), shape=(1049, 1049, 1049))\n >>> x2 = as_strided(\n ... x[64023025:], strides=(12223, 12224, 1), shape=(1049, 1049, 1))\n >>> np.shares_memory(x1, x2, max_work=1000)\n Traceback (most recent call last):\n ...\n numpy.exceptions.TooHardError: Exceeded max_work\n\n Running ``np.shares_memory(x1, x2)`` without `max_work` set takes\n around 1 minute for this case. It is possible to find problems\n that take still significantly longer.\n\n \"\"\"\n return (a, b)\n\n\n@array_function_from_c_func_and_dispatcher(_multiarray_umath.may_share_memory)\ndef may_share_memory(a, b, max_work=None):\n \"\"\"\n may_share_memory(a, b, /, max_work=None)\n\n Determine if two arrays might share memory\n\n A return of True does not necessarily mean that the two arrays\n share any element. It just means that they *might*.\n\n Only the memory bounds of a and b are checked by default.\n\n Parameters\n ----------\n a, b : ndarray\n Input arrays\n max_work : int, optional\n Effort to spend on solving the overlap problem. See\n `shares_memory` for details. Default for ``may_share_memory``\n is to do a bounds check.\n\n Returns\n -------\n out : bool\n\n See Also\n --------\n shares_memory\n\n Examples\n --------\n >>> import numpy as np\n >>> np.may_share_memory(np.array([1,2]), np.array([5,8,9]))\n False\n >>> x = np.zeros([3, 4])\n >>> np.may_share_memory(x[:,0], x[:,1])\n True\n\n \"\"\"\n return (a, b)\n\n\n@array_function_from_c_func_and_dispatcher(_multiarray_umath.is_busday)\ndef is_busday(dates, weekmask=None, holidays=None, busdaycal=None, out=None):\n \"\"\"\n is_busday(\n dates, \n weekmask='1111100', \n holidays=None, \n busdaycal=None, \n out=None\n )\n\n Calculates which of the given dates are valid days, and which are not.\n\n .. versionadded:: 1.7.0\n\n Parameters\n ----------\n dates : array_like of datetime64[D]\n The array of dates to process.\n weekmask : str or array_like of bool, optional\n A seven-element array indicating which of Monday through Sunday are\n valid days. May be specified as a length-seven list or array, like\n [1,1,1,1,1,0,0]; a length-seven string, like '1111100'; or a string\n like \"Mon Tue Wed Thu Fri\", made up of 3-character abbreviations for\n weekdays, optionally separated by white space. Valid abbreviations\n are: Mon Tue Wed Thu Fri Sat Sun\n holidays : array_like of datetime64[D], optional\n An array of dates to consider as invalid dates. They may be\n specified in any order, and NaT (not-a-time) dates are ignored.\n This list is saved in a normalized form that is suited for\n fast calculations of valid days.\n busdaycal : busdaycalendar, optional\n A `busdaycalendar` object which specifies the valid days. If this\n parameter is provided, neither weekmask nor holidays may be\n provided.\n out : array of bool, optional\n If provided, this array is filled with the result.\n\n Returns\n -------\n out : array of bool\n An array with the same shape as ``dates``, containing True for\n each valid day, and False for each invalid day.\n\n See Also\n --------\n busdaycalendar : An object that specifies a custom set of valid days.\n busday_offset : Applies an offset counted in valid days.\n busday_count : Counts how many valid days are in a half-open date range.\n\n Examples\n --------\n >>> import numpy as np\n >>> # The weekdays are Friday, Saturday, and Monday\n ... np.is_busday(['2011-07-01', '2011-07-02', '2011-07-18'],\n ... holidays=['2011-07-01', '2011-07-04', '2011-07-17'])\n array([False, False, True])\n \"\"\"\n return (dates, weekmask, holidays, out)\n\n\n@array_function_from_c_func_and_dispatcher(_multiarray_umath.busday_offset)\ndef busday_offset(dates, offsets, roll=None, weekmask=None, holidays=None,\n busdaycal=None, out=None):\n \"\"\"\n busday_offset(\n dates, \n offsets, \n roll='raise', \n weekmask='1111100', \n holidays=None, \n busdaycal=None, \n out=None\n )\n\n First adjusts the date to fall on a valid day according to\n the ``roll`` rule, then applies offsets to the given dates\n counted in valid days.\n\n .. versionadded:: 1.7.0\n\n Parameters\n ----------\n dates : array_like of datetime64[D]\n The array of dates to process.\n offsets : array_like of int\n The array of offsets, which is broadcast with ``dates``.\n roll : {'raise', 'nat', 'forward', 'following', 'backward', 'preceding', \\\n 'modifiedfollowing', 'modifiedpreceding'}, optional\n How to treat dates that do not fall on a valid day. The default\n is 'raise'.\n\n * 'raise' means to raise an exception for an invalid day.\n * 'nat' means to return a NaT (not-a-time) for an invalid day.\n * 'forward' and 'following' mean to take the first valid day\n later in time.\n * 'backward' and 'preceding' mean to take the first valid day\n earlier in time.\n * 'modifiedfollowing' means to take the first valid day\n later in time unless it is across a Month boundary, in which\n case to take the first valid day earlier in time.\n * 'modifiedpreceding' means to take the first valid day\n earlier in time unless it is across a Month boundary, in which\n case to take the first valid day later in time.\n weekmask : str or array_like of bool, optional\n A seven-element array indicating which of Monday through Sunday are\n valid days. May be specified as a length-seven list or array, like\n [1,1,1,1,1,0,0]; a length-seven string, like '1111100'; or a string\n like \"Mon Tue Wed Thu Fri\", made up of 3-character abbreviations for\n weekdays, optionally separated by white space. Valid abbreviations\n are: Mon Tue Wed Thu Fri Sat Sun\n holidays : array_like of datetime64[D], optional\n An array of dates to consider as invalid dates. They may be\n specified in any order, and NaT (not-a-time) dates are ignored.\n This list is saved in a normalized form that is suited for\n fast calculations of valid days.\n busdaycal : busdaycalendar, optional\n A `busdaycalendar` object which specifies the valid days. If this\n parameter is provided, neither weekmask nor holidays may be\n provided.\n out : array of datetime64[D], optional\n If provided, this array is filled with the result.\n\n Returns\n -------\n out : array of datetime64[D]\n An array with a shape from broadcasting ``dates`` and ``offsets``\n together, containing the dates with offsets applied.\n\n See Also\n --------\n busdaycalendar : An object that specifies a custom set of valid days.\n is_busday : Returns a boolean array indicating valid days.\n busday_count : Counts how many valid days are in a half-open date range.\n\n Examples\n --------\n >>> import numpy as np\n >>> # First business day in October 2011 (not accounting for holidays)\n ... np.busday_offset('2011-10', 0, roll='forward')\n np.datetime64('2011-10-03')\n >>> # Last business day in February 2012 (not accounting for holidays)\n ... np.busday_offset('2012-03', -1, roll='forward')\n np.datetime64('2012-02-29')\n >>> # Third Wednesday in January 2011\n ... np.busday_offset('2011-01', 2, roll='forward', weekmask='Wed')\n np.datetime64('2011-01-19')\n >>> # 2012 Mother's Day in Canada and the U.S.\n ... np.busday_offset('2012-05', 1, roll='forward', weekmask='Sun')\n np.datetime64('2012-05-13')\n\n >>> # First business day on or after a date\n ... np.busday_offset('2011-03-20', 0, roll='forward')\n np.datetime64('2011-03-21')\n >>> np.busday_offset('2011-03-22', 0, roll='forward')\n np.datetime64('2011-03-22')\n >>> # First business day after a date\n ... np.busday_offset('2011-03-20', 1, roll='backward')\n np.datetime64('2011-03-21')\n >>> np.busday_offset('2011-03-22', 1, roll='backward')\n np.datetime64('2011-03-23')\n \"\"\"\n return (dates, offsets, weekmask, holidays, out)\n\n\n@array_function_from_c_func_and_dispatcher(_multiarray_umath.busday_count)\ndef busday_count(begindates, enddates, weekmask=None, holidays=None,\n busdaycal=None, out=None):\n \"\"\"\n busday_count(\n begindates, \n enddates, \n weekmask='1111100', \n holidays=[], \n busdaycal=None, \n out=None\n )\n\n Counts the number of valid days between `begindates` and\n `enddates`, not including the day of `enddates`.\n\n If ``enddates`` specifies a date value that is earlier than the\n corresponding ``begindates`` date value, the count will be negative.\n\n .. versionadded:: 1.7.0\n\n Parameters\n ----------\n begindates : array_like of datetime64[D]\n The array of the first dates for counting.\n enddates : array_like of datetime64[D]\n The array of the end dates for counting, which are excluded\n from the count themselves.\n weekmask : str or array_like of bool, optional\n A seven-element array indicating which of Monday through Sunday are\n valid days. May be specified as a length-seven list or array, like\n [1,1,1,1,1,0,0]; a length-seven string, like '1111100'; or a string\n like \"Mon Tue Wed Thu Fri\", made up of 3-character abbreviations for\n weekdays, optionally separated by white space. Valid abbreviations\n are: Mon Tue Wed Thu Fri Sat Sun\n holidays : array_like of datetime64[D], optional\n An array of dates to consider as invalid dates. They may be\n specified in any order, and NaT (not-a-time) dates are ignored.\n This list is saved in a normalized form that is suited for\n fast calculations of valid days.\n busdaycal : busdaycalendar, optional\n A `busdaycalendar` object which specifies the valid days. If this\n parameter is provided, neither weekmask nor holidays may be\n provided.\n out : array of int, optional\n If provided, this array is filled with the result.\n\n Returns\n -------\n out : array of int\n An array with a shape from broadcasting ``begindates`` and ``enddates``\n together, containing the number of valid days between\n the begin and end dates.\n\n See Also\n --------\n busdaycalendar : An object that specifies a custom set of valid days.\n is_busday : Returns a boolean array indicating valid days.\n busday_offset : Applies an offset counted in valid days.\n\n Examples\n --------\n >>> import numpy as np\n >>> # Number of weekdays in January 2011\n ... np.busday_count('2011-01', '2011-02')\n 21\n >>> # Number of weekdays in 2011\n >>> np.busday_count('2011', '2012')\n 260\n >>> # Number of Saturdays in 2011\n ... np.busday_count('2011', '2012', weekmask='Sat')\n 53\n \"\"\"\n return (begindates, enddates, weekmask, holidays, out)\n\n\n@array_function_from_c_func_and_dispatcher(\n _multiarray_umath.datetime_as_string)\ndef datetime_as_string(arr, unit=None, timezone=None, casting=None):\n \"\"\"\n datetime_as_string(arr, unit=None, timezone='naive', casting='same_kind')\n\n Convert an array of datetimes into an array of strings.\n\n Parameters\n ----------\n arr : array_like of datetime64\n The array of UTC timestamps to format.\n unit : str\n One of None, 'auto', or \n a :ref:`datetime unit `.\n timezone : {'naive', 'UTC', 'local'} or tzinfo\n Timezone information to use when displaying the datetime. If 'UTC',\n end with a Z to indicate UTC time. If 'local', convert to the local\n timezone first, and suffix with a +-#### timezone offset. If a tzinfo\n object, then do as with 'local', but use the specified timezone.\n casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}\n Casting to allow when changing between datetime units.\n\n Returns\n -------\n str_arr : ndarray\n An array of strings the same shape as `arr`.\n\n Examples\n --------\n >>> import numpy as np\n >>> import pytz\n >>> d = np.arange('2002-10-27T04:30', 4*60, 60, dtype='M8[m]')\n >>> d\n array(['2002-10-27T04:30', '2002-10-27T05:30', '2002-10-27T06:30',\n '2002-10-27T07:30'], dtype='datetime64[m]')\n\n Setting the timezone to UTC shows the same information, but with a Z suffix\n\n >>> np.datetime_as_string(d, timezone='UTC')\n array(['2002-10-27T04:30Z', '2002-10-27T05:30Z', '2002-10-27T06:30Z',\n '2002-10-27T07:30Z'], dtype='>> np.datetime_as_string(d, timezone=pytz.timezone('US/Eastern'))\n array(['2002-10-27T00:30-0400', '2002-10-27T01:30-0400',\n '2002-10-27T01:30-0500', '2002-10-27T02:30-0500'], dtype='>> np.datetime_as_string(d, unit='h')\n array(['2002-10-27T04', '2002-10-27T05', '2002-10-27T06', '2002-10-27T07'],\n dtype='>> np.datetime_as_string(d, unit='s')\n array(['2002-10-27T04:30:00', '2002-10-27T05:30:00', '2002-10-27T06:30:00',\n '2002-10-27T07:30:00'], dtype='>> np.datetime_as_string(d, unit='h', casting='safe')\n Traceback (most recent call last):\n ...\n TypeError: Cannot create a datetime string as units 'h' from a NumPy\n datetime with units 'm' according to the rule 'safe'\n \"\"\"\n return (arr,)\n", 1772], "/home/gaogaotiantian/programs/viztracer/venv3.13/lib/python3.13/site-packages/numpy/_core/_ufunc_config.py": ["\"\"\"\nFunctions for changing global ufunc configuration\n\nThis provides helpers which wrap `_get_extobj_dict` and `_make_extobj`, and\n`_extobj_contextvar` from umath.\n\"\"\"\nimport collections.abc\nimport contextlib\nimport contextvars\nimport functools\n\nfrom .._utils import set_module\nfrom .umath import _make_extobj, _get_extobj_dict, _extobj_contextvar\n\n__all__ = [\n \"seterr\", \"geterr\", \"setbufsize\", \"getbufsize\", \"seterrcall\", \"geterrcall\",\n \"errstate\", '_no_nep50_warning'\n]\n\n\n@set_module('numpy')\ndef seterr(all=None, divide=None, over=None, under=None, invalid=None):\n \"\"\"\n Set how floating-point errors are handled.\n\n Note that operations on integer scalar types (such as `int16`) are\n handled like floating point, and are affected by these settings.\n\n Parameters\n ----------\n all : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional\n Set treatment for all types of floating-point errors at once:\n\n - ignore: Take no action when the exception occurs.\n - warn: Print a :exc:`RuntimeWarning` (via the Python `warnings`\n module).\n - raise: Raise a :exc:`FloatingPointError`.\n - call: Call a function specified using the `seterrcall` function.\n - print: Print a warning directly to ``stdout``.\n - log: Record error in a Log object specified by `seterrcall`.\n\n The default is not to change the current behavior.\n divide : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional\n Treatment for division by zero.\n over : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional\n Treatment for floating-point overflow.\n under : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional\n Treatment for floating-point underflow.\n invalid : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional\n Treatment for invalid floating-point operation.\n\n Returns\n -------\n old_settings : dict\n Dictionary containing the old settings.\n\n See also\n --------\n seterrcall : Set a callback function for the 'call' mode.\n geterr, geterrcall, errstate\n\n Notes\n -----\n The floating-point exceptions are defined in the IEEE 754 standard [1]_:\n\n - Division by zero: infinite result obtained from finite numbers.\n - Overflow: result too large to be expressed.\n - Underflow: result so close to zero that some precision\n was lost.\n - Invalid operation: result is not an expressible number, typically\n indicates that a NaN was produced.\n\n .. [1] https://en.wikipedia.org/wiki/IEEE_754\n\n Examples\n --------\n >>> import numpy as np\n >>> orig_settings = np.seterr(all='ignore') # seterr to known value\n >>> np.int16(32000) * np.int16(3)\n 30464\n >>> np.seterr(over='raise')\n {'divide': 'ignore', 'over': 'ignore', 'under': 'ignore', 'invalid': 'ignore'}\n >>> old_settings = np.seterr(all='warn', over='raise')\n >>> np.int16(32000) * np.int16(3)\n Traceback (most recent call last):\n File \"\", line 1, in \n FloatingPointError: overflow encountered in scalar multiply\n\n >>> old_settings = np.seterr(all='print')\n >>> np.geterr()\n {'divide': 'print', 'over': 'print', 'under': 'print', 'invalid': 'print'}\n >>> np.int16(32000) * np.int16(3)\n 30464\n >>> np.seterr(**orig_settings) # restore original\n {'divide': 'print', 'over': 'print', 'under': 'print', 'invalid': 'print'}\n\n \"\"\"\n\n old = _get_extobj_dict()\n # The errstate doesn't include call and bufsize, so pop them:\n old.pop(\"call\", None)\n old.pop(\"bufsize\", None)\n\n extobj = _make_extobj(\n all=all, divide=divide, over=over, under=under, invalid=invalid)\n _extobj_contextvar.set(extobj)\n return old\n\n\n@set_module('numpy')\ndef geterr():\n \"\"\"\n Get the current way of handling floating-point errors.\n\n Returns\n -------\n res : dict\n A dictionary with keys \"divide\", \"over\", \"under\", and \"invalid\",\n whose values are from the strings \"ignore\", \"print\", \"log\", \"warn\",\n \"raise\", and \"call\". The keys represent possible floating-point\n exceptions, and the values define how these exceptions are handled.\n\n See Also\n --------\n geterrcall, seterr, seterrcall\n\n Notes\n -----\n For complete documentation of the types of floating-point exceptions and\n treatment options, see `seterr`.\n\n Examples\n --------\n >>> import numpy as np\n >>> np.geterr()\n {'divide': 'warn', 'over': 'warn', 'under': 'ignore', 'invalid': 'warn'}\n >>> np.arange(3.) / np.arange(3.) # doctest: +SKIP\n array([nan, 1., 1.])\n RuntimeWarning: invalid value encountered in divide\n\n >>> oldsettings = np.seterr(all='warn', invalid='raise')\n >>> np.geterr()\n {'divide': 'warn', 'over': 'warn', 'under': 'warn', 'invalid': 'raise'}\n >>> np.arange(3.) / np.arange(3.)\n Traceback (most recent call last):\n ...\n FloatingPointError: invalid value encountered in divide\n >>> oldsettings = np.seterr(**oldsettings) # restore original\n\n \"\"\"\n res = _get_extobj_dict()\n # The \"geterr\" doesn't include call and bufsize,:\n res.pop(\"call\", None)\n res.pop(\"bufsize\", None)\n return res\n\n\n@set_module('numpy')\ndef setbufsize(size):\n \"\"\"\n Set the size of the buffer used in ufuncs.\n\n .. versionchanged:: 2.0\n The scope of setting the buffer is tied to the `numpy.errstate`\n context. Exiting a ``with errstate():`` will also restore the bufsize.\n\n Parameters\n ----------\n size : int\n Size of buffer.\n\n Returns\n -------\n bufsize : int\n Previous size of ufunc buffer in bytes.\n\n Examples\n --------\n When exiting a `numpy.errstate` context manager the bufsize is restored:\n\n >>> import numpy as np\n >>> with np.errstate():\n ... np.setbufsize(4096)\n ... print(np.getbufsize())\n ...\n 8192\n 4096\n >>> np.getbufsize()\n 8192\n\n \"\"\"\n old = _get_extobj_dict()[\"bufsize\"]\n extobj = _make_extobj(bufsize=size)\n _extobj_contextvar.set(extobj)\n return old\n\n\n@set_module('numpy')\ndef getbufsize():\n \"\"\"\n Return the size of the buffer used in ufuncs.\n\n Returns\n -------\n getbufsize : int\n Size of ufunc buffer in bytes.\n\n Examples\n --------\n >>> import numpy as np\n >>> np.getbufsize()\n 8192\n\n \"\"\"\n return _get_extobj_dict()[\"bufsize\"]\n\n\n@set_module('numpy')\ndef seterrcall(func):\n \"\"\"\n Set the floating-point error callback function or log object.\n\n There are two ways to capture floating-point error messages. The first\n is to set the error-handler to 'call', using `seterr`. Then, set\n the function to call using this function.\n\n The second is to set the error-handler to 'log', using `seterr`.\n Floating-point errors then trigger a call to the 'write' method of\n the provided object.\n\n Parameters\n ----------\n func : callable f(err, flag) or object with write method\n Function to call upon floating-point errors ('call'-mode) or\n object whose 'write' method is used to log such message ('log'-mode).\n\n The call function takes two arguments. The first is a string describing\n the type of error (such as \"divide by zero\", \"overflow\", \"underflow\",\n or \"invalid value\"), and the second is the status flag. The flag is a\n byte, whose four least-significant bits indicate the type of error, one\n of \"divide\", \"over\", \"under\", \"invalid\"::\n\n [0 0 0 0 divide over under invalid]\n\n In other words, ``flags = divide + 2*over + 4*under + 8*invalid``.\n\n If an object is provided, its write method should take one argument,\n a string.\n\n Returns\n -------\n h : callable, log instance or None\n The old error handler.\n\n See Also\n --------\n seterr, geterr, geterrcall\n\n Examples\n --------\n Callback upon error:\n\n >>> def err_handler(type, flag):\n ... print(\"Floating point error (%s), with flag %s\" % (type, flag))\n ...\n\n >>> import numpy as np\n\n >>> orig_handler = np.seterrcall(err_handler)\n >>> orig_err = np.seterr(all='call')\n\n >>> np.array([1, 2, 3]) / 0.0\n Floating point error (divide by zero), with flag 1\n array([inf, inf, inf])\n\n >>> np.seterrcall(orig_handler)\n \n >>> np.seterr(**orig_err)\n {'divide': 'call', 'over': 'call', 'under': 'call', 'invalid': 'call'}\n\n Log error message:\n\n >>> class Log:\n ... def write(self, msg):\n ... print(\"LOG: %s\" % msg)\n ...\n\n >>> log = Log()\n >>> saved_handler = np.seterrcall(log)\n >>> save_err = np.seterr(all='log')\n\n >>> np.array([1, 2, 3]) / 0.0\n LOG: Warning: divide by zero encountered in divide\n array([inf, inf, inf])\n\n >>> np.seterrcall(orig_handler)\n \n >>> np.seterr(**orig_err)\n {'divide': 'log', 'over': 'log', 'under': 'log', 'invalid': 'log'}\n\n \"\"\"\n old = _get_extobj_dict()[\"call\"]\n extobj = _make_extobj(call=func)\n _extobj_contextvar.set(extobj)\n return old\n\n\n@set_module('numpy')\ndef geterrcall():\n \"\"\"\n Return the current callback function used on floating-point errors.\n\n When the error handling for a floating-point error (one of \"divide\",\n \"over\", \"under\", or \"invalid\") is set to 'call' or 'log', the function\n that is called or the log instance that is written to is returned by\n `geterrcall`. This function or log instance has been set with\n `seterrcall`.\n\n Returns\n -------\n errobj : callable, log instance or None\n The current error handler. If no handler was set through `seterrcall`,\n ``None`` is returned.\n\n See Also\n --------\n seterrcall, seterr, geterr\n\n Notes\n -----\n For complete documentation of the types of floating-point exceptions and\n treatment options, see `seterr`.\n\n Examples\n --------\n >>> import numpy as np\n >>> np.geterrcall() # we did not yet set a handler, returns None\n\n >>> orig_settings = np.seterr(all='call')\n >>> def err_handler(type, flag):\n ... print(\"Floating point error (%s), with flag %s\" % (type, flag))\n >>> old_handler = np.seterrcall(err_handler)\n >>> np.array([1, 2, 3]) / 0.0\n Floating point error (divide by zero), with flag 1\n array([inf, inf, inf])\n\n >>> cur_handler = np.geterrcall()\n >>> cur_handler is err_handler\n True\n >>> old_settings = np.seterr(**orig_settings) # restore original\n >>> old_handler = np.seterrcall(None) # restore original\n\n \"\"\"\n return _get_extobj_dict()[\"call\"]\n\n\nclass _unspecified:\n pass\n\n\n_Unspecified = _unspecified()\n\n\n@set_module('numpy')\nclass errstate:\n \"\"\"\n errstate(**kwargs)\n\n Context manager for floating-point error handling.\n\n Using an instance of `errstate` as a context manager allows statements in\n that context to execute with a known error handling behavior. Upon entering\n the context the error handling is set with `seterr` and `seterrcall`, and\n upon exiting it is reset to what it was before.\n\n .. versionchanged:: 1.17.0\n `errstate` is also usable as a function decorator, saving\n a level of indentation if an entire function is wrapped.\n\n .. versionchanged:: 2.0\n `errstate` is now fully thread and asyncio safe, but may not be\n entered more than once.\n It is not safe to decorate async functions using ``errstate``.\n\n Parameters\n ----------\n kwargs : {divide, over, under, invalid}\n Keyword arguments. The valid keywords are the possible floating-point\n exceptions. Each keyword should have a string value that defines the\n treatment for the particular error. Possible values are\n {'ignore', 'warn', 'raise', 'call', 'print', 'log'}.\n\n See Also\n --------\n seterr, geterr, seterrcall, geterrcall\n\n Notes\n -----\n For complete documentation of the types of floating-point exceptions and\n treatment options, see `seterr`.\n\n Examples\n --------\n >>> import numpy as np\n >>> olderr = np.seterr(all='ignore') # Set error handling to known state.\n\n >>> np.arange(3) / 0.\n array([nan, inf, inf])\n >>> with np.errstate(divide='ignore'):\n ... np.arange(3) / 0.\n array([nan, inf, inf])\n\n >>> np.sqrt(-1)\n np.float64(nan)\n >>> with np.errstate(invalid='raise'):\n ... np.sqrt(-1)\n Traceback (most recent call last):\n File \"\", line 2, in \n FloatingPointError: invalid value encountered in sqrt\n\n Outside the context the error handling behavior has not changed:\n\n >>> np.geterr()\n {'divide': 'ignore', 'over': 'ignore', 'under': 'ignore', 'invalid': 'ignore'}\n >>> olderr = np.seterr(**olderr) # restore original state\n\n \"\"\"\n __slots__ = (\n \"_call\", \"_all\", \"_divide\", \"_over\", \"_under\", \"_invalid\", \"_token\")\n\n def __init__(self, *, call=_Unspecified,\n all=None, divide=None, over=None, under=None, invalid=None):\n self._token = None\n self._call = call\n self._all = all\n self._divide = divide\n self._over = over\n self._under = under\n self._invalid = invalid\n\n def __enter__(self):\n # Note that __call__ duplicates much of this logic\n if self._token is not None:\n raise TypeError(\"Cannot enter `np.errstate` twice.\")\n if self._call is _Unspecified:\n extobj = _make_extobj(\n all=self._all, divide=self._divide, over=self._over,\n under=self._under, invalid=self._invalid)\n else:\n extobj = _make_extobj(\n call=self._call,\n all=self._all, divide=self._divide, over=self._over,\n under=self._under, invalid=self._invalid)\n\n self._token = _extobj_contextvar.set(extobj)\n\n def __exit__(self, *exc_info):\n _extobj_contextvar.reset(self._token)\n\n def __call__(self, func):\n # We need to customize `__call__` compared to `ContextDecorator`\n # because we must store the token per-thread so cannot store it on\n # the instance (we could create a new instance for this).\n # This duplicates the code from `__enter__`.\n @functools.wraps(func)\n def inner(*args, **kwargs):\n if self._call is _Unspecified:\n extobj = _make_extobj(\n all=self._all, divide=self._divide, over=self._over,\n under=self._under, invalid=self._invalid)\n else:\n extobj = _make_extobj(\n call=self._call,\n all=self._all, divide=self._divide, over=self._over,\n under=self._under, invalid=self._invalid)\n\n _token = _extobj_contextvar.set(extobj)\n try:\n # Call the original, decorated, function:\n return func(*args, **kwargs)\n finally:\n _extobj_contextvar.reset(_token)\n\n return inner\n\n\nNO_NEP50_WARNING = contextvars.ContextVar(\"_no_nep50_warning\", default=False)\n\n@set_module('numpy')\n@contextlib.contextmanager\ndef _no_nep50_warning():\n \"\"\"\n Context manager to disable NEP 50 warnings. This context manager is\n only relevant if the NEP 50 warnings are enabled globally (which is not\n thread/context safe).\n\n This warning context manager itself is fully safe, however.\n \"\"\"\n token = NO_NEP50_WARNING.set(True)\n try:\n yield\n finally:\n NO_NEP50_WARNING.reset(token)\n", 503], "/usr/lib/python3.13/contextlib.py": ["\"\"\"Utilities for with-statement contexts. See PEP 343.\"\"\"\nimport abc\nimport os\nimport sys\nimport _collections_abc\nfrom collections import deque\nfrom functools import wraps\nfrom types import MethodType, GenericAlias\n\n__all__ = [\"asynccontextmanager\", \"contextmanager\", \"closing\", \"nullcontext\",\n \"AbstractContextManager\", \"AbstractAsyncContextManager\",\n \"AsyncExitStack\", \"ContextDecorator\", \"ExitStack\",\n \"redirect_stdout\", \"redirect_stderr\", \"suppress\", \"aclosing\",\n \"chdir\"]\n\n\nclass AbstractContextManager(abc.ABC):\n\n \"\"\"An abstract base class for context managers.\"\"\"\n\n __class_getitem__ = classmethod(GenericAlias)\n\n __slots__ = ()\n\n def __enter__(self):\n \"\"\"Return `self` upon entering the runtime context.\"\"\"\n return self\n\n @abc.abstractmethod\n def __exit__(self, exc_type, exc_value, traceback):\n \"\"\"Raise any exception triggered within the runtime context.\"\"\"\n return None\n\n @classmethod\n def __subclasshook__(cls, C):\n if cls is AbstractContextManager:\n return _collections_abc._check_methods(C, \"__enter__\", \"__exit__\")\n return NotImplemented\n\n\nclass AbstractAsyncContextManager(abc.ABC):\n\n \"\"\"An abstract base class for asynchronous context managers.\"\"\"\n\n __class_getitem__ = classmethod(GenericAlias)\n\n __slots__ = ()\n\n async def __aenter__(self):\n \"\"\"Return `self` upon entering the runtime context.\"\"\"\n return self\n\n @abc.abstractmethod\n async def __aexit__(self, exc_type, exc_value, traceback):\n \"\"\"Raise any exception triggered within the runtime context.\"\"\"\n return None\n\n @classmethod\n def __subclasshook__(cls, C):\n if cls is AbstractAsyncContextManager:\n return _collections_abc._check_methods(C, \"__aenter__\",\n \"__aexit__\")\n return NotImplemented\n\n\nclass ContextDecorator(object):\n \"A base class or mixin that enables context managers to work as decorators.\"\n\n def _recreate_cm(self):\n \"\"\"Return a recreated instance of self.\n\n Allows an otherwise one-shot context manager like\n _GeneratorContextManager to support use as\n a decorator via implicit recreation.\n\n This is a private interface just for _GeneratorContextManager.\n See issue #11647 for details.\n \"\"\"\n return self\n\n def __call__(self, func):\n @wraps(func)\n def inner(*args, **kwds):\n with self._recreate_cm():\n return func(*args, **kwds)\n return inner\n\n\nclass AsyncContextDecorator(object):\n \"A base class or mixin that enables async context managers to work as decorators.\"\n\n def _recreate_cm(self):\n \"\"\"Return a recreated instance of self.\n \"\"\"\n return self\n\n def __call__(self, func):\n @wraps(func)\n async def inner(*args, **kwds):\n async with self._recreate_cm():\n return await func(*args, **kwds)\n return inner\n\n\nclass _GeneratorContextManagerBase:\n \"\"\"Shared functionality for @contextmanager and @asynccontextmanager.\"\"\"\n\n def __init__(self, func, args, kwds):\n self.gen = func(*args, **kwds)\n self.func, self.args, self.kwds = func, args, kwds\n # Issue 19330: ensure context manager instances have good docstrings\n doc = getattr(func, \"__doc__\", None)\n if doc is None:\n doc = type(self).__doc__\n self.__doc__ = doc\n # Unfortunately, this still doesn't provide good help output when\n # inspecting the created context manager instances, since pydoc\n # currently bypasses the instance docstring and shows the docstring\n # for the class instead.\n # See http://bugs.python.org/issue19404 for more details.\n\n def _recreate_cm(self):\n # _GCMB instances are one-shot context managers, so the\n # CM must be recreated each time a decorated function is\n # called\n return self.__class__(self.func, self.args, self.kwds)\n\n\nclass _GeneratorContextManager(\n _GeneratorContextManagerBase,\n AbstractContextManager,\n ContextDecorator,\n):\n \"\"\"Helper for @contextmanager decorator.\"\"\"\n\n def __enter__(self):\n # do not keep args and kwds alive unnecessarily\n # they are only needed for recreation, which is not possible anymore\n del self.args, self.kwds, self.func\n try:\n return next(self.gen)\n except StopIteration:\n raise RuntimeError(\"generator didn't yield\") from None\n\n def __exit__(self, typ, value, traceback):\n if typ is None:\n try:\n next(self.gen)\n except StopIteration:\n return False\n else:\n try:\n raise RuntimeError(\"generator didn't stop\")\n finally:\n self.gen.close()\n else:\n if value is None:\n # Need to force instantiation so we can reliably\n # tell if we get the same exception back\n value = typ()\n try:\n self.gen.throw(value)\n except StopIteration as exc:\n # Suppress StopIteration *unless* it's the same exception that\n # was passed to throw(). This prevents a StopIteration\n # raised inside the \"with\" statement from being suppressed.\n return exc is not value\n except RuntimeError as exc:\n # Don't re-raise the passed in exception. (issue27122)\n if exc is value:\n exc.__traceback__ = traceback\n return False\n # Avoid suppressing if a StopIteration exception\n # was passed to throw() and later wrapped into a RuntimeError\n # (see PEP 479 for sync generators; async generators also\n # have this behavior). But do this only if the exception wrapped\n # by the RuntimeError is actually Stop(Async)Iteration (see\n # issue29692).\n if (\n isinstance(value, StopIteration)\n and exc.__cause__ is value\n ):\n value.__traceback__ = traceback\n return False\n raise\n except BaseException as exc:\n # only re-raise if it's *not* the exception that was\n # passed to throw(), because __exit__() must not raise\n # an exception unless __exit__() itself failed. But throw()\n # has to raise the exception to signal propagation, so this\n # fixes the impedance mismatch between the throw() protocol\n # and the __exit__() protocol.\n if exc is not value:\n raise\n exc.__traceback__ = traceback\n return False\n try:\n raise RuntimeError(\"generator didn't stop after throw()\")\n finally:\n self.gen.close()\n\nclass _AsyncGeneratorContextManager(\n _GeneratorContextManagerBase,\n AbstractAsyncContextManager,\n AsyncContextDecorator,\n):\n \"\"\"Helper for @asynccontextmanager decorator.\"\"\"\n\n async def __aenter__(self):\n # do not keep args and kwds alive unnecessarily\n # they are only needed for recreation, which is not possible anymore\n del self.args, self.kwds, self.func\n try:\n return await anext(self.gen)\n except StopAsyncIteration:\n raise RuntimeError(\"generator didn't yield\") from None\n\n async def __aexit__(self, typ, value, traceback):\n if typ is None:\n try:\n await anext(self.gen)\n except StopAsyncIteration:\n return False\n else:\n try:\n raise RuntimeError(\"generator didn't stop\")\n finally:\n await self.gen.aclose()\n else:\n if value is None:\n # Need to force instantiation so we can reliably\n # tell if we get the same exception back\n value = typ()\n try:\n await self.gen.athrow(value)\n except StopAsyncIteration as exc:\n # Suppress StopIteration *unless* it's the same exception that\n # was passed to throw(). This prevents a StopIteration\n # raised inside the \"with\" statement from being suppressed.\n return exc is not value\n except RuntimeError as exc:\n # Don't re-raise the passed in exception. (issue27122)\n if exc is value:\n exc.__traceback__ = traceback\n return False\n # Avoid suppressing if a Stop(Async)Iteration exception\n # was passed to athrow() and later wrapped into a RuntimeError\n # (see PEP 479 for sync generators; async generators also\n # have this behavior). But do this only if the exception wrapped\n # by the RuntimeError is actually Stop(Async)Iteration (see\n # issue29692).\n if (\n isinstance(value, (StopIteration, StopAsyncIteration))\n and exc.__cause__ is value\n ):\n value.__traceback__ = traceback\n return False\n raise\n except BaseException as exc:\n # only re-raise if it's *not* the exception that was\n # passed to throw(), because __exit__() must not raise\n # an exception unless __exit__() itself failed. But throw()\n # has to raise the exception to signal propagation, so this\n # fixes the impedance mismatch between the throw() protocol\n # and the __exit__() protocol.\n if exc is not value:\n raise\n exc.__traceback__ = traceback\n return False\n try:\n raise RuntimeError(\"generator didn't stop after athrow()\")\n finally:\n await self.gen.aclose()\n\n\ndef contextmanager(func):\n \"\"\"@contextmanager decorator.\n\n Typical usage:\n\n @contextmanager\n def some_generator():\n \n try:\n yield \n finally:\n \n\n This makes this:\n\n with some_generator() as :\n \n\n equivalent to this:\n\n \n try:\n = \n \n finally:\n \n \"\"\"\n @wraps(func)\n def helper(*args, **kwds):\n return _GeneratorContextManager(func, args, kwds)\n return helper\n\n\ndef asynccontextmanager(func):\n \"\"\"@asynccontextmanager decorator.\n\n Typical usage:\n\n @asynccontextmanager\n async def some_async_generator():\n \n try:\n yield \n finally:\n \n\n This makes this:\n\n async with some_async_generator() as :\n \n\n equivalent to this:\n\n \n try:\n = \n \n finally:\n \n \"\"\"\n @wraps(func)\n def helper(*args, **kwds):\n return _AsyncGeneratorContextManager(func, args, kwds)\n return helper\n\n\nclass closing(AbstractContextManager):\n \"\"\"Context to automatically close something at the end of a block.\n\n Code like this:\n\n with closing(.open()) as f:\n \n\n is equivalent to this:\n\n f = .open()\n try:\n \n finally:\n f.close()\n\n \"\"\"\n def __init__(self, thing):\n self.thing = thing\n def __enter__(self):\n return self.thing\n def __exit__(self, *exc_info):\n self.thing.close()\n\n\nclass aclosing(AbstractAsyncContextManager):\n \"\"\"Async context manager for safely finalizing an asynchronously cleaned-up\n resource such as an async generator, calling its ``aclose()`` method.\n\n Code like this:\n\n async with aclosing(.fetch()) as agen:\n \n\n is equivalent to this:\n\n agen = .fetch()\n try:\n \n finally:\n await agen.aclose()\n\n \"\"\"\n def __init__(self, thing):\n self.thing = thing\n async def __aenter__(self):\n return self.thing\n async def __aexit__(self, *exc_info):\n await self.thing.aclose()\n\n\nclass _RedirectStream(AbstractContextManager):\n\n _stream = None\n\n def __init__(self, new_target):\n self._new_target = new_target\n # We use a list of old targets to make this CM re-entrant\n self._old_targets = []\n\n def __enter__(self):\n self._old_targets.append(getattr(sys, self._stream))\n setattr(sys, self._stream, self._new_target)\n return self._new_target\n\n def __exit__(self, exctype, excinst, exctb):\n setattr(sys, self._stream, self._old_targets.pop())\n\n\nclass redirect_stdout(_RedirectStream):\n \"\"\"Context manager for temporarily redirecting stdout to another file.\n\n # How to send help() to stderr\n with redirect_stdout(sys.stderr):\n help(dir)\n\n # How to write help() to a file\n with open('help.txt', 'w') as f:\n with redirect_stdout(f):\n help(pow)\n \"\"\"\n\n _stream = \"stdout\"\n\n\nclass redirect_stderr(_RedirectStream):\n \"\"\"Context manager for temporarily redirecting stderr to another file.\"\"\"\n\n _stream = \"stderr\"\n\n\nclass suppress(AbstractContextManager):\n \"\"\"Context manager to suppress specified exceptions\n\n After the exception is suppressed, execution proceeds with the next\n statement following the with statement.\n\n with suppress(FileNotFoundError):\n os.remove(somefile)\n # Execution still resumes here if the file was already removed\n \"\"\"\n\n def __init__(self, *exceptions):\n self._exceptions = exceptions\n\n def __enter__(self):\n pass\n\n def __exit__(self, exctype, excinst, exctb):\n # Unlike isinstance and issubclass, CPython exception handling\n # currently only looks at the concrete type hierarchy (ignoring\n # the instance and subclass checking hooks). While Guido considers\n # that a bug rather than a feature, it's a fairly hard one to fix\n # due to various internal implementation details. suppress provides\n # the simpler issubclass based semantics, rather than trying to\n # exactly reproduce the limitations of the CPython interpreter.\n #\n # See http://bugs.python.org/issue12029 for more details\n if exctype is None:\n return\n if issubclass(exctype, self._exceptions):\n return True\n if issubclass(exctype, BaseExceptionGroup):\n match, rest = excinst.split(self._exceptions)\n if rest is None:\n return True\n raise rest\n return False\n\n\nclass _BaseExitStack:\n \"\"\"A base class for ExitStack and AsyncExitStack.\"\"\"\n\n @staticmethod\n def _create_exit_wrapper(cm, cm_exit):\n return MethodType(cm_exit, cm)\n\n @staticmethod\n def _create_cb_wrapper(callback, /, *args, **kwds):\n def _exit_wrapper(exc_type, exc, tb):\n callback(*args, **kwds)\n return _exit_wrapper\n\n def __init__(self):\n self._exit_callbacks = deque()\n\n def pop_all(self):\n \"\"\"Preserve the context stack by transferring it to a new instance.\"\"\"\n new_stack = type(self)()\n new_stack._exit_callbacks = self._exit_callbacks\n self._exit_callbacks = deque()\n return new_stack\n\n def push(self, exit):\n \"\"\"Registers a callback with the standard __exit__ method signature.\n\n Can suppress exceptions the same way __exit__ method can.\n Also accepts any object with an __exit__ method (registering a call\n to the method instead of the object itself).\n \"\"\"\n # We use an unbound method rather than a bound method to follow\n # the standard lookup behaviour for special methods.\n _cb_type = type(exit)\n\n try:\n exit_method = _cb_type.__exit__\n except AttributeError:\n # Not a context manager, so assume it's a callable.\n self._push_exit_callback(exit)\n else:\n self._push_cm_exit(exit, exit_method)\n return exit # Allow use as a decorator.\n\n def enter_context(self, cm):\n \"\"\"Enters the supplied context manager.\n\n If successful, also pushes its __exit__ method as a callback and\n returns the result of the __enter__ method.\n \"\"\"\n # We look up the special methods on the type to match the with\n # statement.\n cls = type(cm)\n try:\n _enter = cls.__enter__\n _exit = cls.__exit__\n except AttributeError:\n raise TypeError(f\"'{cls.__module__}.{cls.__qualname__}' object does \"\n f\"not support the context manager protocol\") from None\n result = _enter(cm)\n self._push_cm_exit(cm, _exit)\n return result\n\n def callback(self, callback, /, *args, **kwds):\n \"\"\"Registers an arbitrary callback and arguments.\n\n Cannot suppress exceptions.\n \"\"\"\n _exit_wrapper = self._create_cb_wrapper(callback, *args, **kwds)\n\n # We changed the signature, so using @wraps is not appropriate, but\n # setting __wrapped__ may still help with introspection.\n _exit_wrapper.__wrapped__ = callback\n self._push_exit_callback(_exit_wrapper)\n return callback # Allow use as a decorator\n\n def _push_cm_exit(self, cm, cm_exit):\n \"\"\"Helper to correctly register callbacks to __exit__ methods.\"\"\"\n _exit_wrapper = self._create_exit_wrapper(cm, cm_exit)\n self._push_exit_callback(_exit_wrapper, True)\n\n def _push_exit_callback(self, callback, is_sync=True):\n self._exit_callbacks.append((is_sync, callback))\n\n\n# Inspired by discussions on http://bugs.python.org/issue13585\nclass ExitStack(_BaseExitStack, AbstractContextManager):\n \"\"\"Context manager for dynamic management of a stack of exit callbacks.\n\n For example:\n with ExitStack() as stack:\n files = [stack.enter_context(open(fname)) for fname in filenames]\n # All opened files will automatically be closed at the end of\n # the with statement, even if attempts to open files later\n # in the list raise an exception.\n \"\"\"\n\n def __enter__(self):\n return self\n\n def __exit__(self, *exc_details):\n exc = exc_details[1]\n received_exc = exc is not None\n\n # We manipulate the exception state so it behaves as though\n # we were actually nesting multiple with statements\n frame_exc = sys.exception()\n def _fix_exception_context(new_exc, old_exc):\n # Context may not be correct, so find the end of the chain\n while 1:\n exc_context = new_exc.__context__\n if exc_context is None or exc_context is old_exc:\n # Context is already set correctly (see issue 20317)\n return\n if exc_context is frame_exc:\n break\n new_exc = exc_context\n # Change the end of the chain to point to the exception\n # we expect it to reference\n new_exc.__context__ = old_exc\n\n # Callbacks are invoked in LIFO order to match the behaviour of\n # nested context managers\n suppressed_exc = False\n pending_raise = False\n while self._exit_callbacks:\n is_sync, cb = self._exit_callbacks.pop()\n assert is_sync\n try:\n if exc is None:\n exc_details = None, None, None\n else:\n exc_details = type(exc), exc, exc.__traceback__\n if cb(*exc_details):\n suppressed_exc = True\n pending_raise = False\n exc = None\n except BaseException as new_exc:\n # simulate the stack of exceptions by setting the context\n _fix_exception_context(new_exc, exc)\n pending_raise = True\n exc = new_exc\n\n if pending_raise:\n try:\n # bare \"raise exc\" replaces our carefully\n # set-up context\n fixed_ctx = exc.__context__\n raise exc\n except BaseException:\n exc.__context__ = fixed_ctx\n raise\n return received_exc and suppressed_exc\n\n def close(self):\n \"\"\"Immediately unwind the context stack.\"\"\"\n self.__exit__(None, None, None)\n\n\n# Inspired by discussions on https://bugs.python.org/issue29302\nclass AsyncExitStack(_BaseExitStack, AbstractAsyncContextManager):\n \"\"\"Async context manager for dynamic management of a stack of exit\n callbacks.\n\n For example:\n async with AsyncExitStack() as stack:\n connections = [await stack.enter_async_context(get_connection())\n for i in range(5)]\n # All opened connections will automatically be released at the\n # end of the async with statement, even if attempts to open a\n # connection later in the list raise an exception.\n \"\"\"\n\n @staticmethod\n def _create_async_exit_wrapper(cm, cm_exit):\n return MethodType(cm_exit, cm)\n\n @staticmethod\n def _create_async_cb_wrapper(callback, /, *args, **kwds):\n async def _exit_wrapper(exc_type, exc, tb):\n await callback(*args, **kwds)\n return _exit_wrapper\n\n async def enter_async_context(self, cm):\n \"\"\"Enters the supplied async context manager.\n\n If successful, also pushes its __aexit__ method as a callback and\n returns the result of the __aenter__ method.\n \"\"\"\n cls = type(cm)\n try:\n _enter = cls.__aenter__\n _exit = cls.__aexit__\n except AttributeError:\n raise TypeError(f\"'{cls.__module__}.{cls.__qualname__}' object does \"\n f\"not support the asynchronous context manager protocol\"\n ) from None\n result = await _enter(cm)\n self._push_async_cm_exit(cm, _exit)\n return result\n\n def push_async_exit(self, exit):\n \"\"\"Registers a coroutine function with the standard __aexit__ method\n signature.\n\n Can suppress exceptions the same way __aexit__ method can.\n Also accepts any object with an __aexit__ method (registering a call\n to the method instead of the object itself).\n \"\"\"\n _cb_type = type(exit)\n try:\n exit_method = _cb_type.__aexit__\n except AttributeError:\n # Not an async context manager, so assume it's a coroutine function\n self._push_exit_callback(exit, False)\n else:\n self._push_async_cm_exit(exit, exit_method)\n return exit # Allow use as a decorator\n\n def push_async_callback(self, callback, /, *args, **kwds):\n \"\"\"Registers an arbitrary coroutine function and arguments.\n\n Cannot suppress exceptions.\n \"\"\"\n _exit_wrapper = self._create_async_cb_wrapper(callback, *args, **kwds)\n\n # We changed the signature, so using @wraps is not appropriate, but\n # setting __wrapped__ may still help with introspection.\n _exit_wrapper.__wrapped__ = callback\n self._push_exit_callback(_exit_wrapper, False)\n return callback # Allow use as a decorator\n\n async def aclose(self):\n \"\"\"Immediately unwind the context stack.\"\"\"\n await self.__aexit__(None, None, None)\n\n def _push_async_cm_exit(self, cm, cm_exit):\n \"\"\"Helper to correctly register coroutine function to __aexit__\n method.\"\"\"\n _exit_wrapper = self._create_async_exit_wrapper(cm, cm_exit)\n self._push_exit_callback(_exit_wrapper, False)\n\n async def __aenter__(self):\n return self\n\n async def __aexit__(self, *exc_details):\n exc = exc_details[1]\n received_exc = exc is not None\n\n # We manipulate the exception state so it behaves as though\n # we were actually nesting multiple with statements\n frame_exc = sys.exception()\n def _fix_exception_context(new_exc, old_exc):\n # Context may not be correct, so find the end of the chain\n while 1:\n exc_context = new_exc.__context__\n if exc_context is None or exc_context is old_exc:\n # Context is already set correctly (see issue 20317)\n return\n if exc_context is frame_exc:\n break\n new_exc = exc_context\n # Change the end of the chain to point to the exception\n # we expect it to reference\n new_exc.__context__ = old_exc\n\n # Callbacks are invoked in LIFO order to match the behaviour of\n # nested context managers\n suppressed_exc = False\n pending_raise = False\n while self._exit_callbacks:\n is_sync, cb = self._exit_callbacks.pop()\n try:\n if exc is None:\n exc_details = None, None, None\n else:\n exc_details = type(exc), exc, exc.__traceback__\n if is_sync:\n cb_suppress = cb(*exc_details)\n else:\n cb_suppress = await cb(*exc_details)\n\n if cb_suppress:\n suppressed_exc = True\n pending_raise = False\n exc = None\n except BaseException as new_exc:\n # simulate the stack of exceptions by setting the context\n _fix_exception_context(new_exc, exc)\n pending_raise = True\n exc = new_exc\n\n if pending_raise:\n try:\n # bare \"raise exc\" replaces our carefully\n # set-up context\n fixed_ctx = exc.__context__\n raise exc\n except BaseException:\n exc.__context__ = fixed_ctx\n raise\n return received_exc and suppressed_exc\n\n\nclass nullcontext(AbstractContextManager, AbstractAsyncContextManager):\n \"\"\"Context manager that does no additional processing.\n\n Used as a stand-in for a normal context manager, when a particular\n block of code is only sometimes used with a normal context manager:\n\n cm = optional_cm if condition else nullcontext()\n with cm:\n # Perform operation, using optional_cm if condition is True\n \"\"\"\n\n def __init__(self, enter_result=None):\n self.enter_result = enter_result\n\n def __enter__(self):\n return self.enter_result\n\n def __exit__(self, *excinfo):\n pass\n\n async def __aenter__(self):\n return self.enter_result\n\n async def __aexit__(self, *excinfo):\n pass\n\n\nclass chdir(AbstractContextManager):\n \"\"\"Non thread-safe context manager to change the current working directory.\"\"\"\n\n def __init__(self, path):\n self.path = path\n self._old_cwd = []\n\n def __enter__(self):\n self._old_cwd.append(os.getcwd())\n os.chdir(self.path)\n\n def __exit__(self, *excinfo):\n os.chdir(self._old_cwd.pop())\n", 814], "/home/gaogaotiantian/programs/viztracer/venv3.13/lib/python3.13/site-packages/numpy/_core/fromnumeric.py": ["\"\"\"Module containing non-deprecated functions borrowed from Numeric.\n\n\"\"\"\nimport functools\nimport types\nimport warnings\n\nimport numpy as np\nfrom .._utils import set_module\nfrom . import multiarray as mu\nfrom . import overrides\nfrom . import umath as um\nfrom . import numerictypes as nt\nfrom .multiarray import asarray, array, asanyarray, concatenate\nfrom ._multiarray_umath import _array_converter\nfrom . import _methods\n\n_dt_ = nt.sctype2char\n\n# functions that are methods\n__all__ = [\n 'all', 'amax', 'amin', 'any', 'argmax',\n 'argmin', 'argpartition', 'argsort', 'around', 'choose', 'clip',\n 'compress', 'cumprod', 'cumsum', 'cumulative_prod', 'cumulative_sum',\n 'diagonal', 'mean', 'max', 'min', 'matrix_transpose',\n 'ndim', 'nonzero', 'partition', 'prod', 'ptp', 'put',\n 'ravel', 'repeat', 'reshape', 'resize', 'round',\n 'searchsorted', 'shape', 'size', 'sort', 'squeeze',\n 'std', 'sum', 'swapaxes', 'take', 'trace', 'transpose', 'var',\n]\n\n_gentype = types.GeneratorType\n# save away Python sum\n_sum_ = sum\n\narray_function_dispatch = functools.partial(\n overrides.array_function_dispatch, module='numpy')\n\n\n# functions that are now methods\ndef _wrapit(obj, method, *args, **kwds):\n conv = _array_converter(obj)\n # As this already tried the method, subok is maybe quite reasonable here\n # but this follows what was done before. TODO: revisit this.\n arr, = conv.as_arrays(subok=False)\n result = getattr(arr, method)(*args, **kwds)\n\n return conv.wrap(result, to_scalar=False)\n\n\ndef _wrapfunc(obj, method, *args, **kwds):\n bound = getattr(obj, method, None)\n if bound is None:\n return _wrapit(obj, method, *args, **kwds)\n\n try:\n return bound(*args, **kwds)\n except TypeError:\n # A TypeError occurs if the object does have such a method in its\n # class, but its signature is not identical to that of NumPy's. This\n # situation has occurred in the case of a downstream library like\n # 'pandas'.\n #\n # Call _wrapit from within the except clause to ensure a potential\n # exception has a traceback chain.\n return _wrapit(obj, method, *args, **kwds)\n\n\ndef _wrapreduction(obj, ufunc, method, axis, dtype, out, **kwargs):\n passkwargs = {k: v for k, v in kwargs.items()\n if v is not np._NoValue}\n\n if type(obj) is not mu.ndarray:\n try:\n reduction = getattr(obj, method)\n except AttributeError:\n pass\n else:\n # This branch is needed for reductions like any which don't\n # support a dtype.\n if dtype is not None:\n return reduction(axis=axis, dtype=dtype, out=out, **passkwargs)\n else:\n return reduction(axis=axis, out=out, **passkwargs)\n\n return ufunc.reduce(obj, axis, dtype, out, **passkwargs)\n\n\ndef _wrapreduction_any_all(obj, ufunc, method, axis, out, **kwargs):\n # Same as above function, but dtype is always bool (but never passed on)\n passkwargs = {k: v for k, v in kwargs.items()\n if v is not np._NoValue}\n\n if type(obj) is not mu.ndarray:\n try:\n reduction = getattr(obj, method)\n except AttributeError:\n pass\n else:\n return reduction(axis=axis, out=out, **passkwargs)\n\n return ufunc.reduce(obj, axis, bool, out, **passkwargs)\n\n\ndef _take_dispatcher(a, indices, axis=None, out=None, mode=None):\n return (a, out)\n\n\n@array_function_dispatch(_take_dispatcher)\ndef take(a, indices, axis=None, out=None, mode='raise'):\n \"\"\"\n Take elements from an array along an axis.\n\n When axis is not None, this function does the same thing as \"fancy\"\n indexing (indexing arrays using arrays); however, it can be easier to use\n if you need elements along a given axis. A call such as\n ``np.take(arr, indices, axis=3)`` is equivalent to\n ``arr[:,:,:,indices,...]``.\n\n Explained without fancy indexing, this is equivalent to the following use\n of `ndindex`, which sets each of ``ii``, ``jj``, and ``kk`` to a tuple of\n indices::\n\n Ni, Nk = a.shape[:axis], a.shape[axis+1:]\n Nj = indices.shape\n for ii in ndindex(Ni):\n for jj in ndindex(Nj):\n for kk in ndindex(Nk):\n out[ii + jj + kk] = a[ii + (indices[jj],) + kk]\n\n Parameters\n ----------\n a : array_like (Ni..., M, Nk...)\n The source array.\n indices : array_like (Nj...)\n The indices of the values to extract.\n\n .. versionadded:: 1.8.0\n\n Also allow scalars for indices.\n axis : int, optional\n The axis over which to select values. By default, the flattened\n input array is used.\n out : ndarray, optional (Ni..., Nj..., Nk...)\n If provided, the result will be placed in this array. It should\n be of the appropriate shape and dtype. Note that `out` is always\n buffered if `mode='raise'`; use other modes for better performance.\n mode : {'raise', 'wrap', 'clip'}, optional\n Specifies how out-of-bounds indices will behave.\n\n * 'raise' -- raise an error (default)\n * 'wrap' -- wrap around\n * 'clip' -- clip to the range\n\n 'clip' mode means that all indices that are too large are replaced\n by the index that addresses the last element along that axis. Note\n that this disables indexing with negative numbers.\n\n Returns\n -------\n out : ndarray (Ni..., Nj..., Nk...)\n The returned array has the same type as `a`.\n\n See Also\n --------\n compress : Take elements using a boolean mask\n ndarray.take : equivalent method\n take_along_axis : Take elements by matching the array and the index arrays\n\n Notes\n -----\n\n By eliminating the inner loop in the description above, and using `s_` to\n build simple slice objects, `take` can be expressed in terms of applying\n fancy indexing to each 1-d slice::\n\n Ni, Nk = a.shape[:axis], a.shape[axis+1:]\n for ii in ndindex(Ni):\n for kk in ndindex(Nj):\n out[ii + s_[...,] + kk] = a[ii + s_[:,] + kk][indices]\n\n For this reason, it is equivalent to (but faster than) the following use\n of `apply_along_axis`::\n\n out = np.apply_along_axis(lambda a_1d: a_1d[indices], axis, a)\n\n Examples\n --------\n >>> import numpy as np\n >>> a = [4, 3, 5, 7, 6, 8]\n >>> indices = [0, 1, 4]\n >>> np.take(a, indices)\n array([4, 3, 6])\n\n In this example if `a` is an ndarray, \"fancy\" indexing can be used.\n\n >>> a = np.array(a)\n >>> a[indices]\n array([4, 3, 6])\n\n If `indices` is not one dimensional, the output also has these dimensions.\n\n >>> np.take(a, [[0, 1], [2, 3]])\n array([[4, 3],\n [5, 7]])\n \"\"\"\n return _wrapfunc(a, 'take', indices, axis=axis, out=out, mode=mode)\n\n\ndef _reshape_dispatcher(a, /, shape=None, order=None, *, newshape=None,\n copy=None):\n return (a,)\n\n\n@array_function_dispatch(_reshape_dispatcher)\ndef reshape(a, /, shape=None, order='C', *, newshape=None, copy=None):\n \"\"\"\n Gives a new shape to an array without changing its data.\n\n Parameters\n ----------\n a : array_like\n Array to be reshaped.\n shape : int or tuple of ints\n The new shape should be compatible with the original shape. If\n an integer, then the result will be a 1-D array of that length.\n One shape dimension can be -1. In this case, the value is\n inferred from the length of the array and remaining dimensions.\n order : {'C', 'F', 'A'}, optional\n Read the elements of ``a`` using this index order, and place the\n elements into the reshaped array using this index order. 'C'\n means to read / write the elements using C-like index order,\n with the last axis index changing fastest, back to the first\n axis index changing slowest. 'F' means to read / write the\n elements using Fortran-like index order, with the first index\n changing fastest, and the last index changing slowest. Note that\n the 'C' and 'F' options take no account of the memory layout of\n the underlying array, and only refer to the order of indexing.\n 'A' means to read / write the elements in Fortran-like index\n order if ``a`` is Fortran *contiguous* in memory, C-like order\n otherwise.\n newshape : int or tuple of ints\n .. deprecated:: 2.1\n Replaced by ``shape`` argument. Retained for backward\n compatibility.\n copy : bool, optional\n If ``True``, then the array data is copied. If ``None``, a copy will\n only be made if it's required by ``order``. For ``False`` it raises\n a ``ValueError`` if a copy cannot be avoided. Default: ``None``.\n\n Returns\n -------\n reshaped_array : ndarray\n This will be a new view object if possible; otherwise, it will\n be a copy. Note there is no guarantee of the *memory layout* (C- or\n Fortran- contiguous) of the returned array.\n\n See Also\n --------\n ndarray.reshape : Equivalent method.\n\n Notes\n -----\n It is not always possible to change the shape of an array without copying\n the data.\n\n The ``order`` keyword gives the index ordering both for *fetching*\n the values from ``a``, and then *placing* the values into the output\n array. For example, let's say you have an array:\n\n >>> a = np.arange(6).reshape((3, 2))\n >>> a\n array([[0, 1],\n [2, 3],\n [4, 5]])\n\n You can think of reshaping as first raveling the array (using the given\n index order), then inserting the elements from the raveled array into the\n new array using the same kind of index ordering as was used for the\n raveling.\n\n >>> np.reshape(a, (2, 3)) # C-like index ordering\n array([[0, 1, 2],\n [3, 4, 5]])\n >>> np.reshape(np.ravel(a), (2, 3)) # equivalent to C ravel then C reshape\n array([[0, 1, 2],\n [3, 4, 5]])\n >>> np.reshape(a, (2, 3), order='F') # Fortran-like index ordering\n array([[0, 4, 3],\n [2, 1, 5]])\n >>> np.reshape(np.ravel(a, order='F'), (2, 3), order='F')\n array([[0, 4, 3],\n [2, 1, 5]])\n\n Examples\n --------\n >>> import numpy as np\n >>> a = np.array([[1,2,3], [4,5,6]])\n >>> np.reshape(a, 6)\n array([1, 2, 3, 4, 5, 6])\n >>> np.reshape(a, 6, order='F')\n array([1, 4, 2, 5, 3, 6])\n\n >>> np.reshape(a, (3,-1)) # the unspecified value is inferred to be 2\n array([[1, 2],\n [3, 4],\n [5, 6]])\n \"\"\"\n if newshape is None and shape is None:\n raise TypeError(\n \"reshape() missing 1 required positional argument: 'shape'\")\n if newshape is not None:\n if shape is not None:\n raise TypeError(\n \"You cannot specify 'newshape' and 'shape' arguments \"\n \"at the same time.\")\n # Deprecated in NumPy 2.1, 2024-04-18\n warnings.warn(\n \"`newshape` keyword argument is deprecated, \"\n \"use `shape=...` or pass shape positionally instead. \"\n \"(deprecated in NumPy 2.1)\",\n DeprecationWarning,\n stacklevel=2,\n )\n shape = newshape\n if copy is not None:\n return _wrapfunc(a, 'reshape', shape, order=order, copy=copy)\n return _wrapfunc(a, 'reshape', shape, order=order)\n\n\ndef _choose_dispatcher(a, choices, out=None, mode=None):\n yield a\n yield from choices\n yield out\n\n\n@array_function_dispatch(_choose_dispatcher)\ndef choose(a, choices, out=None, mode='raise'):\n \"\"\"\n Construct an array from an index array and a list of arrays to choose from.\n\n First of all, if confused or uncertain, definitely look at the Examples -\n in its full generality, this function is less simple than it might\n seem from the following code description (below ndi =\n `numpy.lib.index_tricks`):\n\n ``np.choose(a,c) == np.array([c[a[I]][I] for I in ndi.ndindex(a.shape)])``.\n\n But this omits some subtleties. Here is a fully general summary:\n\n Given an \"index\" array (`a`) of integers and a sequence of ``n`` arrays\n (`choices`), `a` and each choice array are first broadcast, as necessary,\n to arrays of a common shape; calling these *Ba* and *Bchoices[i], i =\n 0,...,n-1* we have that, necessarily, ``Ba.shape == Bchoices[i].shape``\n for each ``i``. Then, a new array with shape ``Ba.shape`` is created as\n follows:\n\n * if ``mode='raise'`` (the default), then, first of all, each element of\n ``a`` (and thus ``Ba``) must be in the range ``[0, n-1]``; now, suppose\n that ``i`` (in that range) is the value at the ``(j0, j1, ..., jm)``\n position in ``Ba`` - then the value at the same position in the new array\n is the value in ``Bchoices[i]`` at that same position;\n\n * if ``mode='wrap'``, values in `a` (and thus `Ba`) may be any (signed)\n integer; modular arithmetic is used to map integers outside the range\n `[0, n-1]` back into that range; and then the new array is constructed\n as above;\n\n * if ``mode='clip'``, values in `a` (and thus ``Ba``) may be any (signed)\n integer; negative integers are mapped to 0; values greater than ``n-1``\n are mapped to ``n-1``; and then the new array is constructed as above.\n\n Parameters\n ----------\n a : int array\n This array must contain integers in ``[0, n-1]``, where ``n`` is the\n number of choices, unless ``mode=wrap`` or ``mode=clip``, in which\n cases any integers are permissible.\n choices : sequence of arrays\n Choice arrays. `a` and all of the choices must be broadcastable to the\n same shape. If `choices` is itself an array (not recommended), then\n its outermost dimension (i.e., the one corresponding to\n ``choices.shape[0]``) is taken as defining the \"sequence\".\n out : array, optional\n If provided, the result will be inserted into this array. It should\n be of the appropriate shape and dtype. Note that `out` is always\n buffered if ``mode='raise'``; use other modes for better performance.\n mode : {'raise' (default), 'wrap', 'clip'}, optional\n Specifies how indices outside ``[0, n-1]`` will be treated:\n\n * 'raise' : an exception is raised\n * 'wrap' : value becomes value mod ``n``\n * 'clip' : values < 0 are mapped to 0, values > n-1 are mapped to n-1\n\n Returns\n -------\n merged_array : array\n The merged result.\n\n Raises\n ------\n ValueError: shape mismatch\n If `a` and each choice array are not all broadcastable to the same\n shape.\n\n See Also\n --------\n ndarray.choose : equivalent method\n numpy.take_along_axis : Preferable if `choices` is an array\n\n Notes\n -----\n To reduce the chance of misinterpretation, even though the following\n \"abuse\" is nominally supported, `choices` should neither be, nor be\n thought of as, a single array, i.e., the outermost sequence-like container\n should be either a list or a tuple.\n\n Examples\n --------\n\n >>> import numpy as np\n >>> choices = [[0, 1, 2, 3], [10, 11, 12, 13],\n ... [20, 21, 22, 23], [30, 31, 32, 33]]\n >>> np.choose([2, 3, 1, 0], choices\n ... # the first element of the result will be the first element of the\n ... # third (2+1) \"array\" in choices, namely, 20; the second element\n ... # will be the second element of the fourth (3+1) choice array, i.e.,\n ... # 31, etc.\n ... )\n array([20, 31, 12, 3])\n >>> np.choose([2, 4, 1, 0], choices, mode='clip') # 4 goes to 3 (4-1)\n array([20, 31, 12, 3])\n >>> # because there are 4 choice arrays\n >>> np.choose([2, 4, 1, 0], choices, mode='wrap') # 4 goes to (4 mod 4)\n array([20, 1, 12, 3])\n >>> # i.e., 0\n\n A couple examples illustrating how choose broadcasts:\n\n >>> a = [[1, 0, 1], [0, 1, 0], [1, 0, 1]]\n >>> choices = [-10, 10]\n >>> np.choose(a, choices)\n array([[ 10, -10, 10],\n [-10, 10, -10],\n [ 10, -10, 10]])\n\n >>> # With thanks to Anne Archibald\n >>> a = np.array([0, 1]).reshape((2,1,1))\n >>> c1 = np.array([1, 2, 3]).reshape((1,3,1))\n >>> c2 = np.array([-1, -2, -3, -4, -5]).reshape((1,1,5))\n >>> np.choose(a, (c1, c2)) # result is 2x3x5, res[0,:,:]=c1, res[1,:,:]=c2\n array([[[ 1, 1, 1, 1, 1],\n [ 2, 2, 2, 2, 2],\n [ 3, 3, 3, 3, 3]],\n [[-1, -2, -3, -4, -5],\n [-1, -2, -3, -4, -5],\n [-1, -2, -3, -4, -5]]])\n\n \"\"\"\n return _wrapfunc(a, 'choose', choices, out=out, mode=mode)\n\n\ndef _repeat_dispatcher(a, repeats, axis=None):\n return (a,)\n\n\n@array_function_dispatch(_repeat_dispatcher)\ndef repeat(a, repeats, axis=None):\n \"\"\"\n Repeat each element of an array after themselves\n\n Parameters\n ----------\n a : array_like\n Input array.\n repeats : int or array of ints\n The number of repetitions for each element. `repeats` is broadcasted\n to fit the shape of the given axis.\n axis : int, optional\n The axis along which to repeat values. By default, use the\n flattened input array, and return a flat output array.\n\n Returns\n -------\n repeated_array : ndarray\n Output array which has the same shape as `a`, except along\n the given axis.\n\n See Also\n --------\n tile : Tile an array.\n unique : Find the unique elements of an array.\n\n Examples\n --------\n >>> import numpy as np\n >>> np.repeat(3, 4)\n array([3, 3, 3, 3])\n >>> x = np.array([[1,2],[3,4]])\n >>> np.repeat(x, 2)\n array([1, 1, 2, 2, 3, 3, 4, 4])\n >>> np.repeat(x, 3, axis=1)\n array([[1, 1, 1, 2, 2, 2],\n [3, 3, 3, 4, 4, 4]])\n >>> np.repeat(x, [1, 2], axis=0)\n array([[1, 2],\n [3, 4],\n [3, 4]])\n\n \"\"\"\n return _wrapfunc(a, 'repeat', repeats, axis=axis)\n\n\ndef _put_dispatcher(a, ind, v, mode=None):\n return (a, ind, v)\n\n\n@array_function_dispatch(_put_dispatcher)\ndef put(a, ind, v, mode='raise'):\n \"\"\"\n Replaces specified elements of an array with given values.\n\n The indexing works on the flattened target array. `put` is roughly\n equivalent to:\n\n ::\n\n a.flat[ind] = v\n\n Parameters\n ----------\n a : ndarray\n Target array.\n ind : array_like\n Target indices, interpreted as integers.\n v : array_like\n Values to place in `a` at target indices. If `v` is shorter than\n `ind` it will be repeated as necessary.\n mode : {'raise', 'wrap', 'clip'}, optional\n Specifies how out-of-bounds indices will behave.\n\n * 'raise' -- raise an error (default)\n * 'wrap' -- wrap around\n * 'clip' -- clip to the range\n\n 'clip' mode means that all indices that are too large are replaced\n by the index that addresses the last element along that axis. Note\n that this disables indexing with negative numbers. In 'raise' mode,\n if an exception occurs the target array may still be modified.\n\n See Also\n --------\n putmask, place\n put_along_axis : Put elements by matching the array and the index arrays\n\n Examples\n --------\n >>> import numpy as np\n >>> a = np.arange(5)\n >>> np.put(a, [0, 2], [-44, -55])\n >>> a\n array([-44, 1, -55, 3, 4])\n\n >>> a = np.arange(5)\n >>> np.put(a, 22, -5, mode='clip')\n >>> a\n array([ 0, 1, 2, 3, -5])\n\n \"\"\"\n try:\n put = a.put\n except AttributeError as e:\n raise TypeError(\"argument 1 must be numpy.ndarray, \"\n \"not {name}\".format(name=type(a).__name__)) from e\n\n return put(ind, v, mode=mode)\n\n\ndef _swapaxes_dispatcher(a, axis1, axis2):\n return (a,)\n\n\n@array_function_dispatch(_swapaxes_dispatcher)\ndef swapaxes(a, axis1, axis2):\n \"\"\"\n Interchange two axes of an array.\n\n Parameters\n ----------\n a : array_like\n Input array.\n axis1 : int\n First axis.\n axis2 : int\n Second axis.\n\n Returns\n -------\n a_swapped : ndarray\n For NumPy >= 1.10.0, if `a` is an ndarray, then a view of `a` is\n returned; otherwise a new array is created. For earlier NumPy\n versions a view of `a` is returned only if the order of the\n axes is changed, otherwise the input array is returned.\n\n Examples\n --------\n >>> import numpy as np\n >>> x = np.array([[1,2,3]])\n >>> np.swapaxes(x,0,1)\n array([[1],\n [2],\n [3]])\n\n >>> x = np.array([[[0,1],[2,3]],[[4,5],[6,7]]])\n >>> x\n array([[[0, 1],\n [2, 3]],\n [[4, 5],\n [6, 7]]])\n\n >>> np.swapaxes(x,0,2)\n array([[[0, 4],\n [2, 6]],\n [[1, 5],\n [3, 7]]])\n\n \"\"\"\n return _wrapfunc(a, 'swapaxes', axis1, axis2)\n\n\ndef _transpose_dispatcher(a, axes=None):\n return (a,)\n\n\n@array_function_dispatch(_transpose_dispatcher)\ndef transpose(a, axes=None):\n \"\"\"\n Returns an array with axes transposed.\n\n For a 1-D array, this returns an unchanged view of the original array, as a\n transposed vector is simply the same vector.\n To convert a 1-D array into a 2-D column vector, an additional dimension\n must be added, e.g., ``np.atleast_2d(a).T`` achieves this, as does\n ``a[:, np.newaxis]``.\n For a 2-D array, this is the standard matrix transpose.\n For an n-D array, if axes are given, their order indicates how the\n axes are permuted (see Examples). If axes are not provided, then\n ``transpose(a).shape == a.shape[::-1]``.\n\n Parameters\n ----------\n a : array_like\n Input array.\n axes : tuple or list of ints, optional\n If specified, it must be a tuple or list which contains a permutation\n of [0,1,...,N-1] where N is the number of axes of `a`. The `i`'th axis\n of the returned array will correspond to the axis numbered ``axes[i]``\n of the input. If not specified, defaults to ``range(a.ndim)[::-1]``,\n which reverses the order of the axes.\n\n Returns\n -------\n p : ndarray\n `a` with its axes permuted. A view is returned whenever possible.\n\n See Also\n --------\n ndarray.transpose : Equivalent method.\n moveaxis : Move axes of an array to new positions.\n argsort : Return the indices that would sort an array.\n\n Notes\n -----\n Use ``transpose(a, argsort(axes))`` to invert the transposition of tensors\n when using the `axes` keyword argument.\n\n Examples\n --------\n >>> import numpy as np\n >>> a = np.array([[1, 2], [3, 4]])\n >>> a\n array([[1, 2],\n [3, 4]])\n >>> np.transpose(a)\n array([[1, 3],\n [2, 4]])\n\n >>> a = np.array([1, 2, 3, 4])\n >>> a\n array([1, 2, 3, 4])\n >>> np.transpose(a)\n array([1, 2, 3, 4])\n\n >>> a = np.ones((1, 2, 3))\n >>> np.transpose(a, (1, 0, 2)).shape\n (2, 1, 3)\n\n >>> a = np.ones((2, 3, 4, 5))\n >>> np.transpose(a).shape\n (5, 4, 3, 2)\n\n \"\"\"\n return _wrapfunc(a, 'transpose', axes)\n\n\ndef _matrix_transpose_dispatcher(x):\n return (x,)\n\n@array_function_dispatch(_matrix_transpose_dispatcher)\ndef matrix_transpose(x, /):\n \"\"\"\n Transposes a matrix (or a stack of matrices) ``x``.\n\n This function is Array API compatible.\n\n Parameters\n ----------\n x : array_like\n Input array having shape (..., M, N) and whose two innermost\n dimensions form ``MxN`` matrices.\n\n Returns\n -------\n out : ndarray\n An array containing the transpose for each matrix and having shape\n (..., N, M).\n\n See Also\n --------\n transpose : Generic transpose method.\n\n Examples\n --------\n >>> import numpy as np\n >>> np.matrix_transpose([[1, 2], [3, 4]])\n array([[1, 3],\n [2, 4]])\n\n >>> np.matrix_transpose([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])\n array([[[1, 3],\n [2, 4]],\n [[5, 7],\n [6, 8]]])\n\n \"\"\"\n x = asanyarray(x)\n if x.ndim < 2:\n raise ValueError(\n f\"Input array must be at least 2-dimensional, but it is {x.ndim}\"\n )\n return swapaxes(x, -1, -2)\n\n\ndef _partition_dispatcher(a, kth, axis=None, kind=None, order=None):\n return (a,)\n\n\n@array_function_dispatch(_partition_dispatcher)\ndef partition(a, kth, axis=-1, kind='introselect', order=None):\n \"\"\"\n Return a partitioned copy of an array.\n\n Creates a copy of the array and partially sorts it in such a way that\n the value of the element in k-th position is in the position it would be\n in a sorted array. In the output array, all elements smaller than the k-th\n element are located to the left of this element and all equal or greater\n are located to its right. The ordering of the elements in the two\n partitions on the either side of the k-th element in the output array is\n undefined.\n\n .. versionadded:: 1.8.0\n\n Parameters\n ----------\n a : array_like\n Array to be sorted.\n kth : int or sequence of ints\n Element index to partition by. The k-th value of the element\n will be in its final sorted position and all smaller elements\n will be moved before it and all equal or greater elements behind\n it. The order of all elements in the partitions is undefined. If\n provided with a sequence of k-th it will partition all elements\n indexed by k-th of them into their sorted position at once.\n\n .. deprecated:: 1.22.0\n Passing booleans as index is deprecated.\n axis : int or None, optional\n Axis along which to sort. If None, the array is flattened before\n sorting. The default is -1, which sorts along the last axis.\n kind : {'introselect'}, optional\n Selection algorithm. Default is 'introselect'.\n order : str or list of str, optional\n When `a` is an array with fields defined, this argument\n specifies which fields to compare first, second, etc. A single\n field can be specified as a string. Not all fields need be\n specified, but unspecified fields will still be used, in the\n order in which they come up in the dtype, to break ties.\n\n Returns\n -------\n partitioned_array : ndarray\n Array of the same type and shape as `a`.\n\n See Also\n --------\n ndarray.partition : Method to sort an array in-place.\n argpartition : Indirect partition.\n sort : Full sorting\n\n Notes\n -----\n The various selection algorithms are characterized by their average\n speed, worst case performance, work space size, and whether they are\n stable. A stable sort keeps items with the same key in the same\n relative order. The available algorithms have the following\n properties:\n\n ================= ======= ============= ============ =======\n kind speed worst case work space stable\n ================= ======= ============= ============ =======\n 'introselect' 1 O(n) 0 no\n ================= ======= ============= ============ =======\n\n All the partition algorithms make temporary copies of the data when\n partitioning along any but the last axis. Consequently,\n partitioning along the last axis is faster and uses less space than\n partitioning along any other axis.\n\n The sort order for complex numbers is lexicographic. If both the\n real and imaginary parts are non-nan then the order is determined by\n the real parts except when they are equal, in which case the order\n is determined by the imaginary parts.\n\n The sort order of ``np.nan`` is bigger than ``np.inf``.\n\n Examples\n --------\n >>> import numpy as np\n >>> a = np.array([7, 1, 7, 7, 1, 5, 7, 2, 3, 2, 6, 2, 3, 0])\n >>> p = np.partition(a, 4)\n >>> p\n array([0, 1, 2, 1, 2, 5, 2, 3, 3, 6, 7, 7, 7, 7]) # may vary\n\n ``p[4]`` is 2; all elements in ``p[:4]`` are less than or equal\n to ``p[4]``, and all elements in ``p[5:]`` are greater than or\n equal to ``p[4]``. The partition is::\n\n [0, 1, 2, 1], [2], [5, 2, 3, 3, 6, 7, 7, 7, 7]\n\n The next example shows the use of multiple values passed to `kth`.\n\n >>> p2 = np.partition(a, (4, 8))\n >>> p2\n array([0, 1, 2, 1, 2, 3, 3, 2, 5, 6, 7, 7, 7, 7])\n\n ``p2[4]`` is 2 and ``p2[8]`` is 5. All elements in ``p2[:4]``\n are less than or equal to ``p2[4]``, all elements in ``p2[5:8]``\n are greater than or equal to ``p2[4]`` and less than or equal to\n ``p2[8]``, and all elements in ``p2[9:]`` are greater than or\n equal to ``p2[8]``. The partition is::\n\n [0, 1, 2, 1], [2], [3, 3, 2], [5], [6, 7, 7, 7, 7]\n \"\"\"\n if axis is None:\n # flatten returns (1, N) for np.matrix, so always use the last axis\n a = asanyarray(a).flatten()\n axis = -1\n else:\n a = asanyarray(a).copy(order=\"K\")\n a.partition(kth, axis=axis, kind=kind, order=order)\n return a\n\n\ndef _argpartition_dispatcher(a, kth, axis=None, kind=None, order=None):\n return (a,)\n\n\n@array_function_dispatch(_argpartition_dispatcher)\ndef argpartition(a, kth, axis=-1, kind='introselect', order=None):\n \"\"\"\n Perform an indirect partition along the given axis using the\n algorithm specified by the `kind` keyword. It returns an array of\n indices of the same shape as `a` that index data along the given\n axis in partitioned order.\n\n .. versionadded:: 1.8.0\n\n Parameters\n ----------\n a : array_like\n Array to sort.\n kth : int or sequence of ints\n Element index to partition by. The k-th element will be in its\n final sorted position and all smaller elements will be moved\n before it and all larger elements behind it. The order of all\n elements in the partitions is undefined. If provided with a\n sequence of k-th it will partition all of them into their sorted\n position at once.\n\n .. deprecated:: 1.22.0\n Passing booleans as index is deprecated.\n axis : int or None, optional\n Axis along which to sort. The default is -1 (the last axis). If\n None, the flattened array is used.\n kind : {'introselect'}, optional\n Selection algorithm. Default is 'introselect'\n order : str or list of str, optional\n When `a` is an array with fields defined, this argument\n specifies which fields to compare first, second, etc. A single\n field can be specified as a string, and not all fields need be\n specified, but unspecified fields will still be used, in the\n order in which they come up in the dtype, to break ties.\n\n Returns\n -------\n index_array : ndarray, int\n Array of indices that partition `a` along the specified axis.\n If `a` is one-dimensional, ``a[index_array]`` yields a partitioned `a`.\n More generally, ``np.take_along_axis(a, index_array, axis=axis)``\n always yields the partitioned `a`, irrespective of dimensionality.\n\n See Also\n --------\n partition : Describes partition algorithms used.\n ndarray.partition : Inplace partition.\n argsort : Full indirect sort.\n take_along_axis : Apply ``index_array`` from argpartition\n to an array as if by calling partition.\n\n Notes\n -----\n The returned indices are not guaranteed to be sorted according to\n the values. Furthermore, the default selection algorithm ``introselect``\n is unstable, and hence the returned indices are not guaranteed\n to be the earliest/latest occurrence of the element.\n\n `argpartition` works for real/complex inputs with nan values,\n see `partition` for notes on the enhanced sort order and\n different selection algorithms.\n\n Examples\n --------\n One dimensional array:\n\n >>> import numpy as np\n >>> x = np.array([3, 4, 2, 1])\n >>> x[np.argpartition(x, 3)]\n array([2, 1, 3, 4]) # may vary\n >>> x[np.argpartition(x, (1, 3))]\n array([1, 2, 3, 4]) # may vary\n\n >>> x = [3, 4, 2, 1]\n >>> np.array(x)[np.argpartition(x, 3)]\n array([2, 1, 3, 4]) # may vary\n\n Multi-dimensional array:\n\n >>> x = np.array([[3, 4, 2], [1, 3, 1]])\n >>> index_array = np.argpartition(x, kth=1, axis=-1)\n >>> # below is the same as np.partition(x, kth=1)\n >>> np.take_along_axis(x, index_array, axis=-1)\n array([[2, 3, 4],\n [1, 1, 3]])\n\n \"\"\"\n return _wrapfunc(a, 'argpartition', kth, axis=axis, kind=kind, order=order)\n\n\ndef _sort_dispatcher(a, axis=None, kind=None, order=None, *, stable=None):\n return (a,)\n\n\n@array_function_dispatch(_sort_dispatcher)\ndef sort(a, axis=-1, kind=None, order=None, *, stable=None):\n \"\"\"\n Return a sorted copy of an array.\n\n Parameters\n ----------\n a : array_like\n Array to be sorted.\n axis : int or None, optional\n Axis along which to sort. If None, the array is flattened before\n sorting. The default is -1, which sorts along the last axis.\n kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, optional\n Sorting algorithm. The default is 'quicksort'. Note that both 'stable'\n and 'mergesort' use timsort or radix sort under the covers and,\n in general, the actual implementation will vary with data type.\n The 'mergesort' option is retained for backwards compatibility.\n\n .. versionchanged:: 1.15.0.\n The 'stable' option was added.\n\n order : str or list of str, optional\n When `a` is an array with fields defined, this argument specifies\n which fields to compare first, second, etc. A single field can\n be specified as a string, and not all fields need be specified,\n but unspecified fields will still be used, in the order in which\n they come up in the dtype, to break ties.\n stable : bool, optional\n Sort stability. If ``True``, the returned array will maintain\n the relative order of ``a`` values which compare as equal.\n If ``False`` or ``None``, this is not guaranteed. Internally,\n this option selects ``kind='stable'``. Default: ``None``.\n\n .. versionadded:: 2.0.0\n\n Returns\n -------\n sorted_array : ndarray\n Array of the same type and shape as `a`.\n\n See Also\n --------\n ndarray.sort : Method to sort an array in-place.\n argsort : Indirect sort.\n lexsort : Indirect stable sort on multiple keys.\n searchsorted : Find elements in a sorted array.\n partition : Partial sort.\n\n Notes\n -----\n The various sorting algorithms are characterized by their average speed,\n worst case performance, work space size, and whether they are stable. A\n stable sort keeps items with the same key in the same relative\n order. The four algorithms implemented in NumPy have the following\n properties:\n\n =========== ======= ============= ============ ========\n kind speed worst case work space stable\n =========== ======= ============= ============ ========\n 'quicksort' 1 O(n^2) 0 no\n 'heapsort' 3 O(n*log(n)) 0 no\n 'mergesort' 2 O(n*log(n)) ~n/2 yes\n 'timsort' 2 O(n*log(n)) ~n/2 yes\n =========== ======= ============= ============ ========\n\n .. note:: The datatype determines which of 'mergesort' or 'timsort'\n is actually used, even if 'mergesort' is specified. User selection\n at a finer scale is not currently available.\n\n For performance, ``sort`` makes a temporary copy if needed to make the data\n `contiguous `_\n in memory along the sort axis. For even better performance and reduced\n memory consumption, ensure that the array is already contiguous along the\n sort axis.\n\n The sort order for complex numbers is lexicographic. If both the real\n and imaginary parts are non-nan then the order is determined by the\n real parts except when they are equal, in which case the order is\n determined by the imaginary parts.\n\n Previous to numpy 1.4.0 sorting real and complex arrays containing nan\n values led to undefined behaviour. In numpy versions >= 1.4.0 nan\n values are sorted to the end. The extended sort order is:\n\n * Real: [R, nan]\n * Complex: [R + Rj, R + nanj, nan + Rj, nan + nanj]\n\n where R is a non-nan real value. Complex values with the same nan\n placements are sorted according to the non-nan part if it exists.\n Non-nan values are sorted as before.\n\n .. versionadded:: 1.12.0\n\n quicksort has been changed to:\n `introsort `_.\n When sorting does not make enough progress it switches to\n `heapsort `_.\n This implementation makes quicksort O(n*log(n)) in the worst case.\n\n 'stable' automatically chooses the best stable sorting algorithm\n for the data type being sorted.\n It, along with 'mergesort' is currently mapped to\n `timsort `_\n or `radix sort `_\n depending on the data type.\n API forward compatibility currently limits the\n ability to select the implementation and it is hardwired for the different\n data types.\n\n .. versionadded:: 1.17.0\n\n Timsort is added for better performance on already or nearly\n sorted data. On random data timsort is almost identical to\n mergesort. It is now used for stable sort while quicksort is still the\n default sort if none is chosen. For timsort details, refer to\n `CPython listsort.txt\n `_\n 'mergesort' and 'stable' are mapped to radix sort for integer data types.\n Radix sort is an O(n) sort instead of O(n log n).\n\n .. versionchanged:: 1.18.0\n\n NaT now sorts to the end of arrays for consistency with NaN.\n\n Examples\n --------\n >>> import numpy as np\n >>> a = np.array([[1,4],[3,1]])\n >>> np.sort(a) # sort along the last axis\n array([[1, 4],\n [1, 3]])\n >>> np.sort(a, axis=None) # sort the flattened array\n array([1, 1, 3, 4])\n >>> np.sort(a, axis=0) # sort along the first axis\n array([[1, 1],\n [3, 4]])\n\n Use the `order` keyword to specify a field to use when sorting a\n structured array:\n\n >>> dtype = [('name', 'S10'), ('height', float), ('age', int)]\n >>> values = [('Arthur', 1.8, 41), ('Lancelot', 1.9, 38),\n ... ('Galahad', 1.7, 38)]\n >>> a = np.array(values, dtype=dtype) # create a structured array\n >>> np.sort(a, order='height') # doctest: +SKIP\n array([('Galahad', 1.7, 38), ('Arthur', 1.8, 41),\n ('Lancelot', 1.8999999999999999, 38)],\n dtype=[('name', '|S10'), ('height', '>> np.sort(a, order=['age', 'height']) # doctest: +SKIP\n array([('Galahad', 1.7, 38), ('Lancelot', 1.8999999999999999, 38),\n ('Arthur', 1.8, 41)],\n dtype=[('name', '|S10'), ('height', '>> import numpy as np\n >>> x = np.array([3, 1, 2])\n >>> np.argsort(x)\n array([1, 2, 0])\n\n Two-dimensional array:\n\n >>> x = np.array([[0, 3], [2, 2]])\n >>> x\n array([[0, 3],\n [2, 2]])\n\n >>> ind = np.argsort(x, axis=0) # sorts along first axis (down)\n >>> ind\n array([[0, 1],\n [1, 0]])\n >>> np.take_along_axis(x, ind, axis=0) # same as np.sort(x, axis=0)\n array([[0, 2],\n [2, 3]])\n\n >>> ind = np.argsort(x, axis=1) # sorts along last axis (across)\n >>> ind\n array([[0, 1],\n [0, 1]])\n >>> np.take_along_axis(x, ind, axis=1) # same as np.sort(x, axis=1)\n array([[0, 3],\n [2, 2]])\n\n Indices of the sorted elements of a N-dimensional array:\n\n >>> ind = np.unravel_index(np.argsort(x, axis=None), x.shape)\n >>> ind\n (array([0, 1, 1, 0]), array([0, 0, 1, 1]))\n >>> x[ind] # same as np.sort(x, axis=None)\n array([0, 2, 2, 3])\n\n Sorting with keys:\n\n >>> x = np.array([(1, 0), (0, 1)], dtype=[('x', '>> x\n array([(1, 0), (0, 1)],\n dtype=[('x', '>> np.argsort(x, order=('x','y'))\n array([1, 0])\n\n >>> np.argsort(x, order=('y','x'))\n array([0, 1])\n\n \"\"\"\n return _wrapfunc(\n a, 'argsort', axis=axis, kind=kind, order=order, stable=stable\n )\n\ndef _argmax_dispatcher(a, axis=None, out=None, *, keepdims=np._NoValue):\n return (a, out)\n\n\n@array_function_dispatch(_argmax_dispatcher)\ndef argmax(a, axis=None, out=None, *, keepdims=np._NoValue):\n \"\"\"\n Returns the indices of the maximum values along an axis.\n\n Parameters\n ----------\n a : array_like\n Input array.\n axis : int, optional\n By default, the index is into the flattened array, otherwise\n along the specified axis.\n out : array, optional\n If provided, the result will be inserted into this array. It should\n be of the appropriate shape and dtype.\n keepdims : bool, optional\n If this is set to True, the axes which are reduced are left\n in the result as dimensions with size one. With this option,\n the result will broadcast correctly against the array.\n\n .. versionadded:: 1.22.0\n\n Returns\n -------\n index_array : ndarray of ints\n Array of indices into the array. It has the same shape as ``a.shape``\n with the dimension along `axis` removed. If `keepdims` is set to True,\n then the size of `axis` will be 1 with the resulting array having same\n shape as ``a.shape``.\n\n See Also\n --------\n ndarray.argmax, argmin\n amax : The maximum value along a given axis.\n unravel_index : Convert a flat index into an index tuple.\n take_along_axis : Apply ``np.expand_dims(index_array, axis)``\n from argmax to an array as if by calling max.\n\n Notes\n -----\n In case of multiple occurrences of the maximum values, the indices\n corresponding to the first occurrence are returned.\n\n Examples\n --------\n >>> import numpy as np\n >>> a = np.arange(6).reshape(2,3) + 10\n >>> a\n array([[10, 11, 12],\n [13, 14, 15]])\n >>> np.argmax(a)\n 5\n >>> np.argmax(a, axis=0)\n array([1, 1, 1])\n >>> np.argmax(a, axis=1)\n array([2, 2])\n\n Indexes of the maximal elements of a N-dimensional array:\n\n >>> ind = np.unravel_index(np.argmax(a, axis=None), a.shape)\n >>> ind\n (1, 2)\n >>> a[ind]\n 15\n\n >>> b = np.arange(6)\n >>> b[1] = 5\n >>> b\n array([0, 5, 2, 3, 4, 5])\n >>> np.argmax(b) # Only the first occurrence is returned.\n 1\n\n >>> x = np.array([[4,2,3], [1,0,3]])\n >>> index_array = np.argmax(x, axis=-1)\n >>> # Same as np.amax(x, axis=-1, keepdims=True)\n >>> np.take_along_axis(x, np.expand_dims(index_array, axis=-1), axis=-1)\n array([[4],\n [3]])\n >>> # Same as np.amax(x, axis=-1)\n >>> np.take_along_axis(x, np.expand_dims(index_array, axis=-1),\n ... axis=-1).squeeze(axis=-1)\n array([4, 3])\n\n Setting `keepdims` to `True`,\n\n >>> x = np.arange(24).reshape((2, 3, 4))\n >>> res = np.argmax(x, axis=1, keepdims=True)\n >>> res.shape\n (2, 1, 4)\n \"\"\"\n kwds = {'keepdims': keepdims} if keepdims is not np._NoValue else {}\n return _wrapfunc(a, 'argmax', axis=axis, out=out, **kwds)\n\n\ndef _argmin_dispatcher(a, axis=None, out=None, *, keepdims=np._NoValue):\n return (a, out)\n\n\n@array_function_dispatch(_argmin_dispatcher)\ndef argmin(a, axis=None, out=None, *, keepdims=np._NoValue):\n \"\"\"\n Returns the indices of the minimum values along an axis.\n\n Parameters\n ----------\n a : array_like\n Input array.\n axis : int, optional\n By default, the index is into the flattened array, otherwise\n along the specified axis.\n out : array, optional\n If provided, the result will be inserted into this array. It should\n be of the appropriate shape and dtype.\n keepdims : bool, optional\n If this is set to True, the axes which are reduced are left\n in the result as dimensions with size one. With this option,\n the result will broadcast correctly against the array.\n\n .. versionadded:: 1.22.0\n\n Returns\n -------\n index_array : ndarray of ints\n Array of indices into the array. It has the same shape as `a.shape`\n with the dimension along `axis` removed. If `keepdims` is set to True,\n then the size of `axis` will be 1 with the resulting array having same\n shape as `a.shape`.\n\n See Also\n --------\n ndarray.argmin, argmax\n amin : The minimum value along a given axis.\n unravel_index : Convert a flat index into an index tuple.\n take_along_axis : Apply ``np.expand_dims(index_array, axis)``\n from argmin to an array as if by calling min.\n\n Notes\n -----\n In case of multiple occurrences of the minimum values, the indices\n corresponding to the first occurrence are returned.\n\n Examples\n --------\n >>> import numpy as np\n >>> a = np.arange(6).reshape(2,3) + 10\n >>> a\n array([[10, 11, 12],\n [13, 14, 15]])\n >>> np.argmin(a)\n 0\n >>> np.argmin(a, axis=0)\n array([0, 0, 0])\n >>> np.argmin(a, axis=1)\n array([0, 0])\n\n Indices of the minimum elements of a N-dimensional array:\n\n >>> ind = np.unravel_index(np.argmin(a, axis=None), a.shape)\n >>> ind\n (0, 0)\n >>> a[ind]\n 10\n\n >>> b = np.arange(6) + 10\n >>> b[4] = 10\n >>> b\n array([10, 11, 12, 13, 10, 15])\n >>> np.argmin(b) # Only the first occurrence is returned.\n 0\n\n >>> x = np.array([[4,2,3], [1,0,3]])\n >>> index_array = np.argmin(x, axis=-1)\n >>> # Same as np.amin(x, axis=-1, keepdims=True)\n >>> np.take_along_axis(x, np.expand_dims(index_array, axis=-1), axis=-1)\n array([[2],\n [0]])\n >>> # Same as np.amax(x, axis=-1)\n >>> np.take_along_axis(x, np.expand_dims(index_array, axis=-1),\n ... axis=-1).squeeze(axis=-1)\n array([2, 0])\n\n Setting `keepdims` to `True`,\n\n >>> x = np.arange(24).reshape((2, 3, 4))\n >>> res = np.argmin(x, axis=1, keepdims=True)\n >>> res.shape\n (2, 1, 4)\n \"\"\"\n kwds = {'keepdims': keepdims} if keepdims is not np._NoValue else {}\n return _wrapfunc(a, 'argmin', axis=axis, out=out, **kwds)\n\n\ndef _searchsorted_dispatcher(a, v, side=None, sorter=None):\n return (a, v, sorter)\n\n\n@array_function_dispatch(_searchsorted_dispatcher)\ndef searchsorted(a, v, side='left', sorter=None):\n \"\"\"\n Find indices where elements should be inserted to maintain order.\n\n Find the indices into a sorted array `a` such that, if the\n corresponding elements in `v` were inserted before the indices, the\n order of `a` would be preserved.\n\n Assuming that `a` is sorted:\n\n ====== ============================\n `side` returned index `i` satisfies\n ====== ============================\n left ``a[i-1] < v <= a[i]``\n right ``a[i-1] <= v < a[i]``\n ====== ============================\n\n Parameters\n ----------\n a : 1-D array_like\n Input array. If `sorter` is None, then it must be sorted in\n ascending order, otherwise `sorter` must be an array of indices\n that sort it.\n v : array_like\n Values to insert into `a`.\n side : {'left', 'right'}, optional\n If 'left', the index of the first suitable location found is given.\n If 'right', return the last such index. If there is no suitable\n index, return either 0 or N (where N is the length of `a`).\n sorter : 1-D array_like, optional\n Optional array of integer indices that sort array a into ascending\n order. They are typically the result of argsort.\n\n .. versionadded:: 1.7.0\n\n Returns\n -------\n indices : int or array of ints\n Array of insertion points with the same shape as `v`,\n or an integer if `v` is a scalar.\n\n See Also\n --------\n sort : Return a sorted copy of an array.\n histogram : Produce histogram from 1-D data.\n\n Notes\n -----\n Binary search is used to find the required insertion points.\n\n As of NumPy 1.4.0 `searchsorted` works with real/complex arrays containing\n `nan` values. The enhanced sort order is documented in `sort`.\n\n This function uses the same algorithm as the builtin python\n `bisect.bisect_left` (``side='left'``) and `bisect.bisect_right`\n (``side='right'``) functions, which is also vectorized\n in the `v` argument.\n\n Examples\n --------\n >>> import numpy as np\n >>> np.searchsorted([11,12,13,14,15], 13)\n 2\n >>> np.searchsorted([11,12,13,14,15], 13, side='right')\n 3\n >>> np.searchsorted([11,12,13,14,15], [-10, 20, 12, 13])\n array([0, 5, 1, 2])\n\n \"\"\"\n return _wrapfunc(a, 'searchsorted', v, side=side, sorter=sorter)\n\n\ndef _resize_dispatcher(a, new_shape):\n return (a,)\n\n\n@array_function_dispatch(_resize_dispatcher)\ndef resize(a, new_shape):\n \"\"\"\n Return a new array with the specified shape.\n\n If the new array is larger than the original array, then the new\n array is filled with repeated copies of `a`. Note that this behavior\n is different from a.resize(new_shape) which fills with zeros instead\n of repeated copies of `a`.\n\n Parameters\n ----------\n a : array_like\n Array to be resized.\n\n new_shape : int or tuple of int\n Shape of resized array.\n\n Returns\n -------\n reshaped_array : ndarray\n The new array is formed from the data in the old array, repeated\n if necessary to fill out the required number of elements. The\n data are repeated iterating over the array in C-order.\n\n See Also\n --------\n numpy.reshape : Reshape an array without changing the total size.\n numpy.pad : Enlarge and pad an array.\n numpy.repeat : Repeat elements of an array.\n ndarray.resize : resize an array in-place.\n\n Notes\n -----\n When the total size of the array does not change `~numpy.reshape` should\n be used. In most other cases either indexing (to reduce the size)\n or padding (to increase the size) may be a more appropriate solution.\n\n Warning: This functionality does **not** consider axes separately,\n i.e. it does not apply interpolation/extrapolation.\n It fills the return array with the required number of elements, iterating\n over `a` in C-order, disregarding axes (and cycling back from the start if\n the new shape is larger). This functionality is therefore not suitable to\n resize images, or data where each axis represents a separate and distinct\n entity.\n\n Examples\n --------\n >>> import numpy as np\n >>> a = np.array([[0,1],[2,3]])\n >>> np.resize(a,(2,3))\n array([[0, 1, 2],\n [3, 0, 1]])\n >>> np.resize(a,(1,4))\n array([[0, 1, 2, 3]])\n >>> np.resize(a,(2,4))\n array([[0, 1, 2, 3],\n [0, 1, 2, 3]])\n\n \"\"\"\n if isinstance(new_shape, (int, nt.integer)):\n new_shape = (new_shape,)\n\n a = ravel(a)\n\n new_size = 1\n for dim_length in new_shape:\n new_size *= dim_length\n if dim_length < 0:\n raise ValueError(\n 'all elements of `new_shape` must be non-negative'\n )\n\n if a.size == 0 or new_size == 0:\n # First case must zero fill. The second would have repeats == 0.\n return np.zeros_like(a, shape=new_shape)\n\n repeats = -(-new_size // a.size) # ceil division\n a = concatenate((a,) * repeats)[:new_size]\n\n return reshape(a, new_shape)\n\n\ndef _squeeze_dispatcher(a, axis=None):\n return (a,)\n\n\n@array_function_dispatch(_squeeze_dispatcher)\ndef squeeze(a, axis=None):\n \"\"\"\n Remove axes of length one from `a`.\n\n Parameters\n ----------\n a : array_like\n Input data.\n axis : None or int or tuple of ints, optional\n .. versionadded:: 1.7.0\n\n Selects a subset of the entries of length one in the\n shape. If an axis is selected with shape entry greater than\n one, an error is raised.\n\n Returns\n -------\n squeezed : ndarray\n The input array, but with all or a subset of the\n dimensions of length 1 removed. This is always `a` itself\n or a view into `a`. Note that if all axes are squeezed,\n the result is a 0d array and not a scalar.\n\n Raises\n ------\n ValueError\n If `axis` is not None, and an axis being squeezed is not of length 1\n\n See Also\n --------\n expand_dims : The inverse operation, adding entries of length one\n reshape : Insert, remove, and combine dimensions, and resize existing ones\n\n Examples\n --------\n >>> import numpy as np\n >>> x = np.array([[[0], [1], [2]]])\n >>> x.shape\n (1, 3, 1)\n >>> np.squeeze(x).shape\n (3,)\n >>> np.squeeze(x, axis=0).shape\n (3, 1)\n >>> np.squeeze(x, axis=1).shape\n Traceback (most recent call last):\n ...\n ValueError: cannot select an axis to squeeze out which has size\n not equal to one\n >>> np.squeeze(x, axis=2).shape\n (1, 3)\n >>> x = np.array([[1234]])\n >>> x.shape\n (1, 1)\n >>> np.squeeze(x)\n array(1234) # 0d array\n >>> np.squeeze(x).shape\n ()\n >>> np.squeeze(x)[()]\n 1234\n\n \"\"\"\n try:\n squeeze = a.squeeze\n except AttributeError:\n return _wrapit(a, 'squeeze', axis=axis)\n if axis is None:\n return squeeze()\n else:\n return squeeze(axis=axis)\n\n\ndef _diagonal_dispatcher(a, offset=None, axis1=None, axis2=None):\n return (a,)\n\n\n@array_function_dispatch(_diagonal_dispatcher)\ndef diagonal(a, offset=0, axis1=0, axis2=1):\n \"\"\"\n Return specified diagonals.\n\n If `a` is 2-D, returns the diagonal of `a` with the given offset,\n i.e., the collection of elements of the form ``a[i, i+offset]``. If\n `a` has more than two dimensions, then the axes specified by `axis1`\n and `axis2` are used to determine the 2-D sub-array whose diagonal is\n returned. The shape of the resulting array can be determined by\n removing `axis1` and `axis2` and appending an index to the right equal\n to the size of the resulting diagonals.\n\n In versions of NumPy prior to 1.7, this function always returned a new,\n independent array containing a copy of the values in the diagonal.\n\n In NumPy 1.7 and 1.8, it continues to return a copy of the diagonal,\n but depending on this fact is deprecated. Writing to the resulting\n array continues to work as it used to, but a FutureWarning is issued.\n\n Starting in NumPy 1.9 it returns a read-only view on the original array.\n Attempting to write to the resulting array will produce an error.\n\n In some future release, it will return a read/write view and writing to\n the returned array will alter your original array. The returned array\n will have the same type as the input array.\n\n If you don't write to the array returned by this function, then you can\n just ignore all of the above.\n\n If you depend on the current behavior, then we suggest copying the\n returned array explicitly, i.e., use ``np.diagonal(a).copy()`` instead\n of just ``np.diagonal(a)``. This will work with both past and future\n versions of NumPy.\n\n Parameters\n ----------\n a : array_like\n Array from which the diagonals are taken.\n offset : int, optional\n Offset of the diagonal from the main diagonal. Can be positive or\n negative. Defaults to main diagonal (0).\n axis1 : int, optional\n Axis to be used as the first axis of the 2-D sub-arrays from which\n the diagonals should be taken. Defaults to first axis (0).\n axis2 : int, optional\n Axis to be used as the second axis of the 2-D sub-arrays from\n which the diagonals should be taken. Defaults to second axis (1).\n\n Returns\n -------\n array_of_diagonals : ndarray\n If `a` is 2-D, then a 1-D array containing the diagonal and of the\n same type as `a` is returned unless `a` is a `matrix`, in which case\n a 1-D array rather than a (2-D) `matrix` is returned in order to\n maintain backward compatibility.\n\n If ``a.ndim > 2``, then the dimensions specified by `axis1` and `axis2`\n are removed, and a new axis inserted at the end corresponding to the\n diagonal.\n\n Raises\n ------\n ValueError\n If the dimension of `a` is less than 2.\n\n See Also\n --------\n diag : MATLAB work-a-like for 1-D and 2-D arrays.\n diagflat : Create diagonal arrays.\n trace : Sum along diagonals.\n\n Examples\n --------\n >>> import numpy as np\n >>> a = np.arange(4).reshape(2,2)\n >>> a\n array([[0, 1],\n [2, 3]])\n >>> a.diagonal()\n array([0, 3])\n >>> a.diagonal(1)\n array([1])\n\n A 3-D example:\n\n >>> a = np.arange(8).reshape(2,2,2); a\n array([[[0, 1],\n [2, 3]],\n [[4, 5],\n [6, 7]]])\n >>> a.diagonal(0, # Main diagonals of two arrays created by skipping\n ... 0, # across the outer(left)-most axis last and\n ... 1) # the \"middle\" (row) axis first.\n array([[0, 6],\n [1, 7]])\n\n The sub-arrays whose main diagonals we just obtained; note that each\n corresponds to fixing the right-most (column) axis, and that the\n diagonals are \"packed\" in rows.\n\n >>> a[:,:,0] # main diagonal is [0 6]\n array([[0, 2],\n [4, 6]])\n >>> a[:,:,1] # main diagonal is [1 7]\n array([[1, 3],\n [5, 7]])\n\n The anti-diagonal can be obtained by reversing the order of elements\n using either `numpy.flipud` or `numpy.fliplr`.\n\n >>> a = np.arange(9).reshape(3, 3)\n >>> a\n array([[0, 1, 2],\n [3, 4, 5],\n [6, 7, 8]])\n >>> np.fliplr(a).diagonal() # Horizontal flip\n array([2, 4, 6])\n >>> np.flipud(a).diagonal() # Vertical flip\n array([6, 4, 2])\n\n Note that the order in which the diagonal is retrieved varies depending\n on the flip function.\n \"\"\"\n if isinstance(a, np.matrix):\n # Make diagonal of matrix 1-D to preserve backward compatibility.\n return asarray(a).diagonal(offset=offset, axis1=axis1, axis2=axis2)\n else:\n return asanyarray(a).diagonal(offset=offset, axis1=axis1, axis2=axis2)\n\n\ndef _trace_dispatcher(\n a, offset=None, axis1=None, axis2=None, dtype=None, out=None):\n return (a, out)\n\n\n@array_function_dispatch(_trace_dispatcher)\ndef trace(a, offset=0, axis1=0, axis2=1, dtype=None, out=None):\n \"\"\"\n Return the sum along diagonals of the array.\n\n If `a` is 2-D, the sum along its diagonal with the given offset\n is returned, i.e., the sum of elements ``a[i,i+offset]`` for all i.\n\n If `a` has more than two dimensions, then the axes specified by axis1 and\n axis2 are used to determine the 2-D sub-arrays whose traces are returned.\n The shape of the resulting array is the same as that of `a` with `axis1`\n and `axis2` removed.\n\n Parameters\n ----------\n a : array_like\n Input array, from which the diagonals are taken.\n offset : int, optional\n Offset of the diagonal from the main diagonal. Can be both positive\n and negative. Defaults to 0.\n axis1, axis2 : int, optional\n Axes to be used as the first and second axis of the 2-D sub-arrays\n from which the diagonals should be taken. Defaults are the first two\n axes of `a`.\n dtype : dtype, optional\n Determines the data-type of the returned array and of the accumulator\n where the elements are summed. If dtype has the value None and `a` is\n of integer type of precision less than the default integer\n precision, then the default integer precision is used. Otherwise,\n the precision is the same as that of `a`.\n out : ndarray, optional\n Array into which the output is placed. Its type is preserved and\n it must be of the right shape to hold the output.\n\n Returns\n -------\n sum_along_diagonals : ndarray\n If `a` is 2-D, the sum along the diagonal is returned. If `a` has\n larger dimensions, then an array of sums along diagonals is returned.\n\n See Also\n --------\n diag, diagonal, diagflat\n\n Examples\n --------\n >>> import numpy as np\n >>> np.trace(np.eye(3))\n 3.0\n >>> a = np.arange(8).reshape((2,2,2))\n >>> np.trace(a)\n array([6, 8])\n\n >>> a = np.arange(24).reshape((2,2,2,3))\n >>> np.trace(a).shape\n (2, 3)\n\n \"\"\"\n if isinstance(a, np.matrix):\n # Get trace of matrix via an array to preserve backward compatibility.\n return asarray(a).trace(\n offset=offset, axis1=axis1, axis2=axis2, dtype=dtype, out=out\n )\n else:\n return asanyarray(a).trace(\n offset=offset, axis1=axis1, axis2=axis2, dtype=dtype, out=out\n )\n\n\ndef _ravel_dispatcher(a, order=None):\n return (a,)\n\n\n@array_function_dispatch(_ravel_dispatcher)\ndef ravel(a, order='C'):\n \"\"\"Return a contiguous flattened array.\n\n A 1-D array, containing the elements of the input, is returned. A copy is\n made only if needed.\n\n As of NumPy 1.10, the returned array will have the same type as the input\n array. (for example, a masked array will be returned for a masked array\n input)\n\n Parameters\n ----------\n a : array_like\n Input array. The elements in `a` are read in the order specified by\n `order`, and packed as a 1-D array.\n order : {'C','F', 'A', 'K'}, optional\n\n The elements of `a` are read using this index order. 'C' means\n to index the elements in row-major, C-style order,\n with the last axis index changing fastest, back to the first\n axis index changing slowest. 'F' means to index the elements\n in column-major, Fortran-style order, with the\n first index changing fastest, and the last index changing\n slowest. Note that the 'C' and 'F' options take no account of\n the memory layout of the underlying array, and only refer to\n the order of axis indexing. 'A' means to read the elements in\n Fortran-like index order if `a` is Fortran *contiguous* in\n memory, C-like order otherwise. 'K' means to read the\n elements in the order they occur in memory, except for\n reversing the data when strides are negative. By default, 'C'\n index order is used.\n\n Returns\n -------\n y : array_like\n y is a contiguous 1-D array of the same subtype as `a`,\n with shape ``(a.size,)``.\n Note that matrices are special cased for backward compatibility,\n if `a` is a matrix, then y is a 1-D ndarray.\n\n See Also\n --------\n ndarray.flat : 1-D iterator over an array.\n ndarray.flatten : 1-D array copy of the elements of an array\n in row-major order.\n ndarray.reshape : Change the shape of an array without changing its data.\n\n Notes\n -----\n In row-major, C-style order, in two dimensions, the row index\n varies the slowest, and the column index the quickest. This can\n be generalized to multiple dimensions, where row-major order\n implies that the index along the first axis varies slowest, and\n the index along the last quickest. The opposite holds for\n column-major, Fortran-style index ordering.\n\n When a view is desired in as many cases as possible, ``arr.reshape(-1)``\n may be preferable. However, ``ravel`` supports ``K`` in the optional\n ``order`` argument while ``reshape`` does not.\n\n Examples\n --------\n It is equivalent to ``reshape(-1, order=order)``.\n\n >>> import numpy as np\n >>> x = np.array([[1, 2, 3], [4, 5, 6]])\n >>> np.ravel(x)\n array([1, 2, 3, 4, 5, 6])\n\n >>> x.reshape(-1)\n array([1, 2, 3, 4, 5, 6])\n\n >>> np.ravel(x, order='F')\n array([1, 4, 2, 5, 3, 6])\n\n When ``order`` is 'A', it will preserve the array's 'C' or 'F' ordering:\n\n >>> np.ravel(x.T)\n array([1, 4, 2, 5, 3, 6])\n >>> np.ravel(x.T, order='A')\n array([1, 2, 3, 4, 5, 6])\n\n When ``order`` is 'K', it will preserve orderings that are neither 'C'\n nor 'F', but won't reverse axes:\n\n >>> a = np.arange(3)[::-1]; a\n array([2, 1, 0])\n >>> a.ravel(order='C')\n array([2, 1, 0])\n >>> a.ravel(order='K')\n array([2, 1, 0])\n\n >>> a = np.arange(12).reshape(2,3,2).swapaxes(1,2); a\n array([[[ 0, 2, 4],\n [ 1, 3, 5]],\n [[ 6, 8, 10],\n [ 7, 9, 11]]])\n >>> a.ravel(order='C')\n array([ 0, 2, 4, 1, 3, 5, 6, 8, 10, 7, 9, 11])\n >>> a.ravel(order='K')\n array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])\n\n \"\"\"\n if isinstance(a, np.matrix):\n return asarray(a).ravel(order=order)\n else:\n return asanyarray(a).ravel(order=order)\n\n\ndef _nonzero_dispatcher(a):\n return (a,)\n\n\n@array_function_dispatch(_nonzero_dispatcher)\ndef nonzero(a):\n \"\"\"\n Return the indices of the elements that are non-zero.\n\n Returns a tuple of arrays, one for each dimension of `a`,\n containing the indices of the non-zero elements in that\n dimension. The values in `a` are always tested and returned in\n row-major, C-style order.\n\n To group the indices by element, rather than dimension, use `argwhere`,\n which returns a row for each non-zero element.\n\n .. note::\n\n When called on a zero-d array or scalar, ``nonzero(a)`` is treated\n as ``nonzero(atleast_1d(a))``.\n\n .. deprecated:: 1.17.0\n\n Use `atleast_1d` explicitly if this behavior is deliberate.\n\n Parameters\n ----------\n a : array_like\n Input array.\n\n Returns\n -------\n tuple_of_arrays : tuple\n Indices of elements that are non-zero.\n\n See Also\n --------\n flatnonzero :\n Return indices that are non-zero in the flattened version of the input\n array.\n ndarray.nonzero :\n Equivalent ndarray method.\n count_nonzero :\n Counts the number of non-zero elements in the input array.\n\n Notes\n -----\n While the nonzero values can be obtained with ``a[nonzero(a)]``, it is\n recommended to use ``x[x.astype(bool)]`` or ``x[x != 0]`` instead, which\n will correctly handle 0-d arrays.\n\n Examples\n --------\n >>> import numpy as np\n >>> x = np.array([[3, 0, 0], [0, 4, 0], [5, 6, 0]])\n >>> x\n array([[3, 0, 0],\n [0, 4, 0],\n [5, 6, 0]])\n >>> np.nonzero(x)\n (array([0, 1, 2, 2]), array([0, 1, 0, 1]))\n\n >>> x[np.nonzero(x)]\n array([3, 4, 5, 6])\n >>> np.transpose(np.nonzero(x))\n array([[0, 0],\n [1, 1],\n [2, 0],\n [2, 1]])\n\n A common use for ``nonzero`` is to find the indices of an array, where\n a condition is True. Given an array `a`, the condition `a` > 3 is a\n boolean array and since False is interpreted as 0, np.nonzero(a > 3)\n yields the indices of the `a` where the condition is true.\n\n >>> a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n >>> a > 3\n array([[False, False, False],\n [ True, True, True],\n [ True, True, True]])\n >>> np.nonzero(a > 3)\n (array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))\n\n Using this result to index `a` is equivalent to using the mask directly:\n\n >>> a[np.nonzero(a > 3)]\n array([4, 5, 6, 7, 8, 9])\n >>> a[a > 3] # prefer this spelling\n array([4, 5, 6, 7, 8, 9])\n\n ``nonzero`` can also be called as a method of the array.\n\n >>> (a > 3).nonzero()\n (array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))\n\n \"\"\"\n return _wrapfunc(a, 'nonzero')\n\n\ndef _shape_dispatcher(a):\n return (a,)\n\n\n@array_function_dispatch(_shape_dispatcher)\ndef shape(a):\n \"\"\"\n Return the shape of an array.\n\n Parameters\n ----------\n a : array_like\n Input array.\n\n Returns\n -------\n shape : tuple of ints\n The elements of the shape tuple give the lengths of the\n corresponding array dimensions.\n\n See Also\n --------\n len : ``len(a)`` is equivalent to ``np.shape(a)[0]`` for N-D arrays with\n ``N>=1``.\n ndarray.shape : Equivalent array method.\n\n Examples\n --------\n >>> import numpy as np\n >>> np.shape(np.eye(3))\n (3, 3)\n >>> np.shape([[1, 3]])\n (1, 2)\n >>> np.shape([0])\n (1,)\n >>> np.shape(0)\n ()\n\n >>> a = np.array([(1, 2), (3, 4), (5, 6)],\n ... dtype=[('x', 'i4'), ('y', 'i4')])\n >>> np.shape(a)\n (3,)\n >>> a.shape\n (3,)\n\n \"\"\"\n try:\n result = a.shape\n except AttributeError:\n result = asarray(a).shape\n return result\n\n\ndef _compress_dispatcher(condition, a, axis=None, out=None):\n return (condition, a, out)\n\n\n@array_function_dispatch(_compress_dispatcher)\ndef compress(condition, a, axis=None, out=None):\n \"\"\"\n Return selected slices of an array along given axis.\n\n When working along a given axis, a slice along that axis is returned in\n `output` for each index where `condition` evaluates to True. When\n working on a 1-D array, `compress` is equivalent to `extract`.\n\n Parameters\n ----------\n condition : 1-D array of bools\n Array that selects which entries to return. If len(condition)\n is less than the size of `a` along the given axis, then output is\n truncated to the length of the condition array.\n a : array_like\n Array from which to extract a part.\n axis : int, optional\n Axis along which to take slices. If None (default), work on the\n flattened array.\n out : ndarray, optional\n Output array. Its type is preserved and it must be of the right\n shape to hold the output.\n\n Returns\n -------\n compressed_array : ndarray\n A copy of `a` without the slices along axis for which `condition`\n is false.\n\n See Also\n --------\n take, choose, diag, diagonal, select\n ndarray.compress : Equivalent method in ndarray\n extract : Equivalent method when working on 1-D arrays\n :ref:`ufuncs-output-type`\n\n Examples\n --------\n >>> import numpy as np\n >>> a = np.array([[1, 2], [3, 4], [5, 6]])\n >>> a\n array([[1, 2],\n [3, 4],\n [5, 6]])\n >>> np.compress([0, 1], a, axis=0)\n array([[3, 4]])\n >>> np.compress([False, True, True], a, axis=0)\n array([[3, 4],\n [5, 6]])\n >>> np.compress([False, True], a, axis=1)\n array([[2],\n [4],\n [6]])\n\n Working on the flattened array does not return slices along an axis but\n selects elements.\n\n >>> np.compress([False, True], a)\n array([2])\n\n \"\"\"\n return _wrapfunc(a, 'compress', condition, axis=axis, out=out)\n\n\ndef _clip_dispatcher(a, a_min=None, a_max=None, out=None, *, min=None,\n max=None, **kwargs):\n return (a, a_min, a_max, out, min, max)\n\n\n@array_function_dispatch(_clip_dispatcher)\ndef clip(a, a_min=np._NoValue, a_max=np._NoValue, out=None, *,\n min=np._NoValue, max=np._NoValue, **kwargs):\n \"\"\"\n Clip (limit) the values in an array.\n\n Given an interval, values outside the interval are clipped to\n the interval edges. For example, if an interval of ``[0, 1]``\n is specified, values smaller than 0 become 0, and values larger\n than 1 become 1.\n\n Equivalent to but faster than ``np.minimum(a_max, np.maximum(a, a_min))``.\n\n No check is performed to ensure ``a_min < a_max``.\n\n Parameters\n ----------\n a : array_like\n Array containing elements to clip.\n a_min, a_max : array_like or None\n Minimum and maximum value. If ``None``, clipping is not performed on\n the corresponding edge. If both ``a_min`` and ``a_max`` are ``None``,\n the elements of the returned array stay the same. Both are broadcasted\n against ``a``.\n out : ndarray, optional\n The results will be placed in this array. It may be the input\n array for in-place clipping. `out` must be of the right shape\n to hold the output. Its type is preserved.\n min, max : array_like or None\n Array API compatible alternatives for ``a_min`` and ``a_max``\n arguments. Either ``a_min`` and ``a_max`` or ``min`` and ``max``\n can be passed at the same time. Default: ``None``.\n\n .. versionadded:: 2.1.0\n **kwargs\n For other keyword-only arguments, see the\n :ref:`ufunc docs `.\n\n .. versionadded:: 1.17.0\n\n Returns\n -------\n clipped_array : ndarray\n An array with the elements of `a`, but where values\n < `a_min` are replaced with `a_min`, and those > `a_max`\n with `a_max`.\n\n See Also\n --------\n :ref:`ufuncs-output-type`\n\n Notes\n -----\n When `a_min` is greater than `a_max`, `clip` returns an\n array in which all values are equal to `a_max`,\n as shown in the second example.\n\n Examples\n --------\n >>> import numpy as np\n >>> a = np.arange(10)\n >>> a\n array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n >>> np.clip(a, 1, 8)\n array([1, 1, 2, 3, 4, 5, 6, 7, 8, 8])\n >>> np.clip(a, 8, 1)\n array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])\n >>> np.clip(a, 3, 6, out=a)\n array([3, 3, 3, 3, 4, 5, 6, 6, 6, 6])\n >>> a\n array([3, 3, 3, 3, 4, 5, 6, 6, 6, 6])\n >>> a = np.arange(10)\n >>> a\n array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n >>> np.clip(a, [3, 4, 1, 1, 1, 4, 4, 4, 4, 4], 8)\n array([3, 4, 2, 3, 4, 5, 6, 7, 8, 8])\n\n \"\"\"\n if a_min is np._NoValue and a_max is np._NoValue:\n a_min = None if min is np._NoValue else min\n a_max = None if max is np._NoValue else max\n elif a_min is np._NoValue:\n raise TypeError(\"clip() missing 1 required positional \"\n \"argument: 'a_min'\")\n elif a_max is np._NoValue:\n raise TypeError(\"clip() missing 1 required positional \"\n \"argument: 'a_max'\")\n elif min is not np._NoValue or max is not np._NoValue:\n raise ValueError(\"Passing `min` or `max` keyword argument when \"\n \"`a_min` and `a_max` are provided is forbidden.\")\n\n return _wrapfunc(a, 'clip', a_min, a_max, out=out, **kwargs)\n\n\ndef _sum_dispatcher(a, axis=None, dtype=None, out=None, keepdims=None,\n initial=None, where=None):\n return (a, out)\n\n\n@array_function_dispatch(_sum_dispatcher)\ndef sum(a, axis=None, dtype=None, out=None, keepdims=np._NoValue,\n initial=np._NoValue, where=np._NoValue):\n \"\"\"\n Sum of array elements over a given axis.\n\n Parameters\n ----------\n a : array_like\n Elements to sum.\n axis : None or int or tuple of ints, optional\n Axis or axes along which a sum is performed. The default,\n axis=None, will sum all of the elements of the input array. If\n axis is negative it counts from the last to the first axis.\n\n .. versionadded:: 1.7.0\n\n If axis is a tuple of ints, a sum is performed on all of the axes\n specified in the tuple instead of a single axis or all the axes as\n before.\n dtype : dtype, optional\n The type of the returned array and of the accumulator in which the\n elements are summed. The dtype of `a` is used by default unless `a`\n has an integer dtype of less precision than the default platform\n integer. In that case, if `a` is signed then the platform integer\n is used while if `a` is unsigned then an unsigned integer of the\n same precision as the platform integer is used.\n out : ndarray, optional\n Alternative output array in which to place the result. It must have\n the same shape as the expected output, but the type of the output\n values will be cast if necessary.\n keepdims : bool, optional\n If this is set to True, the axes which are reduced are left\n in the result as dimensions with size one. With this option,\n the result will broadcast correctly against the input array.\n\n If the default value is passed, then `keepdims` will not be\n passed through to the `sum` method of sub-classes of\n `ndarray`, however any non-default value will be. If the\n sub-class' method does not implement `keepdims` any\n exceptions will be raised.\n initial : scalar, optional\n Starting value for the sum. See `~numpy.ufunc.reduce` for details.\n\n .. versionadded:: 1.15.0\n\n where : array_like of bool, optional\n Elements to include in the sum. See `~numpy.ufunc.reduce` for details.\n\n .. versionadded:: 1.17.0\n\n Returns\n -------\n sum_along_axis : ndarray\n An array with the same shape as `a`, with the specified\n axis removed. If `a` is a 0-d array, or if `axis` is None, a scalar\n is returned. If an output array is specified, a reference to\n `out` is returned.\n\n See Also\n --------\n ndarray.sum : Equivalent method.\n add: ``numpy.add.reduce`` equivalent function.\n cumsum : Cumulative sum of array elements.\n trapezoid : Integration of array values using composite trapezoidal rule.\n\n mean, average\n\n Notes\n -----\n Arithmetic is modular when using integer types, and no error is\n raised on overflow.\n\n The sum of an empty array is the neutral element 0:\n\n >>> np.sum([])\n 0.0\n\n For floating point numbers the numerical precision of sum (and\n ``np.add.reduce``) is in general limited by directly adding each number\n individually to the result causing rounding errors in every step.\n However, often numpy will use a numerically better approach (partial\n pairwise summation) leading to improved precision in many use-cases.\n This improved precision is always provided when no ``axis`` is given.\n When ``axis`` is given, it will depend on which axis is summed.\n Technically, to provide the best speed possible, the improved precision\n is only used when the summation is along the fast axis in memory.\n Note that the exact precision may vary depending on other parameters.\n In contrast to NumPy, Python's ``math.fsum`` function uses a slower but\n more precise approach to summation.\n Especially when summing a large number of lower precision floating point\n numbers, such as ``float32``, numerical errors can become significant.\n In such cases it can be advisable to use `dtype=\"float64\"` to use a higher\n precision for the output.\n\n Examples\n --------\n >>> import numpy as np\n >>> np.sum([0.5, 1.5])\n 2.0\n >>> np.sum([0.5, 0.7, 0.2, 1.5], dtype=np.int32)\n 1\n >>> np.sum([[0, 1], [0, 5]])\n 6\n >>> np.sum([[0, 1], [0, 5]], axis=0)\n array([0, 6])\n >>> np.sum([[0, 1], [0, 5]], axis=1)\n array([1, 5])\n >>> np.sum([[0, 1], [np.nan, 5]], where=[False, True], axis=1)\n array([1., 5.])\n\n If the accumulator is too small, overflow occurs:\n\n >>> np.ones(128, dtype=np.int8).sum(dtype=np.int8)\n -128\n\n You can also start the sum with a value other than zero:\n\n >>> np.sum([10], initial=5)\n 15\n \"\"\"\n if isinstance(a, _gentype):\n # 2018-02-25, 1.15.0\n warnings.warn(\n \"Calling np.sum(generator) is deprecated, and in the future will \"\n \"give a different result. Use np.sum(np.fromiter(generator)) or \"\n \"the python sum builtin instead.\",\n DeprecationWarning, stacklevel=2\n )\n\n res = _sum_(a)\n if out is not None:\n out[...] = res\n return out\n return res\n\n return _wrapreduction(\n a, np.add, 'sum', axis, dtype, out,\n keepdims=keepdims, initial=initial, where=where\n )\n\n\ndef _any_dispatcher(a, axis=None, out=None, keepdims=None, *,\n where=np._NoValue):\n return (a, where, out)\n\n\n@array_function_dispatch(_any_dispatcher)\ndef any(a, axis=None, out=None, keepdims=np._NoValue, *, where=np._NoValue):\n \"\"\"\n Test whether any array element along a given axis evaluates to True.\n\n Returns single boolean if `axis` is ``None``\n\n Parameters\n ----------\n a : array_like\n Input array or object that can be converted to an array.\n axis : None or int or tuple of ints, optional\n Axis or axes along which a logical OR reduction is performed.\n The default (``axis=None``) is to perform a logical OR over all\n the dimensions of the input array. `axis` may be negative, in\n which case it counts from the last to the first axis.\n\n .. versionadded:: 1.7.0\n\n If this is a tuple of ints, a reduction is performed on multiple\n axes, instead of a single axis or all the axes as before.\n out : ndarray, optional\n Alternate output array in which to place the result. It must have\n the same shape as the expected output and its type is preserved\n (e.g., if it is of type float, then it will remain so, returning\n 1.0 for True and 0.0 for False, regardless of the type of `a`).\n See :ref:`ufuncs-output-type` for more details.\n\n keepdims : bool, optional\n If this is set to True, the axes which are reduced are left\n in the result as dimensions with size one. With this option,\n the result will broadcast correctly against the input array.\n\n If the default value is passed, then `keepdims` will not be\n passed through to the `any` method of sub-classes of\n `ndarray`, however any non-default value will be. If the\n sub-class' method does not implement `keepdims` any\n exceptions will be raised.\n\n where : array_like of bool, optional\n Elements to include in checking for any `True` values.\n See `~numpy.ufunc.reduce` for details.\n\n .. versionadded:: 1.20.0\n\n Returns\n -------\n any : bool or ndarray\n A new boolean or `ndarray` is returned unless `out` is specified,\n in which case a reference to `out` is returned.\n\n See Also\n --------\n ndarray.any : equivalent method\n\n all : Test whether all elements along a given axis evaluate to True.\n\n Notes\n -----\n Not a Number (NaN), positive infinity and negative infinity evaluate\n to `True` because these are not equal to zero.\n\n .. versionchanged:: 2.0\n Before NumPy 2.0, ``any`` did not return booleans for object dtype\n input arrays.\n This behavior is still available via ``np.logical_or.reduce``.\n\n Examples\n --------\n >>> import numpy as np\n >>> np.any([[True, False], [True, True]])\n True\n\n >>> np.any([[True, False, True ],\n ... [False, False, False]], axis=0)\n array([ True, False, True])\n\n >>> np.any([-1, 0, 5])\n True\n\n >>> np.any([[np.nan], [np.inf]], axis=1, keepdims=True)\n array([[ True],\n [ True]])\n\n >>> np.any([[True, False], [False, False]], where=[[False], [True]])\n False\n\n >>> a = np.array([[1, 0, 0],\n ... [0, 0, 1],\n ... [0, 0, 0]])\n >>> np.any(a, axis=0)\n array([ True, False, True])\n >>> np.any(a, axis=1)\n array([ True, True, False])\n\n >>> o=np.array(False)\n >>> z=np.any([-1, 4, 5], out=o)\n >>> z, o\n (array(True), array(True))\n >>> # Check now that z is a reference to o\n >>> z is o\n True\n >>> id(z), id(o) # identity of z and o # doctest: +SKIP\n (191614240, 191614240)\n\n \"\"\"\n return _wrapreduction_any_all(a, np.logical_or, 'any', axis, out,\n keepdims=keepdims, where=where)\n\n\ndef _all_dispatcher(a, axis=None, out=None, keepdims=None, *,\n where=None):\n return (a, where, out)\n\n\n@array_function_dispatch(_all_dispatcher)\ndef all(a, axis=None, out=None, keepdims=np._NoValue, *, where=np._NoValue):\n \"\"\"\n Test whether all array elements along a given axis evaluate to True.\n\n Parameters\n ----------\n a : array_like\n Input array or object that can be converted to an array.\n axis : None or int or tuple of ints, optional\n Axis or axes along which a logical AND reduction is performed.\n The default (``axis=None``) is to perform a logical AND over all\n the dimensions of the input array. `axis` may be negative, in\n which case it counts from the last to the first axis.\n\n .. versionadded:: 1.7.0\n\n If this is a tuple of ints, a reduction is performed on multiple\n axes, instead of a single axis or all the axes as before.\n out : ndarray, optional\n Alternate output array in which to place the result.\n It must have the same shape as the expected output and its\n type is preserved (e.g., if ``dtype(out)`` is float, the result\n will consist of 0.0's and 1.0's). See :ref:`ufuncs-output-type`\n for more details.\n\n keepdims : bool, optional\n If this is set to True, the axes which are reduced are left\n in the result as dimensions with size one. With this option,\n the result will broadcast correctly against the input array.\n\n If the default value is passed, then `keepdims` will not be\n passed through to the `all` method of sub-classes of\n `ndarray`, however any non-default value will be. If the\n sub-class' method does not implement `keepdims` any\n exceptions will be raised.\n\n where : array_like of bool, optional\n Elements to include in checking for all `True` values.\n See `~numpy.ufunc.reduce` for details.\n\n .. versionadded:: 1.20.0\n\n Returns\n -------\n all : ndarray, bool\n A new boolean or array is returned unless `out` is specified,\n in which case a reference to `out` is returned.\n\n See Also\n --------\n ndarray.all : equivalent method\n\n any : Test whether any element along a given axis evaluates to True.\n\n Notes\n -----\n Not a Number (NaN), positive infinity and negative infinity\n evaluate to `True` because these are not equal to zero.\n\n .. versionchanged:: 2.0\n Before NumPy 2.0, ``all`` did not return booleans for object dtype\n input arrays.\n This behavior is still available via ``np.logical_and.reduce``.\n\n Examples\n --------\n >>> import numpy as np\n >>> np.all([[True,False],[True,True]])\n False\n\n >>> np.all([[True,False],[True,True]], axis=0)\n array([ True, False])\n\n >>> np.all([-1, 4, 5])\n True\n\n >>> np.all([1.0, np.nan])\n True\n\n >>> np.all([[True, True], [False, True]], where=[[True], [False]])\n True\n\n >>> o=np.array(False)\n >>> z=np.all([-1, 4, 5], out=o)\n >>> id(z), id(o), z\n (28293632, 28293632, array(True)) # may vary\n\n \"\"\"\n return _wrapreduction_any_all(a, np.logical_and, 'all', axis, out,\n keepdims=keepdims, where=where)\n\n\ndef _cumulative_func(x, func, axis, dtype, out, include_initial):\n x = np.atleast_1d(x)\n x_ndim = x.ndim\n if axis is None:\n if x_ndim >= 2:\n raise ValueError(\"For arrays which have more than one dimension \"\n \"``axis`` argument is required.\")\n axis = 0\n\n if out is not None and include_initial:\n item = [slice(None)] * x_ndim\n item[axis] = slice(1, None)\n func.accumulate(x, axis=axis, dtype=dtype, out=out[tuple(item)])\n item[axis] = 0\n out[tuple(item)] = func.identity\n return out\n\n res = func.accumulate(x, axis=axis, dtype=dtype, out=out)\n if include_initial:\n initial_shape = list(x.shape)\n initial_shape[axis] = 1\n res = np.concat(\n [np.full_like(res, func.identity, shape=initial_shape), res],\n axis=axis,\n )\n\n return res\n\n\ndef _cumulative_prod_dispatcher(x, /, *, axis=None, dtype=None, out=None,\n include_initial=None):\n return (x, out)\n\n\n@array_function_dispatch(_cumulative_prod_dispatcher)\ndef cumulative_prod(x, /, *, axis=None, dtype=None, out=None,\n include_initial=False):\n \"\"\"\n Return the cumulative product of elements along a given axis.\n\n This function is an Array API compatible alternative to `numpy.cumprod`.\n\n Parameters\n ----------\n x : array_like\n Input array.\n axis : int, optional\n Axis along which the cumulative product is computed. The default\n (None) is only allowed for one-dimensional arrays. For arrays\n with more than one dimension ``axis`` is required.\n dtype : dtype, optional\n Type of the returned array, as well as of the accumulator in which\n the elements are multiplied. If ``dtype`` is not specified, it\n defaults to the dtype of ``x``, unless ``x`` has an integer dtype\n with a precision less than that of the default platform integer.\n In that case, the default platform integer is used instead.\n out : ndarray, optional\n Alternative output array in which to place the result. It must\n have the same shape and buffer length as the expected output\n but the type of the resulting values will be cast if necessary.\n See :ref:`ufuncs-output-type` for more details.\n include_initial : bool, optional\n Boolean indicating whether to include the initial value (ones) as\n the first value in the output. With ``include_initial=True``\n the shape of the output is different than the shape of the input.\n Default: ``False``.\n\n Returns\n -------\n cumulative_prod_along_axis : ndarray\n A new array holding the result is returned unless ``out`` is\n specified, in which case a reference to ``out`` is returned. The\n result has the same shape as ``x`` if ``include_initial=False``.\n\n Notes\n -----\n Arithmetic is modular when using integer types, and no error is\n raised on overflow.\n\n Examples\n --------\n >>> a = np.array([1, 2, 3])\n >>> np.cumulative_prod(a) # intermediate results 1, 1*2\n ... # total product 1*2*3 = 6\n array([1, 2, 6])\n >>> a = np.array([1, 2, 3, 4, 5, 6])\n >>> np.cumulative_prod(a, dtype=float) # specify type of output\n array([ 1., 2., 6., 24., 120., 720.])\n\n The cumulative product for each column (i.e., over the rows) of ``b``:\n\n >>> b = np.array([[1, 2, 3], [4, 5, 6]])\n >>> np.cumulative_prod(b, axis=0)\n array([[ 1, 2, 3],\n [ 4, 10, 18]])\n\n The cumulative product for each row (i.e. over the columns) of ``b``:\n\n >>> np.cumulative_prod(b, axis=1)\n array([[ 1, 2, 6],\n [ 4, 20, 120]])\n\n \"\"\"\n return _cumulative_func(x, um.multiply, axis, dtype, out, include_initial)\n\n\ndef _cumulative_sum_dispatcher(x, /, *, axis=None, dtype=None, out=None,\n include_initial=None):\n return (x, out)\n\n\n@array_function_dispatch(_cumulative_sum_dispatcher)\ndef cumulative_sum(x, /, *, axis=None, dtype=None, out=None,\n include_initial=False):\n \"\"\"\n Return the cumulative sum of the elements along a given axis.\n\n This function is an Array API compatible alternative to `numpy.cumsum`.\n\n Parameters\n ----------\n x : array_like\n Input array.\n axis : int, optional\n Axis along which the cumulative sum is computed. The default\n (None) is only allowed for one-dimensional arrays. For arrays\n with more than one dimension ``axis`` is required.\n dtype : dtype, optional\n Type of the returned array and of the accumulator in which the\n elements are summed. If ``dtype`` is not specified, it defaults\n to the dtype of ``x``, unless ``x`` has an integer dtype with\n a precision less than that of the default platform integer.\n In that case, the default platform integer is used.\n out : ndarray, optional\n Alternative output array in which to place the result. It must\n have the same shape and buffer length as the expected output\n but the type will be cast if necessary. See :ref:`ufuncs-output-type`\n for more details.\n include_initial : bool, optional\n Boolean indicating whether to include the initial value (ones) as\n the first value in the output. With ``include_initial=True``\n the shape of the output is different than the shape of the input.\n Default: ``False``.\n\n Returns\n -------\n cumulative_sum_along_axis : ndarray\n A new array holding the result is returned unless ``out`` is\n specified, in which case a reference to ``out`` is returned. The\n result has the same shape as ``x`` if ``include_initial=False``.\n\n See Also\n --------\n sum : Sum array elements.\n trapezoid : Integration of array values using composite trapezoidal rule.\n diff : Calculate the n-th discrete difference along given axis.\n\n Notes\n -----\n Arithmetic is modular when using integer types, and no error is\n raised on overflow.\n\n ``cumulative_sum(a)[-1]`` may not be equal to ``sum(a)`` for\n floating-point values since ``sum`` may use a pairwise summation routine,\n reducing the roundoff-error. See `sum` for more information.\n\n Examples\n --------\n >>> a = np.array([1, 2, 3, 4, 5, 6])\n >>> a\n array([1, 2, 3, 4, 5, 6])\n >>> np.cumulative_sum(a)\n array([ 1, 3, 6, 10, 15, 21])\n >>> np.cumulative_sum(a, dtype=float) # specifies type of output value(s)\n array([ 1., 3., 6., 10., 15., 21.])\n\n >>> b = np.array([[1, 2, 3], [4, 5, 6]])\n >>> np.cumulative_sum(b,axis=0) # sum over rows for each of the 3 columns\n array([[1, 2, 3],\n [5, 7, 9]])\n >>> np.cumulative_sum(b,axis=1) # sum over columns for each of the 2 rows\n array([[ 1, 3, 6],\n [ 4, 9, 15]])\n\n ``cumulative_sum(c)[-1]`` may not be equal to ``sum(c)``\n\n >>> c = np.array([1, 2e-9, 3e-9] * 1000000)\n >>> np.cumulative_sum(c)[-1]\n 1000000.0050045159\n >>> c.sum()\n 1000000.0050000029\n\n \"\"\"\n return _cumulative_func(x, um.add, axis, dtype, out, include_initial)\n\n\ndef _cumsum_dispatcher(a, axis=None, dtype=None, out=None):\n return (a, out)\n\n\n@array_function_dispatch(_cumsum_dispatcher)\ndef cumsum(a, axis=None, dtype=None, out=None):\n \"\"\"\n Return the cumulative sum of the elements along a given axis.\n\n Parameters\n ----------\n a : array_like\n Input array.\n axis : int, optional\n Axis along which the cumulative sum is computed. The default\n (None) is to compute the cumsum over the flattened array.\n dtype : dtype, optional\n Type of the returned array and of the accumulator in which the\n elements are summed. If `dtype` is not specified, it defaults\n to the dtype of `a`, unless `a` has an integer dtype with a\n precision less than that of the default platform integer. In\n that case, the default platform integer is used.\n out : ndarray, optional\n Alternative output array in which to place the result. It must\n have the same shape and buffer length as the expected output\n but the type will be cast if necessary. See :ref:`ufuncs-output-type`\n for more details.\n\n Returns\n -------\n cumsum_along_axis : ndarray.\n A new array holding the result is returned unless `out` is\n specified, in which case a reference to `out` is returned. The\n result has the same size as `a`, and the same shape as `a` if\n `axis` is not None or `a` is a 1-d array.\n\n See Also\n --------\n cumulative_sum : Array API compatible alternative for ``cumsum``.\n sum : Sum array elements.\n trapezoid : Integration of array values using composite trapezoidal rule.\n diff : Calculate the n-th discrete difference along given axis.\n\n Notes\n -----\n Arithmetic is modular when using integer types, and no error is\n raised on overflow.\n\n ``cumsum(a)[-1]`` may not be equal to ``sum(a)`` for floating-point\n values since ``sum`` may use a pairwise summation routine, reducing\n the roundoff-error. See `sum` for more information.\n\n Examples\n --------\n >>> import numpy as np\n >>> a = np.array([[1,2,3], [4,5,6]])\n >>> a\n array([[1, 2, 3],\n [4, 5, 6]])\n >>> np.cumsum(a)\n array([ 1, 3, 6, 10, 15, 21])\n >>> np.cumsum(a, dtype=float) # specifies type of output value(s)\n array([ 1., 3., 6., 10., 15., 21.])\n\n >>> np.cumsum(a,axis=0) # sum over rows for each of the 3 columns\n array([[1, 2, 3],\n [5, 7, 9]])\n >>> np.cumsum(a,axis=1) # sum over columns for each of the 2 rows\n array([[ 1, 3, 6],\n [ 4, 9, 15]])\n\n ``cumsum(b)[-1]`` may not be equal to ``sum(b)``\n\n >>> b = np.array([1, 2e-9, 3e-9] * 1000000)\n >>> b.cumsum()[-1]\n 1000000.0050045159\n >>> b.sum()\n 1000000.0050000029\n\n \"\"\"\n return _wrapfunc(a, 'cumsum', axis=axis, dtype=dtype, out=out)\n\n\ndef _ptp_dispatcher(a, axis=None, out=None, keepdims=None):\n return (a, out)\n\n\n@array_function_dispatch(_ptp_dispatcher)\ndef ptp(a, axis=None, out=None, keepdims=np._NoValue):\n \"\"\"\n Range of values (maximum - minimum) along an axis.\n\n The name of the function comes from the acronym for 'peak to peak'.\n\n .. warning::\n `ptp` preserves the data type of the array. This means the\n return value for an input of signed integers with n bits\n (e.g. `numpy.int8`, `numpy.int16`, etc) is also a signed integer\n with n bits. In that case, peak-to-peak values greater than\n ``2**(n-1)-1`` will be returned as negative values. An example\n with a work-around is shown below.\n\n Parameters\n ----------\n a : array_like\n Input values.\n axis : None or int or tuple of ints, optional\n Axis along which to find the peaks. By default, flatten the\n array. `axis` may be negative, in\n which case it counts from the last to the first axis.\n\n .. versionadded:: 1.15.0\n\n If this is a tuple of ints, a reduction is performed on multiple\n axes, instead of a single axis or all the axes as before.\n out : array_like\n Alternative output array in which to place the result. It must\n have the same shape and buffer length as the expected output,\n but the type of the output values will be cast if necessary.\n\n keepdims : bool, optional\n If this is set to True, the axes which are reduced are left\n in the result as dimensions with size one. With this option,\n the result will broadcast correctly against the input array.\n\n If the default value is passed, then `keepdims` will not be\n passed through to the `ptp` method of sub-classes of\n `ndarray`, however any non-default value will be. If the\n sub-class' method does not implement `keepdims` any\n exceptions will be raised.\n\n Returns\n -------\n ptp : ndarray or scalar\n The range of a given array - `scalar` if array is one-dimensional\n or a new array holding the result along the given axis\n\n Examples\n --------\n >>> import numpy as np\n >>> x = np.array([[4, 9, 2, 10],\n ... [6, 9, 7, 12]])\n\n >>> np.ptp(x, axis=1)\n array([8, 6])\n\n >>> np.ptp(x, axis=0)\n array([2, 0, 5, 2])\n\n >>> np.ptp(x)\n 10\n\n This example shows that a negative value can be returned when\n the input is an array of signed integers.\n\n >>> y = np.array([[1, 127],\n ... [0, 127],\n ... [-1, 127],\n ... [-2, 127]], dtype=np.int8)\n >>> np.ptp(y, axis=1)\n array([ 126, 127, -128, -127], dtype=int8)\n\n A work-around is to use the `view()` method to view the result as\n unsigned integers with the same bit width:\n\n >>> np.ptp(y, axis=1).view(np.uint8)\n array([126, 127, 128, 129], dtype=uint8)\n\n \"\"\"\n kwargs = {}\n if keepdims is not np._NoValue:\n kwargs['keepdims'] = keepdims\n return _methods._ptp(a, axis=axis, out=out, **kwargs)\n\n\ndef _max_dispatcher(a, axis=None, out=None, keepdims=None, initial=None,\n where=None):\n return (a, out)\n\n\n@array_function_dispatch(_max_dispatcher)\n@set_module('numpy')\ndef max(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue,\n where=np._NoValue):\n \"\"\"\n Return the maximum of an array or maximum along an axis.\n\n Parameters\n ----------\n a : array_like\n Input data.\n axis : None or int or tuple of ints, optional\n Axis or axes along which to operate. By default, flattened input is\n used.\n\n .. versionadded:: 1.7.0\n\n If this is a tuple of ints, the maximum is selected over multiple axes,\n instead of a single axis or all the axes as before.\n out : ndarray, optional\n Alternative output array in which to place the result. Must\n be of the same shape and buffer length as the expected output.\n See :ref:`ufuncs-output-type` for more details.\n\n keepdims : bool, optional\n If this is set to True, the axes which are reduced are left\n in the result as dimensions with size one. With this option,\n the result will broadcast correctly against the input array.\n\n If the default value is passed, then `keepdims` will not be\n passed through to the ``max`` method of sub-classes of\n `ndarray`, however any non-default value will be. If the\n sub-class' method does not implement `keepdims` any\n exceptions will be raised.\n\n initial : scalar, optional\n The minimum value of an output element. Must be present to allow\n computation on empty slice. See `~numpy.ufunc.reduce` for details.\n\n .. versionadded:: 1.15.0\n\n where : array_like of bool, optional\n Elements to compare for the maximum. See `~numpy.ufunc.reduce`\n for details.\n\n .. versionadded:: 1.17.0\n\n Returns\n -------\n max : ndarray or scalar\n Maximum of `a`. If `axis` is None, the result is a scalar value.\n If `axis` is an int, the result is an array of dimension\n ``a.ndim - 1``. If `axis` is a tuple, the result is an array of\n dimension ``a.ndim - len(axis)``.\n\n See Also\n --------\n amin :\n The minimum value of an array along a given axis, propagating any NaNs.\n nanmax :\n The maximum value of an array along a given axis, ignoring any NaNs.\n maximum :\n Element-wise maximum of two arrays, propagating any NaNs.\n fmax :\n Element-wise maximum of two arrays, ignoring any NaNs.\n argmax :\n Return the indices of the maximum values.\n\n nanmin, minimum, fmin\n\n Notes\n -----\n NaN values are propagated, that is if at least one item is NaN, the\n corresponding max value will be NaN as well. To ignore NaN values\n (MATLAB behavior), please use nanmax.\n\n Don't use `~numpy.max` for element-wise comparison of 2 arrays; when\n ``a.shape[0]`` is 2, ``maximum(a[0], a[1])`` is faster than\n ``max(a, axis=0)``.\n\n Examples\n --------\n >>> import numpy as np\n >>> a = np.arange(4).reshape((2,2))\n >>> a\n array([[0, 1],\n [2, 3]])\n >>> np.max(a) # Maximum of the flattened array\n 3\n >>> np.max(a, axis=0) # Maxima along the first axis\n array([2, 3])\n >>> np.max(a, axis=1) # Maxima along the second axis\n array([1, 3])\n >>> np.max(a, where=[False, True], initial=-1, axis=0)\n array([-1, 3])\n >>> b = np.arange(5, dtype=float)\n >>> b[2] = np.nan\n >>> np.max(b)\n np.float64(nan)\n >>> np.max(b, where=~np.isnan(b), initial=-1)\n 4.0\n >>> np.nanmax(b)\n 4.0\n\n You can use an initial value to compute the maximum of an empty slice, or\n to initialize it to a different value:\n\n >>> np.max([[-50], [10]], axis=-1, initial=0)\n array([ 0, 10])\n\n Notice that the initial value is used as one of the elements for which the\n maximum is determined, unlike for the default argument Python's max\n function, which is only used for empty iterables.\n\n >>> np.max([5], initial=6)\n 6\n >>> max([5], default=6)\n 5\n \"\"\"\n return _wrapreduction(a, np.maximum, 'max', axis, None, out,\n keepdims=keepdims, initial=initial, where=where)\n\n\n@array_function_dispatch(_max_dispatcher)\ndef amax(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue,\n where=np._NoValue):\n \"\"\"\n Return the maximum of an array or maximum along an axis.\n\n `amax` is an alias of `~numpy.max`.\n\n See Also\n --------\n max : alias of this function\n ndarray.max : equivalent method\n \"\"\"\n return _wrapreduction(a, np.maximum, 'max', axis, None, out,\n keepdims=keepdims, initial=initial, where=where)\n\n\ndef _min_dispatcher(a, axis=None, out=None, keepdims=None, initial=None,\n where=None):\n return (a, out)\n\n\n@array_function_dispatch(_min_dispatcher)\ndef min(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue,\n where=np._NoValue):\n \"\"\"\n Return the minimum of an array or minimum along an axis.\n\n Parameters\n ----------\n a : array_like\n Input data.\n axis : None or int or tuple of ints, optional\n Axis or axes along which to operate. By default, flattened input is\n used.\n\n .. versionadded:: 1.7.0\n\n If this is a tuple of ints, the minimum is selected over multiple axes,\n instead of a single axis or all the axes as before.\n out : ndarray, optional\n Alternative output array in which to place the result. Must\n be of the same shape and buffer length as the expected output.\n See :ref:`ufuncs-output-type` for more details.\n\n keepdims : bool, optional\n If this is set to True, the axes which are reduced are left\n in the result as dimensions with size one. With this option,\n the result will broadcast correctly against the input array.\n\n If the default value is passed, then `keepdims` will not be\n passed through to the ``min`` method of sub-classes of\n `ndarray`, however any non-default value will be. If the\n sub-class' method does not implement `keepdims` any\n exceptions will be raised.\n\n initial : scalar, optional\n The maximum value of an output element. Must be present to allow\n computation on empty slice. See `~numpy.ufunc.reduce` for details.\n\n .. versionadded:: 1.15.0\n\n where : array_like of bool, optional\n Elements to compare for the minimum. See `~numpy.ufunc.reduce`\n for details.\n\n .. versionadded:: 1.17.0\n\n Returns\n -------\n min : ndarray or scalar\n Minimum of `a`. If `axis` is None, the result is a scalar value.\n If `axis` is an int, the result is an array of dimension\n ``a.ndim - 1``. If `axis` is a tuple, the result is an array of\n dimension ``a.ndim - len(axis)``.\n\n See Also\n --------\n amax :\n The maximum value of an array along a given axis, propagating any NaNs.\n nanmin :\n The minimum value of an array along a given axis, ignoring any NaNs.\n minimum :\n Element-wise minimum of two arrays, propagating any NaNs.\n fmin :\n Element-wise minimum of two arrays, ignoring any NaNs.\n argmin :\n Return the indices of the minimum values.\n\n nanmax, maximum, fmax\n\n Notes\n -----\n NaN values are propagated, that is if at least one item is NaN, the\n corresponding min value will be NaN as well. To ignore NaN values\n (MATLAB behavior), please use nanmin.\n\n Don't use `~numpy.min` for element-wise comparison of 2 arrays; when\n ``a.shape[0]`` is 2, ``minimum(a[0], a[1])`` is faster than\n ``min(a, axis=0)``.\n\n Examples\n --------\n >>> import numpy as np\n >>> a = np.arange(4).reshape((2,2))\n >>> a\n array([[0, 1],\n [2, 3]])\n >>> np.min(a) # Minimum of the flattened array\n 0\n >>> np.min(a, axis=0) # Minima along the first axis\n array([0, 1])\n >>> np.min(a, axis=1) # Minima along the second axis\n array([0, 2])\n >>> np.min(a, where=[False, True], initial=10, axis=0)\n array([10, 1])\n\n >>> b = np.arange(5, dtype=float)\n >>> b[2] = np.nan\n >>> np.min(b)\n np.float64(nan)\n >>> np.min(b, where=~np.isnan(b), initial=10)\n 0.0\n >>> np.nanmin(b)\n 0.0\n\n >>> np.min([[-50], [10]], axis=-1, initial=0)\n array([-50, 0])\n\n Notice that the initial value is used as one of the elements for which the\n minimum is determined, unlike for the default argument Python's max\n function, which is only used for empty iterables.\n\n Notice that this isn't the same as Python's ``default`` argument.\n\n >>> np.min([6], initial=5)\n 5\n >>> min([6], default=5)\n 6\n \"\"\"\n return _wrapreduction(a, np.minimum, 'min', axis, None, out,\n keepdims=keepdims, initial=initial, where=where)\n\n\n@array_function_dispatch(_min_dispatcher)\ndef amin(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue,\n where=np._NoValue):\n \"\"\"\n Return the minimum of an array or minimum along an axis.\n\n `amin` is an alias of `~numpy.min`.\n\n See Also\n --------\n min : alias of this function\n ndarray.min : equivalent method\n \"\"\"\n return _wrapreduction(a, np.minimum, 'min', axis, None, out,\n keepdims=keepdims, initial=initial, where=where)\n\n\ndef _prod_dispatcher(a, axis=None, dtype=None, out=None, keepdims=None,\n initial=None, where=None):\n return (a, out)\n\n\n@array_function_dispatch(_prod_dispatcher)\ndef prod(a, axis=None, dtype=None, out=None, keepdims=np._NoValue,\n initial=np._NoValue, where=np._NoValue):\n \"\"\"\n Return the product of array elements over a given axis.\n\n Parameters\n ----------\n a : array_like\n Input data.\n axis : None or int or tuple of ints, optional\n Axis or axes along which a product is performed. The default,\n axis=None, will calculate the product of all the elements in the\n input array. If axis is negative it counts from the last to the\n first axis.\n\n .. versionadded:: 1.7.0\n\n If axis is a tuple of ints, a product is performed on all of the\n axes specified in the tuple instead of a single axis or all the\n axes as before.\n dtype : dtype, optional\n The type of the returned array, as well as of the accumulator in\n which the elements are multiplied. The dtype of `a` is used by\n default unless `a` has an integer dtype of less precision than the\n default platform integer. In that case, if `a` is signed then the\n platform integer is used while if `a` is unsigned then an unsigned\n integer of the same precision as the platform integer is used.\n out : ndarray, optional\n Alternative output array in which to place the result. It must have\n the same shape as the expected output, but the type of the output\n values will be cast if necessary.\n keepdims : bool, optional\n If this is set to True, the axes which are reduced are left in the\n result as dimensions with size one. With this option, the result\n will broadcast correctly against the input array.\n\n If the default value is passed, then `keepdims` will not be\n passed through to the `prod` method of sub-classes of\n `ndarray`, however any non-default value will be. If the\n sub-class' method does not implement `keepdims` any\n exceptions will be raised.\n initial : scalar, optional\n The starting value for this product. See `~numpy.ufunc.reduce`\n for details.\n\n .. versionadded:: 1.15.0\n\n where : array_like of bool, optional\n Elements to include in the product. See `~numpy.ufunc.reduce`\n for details.\n\n .. versionadded:: 1.17.0\n\n Returns\n -------\n product_along_axis : ndarray, see `dtype` parameter above.\n An array shaped as `a` but with the specified axis removed.\n Returns a reference to `out` if specified.\n\n See Also\n --------\n ndarray.prod : equivalent method\n :ref:`ufuncs-output-type`\n\n Notes\n -----\n Arithmetic is modular when using integer types, and no error is\n raised on overflow. That means that, on a 32-bit platform:\n\n >>> x = np.array([536870910, 536870910, 536870910, 536870910])\n >>> np.prod(x)\n 16 # may vary\n\n The product of an empty array is the neutral element 1:\n\n >>> np.prod([])\n 1.0\n\n Examples\n --------\n By default, calculate the product of all elements:\n\n >>> import numpy as np\n >>> np.prod([1.,2.])\n 2.0\n\n Even when the input array is two-dimensional:\n\n >>> a = np.array([[1., 2.], [3., 4.]])\n >>> np.prod(a)\n 24.0\n\n But we can also specify the axis over which to multiply:\n\n >>> np.prod(a, axis=1)\n array([ 2., 12.])\n >>> np.prod(a, axis=0)\n array([3., 8.])\n\n Or select specific elements to include:\n\n >>> np.prod([1., np.nan, 3.], where=[True, False, True])\n 3.0\n\n If the type of `x` is unsigned, then the output type is\n the unsigned platform integer:\n\n >>> x = np.array([1, 2, 3], dtype=np.uint8)\n >>> np.prod(x).dtype == np.uint\n True\n\n If `x` is of a signed integer type, then the output type\n is the default platform integer:\n\n >>> x = np.array([1, 2, 3], dtype=np.int8)\n >>> np.prod(x).dtype == int\n True\n\n You can also start the product with a value other than one:\n\n >>> np.prod([1, 2], initial=5)\n 10\n \"\"\"\n return _wrapreduction(a, np.multiply, 'prod', axis, dtype, out,\n keepdims=keepdims, initial=initial, where=where)\n\n\ndef _cumprod_dispatcher(a, axis=None, dtype=None, out=None):\n return (a, out)\n\n\n@array_function_dispatch(_cumprod_dispatcher)\ndef cumprod(a, axis=None, dtype=None, out=None):\n \"\"\"\n Return the cumulative product of elements along a given axis.\n\n Parameters\n ----------\n a : array_like\n Input array.\n axis : int, optional\n Axis along which the cumulative product is computed. By default\n the input is flattened.\n dtype : dtype, optional\n Type of the returned array, as well as of the accumulator in which\n the elements are multiplied. If *dtype* is not specified, it\n defaults to the dtype of `a`, unless `a` has an integer dtype with\n a precision less than that of the default platform integer. In\n that case, the default platform integer is used instead.\n out : ndarray, optional\n Alternative output array in which to place the result. It must\n have the same shape and buffer length as the expected output\n but the type of the resulting values will be cast if necessary.\n\n Returns\n -------\n cumprod : ndarray\n A new array holding the result is returned unless `out` is\n specified, in which case a reference to out is returned.\n\n See Also\n --------\n cumulative_prod : Array API compatible alternative for ``cumprod``.\n :ref:`ufuncs-output-type`\n\n Notes\n -----\n Arithmetic is modular when using integer types, and no error is\n raised on overflow.\n\n Examples\n --------\n >>> import numpy as np\n >>> a = np.array([1,2,3])\n >>> np.cumprod(a) # intermediate results 1, 1*2\n ... # total product 1*2*3 = 6\n array([1, 2, 6])\n >>> a = np.array([[1, 2, 3], [4, 5, 6]])\n >>> np.cumprod(a, dtype=float) # specify type of output\n array([ 1., 2., 6., 24., 120., 720.])\n\n The cumulative product for each column (i.e., over the rows) of `a`:\n\n >>> np.cumprod(a, axis=0)\n array([[ 1, 2, 3],\n [ 4, 10, 18]])\n\n The cumulative product for each row (i.e. over the columns) of `a`:\n\n >>> np.cumprod(a,axis=1)\n array([[ 1, 2, 6],\n [ 4, 20, 120]])\n\n \"\"\"\n return _wrapfunc(a, 'cumprod', axis=axis, dtype=dtype, out=out)\n\n\ndef _ndim_dispatcher(a):\n return (a,)\n\n\n@array_function_dispatch(_ndim_dispatcher)\ndef ndim(a):\n \"\"\"\n Return the number of dimensions of an array.\n\n Parameters\n ----------\n a : array_like\n Input array. If it is not already an ndarray, a conversion is\n attempted.\n\n Returns\n -------\n number_of_dimensions : int\n The number of dimensions in `a`. Scalars are zero-dimensional.\n\n See Also\n --------\n ndarray.ndim : equivalent method\n shape : dimensions of array\n ndarray.shape : dimensions of array\n\n Examples\n --------\n >>> import numpy as np\n >>> np.ndim([[1,2,3],[4,5,6]])\n 2\n >>> np.ndim(np.array([[1,2,3],[4,5,6]]))\n 2\n >>> np.ndim(1)\n 0\n\n \"\"\"\n try:\n return a.ndim\n except AttributeError:\n return asarray(a).ndim\n\n\ndef _size_dispatcher(a, axis=None):\n return (a,)\n\n\n@array_function_dispatch(_size_dispatcher)\ndef size(a, axis=None):\n \"\"\"\n Return the number of elements along a given axis.\n\n Parameters\n ----------\n a : array_like\n Input data.\n axis : int, optional\n Axis along which the elements are counted. By default, give\n the total number of elements.\n\n Returns\n -------\n element_count : int\n Number of elements along the specified axis.\n\n See Also\n --------\n shape : dimensions of array\n ndarray.shape : dimensions of array\n ndarray.size : number of elements in array\n\n Examples\n --------\n >>> import numpy as np\n >>> a = np.array([[1,2,3],[4,5,6]])\n >>> np.size(a)\n 6\n >>> np.size(a,1)\n 3\n >>> np.size(a,0)\n 2\n\n \"\"\"\n if axis is None:\n try:\n return a.size\n except AttributeError:\n return asarray(a).size\n else:\n try:\n return a.shape[axis]\n except AttributeError:\n return asarray(a).shape[axis]\n\n\ndef _round_dispatcher(a, decimals=None, out=None):\n return (a, out)\n\n\n@array_function_dispatch(_round_dispatcher)\ndef round(a, decimals=0, out=None):\n \"\"\"\n Evenly round to the given number of decimals.\n\n Parameters\n ----------\n a : array_like\n Input data.\n decimals : int, optional\n Number of decimal places to round to (default: 0). If\n decimals is negative, it specifies the number of positions to\n the left of the decimal point.\n out : ndarray, optional\n Alternative output array in which to place the result. It must have\n the same shape as the expected output, but the type of the output\n values will be cast if necessary. See :ref:`ufuncs-output-type`\n for more details.\n\n Returns\n -------\n rounded_array : ndarray\n An array of the same type as `a`, containing the rounded values.\n Unless `out` was specified, a new array is created. A reference to\n the result is returned.\n\n The real and imaginary parts of complex numbers are rounded\n separately. The result of rounding a float is a float.\n\n See Also\n --------\n ndarray.round : equivalent method\n around : an alias for this function\n ceil, fix, floor, rint, trunc\n\n\n Notes\n -----\n For values exactly halfway between rounded decimal values, NumPy\n rounds to the nearest even value. Thus 1.5 and 2.5 round to 2.0,\n -0.5 and 0.5 round to 0.0, etc.\n\n ``np.round`` uses a fast but sometimes inexact algorithm to round\n floating-point datatypes. For positive `decimals` it is equivalent to\n ``np.true_divide(np.rint(a * 10**decimals), 10**decimals)``, which has\n error due to the inexact representation of decimal fractions in the IEEE\n floating point standard [1]_ and errors introduced when scaling by powers\n of ten. For instance, note the extra \"1\" in the following:\n\n >>> np.round(56294995342131.5, 3)\n 56294995342131.51\n\n If your goal is to print such values with a fixed number of decimals, it is\n preferable to use numpy's float printing routines to limit the number of\n printed decimals:\n\n >>> np.format_float_positional(56294995342131.5, precision=3)\n '56294995342131.5'\n\n The float printing routines use an accurate but much more computationally\n demanding algorithm to compute the number of digits after the decimal\n point.\n\n Alternatively, Python's builtin `round` function uses a more accurate\n but slower algorithm for 64-bit floating point values:\n\n >>> round(56294995342131.5, 3)\n 56294995342131.5\n >>> np.round(16.055, 2), round(16.055, 2) # equals 16.0549999999999997\n (16.06, 16.05)\n\n\n References\n ----------\n .. [1] \"Lecture Notes on the Status of IEEE 754\", William Kahan,\n https://people.eecs.berkeley.edu/~wkahan/ieee754status/IEEE754.PDF\n\n Examples\n --------\n >>> import numpy as np\n >>> np.round([0.37, 1.64])\n array([0., 2.])\n >>> np.round([0.37, 1.64], decimals=1)\n array([0.4, 1.6])\n >>> np.round([.5, 1.5, 2.5, 3.5, 4.5]) # rounds to nearest even value\n array([0., 2., 2., 4., 4.])\n >>> np.round([1,2,3,11], decimals=1) # ndarray of ints is returned\n array([ 1, 2, 3, 11])\n >>> np.round([1,2,3,11], decimals=-1)\n array([ 0, 0, 0, 10])\n\n \"\"\"\n return _wrapfunc(a, 'round', decimals=decimals, out=out)\n\n\n@array_function_dispatch(_round_dispatcher)\ndef around(a, decimals=0, out=None):\n \"\"\"\n Round an array to the given number of decimals.\n\n `around` is an alias of `~numpy.round`.\n\n See Also\n --------\n ndarray.round : equivalent method\n round : alias for this function\n ceil, fix, floor, rint, trunc\n\n \"\"\"\n return _wrapfunc(a, 'round', decimals=decimals, out=out)\n\n\ndef _mean_dispatcher(a, axis=None, dtype=None, out=None, keepdims=None, *,\n where=None):\n return (a, where, out)\n\n\n@array_function_dispatch(_mean_dispatcher)\ndef mean(a, axis=None, dtype=None, out=None, keepdims=np._NoValue, *,\n where=np._NoValue):\n \"\"\"\n Compute the arithmetic mean along the specified axis.\n\n Returns the average of the array elements. The average is taken over\n the flattened array by default, otherwise over the specified axis.\n `float64` intermediate and return values are used for integer inputs.\n\n Parameters\n ----------\n a : array_like\n Array containing numbers whose mean is desired. If `a` is not an\n array, a conversion is attempted.\n axis : None or int or tuple of ints, optional\n Axis or axes along which the means are computed. The default is to\n compute the mean of the flattened array.\n\n .. versionadded:: 1.7.0\n\n If this is a tuple of ints, a mean is performed over multiple axes,\n instead of a single axis or all the axes as before.\n dtype : data-type, optional\n Type to use in computing the mean. For integer inputs, the default\n is `float64`; for floating point inputs, it is the same as the\n input dtype.\n out : ndarray, optional\n Alternate output array in which to place the result. The default\n is ``None``; if provided, it must have the same shape as the\n expected output, but the type will be cast if necessary.\n See :ref:`ufuncs-output-type` for more details.\n See :ref:`ufuncs-output-type` for more details.\n\n keepdims : bool, optional\n If this is set to True, the axes which are reduced are left\n in the result as dimensions with size one. With this option,\n the result will broadcast correctly against the input array.\n\n If the default value is passed, then `keepdims` will not be\n passed through to the `mean` method of sub-classes of\n `ndarray`, however any non-default value will be. If the\n sub-class' method does not implement `keepdims` any\n exceptions will be raised.\n\n where : array_like of bool, optional\n Elements to include in the mean. See `~numpy.ufunc.reduce` for details.\n\n .. versionadded:: 1.20.0\n\n Returns\n -------\n m : ndarray, see dtype parameter above\n If `out=None`, returns a new array containing the mean values,\n otherwise a reference to the output array is returned.\n\n See Also\n --------\n average : Weighted average\n std, var, nanmean, nanstd, nanvar\n\n Notes\n -----\n The arithmetic mean is the sum of the elements along the axis divided\n by the number of elements.\n\n Note that for floating-point input, the mean is computed using the\n same precision the input has. Depending on the input data, this can\n cause the results to be inaccurate, especially for `float32` (see\n example below). Specifying a higher-precision accumulator using the\n `dtype` keyword can alleviate this issue.\n\n By default, `float16` results are computed using `float32` intermediates\n for extra precision.\n\n Examples\n --------\n >>> import numpy as np\n >>> a = np.array([[1, 2], [3, 4]])\n >>> np.mean(a)\n 2.5\n >>> np.mean(a, axis=0)\n array([2., 3.])\n >>> np.mean(a, axis=1)\n array([1.5, 3.5])\n\n In single precision, `mean` can be inaccurate:\n\n >>> a = np.zeros((2, 512*512), dtype=np.float32)\n >>> a[0, :] = 1.0\n >>> a[1, :] = 0.1\n >>> np.mean(a)\n 0.54999924\n\n Computing the mean in float64 is more accurate:\n\n >>> np.mean(a, dtype=np.float64)\n 0.55000000074505806 # may vary\n\n Specifying a where argument:\n\n >>> a = np.array([[5, 9, 13], [14, 10, 12], [11, 15, 19]])\n >>> np.mean(a)\n 12.0\n >>> np.mean(a, where=[[True], [False], [False]])\n 9.0\n\n \"\"\"\n kwargs = {}\n if keepdims is not np._NoValue:\n kwargs['keepdims'] = keepdims\n if where is not np._NoValue:\n kwargs['where'] = where\n if type(a) is not mu.ndarray:\n try:\n mean = a.mean\n except AttributeError:\n pass\n else:\n return mean(axis=axis, dtype=dtype, out=out, **kwargs)\n\n return _methods._mean(a, axis=axis, dtype=dtype,\n out=out, **kwargs)\n\n\ndef _std_dispatcher(a, axis=None, dtype=None, out=None, ddof=None,\n keepdims=None, *, where=None, mean=None, correction=None):\n return (a, where, out, mean)\n\n\n@array_function_dispatch(_std_dispatcher)\ndef std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue, *,\n where=np._NoValue, mean=np._NoValue, correction=np._NoValue):\n r\"\"\"\n Compute the standard deviation along the specified axis.\n\n Returns the standard deviation, a measure of the spread of a distribution,\n of the array elements. The standard deviation is computed for the\n flattened array by default, otherwise over the specified axis.\n\n Parameters\n ----------\n a : array_like\n Calculate the standard deviation of these values.\n axis : None or int or tuple of ints, optional\n Axis or axes along which the standard deviation is computed. The\n default is to compute the standard deviation of the flattened array.\n\n .. versionadded:: 1.7.0\n\n If this is a tuple of ints, a standard deviation is performed over\n multiple axes, instead of a single axis or all the axes as before.\n dtype : dtype, optional\n Type to use in computing the standard deviation. For arrays of\n integer type the default is float64, for arrays of float types it is\n the same as the array type.\n out : ndarray, optional\n Alternative output array in which to place the result. It must have\n the same shape as the expected output but the type (of the calculated\n values) will be cast if necessary.\n See :ref:`ufuncs-output-type` for more details.\n ddof : {int, float}, optional\n Means Delta Degrees of Freedom. The divisor used in calculations\n is ``N - ddof``, where ``N`` represents the number of elements.\n By default `ddof` is zero. See Notes for details about use of `ddof`.\n keepdims : bool, optional\n If this is set to True, the axes which are reduced are left\n in the result as dimensions with size one. With this option,\n the result will broadcast correctly against the input array.\n\n If the default value is passed, then `keepdims` will not be\n passed through to the `std` method of sub-classes of\n `ndarray`, however any non-default value will be. If the\n sub-class' method does not implement `keepdims` any\n exceptions will be raised.\n where : array_like of bool, optional\n Elements to include in the standard deviation.\n See `~numpy.ufunc.reduce` for details.\n\n .. versionadded:: 1.20.0\n\n mean : array_like, optional\n Provide the mean to prevent its recalculation. The mean should have\n a shape as if it was calculated with ``keepdims=True``.\n The axis for the calculation of the mean should be the same as used in\n the call to this std function.\n\n .. versionadded:: 1.26.0\n\n correction : {int, float}, optional\n Array API compatible name for the ``ddof`` parameter. Only one of them\n can be provided at the same time.\n\n .. versionadded:: 2.0.0\n\n Returns\n -------\n standard_deviation : ndarray, see dtype parameter above.\n If `out` is None, return a new array containing the standard deviation,\n otherwise return a reference to the output array.\n\n See Also\n --------\n var, mean, nanmean, nanstd, nanvar\n :ref:`ufuncs-output-type`\n\n Notes\n -----\n There are several common variants of the array standard deviation\n calculation. Assuming the input `a` is a one-dimensional NumPy array\n and ``mean`` is either provided as an argument or computed as\n ``a.mean()``, NumPy computes the standard deviation of an array as::\n\n N = len(a)\n d2 = abs(a - mean)**2 # abs is for complex `a`\n var = d2.sum() / (N - ddof) # note use of `ddof`\n std = var**0.5\n\n Different values of the argument `ddof` are useful in different\n contexts. NumPy's default ``ddof=0`` corresponds with the expression:\n\n .. math::\n\n \\sqrt{\\frac{\\sum_i{|a_i - \\bar{a}|^2 }}{N}}\n\n which is sometimes called the \"population standard deviation\" in the field\n of statistics because it applies the definition of standard deviation to\n `a` as if `a` were a complete population of possible observations.\n\n Many other libraries define the standard deviation of an array\n differently, e.g.:\n\n .. math::\n\n \\sqrt{\\frac{\\sum_i{|a_i - \\bar{a}|^2 }}{N - 1}}\n\n In statistics, the resulting quantity is sometimed called the \"sample\n standard deviation\" because if `a` is a random sample from a larger\n population, this calculation provides the square root of an unbiased\n estimate of the variance of the population. The use of :math:`N-1` in the\n denominator is often called \"Bessel's correction\" because it corrects for\n bias (toward lower values) in the variance estimate introduced when the\n sample mean of `a` is used in place of the true mean of the population.\n The resulting estimate of the standard deviation is still biased, but less\n than it would have been without the correction. For this quantity, use\n ``ddof=1``.\n\n Note that, for complex numbers, `std` takes the absolute\n value before squaring, so that the result is always real and nonnegative.\n\n For floating-point input, the standard deviation is computed using the same\n precision the input has. Depending on the input data, this can cause\n the results to be inaccurate, especially for float32 (see example below).\n Specifying a higher-accuracy accumulator using the `dtype` keyword can\n alleviate this issue.\n\n Examples\n --------\n >>> import numpy as np\n >>> a = np.array([[1, 2], [3, 4]])\n >>> np.std(a)\n 1.1180339887498949 # may vary\n >>> np.std(a, axis=0)\n array([1., 1.])\n >>> np.std(a, axis=1)\n array([0.5, 0.5])\n\n In single precision, std() can be inaccurate:\n\n >>> a = np.zeros((2, 512*512), dtype=np.float32)\n >>> a[0, :] = 1.0\n >>> a[1, :] = 0.1\n >>> np.std(a)\n 0.45000005\n\n Computing the standard deviation in float64 is more accurate:\n\n >>> np.std(a, dtype=np.float64)\n 0.44999999925494177 # may vary\n\n Specifying a where argument:\n\n >>> a = np.array([[14, 8, 11, 10], [7, 9, 10, 11], [10, 15, 5, 10]])\n >>> np.std(a)\n 2.614064523559687 # may vary\n >>> np.std(a, where=[[True], [True], [False]])\n 2.0\n\n Using the mean keyword to save computation time:\n\n >>> import numpy as np\n >>> from timeit import timeit\n >>> a = np.array([[14, 8, 11, 10], [7, 9, 10, 11], [10, 15, 5, 10]])\n >>> mean = np.mean(a, axis=1, keepdims=True)\n >>>\n >>> g = globals()\n >>> n = 10000\n >>> t1 = timeit(\"std = np.std(a, axis=1, mean=mean)\", globals=g, number=n)\n >>> t2 = timeit(\"std = np.std(a, axis=1)\", globals=g, number=n)\n >>> print(f'Percentage execution time saved {100*(t2-t1)/t2:.0f}%')\n #doctest: +SKIP\n Percentage execution time saved 30%\n\n \"\"\"\n kwargs = {}\n if keepdims is not np._NoValue:\n kwargs['keepdims'] = keepdims\n if where is not np._NoValue:\n kwargs['where'] = where\n if mean is not np._NoValue:\n kwargs['mean'] = mean\n\n if correction != np._NoValue:\n if ddof != 0:\n raise ValueError(\n \"ddof and correction can't be provided simultaneously.\"\n )\n else:\n ddof = correction\n\n if type(a) is not mu.ndarray:\n try:\n std = a.std\n except AttributeError:\n pass\n else:\n return std(axis=axis, dtype=dtype, out=out, ddof=ddof, **kwargs)\n\n return _methods._std(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n **kwargs)\n\n\ndef _var_dispatcher(a, axis=None, dtype=None, out=None, ddof=None,\n keepdims=None, *, where=None, mean=None, correction=None):\n return (a, where, out, mean)\n\n\n@array_function_dispatch(_var_dispatcher)\ndef var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue, *,\n where=np._NoValue, mean=np._NoValue, correction=np._NoValue):\n r\"\"\"\n Compute the variance along the specified axis.\n\n Returns the variance of the array elements, a measure of the spread of a\n distribution. The variance is computed for the flattened array by\n default, otherwise over the specified axis.\n\n Parameters\n ----------\n a : array_like\n Array containing numbers whose variance is desired. If `a` is not an\n array, a conversion is attempted.\n axis : None or int or tuple of ints, optional\n Axis or axes along which the variance is computed. The default is to\n compute the variance of the flattened array.\n\n .. versionadded:: 1.7.0\n\n If this is a tuple of ints, a variance is performed over multiple axes,\n instead of a single axis or all the axes as before.\n dtype : data-type, optional\n Type to use in computing the variance. For arrays of integer type\n the default is `float64`; for arrays of float types it is the same as\n the array type.\n out : ndarray, optional\n Alternate output array in which to place the result. It must have\n the same shape as the expected output, but the type is cast if\n necessary.\n ddof : {int, float}, optional\n \"Delta Degrees of Freedom\": the divisor used in the calculation is\n ``N - ddof``, where ``N`` represents the number of elements. By\n default `ddof` is zero. See notes for details about use of `ddof`.\n keepdims : bool, optional\n If this is set to True, the axes which are reduced are left\n in the result as dimensions with size one. With this option,\n the result will broadcast correctly against the input array.\n\n If the default value is passed, then `keepdims` will not be\n passed through to the `var` method of sub-classes of\n `ndarray`, however any non-default value will be. If the\n sub-class' method does not implement `keepdims` any\n exceptions will be raised.\n where : array_like of bool, optional\n Elements to include in the variance. See `~numpy.ufunc.reduce` for\n details.\n\n .. versionadded:: 1.20.0\n\n mean : array like, optional\n Provide the mean to prevent its recalculation. The mean should have\n a shape as if it was calculated with ``keepdims=True``.\n The axis for the calculation of the mean should be the same as used in\n the call to this var function.\n\n .. versionadded:: 1.26.0\n\n correction : {int, float}, optional\n Array API compatible name for the ``ddof`` parameter. Only one of them\n can be provided at the same time.\n\n .. versionadded:: 2.0.0\n\n Returns\n -------\n variance : ndarray, see dtype parameter above\n If ``out=None``, returns a new array containing the variance;\n otherwise, a reference to the output array is returned.\n\n See Also\n --------\n std, mean, nanmean, nanstd, nanvar\n :ref:`ufuncs-output-type`\n\n Notes\n -----\n There are several common variants of the array variance calculation.\n Assuming the input `a` is a one-dimensional NumPy array and ``mean`` is\n either provided as an argument or computed as ``a.mean()``, NumPy\n computes the variance of an array as::\n\n N = len(a)\n d2 = abs(a - mean)**2 # abs is for complex `a`\n var = d2.sum() / (N - ddof) # note use of `ddof`\n\n Different values of the argument `ddof` are useful in different\n contexts. NumPy's default ``ddof=0`` corresponds with the expression:\n\n .. math::\n\n \\frac{\\sum_i{|a_i - \\bar{a}|^2 }}{N}\n\n which is sometimes called the \"population variance\" in the field of\n statistics because it applies the definition of variance to `a` as if `a`\n were a complete population of possible observations.\n\n Many other libraries define the variance of an array differently, e.g.:\n\n .. math::\n\n \\frac{\\sum_i{|a_i - \\bar{a}|^2}}{N - 1}\n\n In statistics, the resulting quantity is sometimed called the \"sample\n variance\" because if `a` is a random sample from a larger population,\n this calculation provides an unbiased estimate of the variance of the\n population. The use of :math:`N-1` in the denominator is often called\n \"Bessel's correction\" because it corrects for bias (toward lower values)\n in the variance estimate introduced when the sample mean of `a` is used\n in place of the true mean of the population. For this quantity, use\n ``ddof=1``.\n\n Note that for complex numbers, the absolute value is taken before\n squaring, so that the result is always real and nonnegative.\n\n For floating-point input, the variance is computed using the same\n precision the input has. Depending on the input data, this can cause\n the results to be inaccurate, especially for `float32` (see example\n below). Specifying a higher-accuracy accumulator using the ``dtype``\n keyword can alleviate this issue.\n\n Examples\n --------\n >>> import numpy as np\n >>> a = np.array([[1, 2], [3, 4]])\n >>> np.var(a)\n 1.25\n >>> np.var(a, axis=0)\n array([1., 1.])\n >>> np.var(a, axis=1)\n array([0.25, 0.25])\n\n In single precision, var() can be inaccurate:\n\n >>> a = np.zeros((2, 512*512), dtype=np.float32)\n >>> a[0, :] = 1.0\n >>> a[1, :] = 0.1\n >>> np.var(a)\n 0.20250003\n\n Computing the variance in float64 is more accurate:\n\n >>> np.var(a, dtype=np.float64)\n 0.20249999932944759 # may vary\n >>> ((1-0.55)**2 + (0.1-0.55)**2)/2\n 0.2025\n\n Specifying a where argument:\n\n >>> a = np.array([[14, 8, 11, 10], [7, 9, 10, 11], [10, 15, 5, 10]])\n >>> np.var(a)\n 6.833333333333333 # may vary\n >>> np.var(a, where=[[True], [True], [False]])\n 4.0\n\n Using the mean keyword to save computation time:\n\n >>> import numpy as np\n >>> from timeit import timeit\n >>>\n >>> a = np.array([[14, 8, 11, 10], [7, 9, 10, 11], [10, 15, 5, 10]])\n >>> mean = np.mean(a, axis=1, keepdims=True)\n >>>\n >>> g = globals()\n >>> n = 10000\n >>> t1 = timeit(\"var = np.var(a, axis=1, mean=mean)\", globals=g, number=n)\n >>> t2 = timeit(\"var = np.var(a, axis=1)\", globals=g, number=n)\n >>> print(f'Percentage execution time saved {100*(t2-t1)/t2:.0f}%')\n #doctest: +SKIP\n Percentage execution time saved 32%\n\n \"\"\"\n kwargs = {}\n if keepdims is not np._NoValue:\n kwargs['keepdims'] = keepdims\n if where is not np._NoValue:\n kwargs['where'] = where\n if mean is not np._NoValue:\n kwargs['mean'] = mean\n\n if correction != np._NoValue:\n if ddof != 0:\n raise ValueError(\n \"ddof and correction can't be provided simultaneously.\"\n )\n else:\n ddof = correction\n\n if type(a) is not mu.ndarray:\n try:\n var = a.var\n\n except AttributeError:\n pass\n else:\n return var(axis=axis, dtype=dtype, out=out, ddof=ddof, **kwargs)\n\n return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n **kwargs)\n\n", 4320]}, "functions": {"_hypothesis_value (/home/gaogaotiantian/programs/viztracer/example/src/gradient_descent.py:35)": ["/home/gaogaotiantian/programs/viztracer/example/src/gradient_descent.py", 35], "calculate_hypothesis_value (/home/gaogaotiantian/programs/viztracer/example/src/gradient_descent.py:63)": ["/home/gaogaotiantian/programs/viztracer/example/src/gradient_descent.py", 63], "output (/home/gaogaotiantian/programs/viztracer/example/src/gradient_descent.py:51)": ["/home/gaogaotiantian/programs/viztracer/example/src/gradient_descent.py", 51], "_error (/home/gaogaotiantian/programs/viztracer/example/src/gradient_descent.py:25)": ["/home/gaogaotiantian/programs/viztracer/example/src/gradient_descent.py", 25], "summation_of_cost_derivative (/home/gaogaotiantian/programs/viztracer/example/src/gradient_descent.py:76)": ["/home/gaogaotiantian/programs/viztracer/example/src/gradient_descent.py", 76], "get_cost_derivative (/home/gaogaotiantian/programs/viztracer/example/src/gradient_descent.py:94)": ["/home/gaogaotiantian/programs/viztracer/example/src/gradient_descent.py", 94], "_allclose_dispatcher (/home/gaogaotiantian/programs/viztracer/venv3.13/lib/python3.13/site-packages/numpy/_core/numeric.py:2249)": ["/home/gaogaotiantian/programs/viztracer/venv3.13/lib/python3.13/site-packages/numpy/_core/numeric.py", 2249], "_isclose_dispatcher (/home/gaogaotiantian/programs/viztracer/venv3.13/lib/python3.13/site-packages/numpy/_core/numeric.py:2345)": ["/home/gaogaotiantian/programs/viztracer/venv3.13/lib/python3.13/site-packages/numpy/_core/numeric.py", 2345], "isclose.. 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def __init__(self, timeLimit=None, iterationLimit=None, explorationConstant=1 / math.sqrt(2),\n rolloutPolicy=randomPolicy):\n if timeLimit != None:\n if iterationLimit != None:\n raise ValueError(\"Cannot have both a time limit and an iteration limit\")\n # time taken for each MCTS search in milliseconds\n self.timeLimit = timeLimit\n self.limitType = 'time'\n else:\n if iterationLimit == None:\n raise ValueError(\"Must have either a time limit or an iteration limit\")\n # number of iterations of the search\n if iterationLimit < 1:\n raise ValueError(\"Iteration limit must be greater than one\")\n self.searchLimit = iterationLimit\n self.limitType = 'iterations'\n self.explorationConstant = explorationConstant\n self.rollout = rolloutPolicy\n\n def search(self, initialState):\n self.root = treeNode(initialState, None)\n\n if self.limitType == 'time':\n timeLimit = time.time() + self.timeLimit / 1000\n while time.time() < timeLimit:\n self.executeRound()\n else:\n for i in range(self.searchLimit):\n self.executeRound()\n\n bestChild = self.getBestChild(self.root, 0)\n return self.getAction(self.root, bestChild)\n\n def executeRound(self):\n node = self.selectNode(self.root)\n reward = self.rollout(node.state)\n self.backpropogate(node, reward)\n\n def selectNode(self, node):\n while not node.isTerminal:\n if node.isFullyExpanded:\n node = self.getBestChild(node, self.explorationConstant)\n else:\n return self.expand(node)\n return node\n\n def expand(self, node):\n actions = node.state.getPossibleActions()\n for action in actions:\n if action not in node.children.keys():\n newNode = treeNode(node.state.takeAction(action), node)\n node.children[action] = newNode\n if len(actions) == len(node.children):\n node.isFullyExpanded = True\n return newNode\n\n raise Exception(\"Should never reach here\")\n\n def backpropogate(self, node, reward):\n while node is not None:\n node.numVisits += 1\n node.totalReward += reward\n node = node.parent\n\n def getBestChild(self, node, explorationValue):\n bestValue = float(\"-inf\")\n bestNodes = []\n for child in node.children.values():\n nodeValue = child.totalReward / child.numVisits + explorationValue * math.sqrt(\n 2 * math.log(node.numVisits) / child.numVisits)\n if nodeValue > bestValue:\n bestValue = nodeValue\n bestNodes = [child]\n elif nodeValue == bestValue:\n bestNodes.append(child)\n return random.choice(bestNodes)\n\n def getAction(self, root, bestChild):\n for action, node in root.children.items():\n if node is bestChild:\n return action\n",110],"/home/gaogaotiantian/programs/codesnap/example/src/mcts_game.py":["\nfrom __future__ import division\n\nfrom copy import deepcopy\nfrom mcts import mcts\nfrom functools import reduce\nimport operator\n\n\nclass NaughtsAndCrossesState():\n def __init__(self):\n self.board = [[0, 0, 0], [0, 0, 0], [0, 0, 0]]\n self.currentPlayer = 1\n\n def getCurrentPlayer(self):\n return self.currentPlayer\n\n def getPossibleActions(self):\n possibleActions = []\n for i in range(len(self.board)):\n for j in range(len(self.board[i])):\n if self.board[i][j] == 0:\n possibleActions.append(Action(player=self.currentPlayer, x=i, y=j))\n return possibleActions\n\n def takeAction(self, action):\n newState = deepcopy(self)\n newState.board[action.x][action.y] = action.player\n newState.currentPlayer = self.currentPlayer * -1\n return newState\n\n def isTerminal(self):\n for row in self.board:\n if abs(sum(row)) == 3:\n return True\n for column in list(map(list, zip(*self.board))):\n if abs(sum(column)) == 3:\n return True\n for diagonal in [[self.board[i][i] for i in range(len(self.board))],\n [self.board[i][len(self.board) - i - 1] for i in range(len(self.board))]]:\n if abs(sum(diagonal)) == 3:\n return True\n return reduce(operator.mul, sum(self.board, []), 1)\n\n def getReward(self):\n for row in self.board:\n if abs(sum(row)) == 3:\n return sum(row) / 3\n for column in list(map(list, zip(*self.board))):\n if abs(sum(column)) == 3:\n return sum(column) / 3\n for diagonal in [[self.board[i][i] for i in range(len(self.board))],\n [self.board[i][len(self.board) - i - 1] for i in range(len(self.board))]]:\n if abs(sum(diagonal)) == 3:\n return sum(diagonal) / 3\n return False\n\n\nclass Action():\n def __init__(self, player, x, y):\n self.player = player\n self.x = x\n self.y = y\n\n def __str__(self):\n return str((self.x, self.y))\n\n def __repr__(self):\n return str(self)\n\n def __eq__(self, other):\n return self.__class__ == other.__class__ and self.x == other.x and self.y == other.y and self.player == other.player\n\n def __hash__(self):\n return hash((self.x, self.y, self.player))\n\n\ninitialState = NaughtsAndCrossesState()\nmcts = mcts(timeLimit=10)\naction = mcts.search(initialState=initialState)\n",80],"/usr/lib/python3.6/copyreg.py":["\"\"\"Helper to provide extensibility for pickle.\n\nThis is only useful to add pickle support for extension types defined in\nC, not for instances of user-defined classes.\n\"\"\"\n\n__all__ = [\"pickle\", \"constructor\",\n \"add_extension\", \"remove_extension\", \"clear_extension_cache\"]\n\ndispatch_table = {}\n\ndef pickle(ob_type, pickle_function, constructor_ob=None):\n if not callable(pickle_function):\n raise TypeError(\"reduction functions must be callable\")\n dispatch_table[ob_type] = pickle_function\n\n # The constructor_ob function is a vestige of safe for unpickling.\n # There is no reason for the caller to pass it anymore.\n if constructor_ob is not None:\n constructor(constructor_ob)\n\ndef constructor(object):\n if not callable(object):\n raise TypeError(\"constructors must be callable\")\n\n# Example: provide pickling support for complex numbers.\n\ntry:\n complex\nexcept NameError:\n pass\nelse:\n\n def pickle_complex(c):\n return complex, (c.real, c.imag)\n\n pickle(complex, pickle_complex, complex)\n\n# Support for pickling new-style objects\n\ndef _reconstructor(cls, base, state):\n if base is object:\n obj = object.__new__(cls)\n else:\n obj = base.__new__(cls, state)\n if base.__init__ != object.__init__:\n base.__init__(obj, state)\n return obj\n\n_HEAPTYPE = 1<<9\n\n# Python code for object.__reduce_ex__ for protocols 0 and 1\n\ndef _reduce_ex(self, proto):\n assert proto < 2\n for base in self.__class__.__mro__:\n if hasattr(base, '__flags__') and not base.__flags__ & _HEAPTYPE:\n break\n else:\n base = object # not really reachable\n if base is object:\n state = None\n else:\n if base is self.__class__:\n raise TypeError(\"can't pickle %s objects\" % base.__name__)\n state = base(self)\n args = (self.__class__, base, state)\n try:\n getstate = self.__getstate__\n except AttributeError:\n if getattr(self, \"__slots__\", None):\n raise TypeError(\"a class that defines __slots__ without \"\n \"defining __getstate__ cannot be pickled\")\n try:\n dict = self.__dict__\n except AttributeError:\n dict = None\n else:\n dict = getstate()\n if dict:\n return _reconstructor, args, dict\n else:\n return _reconstructor, args\n\n# Helper for __reduce_ex__ protocol 2\n\ndef __newobj__(cls, *args):\n return cls.__new__(cls, *args)\n\ndef __newobj_ex__(cls, args, kwargs):\n \"\"\"Used by pickle protocol 4, instead of __newobj__ to allow classes with\n keyword-only arguments to be pickled correctly.\n \"\"\"\n return cls.__new__(cls, *args, **kwargs)\n\ndef _slotnames(cls):\n \"\"\"Return a list of slot names for a given class.\n\n This needs to find slots defined by the class and its bases, so we\n can't simply return the __slots__ attribute. We must walk down\n the Method Resolution Order and concatenate the __slots__ of each\n class found there. (This assumes classes don't modify their\n __slots__ attribute to misrepresent their slots after the class is\n defined.)\n \"\"\"\n\n # Get the value from a cache in the class if possible\n names = cls.__dict__.get(\"__slotnames__\")\n if names is not None:\n return names\n\n # Not cached -- calculate the value\n names = []\n if not hasattr(cls, \"__slots__\"):\n # This class has no slots\n pass\n else:\n # Slots found -- gather slot names from all base classes\n for c in cls.__mro__:\n if \"__slots__\" in c.__dict__:\n slots = c.__dict__['__slots__']\n # if class has a single slot, it can be given as a string\n if isinstance(slots, str):\n slots = (slots,)\n for name in slots:\n # special descriptors\n if name in (\"__dict__\", \"__weakref__\"):\n continue\n # mangled names\n elif name.startswith('__') and not name.endswith('__'):\n stripped = c.__name__.lstrip('_')\n if stripped:\n names.append('_%s%s' % (stripped, name))\n else:\n names.append(name)\n else:\n names.append(name)\n\n # Cache the outcome in the class if at all possible\n try:\n cls.__slotnames__ = names\n except:\n pass # But don't die if we can't\n\n return names\n\n# A registry of extension codes. This is an ad-hoc compression\n# mechanism. Whenever a global reference to , is about\n# to be pickled, the (, ) tuple is looked up here to see\n# if it is a registered extension code for it. Extension codes are\n# universal, so that the meaning of a pickle does not depend on\n# context. (There are also some codes reserved for local use that\n# don't have this restriction.) Codes are positive ints; 0 is\n# reserved.\n\n_extension_registry = {} # key -> code\n_inverted_registry = {} # code -> key\n_extension_cache = {} # code -> object\n# Don't ever rebind those names: pickling grabs a reference to them when\n# it's initialized, and won't see a rebinding.\n\ndef add_extension(module, name, code):\n \"\"\"Register an extension code.\"\"\"\n code = int(code)\n if not 1 <= code <= 0x7fffffff:\n raise ValueError(\"code out of range\")\n key = (module, name)\n if (_extension_registry.get(key) == code and\n _inverted_registry.get(code) == key):\n return # Redundant registrations are benign\n if key in _extension_registry:\n raise ValueError(\"key %s is already registered with code %s\" %\n (key, _extension_registry[key]))\n if code in _inverted_registry:\n raise ValueError(\"code %s is already in use for key %s\" %\n (code, _inverted_registry[code]))\n _extension_registry[key] = code\n _inverted_registry[code] = key\n\ndef remove_extension(module, name, code):\n \"\"\"Unregister an extension code. For testing only.\"\"\"\n key = (module, name)\n if (_extension_registry.get(key) != code or\n _inverted_registry.get(code) != key):\n raise ValueError(\"key %s is not registered with code %s\" %\n (key, code))\n del _extension_registry[key]\n del _inverted_registry[code]\n if code in _extension_cache:\n del _extension_cache[code]\n\ndef clear_extension_cache():\n _extension_cache.clear()\n\n# Standard extension code assignments\n\n# Reserved ranges\n\n# First Last Count Purpose\n# 1 127 127 Reserved for Python standard library\n# 128 191 64 Reserved for Zope\n# 192 239 48 Reserved for 3rd parties\n# 240 255 16 Reserved for private use (will never be assigned)\n# 256 Inf Inf Reserved for future assignment\n\n# Extension codes are assigned by the Python Software Foundation.\n",206],"/usr/lib/python3.6/copy.py":["\"\"\"Generic (shallow and deep) copying operations.\n\nInterface summary:\n\n import copy\n\n x = copy.copy(y) # make a shallow copy of y\n x = copy.deepcopy(y) # make a deep copy of y\n\nFor module specific errors, copy.Error is raised.\n\nThe difference between shallow and deep copying is only relevant for\ncompound objects (objects that contain other objects, like lists or\nclass instances).\n\n- A shallow copy constructs a new compound object and then (to the\n extent possible) inserts *the same objects* into it that the\n original contains.\n\n- A deep copy constructs a new compound object and then, recursively,\n inserts *copies* into it of the objects found in the original.\n\nTwo problems often exist with deep copy operations that don't exist\nwith shallow copy operations:\n\n a) recursive objects (compound objects that, directly or indirectly,\n contain a reference to themselves) may cause a recursive loop\n\n b) because deep copy copies *everything* it may copy too much, e.g.\n administrative data structures that should be shared even between\n copies\n\nPython's deep copy operation avoids these problems by:\n\n a) keeping a table of objects already copied during the current\n copying pass\n\n b) letting user-defined classes override the copying operation or the\n set of components copied\n\nThis version does not copy types like module, class, function, method,\nnor stack trace, stack frame, nor file, socket, window, nor array, nor\nany similar types.\n\nClasses can use the same interfaces to control copying that they use\nto control pickling: they can define methods called __getinitargs__(),\n__getstate__() and __setstate__(). See the documentation for module\n\"pickle\" for information on these methods.\n\"\"\"\n\nimport types\nimport weakref\nfrom copyreg import dispatch_table\n\nclass Error(Exception):\n pass\nerror = Error # backward compatibility\n\ntry:\n from org.python.core import PyStringMap\nexcept ImportError:\n PyStringMap = None\n\n__all__ = [\"Error\", \"copy\", \"deepcopy\"]\n\ndef copy(x):\n \"\"\"Shallow copy operation on arbitrary Python objects.\n\n See the module's __doc__ string for more info.\n \"\"\"\n\n cls = type(x)\n\n copier = _copy_dispatch.get(cls)\n if copier:\n return copier(x)\n\n try:\n issc = issubclass(cls, type)\n except TypeError: # cls is not a class\n issc = False\n if issc:\n # treat it as a regular class:\n return _copy_immutable(x)\n\n copier = getattr(cls, \"__copy__\", None)\n if copier:\n return copier(x)\n\n reductor = dispatch_table.get(cls)\n if reductor:\n rv = reductor(x)\n else:\n reductor = getattr(x, \"__reduce_ex__\", None)\n if reductor:\n rv = reductor(4)\n else:\n reductor = getattr(x, \"__reduce__\", None)\n if reductor:\n rv = reductor()\n else:\n raise Error(\"un(shallow)copyable object of type %s\" % cls)\n\n if isinstance(rv, str):\n return x\n return _reconstruct(x, None, *rv)\n\n\n_copy_dispatch = d = {}\n\ndef _copy_immutable(x):\n return x\nfor t in (type(None), int, float, bool, complex, str, tuple,\n bytes, frozenset, type, range, slice,\n types.BuiltinFunctionType, type(Ellipsis), type(NotImplemented),\n types.FunctionType, weakref.ref):\n d[t] = _copy_immutable\nt = getattr(types, \"CodeType\", None)\nif t is not None:\n d[t] = _copy_immutable\n\nd[list] = list.copy\nd[dict] = dict.copy\nd[set] = set.copy\nd[bytearray] = bytearray.copy\n\nif PyStringMap is not None:\n d[PyStringMap] = PyStringMap.copy\n\ndel d, t\n\ndef deepcopy(x, memo=None, _nil=[]):\n \"\"\"Deep copy operation on arbitrary Python objects.\n\n See the module's __doc__ string for more info.\n \"\"\"\n\n if memo is None:\n memo = {}\n\n d = id(x)\n y = memo.get(d, _nil)\n if y is not _nil:\n return y\n\n cls = type(x)\n\n copier = _deepcopy_dispatch.get(cls)\n if copier:\n y = copier(x, memo)\n else:\n try:\n issc = issubclass(cls, type)\n except TypeError: # cls is not a class (old Boost; see SF #502085)\n issc = 0\n if issc:\n y = _deepcopy_atomic(x, memo)\n else:\n copier = getattr(x, \"__deepcopy__\", None)\n if copier:\n y = copier(memo)\n else:\n reductor = dispatch_table.get(cls)\n if reductor:\n rv = reductor(x)\n else:\n reductor = getattr(x, \"__reduce_ex__\", None)\n if reductor:\n rv = reductor(4)\n else:\n reductor = getattr(x, \"__reduce__\", None)\n if reductor:\n rv = reductor()\n else:\n raise Error(\n \"un(deep)copyable object of type %s\" % cls)\n if isinstance(rv, str):\n y = x\n else:\n y = _reconstruct(x, memo, *rv)\n\n # If is its own copy, don't memoize.\n if y is not x:\n memo[d] = y\n _keep_alive(x, memo) # Make sure x lives at least as long as d\n return y\n\n_deepcopy_dispatch = d = {}\n\ndef _deepcopy_atomic(x, memo):\n return x\nd[type(None)] = _deepcopy_atomic\nd[type(Ellipsis)] = _deepcopy_atomic\nd[type(NotImplemented)] = _deepcopy_atomic\nd[int] = _deepcopy_atomic\nd[float] = _deepcopy_atomic\nd[bool] = _deepcopy_atomic\nd[complex] = _deepcopy_atomic\nd[bytes] = _deepcopy_atomic\nd[str] = _deepcopy_atomic\ntry:\n d[types.CodeType] = _deepcopy_atomic\nexcept AttributeError:\n pass\nd[type] = _deepcopy_atomic\nd[types.BuiltinFunctionType] = _deepcopy_atomic\nd[types.FunctionType] = _deepcopy_atomic\nd[weakref.ref] = _deepcopy_atomic\n\ndef _deepcopy_list(x, memo, deepcopy=deepcopy):\n y = []\n memo[id(x)] = y\n append = y.append\n for a in x:\n append(deepcopy(a, memo))\n return y\nd[list] = _deepcopy_list\n\ndef _deepcopy_tuple(x, memo, deepcopy=deepcopy):\n y = [deepcopy(a, memo) for a in x]\n # We're not going to put the tuple in the memo, but it's still important we\n # check for it, in case the tuple contains recursive mutable structures.\n try:\n return memo[id(x)]\n except KeyError:\n pass\n for k, j in zip(x, y):\n if k is not j:\n y = tuple(y)\n break\n else:\n y = x\n return y\nd[tuple] = _deepcopy_tuple\n\ndef _deepcopy_dict(x, memo, deepcopy=deepcopy):\n y = {}\n memo[id(x)] = y\n for key, value in x.items():\n y[deepcopy(key, memo)] = deepcopy(value, memo)\n return y\nd[dict] = _deepcopy_dict\nif PyStringMap is not None:\n d[PyStringMap] = _deepcopy_dict\n\ndef _deepcopy_method(x, memo): # Copy instance methods\n return type(x)(x.__func__, deepcopy(x.__self__, memo))\nd[types.MethodType] = _deepcopy_method\n\ndel d\n\ndef _keep_alive(x, memo):\n \"\"\"Keeps a reference to the object x in the memo.\n\n Because we remember objects by their id, we have\n to assure that possibly temporary objects are kept\n alive by referencing them.\n We store a reference at the id of the memo, which should\n normally not be used unless someone tries to deepcopy\n the memo itself...\n \"\"\"\n try:\n memo[id(memo)].append(x)\n except KeyError:\n # aha, this is the first one :-)\n memo[id(memo)]=[x]\n\ndef _reconstruct(x, memo, func, args,\n state=None, listiter=None, dictiter=None,\n deepcopy=deepcopy):\n deep = memo is not None\n if deep and args:\n args = (deepcopy(arg, memo) for arg in args)\n y = func(*args)\n if deep:\n memo[id(x)] = y\n\n if state is not None:\n if deep:\n state = deepcopy(state, memo)\n if hasattr(y, '__setstate__'):\n y.__setstate__(state)\n else:\n if isinstance(state, tuple) and len(state) == 2:\n state, slotstate = state\n else:\n slotstate = None\n if state is not None:\n y.__dict__.update(state)\n if slotstate is not None:\n for key, value in slotstate.items():\n setattr(y, key, value)\n\n if listiter is not None:\n if deep:\n for item in listiter:\n item = deepcopy(item, memo)\n y.append(item)\n else:\n for item in listiter:\n y.append(item)\n if dictiter is not None:\n if deep:\n for key, value in dictiter:\n key = deepcopy(key, memo)\n value = deepcopy(value, memo)\n y[key] = value\n else:\n for key, value in dictiter:\n y[key] = value\n return y\n\ndel types, weakref, PyStringMap\n",313],"/usr/lib/python3.6/random.py":["\"\"\"Random variable generators.\n\n integers\n --------\n uniform within range\n\n sequences\n ---------\n pick random element\n pick random sample\n pick weighted random sample\n generate random permutation\n\n distributions on the real line:\n ------------------------------\n uniform\n triangular\n normal (Gaussian)\n lognormal\n negative exponential\n gamma\n beta\n pareto\n Weibull\n\n distributions on the circle (angles 0 to 2pi)\n ---------------------------------------------\n circular uniform\n von Mises\n\nGeneral notes on the underlying Mersenne Twister core generator:\n\n* The period is 2**19937-1.\n* It is one of the most extensively tested generators in existence.\n* The random() method is implemented in C, executes in a single Python step,\n and is, therefore, threadsafe.\n\n\"\"\"\n\nfrom warnings import warn as _warn\nfrom types import MethodType as _MethodType, BuiltinMethodType as _BuiltinMethodType\nfrom math import log as _log, exp as _exp, pi as _pi, e as _e, ceil as _ceil\nfrom math import sqrt as _sqrt, acos as _acos, cos as _cos, sin as _sin\nfrom os import urandom as _urandom\nfrom _collections_abc import Set as _Set, Sequence as _Sequence\nfrom hashlib import sha512 as _sha512\nimport itertools as _itertools\nimport bisect as _bisect\n\n__all__ = [\"Random\",\"seed\",\"random\",\"uniform\",\"randint\",\"choice\",\"sample\",\n \"randrange\",\"shuffle\",\"normalvariate\",\"lognormvariate\",\n \"expovariate\",\"vonmisesvariate\",\"gammavariate\",\"triangular\",\n \"gauss\",\"betavariate\",\"paretovariate\",\"weibullvariate\",\n \"getstate\",\"setstate\", \"getrandbits\", \"choices\",\n \"SystemRandom\"]\n\nNV_MAGICCONST = 4 * _exp(-0.5)/_sqrt(2.0)\nTWOPI = 2.0*_pi\nLOG4 = _log(4.0)\nSG_MAGICCONST = 1.0 + _log(4.5)\nBPF = 53 # Number of bits in a float\nRECIP_BPF = 2**-BPF\n\n\n# Translated by Guido van Rossum from C source provided by\n# Adrian Baddeley. Adapted by Raymond Hettinger for use with\n# the Mersenne Twister and os.urandom() core generators.\n\nimport _random\n\nclass Random(_random.Random):\n \"\"\"Random number generator base class used by bound module functions.\n\n Used to instantiate instances of Random to get generators that don't\n share state.\n\n Class Random can also be subclassed if you want to use a different basic\n generator of your own devising: in that case, override the following\n methods: random(), seed(), getstate(), and setstate().\n Optionally, implement a getrandbits() method so that randrange()\n can cover arbitrarily large ranges.\n\n \"\"\"\n\n VERSION = 3 # used by getstate/setstate\n\n def __init__(self, x=None):\n \"\"\"Initialize an instance.\n\n Optional argument x controls seeding, as for Random.seed().\n \"\"\"\n\n self.seed(x)\n self.gauss_next = None\n\n def seed(self, a=None, version=2):\n \"\"\"Initialize internal state from hashable object.\n\n None or no argument seeds from current time or from an operating\n system specific randomness source if available.\n\n If *a* is an int, all bits are used.\n\n For version 2 (the default), all of the bits are used if *a* is a str,\n bytes, or bytearray. For version 1 (provided for reproducing random\n sequences from older versions of Python), the algorithm for str and\n bytes generates a narrower range of seeds.\n\n \"\"\"\n\n if version == 1 and isinstance(a, (str, bytes)):\n a = a.decode('latin-1') if isinstance(a, bytes) else a\n x = ord(a[0]) << 7 if a else 0\n for c in map(ord, a):\n x = ((1000003 * x) ^ c) & 0xFFFFFFFFFFFFFFFF\n x ^= len(a)\n a = -2 if x == -1 else x\n\n if version == 2 and isinstance(a, (str, bytes, bytearray)):\n if isinstance(a, str):\n a = a.encode()\n a += _sha512(a).digest()\n a = int.from_bytes(a, 'big')\n\n super().seed(a)\n self.gauss_next = None\n\n def getstate(self):\n \"\"\"Return internal state; can be passed to setstate() later.\"\"\"\n return self.VERSION, super().getstate(), self.gauss_next\n\n def setstate(self, state):\n \"\"\"Restore internal state from object returned by getstate().\"\"\"\n version = state[0]\n if version == 3:\n version, internalstate, self.gauss_next = state\n super().setstate(internalstate)\n elif version == 2:\n version, internalstate, self.gauss_next = state\n # In version 2, the state was saved as signed ints, which causes\n # inconsistencies between 32/64-bit systems. The state is\n # really unsigned 32-bit ints, so we convert negative ints from\n # version 2 to positive longs for version 3.\n try:\n internalstate = tuple(x % (2**32) for x in internalstate)\n except ValueError as e:\n raise TypeError from e\n super().setstate(internalstate)\n else:\n raise ValueError(\"state with version %s passed to \"\n \"Random.setstate() of version %s\" %\n (version, self.VERSION))\n\n## ---- Methods below this point do not need to be overridden when\n## ---- subclassing for the purpose of using a different core generator.\n\n## -------------------- pickle support -------------------\n\n # Issue 17489: Since __reduce__ was defined to fix #759889 this is no\n # longer called; we leave it here because it has been here since random was\n # rewritten back in 2001 and why risk breaking something.\n def __getstate__(self): # for pickle\n return self.getstate()\n\n def __setstate__(self, state): # for pickle\n self.setstate(state)\n\n def __reduce__(self):\n return self.__class__, (), self.getstate()\n\n## -------------------- integer methods -------------------\n\n def randrange(self, start, stop=None, step=1, _int=int):\n \"\"\"Choose a random item from range(start, stop[, step]).\n\n This fixes the problem with randint() which includes the\n endpoint; in Python this is usually not what you want.\n\n \"\"\"\n\n # This code is a bit messy to make it fast for the\n # common case while still doing adequate error checking.\n istart = _int(start)\n if istart != start:\n raise ValueError(\"non-integer arg 1 for randrange()\")\n if stop is None:\n if istart > 0:\n return self._randbelow(istart)\n raise ValueError(\"empty range for randrange()\")\n\n # stop argument supplied.\n istop = _int(stop)\n if istop != stop:\n raise ValueError(\"non-integer stop for randrange()\")\n width = istop - istart\n if step == 1 and width > 0:\n return istart + self._randbelow(width)\n if step == 1:\n raise ValueError(\"empty range for randrange() (%d,%d, %d)\" % (istart, istop, width))\n\n # Non-unit step argument supplied.\n istep = _int(step)\n if istep != step:\n raise ValueError(\"non-integer step for randrange()\")\n if istep > 0:\n n = (width + istep - 1) // istep\n elif istep < 0:\n n = (width + istep + 1) // istep\n else:\n raise ValueError(\"zero step for randrange()\")\n\n if n <= 0:\n raise ValueError(\"empty range for randrange()\")\n\n return istart + istep*self._randbelow(n)\n\n def randint(self, a, b):\n \"\"\"Return random integer in range [a, b], including both end points.\n \"\"\"\n\n return self.randrange(a, b+1)\n\n def _randbelow(self, n, int=int, maxsize=1<= n:\n r = getrandbits(k)\n return r\n # There's an overridden random() method but no new getrandbits() method,\n # so we can only use random() from here.\n if n >= maxsize:\n _warn(\"Underlying random() generator does not supply \\n\"\n \"enough bits to choose from a population range this large.\\n\"\n \"To remove the range limitation, add a getrandbits() method.\")\n return int(random() * n)\n if n == 0:\n raise ValueError(\"Boundary cannot be zero\")\n rem = maxsize % n\n limit = (maxsize - rem) / maxsize # int(limit * maxsize) % n == 0\n r = random()\n while r >= limit:\n r = random()\n return int(r*maxsize) % n\n\n## -------------------- sequence methods -------------------\n\n def choice(self, seq):\n \"\"\"Choose a random element from a non-empty sequence.\"\"\"\n try:\n i = self._randbelow(len(seq))\n except ValueError:\n raise IndexError('Cannot choose from an empty sequence') from None\n return seq[i]\n\n def shuffle(self, x, random=None):\n \"\"\"Shuffle list x in place, and return None.\n\n Optional argument random is a 0-argument function returning a\n random float in [0.0, 1.0); if it is the default None, the\n standard random.random will be used.\n\n \"\"\"\n\n if random is None:\n randbelow = self._randbelow\n for i in reversed(range(1, len(x))):\n # pick an element in x[:i+1] with which to exchange x[i]\n j = randbelow(i+1)\n x[i], x[j] = x[j], x[i]\n else:\n _int = int\n for i in reversed(range(1, len(x))):\n # pick an element in x[:i+1] with which to exchange x[i]\n j = _int(random() * (i+1))\n x[i], x[j] = x[j], x[i]\n\n def sample(self, population, k):\n \"\"\"Chooses k unique random elements from a population sequence or set.\n\n Returns a new list containing elements from the population while\n leaving the original population unchanged. The resulting list is\n in selection order so that all sub-slices will also be valid random\n samples. This allows raffle winners (the sample) to be partitioned\n into grand prize and second place winners (the subslices).\n\n Members of the population need not be hashable or unique. If the\n population contains repeats, then each occurrence is a possible\n selection in the sample.\n\n To choose a sample in a range of integers, use range as an argument.\n This is especially fast and space efficient for sampling from a\n large population: sample(range(10000000), 60)\n \"\"\"\n\n # Sampling without replacement entails tracking either potential\n # selections (the pool) in a list or previous selections in a set.\n\n # When the number of selections is small compared to the\n # population, then tracking selections is efficient, requiring\n # only a small set and an occasional reselection. For\n # a larger number of selections, the pool tracking method is\n # preferred since the list takes less space than the\n # set and it doesn't suffer from frequent reselections.\n\n if isinstance(population, _Set):\n population = tuple(population)\n if not isinstance(population, _Sequence):\n raise TypeError(\"Population must be a sequence or set. For dicts, use list(d).\")\n randbelow = self._randbelow\n n = len(population)\n if not 0 <= k <= n:\n raise ValueError(\"Sample larger than population or is negative\")\n result = [None] * k\n setsize = 21 # size of a small set minus size of an empty list\n if k > 5:\n setsize += 4 ** _ceil(_log(k * 3, 4)) # table size for big sets\n if n <= setsize:\n # An n-length list is smaller than a k-length set\n pool = list(population)\n for i in range(k): # invariant: non-selected at [0,n-i)\n j = randbelow(n-i)\n result[i] = pool[j]\n pool[j] = pool[n-i-1] # move non-selected item into vacancy\n else:\n selected = set()\n selected_add = selected.add\n for i in range(k):\n j = randbelow(n)\n while j in selected:\n j = randbelow(n)\n selected_add(j)\n result[i] = population[j]\n return result\n\n def choices(self, population, weights=None, *, cum_weights=None, k=1):\n \"\"\"Return a k sized list of population elements chosen with replacement.\n\n If the relative weights or cumulative weights are not specified,\n the selections are made with equal probability.\n\n \"\"\"\n random = self.random\n if cum_weights is None:\n if weights is None:\n _int = int\n total = len(population)\n return [population[_int(random() * total)] for i in range(k)]\n cum_weights = list(_itertools.accumulate(weights))\n elif weights is not None:\n raise TypeError('Cannot specify both weights and cumulative weights')\n if len(cum_weights) != len(population):\n raise ValueError('The number of weights does not match the population')\n bisect = _bisect.bisect\n total = cum_weights[-1]\n hi = len(cum_weights) - 1\n return [population[bisect(cum_weights, random() * total, 0, hi)]\n for i in range(k)]\n\n## -------------------- real-valued distributions -------------------\n\n## -------------------- uniform distribution -------------------\n\n def uniform(self, a, b):\n \"Get a random number in the range [a, b) or [a, b] depending on rounding.\"\n return a + (b-a) * self.random()\n\n## -------------------- triangular --------------------\n\n def triangular(self, low=0.0, high=1.0, mode=None):\n \"\"\"Triangular distribution.\n\n Continuous distribution bounded by given lower and upper limits,\n and having a given mode value in-between.\n\n http://en.wikipedia.org/wiki/Triangular_distribution\n\n \"\"\"\n u = self.random()\n try:\n c = 0.5 if mode is None else (mode - low) / (high - low)\n except ZeroDivisionError:\n return low\n if u > c:\n u = 1.0 - u\n c = 1.0 - c\n low, high = high, low\n return low + (high - low) * (u * c) ** 0.5\n\n## -------------------- normal distribution --------------------\n\n def normalvariate(self, mu, sigma):\n \"\"\"Normal distribution.\n\n mu is the mean, and sigma is the standard deviation.\n\n \"\"\"\n # mu = mean, sigma = standard deviation\n\n # Uses Kinderman and Monahan method. Reference: Kinderman,\n # A.J. and Monahan, J.F., \"Computer generation of random\n # variables using the ratio of uniform deviates\", ACM Trans\n # Math Software, 3, (1977), pp257-260.\n\n random = self.random\n while 1:\n u1 = random()\n u2 = 1.0 - random()\n z = NV_MAGICCONST*(u1-0.5)/u2\n zz = z*z/4.0\n if zz <= -_log(u2):\n break\n return mu + z*sigma\n\n## -------------------- lognormal distribution --------------------\n\n def lognormvariate(self, mu, sigma):\n \"\"\"Log normal distribution.\n\n If you take the natural logarithm of this distribution, you'll get a\n normal distribution with mean mu and standard deviation sigma.\n mu can have any value, and sigma must be greater than zero.\n\n \"\"\"\n return _exp(self.normalvariate(mu, sigma))\n\n## -------------------- exponential distribution --------------------\n\n def expovariate(self, lambd):\n \"\"\"Exponential distribution.\n\n lambd is 1.0 divided by the desired mean. It should be\n nonzero. (The parameter would be called \"lambda\", but that is\n a reserved word in Python.) Returned values range from 0 to\n positive infinity if lambd is positive, and from negative\n infinity to 0 if lambd is negative.\n\n \"\"\"\n # lambd: rate lambd = 1/mean\n # ('lambda' is a Python reserved word)\n\n # we use 1-random() instead of random() to preclude the\n # possibility of taking the log of zero.\n return -_log(1.0 - self.random())/lambd\n\n## -------------------- von Mises distribution --------------------\n\n def vonmisesvariate(self, mu, kappa):\n \"\"\"Circular data distribution.\n\n mu is the mean angle, expressed in radians between 0 and 2*pi, and\n kappa is the concentration parameter, which must be greater than or\n equal to zero. If kappa is equal to zero, this distribution reduces\n to a uniform random angle over the range 0 to 2*pi.\n\n \"\"\"\n # mu: mean angle (in radians between 0 and 2*pi)\n # kappa: concentration parameter kappa (>= 0)\n # if kappa = 0 generate uniform random angle\n\n # Based upon an algorithm published in: Fisher, N.I.,\n # \"Statistical Analysis of Circular Data\", Cambridge\n # University Press, 1993.\n\n # Thanks to Magnus Kessler for a correction to the\n # implementation of step 4.\n\n random = self.random\n if kappa <= 1e-6:\n return TWOPI * random()\n\n s = 0.5 / kappa\n r = s + _sqrt(1.0 + s * s)\n\n while 1:\n u1 = random()\n z = _cos(_pi * u1)\n\n d = z / (r + z)\n u2 = random()\n if u2 < 1.0 - d * d or u2 <= (1.0 - d) * _exp(d):\n break\n\n q = 1.0 / r\n f = (q + z) / (1.0 + q * z)\n u3 = random()\n if u3 > 0.5:\n theta = (mu + _acos(f)) % TWOPI\n else:\n theta = (mu - _acos(f)) % TWOPI\n\n return theta\n\n## -------------------- gamma distribution --------------------\n\n def gammavariate(self, alpha, beta):\n \"\"\"Gamma distribution. Not the gamma function!\n\n Conditions on the parameters are alpha > 0 and beta > 0.\n\n The probability distribution function is:\n\n x ** (alpha - 1) * math.exp(-x / beta)\n pdf(x) = --------------------------------------\n math.gamma(alpha) * beta ** alpha\n\n \"\"\"\n\n # alpha > 0, beta > 0, mean is alpha*beta, variance is alpha*beta**2\n\n # Warning: a few older sources define the gamma distribution in terms\n # of alpha > -1.0\n if alpha <= 0.0 or beta <= 0.0:\n raise ValueError('gammavariate: alpha and beta must be > 0.0')\n\n random = self.random\n if alpha > 1.0:\n\n # Uses R.C.H. Cheng, \"The generation of Gamma\n # variables with non-integral shape parameters\",\n # Applied Statistics, (1977), 26, No. 1, p71-74\n\n ainv = _sqrt(2.0 * alpha - 1.0)\n bbb = alpha - LOG4\n ccc = alpha + ainv\n\n while 1:\n u1 = random()\n if not 1e-7 < u1 < .9999999:\n continue\n u2 = 1.0 - random()\n v = _log(u1/(1.0-u1))/ainv\n x = alpha*_exp(v)\n z = u1*u1*u2\n r = bbb+ccc*v-x\n if r + SG_MAGICCONST - 4.5*z >= 0.0 or r >= _log(z):\n return x * beta\n\n elif alpha == 1.0:\n # expovariate(1)\n u = random()\n while u <= 1e-7:\n u = random()\n return -_log(u) * beta\n\n else: # alpha is between 0 and 1 (exclusive)\n\n # Uses ALGORITHM GS of Statistical Computing - Kennedy & Gentle\n\n while 1:\n u = random()\n b = (_e + alpha)/_e\n p = b*u\n if p <= 1.0:\n x = p ** (1.0/alpha)\n else:\n x = -_log((b-p)/alpha)\n u1 = random()\n if p > 1.0:\n if u1 <= x ** (alpha - 1.0):\n break\n elif u1 <= _exp(-x):\n break\n return x * beta\n\n## -------------------- Gauss (faster alternative) --------------------\n\n def gauss(self, mu, sigma):\n \"\"\"Gaussian distribution.\n\n mu is the mean, and sigma is the standard deviation. This is\n slightly faster than the normalvariate() function.\n\n Not thread-safe without a lock around calls.\n\n \"\"\"\n\n # When x and y are two variables from [0, 1), uniformly\n # distributed, then\n #\n # cos(2*pi*x)*sqrt(-2*log(1-y))\n # sin(2*pi*x)*sqrt(-2*log(1-y))\n #\n # are two *independent* variables with normal distribution\n # (mu = 0, sigma = 1).\n # (Lambert Meertens)\n # (corrected version; bug discovered by Mike Miller, fixed by LM)\n\n # Multithreading note: When two threads call this function\n # simultaneously, it is possible that they will receive the\n # same return value. The window is very small though. To\n # avoid this, you have to use a lock around all calls. (I\n # didn't want to slow this down in the serial case by using a\n # lock here.)\n\n random = self.random\n z = self.gauss_next\n self.gauss_next = None\n if z is None:\n x2pi = random() * TWOPI\n g2rad = _sqrt(-2.0 * _log(1.0 - random()))\n z = _cos(x2pi) * g2rad\n self.gauss_next = _sin(x2pi) * g2rad\n\n return mu + z*sigma\n\n## -------------------- beta --------------------\n## See\n## http://mail.python.org/pipermail/python-bugs-list/2001-January/003752.html\n## for Ivan Frohne's insightful analysis of why the original implementation:\n##\n## def betavariate(self, alpha, beta):\n## # Discrete Event Simulation in C, pp 87-88.\n##\n## y = self.expovariate(alpha)\n## z = self.expovariate(1.0/beta)\n## return z/(y+z)\n##\n## was dead wrong, and how it probably got that way.\n\n def betavariate(self, alpha, beta):\n \"\"\"Beta distribution.\n\n Conditions on the parameters are alpha > 0 and beta > 0.\n Returned values range between 0 and 1.\n\n \"\"\"\n\n # This version due to Janne Sinkkonen, and matches all the std\n # texts (e.g., Knuth Vol 2 Ed 3 pg 134 \"the beta distribution\").\n y = self.gammavariate(alpha, 1.0)\n if y == 0:\n return 0.0\n else:\n return y / (y + self.gammavariate(beta, 1.0))\n\n## -------------------- Pareto --------------------\n\n def paretovariate(self, alpha):\n \"\"\"Pareto distribution. alpha is the shape parameter.\"\"\"\n # Jain, pg. 495\n\n u = 1.0 - self.random()\n return 1.0 / u ** (1.0/alpha)\n\n## -------------------- Weibull --------------------\n\n def weibullvariate(self, alpha, beta):\n \"\"\"Weibull distribution.\n\n alpha is the scale parameter and beta is the shape parameter.\n\n \"\"\"\n # Jain, pg. 499; bug fix courtesy Bill Arms\n\n u = 1.0 - self.random()\n return alpha * (-_log(u)) ** (1.0/beta)\n\n## --------------- Operating System Random Source ------------------\n\nclass SystemRandom(Random):\n \"\"\"Alternate random number generator using sources provided\n by the operating system (such as /dev/urandom on Unix or\n CryptGenRandom on Windows).\n\n Not available on all systems (see os.urandom() for details).\n \"\"\"\n\n def random(self):\n \"\"\"Get the next random number in the range [0.0, 1.0).\"\"\"\n return (int.from_bytes(_urandom(7), 'big') >> 3) * RECIP_BPF\n\n def getrandbits(self, k):\n \"\"\"getrandbits(k) -> x. Generates an int with k random bits.\"\"\"\n if k <= 0:\n raise ValueError('number of bits must be greater than zero')\n if k != int(k):\n raise TypeError('number of bits should be an integer')\n numbytes = (k + 7) // 8 # bits / 8 and rounded up\n x = int.from_bytes(_urandom(numbytes), 'big')\n return x >> (numbytes * 8 - k) # trim excess bits\n\n def seed(self, *args, **kwds):\n \"Stub method. Not used for a system random number generator.\"\n return None\n\n def _notimplemented(self, *args, **kwds):\n \"Method should not be called for a system random number generator.\"\n raise NotImplementedError('System entropy source does not have state.')\n getstate = setstate = _notimplemented\n\n## -------------------- test program --------------------\n\ndef _test_generator(n, func, args):\n import time\n print(n, 'times', func.__name__)\n total = 0.0\n sqsum = 0.0\n smallest = 1e10\n largest = -1e10\n t0 = time.time()\n for i in range(n):\n x = func(*args)\n total += x\n sqsum = sqsum + x*x\n smallest = min(x, smallest)\n largest = max(x, largest)\n t1 = time.time()\n print(round(t1-t0, 3), 'sec,', end=' ')\n avg = total/n\n stddev = _sqrt(sqsum/n - avg*avg)\n print('avg %g, stddev %g, min %g, max %g\\n' % \\\n (avg, stddev, smallest, largest))\n\n\ndef _test(N=2000):\n _test_generator(N, random, ())\n _test_generator(N, normalvariate, (0.0, 1.0))\n _test_generator(N, lognormvariate, (0.0, 1.0))\n _test_generator(N, vonmisesvariate, (0.0, 1.0))\n _test_generator(N, gammavariate, (0.01, 1.0))\n _test_generator(N, gammavariate, (0.1, 1.0))\n _test_generator(N, gammavariate, (0.1, 2.0))\n _test_generator(N, gammavariate, (0.5, 1.0))\n _test_generator(N, gammavariate, (0.9, 1.0))\n _test_generator(N, gammavariate, (1.0, 1.0))\n _test_generator(N, gammavariate, (2.0, 1.0))\n _test_generator(N, gammavariate, (20.0, 1.0))\n _test_generator(N, gammavariate, (200.0, 1.0))\n _test_generator(N, gauss, (0.0, 1.0))\n _test_generator(N, betavariate, (3.0, 3.0))\n _test_generator(N, triangular, (0.0, 1.0, 1.0/3.0))\n\n# Create one instance, seeded from current time, and export its methods\n# as module-level functions. The functions share state across all uses\n#(both in the user's code and in the Python libraries), but that's fine\n# for most programs and is easier for the casual user than making them\n# instantiate their own Random() instance.\n\n_inst = Random()\nseed = _inst.seed\nrandom = _inst.random\nuniform = _inst.uniform\ntriangular = _inst.triangular\nrandint = _inst.randint\nchoice = _inst.choice\nrandrange = _inst.randrange\nsample = _inst.sample\nshuffle = _inst.shuffle\nchoices = _inst.choices\nnormalvariate = _inst.normalvariate\nlognormvariate = _inst.lognormvariate\nexpovariate = _inst.expovariate\nvonmisesvariate = _inst.vonmisesvariate\ngammavariate = _inst.gammavariate\ngauss = _inst.gauss\nbetavariate = _inst.betavariate\nparetovariate = _inst.paretovariate\nweibullvariate = _inst.weibullvariate\ngetstate = _inst.getstate\nsetstate = _inst.setstate\ngetrandbits = _inst.getrandbits\n\nif __name__ == '__main__':\n _test()\n",772]},"functions":{"treeNode 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warn_distutils_present():\n if 'distutils' not in sys.modules:\n return\n import warnings\n\n warnings.warn(\n \"Distutils was imported before Setuptools, but importing Setuptools \"\n \"also replaces the `distutils` module in `sys.modules`. This may lead \"\n \"to undesirable behaviors or errors. To avoid these issues, avoid \"\n \"using distutils directly, ensure that setuptools is installed in the \"\n \"traditional way (e.g. not an editable install), and/or make sure \"\n \"that setuptools is always imported before distutils.\"\n )\n\n\ndef clear_distutils():\n if 'distutils' not in sys.modules:\n return\n import warnings\n\n warnings.warn(\n \"Setuptools is replacing distutils. Support for replacing \"\n \"an already imported distutils is deprecated. In the future, \"\n \"this condition will fail. \"\n f\"Register concerns at {report_url}\"\n )\n mods = [\n name\n for name in sys.modules\n if name == \"distutils\" or name.startswith(\"distutils.\")\n ]\n for name in mods:\n del sys.modules[name]\n\n\ndef enabled():\n \"\"\"\n Allow selection of distutils by environment variable.\n \"\"\"\n which = os.environ.get('SETUPTOOLS_USE_DISTUTILS', 'local')\n if which == 'stdlib':\n import warnings\n\n warnings.warn(\n \"Reliance on distutils from stdlib is deprecated. Users \"\n \"must rely on setuptools to provide the distutils module. \"\n \"Avoid importing distutils or import setuptools first, \"\n \"and avoid setting SETUPTOOLS_USE_DISTUTILS=stdlib. \"\n f\"Register concerns at {report_url}\"\n )\n return which == 'local'\n\n\ndef ensure_local_distutils():\n import importlib\n\n clear_distutils()\n\n # With the DistutilsMetaFinder in place,\n # perform an import to cause distutils to be\n # loaded from setuptools._distutils. Ref #2906.\n with shim():\n importlib.import_module('distutils')\n\n # check that submodules load as expected\n core = importlib.import_module('distutils.core')\n assert '_distutils' in core.__file__, core.__file__\n assert 'setuptools._distutils.log' not in sys.modules\n\n\ndef do_override():\n \"\"\"\n Ensure that the local copy of distutils is preferred over stdlib.\n\n See https://github.com/pypa/setuptools/issues/417#issuecomment-392298401\n for more motivation.\n \"\"\"\n if enabled():\n warn_distutils_present()\n ensure_local_distutils()\n\n\nclass _TrivialRe:\n def __init__(self, *patterns):\n self._patterns = patterns\n\n def match(self, string):\n return all(pat in string for pat in self._patterns)\n\n\nclass DistutilsMetaFinder:\n def find_spec(self, fullname, path, target=None):\n # optimization: only consider top level modules and those\n # found in the CPython test suite.\n if path is not None and not fullname.startswith('test.'):\n return None\n\n method_name = 'spec_for_{fullname}'.format(**locals())\n method = getattr(self, method_name, lambda: None)\n return method()\n\n def spec_for_distutils(self):\n if self.is_cpython():\n return None\n\n import importlib\n import importlib.abc\n import importlib.util\n\n try:\n mod = importlib.import_module('setuptools._distutils')\n except Exception:\n # There are a couple of cases where setuptools._distutils\n # may not be present:\n # - An older Setuptools without a local distutils is\n # taking precedence. Ref #2957.\n # - Path manipulation during sitecustomize removes\n # setuptools from the path but only after the hook\n # has been loaded. Ref #2980.\n # In either case, fall back to stdlib behavior.\n return None\n\n class DistutilsLoader(importlib.abc.Loader):\n def create_module(self, spec):\n mod.__name__ = 'distutils'\n return mod\n\n def exec_module(self, module):\n pass\n\n return importlib.util.spec_from_loader(\n 'distutils', DistutilsLoader(), origin=mod.__file__\n )\n\n @staticmethod\n def is_cpython():\n \"\"\"\n Suppress supplying distutils for CPython (build and tests).\n Ref #2965 and #3007.\n \"\"\"\n return os.path.isfile('pybuilddir.txt')\n\n def spec_for_pip(self):\n \"\"\"\n Ensure stdlib distutils when running under pip.\n See pypa/pip#8761 for rationale.\n \"\"\"\n if sys.version_info >= (3, 12) or self.pip_imported_during_build():\n return\n clear_distutils()\n self.spec_for_distutils = lambda: None\n\n @classmethod\n def pip_imported_during_build(cls):\n \"\"\"\n Detect if pip is being imported in a build script. Ref #2355.\n \"\"\"\n import traceback\n\n return any(\n cls.frame_file_is_setup(frame) for frame, line in traceback.walk_stack(None)\n )\n\n @staticmethod\n def frame_file_is_setup(frame):\n \"\"\"\n Return True if the indicated frame suggests a setup.py file.\n \"\"\"\n # some frames may not have __file__ (#2940)\n return frame.f_globals.get('__file__', '').endswith('setup.py')\n\n def spec_for_sensitive_tests(self):\n \"\"\"\n Ensure stdlib distutils when running select tests under CPython.\n\n python/cpython#91169\n \"\"\"\n clear_distutils()\n self.spec_for_distutils = lambda: None\n\n sensitive_tests = (\n [\n 'test.test_distutils',\n 'test.test_peg_generator',\n 'test.test_importlib',\n ]\n if sys.version_info < (3, 10)\n else [\n 'test.test_distutils',\n ]\n )\n\n\nfor name in DistutilsMetaFinder.sensitive_tests:\n setattr(\n DistutilsMetaFinder,\n f'spec_for_{name}',\n DistutilsMetaFinder.spec_for_sensitive_tests,\n )\n\n\nDISTUTILS_FINDER = DistutilsMetaFinder()\n\n\ndef add_shim():\n DISTUTILS_FINDER in sys.meta_path or insert_shim()\n\n\nclass shim:\n def __enter__(self) -> None:\n insert_shim()\n\n def __exit__(self, exc: object, value: object, tb: object) -> None:\n _remove_shim()\n\n\ndef insert_shim():\n sys.meta_path.insert(0, DISTUTILS_FINDER)\n\n\ndef _remove_shim():\n try:\n sys.meta_path.remove(DISTUTILS_FINDER)\n except ValueError:\n pass\n\n\nif sys.version_info < (3, 12):\n # DistutilsMetaFinder can only be disabled in Python < 3.12 (PEP 632)\n remove_shim = _remove_shim\n", 240], "/home/gaogaotiantian/programs/viztracer/venv3.13/lib/python3.13/site-packages/mcts.py": ["from __future__ import division\n\nimport time\nimport math\nimport random\n\n\ndef randomPolicy(state):\n while not state.isTerminal():\n try:\n action = random.choice(state.getPossibleActions())\n except IndexError:\n raise Exception(\"Non-terminal state has no possible actions: \" + str(state))\n state = state.takeAction(action)\n return state.getReward()\n\n\nclass treeNode():\n def __init__(self, state, parent):\n self.state = state\n self.isTerminal = state.isTerminal()\n self.isFullyExpanded = self.isTerminal\n self.parent = parent\n self.numVisits = 0\n self.totalReward = 0\n self.children = {}\n\n\nclass mcts():\n def __init__(self, timeLimit=None, iterationLimit=None, explorationConstant=1 / math.sqrt(2),\n rolloutPolicy=randomPolicy):\n if timeLimit != None:\n if iterationLimit != None:\n raise ValueError(\"Cannot have both a time limit and an iteration limit\")\n # time taken for each MCTS search in milliseconds\n self.timeLimit = timeLimit\n self.limitType = 'time'\n else:\n if iterationLimit == None:\n raise ValueError(\"Must have either a time limit or an iteration limit\")\n # number of iterations of the search\n if iterationLimit < 1:\n raise ValueError(\"Iteration limit must be greater than one\")\n self.searchLimit = iterationLimit\n self.limitType = 'iterations'\n self.explorationConstant = explorationConstant\n self.rollout = rolloutPolicy\n\n def search(self, initialState):\n self.root = treeNode(initialState, None)\n\n if self.limitType == 'time':\n timeLimit = time.time() + self.timeLimit / 1000\n while time.time() < timeLimit:\n self.executeRound()\n else:\n for i in range(self.searchLimit):\n self.executeRound()\n\n bestChild = self.getBestChild(self.root, 0)\n return self.getAction(self.root, bestChild)\n\n def executeRound(self):\n node = self.selectNode(self.root)\n reward = self.rollout(node.state)\n self.backpropogate(node, reward)\n\n def selectNode(self, node):\n while not node.isTerminal:\n if node.isFullyExpanded:\n node = self.getBestChild(node, self.explorationConstant)\n else:\n return self.expand(node)\n return node\n\n def expand(self, node):\n actions = node.state.getPossibleActions()\n for action in actions:\n if action not in node.children.keys():\n newNode = treeNode(node.state.takeAction(action), node)\n node.children[action] = newNode\n if len(actions) == len(node.children):\n node.isFullyExpanded = True\n return newNode\n\n raise Exception(\"Should never reach here\")\n\n def backpropogate(self, node, reward):\n while node is not None:\n node.numVisits += 1\n node.totalReward += reward\n node = node.parent\n\n def getBestChild(self, node, explorationValue):\n bestValue = float(\"-inf\")\n bestNodes = []\n for child in node.children.values():\n nodeValue = child.totalReward / child.numVisits + explorationValue * math.sqrt(\n 2 * math.log(node.numVisits) / child.numVisits)\n if nodeValue > bestValue:\n bestValue = nodeValue\n bestNodes = [child]\n elif nodeValue == bestValue:\n bestNodes.append(child)\n return random.choice(bestNodes)\n\n def getAction(self, root, bestChild):\n for action, node in root.children.items():\n if node is bestChild:\n return action\n", 110], "/home/gaogaotiantian/programs/viztracer/example/src/mcts_game.py": ["\nfrom __future__ import division\n\nimport operator\nfrom copy import deepcopy\nfrom functools import reduce\n\nfrom mcts import mcts\n\n\nclass NaughtsAndCrossesState():\n def __init__(self):\n self.board = [[0, 0, 0], [0, 0, 0], [0, 0, 0]]\n self.currentPlayer = 1\n\n def getCurrentPlayer(self):\n return self.currentPlayer\n\n def getPossibleActions(self):\n possibleActions = []\n for i in range(len(self.board)):\n for j in range(len(self.board[i])):\n if self.board[i][j] == 0:\n possibleActions.append(Action(player=self.currentPlayer, x=i, y=j))\n return possibleActions\n\n def takeAction(self, action):\n newState = deepcopy(self)\n newState.board[action.x][action.y] = action.player\n newState.currentPlayer = self.currentPlayer * -1\n return newState\n\n def isTerminal(self):\n for row in self.board:\n if abs(sum(row)) == 3:\n return True\n for column in list(map(list, zip(*self.board))):\n if abs(sum(column)) == 3:\n return True\n for diagonal in [[self.board[i][i] for i in range(len(self.board))],\n [self.board[i][len(self.board) - i - 1] for i in range(len(self.board))]]:\n if abs(sum(diagonal)) == 3:\n return True\n return reduce(operator.mul, sum(self.board, []), 1)\n\n def getReward(self):\n for row in self.board:\n if abs(sum(row)) == 3:\n return sum(row) / 3\n for column in list(map(list, zip(*self.board))):\n if abs(sum(column)) == 3:\n return sum(column) / 3\n for diagonal in [[self.board[i][i] for i in range(len(self.board))],\n [self.board[i][len(self.board) - i - 1] for i in range(len(self.board))]]:\n if abs(sum(diagonal)) == 3:\n return sum(diagonal) / 3\n return False\n\n\nclass Action():\n def __init__(self, player, x, y):\n self.player = player\n self.x = x\n self.y = y\n\n def __str__(self):\n return str((self.x, self.y))\n\n def __repr__(self):\n return str(self)\n\n def __eq__(self, other):\n return self.__class__ == other.__class__ and self.x == other.x and self.y == other.y and self.player == other.player\n\n def __hash__(self):\n return hash((self.x, self.y, self.player))\n\n\ninitialState = NaughtsAndCrossesState()\nmcts = mcts(timeLimit=10)\naction = mcts.search(initialState=initialState)\n", 81], "/usr/lib/python3.13/copyreg.py": ["\"\"\"Helper to provide extensibility for pickle.\n\nThis is only useful to add pickle support for extension types defined in\nC, not for instances of user-defined classes.\n\"\"\"\n\n__all__ = [\"pickle\", \"constructor\",\n \"add_extension\", \"remove_extension\", \"clear_extension_cache\"]\n\ndispatch_table = {}\n\ndef pickle(ob_type, pickle_function, constructor_ob=None):\n if not callable(pickle_function):\n raise TypeError(\"reduction functions must be callable\")\n dispatch_table[ob_type] = pickle_function\n\n # The constructor_ob function is a vestige of safe for unpickling.\n # There is no reason for the caller to pass it anymore.\n if constructor_ob is not None:\n constructor(constructor_ob)\n\ndef constructor(object):\n if not callable(object):\n raise TypeError(\"constructors must be callable\")\n\n# Example: provide pickling support for complex numbers.\n\ndef pickle_complex(c):\n return complex, (c.real, c.imag)\n\npickle(complex, pickle_complex, complex)\n\ndef pickle_union(obj):\n import functools, operator\n return functools.reduce, (operator.or_, obj.__args__)\n\npickle(type(int | str), pickle_union)\n\n# Support for pickling new-style objects\n\ndef _reconstructor(cls, base, state):\n if base is object:\n obj = object.__new__(cls)\n else:\n obj = base.__new__(cls, state)\n if base.__init__ != object.__init__:\n base.__init__(obj, state)\n return obj\n\n_HEAPTYPE = 1<<9\n_new_type = type(int.__new__)\n\n# Python code for object.__reduce_ex__ for protocols 0 and 1\n\ndef _reduce_ex(self, proto):\n assert proto < 2\n cls = self.__class__\n for base in cls.__mro__:\n if hasattr(base, '__flags__') and not base.__flags__ & _HEAPTYPE:\n break\n new = base.__new__\n if isinstance(new, _new_type) and new.__self__ is base:\n break\n else:\n base = object # not really reachable\n if base is object:\n state = None\n else:\n if base is cls:\n raise TypeError(f\"cannot pickle {cls.__name__!r} object\")\n state = base(self)\n args = (cls, base, state)\n try:\n getstate = self.__getstate__\n except AttributeError:\n if getattr(self, \"__slots__\", None):\n raise TypeError(f\"cannot pickle {cls.__name__!r} object: \"\n f\"a class that defines __slots__ without \"\n f\"defining __getstate__ cannot be pickled \"\n f\"with protocol {proto}\") from None\n try:\n dict = self.__dict__\n except AttributeError:\n dict = None\n else:\n if (type(self).__getstate__ is object.__getstate__ and\n getattr(self, \"__slots__\", None)):\n raise TypeError(\"a class that defines __slots__ without \"\n \"defining __getstate__ cannot be pickled\")\n dict = getstate()\n if dict:\n return _reconstructor, args, dict\n else:\n return _reconstructor, args\n\n# Helper for __reduce_ex__ protocol 2\n\ndef __newobj__(cls, *args):\n return cls.__new__(cls, *args)\n\ndef __newobj_ex__(cls, args, kwargs):\n \"\"\"Used by pickle protocol 4, instead of __newobj__ to allow classes with\n keyword-only arguments to be pickled correctly.\n \"\"\"\n return cls.__new__(cls, *args, **kwargs)\n\ndef _slotnames(cls):\n \"\"\"Return a list of slot names for a given class.\n\n This needs to find slots defined by the class and its bases, so we\n can't simply return the __slots__ attribute. We must walk down\n the Method Resolution Order and concatenate the __slots__ of each\n class found there. (This assumes classes don't modify their\n __slots__ attribute to misrepresent their slots after the class is\n defined.)\n \"\"\"\n\n # Get the value from a cache in the class if possible\n names = cls.__dict__.get(\"__slotnames__\")\n if names is not None:\n return names\n\n # Not cached -- calculate the value\n names = []\n if not hasattr(cls, \"__slots__\"):\n # This class has no slots\n pass\n else:\n # Slots found -- gather slot names from all base classes\n for c in cls.__mro__:\n if \"__slots__\" in c.__dict__:\n slots = c.__dict__['__slots__']\n # if class has a single slot, it can be given as a string\n if isinstance(slots, str):\n slots = (slots,)\n for name in slots:\n # special descriptors\n if name in (\"__dict__\", \"__weakref__\"):\n continue\n # mangled names\n elif name.startswith('__') and not name.endswith('__'):\n stripped = c.__name__.lstrip('_')\n if stripped:\n names.append('_%s%s' % (stripped, name))\n else:\n names.append(name)\n else:\n names.append(name)\n\n # Cache the outcome in the class if at all possible\n try:\n cls.__slotnames__ = names\n except:\n pass # But don't die if we can't\n\n return names\n\n# A registry of extension codes. This is an ad-hoc compression\n# mechanism. Whenever a global reference to , is about\n# to be pickled, the (, ) tuple is looked up here to see\n# if it is a registered extension code for it. Extension codes are\n# universal, so that the meaning of a pickle does not depend on\n# context. (There are also some codes reserved for local use that\n# don't have this restriction.) Codes are positive ints; 0 is\n# reserved.\n\n_extension_registry = {} # key -> code\n_inverted_registry = {} # code -> key\n_extension_cache = {} # code -> object\n# Don't ever rebind those names: pickling grabs a reference to them when\n# it's initialized, and won't see a rebinding.\n\ndef add_extension(module, name, code):\n \"\"\"Register an extension code.\"\"\"\n code = int(code)\n if not 1 <= code <= 0x7fffffff:\n raise ValueError(\"code out of range\")\n key = (module, name)\n if (_extension_registry.get(key) == code and\n _inverted_registry.get(code) == key):\n return # Redundant registrations are benign\n if key in _extension_registry:\n raise ValueError(\"key %s is already registered with code %s\" %\n (key, _extension_registry[key]))\n if code in _inverted_registry:\n raise ValueError(\"code %s is already in use for key %s\" %\n (code, _inverted_registry[code]))\n _extension_registry[key] = code\n _inverted_registry[code] = key\n\ndef remove_extension(module, name, code):\n \"\"\"Unregister an extension code. For testing only.\"\"\"\n key = (module, name)\n if (_extension_registry.get(key) != code or\n _inverted_registry.get(code) != key):\n raise ValueError(\"key %s is not registered with code %s\" %\n (key, code))\n del _extension_registry[key]\n del _inverted_registry[code]\n if code in _extension_cache:\n del _extension_cache[code]\n\ndef clear_extension_cache():\n _extension_cache.clear()\n\n# Standard extension code assignments\n\n# Reserved ranges\n\n# First Last Count Purpose\n# 1 127 127 Reserved for Python standard library\n# 128 191 64 Reserved for Zope\n# 192 239 48 Reserved for 3rd parties\n# 240 255 16 Reserved for private use (will never be assigned)\n# 256 Inf Inf Reserved for future assignment\n\n# Extension codes are assigned by the Python Software Foundation.\n", 217], "/usr/lib/python3.13/copy.py": ["\"\"\"Generic (shallow and deep) copying operations.\n\nInterface summary:\n\n import copy\n\n x = copy.copy(y) # make a shallow copy of y\n x = copy.deepcopy(y) # make a deep copy of y\n x = copy.replace(y, a=1, b=2) # new object with fields replaced, as defined by `__replace__`\n\nFor module specific errors, copy.Error is raised.\n\nThe difference between shallow and deep copying is only relevant for\ncompound objects (objects that contain other objects, like lists or\nclass instances).\n\n- A shallow copy constructs a new compound object and then (to the\n extent possible) inserts *the same objects* into it that the\n original contains.\n\n- A deep copy constructs a new compound object and then, recursively,\n inserts *copies* into it of the objects found in the original.\n\nTwo problems often exist with deep copy operations that don't exist\nwith shallow copy operations:\n\n a) recursive objects (compound objects that, directly or indirectly,\n contain a reference to themselves) may cause a recursive loop\n\n b) because deep copy copies *everything* it may copy too much, e.g.\n administrative data structures that should be shared even between\n copies\n\nPython's deep copy operation avoids these problems by:\n\n a) keeping a table of objects already copied during the current\n copying pass\n\n b) letting user-defined classes override the copying operation or the\n set of components copied\n\nThis version does not copy types like module, class, function, method,\nnor stack trace, stack frame, nor file, socket, window, nor any\nsimilar types.\n\nClasses can use the same interfaces to control copying that they use\nto control pickling: they can define methods called __getinitargs__(),\n__getstate__() and __setstate__(). See the documentation for module\n\"pickle\" for information on these methods.\n\"\"\"\n\nimport types\nimport weakref\nfrom copyreg import dispatch_table\n\nclass Error(Exception):\n pass\nerror = Error # backward compatibility\n\n__all__ = [\"Error\", \"copy\", \"deepcopy\", \"replace\"]\n\ndef copy(x):\n \"\"\"Shallow copy operation on arbitrary Python objects.\n\n See the module's __doc__ string for more info.\n \"\"\"\n\n cls = type(x)\n\n copier = _copy_dispatch.get(cls)\n if copier:\n return copier(x)\n\n if issubclass(cls, type):\n # treat it as a regular class:\n return _copy_immutable(x)\n\n copier = getattr(cls, \"__copy__\", None)\n if copier is not None:\n return copier(x)\n\n reductor = dispatch_table.get(cls)\n if reductor is not None:\n rv = reductor(x)\n else:\n reductor = getattr(x, \"__reduce_ex__\", None)\n if reductor is not None:\n rv = reductor(4)\n else:\n reductor = getattr(x, \"__reduce__\", None)\n if reductor:\n rv = reductor()\n else:\n raise Error(\"un(shallow)copyable object of type %s\" % cls)\n\n if isinstance(rv, str):\n return x\n return _reconstruct(x, None, *rv)\n\n\n_copy_dispatch = d = {}\n\ndef _copy_immutable(x):\n return x\nfor t in (types.NoneType, int, float, bool, complex, str, tuple,\n bytes, frozenset, type, range, slice, property,\n types.BuiltinFunctionType, types.EllipsisType,\n types.NotImplementedType, types.FunctionType, types.CodeType,\n weakref.ref):\n d[t] = _copy_immutable\n\nd[list] = list.copy\nd[dict] = dict.copy\nd[set] = set.copy\nd[bytearray] = bytearray.copy\n\ndel d, t\n\ndef deepcopy(x, memo=None, _nil=[]):\n \"\"\"Deep copy operation on arbitrary Python objects.\n\n See the module's __doc__ string for more info.\n \"\"\"\n\n d = id(x)\n if memo is None:\n memo = {}\n else:\n y = memo.get(d, _nil)\n if y is not _nil:\n return y\n\n cls = type(x)\n\n copier = _deepcopy_dispatch.get(cls)\n if copier is not None:\n y = copier(x, memo)\n else:\n if issubclass(cls, type):\n y = _deepcopy_atomic(x, memo)\n else:\n copier = getattr(x, \"__deepcopy__\", None)\n if copier is not None:\n y = copier(memo)\n else:\n reductor = dispatch_table.get(cls)\n if reductor:\n rv = reductor(x)\n else:\n reductor = getattr(x, \"__reduce_ex__\", None)\n if reductor is not None:\n rv = reductor(4)\n else:\n reductor = getattr(x, \"__reduce__\", None)\n if reductor:\n rv = reductor()\n else:\n raise Error(\n \"un(deep)copyable object of type %s\" % cls)\n if isinstance(rv, str):\n y = x\n else:\n y = _reconstruct(x, memo, *rv)\n\n # If is its own copy, don't memoize.\n if y is not x:\n memo[d] = y\n _keep_alive(x, memo) # Make sure x lives at least as long as d\n return y\n\n_deepcopy_dispatch = d = {}\n\ndef _deepcopy_atomic(x, memo):\n return x\nd[types.NoneType] = _deepcopy_atomic\nd[types.EllipsisType] = _deepcopy_atomic\nd[types.NotImplementedType] = _deepcopy_atomic\nd[int] = _deepcopy_atomic\nd[float] = _deepcopy_atomic\nd[bool] = _deepcopy_atomic\nd[complex] = _deepcopy_atomic\nd[bytes] = _deepcopy_atomic\nd[str] = _deepcopy_atomic\nd[types.CodeType] = _deepcopy_atomic\nd[type] = _deepcopy_atomic\nd[range] = _deepcopy_atomic\nd[types.BuiltinFunctionType] = _deepcopy_atomic\nd[types.FunctionType] = _deepcopy_atomic\nd[weakref.ref] = _deepcopy_atomic\nd[property] = _deepcopy_atomic\n\ndef _deepcopy_list(x, memo, deepcopy=deepcopy):\n y = []\n memo[id(x)] = y\n append = y.append\n for a in x:\n append(deepcopy(a, memo))\n return y\nd[list] = _deepcopy_list\n\ndef _deepcopy_tuple(x, memo, deepcopy=deepcopy):\n y = [deepcopy(a, memo) for a in x]\n # We're not going to put the tuple in the memo, but it's still important we\n # check for it, in case the tuple contains recursive mutable structures.\n try:\n return memo[id(x)]\n except KeyError:\n pass\n for k, j in zip(x, y):\n if k is not j:\n y = tuple(y)\n break\n else:\n y = x\n return y\nd[tuple] = _deepcopy_tuple\n\ndef _deepcopy_dict(x, memo, deepcopy=deepcopy):\n y = {}\n memo[id(x)] = y\n for key, value in x.items():\n y[deepcopy(key, memo)] = deepcopy(value, memo)\n return y\nd[dict] = _deepcopy_dict\n\ndef _deepcopy_method(x, memo): # Copy instance methods\n return type(x)(x.__func__, deepcopy(x.__self__, memo))\nd[types.MethodType] = _deepcopy_method\n\ndel d\n\ndef _keep_alive(x, memo):\n \"\"\"Keeps a reference to the object x in the memo.\n\n Because we remember objects by their id, we have\n to assure that possibly temporary objects are kept\n alive by referencing them.\n We store a reference at the id of the memo, which should\n normally not be used unless someone tries to deepcopy\n the memo itself...\n \"\"\"\n try:\n memo[id(memo)].append(x)\n except KeyError:\n # aha, this is the first one :-)\n memo[id(memo)]=[x]\n\ndef _reconstruct(x, memo, func, args,\n state=None, listiter=None, dictiter=None,\n *, deepcopy=deepcopy):\n deep = memo is not None\n if deep and args:\n args = (deepcopy(arg, memo) for arg in args)\n y = func(*args)\n if deep:\n memo[id(x)] = y\n\n if state is not None:\n if deep:\n state = deepcopy(state, memo)\n if hasattr(y, '__setstate__'):\n y.__setstate__(state)\n else:\n if isinstance(state, tuple) and len(state) == 2:\n state, slotstate = state\n else:\n slotstate = None\n if state is not None:\n y.__dict__.update(state)\n if slotstate is not None:\n for key, value in slotstate.items():\n setattr(y, key, value)\n\n if listiter is not None:\n if deep:\n for item in listiter:\n item = deepcopy(item, memo)\n y.append(item)\n else:\n for item in listiter:\n y.append(item)\n if dictiter is not None:\n if deep:\n for key, value in dictiter:\n key = deepcopy(key, memo)\n value = deepcopy(value, memo)\n y[key] = value\n else:\n for key, value in dictiter:\n y[key] = value\n return y\n\ndel types, weakref\n\n\ndef replace(obj, /, **changes):\n \"\"\"Return a new object replacing specified fields with new values.\n\n This is especially useful for immutable objects, like named tuples or\n frozen dataclasses.\n \"\"\"\n cls = obj.__class__\n func = getattr(cls, '__replace__', None)\n if func is None:\n raise TypeError(f\"replace() does not support {cls.__name__} objects\")\n return func(obj, **changes)\n", 306], "/usr/lib/python3.13/random.py": ["\"\"\"Random variable generators.\n\n bytes\n -----\n uniform bytes (values between 0 and 255)\n\n integers\n --------\n uniform within range\n\n sequences\n ---------\n pick random element\n pick random sample\n pick weighted random sample\n generate random permutation\n\n distributions on the real line:\n ------------------------------\n uniform\n triangular\n normal (Gaussian)\n lognormal\n negative exponential\n gamma\n beta\n pareto\n Weibull\n\n distributions on the circle (angles 0 to 2pi)\n ---------------------------------------------\n circular uniform\n von Mises\n\n discrete distributions\n ----------------------\n binomial\n\n\nGeneral notes on the underlying Mersenne Twister core generator:\n\n* The period is 2**19937-1.\n* It is one of the most extensively tested generators in existence.\n* The random() method is implemented in C, executes in a single Python step,\n and is, therefore, threadsafe.\n\n\"\"\"\n\n# Translated by Guido van Rossum from C source provided by\n# Adrian Baddeley. Adapted by Raymond Hettinger for use with\n# the Mersenne Twister and os.urandom() core generators.\n\nfrom math import log as _log, exp as _exp, pi as _pi, e as _e, ceil as _ceil\nfrom math import sqrt as _sqrt, acos as _acos, cos as _cos, sin as _sin\nfrom math import tau as TWOPI, floor as _floor, isfinite as _isfinite\nfrom math import lgamma as _lgamma, fabs as _fabs, log2 as _log2\nfrom os import urandom as _urandom\nfrom _collections_abc import Sequence as _Sequence\nfrom operator import index as _index\nfrom itertools import accumulate as _accumulate, repeat as _repeat\nfrom bisect import bisect as _bisect\nimport os as _os\nimport _random\n\n__all__ = [\n \"Random\",\n \"SystemRandom\",\n \"betavariate\",\n \"binomialvariate\",\n \"choice\",\n \"choices\",\n \"expovariate\",\n \"gammavariate\",\n \"gauss\",\n \"getrandbits\",\n \"getstate\",\n \"lognormvariate\",\n \"normalvariate\",\n \"paretovariate\",\n \"randbytes\",\n \"randint\",\n \"random\",\n \"randrange\",\n \"sample\",\n \"seed\",\n \"setstate\",\n \"shuffle\",\n \"triangular\",\n \"uniform\",\n \"vonmisesvariate\",\n \"weibullvariate\",\n]\n\nNV_MAGICCONST = 4 * _exp(-0.5) / _sqrt(2.0)\nLOG4 = _log(4.0)\nSG_MAGICCONST = 1.0 + _log(4.5)\nBPF = 53 # Number of bits in a float\nRECIP_BPF = 2 ** -BPF\n_ONE = 1\n_sha512 = None\n\n\nclass Random(_random.Random):\n \"\"\"Random number generator base class used by bound module functions.\n\n Used to instantiate instances of Random to get generators that don't\n share state.\n\n Class Random can also be subclassed if you want to use a different basic\n generator of your own devising: in that case, override the following\n methods: random(), seed(), getstate(), and setstate().\n Optionally, implement a getrandbits() method so that randrange()\n can cover arbitrarily large ranges.\n\n \"\"\"\n\n VERSION = 3 # used by getstate/setstate\n\n def __init__(self, x=None):\n \"\"\"Initialize an instance.\n\n Optional argument x controls seeding, as for Random.seed().\n \"\"\"\n\n self.seed(x)\n self.gauss_next = None\n\n def seed(self, a=None, version=2):\n \"\"\"Initialize internal state from a seed.\n\n The only supported seed types are None, int, float,\n str, bytes, and bytearray.\n\n None or no argument seeds from current time or from an operating\n system specific randomness source if available.\n\n If *a* is an int, all bits are used.\n\n For version 2 (the default), all of the bits are used if *a* is a str,\n bytes, or bytearray. For version 1 (provided for reproducing random\n sequences from older versions of Python), the algorithm for str and\n bytes generates a narrower range of seeds.\n\n \"\"\"\n\n if version == 1 and isinstance(a, (str, bytes)):\n a = a.decode('latin-1') if isinstance(a, bytes) else a\n x = ord(a[0]) << 7 if a else 0\n for c in map(ord, a):\n x = ((1000003 * x) ^ c) & 0xFFFFFFFFFFFFFFFF\n x ^= len(a)\n a = -2 if x == -1 else x\n\n elif version == 2 and isinstance(a, (str, bytes, bytearray)):\n global _sha512\n if _sha512 is None:\n try:\n # hashlib is pretty heavy to load, try lean internal\n # module first\n from _sha2 import sha512 as _sha512\n except ImportError:\n # fallback to official implementation\n from hashlib import sha512 as _sha512\n\n if isinstance(a, str):\n a = a.encode()\n a = int.from_bytes(a + _sha512(a).digest())\n\n elif not isinstance(a, (type(None), int, float, str, bytes, bytearray)):\n raise TypeError('The only supported seed types are:\\n'\n 'None, int, float, str, bytes, and bytearray.')\n\n super().seed(a)\n self.gauss_next = None\n\n def getstate(self):\n \"\"\"Return internal state; can be passed to setstate() later.\"\"\"\n return self.VERSION, super().getstate(), self.gauss_next\n\n def setstate(self, state):\n \"\"\"Restore internal state from object returned by getstate().\"\"\"\n version = state[0]\n if version == 3:\n version, internalstate, self.gauss_next = state\n super().setstate(internalstate)\n elif version == 2:\n version, internalstate, self.gauss_next = state\n # In version 2, the state was saved as signed ints, which causes\n # inconsistencies between 32/64-bit systems. The state is\n # really unsigned 32-bit ints, so we convert negative ints from\n # version 2 to positive longs for version 3.\n try:\n internalstate = tuple(x % (2 ** 32) for x in internalstate)\n except ValueError as e:\n raise TypeError from e\n super().setstate(internalstate)\n else:\n raise ValueError(\"state with version %s passed to \"\n \"Random.setstate() of version %s\" %\n (version, self.VERSION))\n\n\n ## -------------------------------------------------------\n ## ---- Methods below this point do not need to be overridden or extended\n ## ---- when subclassing for the purpose of using a different core generator.\n\n\n ## -------------------- pickle support -------------------\n\n # Issue 17489: Since __reduce__ was defined to fix #759889 this is no\n # longer called; we leave it here because it has been here since random was\n # rewritten back in 2001 and why risk breaking something.\n def __getstate__(self): # for pickle\n return self.getstate()\n\n def __setstate__(self, state): # for pickle\n self.setstate(state)\n\n def __reduce__(self):\n return self.__class__, (), self.getstate()\n\n\n ## ---- internal support method for evenly distributed integers ----\n\n def __init_subclass__(cls, /, **kwargs):\n \"\"\"Control how subclasses generate random integers.\n\n The algorithm a subclass can use depends on the random() and/or\n getrandbits() implementation available to it and determines\n whether it can generate random integers from arbitrarily large\n ranges.\n \"\"\"\n\n for c in cls.__mro__:\n if '_randbelow' in c.__dict__:\n # just inherit it\n break\n if 'getrandbits' in c.__dict__:\n cls._randbelow = cls._randbelow_with_getrandbits\n break\n if 'random' in c.__dict__:\n cls._randbelow = cls._randbelow_without_getrandbits\n break\n\n def _randbelow_with_getrandbits(self, n):\n \"Return a random int in the range [0,n). Defined for n > 0.\"\n\n getrandbits = self.getrandbits\n k = n.bit_length()\n r = getrandbits(k) # 0 <= r < 2**k\n while r >= n:\n r = getrandbits(k)\n return r\n\n def _randbelow_without_getrandbits(self, n, maxsize=1< 0.\n\n The implementation does not use getrandbits, but only random.\n \"\"\"\n\n random = self.random\n if n >= maxsize:\n from warnings import warn\n warn(\"Underlying random() generator does not supply \\n\"\n \"enough bits to choose from a population range this large.\\n\"\n \"To remove the range limitation, add a getrandbits() method.\")\n return _floor(random() * n)\n rem = maxsize % n\n limit = (maxsize - rem) / maxsize # int(limit * maxsize) % n == 0\n r = random()\n while r >= limit:\n r = random()\n return _floor(r * maxsize) % n\n\n _randbelow = _randbelow_with_getrandbits\n\n\n ## --------------------------------------------------------\n ## ---- Methods below this point generate custom distributions\n ## ---- based on the methods defined above. They do not\n ## ---- directly touch the underlying generator and only\n ## ---- access randomness through the methods: random(),\n ## ---- getrandbits(), or _randbelow().\n\n\n ## -------------------- bytes methods ---------------------\n\n def randbytes(self, n):\n \"\"\"Generate n random bytes.\"\"\"\n return self.getrandbits(n * 8).to_bytes(n, 'little')\n\n\n ## -------------------- integer methods -------------------\n\n def randrange(self, start, stop=None, step=_ONE):\n \"\"\"Choose a random item from range(stop) or range(start, stop[, step]).\n\n Roughly equivalent to ``choice(range(start, stop, step))`` but\n supports arbitrarily large ranges and is optimized for common cases.\n\n \"\"\"\n\n # This code is a bit messy to make it fast for the\n # common case while still doing adequate error checking.\n istart = _index(start)\n if stop is None:\n # We don't check for \"step != 1\" because it hasn't been\n # type checked and converted to an integer yet.\n if step is not _ONE:\n raise TypeError(\"Missing a non-None stop argument\")\n if istart > 0:\n return self._randbelow(istart)\n raise ValueError(\"empty range for randrange()\")\n\n # Stop argument supplied.\n istop = _index(stop)\n width = istop - istart\n istep = _index(step)\n # Fast path.\n if istep == 1:\n if width > 0:\n return istart + self._randbelow(width)\n raise ValueError(f\"empty range in randrange({start}, {stop})\")\n\n # Non-unit step argument supplied.\n if istep > 0:\n n = (width + istep - 1) // istep\n elif istep < 0:\n n = (width + istep + 1) // istep\n else:\n raise ValueError(\"zero step for randrange()\")\n if n <= 0:\n raise ValueError(f\"empty range in randrange({start}, {stop}, {step})\")\n return istart + istep * self._randbelow(n)\n\n def randint(self, a, b):\n \"\"\"Return random integer in range [a, b], including both end points.\n \"\"\"\n\n return self.randrange(a, b+1)\n\n\n ## -------------------- sequence methods -------------------\n\n def choice(self, seq):\n \"\"\"Choose a random element from a non-empty sequence.\"\"\"\n\n # As an accommodation for NumPy, we don't use \"if not seq\"\n # because bool(numpy.array()) raises a ValueError.\n if not len(seq):\n raise IndexError('Cannot choose from an empty sequence')\n return seq[self._randbelow(len(seq))]\n\n def shuffle(self, x):\n \"\"\"Shuffle list x in place, and return None.\"\"\"\n\n randbelow = self._randbelow\n for i in reversed(range(1, len(x))):\n # pick an element in x[:i+1] with which to exchange x[i]\n j = randbelow(i + 1)\n x[i], x[j] = x[j], x[i]\n\n def sample(self, population, k, *, counts=None):\n \"\"\"Chooses k unique random elements from a population sequence.\n\n Returns a new list containing elements from the population while\n leaving the original population unchanged. The resulting list is\n in selection order so that all sub-slices will also be valid random\n samples. This allows raffle winners (the sample) to be partitioned\n into grand prize and second place winners (the subslices).\n\n Members of the population need not be hashable or unique. If the\n population contains repeats, then each occurrence is a possible\n selection in the sample.\n\n Repeated elements can be specified one at a time or with the optional\n counts parameter. For example:\n\n sample(['red', 'blue'], counts=[4, 2], k=5)\n\n is equivalent to:\n\n sample(['red', 'red', 'red', 'red', 'blue', 'blue'], k=5)\n\n To choose a sample from a range of integers, use range() for the\n population argument. This is especially fast and space efficient\n for sampling from a large population:\n\n sample(range(10000000), 60)\n\n \"\"\"\n\n # Sampling without replacement entails tracking either potential\n # selections (the pool) in a list or previous selections in a set.\n\n # When the number of selections is small compared to the\n # population, then tracking selections is efficient, requiring\n # only a small set and an occasional reselection. For\n # a larger number of selections, the pool tracking method is\n # preferred since the list takes less space than the\n # set and it doesn't suffer from frequent reselections.\n\n # The number of calls to _randbelow() is kept at or near k, the\n # theoretical minimum. This is important because running time\n # is dominated by _randbelow() and because it extracts the\n # least entropy from the underlying random number generators.\n\n # Memory requirements are kept to the smaller of a k-length\n # set or an n-length list.\n\n # There are other sampling algorithms that do not require\n # auxiliary memory, but they were rejected because they made\n # too many calls to _randbelow(), making them slower and\n # causing them to eat more entropy than necessary.\n\n if not isinstance(population, _Sequence):\n raise TypeError(\"Population must be a sequence. \"\n \"For dicts or sets, use sorted(d).\")\n n = len(population)\n if counts is not None:\n cum_counts = list(_accumulate(counts))\n if len(cum_counts) != n:\n raise ValueError('The number of counts does not match the population')\n total = cum_counts.pop()\n if not isinstance(total, int):\n raise TypeError('Counts must be integers')\n if total <= 0:\n raise ValueError('Total of counts must be greater than zero')\n selections = self.sample(range(total), k=k)\n bisect = _bisect\n return [population[bisect(cum_counts, s)] for s in selections]\n randbelow = self._randbelow\n if not 0 <= k <= n:\n raise ValueError(\"Sample larger than population or is negative\")\n result = [None] * k\n setsize = 21 # size of a small set minus size of an empty list\n if k > 5:\n setsize += 4 ** _ceil(_log(k * 3, 4)) # table size for big sets\n if n <= setsize:\n # An n-length list is smaller than a k-length set.\n # Invariant: non-selected at pool[0 : n-i]\n pool = list(population)\n for i in range(k):\n j = randbelow(n - i)\n result[i] = pool[j]\n pool[j] = pool[n - i - 1] # move non-selected item into vacancy\n else:\n selected = set()\n selected_add = selected.add\n for i in range(k):\n j = randbelow(n)\n while j in selected:\n j = randbelow(n)\n selected_add(j)\n result[i] = population[j]\n return result\n\n def choices(self, population, weights=None, *, cum_weights=None, k=1):\n \"\"\"Return a k sized list of population elements chosen with replacement.\n\n If the relative weights or cumulative weights are not specified,\n the selections are made with equal probability.\n\n \"\"\"\n random = self.random\n n = len(population)\n if cum_weights is None:\n if weights is None:\n floor = _floor\n n += 0.0 # convert to float for a small speed improvement\n return [population[floor(random() * n)] for i in _repeat(None, k)]\n try:\n cum_weights = list(_accumulate(weights))\n except TypeError:\n if not isinstance(weights, int):\n raise\n k = weights\n raise TypeError(\n f'The number of choices must be a keyword argument: {k=}'\n ) from None\n elif weights is not None:\n raise TypeError('Cannot specify both weights and cumulative weights')\n if len(cum_weights) != n:\n raise ValueError('The number of weights does not match the population')\n total = cum_weights[-1] + 0.0 # convert to float\n if total <= 0.0:\n raise ValueError('Total of weights must be greater than zero')\n if not _isfinite(total):\n raise ValueError('Total of weights must be finite')\n bisect = _bisect\n hi = n - 1\n return [population[bisect(cum_weights, random() * total, 0, hi)]\n for i in _repeat(None, k)]\n\n\n ## -------------------- real-valued distributions -------------------\n\n def uniform(self, a, b):\n \"\"\"Get a random number in the range [a, b) or [a, b] depending on rounding.\n\n The mean (expected value) and variance of the random variable are:\n\n E[X] = (a + b) / 2\n Var[X] = (b - a) ** 2 / 12\n\n \"\"\"\n return a + (b - a) * self.random()\n\n def triangular(self, low=0.0, high=1.0, mode=None):\n \"\"\"Triangular distribution.\n\n Continuous distribution bounded by given lower and upper limits,\n and having a given mode value in-between.\n\n http://en.wikipedia.org/wiki/Triangular_distribution\n\n The mean (expected value) and variance of the random variable are:\n\n E[X] = (low + high + mode) / 3\n Var[X] = (low**2 + high**2 + mode**2 - low*high - low*mode - high*mode) / 18\n\n \"\"\"\n u = self.random()\n try:\n c = 0.5 if mode is None else (mode - low) / (high - low)\n except ZeroDivisionError:\n return low\n if u > c:\n u = 1.0 - u\n c = 1.0 - c\n low, high = high, low\n return low + (high - low) * _sqrt(u * c)\n\n def normalvariate(self, mu=0.0, sigma=1.0):\n \"\"\"Normal distribution.\n\n mu is the mean, and sigma is the standard deviation.\n\n \"\"\"\n # Uses Kinderman and Monahan method. Reference: Kinderman,\n # A.J. and Monahan, J.F., \"Computer generation of random\n # variables using the ratio of uniform deviates\", ACM Trans\n # Math Software, 3, (1977), pp257-260.\n\n random = self.random\n while True:\n u1 = random()\n u2 = 1.0 - random()\n z = NV_MAGICCONST * (u1 - 0.5) / u2\n zz = z * z / 4.0\n if zz <= -_log(u2):\n break\n return mu + z * sigma\n\n def gauss(self, mu=0.0, sigma=1.0):\n \"\"\"Gaussian distribution.\n\n mu is the mean, and sigma is the standard deviation. This is\n slightly faster than the normalvariate() function.\n\n Not thread-safe without a lock around calls.\n\n \"\"\"\n # When x and y are two variables from [0, 1), uniformly\n # distributed, then\n #\n # cos(2*pi*x)*sqrt(-2*log(1-y))\n # sin(2*pi*x)*sqrt(-2*log(1-y))\n #\n # are two *independent* variables with normal distribution\n # (mu = 0, sigma = 1).\n # (Lambert Meertens)\n # (corrected version; bug discovered by Mike Miller, fixed by LM)\n\n # Multithreading note: When two threads call this function\n # simultaneously, it is possible that they will receive the\n # same return value. The window is very small though. To\n # avoid this, you have to use a lock around all calls. (I\n # didn't want to slow this down in the serial case by using a\n # lock here.)\n\n random = self.random\n z = self.gauss_next\n self.gauss_next = None\n if z is None:\n x2pi = random() * TWOPI\n g2rad = _sqrt(-2.0 * _log(1.0 - random()))\n z = _cos(x2pi) * g2rad\n self.gauss_next = _sin(x2pi) * g2rad\n\n return mu + z * sigma\n\n def lognormvariate(self, mu, sigma):\n \"\"\"Log normal distribution.\n\n If you take the natural logarithm of this distribution, you'll get a\n normal distribution with mean mu and standard deviation sigma.\n mu can have any value, and sigma must be greater than zero.\n\n \"\"\"\n return _exp(self.normalvariate(mu, sigma))\n\n def expovariate(self, lambd=1.0):\n \"\"\"Exponential distribution.\n\n lambd is 1.0 divided by the desired mean. It should be\n nonzero. (The parameter would be called \"lambda\", but that is\n a reserved word in Python.) Returned values range from 0 to\n positive infinity if lambd is positive, and from negative\n infinity to 0 if lambd is negative.\n\n The mean (expected value) and variance of the random variable are:\n\n E[X] = 1 / lambd\n Var[X] = 1 / lambd ** 2\n\n \"\"\"\n # we use 1-random() instead of random() to preclude the\n # possibility of taking the log of zero.\n\n return -_log(1.0 - self.random()) / lambd\n\n def vonmisesvariate(self, mu, kappa):\n \"\"\"Circular data distribution.\n\n mu is the mean angle, expressed in radians between 0 and 2*pi, and\n kappa is the concentration parameter, which must be greater than or\n equal to zero. If kappa is equal to zero, this distribution reduces\n to a uniform random angle over the range 0 to 2*pi.\n\n \"\"\"\n # Based upon an algorithm published in: Fisher, N.I.,\n # \"Statistical Analysis of Circular Data\", Cambridge\n # University Press, 1993.\n\n # Thanks to Magnus Kessler for a correction to the\n # implementation of step 4.\n\n random = self.random\n if kappa <= 1e-6:\n return TWOPI * random()\n\n s = 0.5 / kappa\n r = s + _sqrt(1.0 + s * s)\n\n while True:\n u1 = random()\n z = _cos(_pi * u1)\n\n d = z / (r + z)\n u2 = random()\n if u2 < 1.0 - d * d or u2 <= (1.0 - d) * _exp(d):\n break\n\n q = 1.0 / r\n f = (q + z) / (1.0 + q * z)\n u3 = random()\n if u3 > 0.5:\n theta = (mu + _acos(f)) % TWOPI\n else:\n theta = (mu - _acos(f)) % TWOPI\n\n return theta\n\n def gammavariate(self, alpha, beta):\n \"\"\"Gamma distribution. Not the gamma function!\n\n Conditions on the parameters are alpha > 0 and beta > 0.\n\n The probability distribution function is:\n\n x ** (alpha - 1) * math.exp(-x / beta)\n pdf(x) = --------------------------------------\n math.gamma(alpha) * beta ** alpha\n\n The mean (expected value) and variance of the random variable are:\n\n E[X] = alpha * beta\n Var[X] = alpha * beta ** 2\n\n \"\"\"\n\n # Warning: a few older sources define the gamma distribution in terms\n # of alpha > -1.0\n if alpha <= 0.0 or beta <= 0.0:\n raise ValueError('gammavariate: alpha and beta must be > 0.0')\n\n random = self.random\n if alpha > 1.0:\n\n # Uses R.C.H. Cheng, \"The generation of Gamma\n # variables with non-integral shape parameters\",\n # Applied Statistics, (1977), 26, No. 1, p71-74\n\n ainv = _sqrt(2.0 * alpha - 1.0)\n bbb = alpha - LOG4\n ccc = alpha + ainv\n\n while True:\n u1 = random()\n if not 1e-7 < u1 < 0.9999999:\n continue\n u2 = 1.0 - random()\n v = _log(u1 / (1.0 - u1)) / ainv\n x = alpha * _exp(v)\n z = u1 * u1 * u2\n r = bbb + ccc * v - x\n if r + SG_MAGICCONST - 4.5 * z >= 0.0 or r >= _log(z):\n return x * beta\n\n elif alpha == 1.0:\n # expovariate(1/beta)\n return -_log(1.0 - random()) * beta\n\n else:\n # alpha is between 0 and 1 (exclusive)\n # Uses ALGORITHM GS of Statistical Computing - Kennedy & Gentle\n while True:\n u = random()\n b = (_e + alpha) / _e\n p = b * u\n if p <= 1.0:\n x = p ** (1.0 / alpha)\n else:\n x = -_log((b - p) / alpha)\n u1 = random()\n if p > 1.0:\n if u1 <= x ** (alpha - 1.0):\n break\n elif u1 <= _exp(-x):\n break\n return x * beta\n\n def betavariate(self, alpha, beta):\n \"\"\"Beta distribution.\n\n Conditions on the parameters are alpha > 0 and beta > 0.\n Returned values range between 0 and 1.\n\n The mean (expected value) and variance of the random variable are:\n\n E[X] = alpha / (alpha + beta)\n Var[X] = alpha * beta / ((alpha + beta)**2 * (alpha + beta + 1))\n\n \"\"\"\n ## See\n ## http://mail.python.org/pipermail/python-bugs-list/2001-January/003752.html\n ## for Ivan Frohne's insightful analysis of why the original implementation:\n ##\n ## def betavariate(self, alpha, beta):\n ## # Discrete Event Simulation in C, pp 87-88.\n ##\n ## y = self.expovariate(alpha)\n ## z = self.expovariate(1.0/beta)\n ## return z/(y+z)\n ##\n ## was dead wrong, and how it probably got that way.\n\n # This version due to Janne Sinkkonen, and matches all the std\n # texts (e.g., Knuth Vol 2 Ed 3 pg 134 \"the beta distribution\").\n y = self.gammavariate(alpha, 1.0)\n if y:\n return y / (y + self.gammavariate(beta, 1.0))\n return 0.0\n\n def paretovariate(self, alpha):\n \"\"\"Pareto distribution. alpha is the shape parameter.\"\"\"\n # Jain, pg. 495\n\n u = 1.0 - self.random()\n return u ** (-1.0 / alpha)\n\n def weibullvariate(self, alpha, beta):\n \"\"\"Weibull distribution.\n\n alpha is the scale parameter and beta is the shape parameter.\n\n \"\"\"\n # Jain, pg. 499; bug fix courtesy Bill Arms\n\n u = 1.0 - self.random()\n return alpha * (-_log(u)) ** (1.0 / beta)\n\n\n ## -------------------- discrete distributions ---------------------\n\n def binomialvariate(self, n=1, p=0.5):\n \"\"\"Binomial random variable.\n\n Gives the number of successes for *n* independent trials\n with the probability of success in each trial being *p*:\n\n sum(random() < p for i in range(n))\n\n Returns an integer in the range: 0 <= X <= n\n\n The mean (expected value) and variance of the random variable are:\n\n E[X] = n * p\n Var[x] = n * p * (1 - p)\n\n \"\"\"\n # Error check inputs and handle edge cases\n if n < 0:\n raise ValueError(\"n must be non-negative\")\n if p <= 0.0 or p >= 1.0:\n if p == 0.0:\n return 0\n if p == 1.0:\n return n\n raise ValueError(\"p must be in the range 0.0 <= p <= 1.0\")\n\n random = self.random\n\n # Fast path for a common case\n if n == 1:\n return _index(random() < p)\n\n # Exploit symmetry to establish: p <= 0.5\n if p > 0.5:\n return n - self.binomialvariate(n, 1.0 - p)\n\n if n * p < 10.0:\n # BG: Geometric method by Devroye with running time of O(np).\n # https://dl.acm.org/doi/pdf/10.1145/42372.42381\n x = y = 0\n c = _log2(1.0 - p)\n if not c:\n return x\n while True:\n y += _floor(_log2(random()) / c) + 1\n if y > n:\n return x\n x += 1\n\n # BTRS: Transformed rejection with squeeze method by Wolfgang H\u00f6rmann\n # https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.47.8407&rep=rep1&type=pdf\n assert n*p >= 10.0 and p <= 0.5\n setup_complete = False\n\n spq = _sqrt(n * p * (1.0 - p)) # Standard deviation of the distribution\n b = 1.15 + 2.53 * spq\n a = -0.0873 + 0.0248 * b + 0.01 * p\n c = n * p + 0.5\n vr = 0.92 - 4.2 / b\n\n while True:\n\n u = random()\n u -= 0.5\n us = 0.5 - _fabs(u)\n k = _floor((2.0 * a / us + b) * u + c)\n if k < 0 or k > n:\n continue\n\n # The early-out \"squeeze\" test substantially reduces\n # the number of acceptance condition evaluations.\n v = random()\n if us >= 0.07 and v <= vr:\n return k\n\n # Acceptance-rejection test.\n # Note, the original paper erroneously omits the call to log(v)\n # when comparing to the log of the rescaled binomial distribution.\n if not setup_complete:\n alpha = (2.83 + 5.1 / b) * spq\n lpq = _log(p / (1.0 - p))\n m = _floor((n + 1) * p) # Mode of the distribution\n h = _lgamma(m + 1) + _lgamma(n - m + 1)\n setup_complete = True # Only needs to be done once\n v *= alpha / (a / (us * us) + b)\n if _log(v) <= h - _lgamma(k + 1) - _lgamma(n - k + 1) + (k - m) * lpq:\n return k\n\n\n## ------------------------------------------------------------------\n## --------------- Operating System Random Source ------------------\n\n\nclass SystemRandom(Random):\n \"\"\"Alternate random number generator using sources provided\n by the operating system (such as /dev/urandom on Unix or\n CryptGenRandom on Windows).\n\n Not available on all systems (see os.urandom() for details).\n\n \"\"\"\n\n def random(self):\n \"\"\"Get the next random number in the range 0.0 <= X < 1.0.\"\"\"\n return (int.from_bytes(_urandom(7)) >> 3) * RECIP_BPF\n\n def getrandbits(self, k):\n \"\"\"getrandbits(k) -> x. Generates an int with k random bits.\"\"\"\n if k < 0:\n raise ValueError('number of bits must be non-negative')\n numbytes = (k + 7) // 8 # bits / 8 and rounded up\n x = int.from_bytes(_urandom(numbytes))\n return x >> (numbytes * 8 - k) # trim excess bits\n\n def randbytes(self, n):\n \"\"\"Generate n random bytes.\"\"\"\n # os.urandom(n) fails with ValueError for n < 0\n # and returns an empty bytes string for n == 0.\n return _urandom(n)\n\n def seed(self, *args, **kwds):\n \"Stub method. Not used for a system random number generator.\"\n return None\n\n def _notimplemented(self, *args, **kwds):\n \"Method should not be called for a system random number generator.\"\n raise NotImplementedError('System entropy source does not have state.')\n getstate = setstate = _notimplemented\n\n\n# ----------------------------------------------------------------------\n# Create one instance, seeded from current time, and export its methods\n# as module-level functions. The functions share state across all uses\n# (both in the user's code and in the Python libraries), but that's fine\n# for most programs and is easier for the casual user than making them\n# instantiate their own Random() instance.\n\n_inst = Random()\nseed = _inst.seed\nrandom = _inst.random\nuniform = _inst.uniform\ntriangular = _inst.triangular\nrandint = _inst.randint\nchoice = _inst.choice\nrandrange = _inst.randrange\nsample = _inst.sample\nshuffle = _inst.shuffle\nchoices = _inst.choices\nnormalvariate = _inst.normalvariate\nlognormvariate = _inst.lognormvariate\nexpovariate = _inst.expovariate\nvonmisesvariate = _inst.vonmisesvariate\ngammavariate = _inst.gammavariate\ngauss = _inst.gauss\nbetavariate = _inst.betavariate\nbinomialvariate = _inst.binomialvariate\nparetovariate = _inst.paretovariate\nweibullvariate = _inst.weibullvariate\ngetstate = _inst.getstate\nsetstate = _inst.setstate\ngetrandbits = _inst.getrandbits\nrandbytes = _inst.randbytes\n\n\n## ------------------------------------------------------\n## ----------------- test program -----------------------\n\ndef _test_generator(n, func, args):\n from statistics import stdev, fmean as mean\n from time import perf_counter\n\n t0 = perf_counter()\n data = [func(*args) for i in _repeat(None, n)]\n t1 = perf_counter()\n\n xbar = mean(data)\n sigma = stdev(data, xbar)\n low = min(data)\n high = max(data)\n\n print(f'{t1 - t0:.3f} sec, {n} times {func.__name__}{args!r}')\n print('avg %g, stddev %g, min %g, max %g\\n' % (xbar, sigma, low, high))\n\n\ndef _test(N=10_000):\n _test_generator(N, random, ())\n _test_generator(N, normalvariate, (0.0, 1.0))\n _test_generator(N, lognormvariate, (0.0, 1.0))\n _test_generator(N, vonmisesvariate, (0.0, 1.0))\n _test_generator(N, binomialvariate, (15, 0.60))\n _test_generator(N, binomialvariate, (100, 0.75))\n _test_generator(N, gammavariate, (0.01, 1.0))\n _test_generator(N, gammavariate, (0.1, 1.0))\n _test_generator(N, gammavariate, (0.1, 2.0))\n _test_generator(N, gammavariate, (0.5, 1.0))\n _test_generator(N, gammavariate, (0.9, 1.0))\n _test_generator(N, gammavariate, (1.0, 1.0))\n _test_generator(N, gammavariate, (2.0, 1.0))\n _test_generator(N, gammavariate, (20.0, 1.0))\n _test_generator(N, gammavariate, (200.0, 1.0))\n _test_generator(N, gauss, (0.0, 1.0))\n _test_generator(N, betavariate, (3.0, 3.0))\n _test_generator(N, triangular, (0.0, 1.0, 1.0 / 3.0))\n\n\n## ------------------------------------------------------\n## ------------------ fork support ---------------------\n\nif hasattr(_os, \"fork\"):\n _os.register_at_fork(after_in_child=_inst.seed)\n\n\n# ------------------------------------------------------\n# -------------- command-line interface ----------------\n\n\ndef _parse_args(arg_list: list[str] | None):\n import argparse\n parser = argparse.ArgumentParser(\n formatter_class=argparse.RawTextHelpFormatter)\n group = parser.add_mutually_exclusive_group()\n group.add_argument(\n \"-c\", \"--choice\", nargs=\"+\",\n help=\"print a random choice\")\n group.add_argument(\n \"-i\", \"--integer\", type=int, metavar=\"N\",\n help=\"print a random integer between 1 and N inclusive\")\n group.add_argument(\n \"-f\", \"--float\", type=float, metavar=\"N\",\n help=\"print a random floating-point number between 0 and N inclusive\")\n group.add_argument(\n \"--test\", type=int, const=10_000, nargs=\"?\",\n help=argparse.SUPPRESS)\n parser.add_argument(\"input\", nargs=\"*\",\n help=\"\"\"\\\nif no options given, output depends on the input\n string or multiple: same as --choice\n integer: same as --integer\n float: same as --float\"\"\")\n args = parser.parse_args(arg_list)\n return args, parser.format_help()\n\n\ndef main(arg_list: list[str] | None = None) -> int | str:\n args, help_text = _parse_args(arg_list)\n\n # Explicit arguments\n if args.choice:\n return choice(args.choice)\n\n if args.integer is not None:\n return randint(1, args.integer)\n\n if args.float is not None:\n return uniform(0, args.float)\n\n if args.test:\n _test(args.test)\n return \"\"\n\n # No explicit argument, select based on input\n if len(args.input) == 1:\n val = args.input[0]\n try:\n # Is it an integer?\n val = int(val)\n return randint(1, val)\n except ValueError:\n try:\n # Is it a float?\n val = float(val)\n return uniform(0, val)\n except ValueError:\n # Split in case of space-separated string: \"a b c\"\n return choice(val.split())\n\n if len(args.input) >= 2:\n return choice(args.input)\n\n return help_text\n\n\nif __name__ == '__main__':\n print(main())\n", 1070]}, "functions": {"DistutilsMetaFinder.find_spec.. (/home/gaogaotiantian/programs/viztracer/venv3.13/lib/python3.13/site-packages/_distutils_hack/__init__.py:109)": ["/home/gaogaotiantian/programs/viztracer/venv3.13/lib/python3.13/site-packages/_distutils_hack/__init__.py", 109], "DistutilsMetaFinder.find_spec (/home/gaogaotiantian/programs/viztracer/venv3.13/lib/python3.13/site-packages/_distutils_hack/__init__.py:102)": ["/home/gaogaotiantian/programs/viztracer/venv3.13/lib/python3.13/site-packages/_distutils_hack/__init__.py", 102], "treeNode (/home/gaogaotiantian/programs/viztracer/venv3.13/lib/python3.13/site-packages/mcts.py:18)": ["/home/gaogaotiantian/programs/viztracer/venv3.13/lib/python3.13/site-packages/mcts.py", 18], "mcts (/home/gaogaotiantian/programs/viztracer/venv3.13/lib/python3.13/site-packages/mcts.py:29)": ["/home/gaogaotiantian/programs/viztracer/venv3.13/lib/python3.13/site-packages/mcts.py", 29], " (/home/gaogaotiantian/programs/viztracer/venv3.13/lib/python3.13/site-packages/mcts.py:1)": ["/home/gaogaotiantian/programs/viztracer/venv3.13/lib/python3.13/site-packages/mcts.py", 1], "NaughtsAndCrossesState (/home/gaogaotiantian/programs/viztracer/example/src/mcts_game.py:11)": ["/home/gaogaotiantian/programs/viztracer/example/src/mcts_game.py", 11], "Action (/home/gaogaotiantian/programs/viztracer/example/src/mcts_game.py:60)": ["/home/gaogaotiantian/programs/viztracer/example/src/mcts_game.py", 60], "NaughtsAndCrossesState.__init__ (/home/gaogaotiantian/programs/viztracer/example/src/mcts_game.py:12)": ["/home/gaogaotiantian/programs/viztracer/example/src/mcts_game.py", 12], "mcts.__init__ (/home/gaogaotiantian/programs/viztracer/venv3.13/lib/python3.13/site-packages/mcts.py:30)": ["/home/gaogaotiantian/programs/viztracer/venv3.13/lib/python3.13/site-packages/mcts.py", 30], "NaughtsAndCrossesState.isTerminal (/home/gaogaotiantian/programs/viztracer/example/src/mcts_game.py:33)": ["/home/gaogaotiantian/programs/viztracer/example/src/mcts_game.py", 33], "treeNode.__init__ (/home/gaogaotiantian/programs/viztracer/venv3.13/lib/python3.13/site-packages/mcts.py:19)": ["/home/gaogaotiantian/programs/viztracer/venv3.13/lib/python3.13/site-packages/mcts.py", 19], "Action.__init__ (/home/gaogaotiantian/programs/viztracer/example/src/mcts_game.py:61)": ["/home/gaogaotiantian/programs/viztracer/example/src/mcts_game.py", 61], "NaughtsAndCrossesState.getPossibleActions (/home/gaogaotiantian/programs/viztracer/example/src/mcts_game.py:19)": ["/home/gaogaotiantian/programs/viztracer/example/src/mcts_game.py", 19], "Action.__hash__ (/home/gaogaotiantian/programs/viztracer/example/src/mcts_game.py:75)": ["/home/gaogaotiantian/programs/viztracer/example/src/mcts_game.py", 75], "_slotnames (/usr/lib/python3.13/copyreg.py:107)": ["/usr/lib/python3.13/copyreg.py", 107], "_deepcopy_atomic (/usr/lib/python3.13/copy.py:173)": ["/usr/lib/python3.13/copy.py", 173], "deepcopy (/usr/lib/python3.13/copy.py:119)": ["/usr/lib/python3.13/copy.py", 119], "_reconstruct.. (/usr/lib/python3.13/copy.py:253)": ["/usr/lib/python3.13/copy.py", 253], "__newobj__ (/usr/lib/python3.13/copyreg.py:98)": ["/usr/lib/python3.13/copyreg.py", 98], "_deepcopy_list (/usr/lib/python3.13/copy.py:192)": ["/usr/lib/python3.13/copy.py", 192], "_keep_alive (/usr/lib/python3.13/copy.py:232)": ["/usr/lib/python3.13/copy.py", 232], "_deepcopy_dict (/usr/lib/python3.13/copy.py:218)": ["/usr/lib/python3.13/copy.py", 218], "_reconstruct (/usr/lib/python3.13/copy.py:248)": ["/usr/lib/python3.13/copy.py", 248], "NaughtsAndCrossesState.takeAction (/home/gaogaotiantian/programs/viztracer/example/src/mcts_game.py:27)": ["/home/gaogaotiantian/programs/viztracer/example/src/mcts_game.py", 27], "mcts.expand (/home/gaogaotiantian/programs/viztracer/venv3.13/lib/python3.13/site-packages/mcts.py:76)": ["/home/gaogaotiantian/programs/viztracer/venv3.13/lib/python3.13/site-packages/mcts.py", 76], "mcts.selectNode 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'Lock', 'RLock', 'Semaphore', 'BoundedSemaphore', 'Condition', 'Event'\n ]\n\nimport threading\nimport sys\nimport tempfile\nimport _multiprocessing\nimport time\n\nfrom . import context\nfrom . import process\nfrom . import util\n\n# Try to import the mp.synchronize module cleanly, if it fails\n# raise ImportError for platforms lacking a working sem_open implementation.\n# See issue 3770\ntry:\n from _multiprocessing import SemLock, sem_unlink\nexcept (ImportError):\n raise ImportError(\"This platform lacks a functioning sem_open\" +\n \" implementation, therefore, the required\" +\n \" synchronization primitives needed will not\" +\n \" function, see issue 3770.\")\n\n#\n# Constants\n#\n\nRECURSIVE_MUTEX, SEMAPHORE = list(range(2))\nSEM_VALUE_MAX = _multiprocessing.SemLock.SEM_VALUE_MAX\n\n#\n# Base class for semaphores and mutexes; wraps `_multiprocessing.SemLock`\n#\n\nclass SemLock(object):\n\n _rand = tempfile._RandomNameSequence()\n\n def __init__(self, kind, value, maxvalue, *, ctx):\n if ctx is None:\n ctx = context._default_context.get_context()\n name = ctx.get_start_method()\n unlink_now = sys.platform == 'win32' or name == 'fork'\n for i in range(100):\n try:\n sl = self._semlock = _multiprocessing.SemLock(\n kind, value, maxvalue, self._make_name(),\n unlink_now)\n except FileExistsError:\n pass\n else:\n break\n else:\n raise FileExistsError('cannot find name for semaphore')\n\n util.debug('created semlock with handle %s' % sl.handle)\n self._make_methods()\n\n if sys.platform != 'win32':\n def _after_fork(obj):\n obj._semlock._after_fork()\n util.register_after_fork(self, _after_fork)\n\n if self._semlock.name is not None:\n # We only get here if we are on Unix with forking\n # disabled. When the object is garbage collected or the\n # process shuts down we unlink the semaphore name\n from .semaphore_tracker import register\n register(self._semlock.name)\n util.Finalize(self, SemLock._cleanup, (self._semlock.name,),\n exitpriority=0)\n\n @staticmethod\n def _cleanup(name):\n from .semaphore_tracker import unregister\n sem_unlink(name)\n unregister(name)\n\n def _make_methods(self):\n self.acquire = self._semlock.acquire\n self.release = self._semlock.release\n\n def __enter__(self):\n return self._semlock.__enter__()\n\n def __exit__(self, *args):\n return self._semlock.__exit__(*args)\n\n def __getstate__(self):\n context.assert_spawning(self)\n sl = self._semlock\n if sys.platform == 'win32':\n h = context.get_spawning_popen().duplicate_for_child(sl.handle)\n else:\n h = sl.handle\n return (h, sl.kind, sl.maxvalue, sl.name)\n\n def __setstate__(self, state):\n self._semlock = _multiprocessing.SemLock._rebuild(*state)\n util.debug('recreated blocker with handle %r' % state[0])\n self._make_methods()\n\n @staticmethod\n def _make_name():\n return '%s-%s' % (process.current_process()._config['semprefix'],\n next(SemLock._rand))\n\n#\n# Semaphore\n#\n\nclass Semaphore(SemLock):\n\n def __init__(self, value=1, *, ctx):\n SemLock.__init__(self, SEMAPHORE, value, SEM_VALUE_MAX, ctx=ctx)\n\n def get_value(self):\n return self._semlock._get_value()\n\n def __repr__(self):\n try:\n value = self._semlock._get_value()\n except Exception:\n value = 'unknown'\n return '<%s(value=%s)>' % (self.__class__.__name__, value)\n\n#\n# Bounded semaphore\n#\n\nclass BoundedSemaphore(Semaphore):\n\n def __init__(self, value=1, *, ctx):\n SemLock.__init__(self, SEMAPHORE, value, value, ctx=ctx)\n\n def __repr__(self):\n try:\n value = self._semlock._get_value()\n except Exception:\n value = 'unknown'\n return '<%s(value=%s, maxvalue=%s)>' % \\\n (self.__class__.__name__, value, self._semlock.maxvalue)\n\n#\n# Non-recursive lock\n#\n\nclass Lock(SemLock):\n\n def __init__(self, *, ctx):\n SemLock.__init__(self, SEMAPHORE, 1, 1, ctx=ctx)\n\n def __repr__(self):\n try:\n if self._semlock._is_mine():\n name = process.current_process().name\n if threading.current_thread().name != 'MainThread':\n name += '|' + threading.current_thread().name\n elif self._semlock._get_value() == 1:\n name = 'None'\n elif self._semlock._count() > 0:\n name = 'SomeOtherThread'\n else:\n name = 'SomeOtherProcess'\n except Exception:\n name = 'unknown'\n return '<%s(owner=%s)>' % (self.__class__.__name__, name)\n\n#\n# Recursive lock\n#\n\nclass RLock(SemLock):\n\n def __init__(self, *, ctx):\n SemLock.__init__(self, RECURSIVE_MUTEX, 1, 1, ctx=ctx)\n\n def __repr__(self):\n try:\n if self._semlock._is_mine():\n name = process.current_process().name\n if threading.current_thread().name != 'MainThread':\n name += '|' + threading.current_thread().name\n count = self._semlock._count()\n elif self._semlock._get_value() == 1:\n name, count = 'None', 0\n elif self._semlock._count() > 0:\n name, count = 'SomeOtherThread', 'nonzero'\n else:\n name, count = 'SomeOtherProcess', 'nonzero'\n except Exception:\n name, count = 'unknown', 'unknown'\n return '<%s(%s, %s)>' % (self.__class__.__name__, name, count)\n\n#\n# Condition variable\n#\n\nclass Condition(object):\n\n def __init__(self, lock=None, *, ctx):\n self._lock = lock or ctx.RLock()\n self._sleeping_count = ctx.Semaphore(0)\n self._woken_count = ctx.Semaphore(0)\n self._wait_semaphore = ctx.Semaphore(0)\n self._make_methods()\n\n def __getstate__(self):\n context.assert_spawning(self)\n return (self._lock, self._sleeping_count,\n self._woken_count, self._wait_semaphore)\n\n def __setstate__(self, state):\n (self._lock, self._sleeping_count,\n self._woken_count, self._wait_semaphore) = state\n self._make_methods()\n\n def __enter__(self):\n return self._lock.__enter__()\n\n def __exit__(self, *args):\n return self._lock.__exit__(*args)\n\n def _make_methods(self):\n self.acquire = self._lock.acquire\n self.release = self._lock.release\n\n def __repr__(self):\n try:\n num_waiters = (self._sleeping_count._semlock._get_value() -\n self._woken_count._semlock._get_value())\n except Exception:\n num_waiters = 'unknown'\n return '<%s(%s, %s)>' % (self.__class__.__name__, self._lock, num_waiters)\n\n def wait(self, timeout=None):\n assert self._lock._semlock._is_mine(), \\\n 'must acquire() condition before using wait()'\n\n # indicate that this thread is going to sleep\n self._sleeping_count.release()\n\n # release lock\n count = self._lock._semlock._count()\n for i in range(count):\n self._lock.release()\n\n try:\n # wait for notification or timeout\n return self._wait_semaphore.acquire(True, timeout)\n finally:\n # indicate that this thread has woken\n self._woken_count.release()\n\n # reacquire lock\n for i in range(count):\n self._lock.acquire()\n\n def notify(self):\n assert self._lock._semlock._is_mine(), 'lock is not owned'\n assert not self._wait_semaphore.acquire(False)\n\n # to take account of timeouts since last notify() we subtract\n # woken_count from sleeping_count and rezero woken_count\n while self._woken_count.acquire(False):\n res = self._sleeping_count.acquire(False)\n assert res\n\n if self._sleeping_count.acquire(False): # try grabbing a sleeper\n self._wait_semaphore.release() # wake up one sleeper\n self._woken_count.acquire() # wait for the sleeper to wake\n\n # rezero _wait_semaphore in case a timeout just happened\n self._wait_semaphore.acquire(False)\n\n def notify_all(self):\n assert self._lock._semlock._is_mine(), 'lock is not owned'\n assert not self._wait_semaphore.acquire(False)\n\n # to take account of timeouts since last notify*() we subtract\n # woken_count from sleeping_count and rezero woken_count\n while self._woken_count.acquire(False):\n res = self._sleeping_count.acquire(False)\n assert res\n\n sleepers = 0\n while self._sleeping_count.acquire(False):\n self._wait_semaphore.release() # wake up one sleeper\n sleepers += 1\n\n if sleepers:\n for i in range(sleepers):\n self._woken_count.acquire() # wait for a sleeper to wake\n\n # rezero wait_semaphore in case some timeouts just happened\n while self._wait_semaphore.acquire(False):\n pass\n\n def wait_for(self, predicate, timeout=None):\n result = predicate()\n if result:\n return result\n if timeout is not None:\n endtime = time.monotonic() + timeout\n else:\n endtime = None\n waittime = None\n while not result:\n if endtime is not None:\n waittime = endtime - time.monotonic()\n if waittime <= 0:\n break\n self.wait(waittime)\n result = predicate()\n return result\n\n#\n# Event\n#\n\nclass Event(object):\n\n def __init__(self, *, ctx):\n self._cond = ctx.Condition(ctx.Lock())\n self._flag = ctx.Semaphore(0)\n\n def is_set(self):\n with self._cond:\n if self._flag.acquire(False):\n self._flag.release()\n return True\n return False\n\n def set(self):\n with self._cond:\n self._flag.acquire(False)\n self._flag.release()\n self._cond.notify_all()\n\n def clear(self):\n with self._cond:\n self._flag.acquire(False)\n\n def wait(self, timeout=None):\n with self._cond:\n if self._flag.acquire(False):\n self._flag.release()\n else:\n self._cond.wait(timeout)\n\n if self._flag.acquire(False):\n self._flag.release()\n return True\n return False\n\n#\n# Barrier\n#\n\nclass Barrier(threading.Barrier):\n\n def __init__(self, parties, action=None, timeout=None, *, ctx):\n import struct\n from .heap import BufferWrapper\n wrapper = BufferWrapper(struct.calcsize('i') * 2)\n cond = ctx.Condition()\n self.__setstate__((parties, action, timeout, cond, wrapper))\n self._state = 0\n self._count = 0\n\n def __setstate__(self, state):\n (self._parties, self._action, self._timeout,\n self._cond, self._wrapper) = state\n self._array = self._wrapper.create_memoryview().cast('i')\n\n def __getstate__(self):\n return (self._parties, self._action, self._timeout,\n self._cond, self._wrapper)\n\n @property\n def _state(self):\n return self._array[0]\n\n @_state.setter\n def _state(self, value):\n self._array[0] = value\n\n @property\n def _count(self):\n return self._array[1]\n\n @_count.setter\n def _count(self, value):\n self._array[1] = value\n",405],"/usr/lib/python3.6/multiprocessing/util.py":["#\n# Module providing various facilities to other parts of the package\n#\n# multiprocessing/util.py\n#\n# Copyright (c) 2006-2008, R Oudkerk\n# Licensed to PSF under a Contributor Agreement.\n#\n\nimport os\nimport itertools\nimport sys\nimport weakref\nimport atexit\nimport threading # we want threading to install it's\n # cleanup function before multiprocessing does\nfrom subprocess import _args_from_interpreter_flags\n\nfrom . import process\n\n__all__ = [\n 'sub_debug', 'debug', 'info', 'sub_warning', 'get_logger',\n 'log_to_stderr', 'get_temp_dir', 'register_after_fork',\n 'is_exiting', 'Finalize', 'ForkAwareThreadLock', 'ForkAwareLocal',\n 'close_all_fds_except', 'SUBDEBUG', 'SUBWARNING',\n ]\n\n#\n# Logging\n#\n\nNOTSET = 0\nSUBDEBUG = 5\nDEBUG = 10\nINFO = 20\nSUBWARNING = 25\n\nLOGGER_NAME = 'multiprocessing'\nDEFAULT_LOGGING_FORMAT = '[%(levelname)s/%(processName)s] %(message)s'\n\n_logger = None\n_log_to_stderr = False\n\ndef sub_debug(msg, *args):\n if _logger:\n _logger.log(SUBDEBUG, msg, *args)\n\ndef debug(msg, *args):\n if _logger:\n _logger.log(DEBUG, msg, *args)\n\ndef info(msg, *args):\n if _logger:\n _logger.log(INFO, msg, *args)\n\ndef sub_warning(msg, *args):\n if _logger:\n _logger.log(SUBWARNING, msg, *args)\n\ndef get_logger():\n '''\n Returns logger used by multiprocessing\n '''\n global _logger\n import logging\n\n logging._acquireLock()\n try:\n if not _logger:\n\n _logger = logging.getLogger(LOGGER_NAME)\n _logger.propagate = 0\n\n # XXX multiprocessing should cleanup before logging\n if hasattr(atexit, 'unregister'):\n atexit.unregister(_exit_function)\n atexit.register(_exit_function)\n else:\n atexit._exithandlers.remove((_exit_function, (), {}))\n atexit._exithandlers.append((_exit_function, (), {}))\n\n finally:\n logging._releaseLock()\n\n return _logger\n\ndef log_to_stderr(level=None):\n '''\n Turn on logging and add a handler which prints to stderr\n '''\n global _log_to_stderr\n import logging\n\n logger = get_logger()\n formatter = logging.Formatter(DEFAULT_LOGGING_FORMAT)\n handler = logging.StreamHandler()\n handler.setFormatter(formatter)\n logger.addHandler(handler)\n\n if level:\n logger.setLevel(level)\n _log_to_stderr = True\n return _logger\n\n#\n# Function returning a temp directory which will be removed on exit\n#\n\ndef get_temp_dir():\n # get name of a temp directory which will be automatically cleaned up\n tempdir = process.current_process()._config.get('tempdir')\n if tempdir is None:\n import shutil, tempfile\n tempdir = tempfile.mkdtemp(prefix='pymp-')\n info('created temp directory %s', tempdir)\n Finalize(None, shutil.rmtree, args=[tempdir], exitpriority=-100)\n process.current_process()._config['tempdir'] = tempdir\n return tempdir\n\n#\n# Support for reinitialization of objects when bootstrapping a child process\n#\n\n_afterfork_registry = weakref.WeakValueDictionary()\n_afterfork_counter = itertools.count()\n\ndef _run_after_forkers():\n items = list(_afterfork_registry.items())\n items.sort()\n for (index, ident, func), obj in items:\n try:\n func(obj)\n except Exception as e:\n info('after forker raised exception %s', e)\n\ndef register_after_fork(obj, func):\n _afterfork_registry[(next(_afterfork_counter), id(obj), func)] = obj\n\n#\n# Finalization using weakrefs\n#\n\n_finalizer_registry = {}\n_finalizer_counter = itertools.count()\n\n\nclass Finalize(object):\n '''\n Class which supports object finalization using weakrefs\n '''\n def __init__(self, obj, callback, args=(), kwargs=None, exitpriority=None):\n assert exitpriority is None or type(exitpriority) is int\n\n if obj is not None:\n self._weakref = weakref.ref(obj, self)\n else:\n assert exitpriority is not None\n\n self._callback = callback\n self._args = args\n self._kwargs = kwargs or {}\n self._key = (exitpriority, next(_finalizer_counter))\n self._pid = os.getpid()\n\n _finalizer_registry[self._key] = self\n\n def __call__(self, wr=None,\n # Need to bind these locally because the globals can have\n # been cleared at shutdown\n _finalizer_registry=_finalizer_registry,\n sub_debug=sub_debug, getpid=os.getpid):\n '''\n Run the callback unless it has already been called or cancelled\n '''\n try:\n del _finalizer_registry[self._key]\n except KeyError:\n sub_debug('finalizer no longer registered')\n else:\n if self._pid != getpid():\n sub_debug('finalizer ignored because different process')\n res = None\n else:\n sub_debug('finalizer calling %s with args %s and kwargs %s',\n self._callback, self._args, self._kwargs)\n res = self._callback(*self._args, **self._kwargs)\n self._weakref = self._callback = self._args = \\\n self._kwargs = self._key = None\n return res\n\n def cancel(self):\n '''\n Cancel finalization of the object\n '''\n try:\n del _finalizer_registry[self._key]\n except KeyError:\n pass\n else:\n self._weakref = self._callback = self._args = \\\n self._kwargs = self._key = None\n\n def still_active(self):\n '''\n Return whether this finalizer is still waiting to invoke callback\n '''\n return self._key in _finalizer_registry\n\n def __repr__(self):\n try:\n obj = self._weakref()\n except (AttributeError, TypeError):\n obj = None\n\n if obj is None:\n return '<%s object, dead>' % self.__class__.__name__\n\n x = '<%s object, callback=%s' % (\n self.__class__.__name__,\n getattr(self._callback, '__name__', self._callback))\n if self._args:\n x += ', args=' + str(self._args)\n if self._kwargs:\n x += ', kwargs=' + str(self._kwargs)\n if self._key[0] is not None:\n x += ', exitprority=' + str(self._key[0])\n return x + '>'\n\n\ndef _run_finalizers(minpriority=None):\n '''\n Run all finalizers whose exit priority is not None and at least minpriority\n\n Finalizers with highest priority are called first; finalizers with\n the same priority will be called in reverse order of creation.\n '''\n if _finalizer_registry is None:\n # This function may be called after this module's globals are\n # destroyed. See the _exit_function function in this module for more\n # notes.\n return\n\n if minpriority is None:\n f = lambda p : p[0] is not None\n else:\n f = lambda p : p[0] is not None and p[0] >= minpriority\n\n # Careful: _finalizer_registry may be mutated while this function\n # is running (either by a GC run or by another thread).\n\n # list(_finalizer_registry) should be atomic, while\n # list(_finalizer_registry.items()) is not.\n keys = [key for key in list(_finalizer_registry) if f(key)]\n keys.sort(reverse=True)\n\n for key in keys:\n finalizer = _finalizer_registry.get(key)\n # key may have been removed from the registry\n if finalizer is not None:\n sub_debug('calling %s', finalizer)\n try:\n finalizer()\n except Exception:\n import traceback\n traceback.print_exc()\n\n if minpriority is None:\n _finalizer_registry.clear()\n\n#\n# Clean up on exit\n#\n\ndef is_exiting():\n '''\n Returns true if the process is shutting down\n '''\n return _exiting or _exiting is None\n\n_exiting = False\n\ndef _exit_function(info=info, debug=debug, _run_finalizers=_run_finalizers,\n active_children=process.active_children,\n current_process=process.current_process):\n # We hold on to references to functions in the arglist due to the\n # situation described below, where this function is called after this\n # module's globals are destroyed.\n\n global _exiting\n\n if not _exiting:\n _exiting = True\n\n info('process shutting down')\n debug('running all \"atexit\" finalizers with priority >= 0')\n _run_finalizers(0)\n\n if current_process() is not None:\n # We check if the current process is None here because if\n # it's None, any call to ``active_children()`` will raise\n # an AttributeError (active_children winds up trying to\n # get attributes from util._current_process). One\n # situation where this can happen is if someone has\n # manipulated sys.modules, causing this module to be\n # garbage collected. The destructor for the module type\n # then replaces all values in the module dict with None.\n # For instance, after setuptools runs a test it replaces\n # sys.modules with a copy created earlier. See issues\n # #9775 and #15881. Also related: #4106, #9205, and\n # #9207.\n\n for p in active_children():\n if p.daemon:\n info('calling terminate() for daemon %s', p.name)\n p._popen.terminate()\n\n for p in active_children():\n info('calling join() for process %s', p.name)\n p.join()\n\n debug('running the remaining \"atexit\" finalizers')\n _run_finalizers()\n\natexit.register(_exit_function)\n\n#\n# Some fork aware types\n#\n\nclass ForkAwareThreadLock(object):\n def __init__(self):\n self._reset()\n register_after_fork(self, ForkAwareThreadLock._reset)\n\n def _reset(self):\n self._lock = threading.Lock()\n self.acquire = self._lock.acquire\n self.release = self._lock.release\n\n def __enter__(self):\n return self._lock.__enter__()\n\n def __exit__(self, *args):\n return self._lock.__exit__(*args)\n\n\nclass ForkAwareLocal(threading.local):\n def __init__(self):\n register_after_fork(self, lambda obj : obj.__dict__.clear())\n def __reduce__(self):\n return type(self), ()\n\n#\n# Close fds except those specified\n#\n\ntry:\n MAXFD = os.sysconf(\"SC_OPEN_MAX\")\nexcept Exception:\n MAXFD = 256\n\ndef close_all_fds_except(fds):\n fds = list(fds) + [-1, MAXFD]\n fds.sort()\n assert fds[-1] == MAXFD, 'fd too large'\n for i in range(len(fds) - 1):\n os.closerange(fds[i]+1, fds[i+1])\n#\n# Close sys.stdin and replace stdin with os.devnull\n#\n\ndef _close_stdin():\n if sys.stdin is None:\n return\n\n try:\n sys.stdin.close()\n except (OSError, ValueError):\n pass\n\n try:\n fd = os.open(os.devnull, os.O_RDONLY)\n try:\n sys.stdin = open(fd, closefd=False)\n except:\n os.close(fd)\n raise\n except (OSError, ValueError):\n pass\n\n#\n# Flush standard streams, if any\n#\n\ndef _flush_std_streams():\n try:\n sys.stdout.flush()\n except (AttributeError, ValueError):\n pass\n try:\n sys.stderr.flush()\n except (AttributeError, ValueError):\n pass\n\n#\n# Start a program with only specified fds kept open\n#\n\ndef spawnv_passfds(path, args, passfds):\n import _posixsubprocess\n passfds = tuple(sorted(map(int, passfds)))\n errpipe_read, errpipe_write = os.pipe()\n try:\n return _posixsubprocess.fork_exec(\n args, [os.fsencode(path)], True, passfds, None, None,\n -1, -1, -1, -1, -1, -1, errpipe_read, errpipe_write,\n False, False, None)\n finally:\n os.close(errpipe_read)\n os.close(errpipe_write)\n",420],"/usr/lib/python3.6/multiprocessing/connection.py":["#\n# A higher level module for using sockets (or Windows named pipes)\n#\n# multiprocessing/connection.py\n#\n# Copyright (c) 2006-2008, R Oudkerk\n# Licensed to PSF under a Contributor Agreement.\n#\n\n__all__ = [ 'Client', 'Listener', 'Pipe', 'wait' ]\n\nimport io\nimport os\nimport sys\nimport socket\nimport struct\nimport time\nimport tempfile\nimport itertools\n\nimport _multiprocessing\n\nfrom . import util\n\nfrom . import AuthenticationError, BufferTooShort\nfrom .context import reduction\n_ForkingPickler = reduction.ForkingPickler\n\ntry:\n import _winapi\n from _winapi import WAIT_OBJECT_0, WAIT_ABANDONED_0, WAIT_TIMEOUT, INFINITE\nexcept ImportError:\n if sys.platform == 'win32':\n raise\n _winapi = None\n\n#\n#\n#\n\nBUFSIZE = 8192\n# A very generous timeout when it comes to local connections...\nCONNECTION_TIMEOUT = 20.\n\n_mmap_counter = itertools.count()\n\ndefault_family = 'AF_INET'\nfamilies = ['AF_INET']\n\nif hasattr(socket, 'AF_UNIX'):\n default_family = 'AF_UNIX'\n families += ['AF_UNIX']\n\nif sys.platform == 'win32':\n default_family = 'AF_PIPE'\n families += ['AF_PIPE']\n\n\ndef _init_timeout(timeout=CONNECTION_TIMEOUT):\n return time.monotonic() + timeout\n\ndef _check_timeout(t):\n return time.monotonic() > t\n\n#\n#\n#\n\ndef arbitrary_address(family):\n '''\n Return an arbitrary free address for the given family\n '''\n if family == 'AF_INET':\n return ('localhost', 0)\n elif family == 'AF_UNIX':\n return tempfile.mktemp(prefix='listener-', dir=util.get_temp_dir())\n elif family == 'AF_PIPE':\n return tempfile.mktemp(prefix=r'\\\\.\\pipe\\pyc-%d-%d-' %\n (os.getpid(), next(_mmap_counter)), dir=\"\")\n else:\n raise ValueError('unrecognized family')\n\ndef _validate_family(family):\n '''\n Checks if the family is valid for the current environment.\n '''\n if sys.platform != 'win32' and family == 'AF_PIPE':\n raise ValueError('Family %s is not recognized.' % family)\n\n if sys.platform == 'win32' and family == 'AF_UNIX':\n # double check\n if not hasattr(socket, family):\n raise ValueError('Family %s is not recognized.' % family)\n\ndef address_type(address):\n '''\n Return the types of the address\n\n This can be 'AF_INET', 'AF_UNIX', or 'AF_PIPE'\n '''\n if type(address) == tuple:\n return 'AF_INET'\n elif type(address) is str and address.startswith('\\\\\\\\'):\n return 'AF_PIPE'\n elif type(address) is str:\n return 'AF_UNIX'\n else:\n raise ValueError('address type of %r unrecognized' % address)\n\n#\n# Connection classes\n#\n\nclass _ConnectionBase:\n _handle = None\n\n def __init__(self, handle, readable=True, writable=True):\n handle = handle.__index__()\n if handle < 0:\n raise ValueError(\"invalid handle\")\n if not readable and not writable:\n raise ValueError(\n \"at least one of `readable` and `writable` must be True\")\n self._handle = handle\n self._readable = readable\n self._writable = writable\n\n # XXX should we use util.Finalize instead of a __del__?\n\n def __del__(self):\n if self._handle is not None:\n self._close()\n\n def _check_closed(self):\n if self._handle is None:\n raise OSError(\"handle is closed\")\n\n def _check_readable(self):\n if not self._readable:\n raise OSError(\"connection is write-only\")\n\n def _check_writable(self):\n if not self._writable:\n raise OSError(\"connection is read-only\")\n\n def _bad_message_length(self):\n if self._writable:\n self._readable = False\n else:\n self.close()\n raise OSError(\"bad message length\")\n\n @property\n def closed(self):\n \"\"\"True if the connection is closed\"\"\"\n return self._handle is None\n\n @property\n def readable(self):\n \"\"\"True if the connection is readable\"\"\"\n return self._readable\n\n @property\n def writable(self):\n \"\"\"True if the connection is writable\"\"\"\n return self._writable\n\n def fileno(self):\n \"\"\"File descriptor or handle of the connection\"\"\"\n self._check_closed()\n return self._handle\n\n def close(self):\n \"\"\"Close the connection\"\"\"\n if self._handle is not None:\n try:\n self._close()\n finally:\n self._handle = None\n\n def send_bytes(self, buf, offset=0, size=None):\n \"\"\"Send the bytes data from a bytes-like object\"\"\"\n self._check_closed()\n self._check_writable()\n m = memoryview(buf)\n # HACK for byte-indexing of non-bytewise buffers (e.g. array.array)\n if m.itemsize > 1:\n m = memoryview(bytes(m))\n n = len(m)\n if offset < 0:\n raise ValueError(\"offset is negative\")\n if n < offset:\n raise ValueError(\"buffer length < offset\")\n if size is None:\n size = n - offset\n elif size < 0:\n raise ValueError(\"size is negative\")\n elif offset + size > n:\n raise ValueError(\"buffer length < offset + size\")\n self._send_bytes(m[offset:offset + size])\n\n def send(self, obj):\n \"\"\"Send a (picklable) object\"\"\"\n self._check_closed()\n self._check_writable()\n self._send_bytes(_ForkingPickler.dumps(obj))\n\n def recv_bytes(self, maxlength=None):\n \"\"\"\n Receive bytes data as a bytes object.\n \"\"\"\n self._check_closed()\n self._check_readable()\n if maxlength is not None and maxlength < 0:\n raise ValueError(\"negative maxlength\")\n buf = self._recv_bytes(maxlength)\n if buf is None:\n self._bad_message_length()\n return buf.getvalue()\n\n def recv_bytes_into(self, buf, offset=0):\n \"\"\"\n Receive bytes data into a writeable bytes-like object.\n Return the number of bytes read.\n \"\"\"\n self._check_closed()\n self._check_readable()\n with memoryview(buf) as m:\n # Get bytesize of arbitrary buffer\n itemsize = m.itemsize\n bytesize = itemsize * len(m)\n if offset < 0:\n raise ValueError(\"negative offset\")\n elif offset > bytesize:\n raise ValueError(\"offset too large\")\n result = self._recv_bytes()\n size = result.tell()\n if bytesize < offset + size:\n raise BufferTooShort(result.getvalue())\n # Message can fit in dest\n result.seek(0)\n result.readinto(m[offset // itemsize :\n (offset + size) // itemsize])\n return size\n\n def recv(self):\n \"\"\"Receive a (picklable) object\"\"\"\n self._check_closed()\n self._check_readable()\n buf = self._recv_bytes()\n return _ForkingPickler.loads(buf.getbuffer())\n\n def poll(self, timeout=0.0):\n \"\"\"Whether there is any input available to be read\"\"\"\n self._check_closed()\n self._check_readable()\n return self._poll(timeout)\n\n def __enter__(self):\n return self\n\n def __exit__(self, exc_type, exc_value, exc_tb):\n self.close()\n\n\nif _winapi:\n\n class PipeConnection(_ConnectionBase):\n \"\"\"\n Connection class based on a Windows named pipe.\n Overlapped I/O is used, so the handles must have been created\n with FILE_FLAG_OVERLAPPED.\n \"\"\"\n _got_empty_message = False\n\n def _close(self, _CloseHandle=_winapi.CloseHandle):\n _CloseHandle(self._handle)\n\n def _send_bytes(self, buf):\n ov, err = _winapi.WriteFile(self._handle, buf, overlapped=True)\n try:\n if err == _winapi.ERROR_IO_PENDING:\n waitres = _winapi.WaitForMultipleObjects(\n [ov.event], False, INFINITE)\n assert waitres == WAIT_OBJECT_0\n except:\n ov.cancel()\n raise\n finally:\n nwritten, err = ov.GetOverlappedResult(True)\n assert err == 0\n assert nwritten == len(buf)\n\n def _recv_bytes(self, maxsize=None):\n if self._got_empty_message:\n self._got_empty_message = False\n return io.BytesIO()\n else:\n bsize = 128 if maxsize is None else min(maxsize, 128)\n try:\n ov, err = _winapi.ReadFile(self._handle, bsize,\n overlapped=True)\n try:\n if err == _winapi.ERROR_IO_PENDING:\n waitres = _winapi.WaitForMultipleObjects(\n [ov.event], False, INFINITE)\n assert waitres == WAIT_OBJECT_0\n except:\n ov.cancel()\n raise\n finally:\n nread, err = ov.GetOverlappedResult(True)\n if err == 0:\n f = io.BytesIO()\n f.write(ov.getbuffer())\n return f\n elif err == _winapi.ERROR_MORE_DATA:\n return self._get_more_data(ov, maxsize)\n except OSError as e:\n if e.winerror == _winapi.ERROR_BROKEN_PIPE:\n raise EOFError\n else:\n raise\n raise RuntimeError(\"shouldn't get here; expected KeyboardInterrupt\")\n\n def _poll(self, timeout):\n if (self._got_empty_message or\n _winapi.PeekNamedPipe(self._handle)[0] != 0):\n return True\n return bool(wait([self], timeout))\n\n def _get_more_data(self, ov, maxsize):\n buf = ov.getbuffer()\n f = io.BytesIO()\n f.write(buf)\n left = _winapi.PeekNamedPipe(self._handle)[1]\n assert left > 0\n if maxsize is not None and len(buf) + left > maxsize:\n self._bad_message_length()\n ov, err = _winapi.ReadFile(self._handle, left, overlapped=True)\n rbytes, err = ov.GetOverlappedResult(True)\n assert err == 0\n assert rbytes == left\n f.write(ov.getbuffer())\n return f\n\n\nclass Connection(_ConnectionBase):\n \"\"\"\n Connection class based on an arbitrary file descriptor (Unix only), or\n a socket handle (Windows).\n \"\"\"\n\n if _winapi:\n def _close(self, _close=_multiprocessing.closesocket):\n _close(self._handle)\n _write = _multiprocessing.send\n _read = _multiprocessing.recv\n else:\n def _close(self, _close=os.close):\n _close(self._handle)\n _write = os.write\n _read = os.read\n\n def _send(self, buf, write=_write):\n remaining = len(buf)\n while True:\n n = write(self._handle, buf)\n remaining -= n\n if remaining == 0:\n break\n buf = buf[n:]\n\n def _recv(self, size, read=_read):\n buf = io.BytesIO()\n handle = self._handle\n remaining = size\n while remaining > 0:\n chunk = read(handle, remaining)\n n = len(chunk)\n if n == 0:\n if remaining == size:\n raise EOFError\n else:\n raise OSError(\"got end of file during message\")\n buf.write(chunk)\n remaining -= n\n return buf\n\n def _send_bytes(self, buf):\n n = len(buf)\n # For wire compatibility with 3.2 and lower\n header = struct.pack(\"!i\", n)\n if n > 16384:\n # The payload is large so Nagle's algorithm won't be triggered\n # and we'd better avoid the cost of concatenation.\n self._send(header)\n self._send(buf)\n else:\n # Issue #20540: concatenate before sending, to avoid delays due\n # to Nagle's algorithm on a TCP socket.\n # Also note we want to avoid sending a 0-length buffer separately,\n # to avoid \"broken pipe\" errors if the other end closed the pipe.\n self._send(header + buf)\n\n def _recv_bytes(self, maxsize=None):\n buf = self._recv(4)\n size, = struct.unpack(\"!i\", buf.getvalue())\n if maxsize is not None and size > maxsize:\n return None\n return self._recv(size)\n\n def _poll(self, timeout):\n r = wait([self], timeout)\n return bool(r)\n\n\n#\n# Public functions\n#\n\nclass Listener(object):\n '''\n Returns a listener object.\n\n This is a wrapper for a bound socket which is 'listening' for\n connections, or for a Windows named pipe.\n '''\n def __init__(self, address=None, family=None, backlog=1, authkey=None):\n family = family or (address and address_type(address)) \\\n or default_family\n address = address or arbitrary_address(family)\n\n _validate_family(family)\n if family == 'AF_PIPE':\n self._listener = PipeListener(address, backlog)\n else:\n self._listener = SocketListener(address, family, backlog)\n\n if authkey is not None and not isinstance(authkey, bytes):\n raise TypeError('authkey should be a byte string')\n\n self._authkey = authkey\n\n def accept(self):\n '''\n Accept a connection on the bound socket or named pipe of `self`.\n\n Returns a `Connection` object.\n '''\n if self._listener is None:\n raise OSError('listener is closed')\n c = self._listener.accept()\n if self._authkey:\n deliver_challenge(c, self._authkey)\n answer_challenge(c, self._authkey)\n return c\n\n def close(self):\n '''\n Close the bound socket or named pipe of `self`.\n '''\n listener = self._listener\n if listener is not None:\n self._listener = None\n listener.close()\n\n address = property(lambda self: self._listener._address)\n last_accepted = property(lambda self: self._listener._last_accepted)\n\n def __enter__(self):\n return self\n\n def __exit__(self, exc_type, exc_value, exc_tb):\n self.close()\n\n\ndef Client(address, family=None, authkey=None):\n '''\n Returns a connection to the address of a `Listener`\n '''\n family = family or address_type(address)\n _validate_family(family)\n if family == 'AF_PIPE':\n c = PipeClient(address)\n else:\n c = SocketClient(address)\n\n if authkey is not None and not isinstance(authkey, bytes):\n raise TypeError('authkey should be a byte string')\n\n if authkey is not None:\n answer_challenge(c, authkey)\n deliver_challenge(c, authkey)\n\n return c\n\n\nif sys.platform != 'win32':\n\n def Pipe(duplex=True):\n '''\n Returns pair of connection objects at either end of a pipe\n '''\n if duplex:\n s1, s2 = socket.socketpair()\n s1.setblocking(True)\n s2.setblocking(True)\n c1 = Connection(s1.detach())\n c2 = Connection(s2.detach())\n else:\n fd1, fd2 = os.pipe()\n c1 = Connection(fd1, writable=False)\n c2 = Connection(fd2, readable=False)\n\n return c1, c2\n\nelse:\n\n def Pipe(duplex=True):\n '''\n Returns pair of connection objects at either end of a pipe\n '''\n address = arbitrary_address('AF_PIPE')\n if duplex:\n openmode = _winapi.PIPE_ACCESS_DUPLEX\n access = _winapi.GENERIC_READ | _winapi.GENERIC_WRITE\n obsize, ibsize = BUFSIZE, BUFSIZE\n else:\n openmode = _winapi.PIPE_ACCESS_INBOUND\n access = _winapi.GENERIC_WRITE\n obsize, ibsize = 0, BUFSIZE\n\n h1 = _winapi.CreateNamedPipe(\n address, openmode | _winapi.FILE_FLAG_OVERLAPPED |\n _winapi.FILE_FLAG_FIRST_PIPE_INSTANCE,\n _winapi.PIPE_TYPE_MESSAGE | _winapi.PIPE_READMODE_MESSAGE |\n _winapi.PIPE_WAIT,\n 1, obsize, ibsize, _winapi.NMPWAIT_WAIT_FOREVER,\n # default security descriptor: the handle cannot be inherited\n _winapi.NULL\n )\n h2 = _winapi.CreateFile(\n address, access, 0, _winapi.NULL, _winapi.OPEN_EXISTING,\n _winapi.FILE_FLAG_OVERLAPPED, _winapi.NULL\n )\n _winapi.SetNamedPipeHandleState(\n h2, _winapi.PIPE_READMODE_MESSAGE, None, None\n )\n\n overlapped = _winapi.ConnectNamedPipe(h1, overlapped=True)\n _, err = overlapped.GetOverlappedResult(True)\n assert err == 0\n\n c1 = PipeConnection(h1, writable=duplex)\n c2 = PipeConnection(h2, readable=duplex)\n\n return c1, c2\n\n#\n# Definitions for connections based on sockets\n#\n\nclass SocketListener(object):\n '''\n Representation of a socket which is bound to an address and listening\n '''\n def __init__(self, address, family, backlog=1):\n self._socket = socket.socket(getattr(socket, family))\n try:\n # SO_REUSEADDR has different semantics on Windows (issue #2550).\n if os.name == 'posix':\n self._socket.setsockopt(socket.SOL_SOCKET,\n socket.SO_REUSEADDR, 1)\n self._socket.setblocking(True)\n self._socket.bind(address)\n self._socket.listen(backlog)\n self._address = self._socket.getsockname()\n except OSError:\n self._socket.close()\n raise\n self._family = family\n self._last_accepted = None\n\n if family == 'AF_UNIX':\n self._unlink = util.Finalize(\n self, os.unlink, args=(address,), exitpriority=0\n )\n else:\n self._unlink = None\n\n def accept(self):\n s, self._last_accepted = self._socket.accept()\n s.setblocking(True)\n return Connection(s.detach())\n\n def close(self):\n try:\n self._socket.close()\n finally:\n unlink = self._unlink\n if unlink is not None:\n self._unlink = None\n unlink()\n\n\ndef SocketClient(address):\n '''\n Return a connection object connected to the socket given by `address`\n '''\n family = address_type(address)\n with socket.socket( getattr(socket, family) ) as s:\n s.setblocking(True)\n s.connect(address)\n return Connection(s.detach())\n\n#\n# Definitions for connections based on named pipes\n#\n\nif sys.platform == 'win32':\n\n class PipeListener(object):\n '''\n Representation of a named pipe\n '''\n def __init__(self, address, backlog=None):\n self._address = address\n self._handle_queue = [self._new_handle(first=True)]\n\n self._last_accepted = None\n util.sub_debug('listener created with address=%r', self._address)\n self.close = util.Finalize(\n self, PipeListener._finalize_pipe_listener,\n args=(self._handle_queue, self._address), exitpriority=0\n )\n\n def _new_handle(self, first=False):\n flags = _winapi.PIPE_ACCESS_DUPLEX | _winapi.FILE_FLAG_OVERLAPPED\n if first:\n flags |= _winapi.FILE_FLAG_FIRST_PIPE_INSTANCE\n return _winapi.CreateNamedPipe(\n self._address, flags,\n _winapi.PIPE_TYPE_MESSAGE | _winapi.PIPE_READMODE_MESSAGE |\n _winapi.PIPE_WAIT,\n _winapi.PIPE_UNLIMITED_INSTANCES, BUFSIZE, BUFSIZE,\n _winapi.NMPWAIT_WAIT_FOREVER, _winapi.NULL\n )\n\n def accept(self):\n self._handle_queue.append(self._new_handle())\n handle = self._handle_queue.pop(0)\n try:\n ov = _winapi.ConnectNamedPipe(handle, overlapped=True)\n except OSError as e:\n if e.winerror != _winapi.ERROR_NO_DATA:\n raise\n # ERROR_NO_DATA can occur if a client has already connected,\n # written data and then disconnected -- see Issue 14725.\n else:\n try:\n res = _winapi.WaitForMultipleObjects(\n [ov.event], False, INFINITE)\n except:\n ov.cancel()\n _winapi.CloseHandle(handle)\n raise\n finally:\n _, err = ov.GetOverlappedResult(True)\n assert err == 0\n return PipeConnection(handle)\n\n @staticmethod\n def _finalize_pipe_listener(queue, address):\n util.sub_debug('closing listener with address=%r', address)\n for handle in queue:\n _winapi.CloseHandle(handle)\n\n def PipeClient(address):\n '''\n Return a connection object connected to the pipe given by `address`\n '''\n t = _init_timeout()\n while 1:\n try:\n _winapi.WaitNamedPipe(address, 1000)\n h = _winapi.CreateFile(\n address, _winapi.GENERIC_READ | _winapi.GENERIC_WRITE,\n 0, _winapi.NULL, _winapi.OPEN_EXISTING,\n _winapi.FILE_FLAG_OVERLAPPED, _winapi.NULL\n )\n except OSError as e:\n if e.winerror not in (_winapi.ERROR_SEM_TIMEOUT,\n _winapi.ERROR_PIPE_BUSY) or _check_timeout(t):\n raise\n else:\n break\n else:\n raise\n\n _winapi.SetNamedPipeHandleState(\n h, _winapi.PIPE_READMODE_MESSAGE, None, None\n )\n return PipeConnection(h)\n\n#\n# Authentication stuff\n#\n\nMESSAGE_LENGTH = 20\n\nCHALLENGE = b'#CHALLENGE#'\nWELCOME = b'#WELCOME#'\nFAILURE = b'#FAILURE#'\n\ndef deliver_challenge(connection, authkey):\n import hmac\n assert isinstance(authkey, bytes)\n message = os.urandom(MESSAGE_LENGTH)\n connection.send_bytes(CHALLENGE + message)\n digest = hmac.new(authkey, message, 'md5').digest()\n response = connection.recv_bytes(256) # reject large message\n if response == digest:\n connection.send_bytes(WELCOME)\n else:\n connection.send_bytes(FAILURE)\n raise AuthenticationError('digest received was wrong')\n\ndef answer_challenge(connection, authkey):\n import hmac\n assert isinstance(authkey, bytes)\n message = connection.recv_bytes(256) # reject large message\n assert message[:len(CHALLENGE)] == CHALLENGE, 'message = %r' % message\n message = message[len(CHALLENGE):]\n digest = hmac.new(authkey, message, 'md5').digest()\n connection.send_bytes(digest)\n response = connection.recv_bytes(256) # reject large message\n if response != WELCOME:\n raise AuthenticationError('digest sent was rejected')\n\n#\n# Support for using xmlrpclib for serialization\n#\n\nclass ConnectionWrapper(object):\n def __init__(self, conn, dumps, loads):\n self._conn = conn\n self._dumps = dumps\n self._loads = loads\n for attr in ('fileno', 'close', 'poll', 'recv_bytes', 'send_bytes'):\n obj = getattr(conn, attr)\n setattr(self, attr, obj)\n def send(self, obj):\n s = self._dumps(obj)\n self._conn.send_bytes(s)\n def recv(self):\n s = self._conn.recv_bytes()\n return self._loads(s)\n\ndef _xml_dumps(obj):\n return xmlrpclib.dumps((obj,), None, None, None, 1).encode('utf-8')\n\ndef _xml_loads(s):\n (obj,), method = xmlrpclib.loads(s.decode('utf-8'))\n return obj\n\nclass XmlListener(Listener):\n def accept(self):\n global xmlrpclib\n import xmlrpc.client as xmlrpclib\n obj = Listener.accept(self)\n return ConnectionWrapper(obj, _xml_dumps, _xml_loads)\n\ndef XmlClient(*args, **kwds):\n global xmlrpclib\n import xmlrpc.client as xmlrpclib\n return ConnectionWrapper(Client(*args, **kwds), _xml_dumps, _xml_loads)\n\n#\n# Wait\n#\n\nif sys.platform == 'win32':\n\n def _exhaustive_wait(handles, timeout):\n # Return ALL handles which are currently signalled. (Only\n # returning the first signalled might create starvation issues.)\n L = list(handles)\n ready = []\n while L:\n res = _winapi.WaitForMultipleObjects(L, False, timeout)\n if res == WAIT_TIMEOUT:\n break\n elif WAIT_OBJECT_0 <= res < WAIT_OBJECT_0 + len(L):\n res -= WAIT_OBJECT_0\n elif WAIT_ABANDONED_0 <= res < WAIT_ABANDONED_0 + len(L):\n res -= WAIT_ABANDONED_0\n else:\n raise RuntimeError('Should not get here')\n ready.append(L[res])\n L = L[res+1:]\n timeout = 0\n return ready\n\n _ready_errors = {_winapi.ERROR_BROKEN_PIPE, _winapi.ERROR_NETNAME_DELETED}\n\n def wait(object_list, timeout=None):\n '''\n Wait till an object in object_list is ready/readable.\n\n Returns list of those objects in object_list which are ready/readable.\n '''\n if timeout is None:\n timeout = INFINITE\n elif timeout < 0:\n timeout = 0\n else:\n timeout = int(timeout * 1000 + 0.5)\n\n object_list = list(object_list)\n waithandle_to_obj = {}\n ov_list = []\n ready_objects = set()\n ready_handles = set()\n\n try:\n for o in object_list:\n try:\n fileno = getattr(o, 'fileno')\n except AttributeError:\n waithandle_to_obj[o.__index__()] = o\n else:\n # start an overlapped read of length zero\n try:\n ov, err = _winapi.ReadFile(fileno(), 0, True)\n except OSError as e:\n ov, err = None, e.winerror\n if err not in _ready_errors:\n raise\n if err == _winapi.ERROR_IO_PENDING:\n ov_list.append(ov)\n waithandle_to_obj[ov.event] = o\n else:\n # If o.fileno() is an overlapped pipe handle and\n # err == 0 then there is a zero length message\n # in the pipe, but it HAS NOT been consumed...\n if ov and sys.getwindowsversion()[:2] >= (6, 2):\n # ... except on Windows 8 and later, where\n # the message HAS been consumed.\n try:\n _, err = ov.GetOverlappedResult(False)\n except OSError as e:\n err = e.winerror\n if not err and hasattr(o, '_got_empty_message'):\n o._got_empty_message = True\n ready_objects.add(o)\n timeout = 0\n\n ready_handles = _exhaustive_wait(waithandle_to_obj.keys(), timeout)\n finally:\n # request that overlapped reads stop\n for ov in ov_list:\n ov.cancel()\n\n # wait for all overlapped reads to stop\n for ov in ov_list:\n try:\n _, err = ov.GetOverlappedResult(True)\n except OSError as e:\n err = e.winerror\n if err not in _ready_errors:\n raise\n if err != _winapi.ERROR_OPERATION_ABORTED:\n o = waithandle_to_obj[ov.event]\n ready_objects.add(o)\n if err == 0:\n # If o.fileno() is an overlapped pipe handle then\n # a zero length message HAS been consumed.\n if hasattr(o, '_got_empty_message'):\n o._got_empty_message = True\n\n ready_objects.update(waithandle_to_obj[h] for h in ready_handles)\n return [o for o in object_list if o in ready_objects]\n\nelse:\n\n import selectors\n\n # poll/select have the advantage of not requiring any extra file\n # descriptor, contrarily to epoll/kqueue (also, they require a single\n # syscall).\n if hasattr(selectors, 'PollSelector'):\n _WaitSelector = selectors.PollSelector\n else:\n _WaitSelector = selectors.SelectSelector\n\n def wait(object_list, timeout=None):\n '''\n Wait till an object in object_list is ready/readable.\n\n Returns list of those objects in object_list which are ready/readable.\n '''\n with _WaitSelector() as selector:\n for obj in object_list:\n selector.register(obj, selectors.EVENT_READ)\n\n if timeout is not None:\n deadline = time.monotonic() + timeout\n\n while True:\n ready = selector.select(timeout)\n if ready:\n return [key.fileobj for (key, events) in ready]\n else:\n if timeout is not None:\n timeout = deadline - time.monotonic()\n if timeout < 0:\n return ready\n\n#\n# Make connection and socket objects sharable if possible\n#\n\nif sys.platform == 'win32':\n def reduce_connection(conn):\n handle = conn.fileno()\n with socket.fromfd(handle, socket.AF_INET, socket.SOCK_STREAM) as s:\n from . import resource_sharer\n ds = resource_sharer.DupSocket(s)\n return rebuild_connection, (ds, conn.readable, conn.writable)\n def rebuild_connection(ds, readable, writable):\n sock = ds.detach()\n return Connection(sock.detach(), readable, writable)\n reduction.register(Connection, reduce_connection)\n\n def reduce_pipe_connection(conn):\n access = ((_winapi.FILE_GENERIC_READ if conn.readable else 0) |\n (_winapi.FILE_GENERIC_WRITE if conn.writable else 0))\n dh = reduction.DupHandle(conn.fileno(), access)\n return rebuild_pipe_connection, (dh, conn.readable, conn.writable)\n def rebuild_pipe_connection(dh, readable, writable):\n handle = dh.detach()\n return PipeConnection(handle, readable, writable)\n reduction.register(PipeConnection, reduce_pipe_connection)\n\nelse:\n def reduce_connection(conn):\n df = reduction.DupFd(conn.fileno())\n return rebuild_connection, (df, conn.readable, conn.writable)\n def rebuild_connection(df, readable, writable):\n fd = df.detach()\n return Connection(fd, readable, writable)\n reduction.register(Connection, reduce_connection)\n",953],"/usr/lib/python3.6/multiprocessing/queues.py":["#\n# Module implementing queues\n#\n# multiprocessing/queues.py\n#\n# Copyright (c) 2006-2008, R Oudkerk\n# Licensed to PSF under a Contributor Agreement.\n#\n\n__all__ = ['Queue', 'SimpleQueue', 'JoinableQueue']\n\nimport sys\nimport os\nimport threading\nimport collections\nimport time\nimport weakref\nimport errno\n\nfrom queue import Empty, Full\n\nimport _multiprocessing\n\nfrom . import connection\nfrom . import context\n_ForkingPickler = context.reduction.ForkingPickler\n\nfrom .util import debug, info, Finalize, register_after_fork, is_exiting\n\n#\n# Queue type using a pipe, buffer and thread\n#\n\nclass Queue(object):\n\n def __init__(self, maxsize=0, *, ctx):\n if maxsize <= 0:\n # Can raise ImportError (see issues #3770 and #23400)\n from .synchronize import SEM_VALUE_MAX as maxsize\n self._maxsize = maxsize\n self._reader, self._writer = connection.Pipe(duplex=False)\n self._rlock = ctx.Lock()\n self._opid = os.getpid()\n if sys.platform == 'win32':\n self._wlock = None\n else:\n self._wlock = ctx.Lock()\n self._sem = ctx.BoundedSemaphore(maxsize)\n # For use by concurrent.futures\n self._ignore_epipe = False\n\n self._after_fork()\n\n if sys.platform != 'win32':\n register_after_fork(self, Queue._after_fork)\n\n def __getstate__(self):\n context.assert_spawning(self)\n return (self._ignore_epipe, self._maxsize, self._reader, self._writer,\n self._rlock, self._wlock, self._sem, self._opid)\n\n def __setstate__(self, state):\n (self._ignore_epipe, self._maxsize, self._reader, self._writer,\n self._rlock, self._wlock, self._sem, self._opid) = state\n self._after_fork()\n\n def _after_fork(self):\n debug('Queue._after_fork()')\n self._notempty = threading.Condition(threading.Lock())\n self._buffer = collections.deque()\n self._thread = None\n self._jointhread = None\n self._joincancelled = False\n self._closed = False\n self._close = None\n self._send_bytes = self._writer.send_bytes\n self._recv_bytes = self._reader.recv_bytes\n self._poll = self._reader.poll\n\n def put(self, obj, block=True, timeout=None):\n assert not self._closed\n if not self._sem.acquire(block, timeout):\n raise Full\n\n with self._notempty:\n if self._thread is None:\n self._start_thread()\n self._buffer.append(obj)\n self._notempty.notify()\n\n def get(self, block=True, timeout=None):\n if block and timeout is None:\n with self._rlock:\n res = self._recv_bytes()\n self._sem.release()\n else:\n if block:\n deadline = time.monotonic() + timeout\n if not self._rlock.acquire(block, timeout):\n raise Empty\n try:\n if block:\n timeout = deadline - time.monotonic()\n if not self._poll(timeout):\n raise Empty\n elif not self._poll():\n raise Empty\n res = self._recv_bytes()\n self._sem.release()\n finally:\n self._rlock.release()\n # unserialize the data after having released the lock\n return _ForkingPickler.loads(res)\n\n def qsize(self):\n # Raises NotImplementedError on Mac OSX because of broken sem_getvalue()\n return self._maxsize - self._sem._semlock._get_value()\n\n def empty(self):\n return not self._poll()\n\n def full(self):\n return self._sem._semlock._is_zero()\n\n def get_nowait(self):\n return self.get(False)\n\n def put_nowait(self, obj):\n return self.put(obj, False)\n\n def close(self):\n self._closed = True\n try:\n self._reader.close()\n finally:\n close = self._close\n if close:\n self._close = None\n close()\n\n def join_thread(self):\n debug('Queue.join_thread()')\n assert self._closed\n if self._jointhread:\n self._jointhread()\n\n def cancel_join_thread(self):\n debug('Queue.cancel_join_thread()')\n self._joincancelled = True\n try:\n self._jointhread.cancel()\n except AttributeError:\n pass\n\n def _start_thread(self):\n debug('Queue._start_thread()')\n\n # Start thread which transfers data from buffer to pipe\n self._buffer.clear()\n self._thread = threading.Thread(\n target=Queue._feed,\n args=(self._buffer, self._notempty, self._send_bytes,\n self._wlock, self._writer.close, self._ignore_epipe),\n name='QueueFeederThread'\n )\n self._thread.daemon = True\n\n debug('doing self._thread.start()')\n self._thread.start()\n debug('... done self._thread.start()')\n\n if not self._joincancelled:\n self._jointhread = Finalize(\n self._thread, Queue._finalize_join,\n [weakref.ref(self._thread)],\n exitpriority=-5\n )\n\n # Send sentinel to the thread queue object when garbage collected\n self._close = Finalize(\n self, Queue._finalize_close,\n [self._buffer, self._notempty],\n exitpriority=10\n )\n\n @staticmethod\n def _finalize_join(twr):\n debug('joining queue thread')\n thread = twr()\n if thread is not None:\n thread.join()\n debug('... queue thread joined')\n else:\n debug('... queue thread already dead')\n\n @staticmethod\n def _finalize_close(buffer, notempty):\n debug('telling queue thread to quit')\n with notempty:\n buffer.append(_sentinel)\n notempty.notify()\n\n @staticmethod\n def _feed(buffer, notempty, send_bytes, writelock, close, ignore_epipe):\n debug('starting thread to feed data to pipe')\n nacquire = notempty.acquire\n nrelease = notempty.release\n nwait = notempty.wait\n bpopleft = buffer.popleft\n sentinel = _sentinel\n if sys.platform != 'win32':\n wacquire = writelock.acquire\n wrelease = writelock.release\n else:\n wacquire = None\n\n while 1:\n try:\n nacquire()\n try:\n if not buffer:\n nwait()\n finally:\n nrelease()\n try:\n while 1:\n obj = bpopleft()\n if obj is sentinel:\n debug('feeder thread got sentinel -- exiting')\n close()\n return\n\n # serialize the data before acquiring the lock\n obj = _ForkingPickler.dumps(obj)\n if wacquire is None:\n send_bytes(obj)\n else:\n wacquire()\n try:\n send_bytes(obj)\n finally:\n wrelease()\n except IndexError:\n pass\n except Exception as e:\n if ignore_epipe and getattr(e, 'errno', 0) == errno.EPIPE:\n return\n # Since this runs in a daemon thread the resources it uses\n # may be become unusable while the process is cleaning up.\n # We ignore errors which happen after the process has\n # started to cleanup.\n if is_exiting():\n info('error in queue thread: %s', e)\n return\n else:\n import traceback\n traceback.print_exc()\n\n_sentinel = object()\n\n#\n# A queue type which also supports join() and task_done() methods\n#\n# Note that if you do not call task_done() for each finished task then\n# eventually the counter's semaphore may overflow causing Bad Things\n# to happen.\n#\n\nclass JoinableQueue(Queue):\n\n def __init__(self, maxsize=0, *, ctx):\n Queue.__init__(self, maxsize, ctx=ctx)\n self._unfinished_tasks = ctx.Semaphore(0)\n self._cond = ctx.Condition()\n\n def __getstate__(self):\n return Queue.__getstate__(self) + (self._cond, self._unfinished_tasks)\n\n def __setstate__(self, state):\n Queue.__setstate__(self, state[:-2])\n self._cond, self._unfinished_tasks = state[-2:]\n\n def put(self, obj, block=True, timeout=None):\n assert not self._closed\n if not self._sem.acquire(block, timeout):\n raise Full\n\n with self._notempty, self._cond:\n if self._thread is None:\n self._start_thread()\n self._buffer.append(obj)\n self._unfinished_tasks.release()\n self._notempty.notify()\n\n def task_done(self):\n with self._cond:\n if not self._unfinished_tasks.acquire(False):\n raise ValueError('task_done() called too many times')\n if self._unfinished_tasks._semlock._is_zero():\n self._cond.notify_all()\n\n def join(self):\n with self._cond:\n if not self._unfinished_tasks._semlock._is_zero():\n self._cond.wait()\n\n#\n# Simplified Queue type -- really just a locked pipe\n#\n\nclass SimpleQueue(object):\n\n def __init__(self, *, ctx):\n self._reader, self._writer = connection.Pipe(duplex=False)\n self._rlock = ctx.Lock()\n self._poll = self._reader.poll\n if sys.platform == 'win32':\n self._wlock = None\n else:\n self._wlock = ctx.Lock()\n\n def empty(self):\n return not self._poll()\n\n def __getstate__(self):\n context.assert_spawning(self)\n return (self._reader, self._writer, self._rlock, self._wlock)\n\n def __setstate__(self, state):\n (self._reader, self._writer, self._rlock, self._wlock) = state\n self._poll = self._reader.poll\n\n def get(self):\n with self._rlock:\n res = self._reader.recv_bytes()\n # unserialize the data after having released the lock\n return _ForkingPickler.loads(res)\n\n def put(self, obj):\n # serialize the data before acquiring the lock\n obj = _ForkingPickler.dumps(obj)\n if self._wlock is None:\n # writes to a message oriented win32 pipe are atomic\n self._writer.send_bytes(obj)\n else:\n with self._wlock:\n self._writer.send_bytes(obj)\n",347],"/home/gaogaotiantian/programs/codesnap/example/src/multi_process_pool.py":["from multiprocessing import Pool\nimport os\n\n\ndef f(x):\n return x ** x\n\n\nif __name__ == \"__main__\":\n process_num = 5\n with Pool(processes=process_num) as pool:\n print(pool.map(f, range(10)))\n\n for i in pool.imap_unordered(f, range(10)):\n print(i)\n\n res = pool.apply_async(f, (20,)) # runs in *only* one process\n print(res.get(timeout=1)) # prints \"400\"\n\n res = pool.apply_async(os.getpid, ()) # runs in *only* one process\n print(res.get(timeout=1)) # prints the PID of that process\n\n multiple_results = [pool.apply_async(os.getpid, ()) for i in range(process_num)]\n print([res.get(timeout=1) for res in multiple_results])\n",24],"/usr/lib/python3.6/multiprocessing/pool.py":["#\n# Module providing the `Pool` class for managing a process pool\n#\n# multiprocessing/pool.py\n#\n# Copyright (c) 2006-2008, R Oudkerk\n# Licensed to PSF under a Contributor Agreement.\n#\n\n__all__ = ['Pool', 'ThreadPool']\n\n#\n# Imports\n#\n\nimport threading\nimport queue\nimport itertools\nimport collections\nimport os\nimport time\nimport traceback\n\n# If threading is available then ThreadPool should be provided. Therefore\n# we avoid top-level imports which are liable to fail on some systems.\nfrom . import util\nfrom . import get_context, TimeoutError\n\n#\n# Constants representing the state of a pool\n#\n\nRUN = 0\nCLOSE = 1\nTERMINATE = 2\n\n#\n# Miscellaneous\n#\n\njob_counter = itertools.count()\n\ndef mapstar(args):\n return list(map(*args))\n\ndef starmapstar(args):\n return list(itertools.starmap(args[0], args[1]))\n\n#\n# Hack to embed stringification of remote traceback in local traceback\n#\n\nclass RemoteTraceback(Exception):\n def __init__(self, tb):\n self.tb = tb\n def __str__(self):\n return self.tb\n\nclass ExceptionWithTraceback:\n def __init__(self, exc, tb):\n tb = traceback.format_exception(type(exc), exc, tb)\n tb = ''.join(tb)\n self.exc = exc\n self.tb = '\\n\"\"\"\\n%s\"\"\"' % tb\n def __reduce__(self):\n return rebuild_exc, (self.exc, self.tb)\n\ndef rebuild_exc(exc, tb):\n exc.__cause__ = RemoteTraceback(tb)\n return exc\n\n#\n# Code run by worker processes\n#\n\nclass MaybeEncodingError(Exception):\n \"\"\"Wraps possible unpickleable errors, so they can be\n safely sent through the socket.\"\"\"\n\n def __init__(self, exc, value):\n self.exc = repr(exc)\n self.value = repr(value)\n super(MaybeEncodingError, self).__init__(self.exc, self.value)\n\n def __str__(self):\n return \"Error sending result: '%s'. Reason: '%s'\" % (self.value,\n self.exc)\n\n def __repr__(self):\n return \"<%s: %s>\" % (self.__class__.__name__, self)\n\n\ndef worker(inqueue, outqueue, initializer=None, initargs=(), maxtasks=None,\n wrap_exception=False):\n assert maxtasks is None or (type(maxtasks) == int and maxtasks > 0)\n put = outqueue.put\n get = inqueue.get\n if hasattr(inqueue, '_writer'):\n inqueue._writer.close()\n outqueue._reader.close()\n\n if initializer is not None:\n initializer(*initargs)\n\n completed = 0\n while maxtasks is None or (maxtasks and completed < maxtasks):\n try:\n task = get()\n except (EOFError, OSError):\n util.debug('worker got EOFError or OSError -- exiting')\n break\n\n if task is None:\n util.debug('worker got sentinel -- exiting')\n break\n\n job, i, func, args, kwds = task\n try:\n result = (True, func(*args, **kwds))\n except Exception as e:\n if wrap_exception and func is not _helper_reraises_exception:\n e = ExceptionWithTraceback(e, e.__traceback__)\n result = (False, e)\n try:\n put((job, i, result))\n except Exception as e:\n wrapped = MaybeEncodingError(e, result[1])\n util.debug(\"Possible encoding error while sending result: %s\" % (\n wrapped))\n put((job, i, (False, wrapped)))\n\n task = job = result = func = args = kwds = None\n completed += 1\n util.debug('worker exiting after %d tasks' % completed)\n\ndef _helper_reraises_exception(ex):\n 'Pickle-able helper function for use by _guarded_task_generation.'\n raise ex\n\n#\n# Class representing a process pool\n#\n\nclass Pool(object):\n '''\n Class which supports an async version of applying functions to arguments.\n '''\n _wrap_exception = True\n\n def Process(self, *args, **kwds):\n return self._ctx.Process(*args, **kwds)\n\n def __init__(self, processes=None, initializer=None, initargs=(),\n maxtasksperchild=None, context=None):\n self._ctx = context or get_context()\n self._setup_queues()\n self._taskqueue = queue.Queue()\n self._cache = {}\n self._state = RUN\n self._maxtasksperchild = maxtasksperchild\n self._initializer = initializer\n self._initargs = initargs\n\n if processes is None:\n processes = os.cpu_count() or 1\n if processes < 1:\n raise ValueError(\"Number of processes must be at least 1\")\n\n if initializer is not None and not callable(initializer):\n raise TypeError('initializer must be a callable')\n\n self._processes = processes\n self._pool = []\n self._repopulate_pool()\n\n self._worker_handler = threading.Thread(\n target=Pool._handle_workers,\n args=(self, )\n )\n self._worker_handler.daemon = True\n self._worker_handler._state = RUN\n self._worker_handler.start()\n\n\n self._task_handler = threading.Thread(\n target=Pool._handle_tasks,\n args=(self._taskqueue, self._quick_put, self._outqueue,\n self._pool, self._cache)\n )\n self._task_handler.daemon = True\n self._task_handler._state = RUN\n self._task_handler.start()\n\n self._result_handler = threading.Thread(\n target=Pool._handle_results,\n args=(self._outqueue, self._quick_get, self._cache)\n )\n self._result_handler.daemon = True\n self._result_handler._state = RUN\n self._result_handler.start()\n\n self._terminate = util.Finalize(\n self, self._terminate_pool,\n args=(self._taskqueue, self._inqueue, self._outqueue, self._pool,\n self._worker_handler, self._task_handler,\n self._result_handler, self._cache),\n exitpriority=15\n )\n\n def _join_exited_workers(self):\n \"\"\"Cleanup after any worker processes which have exited due to reaching\n their specified lifetime. Returns True if any workers were cleaned up.\n \"\"\"\n cleaned = False\n for i in reversed(range(len(self._pool))):\n worker = self._pool[i]\n if worker.exitcode is not None:\n # worker exited\n util.debug('cleaning up worker %d' % i)\n worker.join()\n cleaned = True\n del self._pool[i]\n return cleaned\n\n def _repopulate_pool(self):\n \"\"\"Bring the number of pool processes up to the specified number,\n for use after reaping workers which have exited.\n \"\"\"\n for i in range(self._processes - len(self._pool)):\n w = self.Process(target=worker,\n args=(self._inqueue, self._outqueue,\n self._initializer,\n self._initargs, self._maxtasksperchild,\n self._wrap_exception)\n )\n self._pool.append(w)\n w.name = w.name.replace('Process', 'PoolWorker')\n w.daemon = True\n w.start()\n util.debug('added worker')\n\n def _maintain_pool(self):\n \"\"\"Clean up any exited workers and start replacements for them.\n \"\"\"\n if self._join_exited_workers():\n self._repopulate_pool()\n\n def _setup_queues(self):\n self._inqueue = self._ctx.SimpleQueue()\n self._outqueue = self._ctx.SimpleQueue()\n self._quick_put = self._inqueue._writer.send\n self._quick_get = self._outqueue._reader.recv\n\n def apply(self, func, args=(), kwds={}):\n '''\n Equivalent of `func(*args, **kwds)`.\n '''\n assert self._state == RUN\n return self.apply_async(func, args, kwds).get()\n\n def map(self, func, iterable, chunksize=None):\n '''\n Apply `func` to each element in `iterable`, collecting the results\n in a list that is returned.\n '''\n return self._map_async(func, iterable, mapstar, chunksize).get()\n\n def starmap(self, func, iterable, chunksize=None):\n '''\n Like `map()` method but the elements of the `iterable` are expected to\n be iterables as well and will be unpacked as arguments. Hence\n `func` and (a, b) becomes func(a, b).\n '''\n return self._map_async(func, iterable, starmapstar, chunksize).get()\n\n def starmap_async(self, func, iterable, chunksize=None, callback=None,\n error_callback=None):\n '''\n Asynchronous version of `starmap()` method.\n '''\n return self._map_async(func, iterable, starmapstar, chunksize,\n callback, error_callback)\n\n def _guarded_task_generation(self, result_job, func, iterable):\n '''Provides a generator of tasks for imap and imap_unordered with\n appropriate handling for iterables which throw exceptions during\n iteration.'''\n try:\n i = -1\n for i, x in enumerate(iterable):\n yield (result_job, i, func, (x,), {})\n except Exception as e:\n yield (result_job, i+1, _helper_reraises_exception, (e,), {})\n\n def imap(self, func, iterable, chunksize=1):\n '''\n Equivalent of `map()` -- can be MUCH slower than `Pool.map()`.\n '''\n if self._state != RUN:\n raise ValueError(\"Pool not running\")\n if chunksize == 1:\n result = IMapIterator(self._cache)\n self._taskqueue.put(\n (\n self._guarded_task_generation(result._job, func, iterable),\n result._set_length\n ))\n return result\n else:\n assert chunksize > 1\n task_batches = Pool._get_tasks(func, iterable, chunksize)\n result = IMapIterator(self._cache)\n self._taskqueue.put(\n (\n self._guarded_task_generation(result._job,\n mapstar,\n task_batches),\n result._set_length\n ))\n return (item for chunk in result for item in chunk)\n\n def imap_unordered(self, func, iterable, chunksize=1):\n '''\n Like `imap()` method but ordering of results is arbitrary.\n '''\n if self._state != RUN:\n raise ValueError(\"Pool not running\")\n if chunksize == 1:\n result = IMapUnorderedIterator(self._cache)\n self._taskqueue.put(\n (\n self._guarded_task_generation(result._job, func, iterable),\n result._set_length\n ))\n return result\n else:\n assert chunksize > 1\n task_batches = Pool._get_tasks(func, iterable, chunksize)\n result = IMapUnorderedIterator(self._cache)\n self._taskqueue.put(\n (\n self._guarded_task_generation(result._job,\n mapstar,\n task_batches),\n result._set_length\n ))\n return (item for chunk in result for item in chunk)\n\n def apply_async(self, func, args=(), kwds={}, callback=None,\n error_callback=None):\n '''\n Asynchronous version of `apply()` method.\n '''\n if self._state != RUN:\n raise ValueError(\"Pool not running\")\n result = ApplyResult(self._cache, callback, error_callback)\n self._taskqueue.put(([(result._job, 0, func, args, kwds)], None))\n return result\n\n def map_async(self, func, iterable, chunksize=None, callback=None,\n error_callback=None):\n '''\n Asynchronous version of `map()` method.\n '''\n return self._map_async(func, iterable, mapstar, chunksize, callback,\n error_callback)\n\n def _map_async(self, func, iterable, mapper, chunksize=None, callback=None,\n error_callback=None):\n '''\n Helper function to implement map, starmap and their async counterparts.\n '''\n if self._state != RUN:\n raise ValueError(\"Pool not running\")\n if not hasattr(iterable, '__len__'):\n iterable = list(iterable)\n\n if chunksize is None:\n chunksize, extra = divmod(len(iterable), len(self._pool) * 4)\n if extra:\n chunksize += 1\n if len(iterable) == 0:\n chunksize = 0\n\n task_batches = Pool._get_tasks(func, iterable, chunksize)\n result = MapResult(self._cache, chunksize, len(iterable), callback,\n error_callback=error_callback)\n self._taskqueue.put(\n (\n self._guarded_task_generation(result._job,\n mapper,\n task_batches),\n None\n )\n )\n return result\n\n @staticmethod\n def _handle_workers(pool):\n thread = threading.current_thread()\n\n # Keep maintaining workers until the cache gets drained, unless the pool\n # is terminated.\n while thread._state == RUN or (pool._cache and thread._state != TERMINATE):\n pool._maintain_pool()\n time.sleep(0.1)\n # send sentinel to stop workers\n pool._taskqueue.put(None)\n util.debug('worker handler exiting')\n\n @staticmethod\n def _handle_tasks(taskqueue, put, outqueue, pool, cache):\n thread = threading.current_thread()\n\n for taskseq, set_length in iter(taskqueue.get, None):\n task = None\n try:\n # iterating taskseq cannot fail\n for task in taskseq:\n if thread._state:\n util.debug('task handler found thread._state != RUN')\n break\n try:\n put(task)\n except Exception as e:\n job, idx = task[:2]\n try:\n cache[job]._set(idx, (False, e))\n except KeyError:\n pass\n else:\n if set_length:\n util.debug('doing set_length()')\n idx = task[1] if task else -1\n set_length(idx + 1)\n continue\n break\n finally:\n task = taskseq = job = None\n else:\n util.debug('task handler got sentinel')\n\n try:\n # tell result handler to finish when cache is empty\n util.debug('task handler sending sentinel to result handler')\n outqueue.put(None)\n\n # tell workers there is no more work\n util.debug('task handler sending sentinel to workers')\n for p in pool:\n put(None)\n except OSError:\n util.debug('task handler got OSError when sending sentinels')\n\n util.debug('task handler exiting')\n\n @staticmethod\n def _handle_results(outqueue, get, cache):\n thread = threading.current_thread()\n\n while 1:\n try:\n task = get()\n except (OSError, EOFError):\n util.debug('result handler got EOFError/OSError -- exiting')\n return\n\n if thread._state:\n assert thread._state == TERMINATE\n util.debug('result handler found thread._state=TERMINATE')\n break\n\n if task is None:\n util.debug('result handler got sentinel')\n break\n\n job, i, obj = task\n try:\n cache[job]._set(i, obj)\n except KeyError:\n pass\n task = job = obj = None\n\n while cache and thread._state != TERMINATE:\n try:\n task = get()\n except (OSError, EOFError):\n util.debug('result handler got EOFError/OSError -- exiting')\n return\n\n if task is None:\n util.debug('result handler ignoring extra sentinel')\n continue\n job, i, obj = task\n try:\n cache[job]._set(i, obj)\n except KeyError:\n pass\n task = job = obj = None\n\n if hasattr(outqueue, '_reader'):\n util.debug('ensuring that outqueue is not full')\n # If we don't make room available in outqueue then\n # attempts to add the sentinel (None) to outqueue may\n # block. There is guaranteed to be no more than 2 sentinels.\n try:\n for i in range(10):\n if not outqueue._reader.poll():\n break\n get()\n except (OSError, EOFError):\n pass\n\n util.debug('result handler exiting: len(cache)=%s, thread._state=%s',\n len(cache), thread._state)\n\n @staticmethod\n def _get_tasks(func, it, size):\n it = iter(it)\n while 1:\n x = tuple(itertools.islice(it, size))\n if not x:\n return\n yield (func, x)\n\n def __reduce__(self):\n raise NotImplementedError(\n 'pool objects cannot be passed between processes or pickled'\n )\n\n def close(self):\n util.debug('closing pool')\n if self._state == RUN:\n self._state = CLOSE\n self._worker_handler._state = CLOSE\n\n def terminate(self):\n util.debug('terminating pool')\n self._state = TERMINATE\n self._worker_handler._state = TERMINATE\n self._terminate()\n\n def join(self):\n util.debug('joining pool')\n assert self._state in (CLOSE, TERMINATE)\n self._worker_handler.join()\n self._task_handler.join()\n self._result_handler.join()\n for p in self._pool:\n p.join()\n\n @staticmethod\n def _help_stuff_finish(inqueue, task_handler, size):\n # task_handler may be blocked trying to put items on inqueue\n util.debug('removing tasks from inqueue until task handler finished')\n inqueue._rlock.acquire()\n while task_handler.is_alive() and inqueue._reader.poll():\n inqueue._reader.recv()\n time.sleep(0)\n\n @classmethod\n def _terminate_pool(cls, taskqueue, inqueue, outqueue, pool,\n worker_handler, task_handler, result_handler, cache):\n # this is guaranteed to only be called once\n util.debug('finalizing pool')\n\n worker_handler._state = TERMINATE\n task_handler._state = TERMINATE\n\n util.debug('helping task handler/workers to finish')\n cls._help_stuff_finish(inqueue, task_handler, len(pool))\n\n assert result_handler.is_alive() or len(cache) == 0\n\n result_handler._state = TERMINATE\n outqueue.put(None) # sentinel\n\n # We must wait for the worker handler to exit before terminating\n # workers because we don't want workers to be restarted behind our back.\n util.debug('joining worker handler')\n if threading.current_thread() is not worker_handler:\n worker_handler.join()\n\n # Terminate workers which haven't already finished.\n if pool and hasattr(pool[0], 'terminate'):\n util.debug('terminating workers')\n for p in pool:\n if p.exitcode is None:\n p.terminate()\n\n util.debug('joining task handler')\n if threading.current_thread() is not task_handler:\n task_handler.join()\n\n util.debug('joining result handler')\n if threading.current_thread() is not result_handler:\n result_handler.join()\n\n if pool and hasattr(pool[0], 'terminate'):\n util.debug('joining pool workers')\n for p in pool:\n if p.is_alive():\n # worker has not yet exited\n util.debug('cleaning up worker %d' % p.pid)\n p.join()\n\n def __enter__(self):\n return self\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n self.terminate()\n\n#\n# Class whose instances are returned by `Pool.apply_async()`\n#\n\nclass ApplyResult(object):\n\n def __init__(self, cache, callback, error_callback):\n self._event = threading.Event()\n self._job = next(job_counter)\n self._cache = cache\n self._callback = callback\n self._error_callback = error_callback\n cache[self._job] = self\n\n def ready(self):\n return self._event.is_set()\n\n def successful(self):\n assert self.ready()\n return self._success\n\n def wait(self, timeout=None):\n self._event.wait(timeout)\n\n def get(self, timeout=None):\n self.wait(timeout)\n if not self.ready():\n raise TimeoutError\n if self._success:\n return self._value\n else:\n raise self._value\n\n def _set(self, i, obj):\n self._success, self._value = obj\n if self._callback and self._success:\n self._callback(self._value)\n if self._error_callback and not self._success:\n self._error_callback(self._value)\n self._event.set()\n del self._cache[self._job]\n\nAsyncResult = ApplyResult # create alias -- see #17805\n\n#\n# Class whose instances are returned by `Pool.map_async()`\n#\n\nclass MapResult(ApplyResult):\n\n def __init__(self, cache, chunksize, length, callback, error_callback):\n ApplyResult.__init__(self, cache, callback,\n error_callback=error_callback)\n self._success = True\n self._value = [None] * length\n self._chunksize = chunksize\n if chunksize <= 0:\n self._number_left = 0\n self._event.set()\n del cache[self._job]\n else:\n self._number_left = length//chunksize + bool(length % chunksize)\n\n def _set(self, i, success_result):\n self._number_left -= 1\n success, result = success_result\n if success and self._success:\n self._value[i*self._chunksize:(i+1)*self._chunksize] = result\n if self._number_left == 0:\n if self._callback:\n self._callback(self._value)\n del self._cache[self._job]\n self._event.set()\n else:\n if not success and self._success:\n # only store first exception\n self._success = False\n self._value = result\n if self._number_left == 0:\n # only consider the result ready once all jobs are done\n if self._error_callback:\n self._error_callback(self._value)\n del self._cache[self._job]\n self._event.set()\n\n#\n# Class whose instances are returned by `Pool.imap()`\n#\n\nclass IMapIterator(object):\n\n def __init__(self, cache):\n self._cond = threading.Condition(threading.Lock())\n self._job = next(job_counter)\n self._cache = cache\n self._items = collections.deque()\n self._index = 0\n self._length = None\n self._unsorted = {}\n cache[self._job] = self\n\n def __iter__(self):\n return self\n\n def next(self, timeout=None):\n with self._cond:\n try:\n item = self._items.popleft()\n except IndexError:\n if self._index == self._length:\n raise StopIteration\n self._cond.wait(timeout)\n try:\n item = self._items.popleft()\n except IndexError:\n if self._index == self._length:\n raise StopIteration\n raise TimeoutError\n\n success, value = item\n if success:\n return value\n raise value\n\n __next__ = next # XXX\n\n def _set(self, i, obj):\n with self._cond:\n if self._index == i:\n self._items.append(obj)\n self._index += 1\n while self._index in self._unsorted:\n obj = self._unsorted.pop(self._index)\n self._items.append(obj)\n self._index += 1\n self._cond.notify()\n else:\n self._unsorted[i] = obj\n\n if self._index == self._length:\n del self._cache[self._job]\n\n def _set_length(self, length):\n with self._cond:\n self._length = length\n if self._index == self._length:\n self._cond.notify()\n del self._cache[self._job]\n\n#\n# Class whose instances are returned by `Pool.imap_unordered()`\n#\n\nclass IMapUnorderedIterator(IMapIterator):\n\n def _set(self, i, obj):\n with self._cond:\n self._items.append(obj)\n self._index += 1\n self._cond.notify()\n if self._index == self._length:\n del self._cache[self._job]\n\n#\n#\n#\n\nclass ThreadPool(Pool):\n _wrap_exception = False\n\n @staticmethod\n def Process(*args, **kwds):\n from .dummy import Process\n return Process(*args, **kwds)\n\n def __init__(self, processes=None, initializer=None, initargs=()):\n Pool.__init__(self, processes, initializer, initargs)\n\n def _setup_queues(self):\n self._inqueue = queue.Queue()\n self._outqueue = queue.Queue()\n self._quick_put = self._inqueue.put\n self._quick_get = self._outqueue.get\n\n @staticmethod\n def _help_stuff_finish(inqueue, task_handler, size):\n # put sentinels at head of inqueue to make workers finish\n with inqueue.not_empty:\n inqueue.queue.clear()\n inqueue.queue.extend([None] * size)\n inqueue.not_empty.notify_all()\n",803],"/usr/lib/python3.6/multiprocessing/reduction.py":["#\n# Module which deals with pickling of objects.\n#\n# multiprocessing/reduction.py\n#\n# Copyright (c) 2006-2008, R Oudkerk\n# Licensed to PSF under a Contributor Agreement.\n#\n\nfrom abc import ABCMeta, abstractmethod\nimport copyreg\nimport functools\nimport io\nimport os\nimport pickle\nimport socket\nimport sys\n\nfrom . import context\n\n__all__ = ['send_handle', 'recv_handle', 'ForkingPickler', 'register', 'dump']\n\n\nHAVE_SEND_HANDLE = (sys.platform == 'win32' or\n (hasattr(socket, 'CMSG_LEN') and\n hasattr(socket, 'SCM_RIGHTS') and\n hasattr(socket.socket, 'sendmsg')))\n\n#\n# Pickler subclass\n#\n\nclass ForkingPickler(pickle.Pickler):\n '''Pickler subclass used by multiprocessing.'''\n _extra_reducers = {}\n _copyreg_dispatch_table = copyreg.dispatch_table\n\n def __init__(self, *args):\n super().__init__(*args)\n self.dispatch_table = self._copyreg_dispatch_table.copy()\n self.dispatch_table.update(self._extra_reducers)\n\n @classmethod\n def register(cls, type, reduce):\n '''Register a reduce function for a type.'''\n cls._extra_reducers[type] = reduce\n\n @classmethod\n def dumps(cls, obj, protocol=None):\n buf = io.BytesIO()\n cls(buf, protocol).dump(obj)\n return buf.getbuffer()\n\n loads = pickle.loads\n\nregister = ForkingPickler.register\n\ndef dump(obj, file, protocol=None):\n '''Replacement for pickle.dump() using ForkingPickler.'''\n ForkingPickler(file, protocol).dump(obj)\n\n#\n# Platform specific definitions\n#\n\nif sys.platform == 'win32':\n # Windows\n __all__ += ['DupHandle', 'duplicate', 'steal_handle']\n import _winapi\n\n def duplicate(handle, target_process=None, inheritable=False):\n '''Duplicate a handle. (target_process is a handle not a pid!)'''\n if target_process is None:\n target_process = _winapi.GetCurrentProcess()\n return _winapi.DuplicateHandle(\n _winapi.GetCurrentProcess(), handle, target_process,\n 0, inheritable, _winapi.DUPLICATE_SAME_ACCESS)\n\n def steal_handle(source_pid, handle):\n '''Steal a handle from process identified by source_pid.'''\n source_process_handle = _winapi.OpenProcess(\n _winapi.PROCESS_DUP_HANDLE, False, source_pid)\n try:\n return _winapi.DuplicateHandle(\n source_process_handle, handle,\n _winapi.GetCurrentProcess(), 0, False,\n _winapi.DUPLICATE_SAME_ACCESS | _winapi.DUPLICATE_CLOSE_SOURCE)\n finally:\n _winapi.CloseHandle(source_process_handle)\n\n def send_handle(conn, handle, destination_pid):\n '''Send a handle over a local connection.'''\n dh = DupHandle(handle, _winapi.DUPLICATE_SAME_ACCESS, destination_pid)\n conn.send(dh)\n\n def recv_handle(conn):\n '''Receive a handle over a local connection.'''\n return conn.recv().detach()\n\n class DupHandle(object):\n '''Picklable wrapper for a handle.'''\n def __init__(self, handle, access, pid=None):\n if pid is None:\n # We just duplicate the handle in the current process and\n # let the receiving process steal the handle.\n pid = os.getpid()\n proc = _winapi.OpenProcess(_winapi.PROCESS_DUP_HANDLE, False, pid)\n try:\n self._handle = _winapi.DuplicateHandle(\n _winapi.GetCurrentProcess(),\n handle, proc, access, False, 0)\n finally:\n _winapi.CloseHandle(proc)\n self._access = access\n self._pid = pid\n\n def detach(self):\n '''Get the handle. This should only be called once.'''\n # retrieve handle from process which currently owns it\n if self._pid == os.getpid():\n # The handle has already been duplicated for this process.\n return self._handle\n # We must steal the handle from the process whose pid is self._pid.\n proc = _winapi.OpenProcess(_winapi.PROCESS_DUP_HANDLE, False,\n self._pid)\n try:\n return _winapi.DuplicateHandle(\n proc, self._handle, _winapi.GetCurrentProcess(),\n self._access, False, _winapi.DUPLICATE_CLOSE_SOURCE)\n finally:\n _winapi.CloseHandle(proc)\n\nelse:\n # Unix\n __all__ += ['DupFd', 'sendfds', 'recvfds']\n import array\n\n # On MacOSX we should acknowledge receipt of fds -- see Issue14669\n ACKNOWLEDGE = sys.platform == 'darwin'\n\n def sendfds(sock, fds):\n '''Send an array of fds over an AF_UNIX socket.'''\n fds = array.array('i', fds)\n msg = bytes([len(fds) % 256])\n sock.sendmsg([msg], [(socket.SOL_SOCKET, socket.SCM_RIGHTS, fds)])\n if ACKNOWLEDGE and sock.recv(1) != b'A':\n raise RuntimeError('did not receive acknowledgement of fd')\n\n def recvfds(sock, size):\n '''Receive an array of fds over an AF_UNIX socket.'''\n a = array.array('i')\n bytes_size = a.itemsize * size\n msg, ancdata, flags, addr = sock.recvmsg(1, socket.CMSG_SPACE(bytes_size))\n if not msg and not ancdata:\n raise EOFError\n try:\n if ACKNOWLEDGE:\n sock.send(b'A')\n if len(ancdata) != 1:\n raise RuntimeError('received %d items of ancdata' %\n len(ancdata))\n cmsg_level, cmsg_type, cmsg_data = ancdata[0]\n if (cmsg_level == socket.SOL_SOCKET and\n cmsg_type == socket.SCM_RIGHTS):\n if len(cmsg_data) % a.itemsize != 0:\n raise ValueError\n a.frombytes(cmsg_data)\n assert len(a) % 256 == msg[0]\n return list(a)\n except (ValueError, IndexError):\n pass\n raise RuntimeError('Invalid data received')\n\n def send_handle(conn, handle, destination_pid):\n '''Send a handle over a local connection.'''\n with socket.fromfd(conn.fileno(), socket.AF_UNIX, socket.SOCK_STREAM) as s:\n sendfds(s, [handle])\n\n def recv_handle(conn):\n '''Receive a handle over a local connection.'''\n with socket.fromfd(conn.fileno(), socket.AF_UNIX, socket.SOCK_STREAM) as s:\n return recvfds(s, 1)[0]\n\n def DupFd(fd):\n '''Return a wrapper for an fd.'''\n popen_obj = context.get_spawning_popen()\n if popen_obj is not None:\n return popen_obj.DupFd(popen_obj.duplicate_for_child(fd))\n elif HAVE_SEND_HANDLE:\n from . import resource_sharer\n return resource_sharer.DupFd(fd)\n else:\n raise ValueError('SCM_RIGHTS appears not to be available')\n\n#\n# Try making some callable types picklable\n#\n\ndef _reduce_method(m):\n if m.__self__ is None:\n return getattr, (m.__class__, m.__func__.__name__)\n else:\n return getattr, (m.__self__, m.__func__.__name__)\nclass _C:\n def f(self):\n pass\nregister(type(_C().f), _reduce_method)\n\n\ndef _reduce_method_descriptor(m):\n return getattr, (m.__objclass__, m.__name__)\nregister(type(list.append), _reduce_method_descriptor)\nregister(type(int.__add__), _reduce_method_descriptor)\n\n\ndef _reduce_partial(p):\n return _rebuild_partial, (p.func, p.args, p.keywords or {})\ndef _rebuild_partial(func, args, keywords):\n return functools.partial(func, *args, **keywords)\nregister(functools.partial, _reduce_partial)\n\n#\n# Make sockets picklable\n#\n\nif sys.platform == 'win32':\n def _reduce_socket(s):\n from .resource_sharer import DupSocket\n return _rebuild_socket, (DupSocket(s),)\n def _rebuild_socket(ds):\n return ds.detach()\n register(socket.socket, _reduce_socket)\n\nelse:\n def _reduce_socket(s):\n df = DupFd(s.fileno())\n return _rebuild_socket, (df, s.family, s.type, s.proto)\n def _rebuild_socket(df, family, type, proto):\n fd = df.detach()\n return socket.socket(family, type, proto, fileno=fd)\n register(socket.socket, _reduce_socket)\n\n\nclass AbstractReducer(metaclass=ABCMeta):\n '''Abstract base class for use in implementing a Reduction class\n suitable for use in replacing the standard reduction mechanism\n used in multiprocessing.'''\n ForkingPickler = ForkingPickler\n register = register\n dump = dump\n send_handle = send_handle\n recv_handle = recv_handle\n\n if sys.platform == 'win32':\n steal_handle = steal_handle\n duplicate = duplicate\n DupHandle = DupHandle\n else:\n sendfds = sendfds\n recvfds = recvfds\n DupFd = DupFd\n\n _reduce_method = _reduce_method\n _reduce_method_descriptor = _reduce_method_descriptor\n _rebuild_partial = _rebuild_partial\n _reduce_socket = _reduce_socket\n _rebuild_socket = _rebuild_socket\n\n def __init__(self, *args):\n register(type(_C().f), _reduce_method)\n register(type(list.append), _reduce_method_descriptor)\n register(type(int.__add__), _reduce_method_descriptor)\n register(functools.partial, _reduce_partial)\n register(socket.socket, _reduce_socket)\n",274],"/usr/lib/python3.6/multiprocessing/process.py":["#\n# Module providing the `Process` class which emulates `threading.Thread`\n#\n# multiprocessing/process.py\n#\n# Copyright (c) 2006-2008, R Oudkerk\n# Licensed to PSF under a Contributor Agreement.\n#\n\n__all__ = ['BaseProcess', 'current_process', 'active_children']\n\n#\n# Imports\n#\n\nimport os\nimport sys\nimport signal\nimport itertools\nfrom _weakrefset import WeakSet\n\n#\n#\n#\n\ntry:\n ORIGINAL_DIR = os.path.abspath(os.getcwd())\nexcept OSError:\n ORIGINAL_DIR = None\n\n#\n# Public functions\n#\n\ndef current_process():\n '''\n Return process object representing the current process\n '''\n return _current_process\n\ndef active_children():\n '''\n Return list of process objects corresponding to live child processes\n '''\n _cleanup()\n return list(_children)\n\n#\n#\n#\n\ndef _cleanup():\n # check for processes which have finished\n for p in list(_children):\n if p._popen.poll() is not None:\n _children.discard(p)\n\n#\n# The `Process` class\n#\n\nclass BaseProcess(object):\n '''\n Process objects represent activity that is run in a separate process\n\n The class is analogous to `threading.Thread`\n '''\n def _Popen(self):\n raise NotImplementedError\n\n def __init__(self, group=None, target=None, name=None, args=(), kwargs={},\n *, daemon=None):\n assert group is None, 'group argument must be None for now'\n count = next(_process_counter)\n self._identity = _current_process._identity + (count,)\n self._config = _current_process._config.copy()\n self._parent_pid = os.getpid()\n self._popen = None\n self._target = target\n self._args = tuple(args)\n self._kwargs = dict(kwargs)\n self._name = name or type(self).__name__ + '-' + \\\n ':'.join(str(i) for i in self._identity)\n if daemon is not None:\n self.daemon = daemon\n _dangling.add(self)\n\n def run(self):\n '''\n Method to be run in sub-process; can be overridden in sub-class\n '''\n if self._target:\n self._target(*self._args, **self._kwargs)\n\n def start(self):\n '''\n Start child process\n '''\n assert self._popen is None, 'cannot start a process twice'\n assert self._parent_pid == os.getpid(), \\\n 'can only start a process object created by current process'\n assert not _current_process._config.get('daemon'), \\\n 'daemonic processes are not allowed to have children'\n _cleanup()\n self._popen = self._Popen(self)\n self._sentinel = self._popen.sentinel\n # Avoid a refcycle if the target function holds an indirect\n # reference to the process object (see bpo-30775)\n del self._target, self._args, self._kwargs\n _children.add(self)\n\n def terminate(self):\n '''\n Terminate process; sends SIGTERM signal or uses TerminateProcess()\n '''\n self._popen.terminate()\n\n def join(self, timeout=None):\n '''\n Wait until child process terminates\n '''\n assert self._parent_pid == os.getpid(), 'can only join a child process'\n assert self._popen is not None, 'can only join a started process'\n res = self._popen.wait(timeout)\n if res is not None:\n _children.discard(self)\n\n def is_alive(self):\n '''\n Return whether process is alive\n '''\n if self is _current_process:\n return True\n assert self._parent_pid == os.getpid(), 'can only test a child process'\n\n if self._popen is None:\n return False\n\n returncode = self._popen.poll()\n if returncode is None:\n return True\n else:\n _children.discard(self)\n return False\n\n @property\n def name(self):\n return self._name\n\n @name.setter\n def name(self, name):\n assert isinstance(name, str), 'name must be a string'\n self._name = name\n\n @property\n def daemon(self):\n '''\n Return whether process is a daemon\n '''\n return self._config.get('daemon', False)\n\n @daemon.setter\n def daemon(self, daemonic):\n '''\n Set whether process is a daemon\n '''\n assert self._popen is None, 'process has already started'\n self._config['daemon'] = daemonic\n\n @property\n def authkey(self):\n return self._config['authkey']\n\n @authkey.setter\n def authkey(self, authkey):\n '''\n Set authorization key of process\n '''\n self._config['authkey'] = AuthenticationString(authkey)\n\n @property\n def exitcode(self):\n '''\n Return exit code of process or `None` if it has yet to stop\n '''\n if self._popen is None:\n return self._popen\n return self._popen.poll()\n\n @property\n def ident(self):\n '''\n Return identifier (PID) of process or `None` if it has yet to start\n '''\n if self is _current_process:\n return os.getpid()\n else:\n return self._popen and self._popen.pid\n\n pid = ident\n\n @property\n def sentinel(self):\n '''\n Return a file descriptor (Unix) or handle (Windows) suitable for\n waiting for process termination.\n '''\n try:\n return self._sentinel\n except AttributeError:\n raise ValueError(\"process not started\")\n\n def __repr__(self):\n if self is _current_process:\n status = 'started'\n elif self._parent_pid != os.getpid():\n status = 'unknown'\n elif self._popen is None:\n status = 'initial'\n else:\n if self._popen.poll() is not None:\n status = self.exitcode\n else:\n status = 'started'\n\n if type(status) is int:\n if status == 0:\n status = 'stopped'\n else:\n status = 'stopped[%s]' % _exitcode_to_name.get(status, status)\n\n return '<%s(%s, %s%s)>' % (type(self).__name__, self._name,\n status, self.daemon and ' daemon' or '')\n\n ##\n\n def _bootstrap(self):\n from . import util, context\n global _current_process, _process_counter, _children\n\n try:\n if self._start_method is not None:\n context._force_start_method(self._start_method)\n _process_counter = itertools.count(1)\n _children = set()\n util._close_stdin()\n old_process = _current_process\n _current_process = self\n try:\n util._finalizer_registry.clear()\n util._run_after_forkers()\n finally:\n # delay finalization of the old process object until after\n # _run_after_forkers() is executed\n del old_process\n util.info('child process calling self.run()')\n try:\n self.run()\n exitcode = 0\n finally:\n util._exit_function()\n except SystemExit as e:\n if not e.args:\n exitcode = 1\n elif isinstance(e.args[0], int):\n exitcode = e.args[0]\n else:\n sys.stderr.write(str(e.args[0]) + '\\n')\n exitcode = 1\n except:\n exitcode = 1\n import traceback\n sys.stderr.write('Process %s:\\n' % self.name)\n traceback.print_exc()\n finally:\n util.info('process exiting with exitcode %d' % exitcode)\n util._flush_std_streams()\n\n return exitcode\n\n#\n# We subclass bytes to avoid accidental transmission of auth keys over network\n#\n\nclass AuthenticationString(bytes):\n def __reduce__(self):\n from .context import get_spawning_popen\n if get_spawning_popen() is None:\n raise TypeError(\n 'Pickling an AuthenticationString object is '\n 'disallowed for security reasons'\n )\n return AuthenticationString, (bytes(self),)\n\n#\n# Create object representing the main process\n#\n\nclass _MainProcess(BaseProcess):\n\n def __init__(self):\n self._identity = ()\n self._name = 'MainProcess'\n self._parent_pid = None\n self._popen = None\n self._config = {'authkey': AuthenticationString(os.urandom(32)),\n 'semprefix': '/mp'}\n # Note that some versions of FreeBSD only allow named\n # semaphores to have names of up to 14 characters. Therefore\n # we choose a short prefix.\n #\n # On MacOSX in a sandbox it may be necessary to use a\n # different prefix -- see #19478.\n #\n # Everything in self._config will be inherited by descendant\n # processes.\n\n\n_current_process = _MainProcess()\n_process_counter = itertools.count(1)\n_children = set()\ndel _MainProcess\n\n#\n# Give names to some return codes\n#\n\n_exitcode_to_name = {}\n\nfor name, signum in list(signal.__dict__.items()):\n if name[:3]=='SIG' and '_' not in name:\n _exitcode_to_name[-signum] = name\n\n# For debug and leak testing\n_dangling = WeakSet()\n",335],"/usr/lib/python3.6/multiprocessing/context.py":["import os\nimport sys\nimport threading\n\nfrom . import process\nfrom . import reduction\n\n__all__ = [] # things are copied from here to __init__.py\n\n#\n# Exceptions\n#\n\nclass ProcessError(Exception):\n pass\n\nclass BufferTooShort(ProcessError):\n pass\n\nclass TimeoutError(ProcessError):\n pass\n\nclass AuthenticationError(ProcessError):\n pass\n\n#\n# Base type for contexts\n#\n\nclass BaseContext(object):\n\n ProcessError = ProcessError\n BufferTooShort = BufferTooShort\n TimeoutError = TimeoutError\n AuthenticationError = AuthenticationError\n\n current_process = staticmethod(process.current_process)\n active_children = staticmethod(process.active_children)\n\n def cpu_count(self):\n '''Returns the number of CPUs in the system'''\n num = os.cpu_count()\n if num is None:\n raise NotImplementedError('cannot determine number of cpus')\n else:\n return num\n\n def Manager(self):\n '''Returns a manager associated with a running server process\n\n The managers methods such as `Lock()`, `Condition()` and `Queue()`\n can be used to create shared objects.\n '''\n from .managers import SyncManager\n m = SyncManager(ctx=self.get_context())\n m.start()\n return m\n\n def Pipe(self, duplex=True):\n '''Returns two connection object connected by a pipe'''\n from .connection import Pipe\n return Pipe(duplex)\n\n def Lock(self):\n '''Returns a non-recursive lock object'''\n from .synchronize import Lock\n return Lock(ctx=self.get_context())\n\n def RLock(self):\n '''Returns a recursive lock object'''\n from .synchronize import RLock\n return RLock(ctx=self.get_context())\n\n def Condition(self, lock=None):\n '''Returns a condition object'''\n from .synchronize import Condition\n return Condition(lock, ctx=self.get_context())\n\n def Semaphore(self, value=1):\n '''Returns a semaphore object'''\n from .synchronize import Semaphore\n return Semaphore(value, ctx=self.get_context())\n\n def BoundedSemaphore(self, value=1):\n '''Returns a bounded semaphore object'''\n from .synchronize import BoundedSemaphore\n return BoundedSemaphore(value, ctx=self.get_context())\n\n def Event(self):\n '''Returns an event object'''\n from .synchronize import Event\n return Event(ctx=self.get_context())\n\n def Barrier(self, parties, action=None, timeout=None):\n '''Returns a barrier object'''\n from .synchronize import Barrier\n return Barrier(parties, action, timeout, ctx=self.get_context())\n\n def Queue(self, maxsize=0):\n '''Returns a queue object'''\n from .queues import Queue\n return Queue(maxsize, ctx=self.get_context())\n\n def JoinableQueue(self, maxsize=0):\n '''Returns a queue object'''\n from .queues import JoinableQueue\n return JoinableQueue(maxsize, ctx=self.get_context())\n\n def SimpleQueue(self):\n '''Returns a queue object'''\n from .queues import SimpleQueue\n return SimpleQueue(ctx=self.get_context())\n\n def Pool(self, processes=None, initializer=None, initargs=(),\n maxtasksperchild=None):\n '''Returns a process pool object'''\n from .pool import Pool\n return Pool(processes, initializer, initargs, maxtasksperchild,\n context=self.get_context())\n\n def RawValue(self, typecode_or_type, *args):\n '''Returns a shared object'''\n from .sharedctypes import RawValue\n return RawValue(typecode_or_type, *args)\n\n def RawArray(self, typecode_or_type, size_or_initializer):\n '''Returns a shared array'''\n from .sharedctypes import RawArray\n return RawArray(typecode_or_type, size_or_initializer)\n\n def Value(self, typecode_or_type, *args, lock=True):\n '''Returns a synchronized shared object'''\n from .sharedctypes import Value\n return Value(typecode_or_type, *args, lock=lock,\n ctx=self.get_context())\n\n def Array(self, typecode_or_type, size_or_initializer, *, lock=True):\n '''Returns a synchronized shared array'''\n from .sharedctypes import Array\n return Array(typecode_or_type, size_or_initializer, lock=lock,\n ctx=self.get_context())\n\n def freeze_support(self):\n '''Check whether this is a fake forked process in a frozen executable.\n If so then run code specified by commandline and exit.\n '''\n if sys.platform == 'win32' and getattr(sys, 'frozen', False):\n from .spawn import freeze_support\n freeze_support()\n\n def get_logger(self):\n '''Return package logger -- if it does not already exist then\n it is created.\n '''\n from .util import get_logger\n return get_logger()\n\n def log_to_stderr(self, level=None):\n '''Turn on logging and add a handler which prints to stderr'''\n from .util import log_to_stderr\n return log_to_stderr(level)\n\n def allow_connection_pickling(self):\n '''Install support for sending connections and sockets\n between processes\n '''\n # This is undocumented. In previous versions of multiprocessing\n # its only effect was to make socket objects inheritable on Windows.\n from . import connection\n\n def set_executable(self, executable):\n '''Sets the path to a python.exe or pythonw.exe binary used to run\n child processes instead of sys.executable when using the 'spawn'\n start method. Useful for people embedding Python.\n '''\n from .spawn import set_executable\n set_executable(executable)\n\n def set_forkserver_preload(self, module_names):\n '''Set list of module names to try to load in forkserver process.\n This is really just a hint.\n '''\n from .forkserver import set_forkserver_preload\n set_forkserver_preload(module_names)\n\n def get_context(self, method=None):\n if method is None:\n return self\n try:\n ctx = _concrete_contexts[method]\n except KeyError:\n raise ValueError('cannot find context for %r' % method)\n ctx._check_available()\n return ctx\n\n def get_start_method(self, allow_none=False):\n return self._name\n\n def set_start_method(self, method, force=False):\n raise ValueError('cannot set start method of concrete context')\n\n @property\n def reducer(self):\n '''Controls how objects will be reduced to a form that can be\n shared with other processes.'''\n return globals().get('reduction')\n\n @reducer.setter\n def reducer(self, reduction):\n globals()['reduction'] = reduction\n\n def _check_available(self):\n pass\n\n#\n# Type of default context -- underlying context can be set at most once\n#\n\nclass Process(process.BaseProcess):\n _start_method = None\n @staticmethod\n def _Popen(process_obj):\n return _default_context.get_context().Process._Popen(process_obj)\n\nclass DefaultContext(BaseContext):\n Process = Process\n\n def __init__(self, context):\n self._default_context = context\n self._actual_context = None\n\n def get_context(self, method=None):\n if method is None:\n if self._actual_context is None:\n self._actual_context = self._default_context\n return self._actual_context\n else:\n return super().get_context(method)\n\n def set_start_method(self, method, force=False):\n if self._actual_context is not None and not force:\n raise RuntimeError('context has already been set')\n if method is None and force:\n self._actual_context = None\n return\n self._actual_context = self.get_context(method)\n\n def get_start_method(self, allow_none=False):\n if self._actual_context is None:\n if allow_none:\n return None\n self._actual_context = self._default_context\n return self._actual_context._name\n\n def get_all_start_methods(self):\n if sys.platform == 'win32':\n return ['spawn']\n else:\n if reduction.HAVE_SEND_HANDLE:\n return ['fork', 'spawn', 'forkserver']\n else:\n return ['fork', 'spawn']\n\nDefaultContext.__all__ = list(x for x in dir(DefaultContext) if x[0] != '_')\n\n#\n# Context types for fixed start method\n#\n\nif sys.platform != 'win32':\n\n class ForkProcess(process.BaseProcess):\n _start_method = 'fork'\n @staticmethod\n def _Popen(process_obj):\n from .popen_fork import Popen\n return Popen(process_obj)\n\n class SpawnProcess(process.BaseProcess):\n _start_method = 'spawn'\n @staticmethod\n def _Popen(process_obj):\n from .popen_spawn_posix import Popen\n return Popen(process_obj)\n\n class ForkServerProcess(process.BaseProcess):\n _start_method = 'forkserver'\n @staticmethod\n def _Popen(process_obj):\n from .popen_forkserver import Popen\n return Popen(process_obj)\n\n class ForkContext(BaseContext):\n _name = 'fork'\n Process = ForkProcess\n\n class SpawnContext(BaseContext):\n _name = 'spawn'\n Process = SpawnProcess\n\n class ForkServerContext(BaseContext):\n _name = 'forkserver'\n Process = ForkServerProcess\n def _check_available(self):\n if not reduction.HAVE_SEND_HANDLE:\n raise ValueError('forkserver start method not available')\n\n _concrete_contexts = {\n 'fork': ForkContext(),\n 'spawn': SpawnContext(),\n 'forkserver': ForkServerContext(),\n }\n _default_context = DefaultContext(_concrete_contexts['fork'])\n\nelse:\n\n class SpawnProcess(process.BaseProcess):\n _start_method = 'spawn'\n @staticmethod\n def _Popen(process_obj):\n from .popen_spawn_win32 import Popen\n return Popen(process_obj)\n\n class SpawnContext(BaseContext):\n _name = 'spawn'\n Process = SpawnProcess\n\n _concrete_contexts = {\n 'spawn': SpawnContext(),\n }\n _default_context = DefaultContext(_concrete_contexts['spawn'])\n\n#\n# Force the start method\n#\n\ndef _force_start_method(method):\n _default_context._actual_context = _concrete_contexts[method]\n\n#\n# Check that the current thread is spawning a child process\n#\n\n_tls = threading.local()\n\ndef get_spawning_popen():\n return getattr(_tls, 'spawning_popen', None)\n\ndef set_spawning_popen(popen):\n _tls.spawning_popen = popen\n\ndef assert_spawning(obj):\n if get_spawning_popen() is None:\n raise RuntimeError(\n '%s objects should only be shared between processes'\n ' through inheritance' % type(obj).__name__\n )\n",357],"/usr/lib/python3.6/random.py":["\"\"\"Random variable generators.\n\n integers\n --------\n uniform within range\n\n sequences\n ---------\n pick random element\n pick random sample\n pick weighted random sample\n generate random permutation\n\n distributions on the real line:\n ------------------------------\n uniform\n triangular\n normal (Gaussian)\n lognormal\n negative exponential\n gamma\n beta\n pareto\n Weibull\n\n distributions on the circle (angles 0 to 2pi)\n ---------------------------------------------\n circular uniform\n von Mises\n\nGeneral notes on the underlying Mersenne Twister core generator:\n\n* The period is 2**19937-1.\n* It is one of the most extensively tested generators in existence.\n* The random() method is implemented in C, executes in a single Python step,\n and is, therefore, threadsafe.\n\n\"\"\"\n\nfrom warnings import warn as _warn\nfrom types import MethodType as _MethodType, BuiltinMethodType as _BuiltinMethodType\nfrom math import log as _log, exp as _exp, pi as _pi, e as _e, ceil as _ceil\nfrom math import sqrt as _sqrt, acos as _acos, cos as _cos, sin as _sin\nfrom os import urandom as _urandom\nfrom _collections_abc import Set as _Set, Sequence as _Sequence\nfrom hashlib import sha512 as _sha512\nimport itertools as _itertools\nimport bisect as _bisect\n\n__all__ = [\"Random\",\"seed\",\"random\",\"uniform\",\"randint\",\"choice\",\"sample\",\n \"randrange\",\"shuffle\",\"normalvariate\",\"lognormvariate\",\n \"expovariate\",\"vonmisesvariate\",\"gammavariate\",\"triangular\",\n \"gauss\",\"betavariate\",\"paretovariate\",\"weibullvariate\",\n \"getstate\",\"setstate\", \"getrandbits\", \"choices\",\n \"SystemRandom\"]\n\nNV_MAGICCONST = 4 * _exp(-0.5)/_sqrt(2.0)\nTWOPI = 2.0*_pi\nLOG4 = _log(4.0)\nSG_MAGICCONST = 1.0 + _log(4.5)\nBPF = 53 # Number of bits in a float\nRECIP_BPF = 2**-BPF\n\n\n# Translated by Guido van Rossum from C source provided by\n# Adrian Baddeley. Adapted by Raymond Hettinger for use with\n# the Mersenne Twister and os.urandom() core generators.\n\nimport _random\n\nclass Random(_random.Random):\n \"\"\"Random number generator base class used by bound module functions.\n\n Used to instantiate instances of Random to get generators that don't\n share state.\n\n Class Random can also be subclassed if you want to use a different basic\n generator of your own devising: in that case, override the following\n methods: random(), seed(), getstate(), and setstate().\n Optionally, implement a getrandbits() method so that randrange()\n can cover arbitrarily large ranges.\n\n \"\"\"\n\n VERSION = 3 # used by getstate/setstate\n\n def __init__(self, x=None):\n \"\"\"Initialize an instance.\n\n Optional argument x controls seeding, as for Random.seed().\n \"\"\"\n\n self.seed(x)\n self.gauss_next = None\n\n def seed(self, a=None, version=2):\n \"\"\"Initialize internal state from hashable object.\n\n None or no argument seeds from current time or from an operating\n system specific randomness source if available.\n\n If *a* is an int, all bits are used.\n\n For version 2 (the default), all of the bits are used if *a* is a str,\n bytes, or bytearray. For version 1 (provided for reproducing random\n sequences from older versions of Python), the algorithm for str and\n bytes generates a narrower range of seeds.\n\n \"\"\"\n\n if version == 1 and isinstance(a, (str, bytes)):\n a = a.decode('latin-1') if isinstance(a, bytes) else a\n x = ord(a[0]) << 7 if a else 0\n for c in map(ord, a):\n x = ((1000003 * x) ^ c) & 0xFFFFFFFFFFFFFFFF\n x ^= len(a)\n a = -2 if x == -1 else x\n\n if version == 2 and isinstance(a, (str, bytes, bytearray)):\n if isinstance(a, str):\n a = a.encode()\n a += _sha512(a).digest()\n a = int.from_bytes(a, 'big')\n\n super().seed(a)\n self.gauss_next = None\n\n def getstate(self):\n \"\"\"Return internal state; can be passed to setstate() later.\"\"\"\n return self.VERSION, super().getstate(), self.gauss_next\n\n def setstate(self, state):\n \"\"\"Restore internal state from object returned by getstate().\"\"\"\n version = state[0]\n if version == 3:\n version, internalstate, self.gauss_next = state\n super().setstate(internalstate)\n elif version == 2:\n version, internalstate, self.gauss_next = state\n # In version 2, the state was saved as signed ints, which causes\n # inconsistencies between 32/64-bit systems. The state is\n # really unsigned 32-bit ints, so we convert negative ints from\n # version 2 to positive longs for version 3.\n try:\n internalstate = tuple(x % (2**32) for x in internalstate)\n except ValueError as e:\n raise TypeError from e\n super().setstate(internalstate)\n else:\n raise ValueError(\"state with version %s passed to \"\n \"Random.setstate() of version %s\" %\n (version, self.VERSION))\n\n## ---- Methods below this point do not need to be overridden when\n## ---- subclassing for the purpose of using a different core generator.\n\n## -------------------- pickle support -------------------\n\n # Issue 17489: Since __reduce__ was defined to fix #759889 this is no\n # longer called; we leave it here because it has been here since random was\n # rewritten back in 2001 and why risk breaking something.\n def __getstate__(self): # for pickle\n return self.getstate()\n\n def __setstate__(self, state): # for pickle\n self.setstate(state)\n\n def __reduce__(self):\n return self.__class__, (), self.getstate()\n\n## -------------------- integer methods -------------------\n\n def randrange(self, start, stop=None, step=1, _int=int):\n \"\"\"Choose a random item from range(start, stop[, step]).\n\n This fixes the problem with randint() which includes the\n endpoint; in Python this is usually not what you want.\n\n \"\"\"\n\n # This code is a bit messy to make it fast for the\n # common case while still doing adequate error checking.\n istart = _int(start)\n if istart != start:\n raise ValueError(\"non-integer arg 1 for randrange()\")\n if stop is None:\n if istart > 0:\n return self._randbelow(istart)\n raise ValueError(\"empty range for randrange()\")\n\n # stop argument supplied.\n istop = _int(stop)\n if istop != stop:\n raise ValueError(\"non-integer stop for randrange()\")\n width = istop - istart\n if step == 1 and width > 0:\n return istart + self._randbelow(width)\n if step == 1:\n raise ValueError(\"empty range for randrange() (%d,%d, %d)\" % (istart, istop, width))\n\n # Non-unit step argument supplied.\n istep = _int(step)\n if istep != step:\n raise ValueError(\"non-integer step for randrange()\")\n if istep > 0:\n n = (width + istep - 1) // istep\n elif istep < 0:\n n = (width + istep + 1) // istep\n else:\n raise ValueError(\"zero step for randrange()\")\n\n if n <= 0:\n raise ValueError(\"empty range for randrange()\")\n\n return istart + istep*self._randbelow(n)\n\n def randint(self, a, b):\n \"\"\"Return random integer in range [a, b], including both end points.\n \"\"\"\n\n return self.randrange(a, b+1)\n\n def _randbelow(self, n, int=int, maxsize=1<= n:\n r = getrandbits(k)\n return r\n # There's an overridden random() method but no new getrandbits() method,\n # so we can only use random() from here.\n if n >= maxsize:\n _warn(\"Underlying random() generator does not supply \\n\"\n \"enough bits to choose from a population range this large.\\n\"\n \"To remove the range limitation, add a getrandbits() method.\")\n return int(random() * n)\n if n == 0:\n raise ValueError(\"Boundary cannot be zero\")\n rem = maxsize % n\n limit = (maxsize - rem) / maxsize # int(limit * maxsize) % n == 0\n r = random()\n while r >= limit:\n r = random()\n return int(r*maxsize) % n\n\n## -------------------- sequence methods -------------------\n\n def choice(self, seq):\n \"\"\"Choose a random element from a non-empty sequence.\"\"\"\n try:\n i = self._randbelow(len(seq))\n except ValueError:\n raise IndexError('Cannot choose from an empty sequence') from None\n return seq[i]\n\n def shuffle(self, x, random=None):\n \"\"\"Shuffle list x in place, and return None.\n\n Optional argument random is a 0-argument function returning a\n random float in [0.0, 1.0); if it is the default None, the\n standard random.random will be used.\n\n \"\"\"\n\n if random is None:\n randbelow = self._randbelow\n for i in reversed(range(1, len(x))):\n # pick an element in x[:i+1] with which to exchange x[i]\n j = randbelow(i+1)\n x[i], x[j] = x[j], x[i]\n else:\n _int = int\n for i in reversed(range(1, len(x))):\n # pick an element in x[:i+1] with which to exchange x[i]\n j = _int(random() * (i+1))\n x[i], x[j] = x[j], x[i]\n\n def sample(self, population, k):\n \"\"\"Chooses k unique random elements from a population sequence or set.\n\n Returns a new list containing elements from the population while\n leaving the original population unchanged. The resulting list is\n in selection order so that all sub-slices will also be valid random\n samples. This allows raffle winners (the sample) to be partitioned\n into grand prize and second place winners (the subslices).\n\n Members of the population need not be hashable or unique. If the\n population contains repeats, then each occurrence is a possible\n selection in the sample.\n\n To choose a sample in a range of integers, use range as an argument.\n This is especially fast and space efficient for sampling from a\n large population: sample(range(10000000), 60)\n \"\"\"\n\n # Sampling without replacement entails tracking either potential\n # selections (the pool) in a list or previous selections in a set.\n\n # When the number of selections is small compared to the\n # population, then tracking selections is efficient, requiring\n # only a small set and an occasional reselection. For\n # a larger number of selections, the pool tracking method is\n # preferred since the list takes less space than the\n # set and it doesn't suffer from frequent reselections.\n\n if isinstance(population, _Set):\n population = tuple(population)\n if not isinstance(population, _Sequence):\n raise TypeError(\"Population must be a sequence or set. For dicts, use list(d).\")\n randbelow = self._randbelow\n n = len(population)\n if not 0 <= k <= n:\n raise ValueError(\"Sample larger than population or is negative\")\n result = [None] * k\n setsize = 21 # size of a small set minus size of an empty list\n if k > 5:\n setsize += 4 ** _ceil(_log(k * 3, 4)) # table size for big sets\n if n <= setsize:\n # An n-length list is smaller than a k-length set\n pool = list(population)\n for i in range(k): # invariant: non-selected at [0,n-i)\n j = randbelow(n-i)\n result[i] = pool[j]\n pool[j] = pool[n-i-1] # move non-selected item into vacancy\n else:\n selected = set()\n selected_add = selected.add\n for i in range(k):\n j = randbelow(n)\n while j in selected:\n j = randbelow(n)\n selected_add(j)\n result[i] = population[j]\n return result\n\n def choices(self, population, weights=None, *, cum_weights=None, k=1):\n \"\"\"Return a k sized list of population elements chosen with replacement.\n\n If the relative weights or cumulative weights are not specified,\n the selections are made with equal probability.\n\n \"\"\"\n random = self.random\n if cum_weights is None:\n if weights is None:\n _int = int\n total = len(population)\n return [population[_int(random() * total)] for i in range(k)]\n cum_weights = list(_itertools.accumulate(weights))\n elif weights is not None:\n raise TypeError('Cannot specify both weights and cumulative weights')\n if len(cum_weights) != len(population):\n raise ValueError('The number of weights does not match the population')\n bisect = _bisect.bisect\n total = cum_weights[-1]\n hi = len(cum_weights) - 1\n return [population[bisect(cum_weights, random() * total, 0, hi)]\n for i in range(k)]\n\n## -------------------- real-valued distributions -------------------\n\n## -------------------- uniform distribution -------------------\n\n def uniform(self, a, b):\n \"Get a random number in the range [a, b) or [a, b] depending on rounding.\"\n return a + (b-a) * self.random()\n\n## -------------------- triangular --------------------\n\n def triangular(self, low=0.0, high=1.0, mode=None):\n \"\"\"Triangular distribution.\n\n Continuous distribution bounded by given lower and upper limits,\n and having a given mode value in-between.\n\n http://en.wikipedia.org/wiki/Triangular_distribution\n\n \"\"\"\n u = self.random()\n try:\n c = 0.5 if mode is None else (mode - low) / (high - low)\n except ZeroDivisionError:\n return low\n if u > c:\n u = 1.0 - u\n c = 1.0 - c\n low, high = high, low\n return low + (high - low) * (u * c) ** 0.5\n\n## -------------------- normal distribution --------------------\n\n def normalvariate(self, mu, sigma):\n \"\"\"Normal distribution.\n\n mu is the mean, and sigma is the standard deviation.\n\n \"\"\"\n # mu = mean, sigma = standard deviation\n\n # Uses Kinderman and Monahan method. Reference: Kinderman,\n # A.J. and Monahan, J.F., \"Computer generation of random\n # variables using the ratio of uniform deviates\", ACM Trans\n # Math Software, 3, (1977), pp257-260.\n\n random = self.random\n while 1:\n u1 = random()\n u2 = 1.0 - random()\n z = NV_MAGICCONST*(u1-0.5)/u2\n zz = z*z/4.0\n if zz <= -_log(u2):\n break\n return mu + z*sigma\n\n## -------------------- lognormal distribution --------------------\n\n def lognormvariate(self, mu, sigma):\n \"\"\"Log normal distribution.\n\n If you take the natural logarithm of this distribution, you'll get a\n normal distribution with mean mu and standard deviation sigma.\n mu can have any value, and sigma must be greater than zero.\n\n \"\"\"\n return _exp(self.normalvariate(mu, sigma))\n\n## -------------------- exponential distribution --------------------\n\n def expovariate(self, lambd):\n \"\"\"Exponential distribution.\n\n lambd is 1.0 divided by the desired mean. It should be\n nonzero. (The parameter would be called \"lambda\", but that is\n a reserved word in Python.) Returned values range from 0 to\n positive infinity if lambd is positive, and from negative\n infinity to 0 if lambd is negative.\n\n \"\"\"\n # lambd: rate lambd = 1/mean\n # ('lambda' is a Python reserved word)\n\n # we use 1-random() instead of random() to preclude the\n # possibility of taking the log of zero.\n return -_log(1.0 - self.random())/lambd\n\n## -------------------- von Mises distribution --------------------\n\n def vonmisesvariate(self, mu, kappa):\n \"\"\"Circular data distribution.\n\n mu is the mean angle, expressed in radians between 0 and 2*pi, and\n kappa is the concentration parameter, which must be greater than or\n equal to zero. If kappa is equal to zero, this distribution reduces\n to a uniform random angle over the range 0 to 2*pi.\n\n \"\"\"\n # mu: mean angle (in radians between 0 and 2*pi)\n # kappa: concentration parameter kappa (>= 0)\n # if kappa = 0 generate uniform random angle\n\n # Based upon an algorithm published in: Fisher, N.I.,\n # \"Statistical Analysis of Circular Data\", Cambridge\n # University Press, 1993.\n\n # Thanks to Magnus Kessler for a correction to the\n # implementation of step 4.\n\n random = self.random\n if kappa <= 1e-6:\n return TWOPI * random()\n\n s = 0.5 / kappa\n r = s + _sqrt(1.0 + s * s)\n\n while 1:\n u1 = random()\n z = _cos(_pi * u1)\n\n d = z / (r + z)\n u2 = random()\n if u2 < 1.0 - d * d or u2 <= (1.0 - d) * _exp(d):\n break\n\n q = 1.0 / r\n f = (q + z) / (1.0 + q * z)\n u3 = random()\n if u3 > 0.5:\n theta = (mu + _acos(f)) % TWOPI\n else:\n theta = (mu - _acos(f)) % TWOPI\n\n return theta\n\n## -------------------- gamma distribution --------------------\n\n def gammavariate(self, alpha, beta):\n \"\"\"Gamma distribution. Not the gamma function!\n\n Conditions on the parameters are alpha > 0 and beta > 0.\n\n The probability distribution function is:\n\n x ** (alpha - 1) * math.exp(-x / beta)\n pdf(x) = --------------------------------------\n math.gamma(alpha) * beta ** alpha\n\n \"\"\"\n\n # alpha > 0, beta > 0, mean is alpha*beta, variance is alpha*beta**2\n\n # Warning: a few older sources define the gamma distribution in terms\n # of alpha > -1.0\n if alpha <= 0.0 or beta <= 0.0:\n raise ValueError('gammavariate: alpha and beta must be > 0.0')\n\n random = self.random\n if alpha > 1.0:\n\n # Uses R.C.H. Cheng, \"The generation of Gamma\n # variables with non-integral shape parameters\",\n # Applied Statistics, (1977), 26, No. 1, p71-74\n\n ainv = _sqrt(2.0 * alpha - 1.0)\n bbb = alpha - LOG4\n ccc = alpha + ainv\n\n while 1:\n u1 = random()\n if not 1e-7 < u1 < .9999999:\n continue\n u2 = 1.0 - random()\n v = _log(u1/(1.0-u1))/ainv\n x = alpha*_exp(v)\n z = u1*u1*u2\n r = bbb+ccc*v-x\n if r + SG_MAGICCONST - 4.5*z >= 0.0 or r >= _log(z):\n return x * beta\n\n elif alpha == 1.0:\n # expovariate(1)\n u = random()\n while u <= 1e-7:\n u = random()\n return -_log(u) * beta\n\n else: # alpha is between 0 and 1 (exclusive)\n\n # Uses ALGORITHM GS of Statistical Computing - Kennedy & Gentle\n\n while 1:\n u = random()\n b = (_e + alpha)/_e\n p = b*u\n if p <= 1.0:\n x = p ** (1.0/alpha)\n else:\n x = -_log((b-p)/alpha)\n u1 = random()\n if p > 1.0:\n if u1 <= x ** (alpha - 1.0):\n break\n elif u1 <= _exp(-x):\n break\n return x * beta\n\n## -------------------- Gauss (faster alternative) --------------------\n\n def gauss(self, mu, sigma):\n \"\"\"Gaussian distribution.\n\n mu is the mean, and sigma is the standard deviation. This is\n slightly faster than the normalvariate() function.\n\n Not thread-safe without a lock around calls.\n\n \"\"\"\n\n # When x and y are two variables from [0, 1), uniformly\n # distributed, then\n #\n # cos(2*pi*x)*sqrt(-2*log(1-y))\n # sin(2*pi*x)*sqrt(-2*log(1-y))\n #\n # are two *independent* variables with normal distribution\n # (mu = 0, sigma = 1).\n # (Lambert Meertens)\n # (corrected version; bug discovered by Mike Miller, fixed by LM)\n\n # Multithreading note: When two threads call this function\n # simultaneously, it is possible that they will receive the\n # same return value. The window is very small though. To\n # avoid this, you have to use a lock around all calls. (I\n # didn't want to slow this down in the serial case by using a\n # lock here.)\n\n random = self.random\n z = self.gauss_next\n self.gauss_next = None\n if z is None:\n x2pi = random() * TWOPI\n g2rad = _sqrt(-2.0 * _log(1.0 - random()))\n z = _cos(x2pi) * g2rad\n self.gauss_next = _sin(x2pi) * g2rad\n\n return mu + z*sigma\n\n## -------------------- beta --------------------\n## See\n## http://mail.python.org/pipermail/python-bugs-list/2001-January/003752.html\n## for Ivan Frohne's insightful analysis of why the original implementation:\n##\n## def betavariate(self, alpha, beta):\n## # Discrete Event Simulation in C, pp 87-88.\n##\n## y = self.expovariate(alpha)\n## z = self.expovariate(1.0/beta)\n## return z/(y+z)\n##\n## was dead wrong, and how it probably got that way.\n\n def betavariate(self, alpha, beta):\n \"\"\"Beta distribution.\n\n Conditions on the parameters are alpha > 0 and beta > 0.\n Returned values range between 0 and 1.\n\n \"\"\"\n\n # This version due to Janne Sinkkonen, and matches all the std\n # texts (e.g., Knuth Vol 2 Ed 3 pg 134 \"the beta distribution\").\n y = self.gammavariate(alpha, 1.0)\n if y == 0:\n return 0.0\n else:\n return y / (y + self.gammavariate(beta, 1.0))\n\n## -------------------- Pareto --------------------\n\n def paretovariate(self, alpha):\n \"\"\"Pareto distribution. alpha is the shape parameter.\"\"\"\n # Jain, pg. 495\n\n u = 1.0 - self.random()\n return 1.0 / u ** (1.0/alpha)\n\n## -------------------- Weibull --------------------\n\n def weibullvariate(self, alpha, beta):\n \"\"\"Weibull distribution.\n\n alpha is the scale parameter and beta is the shape parameter.\n\n \"\"\"\n # Jain, pg. 499; bug fix courtesy Bill Arms\n\n u = 1.0 - self.random()\n return alpha * (-_log(u)) ** (1.0/beta)\n\n## --------------- Operating System Random Source ------------------\n\nclass SystemRandom(Random):\n \"\"\"Alternate random number generator using sources provided\n by the operating system (such as /dev/urandom on Unix or\n CryptGenRandom on Windows).\n\n Not available on all systems (see os.urandom() for details).\n \"\"\"\n\n def random(self):\n \"\"\"Get the next random number in the range [0.0, 1.0).\"\"\"\n return (int.from_bytes(_urandom(7), 'big') >> 3) * RECIP_BPF\n\n def getrandbits(self, k):\n \"\"\"getrandbits(k) -> x. Generates an int with k random bits.\"\"\"\n if k <= 0:\n raise ValueError('number of bits must be greater than zero')\n if k != int(k):\n raise TypeError('number of bits should be an integer')\n numbytes = (k + 7) // 8 # bits / 8 and rounded up\n x = int.from_bytes(_urandom(numbytes), 'big')\n return x >> (numbytes * 8 - k) # trim excess bits\n\n def seed(self, *args, **kwds):\n \"Stub method. Not used for a system random number generator.\"\n return None\n\n def _notimplemented(self, *args, **kwds):\n \"Method should not be called for a system random number generator.\"\n raise NotImplementedError('System entropy source does not have state.')\n getstate = setstate = _notimplemented\n\n## -------------------- test program --------------------\n\ndef _test_generator(n, func, args):\n import time\n print(n, 'times', func.__name__)\n total = 0.0\n sqsum = 0.0\n smallest = 1e10\n largest = -1e10\n t0 = time.time()\n for i in range(n):\n x = func(*args)\n total += x\n sqsum = sqsum + x*x\n smallest = min(x, smallest)\n largest = max(x, largest)\n t1 = time.time()\n print(round(t1-t0, 3), 'sec,', end=' ')\n avg = total/n\n stddev = _sqrt(sqsum/n - avg*avg)\n print('avg %g, stddev %g, min %g, max %g\\n' % \\\n (avg, stddev, smallest, largest))\n\n\ndef _test(N=2000):\n _test_generator(N, random, ())\n _test_generator(N, normalvariate, (0.0, 1.0))\n _test_generator(N, lognormvariate, (0.0, 1.0))\n _test_generator(N, vonmisesvariate, (0.0, 1.0))\n _test_generator(N, gammavariate, (0.01, 1.0))\n _test_generator(N, gammavariate, (0.1, 1.0))\n _test_generator(N, gammavariate, (0.1, 2.0))\n _test_generator(N, gammavariate, (0.5, 1.0))\n _test_generator(N, gammavariate, (0.9, 1.0))\n _test_generator(N, gammavariate, (1.0, 1.0))\n _test_generator(N, gammavariate, (2.0, 1.0))\n _test_generator(N, gammavariate, (20.0, 1.0))\n _test_generator(N, gammavariate, (200.0, 1.0))\n _test_generator(N, gauss, (0.0, 1.0))\n _test_generator(N, betavariate, (3.0, 3.0))\n _test_generator(N, triangular, (0.0, 1.0, 1.0/3.0))\n\n# Create one instance, seeded from current time, and export its methods\n# as module-level functions. The functions share state across all uses\n#(both in the user's code and in the Python libraries), but that's fine\n# for most programs and is easier for the casual user than making them\n# instantiate their own Random() instance.\n\n_inst = Random()\nseed = _inst.seed\nrandom = _inst.random\nuniform = _inst.uniform\ntriangular = _inst.triangular\nrandint = _inst.randint\nchoice = _inst.choice\nrandrange = _inst.randrange\nsample = _inst.sample\nshuffle = _inst.shuffle\nchoices = _inst.choices\nnormalvariate = _inst.normalvariate\nlognormvariate = _inst.lognormvariate\nexpovariate = _inst.expovariate\nvonmisesvariate = _inst.vonmisesvariate\ngammavariate = _inst.gammavariate\ngauss = _inst.gauss\nbetavariate = _inst.betavariate\nparetovariate = _inst.paretovariate\nweibullvariate = _inst.weibullvariate\ngetstate = _inst.getstate\nsetstate = _inst.setstate\ngetrandbits = _inst.getrandbits\n\nif __name__ == '__main__':\n _test()\n",772],"/usr/lib/python3.6/tempfile.py":["\"\"\"Temporary files.\n\nThis module provides generic, low- and high-level interfaces for\ncreating temporary files and directories. All of the interfaces\nprovided by this module can be used without fear of race conditions\nexcept for 'mktemp'. 'mktemp' is subject to race conditions and\nshould not be used; it is provided for backward compatibility only.\n\nThe default path names are returned as str. If you supply bytes as\ninput, all return values will be in bytes. Ex:\n\n >>> tempfile.mkstemp()\n (4, '/tmp/tmptpu9nin8')\n >>> tempfile.mkdtemp(suffix=b'')\n b'/tmp/tmppbi8f0hy'\n\nThis module also provides some data items to the user:\n\n TMP_MAX - maximum number of names that will be tried before\n giving up.\n tempdir - If this is set to a string before the first use of\n any routine from this module, it will be considered as\n another candidate location to store temporary files.\n\"\"\"\n\n__all__ = [\n \"NamedTemporaryFile\", \"TemporaryFile\", # high level safe interfaces\n \"SpooledTemporaryFile\", \"TemporaryDirectory\",\n \"mkstemp\", \"mkdtemp\", # low level safe interfaces\n \"mktemp\", # deprecated unsafe interface\n \"TMP_MAX\", \"gettempprefix\", # constants\n \"tempdir\", \"gettempdir\",\n \"gettempprefixb\", \"gettempdirb\",\n ]\n\n\n# Imports.\n\nimport functools as _functools\nimport warnings as _warnings\nimport io as _io\nimport os as _os\ntry:\n import shutil as _shutil\n _rmtree = _shutil.rmtree\nexcept ImportError:\n import sys as _sys\n import stat as _stat\n # version vulnerable to race conditions\n def _rmtree_unsafe(path, onerror):\n try:\n if _os.path.islink(path):\n # symlinks to directories are forbidden, see bug #1669\n raise OSError(\"Cannot call rmtree on a symbolic link\")\n except OSError:\n onerror(_os.path.islink, path, _sys.exc_info())\n # can't continue even if onerror hook returns\n return\n names = []\n try:\n names = _os.listdir(path)\n except OSError:\n onerror(_os.listdir, path, _sys.exc_info())\n for name in names:\n fullname = _os.path.join(path, name)\n try:\n mode = _os.lstat(fullname).st_mode\n except OSError:\n mode = 0\n if _stat.S_ISDIR(mode):\n _rmtree_unsafe(fullname, onerror)\n else:\n try:\n _os.unlink(fullname)\n except OSError:\n onerror(_os.unlink, fullname, _sys.exc_info())\n try:\n _os.rmdir(path)\n except OSError:\n onerror(_os.rmdir, path, _sys.exc_info())\n\n # Version using fd-based APIs to protect against races\n def _rmtree_safe_fd(topfd, path, onerror):\n names = []\n try:\n names = _os.listdir(topfd)\n except OSError as err:\n err.filename = path\n onerror(_os.listdir, path, _sys.exc_info())\n for name in names:\n fullname = _os.path.join(path, name)\n try:\n orig_st = _os.stat(name, dir_fd=topfd, follow_symlinks=False)\n mode = orig_st.st_mode\n except OSError:\n mode = 0\n if _stat.S_ISDIR(mode):\n try:\n dirfd = _os.open(name, _os.O_RDONLY, dir_fd=topfd)\n except OSError:\n onerror(_os.open, fullname, _sys.exc_info())\n else:\n try:\n if _os.path.samestat(orig_st, _os.fstat(dirfd)):\n _rmtree_safe_fd(dirfd, fullname, onerror)\n try:\n _os.rmdir(name, dir_fd=topfd)\n except OSError:\n onerror(_os.rmdir, fullname, _sys.exc_info())\n else:\n try:\n # This can only happen if someone replaces\n # a directory with a symlink after the call to\n # stat.S_ISDIR above.\n raise OSError(\"Cannot call rmtree on a symbolic \"\n \"link\")\n except OSError:\n onerror(_os.path.islink, fullname, _sys.exc_info())\n finally:\n _os.close(dirfd)\n else:\n try:\n _os.unlink(name, dir_fd=topfd)\n except OSError:\n onerror(_os.unlink, fullname, _sys.exc_info())\n\n _use_fd_functions = ({_os.open, _os.stat, _os.unlink, _os.rmdir} <=\n _os.supports_dir_fd and\n _os.listdir in _os.supports_fd and\n _os.stat in _os.supports_follow_symlinks)\n\n def _rmtree(path, ignore_errors=False, onerror=None):\n \"\"\"Recursively delete a directory tree.\n\n If ignore_errors is set, errors are ignored; otherwise, if onerror\n is set, it is called to handle the error with arguments (func,\n path, exc_info) where func is platform and implementation dependent;\n path is the argument to that function that caused it to fail; and\n exc_info is a tuple returned by sys.exc_info(). If ignore_errors\n is false and onerror is None, an exception is raised.\n\n \"\"\"\n if ignore_errors:\n def onerror(*args):\n pass\n elif onerror is None:\n def onerror(*args):\n raise\n if _use_fd_functions:\n # While the unsafe rmtree works fine on bytes, the fd based does not.\n if isinstance(path, bytes):\n path = _os.fsdecode(path)\n # Note: To guard against symlink races, we use the standard\n # lstat()/open()/fstat() trick.\n try:\n orig_st = _os.lstat(path)\n except Exception:\n onerror(_os.lstat, path, _sys.exc_info())\n return\n try:\n fd = _os.open(path, _os.O_RDONLY)\n except Exception:\n onerror(_os.lstat, path, _sys.exc_info())\n return\n try:\n if _os.path.samestat(orig_st, _os.fstat(fd)):\n _rmtree_safe_fd(fd, path, onerror)\n try:\n _os.rmdir(path)\n except OSError:\n onerror(_os.rmdir, path, _sys.exc_info())\n else:\n try:\n # symlinks to directories are forbidden, see bug #1669\n raise OSError(\"Cannot call rmtree on a symbolic link\")\n except OSError:\n onerror(_os.path.islink, path, _sys.exc_info())\n finally:\n _os.close(fd)\n else:\n return _rmtree_unsafe(path, onerror)\n\nimport errno as _errno\nfrom random import Random as _Random\nimport weakref as _weakref\n\ntry:\n import _thread\nexcept ImportError:\n import _dummy_thread as _thread\n_allocate_lock = _thread.allocate_lock\n\n_text_openflags = _os.O_RDWR | _os.O_CREAT | _os.O_EXCL\nif hasattr(_os, 'O_NOFOLLOW'):\n _text_openflags |= _os.O_NOFOLLOW\n\n_bin_openflags = _text_openflags\nif hasattr(_os, 'O_BINARY'):\n _bin_openflags |= _os.O_BINARY\n\nif hasattr(_os, 'TMP_MAX'):\n TMP_MAX = _os.TMP_MAX\nelse:\n TMP_MAX = 10000\n\n# This variable _was_ unused for legacy reasons, see issue 10354.\n# But as of 3.5 we actually use it at runtime so changing it would\n# have a possibly desirable side effect... But we do not want to support\n# that as an API. It is undocumented on purpose. Do not depend on this.\ntemplate = \"tmp\"\n\n# Internal routines.\n\n_once_lock = _allocate_lock()\n\nif hasattr(_os, \"lstat\"):\n _stat = _os.lstat\nelif hasattr(_os, \"stat\"):\n _stat = _os.stat\nelse:\n # Fallback. All we need is something that raises OSError if the\n # file doesn't exist.\n def _stat(fn):\n fd = _os.open(fn, _os.O_RDONLY)\n _os.close(fd)\n\ndef _exists(fn):\n try:\n _stat(fn)\n except OSError:\n return False\n else:\n return True\n\n\ndef _infer_return_type(*args):\n \"\"\"Look at the type of all args and divine their implied return type.\"\"\"\n return_type = None\n for arg in args:\n if arg is None:\n continue\n if isinstance(arg, bytes):\n if return_type is str:\n raise TypeError(\"Can't mix bytes and non-bytes in \"\n \"path components.\")\n return_type = bytes\n else:\n if return_type is bytes:\n raise TypeError(\"Can't mix bytes and non-bytes in \"\n \"path components.\")\n return_type = str\n if return_type is None:\n return str # tempfile APIs return a str by default.\n return return_type\n\n\ndef _sanitize_params(prefix, suffix, dir):\n \"\"\"Common parameter processing for most APIs in this module.\"\"\"\n output_type = _infer_return_type(prefix, suffix, dir)\n if suffix is None:\n suffix = output_type()\n if prefix is None:\n if output_type is str:\n prefix = template\n else:\n prefix = _os.fsencode(template)\n if dir is None:\n if output_type is str:\n dir = gettempdir()\n else:\n dir = gettempdirb()\n return prefix, suffix, dir, output_type\n\n\nclass _RandomNameSequence:\n \"\"\"An instance of _RandomNameSequence generates an endless\n sequence of unpredictable strings which can safely be incorporated\n into file names. Each string is eight characters long. Multiple\n threads can safely use the same instance at the same time.\n\n _RandomNameSequence is an iterator.\"\"\"\n\n characters = \"abcdefghijklmnopqrstuvwxyz0123456789_\"\n\n @property\n def rng(self):\n cur_pid = _os.getpid()\n if cur_pid != getattr(self, '_rng_pid', None):\n self._rng = _Random()\n self._rng_pid = cur_pid\n return self._rng\n\n def __iter__(self):\n return self\n\n def __next__(self):\n c = self.characters\n choose = self.rng.choice\n letters = [choose(c) for dummy in range(8)]\n return ''.join(letters)\n\ndef _candidate_tempdir_list():\n \"\"\"Generate a list of candidate temporary directories which\n _get_default_tempdir will try.\"\"\"\n\n dirlist = []\n\n # First, try the environment.\n for envname in 'TMPDIR', 'TEMP', 'TMP':\n dirname = _os.getenv(envname)\n if dirname: dirlist.append(dirname)\n\n # Failing that, try OS-specific locations.\n if _os.name == 'nt':\n dirlist.extend([ _os.path.expanduser(r'~\\AppData\\Local\\Temp'),\n _os.path.expandvars(r'%SYSTEMROOT%\\Temp'),\n r'c:\\temp', r'c:\\tmp', r'\\temp', r'\\tmp' ])\n else:\n dirlist.extend([ '/tmp', '/var/tmp', '/usr/tmp' ])\n\n # As a last resort, the current directory.\n try:\n dirlist.append(_os.getcwd())\n except (AttributeError, OSError):\n dirlist.append(_os.curdir)\n\n return dirlist\n\ndef _get_default_tempdir():\n \"\"\"Calculate the default directory to use for temporary files.\n This routine should be called exactly once.\n\n We determine whether or not a candidate temp dir is usable by\n trying to create and write to a file in that directory. If this\n is successful, the test file is deleted. To prevent denial of\n service, the name of the test file must be randomized.\"\"\"\n\n namer = _RandomNameSequence()\n dirlist = _candidate_tempdir_list()\n\n for dir in dirlist:\n if dir != _os.curdir:\n dir = _os.path.abspath(dir)\n # Try only a few names per directory.\n for seq in range(100):\n name = next(namer)\n filename = _os.path.join(dir, name)\n try:\n fd = _os.open(filename, _bin_openflags, 0o600)\n try:\n try:\n with _io.open(fd, 'wb', closefd=False) as fp:\n fp.write(b'blat')\n finally:\n _os.close(fd)\n finally:\n _os.unlink(filename)\n return dir\n except FileExistsError:\n pass\n except PermissionError:\n # This exception is thrown when a directory with the chosen name\n # already exists on windows.\n if (_os.name == 'nt' and _os.path.isdir(dir) and\n _os.access(dir, _os.W_OK)):\n continue\n break # no point trying more names in this directory\n except OSError:\n break # no point trying more names in this directory\n raise FileNotFoundError(_errno.ENOENT,\n \"No usable temporary directory found in %s\" %\n dirlist)\n\n_name_sequence = None\n\ndef _get_candidate_names():\n \"\"\"Common setup sequence for all user-callable interfaces.\"\"\"\n\n global _name_sequence\n if _name_sequence is None:\n _once_lock.acquire()\n try:\n if _name_sequence is None:\n _name_sequence = _RandomNameSequence()\n finally:\n _once_lock.release()\n return _name_sequence\n\n\ndef _mkstemp_inner(dir, pre, suf, flags, output_type):\n \"\"\"Code common to mkstemp, TemporaryFile, and NamedTemporaryFile.\"\"\"\n\n names = _get_candidate_names()\n if output_type is bytes:\n names = map(_os.fsencode, names)\n\n for seq in range(TMP_MAX):\n name = next(names)\n file = _os.path.join(dir, pre + name + suf)\n try:\n fd = _os.open(file, flags, 0o600)\n except FileExistsError:\n continue # try again\n except PermissionError:\n # This exception is thrown when a directory with the chosen name\n # already exists on windows.\n if (_os.name == 'nt' and _os.path.isdir(dir) and\n _os.access(dir, _os.W_OK)):\n continue\n else:\n raise\n return (fd, _os.path.abspath(file))\n\n raise FileExistsError(_errno.EEXIST,\n \"No usable temporary file name found\")\n\n\n# User visible interfaces.\n\ndef gettempprefix():\n \"\"\"The default prefix for temporary directories.\"\"\"\n return template\n\ndef gettempprefixb():\n \"\"\"The default prefix for temporary directories as bytes.\"\"\"\n return _os.fsencode(gettempprefix())\n\ntempdir = None\n\ndef gettempdir():\n \"\"\"Accessor for tempfile.tempdir.\"\"\"\n global tempdir\n if tempdir is None:\n _once_lock.acquire()\n try:\n if tempdir is None:\n tempdir = _get_default_tempdir()\n finally:\n _once_lock.release()\n return tempdir\n\ndef gettempdirb():\n \"\"\"A bytes version of tempfile.gettempdir().\"\"\"\n return _os.fsencode(gettempdir())\n\ndef mkstemp(suffix=None, prefix=None, dir=None, text=False):\n \"\"\"User-callable function to create and return a unique temporary\n file. The return value is a pair (fd, name) where fd is the\n file descriptor returned by os.open, and name is the filename.\n\n If 'suffix' is not None, the file name will end with that suffix,\n otherwise there will be no suffix.\n\n If 'prefix' is not None, the file name will begin with that prefix,\n otherwise a default prefix is used.\n\n If 'dir' is not None, the file will be created in that directory,\n otherwise a default directory is used.\n\n If 'text' is specified and true, the file is opened in text\n mode. Else (the default) the file is opened in binary mode. On\n some operating systems, this makes no difference.\n\n If any of 'suffix', 'prefix' and 'dir' are not None, they must be the\n same type. If they are bytes, the returned name will be bytes; str\n otherwise.\n\n The file is readable and writable only by the creating user ID.\n If the operating system uses permission bits to indicate whether a\n file is executable, the file is executable by no one. The file\n descriptor is not inherited by children of this process.\n\n Caller is responsible for deleting the file when done with it.\n \"\"\"\n\n prefix, suffix, dir, output_type = _sanitize_params(prefix, suffix, dir)\n\n if text:\n flags = _text_openflags\n else:\n flags = _bin_openflags\n\n return _mkstemp_inner(dir, prefix, suffix, flags, output_type)\n\n\ndef mkdtemp(suffix=None, prefix=None, dir=None):\n \"\"\"User-callable function to create and return a unique temporary\n directory. The return value is the pathname of the directory.\n\n Arguments are as for mkstemp, except that the 'text' argument is\n not accepted.\n\n The directory is readable, writable, and searchable only by the\n creating user.\n\n Caller is responsible for deleting the directory when done with it.\n \"\"\"\n\n prefix, suffix, dir, output_type = _sanitize_params(prefix, suffix, dir)\n\n names = _get_candidate_names()\n if output_type is bytes:\n names = map(_os.fsencode, names)\n\n for seq in range(TMP_MAX):\n name = next(names)\n file = _os.path.join(dir, prefix + name + suffix)\n try:\n _os.mkdir(file, 0o700)\n except FileExistsError:\n continue # try again\n except PermissionError:\n # This exception is thrown when a directory with the chosen name\n # already exists on windows.\n if (_os.name == 'nt' and _os.path.isdir(dir) and\n _os.access(dir, _os.W_OK)):\n continue\n else:\n raise\n return file\n\n raise FileExistsError(_errno.EEXIST,\n \"No usable temporary directory name found\")\n\ndef mktemp(suffix=\"\", prefix=template, dir=None):\n \"\"\"User-callable function to return a unique temporary file name. The\n file is not created.\n\n Arguments are similar to mkstemp, except that the 'text' argument is\n not accepted, and suffix=None, prefix=None and bytes file names are not\n supported.\n\n THIS FUNCTION IS UNSAFE AND SHOULD NOT BE USED. The file name may\n refer to a file that did not exist at some point, but by the time\n you get around to creating it, someone else may have beaten you to\n the punch.\n \"\"\"\n\n## from warnings import warn as _warn\n## _warn(\"mktemp is a potential security risk to your program\",\n## RuntimeWarning, stacklevel=2)\n\n if dir is None:\n dir = gettempdir()\n\n names = _get_candidate_names()\n for seq in range(TMP_MAX):\n name = next(names)\n file = _os.path.join(dir, prefix + name + suffix)\n if not _exists(file):\n return file\n\n raise FileExistsError(_errno.EEXIST,\n \"No usable temporary filename found\")\n\n\nclass _TemporaryFileCloser:\n \"\"\"A separate object allowing proper closing of a temporary file's\n underlying file object, without adding a __del__ method to the\n temporary file.\"\"\"\n\n file = None # Set here since __del__ checks it\n close_called = False\n\n def __init__(self, file, name, delete=True):\n self.file = file\n self.name = name\n self.delete = delete\n\n # NT provides delete-on-close as a primitive, so we don't need\n # the wrapper to do anything special. We still use it so that\n # file.name is useful (i.e. not \"(fdopen)\") with NamedTemporaryFile.\n if _os.name != 'nt':\n # Cache the unlinker so we don't get spurious errors at\n # shutdown when the module-level \"os\" is None'd out. Note\n # that this must be referenced as self.unlink, because the\n # name TemporaryFileWrapper may also get None'd out before\n # __del__ is called.\n\n def close(self, unlink=_os.unlink):\n if not self.close_called and self.file is not None:\n self.close_called = True\n try:\n self.file.close()\n finally:\n if self.delete:\n unlink(self.name)\n\n # Need to ensure the file is deleted on __del__\n def __del__(self):\n self.close()\n\n else:\n def close(self):\n if not self.close_called:\n self.close_called = True\n self.file.close()\n\n\nclass _TemporaryFileWrapper:\n \"\"\"Temporary file wrapper\n\n This class provides a wrapper around files opened for\n temporary use. In particular, it seeks to automatically\n remove the file when it is no longer needed.\n \"\"\"\n\n def __init__(self, file, name, delete=True):\n self.file = file\n self.name = name\n self.delete = delete\n self._closer = _TemporaryFileCloser(file, name, delete)\n\n def __getattr__(self, name):\n # Attribute lookups are delegated to the underlying file\n # and cached for non-numeric results\n # (i.e. methods are cached, closed and friends are not)\n file = self.__dict__['file']\n a = getattr(file, name)\n if hasattr(a, '__call__'):\n func = a\n @_functools.wraps(func)\n def func_wrapper(*args, **kwargs):\n return func(*args, **kwargs)\n # Avoid closing the file as long as the wrapper is alive,\n # see issue #18879.\n func_wrapper._closer = self._closer\n a = func_wrapper\n if not isinstance(a, int):\n setattr(self, name, a)\n return a\n\n # The underlying __enter__ method returns the wrong object\n # (self.file) so override it to return the wrapper\n def __enter__(self):\n self.file.__enter__()\n return self\n\n # Need to trap __exit__ as well to ensure the file gets\n # deleted when used in a with statement\n def __exit__(self, exc, value, tb):\n result = self.file.__exit__(exc, value, tb)\n self.close()\n return result\n\n def close(self):\n \"\"\"\n Close the temporary file, possibly deleting it.\n \"\"\"\n self._closer.close()\n\n # iter() doesn't use __getattr__ to find the __iter__ method\n def __iter__(self):\n # Don't return iter(self.file), but yield from it to avoid closing\n # file as long as it's being used as iterator (see issue #23700). We\n # can't use 'yield from' here because iter(file) returns the file\n # object itself, which has a close method, and thus the file would get\n # closed when the generator is finalized, due to PEP380 semantics.\n for line in self.file:\n yield line\n\n\ndef NamedTemporaryFile(mode='w+b', buffering=-1, encoding=None,\n newline=None, suffix=None, prefix=None,\n dir=None, delete=True):\n \"\"\"Create and return a temporary file.\n Arguments:\n 'prefix', 'suffix', 'dir' -- as for mkstemp.\n 'mode' -- the mode argument to io.open (default \"w+b\").\n 'buffering' -- the buffer size argument to io.open (default -1).\n 'encoding' -- the encoding argument to io.open (default None)\n 'newline' -- the newline argument to io.open (default None)\n 'delete' -- whether the file is deleted on close (default True).\n The file is created as mkstemp() would do it.\n\n Returns an object with a file-like interface; the name of the file\n is accessible as its 'name' attribute. The file will be automatically\n deleted when it is closed unless the 'delete' argument is set to False.\n \"\"\"\n\n prefix, suffix, dir, output_type = _sanitize_params(prefix, suffix, dir)\n\n flags = _bin_openflags\n\n # Setting O_TEMPORARY in the flags causes the OS to delete\n # the file when it is closed. This is only supported by Windows.\n if _os.name == 'nt' and delete:\n flags |= _os.O_TEMPORARY\n\n (fd, name) = _mkstemp_inner(dir, prefix, suffix, flags, output_type)\n try:\n file = _io.open(fd, mode, buffering=buffering,\n newline=newline, encoding=encoding)\n\n return _TemporaryFileWrapper(file, name, delete)\n except BaseException:\n _os.unlink(name)\n _os.close(fd)\n raise\n\nif _os.name != 'posix' or _os.sys.platform == 'cygwin':\n # On non-POSIX and Cygwin systems, assume that we cannot unlink a file\n # while it is open.\n TemporaryFile = NamedTemporaryFile\n\nelse:\n # Is the O_TMPFILE flag available and does it work?\n # The flag is set to False if os.open(dir, os.O_TMPFILE) raises an\n # IsADirectoryError exception\n _O_TMPFILE_WORKS = hasattr(_os, 'O_TMPFILE')\n\n def TemporaryFile(mode='w+b', buffering=-1, encoding=None,\n newline=None, suffix=None, prefix=None,\n dir=None):\n \"\"\"Create and return a temporary file.\n Arguments:\n 'prefix', 'suffix', 'dir' -- as for mkstemp.\n 'mode' -- the mode argument to io.open (default \"w+b\").\n 'buffering' -- the buffer size argument to io.open (default -1).\n 'encoding' -- the encoding argument to io.open (default None)\n 'newline' -- the newline argument to io.open (default None)\n The file is created as mkstemp() would do it.\n\n Returns an object with a file-like interface. The file has no\n name, and will cease to exist when it is closed.\n \"\"\"\n global _O_TMPFILE_WORKS\n\n prefix, suffix, dir, output_type = _sanitize_params(prefix, suffix, dir)\n\n flags = _bin_openflags\n if _O_TMPFILE_WORKS:\n try:\n flags2 = (flags | _os.O_TMPFILE) & ~_os.O_CREAT\n fd = _os.open(dir, flags2, 0o600)\n except IsADirectoryError:\n # Linux kernel older than 3.11 ignores the O_TMPFILE flag:\n # O_TMPFILE is read as O_DIRECTORY. Trying to open a directory\n # with O_RDWR|O_DIRECTORY fails with IsADirectoryError, a\n # directory cannot be open to write. Set flag to False to not\n # try again.\n _O_TMPFILE_WORKS = False\n except OSError:\n # The filesystem of the directory does not support O_TMPFILE.\n # For example, OSError(95, 'Operation not supported').\n #\n # On Linux kernel older than 3.11, trying to open a regular\n # file (or a symbolic link to a regular file) with O_TMPFILE\n # fails with NotADirectoryError, because O_TMPFILE is read as\n # O_DIRECTORY.\n pass\n else:\n try:\n return _io.open(fd, mode, buffering=buffering,\n newline=newline, encoding=encoding)\n except:\n _os.close(fd)\n raise\n # Fallback to _mkstemp_inner().\n\n (fd, name) = _mkstemp_inner(dir, prefix, suffix, flags, output_type)\n try:\n _os.unlink(name)\n return _io.open(fd, mode, buffering=buffering,\n newline=newline, encoding=encoding)\n except:\n _os.close(fd)\n raise\n\nclass SpooledTemporaryFile:\n \"\"\"Temporary file wrapper, specialized to switch from BytesIO\n or StringIO to a real file when it exceeds a certain size or\n when a fileno is needed.\n \"\"\"\n _rolled = False\n\n def __init__(self, max_size=0, mode='w+b', buffering=-1,\n encoding=None, newline=None,\n suffix=None, prefix=None, dir=None):\n if 'b' in mode:\n self._file = _io.BytesIO()\n else:\n # Setting newline=\"\\n\" avoids newline translation;\n # this is important because otherwise on Windows we'd\n # get double newline translation upon rollover().\n self._file = _io.StringIO(newline=\"\\n\")\n self._max_size = max_size\n self._rolled = False\n self._TemporaryFileArgs = {'mode': mode, 'buffering': buffering,\n 'suffix': suffix, 'prefix': prefix,\n 'encoding': encoding, 'newline': newline,\n 'dir': dir}\n\n def _check(self, file):\n if self._rolled: return\n max_size = self._max_size\n if max_size and file.tell() > max_size:\n self.rollover()\n\n def rollover(self):\n if self._rolled: return\n file = self._file\n newfile = self._file = TemporaryFile(**self._TemporaryFileArgs)\n del self._TemporaryFileArgs\n\n newfile.write(file.getvalue())\n newfile.seek(file.tell(), 0)\n\n self._rolled = True\n\n # The method caching trick from NamedTemporaryFile\n # won't work here, because _file may change from a\n # BytesIO/StringIO instance to a real file. So we list\n # all the methods directly.\n\n # Context management protocol\n def __enter__(self):\n if self._file.closed:\n raise ValueError(\"Cannot enter context with closed file\")\n return self\n\n def __exit__(self, exc, value, tb):\n self._file.close()\n\n # file protocol\n def __iter__(self):\n return self._file.__iter__()\n\n def close(self):\n self._file.close()\n\n @property\n def closed(self):\n return self._file.closed\n\n @property\n def encoding(self):\n try:\n return self._file.encoding\n except AttributeError:\n if 'b' in self._TemporaryFileArgs['mode']:\n raise\n return self._TemporaryFileArgs['encoding']\n\n def fileno(self):\n self.rollover()\n return self._file.fileno()\n\n def flush(self):\n self._file.flush()\n\n def isatty(self):\n return self._file.isatty()\n\n @property\n def mode(self):\n try:\n return self._file.mode\n except AttributeError:\n return self._TemporaryFileArgs['mode']\n\n @property\n def name(self):\n try:\n return self._file.name\n except AttributeError:\n return None\n\n @property\n def newlines(self):\n try:\n return self._file.newlines\n except AttributeError:\n if 'b' in self._TemporaryFileArgs['mode']:\n raise\n return self._TemporaryFileArgs['newline']\n\n def read(self, *args):\n return self._file.read(*args)\n\n def readline(self, *args):\n return self._file.readline(*args)\n\n def readlines(self, *args):\n return self._file.readlines(*args)\n\n def seek(self, *args):\n self._file.seek(*args)\n\n @property\n def softspace(self):\n return self._file.softspace\n\n def tell(self):\n return self._file.tell()\n\n def truncate(self, size=None):\n if size is None:\n self._file.truncate()\n else:\n if size > self._max_size:\n self.rollover()\n self._file.truncate(size)\n\n def write(self, s):\n file = self._file\n rv = file.write(s)\n self._check(file)\n return rv\n\n def writelines(self, iterable):\n file = self._file\n rv = file.writelines(iterable)\n self._check(file)\n return rv\n\n\nclass TemporaryDirectory(object):\n \"\"\"Create and return a temporary directory. This has the same\n behavior as mkdtemp but can be used as a context manager. For\n example:\n\n with TemporaryDirectory() as tmpdir:\n ...\n\n Upon exiting the context, the directory and everything contained\n in it are removed.\n \"\"\"\n\n def __init__(self, suffix=None, prefix=None, dir=None):\n self.name = mkdtemp(suffix, prefix, dir)\n self._finalizer = _weakref.finalize(\n self, self._cleanup, self.name,\n warn_message=\"Implicitly cleaning up {!r}\".format(self))\n\n @classmethod\n def _cleanup(cls, name, warn_message):\n _rmtree(name)\n _warnings.warn(warn_message, ResourceWarning)\n\n def __repr__(self):\n return \"<{} {!r}>\".format(self.__class__.__name__, self.name)\n\n def __enter__(self):\n return self.name\n\n def __exit__(self, exc, value, tb):\n self.cleanup()\n\n def cleanup(self):\n if self._finalizer.detach():\n _rmtree(self.name)\n",952],"/usr/lib/python3.6/weakref.py":["\"\"\"Weak reference support for Python.\n\nThis module is an implementation of PEP 205:\n\nhttp://www.python.org/dev/peps/pep-0205/\n\"\"\"\n\n# Naming convention: Variables named \"wr\" are weak reference objects;\n# they are called this instead of \"ref\" to avoid name collisions with\n# the module-global ref() function imported from _weakref.\n\nfrom _weakref import (\n getweakrefcount,\n getweakrefs,\n ref,\n proxy,\n CallableProxyType,\n ProxyType,\n ReferenceType,\n _remove_dead_weakref)\n\nfrom _weakrefset import WeakSet, _IterationGuard\n\nimport collections # Import after _weakref to avoid circular import.\nimport sys\nimport itertools\n\nProxyTypes = (ProxyType, CallableProxyType)\n\n__all__ = [\"ref\", \"proxy\", \"getweakrefcount\", \"getweakrefs\",\n \"WeakKeyDictionary\", \"ReferenceType\", \"ProxyType\",\n \"CallableProxyType\", \"ProxyTypes\", \"WeakValueDictionary\",\n \"WeakSet\", \"WeakMethod\", \"finalize\"]\n\n\nclass WeakMethod(ref):\n \"\"\"\n A custom `weakref.ref` subclass which simulates a weak reference to\n a bound method, working around the lifetime problem of bound methods.\n \"\"\"\n\n __slots__ = \"_func_ref\", \"_meth_type\", \"_alive\", \"__weakref__\"\n\n def __new__(cls, meth, callback=None):\n try:\n obj = meth.__self__\n func = meth.__func__\n except AttributeError:\n raise TypeError(\"argument should be a bound method, not {}\"\n .format(type(meth))) from None\n def _cb(arg):\n # The self-weakref trick is needed to avoid creating a reference\n # cycle.\n self = self_wr()\n if self._alive:\n self._alive = False\n if callback is not None:\n callback(self)\n self = ref.__new__(cls, obj, _cb)\n self._func_ref = ref(func, _cb)\n self._meth_type = type(meth)\n self._alive = True\n self_wr = ref(self)\n return self\n\n def __call__(self):\n obj = super().__call__()\n func = self._func_ref()\n if obj is None or func is None:\n return None\n return self._meth_type(func, obj)\n\n def __eq__(self, other):\n if isinstance(other, WeakMethod):\n if not self._alive or not other._alive:\n return self is other\n return ref.__eq__(self, other) and self._func_ref == other._func_ref\n return False\n\n def __ne__(self, other):\n if isinstance(other, WeakMethod):\n if not self._alive or not other._alive:\n return self is not other\n return ref.__ne__(self, other) or self._func_ref != other._func_ref\n return True\n\n __hash__ = ref.__hash__\n\n\nclass WeakValueDictionary(collections.MutableMapping):\n \"\"\"Mapping class that references values weakly.\n\n Entries in the dictionary will be discarded when no strong\n reference to the value exists anymore\n \"\"\"\n # We inherit the constructor without worrying about the input\n # dictionary; since it uses our .update() method, we get the right\n # checks (if the other dictionary is a WeakValueDictionary,\n # objects are unwrapped on the way out, and we always wrap on the\n # way in).\n\n def __init__(*args, **kw):\n if not args:\n raise TypeError(\"descriptor '__init__' of 'WeakValueDictionary' \"\n \"object needs an argument\")\n self, *args = args\n if len(args) > 1:\n raise TypeError('expected at most 1 arguments, got %d' % len(args))\n def remove(wr, selfref=ref(self), _atomic_removal=_remove_dead_weakref):\n self = selfref()\n if self is not None:\n if self._iterating:\n self._pending_removals.append(wr.key)\n else:\n # Atomic removal is necessary since this function\n # can be called asynchronously by the GC\n _atomic_removal(d, wr.key)\n self._remove = remove\n # A list of keys to be removed\n self._pending_removals = []\n self._iterating = set()\n self.data = d = {}\n self.update(*args, **kw)\n\n def _commit_removals(self):\n l = self._pending_removals\n d = self.data\n # We shouldn't encounter any KeyError, because this method should\n # always be called *before* mutating the dict.\n while l:\n key = l.pop()\n _remove_dead_weakref(d, key)\n\n def __getitem__(self, key):\n if self._pending_removals:\n self._commit_removals()\n o = self.data[key]()\n if o is None:\n raise KeyError(key)\n else:\n return o\n\n def __delitem__(self, key):\n if self._pending_removals:\n self._commit_removals()\n del self.data[key]\n\n def __len__(self):\n if self._pending_removals:\n self._commit_removals()\n return len(self.data)\n\n def __contains__(self, key):\n if self._pending_removals:\n self._commit_removals()\n try:\n o = self.data[key]()\n except KeyError:\n return False\n return o is not None\n\n def __repr__(self):\n return \"<%s at %#x>\" % (self.__class__.__name__, id(self))\n\n def __setitem__(self, key, value):\n if self._pending_removals:\n self._commit_removals()\n self.data[key] = KeyedRef(value, self._remove, key)\n\n def copy(self):\n if self._pending_removals:\n self._commit_removals()\n new = WeakValueDictionary()\n for key, wr in self.data.items():\n o = wr()\n if o is not None:\n new[key] = o\n return new\n\n __copy__ = copy\n\n def __deepcopy__(self, memo):\n from copy import deepcopy\n if self._pending_removals:\n self._commit_removals()\n new = self.__class__()\n for key, wr in self.data.items():\n o = wr()\n if o is not None:\n new[deepcopy(key, memo)] = o\n return new\n\n def get(self, key, default=None):\n if self._pending_removals:\n self._commit_removals()\n try:\n wr = self.data[key]\n except KeyError:\n return default\n else:\n o = wr()\n if o is None:\n # This should only happen\n return default\n else:\n return o\n\n def items(self):\n if self._pending_removals:\n self._commit_removals()\n with _IterationGuard(self):\n for k, wr in self.data.items():\n v = wr()\n if v is not None:\n yield k, v\n\n def keys(self):\n if self._pending_removals:\n self._commit_removals()\n with _IterationGuard(self):\n for k, wr in self.data.items():\n if wr() is not None:\n yield k\n\n __iter__ = keys\n\n def itervaluerefs(self):\n \"\"\"Return an iterator that yields the weak references to the values.\n\n The references are not guaranteed to be 'live' at the time\n they are used, so the result of calling the references needs\n to be checked before being used. This can be used to avoid\n creating references that will cause the garbage collector to\n keep the values around longer than needed.\n\n \"\"\"\n if self._pending_removals:\n self._commit_removals()\n with _IterationGuard(self):\n yield from self.data.values()\n\n def values(self):\n if self._pending_removals:\n self._commit_removals()\n with _IterationGuard(self):\n for wr in self.data.values():\n obj = wr()\n if obj is not None:\n yield obj\n\n def popitem(self):\n if self._pending_removals:\n self._commit_removals()\n while True:\n key, wr = self.data.popitem()\n o = wr()\n if o is not None:\n return key, o\n\n def pop(self, key, *args):\n if self._pending_removals:\n self._commit_removals()\n try:\n o = self.data.pop(key)()\n except KeyError:\n o = None\n if o is None:\n if args:\n return args[0]\n else:\n raise KeyError(key)\n else:\n return o\n\n def setdefault(self, key, default=None):\n try:\n o = self.data[key]()\n except KeyError:\n o = None\n if o is None:\n if self._pending_removals:\n self._commit_removals()\n self.data[key] = KeyedRef(default, self._remove, key)\n return default\n else:\n return o\n\n def update(*args, **kwargs):\n if not args:\n raise TypeError(\"descriptor 'update' of 'WeakValueDictionary' \"\n \"object needs an argument\")\n self, *args = args\n if len(args) > 1:\n raise TypeError('expected at most 1 arguments, got %d' % len(args))\n dict = args[0] if args else None\n if self._pending_removals:\n self._commit_removals()\n d = self.data\n if dict is not None:\n if not hasattr(dict, \"items\"):\n dict = type({})(dict)\n for key, o in dict.items():\n d[key] = KeyedRef(o, self._remove, key)\n if len(kwargs):\n self.update(kwargs)\n\n def valuerefs(self):\n \"\"\"Return a list of weak references to the values.\n\n The references are not guaranteed to be 'live' at the time\n they are used, so the result of calling the references needs\n to be checked before being used. This can be used to avoid\n creating references that will cause the garbage collector to\n keep the values around longer than needed.\n\n \"\"\"\n if self._pending_removals:\n self._commit_removals()\n return list(self.data.values())\n\n\nclass KeyedRef(ref):\n \"\"\"Specialized reference that includes a key corresponding to the value.\n\n This is used in the WeakValueDictionary to avoid having to create\n a function object for each key stored in the mapping. A shared\n callback object can use the 'key' attribute of a KeyedRef instead\n of getting a reference to the key from an enclosing scope.\n\n \"\"\"\n\n __slots__ = \"key\",\n\n def __new__(type, ob, callback, key):\n self = ref.__new__(type, ob, callback)\n self.key = key\n return self\n\n def __init__(self, ob, callback, key):\n super().__init__(ob, callback)\n\n\nclass WeakKeyDictionary(collections.MutableMapping):\n \"\"\" Mapping class that references keys weakly.\n\n Entries in the dictionary will be discarded when there is no\n longer a strong reference to the key. This can be used to\n associate additional data with an object owned by other parts of\n an application without adding attributes to those objects. This\n can be especially useful with objects that override attribute\n accesses.\n \"\"\"\n\n def __init__(self, dict=None):\n self.data = {}\n def remove(k, selfref=ref(self)):\n self = selfref()\n if self is not None:\n if self._iterating:\n self._pending_removals.append(k)\n else:\n del self.data[k]\n self._remove = remove\n # A list of dead weakrefs (keys to be removed)\n self._pending_removals = []\n self._iterating = set()\n self._dirty_len = False\n if dict is not None:\n self.update(dict)\n\n def _commit_removals(self):\n # NOTE: We don't need to call this method before mutating the dict,\n # because a dead weakref never compares equal to a live weakref,\n # even if they happened to refer to equal objects.\n # However, it means keys may already have been removed.\n l = self._pending_removals\n d = self.data\n while l:\n try:\n del d[l.pop()]\n except KeyError:\n pass\n\n def _scrub_removals(self):\n d = self.data\n self._pending_removals = [k for k in self._pending_removals if k in d]\n self._dirty_len = False\n\n def __delitem__(self, key):\n self._dirty_len = True\n del self.data[ref(key)]\n\n def __getitem__(self, key):\n return self.data[ref(key)]\n\n def __len__(self):\n if self._dirty_len and self._pending_removals:\n # self._pending_removals may still contain keys which were\n # explicitly removed, we have to scrub them (see issue #21173).\n self._scrub_removals()\n return len(self.data) - len(self._pending_removals)\n\n def __repr__(self):\n return \"<%s at %#x>\" % (self.__class__.__name__, id(self))\n\n def __setitem__(self, key, value):\n self.data[ref(key, self._remove)] = value\n\n def copy(self):\n new = WeakKeyDictionary()\n for key, value in self.data.items():\n o = key()\n if o is not None:\n new[o] = value\n return new\n\n __copy__ = copy\n\n def __deepcopy__(self, memo):\n from copy import deepcopy\n new = self.__class__()\n for key, value in self.data.items():\n o = key()\n if o is not None:\n new[o] = deepcopy(value, memo)\n return new\n\n def get(self, key, default=None):\n return self.data.get(ref(key),default)\n\n def __contains__(self, key):\n try:\n wr = ref(key)\n except TypeError:\n return False\n return wr in self.data\n\n def items(self):\n with _IterationGuard(self):\n for wr, value in self.data.items():\n key = wr()\n if key is not None:\n yield key, value\n\n def keys(self):\n with _IterationGuard(self):\n for wr in self.data:\n obj = wr()\n if obj is not None:\n yield obj\n\n __iter__ = keys\n\n def values(self):\n with _IterationGuard(self):\n for wr, value in self.data.items():\n if wr() is not None:\n yield value\n\n def keyrefs(self):\n \"\"\"Return a list of weak references to the keys.\n\n The references are not guaranteed to be 'live' at the time\n they are used, so the result of calling the references needs\n to be checked before being used. This can be used to avoid\n creating references that will cause the garbage collector to\n keep the keys around longer than needed.\n\n \"\"\"\n return list(self.data)\n\n def popitem(self):\n self._dirty_len = True\n while True:\n key, value = self.data.popitem()\n o = key()\n if o is not None:\n return o, value\n\n def pop(self, key, *args):\n self._dirty_len = True\n return self.data.pop(ref(key), *args)\n\n def setdefault(self, key, default=None):\n return self.data.setdefault(ref(key, self._remove),default)\n\n def update(self, dict=None, **kwargs):\n d = self.data\n if dict is not None:\n if not hasattr(dict, \"items\"):\n dict = type({})(dict)\n for key, value in dict.items():\n d[ref(key, self._remove)] = value\n if len(kwargs):\n self.update(kwargs)\n\n\nclass finalize:\n \"\"\"Class for finalization of weakrefable objects\n\n finalize(obj, func, *args, **kwargs) returns a callable finalizer\n object which will be called when obj is garbage collected. The\n first time the finalizer is called it evaluates func(*arg, **kwargs)\n and returns the result. After this the finalizer is dead, and\n calling it just returns None.\n\n When the program exits any remaining finalizers for which the\n atexit attribute is true will be run in reverse order of creation.\n By default atexit is true.\n \"\"\"\n\n # Finalizer objects don't have any state of their own. They are\n # just used as keys to lookup _Info objects in the registry. This\n # ensures that they cannot be part of a ref-cycle.\n\n __slots__ = ()\n _registry = {}\n _shutdown = False\n _index_iter = itertools.count()\n _dirty = False\n _registered_with_atexit = False\n\n class _Info:\n __slots__ = (\"weakref\", \"func\", \"args\", \"kwargs\", \"atexit\", \"index\")\n\n def __init__(self, obj, func, *args, **kwargs):\n if not self._registered_with_atexit:\n # We may register the exit function more than once because\n # of a thread race, but that is harmless\n import atexit\n atexit.register(self._exitfunc)\n finalize._registered_with_atexit = True\n info = self._Info()\n info.weakref = ref(obj, self)\n info.func = func\n info.args = args\n info.kwargs = kwargs or None\n info.atexit = True\n info.index = next(self._index_iter)\n self._registry[self] = info\n finalize._dirty = True\n\n def __call__(self, _=None):\n \"\"\"If alive then mark as dead and return func(*args, **kwargs);\n otherwise return None\"\"\"\n info = self._registry.pop(self, None)\n if info and not self._shutdown:\n return info.func(*info.args, **(info.kwargs or {}))\n\n def detach(self):\n \"\"\"If alive then mark as dead and return (obj, func, args, kwargs);\n otherwise return None\"\"\"\n info = self._registry.get(self)\n obj = info and info.weakref()\n if obj is not None and self._registry.pop(self, None):\n return (obj, info.func, info.args, info.kwargs or {})\n\n def peek(self):\n \"\"\"If alive then return (obj, func, args, kwargs);\n otherwise return None\"\"\"\n info = self._registry.get(self)\n obj = info and info.weakref()\n if obj is not None:\n return (obj, info.func, info.args, info.kwargs or {})\n\n @property\n def alive(self):\n \"\"\"Whether finalizer is alive\"\"\"\n return self in self._registry\n\n @property\n def atexit(self):\n \"\"\"Whether finalizer should be called at exit\"\"\"\n info = self._registry.get(self)\n return bool(info) and info.atexit\n\n @atexit.setter\n def atexit(self, value):\n info = self._registry.get(self)\n if info:\n info.atexit = bool(value)\n\n def __repr__(self):\n info = self._registry.get(self)\n obj = info and info.weakref()\n if obj is None:\n return '<%s object at %#x; dead>' % (type(self).__name__, id(self))\n else:\n return '<%s object at %#x; for %r at %#x>' % \\\n (type(self).__name__, id(self), type(obj).__name__, id(obj))\n\n @classmethod\n def _select_for_exit(cls):\n # Return live finalizers marked for exit, oldest first\n L = [(f,i) for (f,i) in cls._registry.items() if i.atexit]\n L.sort(key=lambda item:item[1].index)\n return [f for (f,i) in L]\n\n @classmethod\n def _exitfunc(cls):\n # At shutdown invoke finalizers for which atexit is true.\n # This is called once all other non-daemonic threads have been\n # joined.\n reenable_gc = False\n try:\n if cls._registry:\n import gc\n if gc.isenabled():\n reenable_gc = True\n gc.disable()\n pending = None\n while True:\n if pending is None or finalize._dirty:\n pending = cls._select_for_exit()\n finalize._dirty = False\n if not pending:\n break\n f = pending.pop()\n try:\n # gc is disabled, so (assuming no daemonic\n # threads) the following is the only line in\n # this function which might trigger creation\n # of a new finalizer\n f()\n except Exception:\n sys.excepthook(*sys.exc_info())\n assert f not in cls._registry\n finally:\n # prevent any more finalizers from executing during shutdown\n finalize._shutdown = True\n if reenable_gc:\n gc.enable()\n",632],"/usr/lib/python3.6/queue.py":["'''A multi-producer, multi-consumer queue.'''\n\ntry:\n import threading\nexcept ImportError:\n import dummy_threading as threading\nfrom collections import deque\nfrom heapq import heappush, heappop\nfrom time import monotonic as time\n\n__all__ = ['Empty', 'Full', 'Queue', 'PriorityQueue', 'LifoQueue']\n\nclass Empty(Exception):\n 'Exception raised by Queue.get(block=0)/get_nowait().'\n pass\n\nclass Full(Exception):\n 'Exception raised by Queue.put(block=0)/put_nowait().'\n pass\n\nclass Queue:\n '''Create a queue object with a given maximum size.\n\n If maxsize is <= 0, the queue size is infinite.\n '''\n\n def __init__(self, maxsize=0):\n self.maxsize = maxsize\n self._init(maxsize)\n\n # mutex must be held whenever the queue is mutating. All methods\n # that acquire mutex must release it before returning. mutex\n # is shared between the three conditions, so acquiring and\n # releasing the conditions also acquires and releases mutex.\n self.mutex = threading.Lock()\n\n # Notify not_empty whenever an item is added to the queue; a\n # thread waiting to get is notified then.\n self.not_empty = threading.Condition(self.mutex)\n\n # Notify not_full whenever an item is removed from the queue;\n # a thread waiting to put is notified then.\n self.not_full = threading.Condition(self.mutex)\n\n # Notify all_tasks_done whenever the number of unfinished tasks\n # drops to zero; thread waiting to join() is notified to resume\n self.all_tasks_done = threading.Condition(self.mutex)\n self.unfinished_tasks = 0\n\n def task_done(self):\n '''Indicate that a formerly enqueued task is complete.\n\n Used by Queue consumer threads. For each get() used to fetch a task,\n a subsequent call to task_done() tells the queue that the processing\n on the task is complete.\n\n If a join() is currently blocking, it will resume when all items\n have been processed (meaning that a task_done() call was received\n for every item that had been put() into the queue).\n\n Raises a ValueError if called more times than there were items\n placed in the queue.\n '''\n with self.all_tasks_done:\n unfinished = self.unfinished_tasks - 1\n if unfinished <= 0:\n if unfinished < 0:\n raise ValueError('task_done() called too many times')\n self.all_tasks_done.notify_all()\n self.unfinished_tasks = unfinished\n\n def join(self):\n '''Blocks until all items in the Queue have been gotten and processed.\n\n The count of unfinished tasks goes up whenever an item is added to the\n queue. The count goes down whenever a consumer thread calls task_done()\n to indicate the item was retrieved and all work on it is complete.\n\n When the count of unfinished tasks drops to zero, join() unblocks.\n '''\n with self.all_tasks_done:\n while self.unfinished_tasks:\n self.all_tasks_done.wait()\n\n def qsize(self):\n '''Return the approximate size of the queue (not reliable!).'''\n with self.mutex:\n return self._qsize()\n\n def empty(self):\n '''Return True if the queue is empty, False otherwise (not reliable!).\n\n This method is likely to be removed at some point. Use qsize() == 0\n as a direct substitute, but be aware that either approach risks a race\n condition where a queue can grow before the result of empty() or\n qsize() can be used.\n\n To create code that needs to wait for all queued tasks to be\n completed, the preferred technique is to use the join() method.\n '''\n with self.mutex:\n return not self._qsize()\n\n def full(self):\n '''Return True if the queue is full, False otherwise (not reliable!).\n\n This method is likely to be removed at some point. Use qsize() >= n\n as a direct substitute, but be aware that either approach risks a race\n condition where a queue can shrink before the result of full() or\n qsize() can be used.\n '''\n with self.mutex:\n return 0 < self.maxsize <= self._qsize()\n\n def put(self, item, block=True, timeout=None):\n '''Put an item into the queue.\n\n If optional args 'block' is true and 'timeout' is None (the default),\n block if necessary until a free slot is available. If 'timeout' is\n a non-negative number, it blocks at most 'timeout' seconds and raises\n the Full exception if no free slot was available within that time.\n Otherwise ('block' is false), put an item on the queue if a free slot\n is immediately available, else raise the Full exception ('timeout'\n is ignored in that case).\n '''\n with self.not_full:\n if self.maxsize > 0:\n if not block:\n if self._qsize() >= self.maxsize:\n raise Full\n elif timeout is None:\n while self._qsize() >= self.maxsize:\n self.not_full.wait()\n elif timeout < 0:\n raise ValueError(\"'timeout' must be a non-negative number\")\n else:\n endtime = time() + timeout\n while self._qsize() >= self.maxsize:\n remaining = endtime - time()\n if remaining <= 0.0:\n raise Full\n self.not_full.wait(remaining)\n self._put(item)\n self.unfinished_tasks += 1\n self.not_empty.notify()\n\n def get(self, block=True, timeout=None):\n '''Remove and return an item from the queue.\n\n If optional args 'block' is true and 'timeout' is None (the default),\n block if necessary until an item is available. If 'timeout' is\n a non-negative number, it blocks at most 'timeout' seconds and raises\n the Empty exception if no item was available within that time.\n Otherwise ('block' is false), return an item if one is immediately\n available, else raise the Empty exception ('timeout' is ignored\n in that case).\n '''\n with self.not_empty:\n if not block:\n if not self._qsize():\n raise Empty\n elif timeout is None:\n while not self._qsize():\n self.not_empty.wait()\n elif timeout < 0:\n raise ValueError(\"'timeout' must be a non-negative number\")\n else:\n endtime = time() + timeout\n while not self._qsize():\n remaining = endtime - time()\n if remaining <= 0.0:\n raise Empty\n self.not_empty.wait(remaining)\n item = self._get()\n self.not_full.notify()\n return item\n\n def put_nowait(self, item):\n '''Put an item into the queue without blocking.\n\n Only enqueue the item if a free slot is immediately available.\n Otherwise raise the Full exception.\n '''\n return self.put(item, block=False)\n\n def get_nowait(self):\n '''Remove and return an item from the queue without blocking.\n\n Only get an item if one is immediately available. Otherwise\n raise the Empty exception.\n '''\n return self.get(block=False)\n\n # Override these methods to implement other queue organizations\n # (e.g. stack or priority queue).\n # These will only be called with appropriate locks held\n\n # Initialize the queue representation\n def _init(self, maxsize):\n self.queue = deque()\n\n def _qsize(self):\n return len(self.queue)\n\n # Put a new item in the queue\n def _put(self, item):\n self.queue.append(item)\n\n # Get an item from the queue\n def _get(self):\n return self.queue.popleft()\n\n\nclass PriorityQueue(Queue):\n '''Variant of Queue that retrieves open entries in priority order (lowest first).\n\n Entries are typically tuples of the form: (priority number, data).\n '''\n\n def _init(self, maxsize):\n self.queue = []\n\n def _qsize(self):\n return len(self.queue)\n\n def _put(self, item):\n heappush(self.queue, item)\n\n def _get(self):\n return heappop(self.queue)\n\n\nclass LifoQueue(Queue):\n '''Variant of Queue that retrieves most recently added entries first.'''\n\n def _init(self, maxsize):\n self.queue = []\n\n def _qsize(self):\n return len(self.queue)\n\n def _put(self, item):\n self.queue.append(item)\n\n def _get(self):\n return self.queue.pop()\n",246],"/usr/lib/python3.6/threading.py":["\"\"\"Thread module emulating a subset of Java's threading model.\"\"\"\n\nimport sys as _sys\nimport _thread\n\nfrom time import monotonic as _time\nfrom traceback import format_exc as _format_exc\nfrom _weakrefset import WeakSet\nfrom itertools import islice as _islice, count as _count\ntry:\n from _collections import deque as _deque\nexcept ImportError:\n from collections import deque as _deque\n\n# Note regarding PEP 8 compliant names\n# This threading model was originally inspired by Java, and inherited\n# the convention of camelCase function and method names from that\n# language. Those original names are not in any imminent danger of\n# being deprecated (even for Py3k),so this module provides them as an\n# alias for the PEP 8 compliant names\n# Note that using the new PEP 8 compliant names facilitates substitution\n# with the multiprocessing module, which doesn't provide the old\n# Java inspired names.\n\n__all__ = ['get_ident', 'active_count', 'Condition', 'current_thread',\n 'enumerate', 'main_thread', 'TIMEOUT_MAX',\n 'Event', 'Lock', 'RLock', 'Semaphore', 'BoundedSemaphore', 'Thread',\n 'Barrier', 'BrokenBarrierError', 'Timer', 'ThreadError',\n 'setprofile', 'settrace', 'local', 'stack_size']\n\n# Rename some stuff so \"from threading import *\" is safe\n_start_new_thread = _thread.start_new_thread\n_allocate_lock = _thread.allocate_lock\n_set_sentinel = _thread._set_sentinel\nget_ident = _thread.get_ident\nThreadError = _thread.error\ntry:\n _CRLock = _thread.RLock\nexcept AttributeError:\n _CRLock = None\nTIMEOUT_MAX = _thread.TIMEOUT_MAX\ndel _thread\n\n\n# Support for profile and trace hooks\n\n_profile_hook = None\n_trace_hook = None\n\ndef setprofile(func):\n \"\"\"Set a profile function for all threads started from the threading module.\n\n The func will be passed to sys.setprofile() for each thread, before its\n run() method is called.\n\n \"\"\"\n global _profile_hook\n _profile_hook = func\n\ndef settrace(func):\n \"\"\"Set a trace function for all threads started from the threading module.\n\n The func will be passed to sys.settrace() for each thread, before its run()\n method is called.\n\n \"\"\"\n global _trace_hook\n _trace_hook = func\n\n# Synchronization classes\n\nLock = _allocate_lock\n\ndef RLock(*args, **kwargs):\n \"\"\"Factory function that returns a new reentrant lock.\n\n A reentrant lock must be released by the thread that acquired it. Once a\n thread has acquired a reentrant lock, the same thread may acquire it again\n without blocking; the thread must release it once for each time it has\n acquired it.\n\n \"\"\"\n if _CRLock is None:\n return _PyRLock(*args, **kwargs)\n return _CRLock(*args, **kwargs)\n\nclass _RLock:\n \"\"\"This class implements reentrant lock objects.\n\n A reentrant lock must be released by the thread that acquired it. Once a\n thread has acquired a reentrant lock, the same thread may acquire it\n again without blocking; the thread must release it once for each time it\n has acquired it.\n\n \"\"\"\n\n def __init__(self):\n self._block = _allocate_lock()\n self._owner = None\n self._count = 0\n\n def __repr__(self):\n owner = self._owner\n try:\n owner = _active[owner].name\n except KeyError:\n pass\n return \"<%s %s.%s object owner=%r count=%d at %s>\" % (\n \"locked\" if self._block.locked() else \"unlocked\",\n self.__class__.__module__,\n self.__class__.__qualname__,\n owner,\n self._count,\n hex(id(self))\n )\n\n def acquire(self, blocking=True, timeout=-1):\n \"\"\"Acquire a lock, blocking or non-blocking.\n\n When invoked without arguments: if this thread already owns the lock,\n increment the recursion level by one, and return immediately. Otherwise,\n if another thread owns the lock, block until the lock is unlocked. Once\n the lock is unlocked (not owned by any thread), then grab ownership, set\n the recursion level to one, and return. If more than one thread is\n blocked waiting until the lock is unlocked, only one at a time will be\n able to grab ownership of the lock. There is no return value in this\n case.\n\n When invoked with the blocking argument set to true, do the same thing\n as when called without arguments, and return true.\n\n When invoked with the blocking argument set to false, do not block. If a\n call without an argument would block, return false immediately;\n otherwise, do the same thing as when called without arguments, and\n return true.\n\n When invoked with the floating-point timeout argument set to a positive\n value, block for at most the number of seconds specified by timeout\n and as long as the lock cannot be acquired. Return true if the lock has\n been acquired, false if the timeout has elapsed.\n\n \"\"\"\n me = get_ident()\n if self._owner == me:\n self._count += 1\n return 1\n rc = self._block.acquire(blocking, timeout)\n if rc:\n self._owner = me\n self._count = 1\n return rc\n\n __enter__ = acquire\n\n def release(self):\n \"\"\"Release a lock, decrementing the recursion level.\n\n If after the decrement it is zero, reset the lock to unlocked (not owned\n by any thread), and if any other threads are blocked waiting for the\n lock to become unlocked, allow exactly one of them to proceed. If after\n the decrement the recursion level is still nonzero, the lock remains\n locked and owned by the calling thread.\n\n Only call this method when the calling thread owns the lock. A\n RuntimeError is raised if this method is called when the lock is\n unlocked.\n\n There is no return value.\n\n \"\"\"\n if self._owner != get_ident():\n raise RuntimeError(\"cannot release un-acquired lock\")\n self._count = count = self._count - 1\n if not count:\n self._owner = None\n self._block.release()\n\n def __exit__(self, t, v, tb):\n self.release()\n\n # Internal methods used by condition variables\n\n def _acquire_restore(self, state):\n self._block.acquire()\n self._count, self._owner = state\n\n def _release_save(self):\n if self._count == 0:\n raise RuntimeError(\"cannot release un-acquired lock\")\n count = self._count\n self._count = 0\n owner = self._owner\n self._owner = None\n self._block.release()\n return (count, owner)\n\n def _is_owned(self):\n return self._owner == get_ident()\n\n_PyRLock = _RLock\n\n\nclass Condition:\n \"\"\"Class that implements a condition variable.\n\n A condition variable allows one or more threads to wait until they are\n notified by another thread.\n\n If the lock argument is given and not None, it must be a Lock or RLock\n object, and it is used as the underlying lock. Otherwise, a new RLock object\n is created and used as the underlying lock.\n\n \"\"\"\n\n def __init__(self, lock=None):\n if lock is None:\n lock = RLock()\n self._lock = lock\n # Export the lock's acquire() and release() methods\n self.acquire = lock.acquire\n self.release = lock.release\n # If the lock defines _release_save() and/or _acquire_restore(),\n # these override the default implementations (which just call\n # release() and acquire() on the lock). Ditto for _is_owned().\n try:\n self._release_save = lock._release_save\n except AttributeError:\n pass\n try:\n self._acquire_restore = lock._acquire_restore\n except AttributeError:\n pass\n try:\n self._is_owned = lock._is_owned\n except AttributeError:\n pass\n self._waiters = _deque()\n\n def __enter__(self):\n return self._lock.__enter__()\n\n def __exit__(self, *args):\n return self._lock.__exit__(*args)\n\n def __repr__(self):\n return \"\" % (self._lock, len(self._waiters))\n\n def _release_save(self):\n self._lock.release() # No state to save\n\n def _acquire_restore(self, x):\n self._lock.acquire() # Ignore saved state\n\n def _is_owned(self):\n # Return True if lock is owned by current_thread.\n # This method is called only if _lock doesn't have _is_owned().\n if self._lock.acquire(0):\n self._lock.release()\n return False\n else:\n return True\n\n def wait(self, timeout=None):\n \"\"\"Wait until notified or until a timeout occurs.\n\n If the calling thread has not acquired the lock when this method is\n called, a RuntimeError is raised.\n\n This method releases the underlying lock, and then blocks until it is\n awakened by a notify() or notify_all() call for the same condition\n variable in another thread, or until the optional timeout occurs. Once\n awakened or timed out, it re-acquires the lock and returns.\n\n When the timeout argument is present and not None, it should be a\n floating point number specifying a timeout for the operation in seconds\n (or fractions thereof).\n\n When the underlying lock is an RLock, it is not released using its\n release() method, since this may not actually unlock the lock when it\n was acquired multiple times recursively. Instead, an internal interface\n of the RLock class is used, which really unlocks it even when it has\n been recursively acquired several times. Another internal interface is\n then used to restore the recursion level when the lock is reacquired.\n\n \"\"\"\n if not self._is_owned():\n raise RuntimeError(\"cannot wait on un-acquired lock\")\n waiter = _allocate_lock()\n waiter.acquire()\n self._waiters.append(waiter)\n saved_state = self._release_save()\n gotit = False\n try: # restore state no matter what (e.g., KeyboardInterrupt)\n if timeout is None:\n waiter.acquire()\n gotit = True\n else:\n if timeout > 0:\n gotit = waiter.acquire(True, timeout)\n else:\n gotit = waiter.acquire(False)\n return gotit\n finally:\n self._acquire_restore(saved_state)\n if not gotit:\n try:\n self._waiters.remove(waiter)\n except ValueError:\n pass\n\n def wait_for(self, predicate, timeout=None):\n \"\"\"Wait until a condition evaluates to True.\n\n predicate should be a callable which result will be interpreted as a\n boolean value. A timeout may be provided giving the maximum time to\n wait.\n\n \"\"\"\n endtime = None\n waittime = timeout\n result = predicate()\n while not result:\n if waittime is not None:\n if endtime is None:\n endtime = _time() + waittime\n else:\n waittime = endtime - _time()\n if waittime <= 0:\n break\n self.wait(waittime)\n result = predicate()\n return result\n\n def notify(self, n=1):\n \"\"\"Wake up one or more threads waiting on this condition, if any.\n\n If the calling thread has not acquired the lock when this method is\n called, a RuntimeError is raised.\n\n This method wakes up at most n of the threads waiting for the condition\n variable; it is a no-op if no threads are waiting.\n\n \"\"\"\n if not self._is_owned():\n raise RuntimeError(\"cannot notify on un-acquired lock\")\n all_waiters = self._waiters\n waiters_to_notify = _deque(_islice(all_waiters, n))\n if not waiters_to_notify:\n return\n for waiter in waiters_to_notify:\n waiter.release()\n try:\n all_waiters.remove(waiter)\n except ValueError:\n pass\n\n def notify_all(self):\n \"\"\"Wake up all threads waiting on this condition.\n\n If the calling thread has not acquired the lock when this method\n is called, a RuntimeError is raised.\n\n \"\"\"\n self.notify(len(self._waiters))\n\n notifyAll = notify_all\n\n\nclass Semaphore:\n \"\"\"This class implements semaphore objects.\n\n Semaphores manage a counter representing the number of release() calls minus\n the number of acquire() calls, plus an initial value. The acquire() method\n blocks if necessary until it can return without making the counter\n negative. If not given, value defaults to 1.\n\n \"\"\"\n\n # After Tim Peters' semaphore class, but not quite the same (no maximum)\n\n def __init__(self, value=1):\n if value < 0:\n raise ValueError(\"semaphore initial value must be >= 0\")\n self._cond = Condition(Lock())\n self._value = value\n\n def acquire(self, blocking=True, timeout=None):\n \"\"\"Acquire a semaphore, decrementing the internal counter by one.\n\n When invoked without arguments: if the internal counter is larger than\n zero on entry, decrement it by one and return immediately. If it is zero\n on entry, block, waiting until some other thread has called release() to\n make it larger than zero. This is done with proper interlocking so that\n if multiple acquire() calls are blocked, release() will wake exactly one\n of them up. The implementation may pick one at random, so the order in\n which blocked threads are awakened should not be relied on. There is no\n return value in this case.\n\n When invoked with blocking set to true, do the same thing as when called\n without arguments, and return true.\n\n When invoked with blocking set to false, do not block. If a call without\n an argument would block, return false immediately; otherwise, do the\n same thing as when called without arguments, and return true.\n\n When invoked with a timeout other than None, it will block for at\n most timeout seconds. If acquire does not complete successfully in\n that interval, return false. Return true otherwise.\n\n \"\"\"\n if not blocking and timeout is not None:\n raise ValueError(\"can't specify timeout for non-blocking acquire\")\n rc = False\n endtime = None\n with self._cond:\n while self._value == 0:\n if not blocking:\n break\n if timeout is not None:\n if endtime is None:\n endtime = _time() + timeout\n else:\n timeout = endtime - _time()\n if timeout <= 0:\n break\n self._cond.wait(timeout)\n else:\n self._value -= 1\n rc = True\n return rc\n\n __enter__ = acquire\n\n def release(self):\n \"\"\"Release a semaphore, incrementing the internal counter by one.\n\n When the counter is zero on entry and another thread is waiting for it\n to become larger than zero again, wake up that thread.\n\n \"\"\"\n with self._cond:\n self._value += 1\n self._cond.notify()\n\n def __exit__(self, t, v, tb):\n self.release()\n\n\nclass BoundedSemaphore(Semaphore):\n \"\"\"Implements a bounded semaphore.\n\n A bounded semaphore checks to make sure its current value doesn't exceed its\n initial value. If it does, ValueError is raised. In most situations\n semaphores are used to guard resources with limited capacity.\n\n If the semaphore is released too many times it's a sign of a bug. If not\n given, value defaults to 1.\n\n Like regular semaphores, bounded semaphores manage a counter representing\n the number of release() calls minus the number of acquire() calls, plus an\n initial value. The acquire() method blocks if necessary until it can return\n without making the counter negative. If not given, value defaults to 1.\n\n \"\"\"\n\n def __init__(self, value=1):\n Semaphore.__init__(self, value)\n self._initial_value = value\n\n def release(self):\n \"\"\"Release a semaphore, incrementing the internal counter by one.\n\n When the counter is zero on entry and another thread is waiting for it\n to become larger than zero again, wake up that thread.\n\n If the number of releases exceeds the number of acquires,\n raise a ValueError.\n\n \"\"\"\n with self._cond:\n if self._value >= self._initial_value:\n raise ValueError(\"Semaphore released too many times\")\n self._value += 1\n self._cond.notify()\n\n\nclass Event:\n \"\"\"Class implementing event objects.\n\n Events manage a flag that can be set to true with the set() method and reset\n to false with the clear() method. The wait() method blocks until the flag is\n true. The flag is initially false.\n\n \"\"\"\n\n # After Tim Peters' event class (without is_posted())\n\n def __init__(self):\n self._cond = Condition(Lock())\n self._flag = False\n\n def _reset_internal_locks(self):\n # private! called by Thread._reset_internal_locks by _after_fork()\n self._cond.__init__(Lock())\n\n def is_set(self):\n \"\"\"Return true if and only if the internal flag is true.\"\"\"\n return self._flag\n\n isSet = is_set\n\n def set(self):\n \"\"\"Set the internal flag to true.\n\n All threads waiting for it to become true are awakened. Threads\n that call wait() once the flag is true will not block at all.\n\n \"\"\"\n with self._cond:\n self._flag = True\n self._cond.notify_all()\n\n def clear(self):\n \"\"\"Reset the internal flag to false.\n\n Subsequently, threads calling wait() will block until set() is called to\n set the internal flag to true again.\n\n \"\"\"\n with self._cond:\n self._flag = False\n\n def wait(self, timeout=None):\n \"\"\"Block until the internal flag is true.\n\n If the internal flag is true on entry, return immediately. Otherwise,\n block until another thread calls set() to set the flag to true, or until\n the optional timeout occurs.\n\n When the timeout argument is present and not None, it should be a\n floating point number specifying a timeout for the operation in seconds\n (or fractions thereof).\n\n This method returns the internal flag on exit, so it will always return\n True except if a timeout is given and the operation times out.\n\n \"\"\"\n with self._cond:\n signaled = self._flag\n if not signaled:\n signaled = self._cond.wait(timeout)\n return signaled\n\n\n# A barrier class. Inspired in part by the pthread_barrier_* api and\n# the CyclicBarrier class from Java. See\n# http://sourceware.org/pthreads-win32/manual/pthread_barrier_init.html and\n# http://java.sun.com/j2se/1.5.0/docs/api/java/util/concurrent/\n# CyclicBarrier.html\n# for information.\n# We maintain two main states, 'filling' and 'draining' enabling the barrier\n# to be cyclic. Threads are not allowed into it until it has fully drained\n# since the previous cycle. In addition, a 'resetting' state exists which is\n# similar to 'draining' except that threads leave with a BrokenBarrierError,\n# and a 'broken' state in which all threads get the exception.\nclass Barrier:\n \"\"\"Implements a Barrier.\n\n Useful for synchronizing a fixed number of threads at known synchronization\n points. Threads block on 'wait()' and are simultaneously once they have all\n made that call.\n\n \"\"\"\n\n def __init__(self, parties, action=None, timeout=None):\n \"\"\"Create a barrier, initialised to 'parties' threads.\n\n 'action' is a callable which, when supplied, will be called by one of\n the threads after they have all entered the barrier and just prior to\n releasing them all. If a 'timeout' is provided, it is uses as the\n default for all subsequent 'wait()' calls.\n\n \"\"\"\n self._cond = Condition(Lock())\n self._action = action\n self._timeout = timeout\n self._parties = parties\n self._state = 0 #0 filling, 1, draining, -1 resetting, -2 broken\n self._count = 0\n\n def wait(self, timeout=None):\n \"\"\"Wait for the barrier.\n\n When the specified number of threads have started waiting, they are all\n simultaneously awoken. If an 'action' was provided for the barrier, one\n of the threads will have executed that callback prior to returning.\n Returns an individual index number from 0 to 'parties-1'.\n\n \"\"\"\n if timeout is None:\n timeout = self._timeout\n with self._cond:\n self._enter() # Block while the barrier drains.\n index = self._count\n self._count += 1\n try:\n if index + 1 == self._parties:\n # We release the barrier\n self._release()\n else:\n # We wait until someone releases us\n self._wait(timeout)\n return index\n finally:\n self._count -= 1\n # Wake up any threads waiting for barrier to drain.\n self._exit()\n\n # Block until the barrier is ready for us, or raise an exception\n # if it is broken.\n def _enter(self):\n while self._state in (-1, 1):\n # It is draining or resetting, wait until done\n self._cond.wait()\n #see if the barrier is in a broken state\n if self._state < 0:\n raise BrokenBarrierError\n assert self._state == 0\n\n # Optionally run the 'action' and release the threads waiting\n # in the barrier.\n def _release(self):\n try:\n if self._action:\n self._action()\n # enter draining state\n self._state = 1\n self._cond.notify_all()\n except:\n #an exception during the _action handler. Break and reraise\n self._break()\n raise\n\n # Wait in the barrier until we are released. Raise an exception\n # if the barrier is reset or broken.\n def _wait(self, timeout):\n if not self._cond.wait_for(lambda : self._state != 0, timeout):\n #timed out. Break the barrier\n self._break()\n raise BrokenBarrierError\n if self._state < 0:\n raise BrokenBarrierError\n assert self._state == 1\n\n # If we are the last thread to exit the barrier, signal any threads\n # waiting for the barrier to drain.\n def _exit(self):\n if self._count == 0:\n if self._state in (-1, 1):\n #resetting or draining\n self._state = 0\n self._cond.notify_all()\n\n def reset(self):\n \"\"\"Reset the barrier to the initial state.\n\n Any threads currently waiting will get the BrokenBarrier exception\n raised.\n\n \"\"\"\n with self._cond:\n if self._count > 0:\n if self._state == 0:\n #reset the barrier, waking up threads\n self._state = -1\n elif self._state == -2:\n #was broken, set it to reset state\n #which clears when the last thread exits\n self._state = -1\n else:\n self._state = 0\n self._cond.notify_all()\n\n def abort(self):\n \"\"\"Place the barrier into a 'broken' state.\n\n Useful in case of error. Any currently waiting threads and threads\n attempting to 'wait()' will have BrokenBarrierError raised.\n\n \"\"\"\n with self._cond:\n self._break()\n\n def _break(self):\n # An internal error was detected. The barrier is set to\n # a broken state all parties awakened.\n self._state = -2\n self._cond.notify_all()\n\n @property\n def parties(self):\n \"\"\"Return the number of threads required to trip the barrier.\"\"\"\n return self._parties\n\n @property\n def n_waiting(self):\n \"\"\"Return the number of threads currently waiting at the barrier.\"\"\"\n # We don't need synchronization here since this is an ephemeral result\n # anyway. It returns the correct value in the steady state.\n if self._state == 0:\n return self._count\n return 0\n\n @property\n def broken(self):\n \"\"\"Return True if the barrier is in a broken state.\"\"\"\n return self._state == -2\n\n# exception raised by the Barrier class\nclass BrokenBarrierError(RuntimeError):\n pass\n\n\n# Helper to generate new thread names\n_counter = _count().__next__\n_counter() # Consume 0 so first non-main thread has id 1.\ndef _newname(template=\"Thread-%d\"):\n return template % _counter()\n\n# Active thread administration\n_active_limbo_lock = _allocate_lock()\n_active = {} # maps thread id to Thread object\n_limbo = {}\n_dangling = WeakSet()\n\n# Main class for threads\n\nclass Thread:\n \"\"\"A class that represents a thread of control.\n\n This class can be safely subclassed in a limited fashion. There are two ways\n to specify the activity: by passing a callable object to the constructor, or\n by overriding the run() method in a subclass.\n\n \"\"\"\n\n _initialized = False\n # Need to store a reference to sys.exc_info for printing\n # out exceptions when a thread tries to use a global var. during interp.\n # shutdown and thus raises an exception about trying to perform some\n # operation on/with a NoneType\n _exc_info = _sys.exc_info\n # Keep sys.exc_clear too to clear the exception just before\n # allowing .join() to return.\n #XXX __exc_clear = _sys.exc_clear\n\n def __init__(self, group=None, target=None, name=None,\n args=(), kwargs=None, *, daemon=None):\n \"\"\"This constructor should always be called with keyword arguments. Arguments are:\n\n *group* should be None; reserved for future extension when a ThreadGroup\n class is implemented.\n\n *target* is the callable object to be invoked by the run()\n method. Defaults to None, meaning nothing is called.\n\n *name* is the thread name. By default, a unique name is constructed of\n the form \"Thread-N\" where N is a small decimal number.\n\n *args* is the argument tuple for the target invocation. Defaults to ().\n\n *kwargs* is a dictionary of keyword arguments for the target\n invocation. Defaults to {}.\n\n If a subclass overrides the constructor, it must make sure to invoke\n the base class constructor (Thread.__init__()) before doing anything\n else to the thread.\n\n \"\"\"\n assert group is None, \"group argument must be None for now\"\n if kwargs is None:\n kwargs = {}\n self._target = target\n self._name = str(name or _newname())\n self._args = args\n self._kwargs = kwargs\n if daemon is not None:\n self._daemonic = daemon\n else:\n self._daemonic = current_thread().daemon\n self._ident = None\n self._tstate_lock = None\n self._started = Event()\n self._is_stopped = False\n self._initialized = True\n # sys.stderr is not stored in the class like\n # sys.exc_info since it can be changed between instances\n self._stderr = _sys.stderr\n # For debugging and _after_fork()\n _dangling.add(self)\n\n def _reset_internal_locks(self, is_alive):\n # private! Called by _after_fork() to reset our internal locks as\n # they may be in an invalid state leading to a deadlock or crash.\n self._started._reset_internal_locks()\n if is_alive:\n self._set_tstate_lock()\n else:\n # The thread isn't alive after fork: it doesn't have a tstate\n # anymore.\n self._is_stopped = True\n self._tstate_lock = None\n\n def __repr__(self):\n assert self._initialized, \"Thread.__init__() was not called\"\n status = \"initial\"\n if self._started.is_set():\n status = \"started\"\n self.is_alive() # easy way to get ._is_stopped set when appropriate\n if self._is_stopped:\n status = \"stopped\"\n if self._daemonic:\n status += \" daemon\"\n if self._ident is not None:\n status += \" %s\" % self._ident\n return \"<%s(%s, %s)>\" % (self.__class__.__name__, self._name, status)\n\n def start(self):\n \"\"\"Start the thread's activity.\n\n It must be called at most once per thread object. It arranges for the\n object's run() method to be invoked in a separate thread of control.\n\n This method will raise a RuntimeError if called more than once on the\n same thread object.\n\n \"\"\"\n if not self._initialized:\n raise RuntimeError(\"thread.__init__() not called\")\n\n if self._started.is_set():\n raise RuntimeError(\"threads can only be started once\")\n with _active_limbo_lock:\n _limbo[self] = self\n try:\n _start_new_thread(self._bootstrap, ())\n except Exception:\n with _active_limbo_lock:\n del _limbo[self]\n raise\n self._started.wait()\n\n def run(self):\n \"\"\"Method representing the thread's activity.\n\n You may override this method in a subclass. The standard run() method\n invokes the callable object passed to the object's constructor as the\n target argument, if any, with sequential and keyword arguments taken\n from the args and kwargs arguments, respectively.\n\n \"\"\"\n try:\n if self._target:\n self._target(*self._args, **self._kwargs)\n finally:\n # Avoid a refcycle if the thread is running a function with\n # an argument that has a member that points to the thread.\n del self._target, self._args, self._kwargs\n\n def _bootstrap(self):\n # Wrapper around the real bootstrap code that ignores\n # exceptions during interpreter cleanup. Those typically\n # happen when a daemon thread wakes up at an unfortunate\n # moment, finds the world around it destroyed, and raises some\n # random exception *** while trying to report the exception in\n # _bootstrap_inner() below ***. Those random exceptions\n # don't help anybody, and they confuse users, so we suppress\n # them. We suppress them only when it appears that the world\n # indeed has already been destroyed, so that exceptions in\n # _bootstrap_inner() during normal business hours are properly\n # reported. Also, we only suppress them for daemonic threads;\n # if a non-daemonic encounters this, something else is wrong.\n try:\n self._bootstrap_inner()\n except:\n if self._daemonic and _sys is None:\n return\n raise\n\n def _set_ident(self):\n self._ident = get_ident()\n\n def _set_tstate_lock(self):\n \"\"\"\n Set a lock object which will be released by the interpreter when\n the underlying thread state (see pystate.h) gets deleted.\n \"\"\"\n self._tstate_lock = _set_sentinel()\n self._tstate_lock.acquire()\n\n def _bootstrap_inner(self):\n try:\n self._set_ident()\n self._set_tstate_lock()\n self._started.set()\n with _active_limbo_lock:\n _active[self._ident] = self\n del _limbo[self]\n\n if _trace_hook:\n _sys.settrace(_trace_hook)\n if _profile_hook:\n _sys.setprofile(_profile_hook)\n\n try:\n self.run()\n except SystemExit:\n pass\n except:\n # If sys.stderr is no more (most likely from interpreter\n # shutdown) use self._stderr. Otherwise still use sys (as in\n # _sys) in case sys.stderr was redefined since the creation of\n # self.\n if _sys and _sys.stderr is not None:\n print(\"Exception in thread %s:\\n%s\" %\n (self.name, _format_exc()), file=_sys.stderr)\n elif self._stderr is not None:\n # Do the best job possible w/o a huge amt. of code to\n # approximate a traceback (code ideas from\n # Lib/traceback.py)\n exc_type, exc_value, exc_tb = self._exc_info()\n try:\n print((\n \"Exception in thread \" + self.name +\n \" (most likely raised during interpreter shutdown):\"), file=self._stderr)\n print((\n \"Traceback (most recent call last):\"), file=self._stderr)\n while exc_tb:\n print((\n ' File \"%s\", line %s, in %s' %\n (exc_tb.tb_frame.f_code.co_filename,\n exc_tb.tb_lineno,\n exc_tb.tb_frame.f_code.co_name)), file=self._stderr)\n exc_tb = exc_tb.tb_next\n print((\"%s: %s\" % (exc_type, exc_value)), file=self._stderr)\n # Make sure that exc_tb gets deleted since it is a memory\n # hog; deleting everything else is just for thoroughness\n finally:\n del exc_type, exc_value, exc_tb\n finally:\n # Prevent a race in\n # test_threading.test_no_refcycle_through_target when\n # the exception keeps the target alive past when we\n # assert that it's dead.\n #XXX self._exc_clear()\n pass\n finally:\n with _active_limbo_lock:\n try:\n # We don't call self._delete() because it also\n # grabs _active_limbo_lock.\n del _active[get_ident()]\n except:\n pass\n\n def _stop(self):\n # After calling ._stop(), .is_alive() returns False and .join() returns\n # immediately. ._tstate_lock must be released before calling ._stop().\n #\n # Normal case: C code at the end of the thread's life\n # (release_sentinel in _threadmodule.c) releases ._tstate_lock, and\n # that's detected by our ._wait_for_tstate_lock(), called by .join()\n # and .is_alive(). Any number of threads _may_ call ._stop()\n # simultaneously (for example, if multiple threads are blocked in\n # .join() calls), and they're not serialized. That's harmless -\n # they'll just make redundant rebindings of ._is_stopped and\n # ._tstate_lock. Obscure: we rebind ._tstate_lock last so that the\n # \"assert self._is_stopped\" in ._wait_for_tstate_lock() always works\n # (the assert is executed only if ._tstate_lock is None).\n #\n # Special case: _main_thread releases ._tstate_lock via this\n # module's _shutdown() function.\n lock = self._tstate_lock\n if lock is not None:\n assert not lock.locked()\n self._is_stopped = True\n self._tstate_lock = None\n\n def _delete(self):\n \"Remove current thread from the dict of currently running threads.\"\n\n # Notes about running with _dummy_thread:\n #\n # Must take care to not raise an exception if _dummy_thread is being\n # used (and thus this module is being used as an instance of\n # dummy_threading). _dummy_thread.get_ident() always returns -1 since\n # there is only one thread if _dummy_thread is being used. Thus\n # len(_active) is always <= 1 here, and any Thread instance created\n # overwrites the (if any) thread currently registered in _active.\n #\n # An instance of _MainThread is always created by 'threading'. This\n # gets overwritten the instant an instance of Thread is created; both\n # threads return -1 from _dummy_thread.get_ident() and thus have the\n # same key in the dict. So when the _MainThread instance created by\n # 'threading' tries to clean itself up when atexit calls this method\n # it gets a KeyError if another Thread instance was created.\n #\n # This all means that KeyError from trying to delete something from\n # _active if dummy_threading is being used is a red herring. But\n # since it isn't if dummy_threading is *not* being used then don't\n # hide the exception.\n\n try:\n with _active_limbo_lock:\n del _active[get_ident()]\n # There must not be any python code between the previous line\n # and after the lock is released. Otherwise a tracing function\n # could try to acquire the lock again in the same thread, (in\n # current_thread()), and would block.\n except KeyError:\n if 'dummy_threading' not in _sys.modules:\n raise\n\n def join(self, timeout=None):\n \"\"\"Wait until the thread terminates.\n\n This blocks the calling thread until the thread whose join() method is\n called terminates -- either normally or through an unhandled exception\n or until the optional timeout occurs.\n\n When the timeout argument is present and not None, it should be a\n floating point number specifying a timeout for the operation in seconds\n (or fractions thereof). As join() always returns None, you must call\n isAlive() after join() to decide whether a timeout happened -- if the\n thread is still alive, the join() call timed out.\n\n When the timeout argument is not present or None, the operation will\n block until the thread terminates.\n\n A thread can be join()ed many times.\n\n join() raises a RuntimeError if an attempt is made to join the current\n thread as that would cause a deadlock. It is also an error to join() a\n thread before it has been started and attempts to do so raises the same\n exception.\n\n \"\"\"\n if not self._initialized:\n raise RuntimeError(\"Thread.__init__() not called\")\n if not self._started.is_set():\n raise RuntimeError(\"cannot join thread before it is started\")\n if self is current_thread():\n raise RuntimeError(\"cannot join current thread\")\n\n if timeout is None:\n self._wait_for_tstate_lock()\n else:\n # the behavior of a negative timeout isn't documented, but\n # historically .join(timeout=x) for x<0 has acted as if timeout=0\n self._wait_for_tstate_lock(timeout=max(timeout, 0))\n\n def _wait_for_tstate_lock(self, block=True, timeout=-1):\n # Issue #18808: wait for the thread state to be gone.\n # At the end of the thread's life, after all knowledge of the thread\n # is removed from C data structures, C code releases our _tstate_lock.\n # This method passes its arguments to _tstate_lock.acquire().\n # If the lock is acquired, the C code is done, and self._stop() is\n # called. That sets ._is_stopped to True, and ._tstate_lock to None.\n lock = self._tstate_lock\n if lock is None: # already determined that the C code is done\n assert self._is_stopped\n elif lock.acquire(block, timeout):\n lock.release()\n self._stop()\n\n @property\n def name(self):\n \"\"\"A string used for identification purposes only.\n\n It has no semantics. Multiple threads may be given the same name. The\n initial name is set by the constructor.\n\n \"\"\"\n assert self._initialized, \"Thread.__init__() not called\"\n return self._name\n\n @name.setter\n def name(self, name):\n assert self._initialized, \"Thread.__init__() not called\"\n self._name = str(name)\n\n @property\n def ident(self):\n \"\"\"Thread identifier of this thread or None if it has not been started.\n\n This is a nonzero integer. See the get_ident() function. Thread\n identifiers may be recycled when a thread exits and another thread is\n created. The identifier is available even after the thread has exited.\n\n \"\"\"\n assert self._initialized, \"Thread.__init__() not called\"\n return self._ident\n\n def is_alive(self):\n \"\"\"Return whether the thread is alive.\n\n This method returns True just before the run() method starts until just\n after the run() method terminates. The module function enumerate()\n returns a list of all alive threads.\n\n \"\"\"\n assert self._initialized, \"Thread.__init__() not called\"\n if self._is_stopped or not self._started.is_set():\n return False\n self._wait_for_tstate_lock(False)\n return not self._is_stopped\n\n isAlive = is_alive\n\n @property\n def daemon(self):\n \"\"\"A boolean value indicating whether this thread is a daemon thread.\n\n This must be set before start() is called, otherwise RuntimeError is\n raised. Its initial value is inherited from the creating thread; the\n main thread is not a daemon thread and therefore all threads created in\n the main thread default to daemon = False.\n\n The entire Python program exits when no alive non-daemon threads are\n left.\n\n \"\"\"\n assert self._initialized, \"Thread.__init__() not called\"\n return self._daemonic\n\n @daemon.setter\n def daemon(self, daemonic):\n if not self._initialized:\n raise RuntimeError(\"Thread.__init__() not called\")\n if self._started.is_set():\n raise RuntimeError(\"cannot set daemon status of active thread\")\n self._daemonic = daemonic\n\n def isDaemon(self):\n return self.daemon\n\n def setDaemon(self, daemonic):\n self.daemon = daemonic\n\n def getName(self):\n return self.name\n\n def setName(self, name):\n self.name = name\n\n# The timer class was contributed by Itamar Shtull-Trauring\n\nclass Timer(Thread):\n \"\"\"Call a function after a specified number of seconds:\n\n t = Timer(30.0, f, args=None, kwargs=None)\n t.start()\n t.cancel() # stop the timer's action if it's still waiting\n\n \"\"\"\n\n def __init__(self, interval, function, args=None, kwargs=None):\n Thread.__init__(self)\n self.interval = interval\n self.function = function\n self.args = args if args is not None else []\n self.kwargs = kwargs if kwargs is not None else {}\n self.finished = Event()\n\n def cancel(self):\n \"\"\"Stop the timer if it hasn't finished yet.\"\"\"\n self.finished.set()\n\n def run(self):\n self.finished.wait(self.interval)\n if not self.finished.is_set():\n self.function(*self.args, **self.kwargs)\n self.finished.set()\n\n\n# Special thread class to represent the main thread\n\nclass _MainThread(Thread):\n\n def __init__(self):\n Thread.__init__(self, name=\"MainThread\", daemon=False)\n self._set_tstate_lock()\n self._started.set()\n self._set_ident()\n with _active_limbo_lock:\n _active[self._ident] = self\n\n\n# Dummy thread class to represent threads not started here.\n# These aren't garbage collected when they die, nor can they be waited for.\n# If they invoke anything in threading.py that calls current_thread(), they\n# leave an entry in the _active dict forever after.\n# Their purpose is to return *something* from current_thread().\n# They are marked as daemon threads so we won't wait for them\n# when we exit (conform previous semantics).\n\nclass _DummyThread(Thread):\n\n def __init__(self):\n Thread.__init__(self, name=_newname(\"Dummy-%d\"), daemon=True)\n\n self._started.set()\n self._set_ident()\n with _active_limbo_lock:\n _active[self._ident] = self\n\n def _stop(self):\n pass\n\n def is_alive(self):\n assert not self._is_stopped and self._started.is_set()\n return True\n\n def join(self, timeout=None):\n assert False, \"cannot join a dummy thread\"\n\n\n# Global API functions\n\ndef current_thread():\n \"\"\"Return the current Thread object, corresponding to the caller's thread of control.\n\n If the caller's thread of control was not created through the threading\n module, a dummy thread object with limited functionality is returned.\n\n \"\"\"\n try:\n return _active[get_ident()]\n except KeyError:\n return _DummyThread()\n\ncurrentThread = current_thread\n\ndef active_count():\n \"\"\"Return the number of Thread objects currently alive.\n\n The returned count is equal to the length of the list returned by\n enumerate().\n\n \"\"\"\n with _active_limbo_lock:\n return len(_active) + len(_limbo)\n\nactiveCount = active_count\n\ndef _enumerate():\n # Same as enumerate(), but without the lock. Internal use only.\n return list(_active.values()) + list(_limbo.values())\n\ndef enumerate():\n \"\"\"Return a list of all Thread objects currently alive.\n\n The list includes daemonic threads, dummy thread objects created by\n current_thread(), and the main thread. It excludes terminated threads and\n threads that have not yet been started.\n\n \"\"\"\n with _active_limbo_lock:\n return list(_active.values()) + list(_limbo.values())\n\nfrom _thread import stack_size\n\n# Create the main thread object,\n# and make it available for the interpreter\n# (Py_Main) as threading._shutdown.\n\n_main_thread = _MainThread()\n\ndef _shutdown():\n # Obscure: other threads may be waiting to join _main_thread. That's\n # dubious, but some code does it. We can't wait for C code to release\n # the main thread's tstate_lock - that won't happen until the interpreter\n # is nearly dead. So we release it here. Note that just calling _stop()\n # isn't enough: other threads may already be waiting on _tstate_lock.\n tlock = _main_thread._tstate_lock\n # The main thread isn't finished yet, so its thread state lock can't have\n # been released.\n assert tlock is not None\n assert tlock.locked()\n tlock.release()\n _main_thread._stop()\n t = _pickSomeNonDaemonThread()\n while t:\n t.join()\n t = _pickSomeNonDaemonThread()\n\ndef _pickSomeNonDaemonThread():\n for t in enumerate():\n if not t.daemon and t.is_alive():\n return t\n return None\n\ndef main_thread():\n \"\"\"Return the main thread object.\n\n In normal conditions, the main thread is the thread from which the\n Python interpreter was started.\n \"\"\"\n return _main_thread\n\n# get thread-local implementation, either from the thread\n# module, or from the python fallback\n\ntry:\n from _thread import _local as local\nexcept ImportError:\n from _threading_local import local\n\n\ndef _after_fork():\n # This function is called by Python/ceval.c:PyEval_ReInitThreads which\n # is called from PyOS_AfterFork. Here we cleanup threading module state\n # that should not exist after a fork.\n\n # Reset _active_limbo_lock, in case we forked while the lock was held\n # by another (non-forked) thread. http://bugs.python.org/issue874900\n global _active_limbo_lock, _main_thread\n _active_limbo_lock = _allocate_lock()\n\n # fork() only copied the current thread; clear references to others.\n new_active = {}\n current = current_thread()\n _main_thread = current\n with _active_limbo_lock:\n # Dangling thread instances must still have their locks reset,\n # because someone may join() them.\n threads = set(_enumerate())\n threads.update(_dangling)\n for thread in threads:\n # Any lock/condition variable may be currently locked or in an\n # invalid state, so we reinitialize them.\n if thread is current:\n # There is only one active thread. We reset the ident to\n # its new value since it can have changed.\n thread._reset_internal_locks(True)\n ident = get_ident()\n thread._ident = ident\n new_active[ident] = thread\n else:\n # All the others are already stopped.\n thread._reset_internal_locks(False)\n thread._stop()\n\n _limbo.clear()\n _active.clear()\n _active.update(new_active)\n assert len(_active) == 1\n",1357],"/usr/lib/python3.6/_weakrefset.py":["# Access WeakSet through the weakref module.\n# This code is separated-out because it is needed\n# by abc.py to load everything else at startup.\n\nfrom _weakref import ref\n\n__all__ = ['WeakSet']\n\n\nclass _IterationGuard:\n # This context manager registers itself in the current iterators of the\n # weak container, such as to delay all removals until the context manager\n # exits.\n # This technique should be relatively thread-safe (since sets are).\n\n def __init__(self, weakcontainer):\n # Don't create cycles\n self.weakcontainer = ref(weakcontainer)\n\n def __enter__(self):\n w = self.weakcontainer()\n if w is not None:\n w._iterating.add(self)\n return self\n\n def __exit__(self, e, t, b):\n w = self.weakcontainer()\n if w is not None:\n s = w._iterating\n s.remove(self)\n if not s:\n w._commit_removals()\n\n\nclass WeakSet:\n def __init__(self, data=None):\n self.data = set()\n def _remove(item, selfref=ref(self)):\n self = selfref()\n if self is not None:\n if self._iterating:\n self._pending_removals.append(item)\n else:\n self.data.discard(item)\n self._remove = _remove\n # A list of keys to be removed\n self._pending_removals = []\n self._iterating = set()\n if data is not None:\n self.update(data)\n\n def _commit_removals(self):\n l = self._pending_removals\n discard = self.data.discard\n while l:\n discard(l.pop())\n\n def __iter__(self):\n with _IterationGuard(self):\n for itemref in self.data:\n item = itemref()\n if item is not None:\n # Caveat: the iterator will keep a strong reference to\n # `item` until it is resumed or closed.\n yield item\n\n def __len__(self):\n return len(self.data) - len(self._pending_removals)\n\n def __contains__(self, item):\n try:\n wr = ref(item)\n except TypeError:\n return False\n return wr in self.data\n\n def __reduce__(self):\n return (self.__class__, (list(self),),\n getattr(self, '__dict__', None))\n\n def add(self, item):\n if self._pending_removals:\n self._commit_removals()\n self.data.add(ref(item, self._remove))\n\n def clear(self):\n if self._pending_removals:\n self._commit_removals()\n self.data.clear()\n\n def copy(self):\n return self.__class__(self)\n\n def pop(self):\n if self._pending_removals:\n self._commit_removals()\n while True:\n try:\n itemref = self.data.pop()\n except KeyError:\n raise KeyError('pop from empty WeakSet')\n item = itemref()\n if item is not None:\n return item\n\n def remove(self, item):\n if self._pending_removals:\n self._commit_removals()\n self.data.remove(ref(item))\n\n def discard(self, item):\n if self._pending_removals:\n self._commit_removals()\n self.data.discard(ref(item))\n\n def update(self, other):\n if self._pending_removals:\n self._commit_removals()\n for element in other:\n self.add(element)\n\n def __ior__(self, other):\n self.update(other)\n return self\n\n def difference(self, other):\n newset = self.copy()\n newset.difference_update(other)\n return newset\n __sub__ = difference\n\n def difference_update(self, other):\n self.__isub__(other)\n def __isub__(self, other):\n if self._pending_removals:\n self._commit_removals()\n if self is other:\n self.data.clear()\n else:\n self.data.difference_update(ref(item) for item in other)\n return self\n\n def intersection(self, other):\n return self.__class__(item for item in other if item in self)\n __and__ = intersection\n\n def intersection_update(self, other):\n self.__iand__(other)\n def __iand__(self, other):\n if self._pending_removals:\n self._commit_removals()\n self.data.intersection_update(ref(item) for item in other)\n return self\n\n def issubset(self, other):\n return self.data.issubset(ref(item) for item in other)\n __le__ = issubset\n\n def __lt__(self, other):\n return self.data < set(ref(item) for item in other)\n\n def issuperset(self, other):\n return self.data.issuperset(ref(item) for item in other)\n __ge__ = issuperset\n\n def __gt__(self, other):\n return self.data > set(ref(item) for item in other)\n\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return NotImplemented\n return self.data == set(ref(item) for item in other)\n\n def symmetric_difference(self, other):\n newset = self.copy()\n newset.symmetric_difference_update(other)\n return newset\n __xor__ = symmetric_difference\n\n def symmetric_difference_update(self, other):\n self.__ixor__(other)\n def __ixor__(self, other):\n if self._pending_removals:\n self._commit_removals()\n if self is other:\n self.data.clear()\n else:\n self.data.symmetric_difference_update(ref(item, self._remove) for item in other)\n return self\n\n def union(self, other):\n return self.__class__(e for s in (self, other) for e in s)\n __or__ = union\n\n def isdisjoint(self, other):\n return len(self.intersection(other)) == 0\n",196],"/usr/lib/python3.6/multiprocessing/popen_fork.py":["import os\nimport sys\nimport signal\n\nfrom . import util\n\n__all__ = ['Popen']\n\n#\n# Start child process using fork\n#\n\nclass Popen(object):\n method = 'fork'\n\n def __init__(self, process_obj):\n util._flush_std_streams()\n self.returncode = None\n self._launch(process_obj)\n\n def duplicate_for_child(self, fd):\n return fd\n\n def poll(self, flag=os.WNOHANG):\n if self.returncode is None:\n while True:\n try:\n pid, sts = os.waitpid(self.pid, flag)\n except OSError as e:\n # Child process not yet created. See #1731717\n # e.errno == errno.ECHILD == 10\n return None\n else:\n break\n if pid == self.pid:\n if os.WIFSIGNALED(sts):\n self.returncode = -os.WTERMSIG(sts)\n else:\n assert os.WIFEXITED(sts)\n self.returncode = os.WEXITSTATUS(sts)\n return self.returncode\n\n def wait(self, timeout=None):\n if self.returncode is None:\n if timeout is not None:\n from multiprocessing.connection import wait\n if not wait([self.sentinel], timeout):\n return None\n # This shouldn't block if wait() returned successfully.\n return self.poll(os.WNOHANG if timeout == 0.0 else 0)\n return self.returncode\n\n def terminate(self):\n if self.returncode is None:\n try:\n os.kill(self.pid, signal.SIGTERM)\n except ProcessLookupError:\n pass\n except OSError:\n if self.wait(timeout=0.1) is None:\n raise\n\n def _launch(self, process_obj):\n code = 1\n parent_r, child_w = os.pipe()\n self.pid = os.fork()\n if self.pid == 0:\n try:\n os.close(parent_r)\n if 'random' in sys.modules:\n import random\n random.seed()\n code = process_obj._bootstrap()\n finally:\n os._exit(code)\n else:\n os.close(child_w)\n util.Finalize(self, os.close, (parent_r,))\n self.sentinel = parent_r\n",79],"/usr/lib/python3.6/selectors.py":["\"\"\"Selectors module.\n\nThis module allows high-level and efficient I/O multiplexing, built upon the\n`select` module primitives.\n\"\"\"\n\n\nfrom abc import ABCMeta, abstractmethod\nfrom collections import namedtuple, Mapping\nimport math\nimport select\nimport sys\n\n\n# generic events, that must be mapped to implementation-specific ones\nEVENT_READ = (1 << 0)\nEVENT_WRITE = (1 << 1)\n\n\ndef _fileobj_to_fd(fileobj):\n \"\"\"Return a file descriptor from a file object.\n\n Parameters:\n fileobj -- file object or file descriptor\n\n Returns:\n corresponding file descriptor\n\n Raises:\n ValueError if the object is invalid\n \"\"\"\n if isinstance(fileobj, int):\n fd = fileobj\n else:\n try:\n fd = int(fileobj.fileno())\n except (AttributeError, TypeError, ValueError):\n raise ValueError(\"Invalid file object: \"\n \"{!r}\".format(fileobj)) from None\n if fd < 0:\n raise ValueError(\"Invalid file descriptor: {}\".format(fd))\n return fd\n\n\nSelectorKey = namedtuple('SelectorKey', ['fileobj', 'fd', 'events', 'data'])\n\nSelectorKey.__doc__ = \"\"\"SelectorKey(fileobj, fd, events, data)\n\n Object used to associate a file object to its backing\n file descriptor, selected event mask, and attached data.\n\"\"\"\nif sys.version_info >= (3, 5):\n SelectorKey.fileobj.__doc__ = 'File object registered.'\n SelectorKey.fd.__doc__ = 'Underlying file descriptor.'\n SelectorKey.events.__doc__ = 'Events that must be waited for on this file object.'\n SelectorKey.data.__doc__ = ('''Optional opaque data associated to this file object.\n For example, this could be used to store a per-client session ID.''')\n\nclass _SelectorMapping(Mapping):\n \"\"\"Mapping of file objects to selector keys.\"\"\"\n\n def __init__(self, selector):\n self._selector = selector\n\n def __len__(self):\n return len(self._selector._fd_to_key)\n\n def __getitem__(self, fileobj):\n try:\n fd = self._selector._fileobj_lookup(fileobj)\n return self._selector._fd_to_key[fd]\n except KeyError:\n raise KeyError(\"{!r} is not registered\".format(fileobj)) from None\n\n def __iter__(self):\n return iter(self._selector._fd_to_key)\n\n\nclass BaseSelector(metaclass=ABCMeta):\n \"\"\"Selector abstract base class.\n\n A selector supports registering file objects to be monitored for specific\n I/O events.\n\n A file object is a file descriptor or any object with a `fileno()` method.\n An arbitrary object can be attached to the file object, which can be used\n for example to store context information, a callback, etc.\n\n A selector can use various implementations (select(), poll(), epoll()...)\n depending on the platform. The default `Selector` class uses the most\n efficient implementation on the current platform.\n \"\"\"\n\n @abstractmethod\n def register(self, fileobj, events, data=None):\n \"\"\"Register a file object.\n\n Parameters:\n fileobj -- file object or file descriptor\n events -- events to monitor (bitwise mask of EVENT_READ|EVENT_WRITE)\n data -- attached data\n\n Returns:\n SelectorKey instance\n\n Raises:\n ValueError if events is invalid\n KeyError if fileobj is already registered\n OSError if fileobj is closed or otherwise is unacceptable to\n the underlying system call (if a system call is made)\n\n Note:\n OSError may or may not be raised\n \"\"\"\n raise NotImplementedError\n\n @abstractmethod\n def unregister(self, fileobj):\n \"\"\"Unregister a file object.\n\n Parameters:\n fileobj -- file object or file descriptor\n\n Returns:\n SelectorKey instance\n\n Raises:\n KeyError if fileobj is not registered\n\n Note:\n If fileobj is registered but has since been closed this does\n *not* raise OSError (even if the wrapped syscall does)\n \"\"\"\n raise NotImplementedError\n\n def modify(self, fileobj, events, data=None):\n \"\"\"Change a registered file object monitored events or attached data.\n\n Parameters:\n fileobj -- file object or file descriptor\n events -- events to monitor (bitwise mask of EVENT_READ|EVENT_WRITE)\n data -- attached data\n\n Returns:\n SelectorKey instance\n\n Raises:\n Anything that unregister() or register() raises\n \"\"\"\n self.unregister(fileobj)\n return self.register(fileobj, events, data)\n\n @abstractmethod\n def select(self, timeout=None):\n \"\"\"Perform the actual selection, until some monitored file objects are\n ready or a timeout expires.\n\n Parameters:\n timeout -- if timeout > 0, this specifies the maximum wait time, in\n seconds\n if timeout <= 0, the select() call won't block, and will\n report the currently ready file objects\n if timeout is None, select() will block until a monitored\n file object becomes ready\n\n Returns:\n list of (key, events) for ready file objects\n `events` is a bitwise mask of EVENT_READ|EVENT_WRITE\n \"\"\"\n raise NotImplementedError\n\n def close(self):\n \"\"\"Close the selector.\n\n This must be called to make sure that any underlying resource is freed.\n \"\"\"\n pass\n\n def get_key(self, fileobj):\n \"\"\"Return the key associated to a registered file object.\n\n Returns:\n SelectorKey for this file object\n \"\"\"\n mapping = self.get_map()\n if mapping is None:\n raise RuntimeError('Selector is closed')\n try:\n return mapping[fileobj]\n except KeyError:\n raise KeyError(\"{!r} is not registered\".format(fileobj)) from None\n\n @abstractmethod\n def get_map(self):\n \"\"\"Return a mapping of file objects to selector keys.\"\"\"\n raise NotImplementedError\n\n def __enter__(self):\n return self\n\n def __exit__(self, *args):\n self.close()\n\n\nclass _BaseSelectorImpl(BaseSelector):\n \"\"\"Base selector implementation.\"\"\"\n\n def __init__(self):\n # this maps file descriptors to keys\n self._fd_to_key = {}\n # read-only mapping returned by get_map()\n self._map = _SelectorMapping(self)\n\n def _fileobj_lookup(self, fileobj):\n \"\"\"Return a file descriptor from a file object.\n\n This wraps _fileobj_to_fd() to do an exhaustive search in case\n the object is invalid but we still have it in our map. This\n is used by unregister() so we can unregister an object that\n was previously registered even if it is closed. It is also\n used by _SelectorMapping.\n \"\"\"\n try:\n return _fileobj_to_fd(fileobj)\n except ValueError:\n # Do an exhaustive search.\n for key in self._fd_to_key.values():\n if key.fileobj is fileobj:\n return key.fd\n # Raise ValueError after all.\n raise\n\n def register(self, fileobj, events, data=None):\n if (not events) or (events & ~(EVENT_READ | EVENT_WRITE)):\n raise ValueError(\"Invalid events: {!r}\".format(events))\n\n key = SelectorKey(fileobj, self._fileobj_lookup(fileobj), events, data)\n\n if key.fd in self._fd_to_key:\n raise KeyError(\"{!r} (FD {}) is already registered\"\n .format(fileobj, key.fd))\n\n self._fd_to_key[key.fd] = key\n return key\n\n def unregister(self, fileobj):\n try:\n key = self._fd_to_key.pop(self._fileobj_lookup(fileobj))\n except KeyError:\n raise KeyError(\"{!r} is not registered\".format(fileobj)) from None\n return key\n\n def modify(self, fileobj, events, data=None):\n # TODO: Subclasses can probably optimize this even further.\n try:\n key = self._fd_to_key[self._fileobj_lookup(fileobj)]\n except KeyError:\n raise KeyError(\"{!r} is not registered\".format(fileobj)) from None\n if events != key.events:\n self.unregister(fileobj)\n key = self.register(fileobj, events, data)\n elif data != key.data:\n # Use a shortcut to update the data.\n key = key._replace(data=data)\n self._fd_to_key[key.fd] = key\n return key\n\n def close(self):\n self._fd_to_key.clear()\n self._map = None\n\n def get_map(self):\n return self._map\n\n def _key_from_fd(self, fd):\n \"\"\"Return the key associated to a given file descriptor.\n\n Parameters:\n fd -- file descriptor\n\n Returns:\n corresponding key, or None if not found\n \"\"\"\n try:\n return self._fd_to_key[fd]\n except KeyError:\n return None\n\n\nclass SelectSelector(_BaseSelectorImpl):\n \"\"\"Select-based selector.\"\"\"\n\n def __init__(self):\n super().__init__()\n self._readers = set()\n self._writers = set()\n\n def register(self, fileobj, events, data=None):\n key = super().register(fileobj, events, data)\n if events & EVENT_READ:\n self._readers.add(key.fd)\n if events & EVENT_WRITE:\n self._writers.add(key.fd)\n return key\n\n def unregister(self, fileobj):\n key = super().unregister(fileobj)\n self._readers.discard(key.fd)\n self._writers.discard(key.fd)\n return key\n\n if sys.platform == 'win32':\n def _select(self, r, w, _, timeout=None):\n r, w, x = select.select(r, w, w, timeout)\n return r, w + x, []\n else:\n _select = select.select\n\n def select(self, timeout=None):\n timeout = None if timeout is None else max(timeout, 0)\n ready = []\n try:\n r, w, _ = self._select(self._readers, self._writers, [], timeout)\n except InterruptedError:\n return ready\n r = set(r)\n w = set(w)\n for fd in r | w:\n events = 0\n if fd in r:\n events |= EVENT_READ\n if fd in w:\n events |= EVENT_WRITE\n\n key = self._key_from_fd(fd)\n if key:\n ready.append((key, events & key.events))\n return ready\n\n\nif hasattr(select, 'poll'):\n\n class PollSelector(_BaseSelectorImpl):\n \"\"\"Poll-based selector.\"\"\"\n\n def __init__(self):\n super().__init__()\n self._poll = select.poll()\n\n def register(self, fileobj, events, data=None):\n key = super().register(fileobj, events, data)\n poll_events = 0\n if events & EVENT_READ:\n poll_events |= select.POLLIN\n if events & EVENT_WRITE:\n poll_events |= select.POLLOUT\n self._poll.register(key.fd, poll_events)\n return key\n\n def unregister(self, fileobj):\n key = super().unregister(fileobj)\n self._poll.unregister(key.fd)\n return key\n\n def select(self, timeout=None):\n if timeout is None:\n timeout = None\n elif timeout <= 0:\n timeout = 0\n else:\n # poll() has a resolution of 1 millisecond, round away from\n # zero to wait *at least* timeout seconds.\n timeout = math.ceil(timeout * 1e3)\n ready = []\n try:\n fd_event_list = self._poll.poll(timeout)\n except InterruptedError:\n return ready\n for fd, event in fd_event_list:\n events = 0\n if event & ~select.POLLIN:\n events |= EVENT_WRITE\n if event & ~select.POLLOUT:\n events |= EVENT_READ\n\n key = self._key_from_fd(fd)\n if key:\n ready.append((key, events & key.events))\n return ready\n\n\nif hasattr(select, 'epoll'):\n\n class EpollSelector(_BaseSelectorImpl):\n \"\"\"Epoll-based selector.\"\"\"\n\n def __init__(self):\n super().__init__()\n self._epoll = select.epoll()\n\n def fileno(self):\n return self._epoll.fileno()\n\n def register(self, fileobj, events, data=None):\n key = super().register(fileobj, events, data)\n epoll_events = 0\n if events & EVENT_READ:\n epoll_events |= select.EPOLLIN\n if events & EVENT_WRITE:\n epoll_events |= select.EPOLLOUT\n try:\n self._epoll.register(key.fd, epoll_events)\n except BaseException:\n super().unregister(fileobj)\n raise\n return key\n\n def unregister(self, fileobj):\n key = super().unregister(fileobj)\n try:\n self._epoll.unregister(key.fd)\n except OSError:\n # This can happen if the FD was closed since it\n # was registered.\n pass\n return key\n\n def select(self, timeout=None):\n if timeout is None:\n timeout = -1\n elif timeout <= 0:\n timeout = 0\n else:\n # epoll_wait() has a resolution of 1 millisecond, round away\n # from zero to wait *at least* timeout seconds.\n timeout = math.ceil(timeout * 1e3) * 1e-3\n\n # epoll_wait() expects `maxevents` to be greater than zero;\n # we want to make sure that `select()` can be called when no\n # FD is registered.\n max_ev = max(len(self._fd_to_key), 1)\n\n ready = []\n try:\n fd_event_list = self._epoll.poll(timeout, max_ev)\n except InterruptedError:\n return ready\n for fd, event in fd_event_list:\n events = 0\n if event & ~select.EPOLLIN:\n events |= EVENT_WRITE\n if event & ~select.EPOLLOUT:\n events |= EVENT_READ\n\n key = self._key_from_fd(fd)\n if key:\n ready.append((key, events & key.events))\n return ready\n\n def close(self):\n self._epoll.close()\n super().close()\n\n\nif hasattr(select, 'devpoll'):\n\n class DevpollSelector(_BaseSelectorImpl):\n \"\"\"Solaris /dev/poll selector.\"\"\"\n\n def __init__(self):\n super().__init__()\n self._devpoll = select.devpoll()\n\n def fileno(self):\n return self._devpoll.fileno()\n\n def register(self, fileobj, events, data=None):\n key = super().register(fileobj, events, data)\n poll_events = 0\n if events & EVENT_READ:\n poll_events |= select.POLLIN\n if events & EVENT_WRITE:\n poll_events |= select.POLLOUT\n self._devpoll.register(key.fd, poll_events)\n return key\n\n def unregister(self, fileobj):\n key = super().unregister(fileobj)\n self._devpoll.unregister(key.fd)\n return key\n\n def select(self, timeout=None):\n if timeout is None:\n timeout = None\n elif timeout <= 0:\n timeout = 0\n else:\n # devpoll() has a resolution of 1 millisecond, round away from\n # zero to wait *at least* timeout seconds.\n timeout = math.ceil(timeout * 1e3)\n ready = []\n try:\n fd_event_list = self._devpoll.poll(timeout)\n except InterruptedError:\n return ready\n for fd, event in fd_event_list:\n events = 0\n if event & ~select.POLLIN:\n events |= EVENT_WRITE\n if event & ~select.POLLOUT:\n events |= EVENT_READ\n\n key = self._key_from_fd(fd)\n if key:\n ready.append((key, events & key.events))\n return ready\n\n def close(self):\n self._devpoll.close()\n super().close()\n\n\nif hasattr(select, 'kqueue'):\n\n class KqueueSelector(_BaseSelectorImpl):\n \"\"\"Kqueue-based selector.\"\"\"\n\n def __init__(self):\n super().__init__()\n self._kqueue = select.kqueue()\n\n def fileno(self):\n return self._kqueue.fileno()\n\n def register(self, fileobj, events, data=None):\n key = super().register(fileobj, events, data)\n try:\n if events & EVENT_READ:\n kev = select.kevent(key.fd, select.KQ_FILTER_READ,\n select.KQ_EV_ADD)\n self._kqueue.control([kev], 0, 0)\n if events & EVENT_WRITE:\n kev = select.kevent(key.fd, select.KQ_FILTER_WRITE,\n select.KQ_EV_ADD)\n self._kqueue.control([kev], 0, 0)\n except BaseException:\n super().unregister(fileobj)\n raise\n return key\n\n def unregister(self, fileobj):\n key = super().unregister(fileobj)\n if key.events & EVENT_READ:\n kev = select.kevent(key.fd, select.KQ_FILTER_READ,\n select.KQ_EV_DELETE)\n try:\n self._kqueue.control([kev], 0, 0)\n except OSError:\n # This can happen if the FD was closed since it\n # was registered.\n pass\n if key.events & EVENT_WRITE:\n kev = select.kevent(key.fd, select.KQ_FILTER_WRITE,\n select.KQ_EV_DELETE)\n try:\n self._kqueue.control([kev], 0, 0)\n except OSError:\n # See comment above.\n pass\n return key\n\n def select(self, timeout=None):\n timeout = None if timeout is None else max(timeout, 0)\n max_ev = len(self._fd_to_key)\n ready = []\n try:\n kev_list = self._kqueue.control(None, max_ev, timeout)\n except InterruptedError:\n return ready\n for kev in kev_list:\n fd = kev.ident\n flag = kev.filter\n events = 0\n if flag == select.KQ_FILTER_READ:\n events |= EVENT_READ\n if flag == select.KQ_FILTER_WRITE:\n events |= EVENT_WRITE\n\n key = self._key_from_fd(fd)\n if key:\n ready.append((key, events & 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When the object is garbage collected or the\n # process shuts down we unlink the semaphore name\n from .resource_tracker import register\n register(self._semlock.name, \"semaphore\")\n util.Finalize(self, SemLock._cleanup, (self._semlock.name,),\n exitpriority=0)\n\n @staticmethod\n def _cleanup(name):\n from .resource_tracker import unregister\n sem_unlink(name)\n unregister(name, \"semaphore\")\n\n def _make_methods(self):\n self.acquire = self._semlock.acquire\n self.release = self._semlock.release\n\n def __enter__(self):\n return self._semlock.__enter__()\n\n def __exit__(self, *args):\n return self._semlock.__exit__(*args)\n\n def __getstate__(self):\n context.assert_spawning(self)\n sl = self._semlock\n if sys.platform == 'win32':\n h = context.get_spawning_popen().duplicate_for_child(sl.handle)\n else:\n if self._is_fork_ctx:\n raise RuntimeError('A SemLock created in a fork context is being '\n 'shared with a process in a spawn context. This is '\n 'not supported. Please use the same context to create '\n 'multiprocessing objects and Process.')\n h = sl.handle\n return (h, sl.kind, sl.maxvalue, sl.name)\n\n def __setstate__(self, state):\n self._semlock = _multiprocessing.SemLock._rebuild(*state)\n util.debug('recreated blocker with handle %r' % state[0])\n self._make_methods()\n # Ensure that deserialized SemLock can be serialized again (gh-108520).\n self._is_fork_ctx = False\n\n @staticmethod\n def _make_name():\n return '%s-%s' % (process.current_process()._config['semprefix'],\n next(SemLock._rand))\n\n#\n# Semaphore\n#\n\nclass Semaphore(SemLock):\n\n def __init__(self, value=1, *, ctx):\n SemLock.__init__(self, SEMAPHORE, value, SEM_VALUE_MAX, ctx=ctx)\n\n def get_value(self):\n return self._semlock._get_value()\n\n def __repr__(self):\n try:\n value = self._semlock._get_value()\n except Exception:\n value = 'unknown'\n return '<%s(value=%s)>' % (self.__class__.__name__, value)\n\n#\n# Bounded semaphore\n#\n\nclass BoundedSemaphore(Semaphore):\n\n def __init__(self, value=1, *, ctx):\n SemLock.__init__(self, SEMAPHORE, value, value, ctx=ctx)\n\n def __repr__(self):\n try:\n value = self._semlock._get_value()\n except Exception:\n value = 'unknown'\n return '<%s(value=%s, maxvalue=%s)>' % \\\n (self.__class__.__name__, value, self._semlock.maxvalue)\n\n#\n# Non-recursive lock\n#\n\nclass Lock(SemLock):\n\n def __init__(self, *, ctx):\n SemLock.__init__(self, SEMAPHORE, 1, 1, ctx=ctx)\n\n def __repr__(self):\n try:\n if self._semlock._is_mine():\n name = process.current_process().name\n if threading.current_thread().name != 'MainThread':\n name += '|' + threading.current_thread().name\n elif self._semlock._get_value() == 1:\n name = 'None'\n elif self._semlock._count() > 0:\n name = 'SomeOtherThread'\n else:\n name = 'SomeOtherProcess'\n except Exception:\n name = 'unknown'\n return '<%s(owner=%s)>' % (self.__class__.__name__, name)\n\n#\n# Recursive lock\n#\n\nclass RLock(SemLock):\n\n def __init__(self, *, ctx):\n SemLock.__init__(self, RECURSIVE_MUTEX, 1, 1, ctx=ctx)\n\n def __repr__(self):\n try:\n if self._semlock._is_mine():\n name = process.current_process().name\n if threading.current_thread().name != 'MainThread':\n name += '|' + threading.current_thread().name\n count = self._semlock._count()\n elif self._semlock._get_value() == 1:\n name, count = 'None', 0\n elif self._semlock._count() > 0:\n name, count = 'SomeOtherThread', 'nonzero'\n else:\n name, count = 'SomeOtherProcess', 'nonzero'\n except Exception:\n name, count = 'unknown', 'unknown'\n return '<%s(%s, %s)>' % (self.__class__.__name__, name, count)\n\n#\n# Condition variable\n#\n\nclass Condition(object):\n\n def __init__(self, lock=None, *, ctx):\n self._lock = lock or ctx.RLock()\n self._sleeping_count = ctx.Semaphore(0)\n self._woken_count = ctx.Semaphore(0)\n self._wait_semaphore = ctx.Semaphore(0)\n self._make_methods()\n\n def __getstate__(self):\n context.assert_spawning(self)\n return (self._lock, self._sleeping_count,\n self._woken_count, self._wait_semaphore)\n\n def __setstate__(self, state):\n (self._lock, self._sleeping_count,\n self._woken_count, self._wait_semaphore) = state\n self._make_methods()\n\n def __enter__(self):\n return self._lock.__enter__()\n\n def __exit__(self, *args):\n return self._lock.__exit__(*args)\n\n def _make_methods(self):\n self.acquire = self._lock.acquire\n self.release = self._lock.release\n\n def __repr__(self):\n try:\n num_waiters = (self._sleeping_count._semlock._get_value() -\n self._woken_count._semlock._get_value())\n except Exception:\n num_waiters = 'unknown'\n return '<%s(%s, %s)>' % (self.__class__.__name__, self._lock, num_waiters)\n\n def wait(self, timeout=None):\n assert self._lock._semlock._is_mine(), \\\n 'must acquire() condition before using wait()'\n\n # indicate that this thread is going to sleep\n self._sleeping_count.release()\n\n # release lock\n count = self._lock._semlock._count()\n for i in range(count):\n self._lock.release()\n\n try:\n # wait for notification or timeout\n return self._wait_semaphore.acquire(True, timeout)\n finally:\n # indicate that this thread has woken\n self._woken_count.release()\n\n # reacquire lock\n for i in range(count):\n self._lock.acquire()\n\n def notify(self, n=1):\n assert self._lock._semlock._is_mine(), 'lock is not owned'\n assert not self._wait_semaphore.acquire(\n False), ('notify: Should not have been able to acquire '\n + '_wait_semaphore')\n\n # to take account of timeouts since last notify*() we subtract\n # woken_count from sleeping_count and rezero woken_count\n while self._woken_count.acquire(False):\n res = self._sleeping_count.acquire(False)\n assert res, ('notify: Bug in sleeping_count.acquire'\n + '- res should not be False')\n\n sleepers = 0\n while sleepers < n and self._sleeping_count.acquire(False):\n self._wait_semaphore.release() # wake up one sleeper\n sleepers += 1\n\n if sleepers:\n for i in range(sleepers):\n self._woken_count.acquire() # wait for a sleeper to wake\n\n # rezero wait_semaphore in case some timeouts just happened\n while self._wait_semaphore.acquire(False):\n pass\n\n def notify_all(self):\n self.notify(n=sys.maxsize)\n\n def wait_for(self, predicate, timeout=None):\n result = predicate()\n if result:\n return result\n if timeout is not None:\n endtime = time.monotonic() + timeout\n else:\n endtime = None\n waittime = None\n while not result:\n if endtime is not None:\n waittime = endtime - time.monotonic()\n if waittime <= 0:\n break\n self.wait(waittime)\n result = predicate()\n return result\n\n#\n# Event\n#\n\nclass Event(object):\n\n def __init__(self, *, ctx):\n self._cond = ctx.Condition(ctx.Lock())\n self._flag = ctx.Semaphore(0)\n\n def is_set(self):\n with self._cond:\n if self._flag.acquire(False):\n self._flag.release()\n return True\n return False\n\n def set(self):\n with self._cond:\n self._flag.acquire(False)\n self._flag.release()\n self._cond.notify_all()\n\n def clear(self):\n with self._cond:\n self._flag.acquire(False)\n\n def wait(self, timeout=None):\n with self._cond:\n if self._flag.acquire(False):\n self._flag.release()\n else:\n self._cond.wait(timeout)\n\n if self._flag.acquire(False):\n self._flag.release()\n return True\n return False\n\n def __repr__(self) -> str:\n set_status = 'set' if self.is_set() else 'unset'\n return f\"<{type(self).__qualname__} at {id(self):#x} {set_status}>\"\n#\n# Barrier\n#\n\nclass Barrier(threading.Barrier):\n\n def __init__(self, parties, action=None, timeout=None, *, ctx):\n import struct\n from .heap import BufferWrapper\n wrapper = BufferWrapper(struct.calcsize('i') * 2)\n cond = ctx.Condition()\n self.__setstate__((parties, action, timeout, cond, wrapper))\n self._state = 0\n self._count = 0\n\n def __setstate__(self, state):\n (self._parties, self._action, self._timeout,\n self._cond, self._wrapper) = state\n self._array = self._wrapper.create_memoryview().cast('i')\n\n def __getstate__(self):\n return (self._parties, self._action, self._timeout,\n self._cond, self._wrapper)\n\n @property\n def _state(self):\n return self._array[0]\n\n @_state.setter\n def _state(self, value):\n self._array[0] = value\n\n @property\n def _count(self):\n return self._array[1]\n\n @_count.setter\n def _count(self, value):\n self._array[1] = value\n", 404], "/usr/lib/python3.13/multiprocessing/util.py": ["#\n# Module providing various facilities to other parts of the package\n#\n# multiprocessing/util.py\n#\n# Copyright (c) 2006-2008, R Oudkerk\n# Licensed to PSF under a Contributor Agreement.\n#\n\nimport os\nimport itertools\nimport sys\nimport weakref\nimport atexit\nimport threading # we want threading to install it's\n # cleanup function before multiprocessing does\nfrom subprocess import _args_from_interpreter_flags\n\nfrom . import process\n\n__all__ = [\n 'sub_debug', 'debug', 'info', 'sub_warning', 'get_logger',\n 'log_to_stderr', 'get_temp_dir', 'register_after_fork',\n 'is_exiting', 'Finalize', 'ForkAwareThreadLock', 'ForkAwareLocal',\n 'close_all_fds_except', 'SUBDEBUG', 'SUBWARNING',\n ]\n\n#\n# Logging\n#\n\nNOTSET = 0\nSUBDEBUG = 5\nDEBUG = 10\nINFO = 20\nSUBWARNING = 25\n\nLOGGER_NAME = 'multiprocessing'\nDEFAULT_LOGGING_FORMAT = '[%(levelname)s/%(processName)s] %(message)s'\n\n_logger = None\n_log_to_stderr = False\n\ndef sub_debug(msg, *args):\n if _logger:\n _logger.log(SUBDEBUG, msg, *args, stacklevel=2)\n\ndef debug(msg, *args):\n if _logger:\n _logger.log(DEBUG, msg, *args, stacklevel=2)\n\ndef info(msg, *args):\n if _logger:\n _logger.log(INFO, msg, *args, stacklevel=2)\n\ndef sub_warning(msg, *args):\n if _logger:\n _logger.log(SUBWARNING, msg, *args, stacklevel=2)\n\ndef get_logger():\n '''\n Returns logger used by multiprocessing\n '''\n global _logger\n import logging\n\n with logging._lock:\n if not _logger:\n\n _logger = logging.getLogger(LOGGER_NAME)\n _logger.propagate = 0\n\n # XXX multiprocessing should cleanup before logging\n if hasattr(atexit, 'unregister'):\n atexit.unregister(_exit_function)\n atexit.register(_exit_function)\n else:\n atexit._exithandlers.remove((_exit_function, (), {}))\n atexit._exithandlers.append((_exit_function, (), {}))\n\n return _logger\n\ndef log_to_stderr(level=None):\n '''\n Turn on logging and add a handler which prints to stderr\n '''\n global _log_to_stderr\n import logging\n\n logger = get_logger()\n formatter = logging.Formatter(DEFAULT_LOGGING_FORMAT)\n handler = logging.StreamHandler()\n handler.setFormatter(formatter)\n logger.addHandler(handler)\n\n if level:\n logger.setLevel(level)\n _log_to_stderr = True\n return _logger\n\n\n# Abstract socket support\n\ndef _platform_supports_abstract_sockets():\n return sys.platform in (\"linux\", \"android\")\n\n\ndef is_abstract_socket_namespace(address):\n if not address:\n return False\n if isinstance(address, bytes):\n return address[0] == 0\n elif isinstance(address, str):\n return address[0] == \"\\0\"\n raise TypeError(f'address type of {address!r} unrecognized')\n\n\nabstract_sockets_supported = _platform_supports_abstract_sockets()\n\n#\n# Function returning a temp directory which will be removed on exit\n#\n\ndef _remove_temp_dir(rmtree, tempdir):\n rmtree(tempdir)\n\n current_process = process.current_process()\n # current_process() can be None if the finalizer is called\n # late during Python finalization\n if current_process is not None:\n current_process._config['tempdir'] = None\n\ndef get_temp_dir():\n # get name of a temp directory which will be automatically cleaned up\n tempdir = process.current_process()._config.get('tempdir')\n if tempdir is None:\n import shutil, tempfile\n tempdir = tempfile.mkdtemp(prefix='pymp-')\n info('created temp directory %s', tempdir)\n # keep a strong reference to shutil.rmtree(), since the finalizer\n # can be called late during Python shutdown\n Finalize(None, _remove_temp_dir, args=(shutil.rmtree, tempdir),\n exitpriority=-100)\n process.current_process()._config['tempdir'] = tempdir\n return tempdir\n\n#\n# Support for reinitialization of objects when bootstrapping a child process\n#\n\n_afterfork_registry = weakref.WeakValueDictionary()\n_afterfork_counter = itertools.count()\n\ndef _run_after_forkers():\n items = list(_afterfork_registry.items())\n items.sort()\n for (index, ident, func), obj in items:\n try:\n func(obj)\n except Exception as e:\n info('after forker raised exception %s', e)\n\ndef register_after_fork(obj, func):\n _afterfork_registry[(next(_afterfork_counter), id(obj), func)] = obj\n\n#\n# Finalization using weakrefs\n#\n\n_finalizer_registry = {}\n_finalizer_counter = itertools.count()\n\n\nclass Finalize(object):\n '''\n Class which supports object finalization using weakrefs\n '''\n def __init__(self, obj, callback, args=(), kwargs=None, exitpriority=None):\n if (exitpriority is not None) and not isinstance(exitpriority,int):\n raise TypeError(\n \"Exitpriority ({0!r}) must be None or int, not {1!s}\".format(\n exitpriority, type(exitpriority)))\n\n if obj is not None:\n self._weakref = weakref.ref(obj, self)\n elif exitpriority is None:\n raise ValueError(\"Without object, exitpriority cannot be None\")\n\n self._callback = callback\n self._args = args\n self._kwargs = kwargs or {}\n self._key = (exitpriority, next(_finalizer_counter))\n self._pid = os.getpid()\n\n _finalizer_registry[self._key] = self\n\n def __call__(self, wr=None,\n # Need to bind these locally because the globals can have\n # been cleared at shutdown\n _finalizer_registry=_finalizer_registry,\n sub_debug=sub_debug, getpid=os.getpid):\n '''\n Run the callback unless it has already been called or cancelled\n '''\n try:\n del _finalizer_registry[self._key]\n except KeyError:\n sub_debug('finalizer no longer registered')\n else:\n if self._pid != getpid():\n sub_debug('finalizer ignored because different process')\n res = None\n else:\n sub_debug('finalizer calling %s with args %s and kwargs %s',\n self._callback, self._args, self._kwargs)\n res = self._callback(*self._args, **self._kwargs)\n self._weakref = self._callback = self._args = \\\n self._kwargs = self._key = None\n return res\n\n def cancel(self):\n '''\n Cancel finalization of the object\n '''\n try:\n del _finalizer_registry[self._key]\n except KeyError:\n pass\n else:\n self._weakref = self._callback = self._args = \\\n self._kwargs = self._key = None\n\n def still_active(self):\n '''\n Return whether this finalizer is still waiting to invoke callback\n '''\n return self._key in _finalizer_registry\n\n def __repr__(self):\n try:\n obj = self._weakref()\n except (AttributeError, TypeError):\n obj = None\n\n if obj is None:\n return '<%s object, dead>' % self.__class__.__name__\n\n x = '<%s object, callback=%s' % (\n self.__class__.__name__,\n getattr(self._callback, '__name__', self._callback))\n if self._args:\n x += ', args=' + str(self._args)\n if self._kwargs:\n x += ', kwargs=' + str(self._kwargs)\n if self._key[0] is not None:\n x += ', exitpriority=' + str(self._key[0])\n return x + '>'\n\n\ndef _run_finalizers(minpriority=None):\n '''\n Run all finalizers whose exit priority is not None and at least minpriority\n\n Finalizers with highest priority are called first; finalizers with\n the same priority will be called in reverse order of creation.\n '''\n if _finalizer_registry is None:\n # This function may be called after this module's globals are\n # destroyed. See the _exit_function function in this module for more\n # notes.\n return\n\n if minpriority is None:\n f = lambda p : p[0] is not None\n else:\n f = lambda p : p[0] is not None and p[0] >= minpriority\n\n # Careful: _finalizer_registry may be mutated while this function\n # is running (either by a GC run or by another thread).\n\n # list(_finalizer_registry) should be atomic, while\n # list(_finalizer_registry.items()) is not.\n keys = [key for key in list(_finalizer_registry) if f(key)]\n keys.sort(reverse=True)\n\n for key in keys:\n finalizer = _finalizer_registry.get(key)\n # key may have been removed from the registry\n if finalizer is not None:\n sub_debug('calling %s', finalizer)\n try:\n finalizer()\n except Exception:\n import traceback\n traceback.print_exc()\n\n if minpriority is None:\n _finalizer_registry.clear()\n\n#\n# Clean up on exit\n#\n\ndef is_exiting():\n '''\n Returns true if the process is shutting down\n '''\n return _exiting or _exiting is None\n\n_exiting = False\n\ndef _exit_function(info=info, debug=debug, _run_finalizers=_run_finalizers,\n active_children=process.active_children,\n current_process=process.current_process):\n # We hold on to references to functions in the arglist due to the\n # situation described below, where this function is called after this\n # module's globals are destroyed.\n\n global _exiting\n\n if not _exiting:\n _exiting = True\n\n info('process shutting down')\n debug('running all \"atexit\" finalizers with priority >= 0')\n _run_finalizers(0)\n\n if current_process() is not None:\n # We check if the current process is None here because if\n # it's None, any call to ``active_children()`` will raise\n # an AttributeError (active_children winds up trying to\n # get attributes from util._current_process). One\n # situation where this can happen is if someone has\n # manipulated sys.modules, causing this module to be\n # garbage collected. The destructor for the module type\n # then replaces all values in the module dict with None.\n # For instance, after setuptools runs a test it replaces\n # sys.modules with a copy created earlier. See issues\n # #9775 and #15881. Also related: #4106, #9205, and\n # #9207.\n\n for p in active_children():\n if p.daemon:\n info('calling terminate() for daemon %s', p.name)\n p._popen.terminate()\n\n for p in active_children():\n info('calling join() for process %s', p.name)\n p.join()\n\n debug('running the remaining \"atexit\" finalizers')\n _run_finalizers()\n\natexit.register(_exit_function)\n\n#\n# Some fork aware types\n#\n\nclass ForkAwareThreadLock(object):\n def __init__(self):\n self._lock = threading.Lock()\n self.acquire = self._lock.acquire\n self.release = self._lock.release\n register_after_fork(self, ForkAwareThreadLock._at_fork_reinit)\n\n def _at_fork_reinit(self):\n self._lock._at_fork_reinit()\n\n def __enter__(self):\n return self._lock.__enter__()\n\n def __exit__(self, *args):\n return self._lock.__exit__(*args)\n\n\nclass ForkAwareLocal(threading.local):\n def __init__(self):\n register_after_fork(self, lambda obj : obj.__dict__.clear())\n def __reduce__(self):\n return type(self), ()\n\n#\n# Close fds except those specified\n#\n\ntry:\n MAXFD = os.sysconf(\"SC_OPEN_MAX\")\nexcept Exception:\n MAXFD = 256\n\ndef close_all_fds_except(fds):\n fds = list(fds) + [-1, MAXFD]\n fds.sort()\n assert fds[-1] == MAXFD, 'fd too large'\n for i in range(len(fds) - 1):\n os.closerange(fds[i]+1, fds[i+1])\n#\n# Close sys.stdin and replace stdin with os.devnull\n#\n\ndef _close_stdin():\n if sys.stdin is None:\n return\n\n try:\n sys.stdin.close()\n except (OSError, ValueError):\n pass\n\n try:\n fd = os.open(os.devnull, os.O_RDONLY)\n try:\n sys.stdin = open(fd, encoding=\"utf-8\", closefd=False)\n except:\n os.close(fd)\n raise\n except (OSError, ValueError):\n pass\n\n#\n# Flush standard streams, if any\n#\n\ndef _flush_std_streams():\n try:\n sys.stdout.flush()\n except (AttributeError, ValueError):\n pass\n try:\n sys.stderr.flush()\n except (AttributeError, ValueError):\n pass\n\n#\n# Start a program with only specified fds kept open\n#\n\ndef spawnv_passfds(path, args, passfds):\n import _posixsubprocess\n import subprocess\n passfds = tuple(sorted(map(int, passfds)))\n errpipe_read, errpipe_write = os.pipe()\n try:\n return _posixsubprocess.fork_exec(\n args, [path], True, passfds, None, None,\n -1, -1, -1, -1, -1, -1, errpipe_read, errpipe_write,\n False, False, -1, None, None, None, -1, None,\n subprocess._USE_VFORK)\n finally:\n os.close(errpipe_read)\n os.close(errpipe_write)\n\n\ndef close_fds(*fds):\n \"\"\"Close each file descriptor given as an argument\"\"\"\n for fd in fds:\n os.close(fd)\n\n\ndef _cleanup_tests():\n \"\"\"Cleanup multiprocessing resources when multiprocessing tests\n completed.\"\"\"\n\n from test import support\n\n # cleanup multiprocessing\n process._cleanup()\n\n # Stop the ForkServer process if it's running\n from multiprocessing import forkserver\n forkserver._forkserver._stop()\n\n # Stop the ResourceTracker process if it's running\n from multiprocessing import resource_tracker\n resource_tracker._resource_tracker._stop()\n\n # bpo-37421: Explicitly call _run_finalizers() to remove immediately\n # temporary directories created by multiprocessing.util.get_temp_dir().\n _run_finalizers()\n support.gc_collect()\n\n support.reap_children()\n", 483], "/usr/lib/python3.13/multiprocessing/process.py": ["#\n# Module providing the `Process` class which emulates `threading.Thread`\n#\n# multiprocessing/process.py\n#\n# Copyright (c) 2006-2008, R Oudkerk\n# Licensed to PSF under a Contributor Agreement.\n#\n\n__all__ = ['BaseProcess', 'current_process', 'active_children',\n 'parent_process']\n\n#\n# Imports\n#\n\nimport os\nimport sys\nimport signal\nimport itertools\nimport threading\nfrom _weakrefset import WeakSet\n\n#\n#\n#\n\ntry:\n ORIGINAL_DIR = os.path.abspath(os.getcwd())\nexcept OSError:\n ORIGINAL_DIR = None\n\n#\n# Public functions\n#\n\ndef current_process():\n '''\n Return process object representing the current process\n '''\n return _current_process\n\ndef active_children():\n '''\n Return list of process objects corresponding to live child processes\n '''\n _cleanup()\n return list(_children)\n\n\ndef parent_process():\n '''\n Return process object representing the parent process\n '''\n return _parent_process\n\n#\n#\n#\n\ndef _cleanup():\n # check for processes which have finished\n for p in list(_children):\n if (child_popen := p._popen) and child_popen.poll() is not None:\n _children.discard(p)\n\n#\n# The `Process` class\n#\n\nclass BaseProcess(object):\n '''\n Process objects represent activity that is run in a separate process\n\n The class is analogous to `threading.Thread`\n '''\n def _Popen(self):\n raise NotImplementedError\n\n def __init__(self, group=None, target=None, name=None, args=(), kwargs={},\n *, daemon=None):\n assert group is None, 'group argument must be None for now'\n count = next(_process_counter)\n self._identity = _current_process._identity + (count,)\n self._config = _current_process._config.copy()\n self._parent_pid = os.getpid()\n self._parent_name = _current_process.name\n self._popen = None\n self._closed = False\n self._target = target\n self._args = tuple(args)\n self._kwargs = dict(kwargs)\n self._name = name or type(self).__name__ + '-' + \\\n ':'.join(str(i) for i in self._identity)\n if daemon is not None:\n self.daemon = daemon\n _dangling.add(self)\n\n def _check_closed(self):\n if self._closed:\n raise ValueError(\"process object is closed\")\n\n def run(self):\n '''\n Method to be run in sub-process; can be overridden in sub-class\n '''\n if self._target:\n self._target(*self._args, **self._kwargs)\n\n def start(self):\n '''\n Start child process\n '''\n self._check_closed()\n assert self._popen is None, 'cannot start a process twice'\n assert self._parent_pid == os.getpid(), \\\n 'can only start a process object created by current process'\n assert not _current_process._config.get('daemon'), \\\n 'daemonic processes are not allowed to have children'\n _cleanup()\n self._popen = self._Popen(self)\n self._sentinel = self._popen.sentinel\n # Avoid a refcycle if the target function holds an indirect\n # reference to the process object (see bpo-30775)\n del self._target, self._args, self._kwargs\n _children.add(self)\n\n def terminate(self):\n '''\n Terminate process; sends SIGTERM signal or uses TerminateProcess()\n '''\n self._check_closed()\n self._popen.terminate()\n\n def kill(self):\n '''\n Terminate process; sends SIGKILL signal or uses TerminateProcess()\n '''\n self._check_closed()\n self._popen.kill()\n\n def join(self, timeout=None):\n '''\n Wait until child process terminates\n '''\n self._check_closed()\n assert self._parent_pid == os.getpid(), 'can only join a child process'\n assert self._popen is not None, 'can only join a started process'\n res = self._popen.wait(timeout)\n if res is not None:\n _children.discard(self)\n\n def is_alive(self):\n '''\n Return whether process is alive\n '''\n self._check_closed()\n if self is _current_process:\n return True\n assert self._parent_pid == os.getpid(), 'can only test a child process'\n\n if self._popen is None:\n return False\n\n returncode = self._popen.poll()\n if returncode is None:\n return True\n else:\n _children.discard(self)\n return False\n\n def close(self):\n '''\n Close the Process object.\n\n This method releases resources held by the Process object. It is\n an error to call this method if the child process is still running.\n '''\n if self._popen is not None:\n if self._popen.poll() is None:\n raise ValueError(\"Cannot close a process while it is still running. \"\n \"You should first call join() or terminate().\")\n self._popen.close()\n self._popen = None\n del self._sentinel\n _children.discard(self)\n self._closed = True\n\n @property\n def name(self):\n return self._name\n\n @name.setter\n def name(self, name):\n assert isinstance(name, str), 'name must be a string'\n self._name = name\n\n @property\n def daemon(self):\n '''\n Return whether process is a daemon\n '''\n return self._config.get('daemon', False)\n\n @daemon.setter\n def daemon(self, daemonic):\n '''\n Set whether process is a daemon\n '''\n assert self._popen is None, 'process has already started'\n self._config['daemon'] = daemonic\n\n @property\n def authkey(self):\n return self._config['authkey']\n\n @authkey.setter\n def authkey(self, authkey):\n '''\n Set authorization key of process\n '''\n self._config['authkey'] = AuthenticationString(authkey)\n\n @property\n def exitcode(self):\n '''\n Return exit code of process or `None` if it has yet to stop\n '''\n self._check_closed()\n if self._popen is None:\n return self._popen\n return self._popen.poll()\n\n @property\n def ident(self):\n '''\n Return identifier (PID) of process or `None` if it has yet to start\n '''\n self._check_closed()\n if self is _current_process:\n return os.getpid()\n else:\n return self._popen and self._popen.pid\n\n pid = ident\n\n @property\n def sentinel(self):\n '''\n Return a file descriptor (Unix) or handle (Windows) suitable for\n waiting for process termination.\n '''\n self._check_closed()\n try:\n return self._sentinel\n except AttributeError:\n raise ValueError(\"process not started\") from None\n\n def __repr__(self):\n exitcode = None\n if self is _current_process:\n status = 'started'\n elif self._closed:\n status = 'closed'\n elif self._parent_pid != os.getpid():\n status = 'unknown'\n elif self._popen is None:\n status = 'initial'\n else:\n exitcode = self._popen.poll()\n if exitcode is not None:\n status = 'stopped'\n else:\n status = 'started'\n\n info = [type(self).__name__, 'name=%r' % self._name]\n if self._popen is not None:\n info.append('pid=%s' % self._popen.pid)\n info.append('parent=%s' % self._parent_pid)\n info.append(status)\n if exitcode is not None:\n exitcode = _exitcode_to_name.get(exitcode, exitcode)\n info.append('exitcode=%s' % exitcode)\n if self.daemon:\n info.append('daemon')\n return '<%s>' % ' '.join(info)\n\n ##\n\n def _bootstrap(self, parent_sentinel=None):\n from . import util, context\n global _current_process, _parent_process, _process_counter, _children\n\n try:\n if self._start_method is not None:\n context._force_start_method(self._start_method)\n _process_counter = itertools.count(1)\n _children = set()\n util._close_stdin()\n old_process = _current_process\n _current_process = self\n _parent_process = _ParentProcess(\n self._parent_name, self._parent_pid, parent_sentinel)\n if threading._HAVE_THREAD_NATIVE_ID:\n threading.main_thread()._set_native_id()\n try:\n self._after_fork()\n finally:\n # delay finalization of the old process object until after\n # _run_after_forkers() is executed\n del old_process\n util.info('child process calling self.run()')\n self.run()\n exitcode = 0\n except SystemExit as e:\n if e.code is None:\n exitcode = 0\n elif isinstance(e.code, int):\n exitcode = e.code\n else:\n sys.stderr.write(str(e.code) + '\\n')\n exitcode = 1\n except:\n exitcode = 1\n import traceback\n sys.stderr.write('Process %s:\\n' % self.name)\n traceback.print_exc()\n finally:\n threading._shutdown()\n util.info('process exiting with exitcode %d' % exitcode)\n util._flush_std_streams()\n\n return exitcode\n\n @staticmethod\n def _after_fork():\n from . import util\n util._finalizer_registry.clear()\n util._run_after_forkers()\n\n\n#\n# We subclass bytes to avoid accidental transmission of auth keys over network\n#\n\nclass AuthenticationString(bytes):\n def __reduce__(self):\n from .context import get_spawning_popen\n if get_spawning_popen() is None:\n raise TypeError(\n 'Pickling an AuthenticationString object is '\n 'disallowed for security reasons'\n )\n return AuthenticationString, (bytes(self),)\n\n\n#\n# Create object representing the parent process\n#\n\nclass _ParentProcess(BaseProcess):\n\n def __init__(self, name, pid, sentinel):\n self._identity = ()\n self._name = name\n self._pid = pid\n self._parent_pid = None\n self._popen = None\n self._closed = False\n self._sentinel = sentinel\n self._config = {}\n\n def is_alive(self):\n from multiprocessing.connection import wait\n return not wait([self._sentinel], timeout=0)\n\n @property\n def ident(self):\n return self._pid\n\n def join(self, timeout=None):\n '''\n Wait until parent process terminates\n '''\n from multiprocessing.connection import wait\n wait([self._sentinel], timeout=timeout)\n\n pid = ident\n\n#\n# Create object representing the main process\n#\n\nclass _MainProcess(BaseProcess):\n\n def __init__(self):\n self._identity = ()\n self._name = 'MainProcess'\n self._parent_pid = None\n self._popen = None\n self._closed = False\n self._config = {'authkey': AuthenticationString(os.urandom(32)),\n 'semprefix': '/mp'}\n # Note that some versions of FreeBSD only allow named\n # semaphores to have names of up to 14 characters. Therefore\n # we choose a short prefix.\n #\n # On MacOSX in a sandbox it may be necessary to use a\n # different prefix -- see #19478.\n #\n # Everything in self._config will be inherited by descendant\n # processes.\n\n def close(self):\n pass\n\n\n_parent_process = None\n_current_process = _MainProcess()\n_process_counter = itertools.count(1)\n_children = set()\ndel _MainProcess\n\n#\n# Give names to some return codes\n#\n\n_exitcode_to_name = {}\n\nfor name, signum in list(signal.__dict__.items()):\n if name[:3]=='SIG' and '_' not in name:\n _exitcode_to_name[-signum] = f'-{name}'\ndel name, signum\n\n# For debug and leak testing\n_dangling = WeakSet()\n", 436], "/usr/lib/python3.13/multiprocessing/connection.py": ["#\n# A higher level module for using sockets (or Windows named pipes)\n#\n# multiprocessing/connection.py\n#\n# Copyright (c) 2006-2008, R Oudkerk\n# Licensed to PSF under a Contributor Agreement.\n#\n\n__all__ = [ 'Client', 'Listener', 'Pipe', 'wait' ]\n\nimport errno\nimport io\nimport os\nimport sys\nimport socket\nimport struct\nimport time\nimport tempfile\nimport itertools\n\n\nfrom . import util\n\nfrom . import AuthenticationError, BufferTooShort\nfrom .context import reduction\n_ForkingPickler = reduction.ForkingPickler\n\ntry:\n import _multiprocessing\n import _winapi\n from _winapi import WAIT_OBJECT_0, WAIT_ABANDONED_0, WAIT_TIMEOUT, INFINITE\nexcept ImportError:\n if sys.platform == 'win32':\n raise\n _winapi = None\n\n#\n#\n#\n\nBUFSIZE = 8192\n# A very generous timeout when it comes to local connections...\nCONNECTION_TIMEOUT = 20.\n\n_mmap_counter = itertools.count()\n\ndefault_family = 'AF_INET'\nfamilies = ['AF_INET']\n\nif hasattr(socket, 'AF_UNIX'):\n default_family = 'AF_UNIX'\n families += ['AF_UNIX']\n\nif sys.platform == 'win32':\n default_family = 'AF_PIPE'\n families += ['AF_PIPE']\n\n\ndef _init_timeout(timeout=CONNECTION_TIMEOUT):\n return time.monotonic() + timeout\n\ndef _check_timeout(t):\n return time.monotonic() > t\n\n#\n#\n#\n\ndef arbitrary_address(family):\n '''\n Return an arbitrary free address for the given family\n '''\n if family == 'AF_INET':\n return ('localhost', 0)\n elif family == 'AF_UNIX':\n return tempfile.mktemp(prefix='listener-', dir=util.get_temp_dir())\n elif family == 'AF_PIPE':\n return tempfile.mktemp(prefix=r'\\\\.\\pipe\\pyc-%d-%d-' %\n (os.getpid(), next(_mmap_counter)), dir=\"\")\n else:\n raise ValueError('unrecognized family')\n\ndef _validate_family(family):\n '''\n Checks if the family is valid for the current environment.\n '''\n if sys.platform != 'win32' and family == 'AF_PIPE':\n raise ValueError('Family %s is not recognized.' % family)\n\n if sys.platform == 'win32' and family == 'AF_UNIX':\n # double check\n if not hasattr(socket, family):\n raise ValueError('Family %s is not recognized.' % family)\n\ndef address_type(address):\n '''\n Return the types of the address\n\n This can be 'AF_INET', 'AF_UNIX', or 'AF_PIPE'\n '''\n if type(address) == tuple:\n return 'AF_INET'\n elif type(address) is str and address.startswith('\\\\\\\\'):\n return 'AF_PIPE'\n elif type(address) is str or util.is_abstract_socket_namespace(address):\n return 'AF_UNIX'\n else:\n raise ValueError('address type of %r unrecognized' % address)\n\n#\n# Connection classes\n#\n\nclass _ConnectionBase:\n _handle = None\n\n def __init__(self, handle, readable=True, writable=True):\n handle = handle.__index__()\n if handle < 0:\n raise ValueError(\"invalid handle\")\n if not readable and not writable:\n raise ValueError(\n \"at least one of `readable` and `writable` must be True\")\n self._handle = handle\n self._readable = readable\n self._writable = writable\n\n # XXX should we use util.Finalize instead of a __del__?\n\n def __del__(self):\n if self._handle is not None:\n self._close()\n\n def _check_closed(self):\n if self._handle is None:\n raise OSError(\"handle is closed\")\n\n def _check_readable(self):\n if not self._readable:\n raise OSError(\"connection is write-only\")\n\n def _check_writable(self):\n if not self._writable:\n raise OSError(\"connection is read-only\")\n\n def _bad_message_length(self):\n if self._writable:\n self._readable = False\n else:\n self.close()\n raise OSError(\"bad message length\")\n\n @property\n def closed(self):\n \"\"\"True if the connection is closed\"\"\"\n return self._handle is None\n\n @property\n def readable(self):\n \"\"\"True if the connection is readable\"\"\"\n return self._readable\n\n @property\n def writable(self):\n \"\"\"True if the connection is writable\"\"\"\n return self._writable\n\n def fileno(self):\n \"\"\"File descriptor or handle of the connection\"\"\"\n self._check_closed()\n return self._handle\n\n def close(self):\n \"\"\"Close the connection\"\"\"\n if self._handle is not None:\n try:\n self._close()\n finally:\n self._handle = None\n\n def send_bytes(self, buf, offset=0, size=None):\n \"\"\"Send the bytes data from a bytes-like object\"\"\"\n self._check_closed()\n self._check_writable()\n m = memoryview(buf)\n if m.itemsize > 1:\n m = m.cast('B')\n n = m.nbytes\n if offset < 0:\n raise ValueError(\"offset is negative\")\n if n < offset:\n raise ValueError(\"buffer length < offset\")\n if size is None:\n size = n - offset\n elif size < 0:\n raise ValueError(\"size is negative\")\n elif offset + size > n:\n raise ValueError(\"buffer length < offset + size\")\n self._send_bytes(m[offset:offset + size])\n\n def send(self, obj):\n \"\"\"Send a (picklable) object\"\"\"\n self._check_closed()\n self._check_writable()\n self._send_bytes(_ForkingPickler.dumps(obj))\n\n def recv_bytes(self, maxlength=None):\n \"\"\"\n Receive bytes data as a bytes object.\n \"\"\"\n self._check_closed()\n self._check_readable()\n if maxlength is not None and maxlength < 0:\n raise ValueError(\"negative maxlength\")\n buf = self._recv_bytes(maxlength)\n if buf is None:\n self._bad_message_length()\n return buf.getvalue()\n\n def recv_bytes_into(self, buf, offset=0):\n \"\"\"\n Receive bytes data into a writeable bytes-like object.\n Return the number of bytes read.\n \"\"\"\n self._check_closed()\n self._check_readable()\n with memoryview(buf) as m:\n # Get bytesize of arbitrary buffer\n itemsize = m.itemsize\n bytesize = itemsize * len(m)\n if offset < 0:\n raise ValueError(\"negative offset\")\n elif offset > bytesize:\n raise ValueError(\"offset too large\")\n result = self._recv_bytes()\n size = result.tell()\n if bytesize < offset + size:\n raise BufferTooShort(result.getvalue())\n # Message can fit in dest\n result.seek(0)\n result.readinto(m[offset // itemsize :\n (offset + size) // itemsize])\n return size\n\n def recv(self):\n \"\"\"Receive a (picklable) object\"\"\"\n self._check_closed()\n self._check_readable()\n buf = self._recv_bytes()\n return _ForkingPickler.loads(buf.getbuffer())\n\n def poll(self, timeout=0.0):\n \"\"\"Whether there is any input available to be read\"\"\"\n self._check_closed()\n self._check_readable()\n return self._poll(timeout)\n\n def __enter__(self):\n return self\n\n def __exit__(self, exc_type, exc_value, exc_tb):\n self.close()\n\n\nif _winapi:\n\n class PipeConnection(_ConnectionBase):\n \"\"\"\n Connection class based on a Windows named pipe.\n Overlapped I/O is used, so the handles must have been created\n with FILE_FLAG_OVERLAPPED.\n \"\"\"\n _got_empty_message = False\n _send_ov = None\n\n def _close(self, _CloseHandle=_winapi.CloseHandle):\n ov = self._send_ov\n if ov is not None:\n # Interrupt WaitForMultipleObjects() in _send_bytes()\n ov.cancel()\n _CloseHandle(self._handle)\n\n def _send_bytes(self, buf):\n if self._send_ov is not None:\n # A connection should only be used by a single thread\n raise ValueError(\"concurrent send_bytes() calls \"\n \"are not supported\")\n ov, err = _winapi.WriteFile(self._handle, buf, overlapped=True)\n self._send_ov = ov\n try:\n if err == _winapi.ERROR_IO_PENDING:\n waitres = _winapi.WaitForMultipleObjects(\n [ov.event], False, INFINITE)\n assert waitres == WAIT_OBJECT_0\n except:\n ov.cancel()\n raise\n finally:\n self._send_ov = None\n nwritten, err = ov.GetOverlappedResult(True)\n if err == _winapi.ERROR_OPERATION_ABORTED:\n # close() was called by another thread while\n # WaitForMultipleObjects() was waiting for the overlapped\n # operation.\n raise OSError(errno.EPIPE, \"handle is closed\")\n assert err == 0\n assert nwritten == len(buf)\n\n def _recv_bytes(self, maxsize=None):\n if self._got_empty_message:\n self._got_empty_message = False\n return io.BytesIO()\n else:\n bsize = 128 if maxsize is None else min(maxsize, 128)\n try:\n ov, err = _winapi.ReadFile(self._handle, bsize,\n overlapped=True)\n try:\n if err == _winapi.ERROR_IO_PENDING:\n waitres = _winapi.WaitForMultipleObjects(\n [ov.event], False, INFINITE)\n assert waitres == WAIT_OBJECT_0\n except:\n ov.cancel()\n raise\n finally:\n nread, err = ov.GetOverlappedResult(True)\n if err == 0:\n f = io.BytesIO()\n f.write(ov.getbuffer())\n return f\n elif err == _winapi.ERROR_MORE_DATA:\n return self._get_more_data(ov, maxsize)\n except OSError as e:\n if e.winerror == _winapi.ERROR_BROKEN_PIPE:\n raise EOFError\n else:\n raise\n raise RuntimeError(\"shouldn't get here; expected KeyboardInterrupt\")\n\n def _poll(self, timeout):\n if (self._got_empty_message or\n _winapi.PeekNamedPipe(self._handle)[0] != 0):\n return True\n return bool(wait([self], timeout))\n\n def _get_more_data(self, ov, maxsize):\n buf = ov.getbuffer()\n f = io.BytesIO()\n f.write(buf)\n left = _winapi.PeekNamedPipe(self._handle)[1]\n assert left > 0\n if maxsize is not None and len(buf) + left > maxsize:\n self._bad_message_length()\n ov, err = _winapi.ReadFile(self._handle, left, overlapped=True)\n rbytes, err = ov.GetOverlappedResult(True)\n assert err == 0\n assert rbytes == left\n f.write(ov.getbuffer())\n return f\n\n\nclass Connection(_ConnectionBase):\n \"\"\"\n Connection class based on an arbitrary file descriptor (Unix only), or\n a socket handle (Windows).\n \"\"\"\n\n if _winapi:\n def _close(self, _close=_multiprocessing.closesocket):\n _close(self._handle)\n _write = _multiprocessing.send\n _read = _multiprocessing.recv\n else:\n def _close(self, _close=os.close):\n _close(self._handle)\n _write = os.write\n _read = os.read\n\n def _send(self, buf, write=_write):\n remaining = len(buf)\n while True:\n n = write(self._handle, buf)\n remaining -= n\n if remaining == 0:\n break\n buf = buf[n:]\n\n def _recv(self, size, read=_read):\n buf = io.BytesIO()\n handle = self._handle\n remaining = size\n while remaining > 0:\n chunk = read(handle, remaining)\n n = len(chunk)\n if n == 0:\n if remaining == size:\n raise EOFError\n else:\n raise OSError(\"got end of file during message\")\n buf.write(chunk)\n remaining -= n\n return buf\n\n def _send_bytes(self, buf):\n n = len(buf)\n if n > 0x7fffffff:\n pre_header = struct.pack(\"!i\", -1)\n header = struct.pack(\"!Q\", n)\n self._send(pre_header)\n self._send(header)\n self._send(buf)\n else:\n # For wire compatibility with 3.7 and lower\n header = struct.pack(\"!i\", n)\n if n > 16384:\n # The payload is large so Nagle's algorithm won't be triggered\n # and we'd better avoid the cost of concatenation.\n self._send(header)\n self._send(buf)\n else:\n # Issue #20540: concatenate before sending, to avoid delays due\n # to Nagle's algorithm on a TCP socket.\n # Also note we want to avoid sending a 0-length buffer separately,\n # to avoid \"broken pipe\" errors if the other end closed the pipe.\n self._send(header + buf)\n\n def _recv_bytes(self, maxsize=None):\n buf = self._recv(4)\n size, = struct.unpack(\"!i\", buf.getvalue())\n if size == -1:\n buf = self._recv(8)\n size, = struct.unpack(\"!Q\", buf.getvalue())\n if maxsize is not None and size > maxsize:\n return None\n return self._recv(size)\n\n def _poll(self, timeout):\n r = wait([self], timeout)\n return bool(r)\n\n\n#\n# Public functions\n#\n\nclass Listener(object):\n '''\n Returns a listener object.\n\n This is a wrapper for a bound socket which is 'listening' for\n connections, or for a Windows named pipe.\n '''\n def __init__(self, address=None, family=None, backlog=1, authkey=None):\n family = family or (address and address_type(address)) \\\n or default_family\n address = address or arbitrary_address(family)\n\n _validate_family(family)\n if family == 'AF_PIPE':\n self._listener = PipeListener(address, backlog)\n else:\n self._listener = SocketListener(address, family, backlog)\n\n if authkey is not None and not isinstance(authkey, bytes):\n raise TypeError('authkey should be a byte string')\n\n self._authkey = authkey\n\n def accept(self):\n '''\n Accept a connection on the bound socket or named pipe of `self`.\n\n Returns a `Connection` object.\n '''\n if self._listener is None:\n raise OSError('listener is closed')\n\n c = self._listener.accept()\n if self._authkey is not None:\n deliver_challenge(c, self._authkey)\n answer_challenge(c, self._authkey)\n return c\n\n def close(self):\n '''\n Close the bound socket or named pipe of `self`.\n '''\n listener = self._listener\n if listener is not None:\n self._listener = None\n listener.close()\n\n @property\n def address(self):\n return self._listener._address\n\n @property\n def last_accepted(self):\n return self._listener._last_accepted\n\n def __enter__(self):\n return self\n\n def __exit__(self, exc_type, exc_value, exc_tb):\n self.close()\n\n\ndef Client(address, family=None, authkey=None):\n '''\n Returns a connection to the address of a `Listener`\n '''\n family = family or address_type(address)\n _validate_family(family)\n if family == 'AF_PIPE':\n c = PipeClient(address)\n else:\n c = SocketClient(address)\n\n if authkey is not None and not isinstance(authkey, bytes):\n raise TypeError('authkey should be a byte string')\n\n if authkey is not None:\n answer_challenge(c, authkey)\n deliver_challenge(c, authkey)\n\n return c\n\n\nif sys.platform != 'win32':\n\n def Pipe(duplex=True):\n '''\n Returns pair of connection objects at either end of a pipe\n '''\n if duplex:\n s1, s2 = socket.socketpair()\n s1.setblocking(True)\n s2.setblocking(True)\n c1 = Connection(s1.detach())\n c2 = Connection(s2.detach())\n else:\n fd1, fd2 = os.pipe()\n c1 = Connection(fd1, writable=False)\n c2 = Connection(fd2, readable=False)\n\n return c1, c2\n\nelse:\n\n def Pipe(duplex=True):\n '''\n Returns pair of connection objects at either end of a pipe\n '''\n address = arbitrary_address('AF_PIPE')\n if duplex:\n openmode = _winapi.PIPE_ACCESS_DUPLEX\n access = _winapi.GENERIC_READ | _winapi.GENERIC_WRITE\n obsize, ibsize = BUFSIZE, BUFSIZE\n else:\n openmode = _winapi.PIPE_ACCESS_INBOUND\n access = _winapi.GENERIC_WRITE\n obsize, ibsize = 0, BUFSIZE\n\n h1 = _winapi.CreateNamedPipe(\n address, openmode | _winapi.FILE_FLAG_OVERLAPPED |\n _winapi.FILE_FLAG_FIRST_PIPE_INSTANCE,\n _winapi.PIPE_TYPE_MESSAGE | _winapi.PIPE_READMODE_MESSAGE |\n _winapi.PIPE_WAIT,\n 1, obsize, ibsize, _winapi.NMPWAIT_WAIT_FOREVER,\n # default security descriptor: the handle cannot be inherited\n _winapi.NULL\n )\n h2 = _winapi.CreateFile(\n address, access, 0, _winapi.NULL, _winapi.OPEN_EXISTING,\n _winapi.FILE_FLAG_OVERLAPPED, _winapi.NULL\n )\n _winapi.SetNamedPipeHandleState(\n h2, _winapi.PIPE_READMODE_MESSAGE, None, None\n )\n\n overlapped = _winapi.ConnectNamedPipe(h1, overlapped=True)\n _, err = overlapped.GetOverlappedResult(True)\n assert err == 0\n\n c1 = PipeConnection(h1, writable=duplex)\n c2 = PipeConnection(h2, readable=duplex)\n\n return c1, c2\n\n#\n# Definitions for connections based on sockets\n#\n\nclass SocketListener(object):\n '''\n Representation of a socket which is bound to an address and listening\n '''\n def __init__(self, address, family, backlog=1):\n self._socket = socket.socket(getattr(socket, family))\n try:\n # SO_REUSEADDR has different semantics on Windows (issue #2550).\n if os.name == 'posix':\n self._socket.setsockopt(socket.SOL_SOCKET,\n socket.SO_REUSEADDR, 1)\n self._socket.setblocking(True)\n self._socket.bind(address)\n self._socket.listen(backlog)\n self._address = self._socket.getsockname()\n except OSError:\n self._socket.close()\n raise\n self._family = family\n self._last_accepted = None\n\n if family == 'AF_UNIX' and not util.is_abstract_socket_namespace(address):\n # Linux abstract socket namespaces do not need to be explicitly unlinked\n self._unlink = util.Finalize(\n self, os.unlink, args=(address,), exitpriority=0\n )\n else:\n self._unlink = None\n\n def accept(self):\n s, self._last_accepted = self._socket.accept()\n s.setblocking(True)\n return Connection(s.detach())\n\n def close(self):\n try:\n self._socket.close()\n finally:\n unlink = self._unlink\n if unlink is not None:\n self._unlink = None\n unlink()\n\n\ndef SocketClient(address):\n '''\n Return a connection object connected to the socket given by `address`\n '''\n family = address_type(address)\n with socket.socket( getattr(socket, family) ) as s:\n s.setblocking(True)\n s.connect(address)\n return Connection(s.detach())\n\n#\n# Definitions for connections based on named pipes\n#\n\nif sys.platform == 'win32':\n\n class PipeListener(object):\n '''\n Representation of a named pipe\n '''\n def __init__(self, address, backlog=None):\n self._address = address\n self._handle_queue = [self._new_handle(first=True)]\n\n self._last_accepted = None\n util.sub_debug('listener created with address=%r', self._address)\n self.close = util.Finalize(\n self, PipeListener._finalize_pipe_listener,\n args=(self._handle_queue, self._address), exitpriority=0\n )\n\n def _new_handle(self, first=False):\n flags = _winapi.PIPE_ACCESS_DUPLEX | _winapi.FILE_FLAG_OVERLAPPED\n if first:\n flags |= _winapi.FILE_FLAG_FIRST_PIPE_INSTANCE\n return _winapi.CreateNamedPipe(\n self._address, flags,\n _winapi.PIPE_TYPE_MESSAGE | _winapi.PIPE_READMODE_MESSAGE |\n _winapi.PIPE_WAIT,\n _winapi.PIPE_UNLIMITED_INSTANCES, BUFSIZE, BUFSIZE,\n _winapi.NMPWAIT_WAIT_FOREVER, _winapi.NULL\n )\n\n def accept(self):\n self._handle_queue.append(self._new_handle())\n handle = self._handle_queue.pop(0)\n try:\n ov = _winapi.ConnectNamedPipe(handle, overlapped=True)\n except OSError as e:\n if e.winerror != _winapi.ERROR_NO_DATA:\n raise\n # ERROR_NO_DATA can occur if a client has already connected,\n # written data and then disconnected -- see Issue 14725.\n else:\n try:\n res = _winapi.WaitForMultipleObjects(\n [ov.event], False, INFINITE)\n except:\n ov.cancel()\n _winapi.CloseHandle(handle)\n raise\n finally:\n _, err = ov.GetOverlappedResult(True)\n assert err == 0\n return PipeConnection(handle)\n\n @staticmethod\n def _finalize_pipe_listener(queue, address):\n util.sub_debug('closing listener with address=%r', address)\n for handle in queue:\n _winapi.CloseHandle(handle)\n\n def PipeClient(address):\n '''\n Return a connection object connected to the pipe given by `address`\n '''\n t = _init_timeout()\n while 1:\n try:\n _winapi.WaitNamedPipe(address, 1000)\n h = _winapi.CreateFile(\n address, _winapi.GENERIC_READ | _winapi.GENERIC_WRITE,\n 0, _winapi.NULL, _winapi.OPEN_EXISTING,\n _winapi.FILE_FLAG_OVERLAPPED, _winapi.NULL\n )\n except OSError as e:\n if e.winerror not in (_winapi.ERROR_SEM_TIMEOUT,\n _winapi.ERROR_PIPE_BUSY) or _check_timeout(t):\n raise\n else:\n break\n else:\n raise\n\n _winapi.SetNamedPipeHandleState(\n h, _winapi.PIPE_READMODE_MESSAGE, None, None\n )\n return PipeConnection(h)\n\n#\n# Authentication stuff\n#\n\nMESSAGE_LENGTH = 40 # MUST be > 20\n\n_CHALLENGE = b'#CHALLENGE#'\n_WELCOME = b'#WELCOME#'\n_FAILURE = b'#FAILURE#'\n\n# multiprocessing.connection Authentication Handshake Protocol Description\n# (as documented for reference after reading the existing code)\n# =============================================================================\n#\n# On Windows: native pipes with \"overlapped IO\" are used to send the bytes,\n# instead of the length prefix SIZE scheme described below. (ie: the OS deals\n# with message sizes for us)\n#\n# Protocol error behaviors:\n#\n# On POSIX, any failure to receive the length prefix into SIZE, for SIZE greater\n# than the requested maxsize to receive, or receiving fewer than SIZE bytes\n# results in the connection being closed and auth to fail.\n#\n# On Windows, receiving too few bytes is never a low level _recv_bytes read\n# error, receiving too many will trigger an error only if receive maxsize\n# value was larger than 128 OR the if the data arrived in smaller pieces.\n#\n# Serving side Client side\n# ------------------------------ ---------------------------------------\n# 0. Open a connection on the pipe.\n# 1. Accept connection.\n# 2. Random 20+ bytes -> MESSAGE\n# Modern servers always send\n# more than 20 bytes and include\n# a {digest} prefix on it with\n# their preferred HMAC digest.\n# Legacy ones send ==20 bytes.\n# 3. send 4 byte length (net order)\n# prefix followed by:\n# b'#CHALLENGE#' + MESSAGE\n# 4. Receive 4 bytes, parse as network byte\n# order integer. If it is -1, receive an\n# additional 8 bytes, parse that as network\n# byte order. The result is the length of\n# the data that follows -> SIZE.\n# 5. Receive min(SIZE, 256) bytes -> M1\n# 6. Assert that M1 starts with:\n# b'#CHALLENGE#'\n# 7. Strip that prefix from M1 into -> M2\n# 7.1. Parse M2: if it is exactly 20 bytes in\n# length this indicates a legacy server\n# supporting only HMAC-MD5. Otherwise the\n# 7.2. preferred digest is looked up from an\n# expected \"{digest}\" prefix on M2. No prefix\n# or unsupported digest? <- AuthenticationError\n# 7.3. Put divined algorithm name in -> D_NAME\n# 8. Compute HMAC-D_NAME of AUTHKEY, M2 -> C_DIGEST\n# 9. Send 4 byte length prefix (net order)\n# followed by C_DIGEST bytes.\n# 10. Receive 4 or 4+8 byte length\n# prefix (#4 dance) -> SIZE.\n# 11. Receive min(SIZE, 256) -> C_D.\n# 11.1. Parse C_D: legacy servers\n# accept it as is, \"md5\" -> D_NAME\n# 11.2. modern servers check the length\n# of C_D, IF it is 16 bytes?\n# 11.2.1. \"md5\" -> D_NAME\n# and skip to step 12.\n# 11.3. longer? expect and parse a \"{digest}\"\n# prefix into -> D_NAME.\n# Strip the prefix and store remaining\n# bytes in -> C_D.\n# 11.4. Don't like D_NAME? <- AuthenticationError\n# 12. Compute HMAC-D_NAME of AUTHKEY,\n# MESSAGE into -> M_DIGEST.\n# 13. Compare M_DIGEST == C_D:\n# 14a: Match? Send length prefix &\n# b'#WELCOME#'\n# <- RETURN\n# 14b: Mismatch? Send len prefix &\n# b'#FAILURE#'\n# <- CLOSE & AuthenticationError\n# 15. Receive 4 or 4+8 byte length prefix (net\n# order) again as in #4 into -> SIZE.\n# 16. Receive min(SIZE, 256) bytes -> M3.\n# 17. Compare M3 == b'#WELCOME#':\n# 17a. Match? <- RETURN\n# 17b. Mismatch? <- CLOSE & AuthenticationError\n#\n# If this RETURNed, the connection remains open: it has been authenticated.\n#\n# Length prefixes are used consistently. Even on the legacy protocol, this\n# was good fortune and allowed us to evolve the protocol by using the length\n# of the opening challenge or length of the returned digest as a signal as\n# to which protocol the other end supports.\n\n_ALLOWED_DIGESTS = frozenset(\n {b'md5', b'sha256', b'sha384', b'sha3_256', b'sha3_384'})\n_MAX_DIGEST_LEN = max(len(_) for _ in _ALLOWED_DIGESTS)\n\n# Old hmac-md5 only server versions from Python <=3.11 sent a message of this\n# length. It happens to not match the length of any supported digest so we can\n# use a message of this length to indicate that we should work in backwards\n# compatible md5-only mode without a {digest_name} prefix on our response.\n_MD5ONLY_MESSAGE_LENGTH = 20\n_MD5_DIGEST_LEN = 16\n_LEGACY_LENGTHS = (_MD5ONLY_MESSAGE_LENGTH, _MD5_DIGEST_LEN)\n\n\ndef _get_digest_name_and_payload(message: bytes) -> (str, bytes):\n \"\"\"Returns a digest name and the payload for a response hash.\n\n If a legacy protocol is detected based on the message length\n or contents the digest name returned will be empty to indicate\n legacy mode where MD5 and no digest prefix should be sent.\n \"\"\"\n # modern message format: b\"{digest}payload\" longer than 20 bytes\n # legacy message format: 16 or 20 byte b\"payload\"\n if len(message) in _LEGACY_LENGTHS:\n # Either this was a legacy server challenge, or we're processing\n # a reply from a legacy client that sent an unprefixed 16-byte\n # HMAC-MD5 response. All messages using the modern protocol will\n # be longer than either of these lengths.\n return '', message\n if (message.startswith(b'{') and\n (curly := message.find(b'}', 1, _MAX_DIGEST_LEN+2)) > 0):\n digest = message[1:curly]\n if digest in _ALLOWED_DIGESTS:\n payload = message[curly+1:]\n return digest.decode('ascii'), payload\n raise AuthenticationError(\n 'unsupported message length, missing digest prefix, '\n f'or unsupported digest: {message=}')\n\n\ndef _create_response(authkey, message):\n \"\"\"Create a MAC based on authkey and message\n\n The MAC algorithm defaults to HMAC-MD5, unless MD5 is not available or\n the message has a '{digest_name}' prefix. For legacy HMAC-MD5, the response\n is the raw MAC, otherwise the response is prefixed with '{digest_name}',\n e.g. b'{sha256}abcdefg...'\n\n Note: The MAC protects the entire message including the digest_name prefix.\n \"\"\"\n import hmac\n digest_name = _get_digest_name_and_payload(message)[0]\n # The MAC protects the entire message: digest header and payload.\n if not digest_name:\n # Legacy server without a {digest} prefix on message.\n # Generate a legacy non-prefixed HMAC-MD5 reply.\n try:\n return hmac.new(authkey, message, 'md5').digest()\n except ValueError:\n # HMAC-MD5 is not available (FIPS mode?), fall back to\n # HMAC-SHA2-256 modern protocol. The legacy server probably\n # doesn't support it and will reject us anyways. :shrug:\n digest_name = 'sha256'\n # Modern protocol, indicate the digest used in the reply.\n response = hmac.new(authkey, message, digest_name).digest()\n return b'{%s}%s' % (digest_name.encode('ascii'), response)\n\n\ndef _verify_challenge(authkey, message, response):\n \"\"\"Verify MAC challenge\n\n If our message did not include a digest_name prefix, the client is allowed\n to select a stronger digest_name from _ALLOWED_DIGESTS.\n\n In case our message is prefixed, a client cannot downgrade to a weaker\n algorithm, because the MAC is calculated over the entire message\n including the '{digest_name}' prefix.\n \"\"\"\n import hmac\n response_digest, response_mac = _get_digest_name_and_payload(response)\n response_digest = response_digest or 'md5'\n try:\n expected = hmac.new(authkey, message, response_digest).digest()\n except ValueError:\n raise AuthenticationError(f'{response_digest=} unsupported')\n if len(expected) != len(response_mac):\n raise AuthenticationError(\n f'expected {response_digest!r} of length {len(expected)} '\n f'got {len(response_mac)}')\n if not hmac.compare_digest(expected, response_mac):\n raise AuthenticationError('digest received was wrong')\n\n\ndef deliver_challenge(connection, authkey: bytes, digest_name='sha256'):\n if not isinstance(authkey, bytes):\n raise ValueError(\n \"Authkey must be bytes, not {0!s}\".format(type(authkey)))\n assert MESSAGE_LENGTH > _MD5ONLY_MESSAGE_LENGTH, \"protocol constraint\"\n message = os.urandom(MESSAGE_LENGTH)\n message = b'{%s}%s' % (digest_name.encode('ascii'), message)\n # Even when sending a challenge to a legacy client that does not support\n # digest prefixes, they'll take the entire thing as a challenge and\n # respond to it with a raw HMAC-MD5.\n connection.send_bytes(_CHALLENGE + message)\n response = connection.recv_bytes(256) # reject large message\n try:\n _verify_challenge(authkey, message, response)\n except AuthenticationError:\n connection.send_bytes(_FAILURE)\n raise\n else:\n connection.send_bytes(_WELCOME)\n\n\ndef answer_challenge(connection, authkey: bytes):\n if not isinstance(authkey, bytes):\n raise ValueError(\n \"Authkey must be bytes, not {0!s}\".format(type(authkey)))\n message = connection.recv_bytes(256) # reject large message\n if not message.startswith(_CHALLENGE):\n raise AuthenticationError(\n f'Protocol error, expected challenge: {message=}')\n message = message[len(_CHALLENGE):]\n if len(message) < _MD5ONLY_MESSAGE_LENGTH:\n raise AuthenticationError('challenge too short: {len(message)} bytes')\n digest = _create_response(authkey, message)\n connection.send_bytes(digest)\n response = connection.recv_bytes(256) # reject large message\n if response != _WELCOME:\n raise AuthenticationError('digest sent was rejected')\n\n#\n# Support for using xmlrpclib for serialization\n#\n\nclass ConnectionWrapper(object):\n def __init__(self, conn, dumps, loads):\n self._conn = conn\n self._dumps = dumps\n self._loads = loads\n for attr in ('fileno', 'close', 'poll', 'recv_bytes', 'send_bytes'):\n obj = getattr(conn, attr)\n setattr(self, attr, obj)\n def send(self, obj):\n s = self._dumps(obj)\n self._conn.send_bytes(s)\n def recv(self):\n s = self._conn.recv_bytes()\n return self._loads(s)\n\ndef _xml_dumps(obj):\n return xmlrpclib.dumps((obj,), None, None, None, 1).encode('utf-8')\n\ndef _xml_loads(s):\n (obj,), method = xmlrpclib.loads(s.decode('utf-8'))\n return obj\n\nclass XmlListener(Listener):\n def accept(self):\n global xmlrpclib\n import xmlrpc.client as xmlrpclib\n obj = Listener.accept(self)\n return ConnectionWrapper(obj, _xml_dumps, _xml_loads)\n\ndef XmlClient(*args, **kwds):\n global xmlrpclib\n import xmlrpc.client as xmlrpclib\n return ConnectionWrapper(Client(*args, **kwds), _xml_dumps, _xml_loads)\n\n#\n# Wait\n#\n\nif sys.platform == 'win32':\n\n def _exhaustive_wait(handles, timeout):\n # Return ALL handles which are currently signalled. (Only\n # returning the first signalled might create starvation issues.)\n L = list(handles)\n ready = []\n # Windows limits WaitForMultipleObjects at 64 handles, and we use a\n # few for synchronisation, so we switch to batched waits at 60.\n if len(L) > 60:\n try:\n res = _winapi.BatchedWaitForMultipleObjects(L, False, timeout)\n except TimeoutError:\n return []\n ready.extend(L[i] for i in res)\n if res:\n L = [h for i, h in enumerate(L) if i > res[0] & i not in res]\n timeout = 0\n while L:\n short_L = L[:60] if len(L) > 60 else L\n res = _winapi.WaitForMultipleObjects(short_L, False, timeout)\n if res == WAIT_TIMEOUT:\n break\n elif WAIT_OBJECT_0 <= res < WAIT_OBJECT_0 + len(L):\n res -= WAIT_OBJECT_0\n elif WAIT_ABANDONED_0 <= res < WAIT_ABANDONED_0 + len(L):\n res -= WAIT_ABANDONED_0\n else:\n raise RuntimeError('Should not get here')\n ready.append(L[res])\n L = L[res+1:]\n timeout = 0\n return ready\n\n _ready_errors = {_winapi.ERROR_BROKEN_PIPE, _winapi.ERROR_NETNAME_DELETED}\n\n def wait(object_list, timeout=None):\n '''\n Wait till an object in object_list is ready/readable.\n\n Returns list of those objects in object_list which are ready/readable.\n '''\n if timeout is None:\n timeout = INFINITE\n elif timeout < 0:\n timeout = 0\n else:\n timeout = int(timeout * 1000 + 0.5)\n\n object_list = list(object_list)\n waithandle_to_obj = {}\n ov_list = []\n ready_objects = set()\n ready_handles = set()\n\n try:\n for o in object_list:\n try:\n fileno = getattr(o, 'fileno')\n except AttributeError:\n waithandle_to_obj[o.__index__()] = o\n else:\n # start an overlapped read of length zero\n try:\n ov, err = _winapi.ReadFile(fileno(), 0, True)\n except OSError as e:\n ov, err = None, e.winerror\n if err not in _ready_errors:\n raise\n if err == _winapi.ERROR_IO_PENDING:\n ov_list.append(ov)\n waithandle_to_obj[ov.event] = o\n else:\n # If o.fileno() is an overlapped pipe handle and\n # err == 0 then there is a zero length message\n # in the pipe, but it HAS NOT been consumed...\n if ov and sys.getwindowsversion()[:2] >= (6, 2):\n # ... except on Windows 8 and later, where\n # the message HAS been consumed.\n try:\n _, err = ov.GetOverlappedResult(False)\n except OSError as e:\n err = e.winerror\n if not err and hasattr(o, '_got_empty_message'):\n o._got_empty_message = True\n ready_objects.add(o)\n timeout = 0\n\n ready_handles = _exhaustive_wait(waithandle_to_obj.keys(), timeout)\n finally:\n # request that overlapped reads stop\n for ov in ov_list:\n ov.cancel()\n\n # wait for all overlapped reads to stop\n for ov in ov_list:\n try:\n _, err = ov.GetOverlappedResult(True)\n except OSError as e:\n err = e.winerror\n if err not in _ready_errors:\n raise\n if err != _winapi.ERROR_OPERATION_ABORTED:\n o = waithandle_to_obj[ov.event]\n ready_objects.add(o)\n if err == 0:\n # If o.fileno() is an overlapped pipe handle then\n # a zero length message HAS been consumed.\n if hasattr(o, '_got_empty_message'):\n o._got_empty_message = True\n\n ready_objects.update(waithandle_to_obj[h] for h in ready_handles)\n return [o for o in object_list if o in ready_objects]\n\nelse:\n\n import selectors\n\n # poll/select have the advantage of not requiring any extra file\n # descriptor, contrarily to epoll/kqueue (also, they require a single\n # syscall).\n if hasattr(selectors, 'PollSelector'):\n _WaitSelector = selectors.PollSelector\n else:\n _WaitSelector = selectors.SelectSelector\n\n def wait(object_list, timeout=None):\n '''\n Wait till an object in object_list is ready/readable.\n\n Returns list of those objects in object_list which are ready/readable.\n '''\n with _WaitSelector() as selector:\n for obj in object_list:\n selector.register(obj, selectors.EVENT_READ)\n\n if timeout is not None:\n deadline = time.monotonic() + timeout\n\n while True:\n ready = selector.select(timeout)\n if ready:\n return [key.fileobj for (key, events) in ready]\n else:\n if timeout is not None:\n timeout = deadline - time.monotonic()\n if timeout < 0:\n return ready\n\n#\n# Make connection and socket objects shareable if possible\n#\n\nif sys.platform == 'win32':\n def reduce_connection(conn):\n handle = conn.fileno()\n with socket.fromfd(handle, socket.AF_INET, socket.SOCK_STREAM) as s:\n from . import resource_sharer\n ds = resource_sharer.DupSocket(s)\n return rebuild_connection, (ds, conn.readable, conn.writable)\n def rebuild_connection(ds, readable, writable):\n sock = ds.detach()\n return Connection(sock.detach(), readable, writable)\n reduction.register(Connection, reduce_connection)\n\n def reduce_pipe_connection(conn):\n access = ((_winapi.FILE_GENERIC_READ if conn.readable else 0) |\n (_winapi.FILE_GENERIC_WRITE if conn.writable else 0))\n dh = reduction.DupHandle(conn.fileno(), access)\n return rebuild_pipe_connection, (dh, conn.readable, conn.writable)\n def rebuild_pipe_connection(dh, readable, writable):\n handle = dh.detach()\n return PipeConnection(handle, readable, writable)\n reduction.register(PipeConnection, reduce_pipe_connection)\n\nelse:\n def reduce_connection(conn):\n df = reduction.DupFd(conn.fileno())\n return rebuild_connection, (df, conn.readable, conn.writable)\n def rebuild_connection(df, readable, writable):\n fd = df.detach()\n return Connection(fd, readable, writable)\n reduction.register(Connection, reduce_connection)\n", 1190], "/usr/lib/python3.13/multiprocessing/queues.py": ["#\n# Module implementing queues\n#\n# multiprocessing/queues.py\n#\n# Copyright (c) 2006-2008, R Oudkerk\n# Licensed to PSF under a Contributor Agreement.\n#\n\n__all__ = ['Queue', 'SimpleQueue', 'JoinableQueue']\n\nimport sys\nimport os\nimport threading\nimport collections\nimport time\nimport types\nimport weakref\nimport errno\n\nfrom queue import Empty, Full\n\nfrom . import connection\nfrom . import context\n_ForkingPickler = context.reduction.ForkingPickler\n\nfrom .util import debug, info, Finalize, register_after_fork, is_exiting\n\n#\n# Queue type using a pipe, buffer and thread\n#\n\nclass Queue(object):\n\n def __init__(self, maxsize=0, *, ctx):\n if maxsize <= 0:\n # Can raise ImportError (see issues #3770 and #23400)\n from .synchronize import SEM_VALUE_MAX as maxsize\n self._maxsize = maxsize\n self._reader, self._writer = connection.Pipe(duplex=False)\n self._rlock = ctx.Lock()\n self._opid = os.getpid()\n if sys.platform == 'win32':\n self._wlock = None\n else:\n self._wlock = ctx.Lock()\n self._sem = ctx.BoundedSemaphore(maxsize)\n # For use by concurrent.futures\n self._ignore_epipe = False\n self._reset()\n\n if sys.platform != 'win32':\n register_after_fork(self, Queue._after_fork)\n\n def __getstate__(self):\n context.assert_spawning(self)\n return (self._ignore_epipe, self._maxsize, self._reader, self._writer,\n self._rlock, self._wlock, self._sem, self._opid)\n\n def __setstate__(self, state):\n (self._ignore_epipe, self._maxsize, self._reader, self._writer,\n self._rlock, self._wlock, self._sem, self._opid) = state\n self._reset()\n\n def _after_fork(self):\n debug('Queue._after_fork()')\n self._reset(after_fork=True)\n\n def _reset(self, after_fork=False):\n if after_fork:\n self._notempty._at_fork_reinit()\n else:\n self._notempty = threading.Condition(threading.Lock())\n self._buffer = collections.deque()\n self._thread = None\n self._jointhread = None\n self._joincancelled = False\n self._closed = False\n self._close = None\n self._send_bytes = self._writer.send_bytes\n self._recv_bytes = self._reader.recv_bytes\n self._poll = self._reader.poll\n\n def put(self, obj, block=True, timeout=None):\n if self._closed:\n raise ValueError(f\"Queue {self!r} is closed\")\n if not self._sem.acquire(block, timeout):\n raise Full\n\n with self._notempty:\n if self._thread is None:\n self._start_thread()\n self._buffer.append(obj)\n self._notempty.notify()\n\n def get(self, block=True, timeout=None):\n if self._closed:\n raise ValueError(f\"Queue {self!r} is closed\")\n if block and timeout is None:\n with self._rlock:\n res = self._recv_bytes()\n self._sem.release()\n else:\n if block:\n deadline = time.monotonic() + timeout\n if not self._rlock.acquire(block, timeout):\n raise Empty\n try:\n if block:\n timeout = deadline - time.monotonic()\n if not self._poll(timeout):\n raise Empty\n elif not self._poll():\n raise Empty\n res = self._recv_bytes()\n self._sem.release()\n finally:\n self._rlock.release()\n # unserialize the data after having released the lock\n return _ForkingPickler.loads(res)\n\n def qsize(self):\n # Raises NotImplementedError on Mac OSX because of broken sem_getvalue()\n return self._maxsize - self._sem._semlock._get_value()\n\n def empty(self):\n return not self._poll()\n\n def full(self):\n return self._sem._semlock._is_zero()\n\n def get_nowait(self):\n return self.get(False)\n\n def put_nowait(self, obj):\n return self.put(obj, False)\n\n def close(self):\n self._closed = True\n close = self._close\n if close:\n self._close = None\n close()\n\n def join_thread(self):\n debug('Queue.join_thread()')\n assert self._closed, \"Queue {0!r} not closed\".format(self)\n if self._jointhread:\n self._jointhread()\n\n def cancel_join_thread(self):\n debug('Queue.cancel_join_thread()')\n self._joincancelled = True\n try:\n self._jointhread.cancel()\n except AttributeError:\n pass\n\n def _terminate_broken(self):\n # Close a Queue on error.\n\n # gh-94777: Prevent queue writing to a pipe which is no longer read.\n self._reader.close()\n\n # gh-107219: Close the connection writer which can unblock\n # Queue._feed() if it was stuck in send_bytes().\n if sys.platform == 'win32':\n self._writer.close()\n\n self.close()\n self.join_thread()\n\n def _start_thread(self):\n debug('Queue._start_thread()')\n\n # Start thread which transfers data from buffer to pipe\n self._buffer.clear()\n self._thread = threading.Thread(\n target=Queue._feed,\n args=(self._buffer, self._notempty, self._send_bytes,\n self._wlock, self._reader.close, self._writer.close,\n self._ignore_epipe, self._on_queue_feeder_error,\n self._sem),\n name='QueueFeederThread',\n daemon=True,\n )\n\n try:\n debug('doing self._thread.start()')\n self._thread.start()\n debug('... done self._thread.start()')\n except:\n # gh-109047: During Python finalization, creating a thread\n # can fail with RuntimeError.\n self._thread = None\n raise\n\n if not self._joincancelled:\n self._jointhread = Finalize(\n self._thread, Queue._finalize_join,\n [weakref.ref(self._thread)],\n exitpriority=-5\n )\n\n # Send sentinel to the thread queue object when garbage collected\n self._close = Finalize(\n self, Queue._finalize_close,\n [self._buffer, self._notempty],\n exitpriority=10\n )\n\n @staticmethod\n def _finalize_join(twr):\n debug('joining queue thread')\n thread = twr()\n if thread is not None:\n thread.join()\n debug('... queue thread joined')\n else:\n debug('... queue thread already dead')\n\n @staticmethod\n def _finalize_close(buffer, notempty):\n debug('telling queue thread to quit')\n with notempty:\n buffer.append(_sentinel)\n notempty.notify()\n\n @staticmethod\n def _feed(buffer, notempty, send_bytes, writelock, reader_close,\n writer_close, ignore_epipe, onerror, queue_sem):\n debug('starting thread to feed data to pipe')\n nacquire = notempty.acquire\n nrelease = notempty.release\n nwait = notempty.wait\n bpopleft = buffer.popleft\n sentinel = _sentinel\n if sys.platform != 'win32':\n wacquire = writelock.acquire\n wrelease = writelock.release\n else:\n wacquire = None\n\n while 1:\n try:\n nacquire()\n try:\n if not buffer:\n nwait()\n finally:\n nrelease()\n try:\n while 1:\n obj = bpopleft()\n if obj is sentinel:\n debug('feeder thread got sentinel -- exiting')\n reader_close()\n writer_close()\n return\n\n # serialize the data before acquiring the lock\n obj = _ForkingPickler.dumps(obj)\n if wacquire is None:\n send_bytes(obj)\n else:\n wacquire()\n try:\n send_bytes(obj)\n finally:\n wrelease()\n except IndexError:\n pass\n except Exception as e:\n if ignore_epipe and getattr(e, 'errno', 0) == errno.EPIPE:\n return\n # Since this runs in a daemon thread the resources it uses\n # may be become unusable while the process is cleaning up.\n # We ignore errors which happen after the process has\n # started to cleanup.\n if is_exiting():\n info('error in queue thread: %s', e)\n return\n else:\n # Since the object has not been sent in the queue, we need\n # to decrease the size of the queue. The error acts as\n # if the object had been silently removed from the queue\n # and this step is necessary to have a properly working\n # queue.\n queue_sem.release()\n onerror(e, obj)\n\n @staticmethod\n def _on_queue_feeder_error(e, obj):\n \"\"\"\n Private API hook called when feeding data in the background thread\n raises an exception. For overriding by concurrent.futures.\n \"\"\"\n import traceback\n traceback.print_exc()\n\n __class_getitem__ = classmethod(types.GenericAlias)\n\n\n_sentinel = object()\n\n#\n# A queue type which also supports join() and task_done() methods\n#\n# Note that if you do not call task_done() for each finished task then\n# eventually the counter's semaphore may overflow causing Bad Things\n# to happen.\n#\n\nclass JoinableQueue(Queue):\n\n def __init__(self, maxsize=0, *, ctx):\n Queue.__init__(self, maxsize, ctx=ctx)\n self._unfinished_tasks = ctx.Semaphore(0)\n self._cond = ctx.Condition()\n\n def __getstate__(self):\n return Queue.__getstate__(self) + (self._cond, self._unfinished_tasks)\n\n def __setstate__(self, state):\n Queue.__setstate__(self, state[:-2])\n self._cond, self._unfinished_tasks = state[-2:]\n\n def put(self, obj, block=True, timeout=None):\n if self._closed:\n raise ValueError(f\"Queue {self!r} is closed\")\n if not self._sem.acquire(block, timeout):\n raise Full\n\n with self._notempty, self._cond:\n if self._thread is None:\n self._start_thread()\n self._buffer.append(obj)\n self._unfinished_tasks.release()\n self._notempty.notify()\n\n def task_done(self):\n with self._cond:\n if not self._unfinished_tasks.acquire(False):\n raise ValueError('task_done() called too many times')\n if self._unfinished_tasks._semlock._is_zero():\n self._cond.notify_all()\n\n def join(self):\n with self._cond:\n if not self._unfinished_tasks._semlock._is_zero():\n self._cond.wait()\n\n#\n# Simplified Queue type -- really just a locked pipe\n#\n\nclass SimpleQueue(object):\n\n def __init__(self, *, ctx):\n self._reader, self._writer = connection.Pipe(duplex=False)\n self._rlock = ctx.Lock()\n self._poll = self._reader.poll\n if sys.platform == 'win32':\n self._wlock = None\n else:\n self._wlock = ctx.Lock()\n\n def close(self):\n self._reader.close()\n self._writer.close()\n\n def empty(self):\n return not self._poll()\n\n def __getstate__(self):\n context.assert_spawning(self)\n return (self._reader, self._writer, self._rlock, self._wlock)\n\n def __setstate__(self, state):\n (self._reader, self._writer, self._rlock, self._wlock) = state\n self._poll = self._reader.poll\n\n def get(self):\n with self._rlock:\n res = self._reader.recv_bytes()\n # unserialize the data after having released the lock\n return _ForkingPickler.loads(res)\n\n def put(self, obj):\n # serialize the data before acquiring the lock\n obj = _ForkingPickler.dumps(obj)\n if self._wlock is None:\n # writes to a message oriented win32 pipe are atomic\n self._writer.send_bytes(obj)\n else:\n with self._wlock:\n self._writer.send_bytes(obj)\n\n __class_getitem__ = classmethod(types.GenericAlias)\n", 399], "/home/gaogaotiantian/programs/viztracer/example/src/multi_process_pool.py": ["import os\nfrom multiprocessing import Pool\n\n\ndef f(x):\n return x ** x\n\n\nif __name__ == \"__main__\":\n process_num = 5\n with Pool(processes=process_num) as pool:\n print(pool.map(f, range(10)))\n\n for i in pool.imap_unordered(f, range(10)):\n print(i)\n\n res = pool.apply_async(f, (20,)) # runs in *only* one process\n print(res.get(timeout=1)) # prints \"400\"\n\n res = pool.apply_async(os.getpid, ()) # runs in *only* one process\n print(res.get(timeout=1)) # prints the PID of that process\n\n multiple_results = [pool.apply_async(os.getpid, ()) for i in range(process_num)]\n print([res.get(timeout=1) for res in multiple_results])\n", 24], "/usr/lib/python3.13/multiprocessing/pool.py": ["#\n# Module providing the `Pool` class for managing a process pool\n#\n# multiprocessing/pool.py\n#\n# Copyright (c) 2006-2008, R Oudkerk\n# Licensed to PSF under a Contributor Agreement.\n#\n\n__all__ = ['Pool', 'ThreadPool']\n\n#\n# Imports\n#\n\nimport collections\nimport itertools\nimport os\nimport queue\nimport threading\nimport time\nimport traceback\nimport types\nimport warnings\n\n# If threading is available then ThreadPool should be provided. Therefore\n# we avoid top-level imports which are liable to fail on some systems.\nfrom . import util\nfrom . import get_context, TimeoutError\nfrom .connection import wait\n\n#\n# Constants representing the state of a pool\n#\n\nINIT = \"INIT\"\nRUN = \"RUN\"\nCLOSE = \"CLOSE\"\nTERMINATE = \"TERMINATE\"\n\n#\n# Miscellaneous\n#\n\njob_counter = itertools.count()\n\ndef mapstar(args):\n return list(map(*args))\n\ndef starmapstar(args):\n return list(itertools.starmap(args[0], args[1]))\n\n#\n# Hack to embed stringification of remote traceback in local traceback\n#\n\nclass RemoteTraceback(Exception):\n def __init__(self, tb):\n self.tb = tb\n def __str__(self):\n return self.tb\n\nclass ExceptionWithTraceback:\n def __init__(self, exc, tb):\n tb = traceback.format_exception(type(exc), exc, tb)\n tb = ''.join(tb)\n self.exc = exc\n self.tb = '\\n\"\"\"\\n%s\"\"\"' % tb\n def __reduce__(self):\n return rebuild_exc, (self.exc, self.tb)\n\ndef rebuild_exc(exc, tb):\n exc.__cause__ = RemoteTraceback(tb)\n return exc\n\n#\n# Code run by worker processes\n#\n\nclass MaybeEncodingError(Exception):\n \"\"\"Wraps possible unpickleable errors, so they can be\n safely sent through the socket.\"\"\"\n\n def __init__(self, exc, value):\n self.exc = repr(exc)\n self.value = repr(value)\n super(MaybeEncodingError, self).__init__(self.exc, self.value)\n\n def __str__(self):\n return \"Error sending result: '%s'. Reason: '%s'\" % (self.value,\n self.exc)\n\n def __repr__(self):\n return \"<%s: %s>\" % (self.__class__.__name__, self)\n\n\ndef worker(inqueue, outqueue, initializer=None, initargs=(), maxtasks=None,\n wrap_exception=False):\n if (maxtasks is not None) and not (isinstance(maxtasks, int)\n and maxtasks >= 1):\n raise AssertionError(\"Maxtasks {!r} is not valid\".format(maxtasks))\n put = outqueue.put\n get = inqueue.get\n if hasattr(inqueue, '_writer'):\n inqueue._writer.close()\n outqueue._reader.close()\n\n if initializer is not None:\n initializer(*initargs)\n\n completed = 0\n while maxtasks is None or (maxtasks and completed < maxtasks):\n try:\n task = get()\n except (EOFError, OSError):\n util.debug('worker got EOFError or OSError -- exiting')\n break\n\n if task is None:\n util.debug('worker got sentinel -- exiting')\n break\n\n job, i, func, args, kwds = task\n try:\n result = (True, func(*args, **kwds))\n except Exception as e:\n if wrap_exception and func is not _helper_reraises_exception:\n e = ExceptionWithTraceback(e, e.__traceback__)\n result = (False, e)\n try:\n put((job, i, result))\n except Exception as e:\n wrapped = MaybeEncodingError(e, result[1])\n util.debug(\"Possible encoding error while sending result: %s\" % (\n wrapped))\n put((job, i, (False, wrapped)))\n\n task = job = result = func = args = kwds = None\n completed += 1\n util.debug('worker exiting after %d tasks' % completed)\n\ndef _helper_reraises_exception(ex):\n 'Pickle-able helper function for use by _guarded_task_generation.'\n raise ex\n\n#\n# Class representing a process pool\n#\n\nclass _PoolCache(dict):\n \"\"\"\n Class that implements a cache for the Pool class that will notify\n the pool management threads every time the cache is emptied. The\n notification is done by the use of a queue that is provided when\n instantiating the cache.\n \"\"\"\n def __init__(self, /, *args, notifier=None, **kwds):\n self.notifier = notifier\n super().__init__(*args, **kwds)\n\n def __delitem__(self, item):\n super().__delitem__(item)\n\n # Notify that the cache is empty. This is important because the\n # pool keeps maintaining workers until the cache gets drained. This\n # eliminates a race condition in which a task is finished after the\n # the pool's _handle_workers method has enter another iteration of the\n # loop. In this situation, the only event that can wake up the pool\n # is the cache to be emptied (no more tasks available).\n if not self:\n self.notifier.put(None)\n\nclass Pool(object):\n '''\n Class which supports an async version of applying functions to arguments.\n '''\n _wrap_exception = True\n\n @staticmethod\n def Process(ctx, *args, **kwds):\n return ctx.Process(*args, **kwds)\n\n def __init__(self, processes=None, initializer=None, initargs=(),\n maxtasksperchild=None, context=None):\n # Attributes initialized early to make sure that they exist in\n # __del__() if __init__() raises an exception\n self._pool = []\n self._state = INIT\n\n self._ctx = context or get_context()\n self._setup_queues()\n self._taskqueue = queue.SimpleQueue()\n # The _change_notifier queue exist to wake up self._handle_workers()\n # when the cache (self._cache) is empty or when there is a change in\n # the _state variable of the thread that runs _handle_workers.\n self._change_notifier = self._ctx.SimpleQueue()\n self._cache = _PoolCache(notifier=self._change_notifier)\n self._maxtasksperchild = maxtasksperchild\n self._initializer = initializer\n self._initargs = initargs\n\n if processes is None:\n processes = os.process_cpu_count() or 1\n if processes < 1:\n raise ValueError(\"Number of processes must be at least 1\")\n if maxtasksperchild is not None:\n if not isinstance(maxtasksperchild, int) or maxtasksperchild <= 0:\n raise ValueError(\"maxtasksperchild must be a positive int or None\")\n\n if initializer is not None and not callable(initializer):\n raise TypeError('initializer must be a callable')\n\n self._processes = processes\n try:\n self._repopulate_pool()\n except Exception:\n for p in self._pool:\n if p.exitcode is None:\n p.terminate()\n for p in self._pool:\n p.join()\n raise\n\n sentinels = self._get_sentinels()\n\n self._worker_handler = threading.Thread(\n target=Pool._handle_workers,\n args=(self._cache, self._taskqueue, self._ctx, self.Process,\n self._processes, self._pool, self._inqueue, self._outqueue,\n self._initializer, self._initargs, self._maxtasksperchild,\n self._wrap_exception, sentinels, self._change_notifier)\n )\n self._worker_handler.daemon = True\n self._worker_handler._state = RUN\n self._worker_handler.start()\n\n\n self._task_handler = threading.Thread(\n target=Pool._handle_tasks,\n args=(self._taskqueue, self._quick_put, self._outqueue,\n self._pool, self._cache)\n )\n self._task_handler.daemon = True\n self._task_handler._state = RUN\n self._task_handler.start()\n\n self._result_handler = threading.Thread(\n target=Pool._handle_results,\n args=(self._outqueue, self._quick_get, self._cache)\n )\n self._result_handler.daemon = True\n self._result_handler._state = RUN\n self._result_handler.start()\n\n self._terminate = util.Finalize(\n self, self._terminate_pool,\n args=(self._taskqueue, self._inqueue, self._outqueue, self._pool,\n self._change_notifier, self._worker_handler, self._task_handler,\n self._result_handler, self._cache),\n exitpriority=15\n )\n self._state = RUN\n\n # Copy globals as function locals to make sure that they are available\n # during Python shutdown when the Pool is destroyed.\n def __del__(self, _warn=warnings.warn, RUN=RUN):\n if self._state == RUN:\n _warn(f\"unclosed running multiprocessing pool {self!r}\",\n ResourceWarning, source=self)\n if getattr(self, '_change_notifier', None) is not None:\n self._change_notifier.put(None)\n\n def __repr__(self):\n cls = self.__class__\n return (f'<{cls.__module__}.{cls.__qualname__} '\n f'state={self._state} '\n f'pool_size={len(self._pool)}>')\n\n def _get_sentinels(self):\n task_queue_sentinels = [self._outqueue._reader]\n self_notifier_sentinels = [self._change_notifier._reader]\n return [*task_queue_sentinels, *self_notifier_sentinels]\n\n @staticmethod\n def _get_worker_sentinels(workers):\n return [worker.sentinel for worker in\n workers if hasattr(worker, \"sentinel\")]\n\n @staticmethod\n def _join_exited_workers(pool):\n \"\"\"Cleanup after any worker processes which have exited due to reaching\n their specified lifetime. Returns True if any workers were cleaned up.\n \"\"\"\n cleaned = False\n for i in reversed(range(len(pool))):\n worker = pool[i]\n if worker.exitcode is not None:\n # worker exited\n util.debug('cleaning up worker %d' % i)\n worker.join()\n cleaned = True\n del pool[i]\n return cleaned\n\n def _repopulate_pool(self):\n return self._repopulate_pool_static(self._ctx, self.Process,\n self._processes,\n self._pool, self._inqueue,\n self._outqueue, self._initializer,\n self._initargs,\n self._maxtasksperchild,\n self._wrap_exception)\n\n @staticmethod\n def _repopulate_pool_static(ctx, Process, processes, pool, inqueue,\n outqueue, initializer, initargs,\n maxtasksperchild, wrap_exception):\n \"\"\"Bring the number of pool processes up to the specified number,\n for use after reaping workers which have exited.\n \"\"\"\n for i in range(processes - len(pool)):\n w = Process(ctx, target=worker,\n args=(inqueue, outqueue,\n initializer,\n initargs, maxtasksperchild,\n wrap_exception))\n w.name = w.name.replace('Process', 'PoolWorker')\n w.daemon = True\n w.start()\n pool.append(w)\n util.debug('added worker')\n\n @staticmethod\n def _maintain_pool(ctx, Process, processes, pool, inqueue, outqueue,\n initializer, initargs, maxtasksperchild,\n wrap_exception):\n \"\"\"Clean up any exited workers and start replacements for them.\n \"\"\"\n if Pool._join_exited_workers(pool):\n Pool._repopulate_pool_static(ctx, Process, processes, pool,\n inqueue, outqueue, initializer,\n initargs, maxtasksperchild,\n wrap_exception)\n\n def _setup_queues(self):\n self._inqueue = self._ctx.SimpleQueue()\n self._outqueue = self._ctx.SimpleQueue()\n self._quick_put = self._inqueue._writer.send\n self._quick_get = self._outqueue._reader.recv\n\n def _check_running(self):\n if self._state != RUN:\n raise ValueError(\"Pool not running\")\n\n def apply(self, func, args=(), kwds={}):\n '''\n Equivalent of `func(*args, **kwds)`.\n Pool must be running.\n '''\n return self.apply_async(func, args, kwds).get()\n\n def map(self, func, iterable, chunksize=None):\n '''\n Apply `func` to each element in `iterable`, collecting the results\n in a list that is returned.\n '''\n return self._map_async(func, iterable, mapstar, chunksize).get()\n\n def starmap(self, func, iterable, chunksize=None):\n '''\n Like `map()` method but the elements of the `iterable` are expected to\n be iterables as well and will be unpacked as arguments. Hence\n `func` and (a, b) becomes func(a, b).\n '''\n return self._map_async(func, iterable, starmapstar, chunksize).get()\n\n def starmap_async(self, func, iterable, chunksize=None, callback=None,\n error_callback=None):\n '''\n Asynchronous version of `starmap()` method.\n '''\n return self._map_async(func, iterable, starmapstar, chunksize,\n callback, error_callback)\n\n def _guarded_task_generation(self, result_job, func, iterable):\n '''Provides a generator of tasks for imap and imap_unordered with\n appropriate handling for iterables which throw exceptions during\n iteration.'''\n try:\n i = -1\n for i, x in enumerate(iterable):\n yield (result_job, i, func, (x,), {})\n except Exception as e:\n yield (result_job, i+1, _helper_reraises_exception, (e,), {})\n\n def imap(self, func, iterable, chunksize=1):\n '''\n Equivalent of `map()` -- can be MUCH slower than `Pool.map()`.\n '''\n self._check_running()\n if chunksize == 1:\n result = IMapIterator(self)\n self._taskqueue.put(\n (\n self._guarded_task_generation(result._job, func, iterable),\n result._set_length\n ))\n return result\n else:\n if chunksize < 1:\n raise ValueError(\n \"Chunksize must be 1+, not {0:n}\".format(\n chunksize))\n task_batches = Pool._get_tasks(func, iterable, chunksize)\n result = IMapIterator(self)\n self._taskqueue.put(\n (\n self._guarded_task_generation(result._job,\n mapstar,\n task_batches),\n result._set_length\n ))\n return (item for chunk in result for item in chunk)\n\n def imap_unordered(self, func, iterable, chunksize=1):\n '''\n Like `imap()` method but ordering of results is arbitrary.\n '''\n self._check_running()\n if chunksize == 1:\n result = IMapUnorderedIterator(self)\n self._taskqueue.put(\n (\n self._guarded_task_generation(result._job, func, iterable),\n result._set_length\n ))\n return result\n else:\n if chunksize < 1:\n raise ValueError(\n \"Chunksize must be 1+, not {0!r}\".format(chunksize))\n task_batches = Pool._get_tasks(func, iterable, chunksize)\n result = IMapUnorderedIterator(self)\n self._taskqueue.put(\n (\n self._guarded_task_generation(result._job,\n mapstar,\n task_batches),\n result._set_length\n ))\n return (item for chunk in result for item in chunk)\n\n def apply_async(self, func, args=(), kwds={}, callback=None,\n error_callback=None):\n '''\n Asynchronous version of `apply()` method.\n '''\n self._check_running()\n result = ApplyResult(self, callback, error_callback)\n self._taskqueue.put(([(result._job, 0, func, args, kwds)], None))\n return result\n\n def map_async(self, func, iterable, chunksize=None, callback=None,\n error_callback=None):\n '''\n Asynchronous version of `map()` method.\n '''\n return self._map_async(func, iterable, mapstar, chunksize, callback,\n error_callback)\n\n def _map_async(self, func, iterable, mapper, chunksize=None, callback=None,\n error_callback=None):\n '''\n Helper function to implement map, starmap and their async counterparts.\n '''\n self._check_running()\n if not hasattr(iterable, '__len__'):\n iterable = list(iterable)\n\n if chunksize is None:\n chunksize, extra = divmod(len(iterable), len(self._pool) * 4)\n if extra:\n chunksize += 1\n if len(iterable) == 0:\n chunksize = 0\n\n task_batches = Pool._get_tasks(func, iterable, chunksize)\n result = MapResult(self, chunksize, len(iterable), callback,\n error_callback=error_callback)\n self._taskqueue.put(\n (\n self._guarded_task_generation(result._job,\n mapper,\n task_batches),\n None\n )\n )\n return result\n\n @staticmethod\n def _wait_for_updates(sentinels, change_notifier, timeout=None):\n wait(sentinels, timeout=timeout)\n while not change_notifier.empty():\n change_notifier.get()\n\n @classmethod\n def _handle_workers(cls, cache, taskqueue, ctx, Process, processes,\n pool, inqueue, outqueue, initializer, initargs,\n maxtasksperchild, wrap_exception, sentinels,\n change_notifier):\n thread = threading.current_thread()\n\n # Keep maintaining workers until the cache gets drained, unless the pool\n # is terminated.\n while thread._state == RUN or (cache and thread._state != TERMINATE):\n cls._maintain_pool(ctx, Process, processes, pool, inqueue,\n outqueue, initializer, initargs,\n maxtasksperchild, wrap_exception)\n\n current_sentinels = [*cls._get_worker_sentinels(pool), *sentinels]\n\n cls._wait_for_updates(current_sentinels, change_notifier)\n # send sentinel to stop workers\n taskqueue.put(None)\n util.debug('worker handler exiting')\n\n @staticmethod\n def _handle_tasks(taskqueue, put, outqueue, pool, cache):\n thread = threading.current_thread()\n\n for taskseq, set_length in iter(taskqueue.get, None):\n task = None\n try:\n # iterating taskseq cannot fail\n for task in taskseq:\n if thread._state != RUN:\n util.debug('task handler found thread._state != RUN')\n break\n try:\n put(task)\n except Exception as e:\n job, idx = task[:2]\n try:\n cache[job]._set(idx, (False, e))\n except KeyError:\n pass\n else:\n if set_length:\n util.debug('doing set_length()')\n idx = task[1] if task else -1\n set_length(idx + 1)\n continue\n break\n finally:\n task = taskseq = job = None\n else:\n util.debug('task handler got sentinel')\n\n try:\n # tell result handler to finish when cache is empty\n util.debug('task handler sending sentinel to result handler')\n outqueue.put(None)\n\n # tell workers there is no more work\n util.debug('task handler sending sentinel to workers')\n for p in pool:\n put(None)\n except OSError:\n util.debug('task handler got OSError when sending sentinels')\n\n util.debug('task handler exiting')\n\n @staticmethod\n def _handle_results(outqueue, get, cache):\n thread = threading.current_thread()\n\n while 1:\n try:\n task = get()\n except (OSError, EOFError):\n util.debug('result handler got EOFError/OSError -- exiting')\n return\n\n if thread._state != RUN:\n assert thread._state == TERMINATE, \"Thread not in TERMINATE\"\n util.debug('result handler found thread._state=TERMINATE')\n break\n\n if task is None:\n util.debug('result handler got sentinel')\n break\n\n job, i, obj = task\n try:\n cache[job]._set(i, obj)\n except KeyError:\n pass\n task = job = obj = None\n\n while cache and thread._state != TERMINATE:\n try:\n task = get()\n except (OSError, EOFError):\n util.debug('result handler got EOFError/OSError -- exiting')\n return\n\n if task is None:\n util.debug('result handler ignoring extra sentinel')\n continue\n job, i, obj = task\n try:\n cache[job]._set(i, obj)\n except KeyError:\n pass\n task = job = obj = None\n\n if hasattr(outqueue, '_reader'):\n util.debug('ensuring that outqueue is not full')\n # If we don't make room available in outqueue then\n # attempts to add the sentinel (None) to outqueue may\n # block. There is guaranteed to be no more than 2 sentinels.\n try:\n for i in range(10):\n if not outqueue._reader.poll():\n break\n get()\n except (OSError, EOFError):\n pass\n\n util.debug('result handler exiting: len(cache)=%s, thread._state=%s',\n len(cache), thread._state)\n\n @staticmethod\n def _get_tasks(func, it, size):\n it = iter(it)\n while 1:\n x = tuple(itertools.islice(it, size))\n if not x:\n return\n yield (func, x)\n\n def __reduce__(self):\n raise NotImplementedError(\n 'pool objects cannot be passed between processes or pickled'\n )\n\n def close(self):\n util.debug('closing pool')\n if self._state == RUN:\n self._state = CLOSE\n self._worker_handler._state = CLOSE\n self._change_notifier.put(None)\n\n def terminate(self):\n util.debug('terminating pool')\n self._state = TERMINATE\n self._terminate()\n\n def join(self):\n util.debug('joining pool')\n if self._state == RUN:\n raise ValueError(\"Pool is still running\")\n elif self._state not in (CLOSE, TERMINATE):\n raise ValueError(\"In unknown state\")\n self._worker_handler.join()\n self._task_handler.join()\n self._result_handler.join()\n for p in self._pool:\n p.join()\n\n @staticmethod\n def _help_stuff_finish(inqueue, task_handler, size):\n # task_handler may be blocked trying to put items on inqueue\n util.debug('removing tasks from inqueue until task handler finished')\n inqueue._rlock.acquire()\n while task_handler.is_alive() and inqueue._reader.poll():\n inqueue._reader.recv()\n time.sleep(0)\n\n @classmethod\n def _terminate_pool(cls, taskqueue, inqueue, outqueue, pool, change_notifier,\n worker_handler, task_handler, result_handler, cache):\n # this is guaranteed to only be called once\n util.debug('finalizing pool')\n\n # Notify that the worker_handler state has been changed so the\n # _handle_workers loop can be unblocked (and exited) in order to\n # send the finalization sentinel all the workers.\n worker_handler._state = TERMINATE\n change_notifier.put(None)\n\n task_handler._state = TERMINATE\n\n util.debug('helping task handler/workers to finish')\n cls._help_stuff_finish(inqueue, task_handler, len(pool))\n\n if (not result_handler.is_alive()) and (len(cache) != 0):\n raise AssertionError(\n \"Cannot have cache with result_handler not alive\")\n\n result_handler._state = TERMINATE\n change_notifier.put(None)\n outqueue.put(None) # sentinel\n\n # We must wait for the worker handler to exit before terminating\n # workers because we don't want workers to be restarted behind our back.\n util.debug('joining worker handler')\n if threading.current_thread() is not worker_handler:\n worker_handler.join()\n\n # Terminate workers which haven't already finished.\n if pool and hasattr(pool[0], 'terminate'):\n util.debug('terminating workers')\n for p in pool:\n if p.exitcode is None:\n p.terminate()\n\n util.debug('joining task handler')\n if threading.current_thread() is not task_handler:\n task_handler.join()\n\n util.debug('joining result handler')\n if threading.current_thread() is not result_handler:\n result_handler.join()\n\n if pool and hasattr(pool[0], 'terminate'):\n util.debug('joining pool workers')\n for p in pool:\n if p.is_alive():\n # worker has not yet exited\n util.debug('cleaning up worker %d' % p.pid)\n p.join()\n\n def __enter__(self):\n self._check_running()\n return self\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n self.terminate()\n\n#\n# Class whose instances are returned by `Pool.apply_async()`\n#\n\nclass ApplyResult(object):\n\n def __init__(self, pool, callback, error_callback):\n self._pool = pool\n self._event = threading.Event()\n self._job = next(job_counter)\n self._cache = pool._cache\n self._callback = callback\n self._error_callback = error_callback\n self._cache[self._job] = self\n\n def ready(self):\n return self._event.is_set()\n\n def successful(self):\n if not self.ready():\n raise ValueError(\"{0!r} not ready\".format(self))\n return self._success\n\n def wait(self, timeout=None):\n self._event.wait(timeout)\n\n def get(self, timeout=None):\n self.wait(timeout)\n if not self.ready():\n raise TimeoutError\n if self._success:\n return self._value\n else:\n raise self._value\n\n def _set(self, i, obj):\n self._success, self._value = obj\n if self._callback and self._success:\n self._callback(self._value)\n if self._error_callback and not self._success:\n self._error_callback(self._value)\n self._event.set()\n del self._cache[self._job]\n self._pool = None\n\n __class_getitem__ = classmethod(types.GenericAlias)\n\nAsyncResult = ApplyResult # create alias -- see #17805\n\n#\n# Class whose instances are returned by `Pool.map_async()`\n#\n\nclass MapResult(ApplyResult):\n\n def __init__(self, pool, chunksize, length, callback, error_callback):\n ApplyResult.__init__(self, pool, callback,\n error_callback=error_callback)\n self._success = True\n self._value = [None] * length\n self._chunksize = chunksize\n if chunksize <= 0:\n self._number_left = 0\n self._event.set()\n del self._cache[self._job]\n else:\n self._number_left = length//chunksize + bool(length % chunksize)\n\n def _set(self, i, success_result):\n self._number_left -= 1\n success, result = success_result\n if success and self._success:\n self._value[i*self._chunksize:(i+1)*self._chunksize] = result\n if self._number_left == 0:\n if self._callback:\n self._callback(self._value)\n del self._cache[self._job]\n self._event.set()\n self._pool = None\n else:\n if not success and self._success:\n # only store first exception\n self._success = False\n self._value = result\n if self._number_left == 0:\n # only consider the result ready once all jobs are done\n if self._error_callback:\n self._error_callback(self._value)\n del self._cache[self._job]\n self._event.set()\n self._pool = None\n\n#\n# Class whose instances are returned by `Pool.imap()`\n#\n\nclass IMapIterator(object):\n\n def __init__(self, pool):\n self._pool = pool\n self._cond = threading.Condition(threading.Lock())\n self._job = next(job_counter)\n self._cache = pool._cache\n self._items = collections.deque()\n self._index = 0\n self._length = None\n self._unsorted = {}\n self._cache[self._job] = self\n\n def __iter__(self):\n return self\n\n def next(self, timeout=None):\n with self._cond:\n try:\n item = self._items.popleft()\n except IndexError:\n if self._index == self._length:\n self._pool = None\n raise StopIteration from None\n self._cond.wait(timeout)\n try:\n item = self._items.popleft()\n except IndexError:\n if self._index == self._length:\n self._pool = None\n raise StopIteration from None\n raise TimeoutError from None\n\n success, value = item\n if success:\n return value\n raise value\n\n __next__ = next # XXX\n\n def _set(self, i, obj):\n with self._cond:\n if self._index == i:\n self._items.append(obj)\n self._index += 1\n while self._index in self._unsorted:\n obj = self._unsorted.pop(self._index)\n self._items.append(obj)\n self._index += 1\n self._cond.notify()\n else:\n self._unsorted[i] = obj\n\n if self._index == self._length:\n del self._cache[self._job]\n self._pool = None\n\n def _set_length(self, length):\n with self._cond:\n self._length = length\n if self._index == self._length:\n self._cond.notify()\n del self._cache[self._job]\n self._pool = None\n\n#\n# Class whose instances are returned by `Pool.imap_unordered()`\n#\n\nclass IMapUnorderedIterator(IMapIterator):\n\n def _set(self, i, obj):\n with self._cond:\n self._items.append(obj)\n self._index += 1\n self._cond.notify()\n if self._index == self._length:\n del self._cache[self._job]\n self._pool = None\n\n#\n#\n#\n\nclass ThreadPool(Pool):\n _wrap_exception = False\n\n @staticmethod\n def Process(ctx, *args, **kwds):\n from .dummy import Process\n return Process(*args, **kwds)\n\n def __init__(self, processes=None, initializer=None, initargs=()):\n Pool.__init__(self, processes, initializer, initargs)\n\n def _setup_queues(self):\n self._inqueue = queue.SimpleQueue()\n self._outqueue = queue.SimpleQueue()\n self._quick_put = self._inqueue.put\n self._quick_get = self._outqueue.get\n\n def _get_sentinels(self):\n return [self._change_notifier._reader]\n\n @staticmethod\n def _get_worker_sentinels(workers):\n return []\n\n @staticmethod\n def _help_stuff_finish(inqueue, task_handler, size):\n # drain inqueue, and put sentinels at its head to make workers finish\n try:\n while True:\n inqueue.get(block=False)\n except queue.Empty:\n pass\n for i in range(size):\n inqueue.put(None)\n\n def _wait_for_updates(self, sentinels, change_notifier, timeout):\n time.sleep(timeout)\n", 957], "/usr/lib/python3.13/multiprocessing/reduction.py": ["#\n# Module which deals with pickling of objects.\n#\n# multiprocessing/reduction.py\n#\n# Copyright (c) 2006-2008, R Oudkerk\n# Licensed to PSF under a Contributor Agreement.\n#\n\nfrom abc import ABCMeta\nimport copyreg\nimport functools\nimport io\nimport os\nimport pickle\nimport socket\nimport sys\n\nfrom . import context\n\n__all__ = ['send_handle', 'recv_handle', 'ForkingPickler', 'register', 'dump']\n\n\nHAVE_SEND_HANDLE = (sys.platform == 'win32' or\n (hasattr(socket, 'CMSG_LEN') and\n hasattr(socket, 'SCM_RIGHTS') and\n hasattr(socket.socket, 'sendmsg')))\n\n#\n# Pickler subclass\n#\n\nclass ForkingPickler(pickle.Pickler):\n '''Pickler subclass used by multiprocessing.'''\n _extra_reducers = {}\n _copyreg_dispatch_table = copyreg.dispatch_table\n\n def __init__(self, *args):\n super().__init__(*args)\n self.dispatch_table = self._copyreg_dispatch_table.copy()\n self.dispatch_table.update(self._extra_reducers)\n\n @classmethod\n def register(cls, type, reduce):\n '''Register a reduce function for a type.'''\n cls._extra_reducers[type] = reduce\n\n @classmethod\n def dumps(cls, obj, protocol=None):\n buf = io.BytesIO()\n cls(buf, protocol).dump(obj)\n return buf.getbuffer()\n\n loads = pickle.loads\n\nregister = ForkingPickler.register\n\ndef dump(obj, file, protocol=None):\n '''Replacement for pickle.dump() using ForkingPickler.'''\n ForkingPickler(file, protocol).dump(obj)\n\n#\n# Platform specific definitions\n#\n\nif sys.platform == 'win32':\n # Windows\n __all__ += ['DupHandle', 'duplicate', 'steal_handle']\n import _winapi\n\n def duplicate(handle, target_process=None, inheritable=False,\n *, source_process=None):\n '''Duplicate a handle. (target_process is a handle not a pid!)'''\n current_process = _winapi.GetCurrentProcess()\n if source_process is None:\n source_process = current_process\n if target_process is None:\n target_process = current_process\n return _winapi.DuplicateHandle(\n source_process, handle, target_process,\n 0, inheritable, _winapi.DUPLICATE_SAME_ACCESS)\n\n def steal_handle(source_pid, handle):\n '''Steal a handle from process identified by source_pid.'''\n source_process_handle = _winapi.OpenProcess(\n _winapi.PROCESS_DUP_HANDLE, False, source_pid)\n try:\n return _winapi.DuplicateHandle(\n source_process_handle, handle,\n _winapi.GetCurrentProcess(), 0, False,\n _winapi.DUPLICATE_SAME_ACCESS | _winapi.DUPLICATE_CLOSE_SOURCE)\n finally:\n _winapi.CloseHandle(source_process_handle)\n\n def send_handle(conn, handle, destination_pid):\n '''Send a handle over a local connection.'''\n dh = DupHandle(handle, _winapi.DUPLICATE_SAME_ACCESS, destination_pid)\n conn.send(dh)\n\n def recv_handle(conn):\n '''Receive a handle over a local connection.'''\n return conn.recv().detach()\n\n class DupHandle(object):\n '''Picklable wrapper for a handle.'''\n def __init__(self, handle, access, pid=None):\n if pid is None:\n # We just duplicate the handle in the current process and\n # let the receiving process steal the handle.\n pid = os.getpid()\n proc = _winapi.OpenProcess(_winapi.PROCESS_DUP_HANDLE, False, pid)\n try:\n self._handle = _winapi.DuplicateHandle(\n _winapi.GetCurrentProcess(),\n handle, proc, access, False, 0)\n finally:\n _winapi.CloseHandle(proc)\n self._access = access\n self._pid = pid\n\n def detach(self):\n '''Get the handle. This should only be called once.'''\n # retrieve handle from process which currently owns it\n if self._pid == os.getpid():\n # The handle has already been duplicated for this process.\n return self._handle\n # We must steal the handle from the process whose pid is self._pid.\n proc = _winapi.OpenProcess(_winapi.PROCESS_DUP_HANDLE, False,\n self._pid)\n try:\n return _winapi.DuplicateHandle(\n proc, self._handle, _winapi.GetCurrentProcess(),\n self._access, False, _winapi.DUPLICATE_CLOSE_SOURCE)\n finally:\n _winapi.CloseHandle(proc)\n\nelse:\n # Unix\n __all__ += ['DupFd', 'sendfds', 'recvfds']\n import array\n\n # On MacOSX we should acknowledge receipt of fds -- see Issue14669\n ACKNOWLEDGE = sys.platform == 'darwin'\n\n def sendfds(sock, fds):\n '''Send an array of fds over an AF_UNIX socket.'''\n fds = array.array('i', fds)\n msg = bytes([len(fds) % 256])\n sock.sendmsg([msg], [(socket.SOL_SOCKET, socket.SCM_RIGHTS, fds)])\n if ACKNOWLEDGE and sock.recv(1) != b'A':\n raise RuntimeError('did not receive acknowledgement of fd')\n\n def recvfds(sock, size):\n '''Receive an array of fds over an AF_UNIX socket.'''\n a = array.array('i')\n bytes_size = a.itemsize * size\n msg, ancdata, flags, addr = sock.recvmsg(1, socket.CMSG_SPACE(bytes_size))\n if not msg and not ancdata:\n raise EOFError\n try:\n if ACKNOWLEDGE:\n sock.send(b'A')\n if len(ancdata) != 1:\n raise RuntimeError('received %d items of ancdata' %\n len(ancdata))\n cmsg_level, cmsg_type, cmsg_data = ancdata[0]\n if (cmsg_level == socket.SOL_SOCKET and\n cmsg_type == socket.SCM_RIGHTS):\n if len(cmsg_data) % a.itemsize != 0:\n raise ValueError\n a.frombytes(cmsg_data)\n if len(a) % 256 != msg[0]:\n raise AssertionError(\n \"Len is {0:n} but msg[0] is {1!r}\".format(\n len(a), msg[0]))\n return list(a)\n except (ValueError, IndexError):\n pass\n raise RuntimeError('Invalid data received')\n\n def send_handle(conn, handle, destination_pid):\n '''Send a handle over a local connection.'''\n with socket.fromfd(conn.fileno(), socket.AF_UNIX, socket.SOCK_STREAM) as s:\n sendfds(s, [handle])\n\n def recv_handle(conn):\n '''Receive a handle over a local connection.'''\n with socket.fromfd(conn.fileno(), socket.AF_UNIX, socket.SOCK_STREAM) as s:\n return recvfds(s, 1)[0]\n\n def DupFd(fd):\n '''Return a wrapper for an fd.'''\n popen_obj = context.get_spawning_popen()\n if popen_obj is not None:\n return popen_obj.DupFd(popen_obj.duplicate_for_child(fd))\n elif HAVE_SEND_HANDLE:\n from . import resource_sharer\n return resource_sharer.DupFd(fd)\n else:\n raise ValueError('SCM_RIGHTS appears not to be available')\n\n#\n# Try making some callable types picklable\n#\n\ndef _reduce_method(m):\n if m.__self__ is None:\n return getattr, (m.__class__, m.__func__.__name__)\n else:\n return getattr, (m.__self__, m.__func__.__name__)\nclass _C:\n def f(self):\n pass\nregister(type(_C().f), _reduce_method)\n\n\ndef _reduce_method_descriptor(m):\n return getattr, (m.__objclass__, m.__name__)\nregister(type(list.append), _reduce_method_descriptor)\nregister(type(int.__add__), _reduce_method_descriptor)\n\n\ndef _reduce_partial(p):\n return _rebuild_partial, (p.func, p.args, p.keywords or {})\ndef _rebuild_partial(func, args, keywords):\n return functools.partial(func, *args, **keywords)\nregister(functools.partial, _reduce_partial)\n\n#\n# Make sockets picklable\n#\n\nif sys.platform == 'win32':\n def _reduce_socket(s):\n from .resource_sharer import DupSocket\n return _rebuild_socket, (DupSocket(s),)\n def _rebuild_socket(ds):\n return ds.detach()\n register(socket.socket, _reduce_socket)\n\nelse:\n def _reduce_socket(s):\n df = DupFd(s.fileno())\n return _rebuild_socket, (df, s.family, s.type, s.proto)\n def _rebuild_socket(df, family, type, proto):\n fd = df.detach()\n return socket.socket(family, type, proto, fileno=fd)\n register(socket.socket, _reduce_socket)\n\n\nclass AbstractReducer(metaclass=ABCMeta):\n '''Abstract base class for use in implementing a Reduction class\n suitable for use in replacing the standard reduction mechanism\n used in multiprocessing.'''\n ForkingPickler = ForkingPickler\n register = register\n dump = dump\n send_handle = send_handle\n recv_handle = recv_handle\n\n if sys.platform == 'win32':\n steal_handle = steal_handle\n duplicate = duplicate\n DupHandle = DupHandle\n else:\n sendfds = sendfds\n recvfds = recvfds\n DupFd = DupFd\n\n _reduce_method = _reduce_method\n _reduce_method_descriptor = _reduce_method_descriptor\n _rebuild_partial = _rebuild_partial\n _reduce_socket = _reduce_socket\n _rebuild_socket = _rebuild_socket\n\n def __init__(self, *args):\n register(type(_C().f), _reduce_method)\n register(type(list.append), _reduce_method_descriptor)\n register(type(int.__add__), _reduce_method_descriptor)\n register(functools.partial, _reduce_partial)\n register(socket.socket, _reduce_socket)\n", 281], "/usr/lib/python3.13/threading.py": ["\"\"\"Thread module emulating a subset of Java's threading model.\"\"\"\n\nimport os as _os\nimport sys as _sys\nimport _thread\nimport warnings\n\nfrom time import monotonic as _time\nfrom _weakrefset import WeakSet\nfrom itertools import count as _count\ntry:\n from _collections import deque as _deque\nexcept ImportError:\n from collections import deque as _deque\n\n# Note regarding PEP 8 compliant names\n# This threading model was originally inspired by Java, and inherited\n# the convention of camelCase function and method names from that\n# language. Those original names are not in any imminent danger of\n# being deprecated (even for Py3k),so this module provides them as an\n# alias for the PEP 8 compliant names\n# Note that using the new PEP 8 compliant names facilitates substitution\n# with the multiprocessing module, which doesn't provide the old\n# Java inspired names.\n\n__all__ = ['get_ident', 'active_count', 'Condition', 'current_thread',\n 'enumerate', 'main_thread', 'TIMEOUT_MAX',\n 'Event', 'Lock', 'RLock', 'Semaphore', 'BoundedSemaphore', 'Thread',\n 'Barrier', 'BrokenBarrierError', 'Timer', 'ThreadError',\n 'setprofile', 'settrace', 'local', 'stack_size',\n 'excepthook', 'ExceptHookArgs', 'gettrace', 'getprofile',\n 'setprofile_all_threads','settrace_all_threads']\n\n# Rename some stuff so \"from threading import *\" is safe\n_start_joinable_thread = _thread.start_joinable_thread\n_daemon_threads_allowed = _thread.daemon_threads_allowed\n_allocate_lock = _thread.allocate_lock\n_LockType = _thread.LockType\n_thread_shutdown = _thread._shutdown\n_make_thread_handle = _thread._make_thread_handle\n_ThreadHandle = _thread._ThreadHandle\nget_ident = _thread.get_ident\n_get_main_thread_ident = _thread._get_main_thread_ident\n_is_main_interpreter = _thread._is_main_interpreter\ntry:\n get_native_id = _thread.get_native_id\n _HAVE_THREAD_NATIVE_ID = True\n __all__.append('get_native_id')\nexcept AttributeError:\n _HAVE_THREAD_NATIVE_ID = False\nThreadError = _thread.error\ntry:\n _CRLock = _thread.RLock\nexcept AttributeError:\n _CRLock = None\nTIMEOUT_MAX = _thread.TIMEOUT_MAX\ndel _thread\n\n# get thread-local implementation, either from the thread\n# module, or from the python fallback\n\ntry:\n from _thread import _local as local\nexcept ImportError:\n from _threading_local import local\n\n# Support for profile and trace hooks\n\n_profile_hook = None\n_trace_hook = None\n\ndef setprofile(func):\n \"\"\"Set a profile function for all threads started from the threading module.\n\n The func will be passed to sys.setprofile() for each thread, before its\n run() method is called.\n \"\"\"\n global _profile_hook\n _profile_hook = func\n\ndef setprofile_all_threads(func):\n \"\"\"Set a profile function for all threads started from the threading module\n and all Python threads that are currently executing.\n\n The func will be passed to sys.setprofile() for each thread, before its\n run() method is called.\n \"\"\"\n setprofile(func)\n _sys._setprofileallthreads(func)\n\ndef getprofile():\n \"\"\"Get the profiler function as set by threading.setprofile().\"\"\"\n return _profile_hook\n\ndef settrace(func):\n \"\"\"Set a trace function for all threads started from the threading module.\n\n The func will be passed to sys.settrace() for each thread, before its run()\n method is called.\n \"\"\"\n global _trace_hook\n _trace_hook = func\n\ndef settrace_all_threads(func):\n \"\"\"Set a trace function for all threads started from the threading module\n and all Python threads that are currently executing.\n\n The func will be passed to sys.settrace() for each thread, before its run()\n method is called.\n \"\"\"\n settrace(func)\n _sys._settraceallthreads(func)\n\ndef gettrace():\n \"\"\"Get the trace function as set by threading.settrace().\"\"\"\n return _trace_hook\n\n# Synchronization classes\n\nLock = _LockType\n\ndef RLock(*args, **kwargs):\n \"\"\"Factory function that returns a new reentrant lock.\n\n A reentrant lock must be released by the thread that acquired it. Once a\n thread has acquired a reentrant lock, the same thread may acquire it again\n without blocking; the thread must release it once for each time it has\n acquired it.\n\n \"\"\"\n if args or kwargs:\n warnings.warn(\n 'Passing arguments to RLock is deprecated and will be removed in 3.15',\n DeprecationWarning,\n stacklevel=2,\n )\n if _CRLock is None:\n return _PyRLock(*args, **kwargs)\n return _CRLock(*args, **kwargs)\n\nclass _RLock:\n \"\"\"This class implements reentrant lock objects.\n\n A reentrant lock must be released by the thread that acquired it. Once a\n thread has acquired a reentrant lock, the same thread may acquire it\n again without blocking; the thread must release it once for each time it\n has acquired it.\n\n \"\"\"\n\n def __init__(self):\n self._block = _allocate_lock()\n self._owner = None\n self._count = 0\n\n def __repr__(self):\n owner = self._owner\n try:\n owner = _active[owner].name\n except KeyError:\n pass\n return \"<%s %s.%s object owner=%r count=%d at %s>\" % (\n \"locked\" if self._block.locked() else \"unlocked\",\n self.__class__.__module__,\n self.__class__.__qualname__,\n owner,\n self._count,\n hex(id(self))\n )\n\n def _at_fork_reinit(self):\n self._block._at_fork_reinit()\n self._owner = None\n self._count = 0\n\n def acquire(self, blocking=True, timeout=-1):\n \"\"\"Acquire a lock, blocking or non-blocking.\n\n When invoked without arguments: if this thread already owns the lock,\n increment the recursion level by one, and return immediately. Otherwise,\n if another thread owns the lock, block until the lock is unlocked. Once\n the lock is unlocked (not owned by any thread), then grab ownership, set\n the recursion level to one, and return. If more than one thread is\n blocked waiting until the lock is unlocked, only one at a time will be\n able to grab ownership of the lock. There is no return value in this\n case.\n\n When invoked with the blocking argument set to true, do the same thing\n as when called without arguments, and return true.\n\n When invoked with the blocking argument set to false, do not block. If a\n call without an argument would block, return false immediately;\n otherwise, do the same thing as when called without arguments, and\n return true.\n\n When invoked with the floating-point timeout argument set to a positive\n value, block for at most the number of seconds specified by timeout\n and as long as the lock cannot be acquired. Return true if the lock has\n been acquired, false if the timeout has elapsed.\n\n \"\"\"\n me = get_ident()\n if self._owner == me:\n self._count += 1\n return 1\n rc = self._block.acquire(blocking, timeout)\n if rc:\n self._owner = me\n self._count = 1\n return rc\n\n __enter__ = acquire\n\n def release(self):\n \"\"\"Release a lock, decrementing the recursion level.\n\n If after the decrement it is zero, reset the lock to unlocked (not owned\n by any thread), and if any other threads are blocked waiting for the\n lock to become unlocked, allow exactly one of them to proceed. If after\n the decrement the recursion level is still nonzero, the lock remains\n locked and owned by the calling thread.\n\n Only call this method when the calling thread owns the lock. A\n RuntimeError is raised if this method is called when the lock is\n unlocked.\n\n There is no return value.\n\n \"\"\"\n if self._owner != get_ident():\n raise RuntimeError(\"cannot release un-acquired lock\")\n self._count = count = self._count - 1\n if not count:\n self._owner = None\n self._block.release()\n\n def __exit__(self, t, v, tb):\n self.release()\n\n # Internal methods used by condition variables\n\n def _acquire_restore(self, state):\n self._block.acquire()\n self._count, self._owner = state\n\n def _release_save(self):\n if self._count == 0:\n raise RuntimeError(\"cannot release un-acquired lock\")\n count = self._count\n self._count = 0\n owner = self._owner\n self._owner = None\n self._block.release()\n return (count, owner)\n\n def _is_owned(self):\n return self._owner == get_ident()\n\n # Internal method used for reentrancy checks\n\n def _recursion_count(self):\n if self._owner != get_ident():\n return 0\n return self._count\n\n_PyRLock = _RLock\n\n\nclass Condition:\n \"\"\"Class that implements a condition variable.\n\n A condition variable allows one or more threads to wait until they are\n notified by another thread.\n\n If the lock argument is given and not None, it must be a Lock or RLock\n object, and it is used as the underlying lock. Otherwise, a new RLock object\n is created and used as the underlying lock.\n\n \"\"\"\n\n def __init__(self, lock=None):\n if lock is None:\n lock = RLock()\n self._lock = lock\n # Export the lock's acquire() and release() methods\n self.acquire = lock.acquire\n self.release = lock.release\n # If the lock defines _release_save() and/or _acquire_restore(),\n # these override the default implementations (which just call\n # release() and acquire() on the lock). Ditto for _is_owned().\n if hasattr(lock, '_release_save'):\n self._release_save = lock._release_save\n if hasattr(lock, '_acquire_restore'):\n self._acquire_restore = lock._acquire_restore\n if hasattr(lock, '_is_owned'):\n self._is_owned = lock._is_owned\n self._waiters = _deque()\n\n def _at_fork_reinit(self):\n self._lock._at_fork_reinit()\n self._waiters.clear()\n\n def __enter__(self):\n return self._lock.__enter__()\n\n def __exit__(self, *args):\n return self._lock.__exit__(*args)\n\n def __repr__(self):\n return \"\" % (self._lock, len(self._waiters))\n\n def _release_save(self):\n self._lock.release() # No state to save\n\n def _acquire_restore(self, x):\n self._lock.acquire() # Ignore saved state\n\n def _is_owned(self):\n # Return True if lock is owned by current_thread.\n # This method is called only if _lock doesn't have _is_owned().\n if self._lock.acquire(False):\n self._lock.release()\n return False\n else:\n return True\n\n def wait(self, timeout=None):\n \"\"\"Wait until notified or until a timeout occurs.\n\n If the calling thread has not acquired the lock when this method is\n called, a RuntimeError is raised.\n\n This method releases the underlying lock, and then blocks until it is\n awakened by a notify() or notify_all() call for the same condition\n variable in another thread, or until the optional timeout occurs. Once\n awakened or timed out, it re-acquires the lock and returns.\n\n When the timeout argument is present and not None, it should be a\n floating-point number specifying a timeout for the operation in seconds\n (or fractions thereof).\n\n When the underlying lock is an RLock, it is not released using its\n release() method, since this may not actually unlock the lock when it\n was acquired multiple times recursively. Instead, an internal interface\n of the RLock class is used, which really unlocks it even when it has\n been recursively acquired several times. Another internal interface is\n then used to restore the recursion level when the lock is reacquired.\n\n \"\"\"\n if not self._is_owned():\n raise RuntimeError(\"cannot wait on un-acquired lock\")\n waiter = _allocate_lock()\n waiter.acquire()\n self._waiters.append(waiter)\n saved_state = self._release_save()\n gotit = False\n try: # restore state no matter what (e.g., KeyboardInterrupt)\n if timeout is None:\n waiter.acquire()\n gotit = True\n else:\n if timeout > 0:\n gotit = waiter.acquire(True, timeout)\n else:\n gotit = waiter.acquire(False)\n return gotit\n finally:\n self._acquire_restore(saved_state)\n if not gotit:\n try:\n self._waiters.remove(waiter)\n except ValueError:\n pass\n\n def wait_for(self, predicate, timeout=None):\n \"\"\"Wait until a condition evaluates to True.\n\n predicate should be a callable which result will be interpreted as a\n boolean value. A timeout may be provided giving the maximum time to\n wait.\n\n \"\"\"\n endtime = None\n waittime = timeout\n result = predicate()\n while not result:\n if waittime is not None:\n if endtime is None:\n endtime = _time() + waittime\n else:\n waittime = endtime - _time()\n if waittime <= 0:\n break\n self.wait(waittime)\n result = predicate()\n return result\n\n def notify(self, n=1):\n \"\"\"Wake up one or more threads waiting on this condition, if any.\n\n If the calling thread has not acquired the lock when this method is\n called, a RuntimeError is raised.\n\n This method wakes up at most n of the threads waiting for the condition\n variable; it is a no-op if no threads are waiting.\n\n \"\"\"\n if not self._is_owned():\n raise RuntimeError(\"cannot notify on un-acquired lock\")\n waiters = self._waiters\n while waiters and n > 0:\n waiter = waiters[0]\n try:\n waiter.release()\n except RuntimeError:\n # gh-92530: The previous call of notify() released the lock,\n # but was interrupted before removing it from the queue.\n # It can happen if a signal handler raises an exception,\n # like CTRL+C which raises KeyboardInterrupt.\n pass\n else:\n n -= 1\n try:\n waiters.remove(waiter)\n except ValueError:\n pass\n\n def notify_all(self):\n \"\"\"Wake up all threads waiting on this condition.\n\n If the calling thread has not acquired the lock when this method\n is called, a RuntimeError is raised.\n\n \"\"\"\n self.notify(len(self._waiters))\n\n def notifyAll(self):\n \"\"\"Wake up all threads waiting on this condition.\n\n This method is deprecated, use notify_all() instead.\n\n \"\"\"\n import warnings\n warnings.warn('notifyAll() is deprecated, use notify_all() instead',\n DeprecationWarning, stacklevel=2)\n self.notify_all()\n\n\nclass Semaphore:\n \"\"\"This class implements semaphore objects.\n\n Semaphores manage a counter representing the number of release() calls minus\n the number of acquire() calls, plus an initial value. The acquire() method\n blocks if necessary until it can return without making the counter\n negative. If not given, value defaults to 1.\n\n \"\"\"\n\n # After Tim Peters' semaphore class, but not quite the same (no maximum)\n\n def __init__(self, value=1):\n if value < 0:\n raise ValueError(\"semaphore initial value must be >= 0\")\n self._cond = Condition(Lock())\n self._value = value\n\n def __repr__(self):\n cls = self.__class__\n return (f\"<{cls.__module__}.{cls.__qualname__} at {id(self):#x}:\"\n f\" value={self._value}>\")\n\n def acquire(self, blocking=True, timeout=None):\n \"\"\"Acquire a semaphore, decrementing the internal counter by one.\n\n When invoked without arguments: if the internal counter is larger than\n zero on entry, decrement it by one and return immediately. If it is zero\n on entry, block, waiting until some other thread has called release() to\n make it larger than zero. This is done with proper interlocking so that\n if multiple acquire() calls are blocked, release() will wake exactly one\n of them up. The implementation may pick one at random, so the order in\n which blocked threads are awakened should not be relied on. There is no\n return value in this case.\n\n When invoked with blocking set to true, do the same thing as when called\n without arguments, and return true.\n\n When invoked with blocking set to false, do not block. If a call without\n an argument would block, return false immediately; otherwise, do the\n same thing as when called without arguments, and return true.\n\n When invoked with a timeout other than None, it will block for at\n most timeout seconds. If acquire does not complete successfully in\n that interval, return false. Return true otherwise.\n\n \"\"\"\n if not blocking and timeout is not None:\n raise ValueError(\"can't specify timeout for non-blocking acquire\")\n rc = False\n endtime = None\n with self._cond:\n while self._value == 0:\n if not blocking:\n break\n if timeout is not None:\n if endtime is None:\n endtime = _time() + timeout\n else:\n timeout = endtime - _time()\n if timeout <= 0:\n break\n self._cond.wait(timeout)\n else:\n self._value -= 1\n rc = True\n return rc\n\n __enter__ = acquire\n\n def release(self, n=1):\n \"\"\"Release a semaphore, incrementing the internal counter by one or more.\n\n When the counter is zero on entry and another thread is waiting for it\n to become larger than zero again, wake up that thread.\n\n \"\"\"\n if n < 1:\n raise ValueError('n must be one or more')\n with self._cond:\n self._value += n\n self._cond.notify(n)\n\n def __exit__(self, t, v, tb):\n self.release()\n\n\nclass BoundedSemaphore(Semaphore):\n \"\"\"Implements a bounded semaphore.\n\n A bounded semaphore checks to make sure its current value doesn't exceed its\n initial value. If it does, ValueError is raised. In most situations\n semaphores are used to guard resources with limited capacity.\n\n If the semaphore is released too many times it's a sign of a bug. If not\n given, value defaults to 1.\n\n Like regular semaphores, bounded semaphores manage a counter representing\n the number of release() calls minus the number of acquire() calls, plus an\n initial value. The acquire() method blocks if necessary until it can return\n without making the counter negative. If not given, value defaults to 1.\n\n \"\"\"\n\n def __init__(self, value=1):\n super().__init__(value)\n self._initial_value = value\n\n def __repr__(self):\n cls = self.__class__\n return (f\"<{cls.__module__}.{cls.__qualname__} at {id(self):#x}:\"\n f\" value={self._value}/{self._initial_value}>\")\n\n def release(self, n=1):\n \"\"\"Release a semaphore, incrementing the internal counter by one or more.\n\n When the counter is zero on entry and another thread is waiting for it\n to become larger than zero again, wake up that thread.\n\n If the number of releases exceeds the number of acquires,\n raise a ValueError.\n\n \"\"\"\n if n < 1:\n raise ValueError('n must be one or more')\n with self._cond:\n if self._value + n > self._initial_value:\n raise ValueError(\"Semaphore released too many times\")\n self._value += n\n self._cond.notify(n)\n\n\nclass Event:\n \"\"\"Class implementing event objects.\n\n Events manage a flag that can be set to true with the set() method and reset\n to false with the clear() method. The wait() method blocks until the flag is\n true. The flag is initially false.\n\n \"\"\"\n\n # After Tim Peters' event class (without is_posted())\n\n def __init__(self):\n self._cond = Condition(Lock())\n self._flag = False\n\n def __repr__(self):\n cls = self.__class__\n status = 'set' if self._flag else 'unset'\n return f\"<{cls.__module__}.{cls.__qualname__} at {id(self):#x}: {status}>\"\n\n def _at_fork_reinit(self):\n # Private method called by Thread._after_fork()\n self._cond._at_fork_reinit()\n\n def is_set(self):\n \"\"\"Return true if and only if the internal flag is true.\"\"\"\n return self._flag\n\n def isSet(self):\n \"\"\"Return true if and only if the internal flag is true.\n\n This method is deprecated, use is_set() instead.\n\n \"\"\"\n import warnings\n warnings.warn('isSet() is deprecated, use is_set() instead',\n DeprecationWarning, stacklevel=2)\n return self.is_set()\n\n def set(self):\n \"\"\"Set the internal flag to true.\n\n All threads waiting for it to become true are awakened. Threads\n that call wait() once the flag is true will not block at all.\n\n \"\"\"\n with self._cond:\n self._flag = True\n self._cond.notify_all()\n\n def clear(self):\n \"\"\"Reset the internal flag to false.\n\n Subsequently, threads calling wait() will block until set() is called to\n set the internal flag to true again.\n\n \"\"\"\n with self._cond:\n self._flag = False\n\n def wait(self, timeout=None):\n \"\"\"Block until the internal flag is true.\n\n If the internal flag is true on entry, return immediately. Otherwise,\n block until another thread calls set() to set the flag to true, or until\n the optional timeout occurs.\n\n When the timeout argument is present and not None, it should be a\n floating-point number specifying a timeout for the operation in seconds\n (or fractions thereof).\n\n This method returns the internal flag on exit, so it will always return\n True except if a timeout is given and the operation times out.\n\n \"\"\"\n with self._cond:\n signaled = self._flag\n if not signaled:\n signaled = self._cond.wait(timeout)\n return signaled\n\n\n# A barrier class. Inspired in part by the pthread_barrier_* api and\n# the CyclicBarrier class from Java. See\n# http://sourceware.org/pthreads-win32/manual/pthread_barrier_init.html and\n# http://java.sun.com/j2se/1.5.0/docs/api/java/util/concurrent/\n# CyclicBarrier.html\n# for information.\n# We maintain two main states, 'filling' and 'draining' enabling the barrier\n# to be cyclic. Threads are not allowed into it until it has fully drained\n# since the previous cycle. In addition, a 'resetting' state exists which is\n# similar to 'draining' except that threads leave with a BrokenBarrierError,\n# and a 'broken' state in which all threads get the exception.\nclass Barrier:\n \"\"\"Implements a Barrier.\n\n Useful for synchronizing a fixed number of threads at known synchronization\n points. Threads block on 'wait()' and are simultaneously awoken once they\n have all made that call.\n\n \"\"\"\n\n def __init__(self, parties, action=None, timeout=None):\n \"\"\"Create a barrier, initialised to 'parties' threads.\n\n 'action' is a callable which, when supplied, will be called by one of\n the threads after they have all entered the barrier and just prior to\n releasing them all. If a 'timeout' is provided, it is used as the\n default for all subsequent 'wait()' calls.\n\n \"\"\"\n if parties < 1:\n raise ValueError(\"parties must be > 0\")\n self._cond = Condition(Lock())\n self._action = action\n self._timeout = timeout\n self._parties = parties\n self._state = 0 # 0 filling, 1 draining, -1 resetting, -2 broken\n self._count = 0\n\n def __repr__(self):\n cls = self.__class__\n if self.broken:\n return f\"<{cls.__module__}.{cls.__qualname__} at {id(self):#x}: broken>\"\n return (f\"<{cls.__module__}.{cls.__qualname__} at {id(self):#x}:\"\n f\" waiters={self.n_waiting}/{self.parties}>\")\n\n def wait(self, timeout=None):\n \"\"\"Wait for the barrier.\n\n When the specified number of threads have started waiting, they are all\n simultaneously awoken. If an 'action' was provided for the barrier, one\n of the threads will have executed that callback prior to returning.\n Returns an individual index number from 0 to 'parties-1'.\n\n \"\"\"\n if timeout is None:\n timeout = self._timeout\n with self._cond:\n self._enter() # Block while the barrier drains.\n index = self._count\n self._count += 1\n try:\n if index + 1 == self._parties:\n # We release the barrier\n self._release()\n else:\n # We wait until someone releases us\n self._wait(timeout)\n return index\n finally:\n self._count -= 1\n # Wake up any threads waiting for barrier to drain.\n self._exit()\n\n # Block until the barrier is ready for us, or raise an exception\n # if it is broken.\n def _enter(self):\n while self._state in (-1, 1):\n # It is draining or resetting, wait until done\n self._cond.wait()\n #see if the barrier is in a broken state\n if self._state < 0:\n raise BrokenBarrierError\n assert self._state == 0\n\n # Optionally run the 'action' and release the threads waiting\n # in the barrier.\n def _release(self):\n try:\n if self._action:\n self._action()\n # enter draining state\n self._state = 1\n self._cond.notify_all()\n except:\n #an exception during the _action handler. Break and reraise\n self._break()\n raise\n\n # Wait in the barrier until we are released. Raise an exception\n # if the barrier is reset or broken.\n def _wait(self, timeout):\n if not self._cond.wait_for(lambda : self._state != 0, timeout):\n #timed out. Break the barrier\n self._break()\n raise BrokenBarrierError\n if self._state < 0:\n raise BrokenBarrierError\n assert self._state == 1\n\n # If we are the last thread to exit the barrier, signal any threads\n # waiting for the barrier to drain.\n def _exit(self):\n if self._count == 0:\n if self._state in (-1, 1):\n #resetting or draining\n self._state = 0\n self._cond.notify_all()\n\n def reset(self):\n \"\"\"Reset the barrier to the initial state.\n\n Any threads currently waiting will get the BrokenBarrier exception\n raised.\n\n \"\"\"\n with self._cond:\n if self._count > 0:\n if self._state == 0:\n #reset the barrier, waking up threads\n self._state = -1\n elif self._state == -2:\n #was broken, set it to reset state\n #which clears when the last thread exits\n self._state = -1\n else:\n self._state = 0\n self._cond.notify_all()\n\n def abort(self):\n \"\"\"Place the barrier into a 'broken' state.\n\n Useful in case of error. Any currently waiting threads and threads\n attempting to 'wait()' will have BrokenBarrierError raised.\n\n \"\"\"\n with self._cond:\n self._break()\n\n def _break(self):\n # An internal error was detected. The barrier is set to\n # a broken state all parties awakened.\n self._state = -2\n self._cond.notify_all()\n\n @property\n def parties(self):\n \"\"\"Return the number of threads required to trip the barrier.\"\"\"\n return self._parties\n\n @property\n def n_waiting(self):\n \"\"\"Return the number of threads currently waiting at the barrier.\"\"\"\n # We don't need synchronization here since this is an ephemeral result\n # anyway. It returns the correct value in the steady state.\n if self._state == 0:\n return self._count\n return 0\n\n @property\n def broken(self):\n \"\"\"Return True if the barrier is in a broken state.\"\"\"\n return self._state == -2\n\n# exception raised by the Barrier class\nclass BrokenBarrierError(RuntimeError):\n pass\n\n\n# Helper to generate new thread names\n_counter = _count(1).__next__\ndef _newname(name_template):\n return name_template % _counter()\n\n# Active thread administration.\n#\n# bpo-44422: Use a reentrant lock to allow reentrant calls to functions like\n# threading.enumerate().\n_active_limbo_lock = RLock()\n_active = {} # maps thread id to Thread object\n_limbo = {}\n_dangling = WeakSet()\n\n\n# Main class for threads\n\nclass Thread:\n \"\"\"A class that represents a thread of control.\n\n This class can be safely subclassed in a limited fashion. There are two ways\n to specify the activity: by passing a callable object to the constructor, or\n by overriding the run() method in a subclass.\n\n \"\"\"\n\n _initialized = False\n\n def __init__(self, group=None, target=None, name=None,\n args=(), kwargs=None, *, daemon=None):\n \"\"\"This constructor should always be called with keyword arguments. Arguments are:\n\n *group* should be None; reserved for future extension when a ThreadGroup\n class is implemented.\n\n *target* is the callable object to be invoked by the run()\n method. Defaults to None, meaning nothing is called.\n\n *name* is the thread name. By default, a unique name is constructed of\n the form \"Thread-N\" where N is a small decimal number.\n\n *args* is a list or tuple of arguments for the target invocation. Defaults to ().\n\n *kwargs* is a dictionary of keyword arguments for the target\n invocation. Defaults to {}.\n\n If a subclass overrides the constructor, it must make sure to invoke\n the base class constructor (Thread.__init__()) before doing anything\n else to the thread.\n\n \"\"\"\n assert group is None, \"group argument must be None for now\"\n if kwargs is None:\n kwargs = {}\n if name:\n name = str(name)\n else:\n name = _newname(\"Thread-%d\")\n if target is not None:\n try:\n target_name = target.__name__\n name += f\" ({target_name})\"\n except AttributeError:\n pass\n\n self._target = target\n self._name = name\n self._args = args\n self._kwargs = kwargs\n if daemon is not None:\n if daemon and not _daemon_threads_allowed():\n raise RuntimeError('daemon threads are disabled in this (sub)interpreter')\n self._daemonic = daemon\n else:\n self._daemonic = current_thread().daemon\n self._ident = None\n if _HAVE_THREAD_NATIVE_ID:\n self._native_id = None\n self._handle = _ThreadHandle()\n self._started = Event()\n self._initialized = True\n # Copy of sys.stderr used by self._invoke_excepthook()\n self._stderr = _sys.stderr\n self._invoke_excepthook = _make_invoke_excepthook()\n # For debugging and _after_fork()\n _dangling.add(self)\n\n def _after_fork(self, new_ident=None):\n # Private! Called by threading._after_fork().\n self._started._at_fork_reinit()\n if new_ident is not None:\n # This thread is alive.\n self._ident = new_ident\n assert self._handle.ident == new_ident\n else:\n # Otherwise, the thread is dead, Jim. _PyThread_AfterFork()\n # already marked our handle done.\n pass\n\n def __repr__(self):\n assert self._initialized, \"Thread.__init__() was not called\"\n status = \"initial\"\n if self._started.is_set():\n status = \"started\"\n if self._handle.is_done():\n status = \"stopped\"\n if self._daemonic:\n status += \" daemon\"\n if self._ident is not None:\n status += \" %s\" % self._ident\n return \"<%s(%s, %s)>\" % (self.__class__.__name__, self._name, status)\n\n def start(self):\n \"\"\"Start the thread's activity.\n\n It must be called at most once per thread object. It arranges for the\n object's run() method to be invoked in a separate thread of control.\n\n This method will raise a RuntimeError if called more than once on the\n same thread object.\n\n \"\"\"\n if not self._initialized:\n raise RuntimeError(\"thread.__init__() not called\")\n\n if self._started.is_set():\n raise RuntimeError(\"threads can only be started once\")\n\n with _active_limbo_lock:\n _limbo[self] = self\n try:\n # Start joinable thread\n _start_joinable_thread(self._bootstrap, handle=self._handle,\n daemon=self.daemon)\n except Exception:\n with _active_limbo_lock:\n del _limbo[self]\n raise\n self._started.wait() # Will set ident and native_id\n\n def run(self):\n \"\"\"Method representing the thread's activity.\n\n You may override this method in a subclass. The standard run() method\n invokes the callable object passed to the object's constructor as the\n target argument, if any, with sequential and keyword arguments taken\n from the args and kwargs arguments, respectively.\n\n \"\"\"\n try:\n if self._target is not None:\n self._target(*self._args, **self._kwargs)\n finally:\n # Avoid a refcycle if the thread is running a function with\n # an argument that has a member that points to the thread.\n del self._target, self._args, self._kwargs\n\n def _bootstrap(self):\n # Wrapper around the real bootstrap code that ignores\n # exceptions during interpreter cleanup. Those typically\n # happen when a daemon thread wakes up at an unfortunate\n # moment, finds the world around it destroyed, and raises some\n # random exception *** while trying to report the exception in\n # _bootstrap_inner() below ***. Those random exceptions\n # don't help anybody, and they confuse users, so we suppress\n # them. We suppress them only when it appears that the world\n # indeed has already been destroyed, so that exceptions in\n # _bootstrap_inner() during normal business hours are properly\n # reported. Also, we only suppress them for daemonic threads;\n # if a non-daemonic encounters this, something else is wrong.\n try:\n self._bootstrap_inner()\n except:\n if self._daemonic and _sys is None:\n return\n raise\n\n def _set_ident(self):\n self._ident = get_ident()\n\n if _HAVE_THREAD_NATIVE_ID:\n def _set_native_id(self):\n self._native_id = get_native_id()\n\n def _bootstrap_inner(self):\n try:\n self._set_ident()\n if _HAVE_THREAD_NATIVE_ID:\n self._set_native_id()\n self._started.set()\n with _active_limbo_lock:\n _active[self._ident] = self\n del _limbo[self]\n\n if _trace_hook:\n _sys.settrace(_trace_hook)\n if _profile_hook:\n _sys.setprofile(_profile_hook)\n\n try:\n self.run()\n except:\n self._invoke_excepthook(self)\n finally:\n self._delete()\n\n def _delete(self):\n \"Remove current thread from the dict of currently running threads.\"\n with _active_limbo_lock:\n del _active[get_ident()]\n # There must not be any python code between the previous line\n # and after the lock is released. Otherwise a tracing function\n # could try to acquire the lock again in the same thread, (in\n # current_thread()), and would block.\n\n def join(self, timeout=None):\n \"\"\"Wait until the thread terminates.\n\n This blocks the calling thread until the thread whose join() method is\n called terminates -- either normally or through an unhandled exception\n or until the optional timeout occurs.\n\n When the timeout argument is present and not None, it should be a\n floating-point number specifying a timeout for the operation in seconds\n (or fractions thereof). As join() always returns None, you must call\n is_alive() after join() to decide whether a timeout happened -- if the\n thread is still alive, the join() call timed out.\n\n When the timeout argument is not present or None, the operation will\n block until the thread terminates.\n\n A thread can be join()ed many times.\n\n join() raises a RuntimeError if an attempt is made to join the current\n thread as that would cause a deadlock. It is also an error to join() a\n thread before it has been started and attempts to do so raises the same\n exception.\n\n \"\"\"\n if not self._initialized:\n raise RuntimeError(\"Thread.__init__() not called\")\n if not self._started.is_set():\n raise RuntimeError(\"cannot join thread before it is started\")\n if self is current_thread():\n raise RuntimeError(\"cannot join current thread\")\n\n # the behavior of a negative timeout isn't documented, but\n # historically .join(timeout=x) for x<0 has acted as if timeout=0\n if timeout is not None:\n timeout = max(timeout, 0)\n\n self._handle.join(timeout)\n\n @property\n def name(self):\n \"\"\"A string used for identification purposes only.\n\n It has no semantics. Multiple threads may be given the same name. The\n initial name is set by the constructor.\n\n \"\"\"\n assert self._initialized, \"Thread.__init__() not called\"\n return self._name\n\n @name.setter\n def name(self, name):\n assert self._initialized, \"Thread.__init__() not called\"\n self._name = str(name)\n\n @property\n def ident(self):\n \"\"\"Thread identifier of this thread or None if it has not been started.\n\n This is a nonzero integer. See the get_ident() function. Thread\n identifiers may be recycled when a thread exits and another thread is\n created. The identifier is available even after the thread has exited.\n\n \"\"\"\n assert self._initialized, \"Thread.__init__() not called\"\n return self._ident\n\n if _HAVE_THREAD_NATIVE_ID:\n @property\n def native_id(self):\n \"\"\"Native integral thread ID of this thread, or None if it has not been started.\n\n This is a non-negative integer. See the get_native_id() function.\n This represents the Thread ID as reported by the kernel.\n\n \"\"\"\n assert self._initialized, \"Thread.__init__() not called\"\n return self._native_id\n\n def is_alive(self):\n \"\"\"Return whether the thread is alive.\n\n This method returns True just before the run() method starts until just\n after the run() method terminates. See also the module function\n enumerate().\n\n \"\"\"\n assert self._initialized, \"Thread.__init__() not called\"\n return self._started.is_set() and not self._handle.is_done()\n\n @property\n def daemon(self):\n \"\"\"A boolean value indicating whether this thread is a daemon thread.\n\n This must be set before start() is called, otherwise RuntimeError is\n raised. Its initial value is inherited from the creating thread; the\n main thread is not a daemon thread and therefore all threads created in\n the main thread default to daemon = False.\n\n The entire Python program exits when only daemon threads are left.\n\n \"\"\"\n assert self._initialized, \"Thread.__init__() not called\"\n return self._daemonic\n\n @daemon.setter\n def daemon(self, daemonic):\n if not self._initialized:\n raise RuntimeError(\"Thread.__init__() not called\")\n if daemonic and not _daemon_threads_allowed():\n raise RuntimeError('daemon threads are disabled in this interpreter')\n if self._started.is_set():\n raise RuntimeError(\"cannot set daemon status of active thread\")\n self._daemonic = daemonic\n\n def isDaemon(self):\n \"\"\"Return whether this thread is a daemon.\n\n This method is deprecated, use the daemon attribute instead.\n\n \"\"\"\n import warnings\n warnings.warn('isDaemon() is deprecated, get the daemon attribute instead',\n DeprecationWarning, stacklevel=2)\n return self.daemon\n\n def setDaemon(self, daemonic):\n \"\"\"Set whether this thread is a daemon.\n\n This method is deprecated, use the .daemon property instead.\n\n \"\"\"\n import warnings\n warnings.warn('setDaemon() is deprecated, set the daemon attribute instead',\n DeprecationWarning, stacklevel=2)\n self.daemon = daemonic\n\n def getName(self):\n \"\"\"Return a string used for identification purposes only.\n\n This method is deprecated, use the name attribute instead.\n\n \"\"\"\n import warnings\n warnings.warn('getName() is deprecated, get the name attribute instead',\n DeprecationWarning, stacklevel=2)\n return self.name\n\n def setName(self, name):\n \"\"\"Set the name string for this thread.\n\n This method is deprecated, use the name attribute instead.\n\n \"\"\"\n import warnings\n warnings.warn('setName() is deprecated, set the name attribute instead',\n DeprecationWarning, stacklevel=2)\n self.name = name\n\n\ntry:\n from _thread import (_excepthook as excepthook,\n _ExceptHookArgs as ExceptHookArgs)\nexcept ImportError:\n # Simple Python implementation if _thread._excepthook() is not available\n from traceback import print_exception as _print_exception\n from collections import namedtuple\n\n _ExceptHookArgs = namedtuple(\n 'ExceptHookArgs',\n 'exc_type exc_value exc_traceback thread')\n\n def ExceptHookArgs(args):\n return _ExceptHookArgs(*args)\n\n def excepthook(args, /):\n \"\"\"\n Handle uncaught Thread.run() exception.\n \"\"\"\n if args.exc_type == SystemExit:\n # silently ignore SystemExit\n return\n\n if _sys is not None and _sys.stderr is not None:\n stderr = _sys.stderr\n elif args.thread is not None:\n stderr = args.thread._stderr\n if stderr is None:\n # do nothing if sys.stderr is None and sys.stderr was None\n # when the thread was created\n return\n else:\n # do nothing if sys.stderr is None and args.thread is None\n return\n\n if args.thread is not None:\n name = args.thread.name\n else:\n name = get_ident()\n print(f\"Exception in thread {name}:\",\n file=stderr, flush=True)\n _print_exception(args.exc_type, args.exc_value, args.exc_traceback,\n file=stderr)\n stderr.flush()\n\n\n# Original value of threading.excepthook\n__excepthook__ = excepthook\n\n\ndef _make_invoke_excepthook():\n # Create a local namespace to ensure that variables remain alive\n # when _invoke_excepthook() is called, even if it is called late during\n # Python shutdown. It is mostly needed for daemon threads.\n\n old_excepthook = excepthook\n old_sys_excepthook = _sys.excepthook\n if old_excepthook is None:\n raise RuntimeError(\"threading.excepthook is None\")\n if old_sys_excepthook is None:\n raise RuntimeError(\"sys.excepthook is None\")\n\n sys_exc_info = _sys.exc_info\n local_print = print\n local_sys = _sys\n\n def invoke_excepthook(thread):\n global excepthook\n try:\n hook = excepthook\n if hook is None:\n hook = old_excepthook\n\n args = ExceptHookArgs([*sys_exc_info(), thread])\n\n hook(args)\n except Exception as exc:\n exc.__suppress_context__ = True\n del exc\n\n if local_sys is not None and local_sys.stderr is not None:\n stderr = local_sys.stderr\n else:\n stderr = thread._stderr\n\n local_print(\"Exception in threading.excepthook:\",\n file=stderr, flush=True)\n\n if local_sys is not None and local_sys.excepthook is not None:\n sys_excepthook = local_sys.excepthook\n else:\n sys_excepthook = old_sys_excepthook\n\n sys_excepthook(*sys_exc_info())\n finally:\n # Break reference cycle (exception stored in a variable)\n args = None\n\n return invoke_excepthook\n\n\n# The timer class was contributed by Itamar Shtull-Trauring\n\nclass Timer(Thread):\n \"\"\"Call a function after a specified number of seconds:\n\n t = Timer(30.0, f, args=None, kwargs=None)\n t.start()\n t.cancel() # stop the timer's action if it's still waiting\n\n \"\"\"\n\n def __init__(self, interval, function, args=None, kwargs=None):\n Thread.__init__(self)\n self.interval = interval\n self.function = function\n self.args = args if args is not None else []\n self.kwargs = kwargs if kwargs is not None else {}\n self.finished = Event()\n\n def cancel(self):\n \"\"\"Stop the timer if it hasn't finished yet.\"\"\"\n self.finished.set()\n\n def run(self):\n self.finished.wait(self.interval)\n if not self.finished.is_set():\n self.function(*self.args, **self.kwargs)\n self.finished.set()\n\n\n# Special thread class to represent the main thread\n\nclass _MainThread(Thread):\n\n def __init__(self):\n Thread.__init__(self, name=\"MainThread\", daemon=False)\n self._started.set()\n self._ident = _get_main_thread_ident()\n self._handle = _make_thread_handle(self._ident)\n if _HAVE_THREAD_NATIVE_ID:\n self._set_native_id()\n with _active_limbo_lock:\n _active[self._ident] = self\n\n\n# Helper thread-local instance to detect when a _DummyThread\n# is collected. Not a part of the public API.\n_thread_local_info = local()\n\n\nclass _DeleteDummyThreadOnDel:\n '''\n Helper class to remove a dummy thread from threading._active on __del__.\n '''\n\n def __init__(self, dummy_thread):\n self._dummy_thread = dummy_thread\n self._tident = dummy_thread.ident\n # Put the thread on a thread local variable so that when\n # the related thread finishes this instance is collected.\n #\n # Note: no other references to this instance may be created.\n # If any client code creates a reference to this instance,\n # the related _DummyThread will be kept forever!\n _thread_local_info._track_dummy_thread_ref = self\n\n def __del__(self):\n with _active_limbo_lock:\n if _active.get(self._tident) is self._dummy_thread:\n _active.pop(self._tident, None)\n\n\n# Dummy thread class to represent threads not started here.\n# These should be added to `_active` and removed automatically\n# when they die, although they can't be waited for.\n# Their purpose is to return *something* from current_thread().\n# They are marked as daemon threads so we won't wait for them\n# when we exit (conform previous semantics).\n\nclass _DummyThread(Thread):\n\n def __init__(self):\n Thread.__init__(self, name=_newname(\"Dummy-%d\"),\n daemon=_daemon_threads_allowed())\n self._started.set()\n self._set_ident()\n self._handle = _make_thread_handle(self._ident)\n if _HAVE_THREAD_NATIVE_ID:\n self._set_native_id()\n with _active_limbo_lock:\n _active[self._ident] = self\n _DeleteDummyThreadOnDel(self)\n\n def is_alive(self):\n if not self._handle.is_done() and self._started.is_set():\n return True\n raise RuntimeError(\"thread is not alive\")\n\n def join(self, timeout=None):\n raise RuntimeError(\"cannot join a dummy thread\")\n\n def _after_fork(self, new_ident=None):\n if new_ident is not None:\n self.__class__ = _MainThread\n self._name = 'MainThread'\n self._daemonic = False\n Thread._after_fork(self, new_ident=new_ident)\n\n\n# Global API functions\n\ndef current_thread():\n \"\"\"Return the current Thread object, corresponding to the caller's thread of control.\n\n If the caller's thread of control was not created through the threading\n module, a dummy thread object with limited functionality is returned.\n\n \"\"\"\n try:\n return _active[get_ident()]\n except KeyError:\n return _DummyThread()\n\ndef currentThread():\n \"\"\"Return the current Thread object, corresponding to the caller's thread of control.\n\n This function is deprecated, use current_thread() instead.\n\n \"\"\"\n import warnings\n warnings.warn('currentThread() is deprecated, use current_thread() instead',\n DeprecationWarning, stacklevel=2)\n return current_thread()\n\ndef active_count():\n \"\"\"Return the number of Thread objects currently alive.\n\n The returned count is equal to the length of the list returned by\n enumerate().\n\n \"\"\"\n # NOTE: if the logic in here ever changes, update Modules/posixmodule.c\n # warn_about_fork_with_threads() to match.\n with _active_limbo_lock:\n return len(_active) + len(_limbo)\n\ndef activeCount():\n \"\"\"Return the number of Thread objects currently alive.\n\n This function is deprecated, use active_count() instead.\n\n \"\"\"\n import warnings\n warnings.warn('activeCount() is deprecated, use active_count() instead',\n DeprecationWarning, stacklevel=2)\n return active_count()\n\ndef _enumerate():\n # Same as enumerate(), but without the lock. Internal use only.\n return list(_active.values()) + list(_limbo.values())\n\ndef enumerate():\n \"\"\"Return a list of all Thread objects currently alive.\n\n The list includes daemonic threads, dummy thread objects created by\n current_thread(), and the main thread. It excludes terminated threads and\n threads that have not yet been started.\n\n \"\"\"\n with _active_limbo_lock:\n return list(_active.values()) + list(_limbo.values())\n\n\n_threading_atexits = []\n_SHUTTING_DOWN = False\n\ndef _register_atexit(func, *arg, **kwargs):\n \"\"\"CPython internal: register *func* to be called before joining threads.\n\n The registered *func* is called with its arguments just before all\n non-daemon threads are joined in `_shutdown()`. It provides a similar\n purpose to `atexit.register()`, but its functions are called prior to\n threading shutdown instead of interpreter shutdown.\n\n For similarity to atexit, the registered functions are called in reverse.\n \"\"\"\n if _SHUTTING_DOWN:\n raise RuntimeError(\"can't register atexit after shutdown\")\n\n _threading_atexits.append(lambda: func(*arg, **kwargs))\n\n\nfrom _thread import stack_size\n\n# Create the main thread object,\n# and make it available for the interpreter\n# (Py_Main) as threading._shutdown.\n\n_main_thread = _MainThread()\n\ndef _shutdown():\n \"\"\"\n Wait until the Python thread state of all non-daemon threads get deleted.\n \"\"\"\n # Obscure: other threads may be waiting to join _main_thread. That's\n # dubious, but some code does it. We can't wait for it to be marked as done\n # normally - that won't happen until the interpreter is nearly dead. So\n # mark it done here.\n if _main_thread._handle.is_done() and _is_main_interpreter():\n # _shutdown() was already called\n return\n\n global _SHUTTING_DOWN\n _SHUTTING_DOWN = True\n\n # Call registered threading atexit functions before threads are joined.\n # Order is reversed, similar to atexit.\n for atexit_call in reversed(_threading_atexits):\n atexit_call()\n\n if _is_main_interpreter():\n _main_thread._handle._set_done()\n\n # Wait for all non-daemon threads to exit.\n _thread_shutdown()\n\n\ndef main_thread():\n \"\"\"Return the main thread object.\n\n In normal conditions, the main thread is the thread from which the\n Python interpreter was started.\n \"\"\"\n # XXX Figure this out for subinterpreters. (See gh-75698.)\n return _main_thread\n\n\ndef _after_fork():\n \"\"\"\n Cleanup threading module state that should not exist after a fork.\n \"\"\"\n # Reset _active_limbo_lock, in case we forked while the lock was held\n # by another (non-forked) thread. http://bugs.python.org/issue874900\n global _active_limbo_lock, _main_thread\n _active_limbo_lock = RLock()\n\n # fork() only copied the current thread; clear references to others.\n new_active = {}\n\n try:\n current = _active[get_ident()]\n except KeyError:\n # fork() was called in a thread which was not spawned\n # by threading.Thread. For example, a thread spawned\n # by thread.start_new_thread().\n current = _MainThread()\n\n _main_thread = current\n\n with _active_limbo_lock:\n # Dangling thread instances must still have their locks reset,\n # because someone may join() them.\n threads = set(_enumerate())\n threads.update(_dangling)\n for thread in threads:\n # Any lock/condition variable may be currently locked or in an\n # invalid state, so we reinitialize them.\n if thread is current:\n # This is the one and only active thread.\n ident = get_ident()\n thread._after_fork(new_ident=ident)\n new_active[ident] = thread\n else:\n # All the others are already stopped.\n thread._after_fork()\n\n _limbo.clear()\n _active.clear()\n _active.update(new_active)\n assert len(_active) == 1\n\n\nif hasattr(_os, \"register_at_fork\"):\n _os.register_at_fork(after_in_child=_after_fork)\n", 1599], "/usr/lib/python3.13/multiprocessing/popen_fork.py": ["import atexit\nimport os\nimport signal\n\nfrom . import util\n\n__all__ = ['Popen']\n\n#\n# Start child process using fork\n#\n\nclass Popen(object):\n method = 'fork'\n\n def __init__(self, process_obj):\n util._flush_std_streams()\n self.returncode = None\n self.finalizer = None\n self._launch(process_obj)\n\n def duplicate_for_child(self, fd):\n return fd\n\n def poll(self, flag=os.WNOHANG):\n if self.returncode is None:\n try:\n pid, sts = os.waitpid(self.pid, flag)\n except OSError:\n # Child process not yet created. See #1731717\n # e.errno == errno.ECHILD == 10\n return None\n if pid == self.pid:\n self.returncode = os.waitstatus_to_exitcode(sts)\n return self.returncode\n\n def wait(self, timeout=None):\n if self.returncode is None:\n if timeout is not None:\n from multiprocessing.connection import wait\n if not wait([self.sentinel], timeout):\n return None\n # This shouldn't block if wait() returned successfully.\n return self.poll(os.WNOHANG if timeout == 0.0 else 0)\n return self.returncode\n\n def _send_signal(self, sig):\n if self.returncode is None:\n try:\n os.kill(self.pid, sig)\n except ProcessLookupError:\n pass\n except OSError:\n if self.wait(timeout=0.1) is None:\n raise\n\n def terminate(self):\n self._send_signal(signal.SIGTERM)\n\n def kill(self):\n self._send_signal(signal.SIGKILL)\n\n def _launch(self, process_obj):\n code = 1\n parent_r, child_w = os.pipe()\n child_r, parent_w = os.pipe()\n self.pid = os.fork()\n if self.pid == 0:\n try:\n atexit._clear()\n atexit.register(util._exit_function)\n os.close(parent_r)\n os.close(parent_w)\n code = process_obj._bootstrap(parent_sentinel=child_r)\n finally:\n atexit._run_exitfuncs()\n os._exit(code)\n else:\n os.close(child_w)\n os.close(child_r)\n self.finalizer = util.Finalize(self, util.close_fds,\n (parent_r, parent_w,))\n self.sentinel = parent_r\n\n def close(self):\n if self.finalizer is not None:\n self.finalizer()\n", 87], "/usr/lib/python3.13/multiprocessing/context.py": ["import os\nimport sys\nimport threading\n\nfrom . import process\nfrom . import reduction\n\n__all__ = ()\n\n#\n# Exceptions\n#\n\nclass ProcessError(Exception):\n pass\n\nclass BufferTooShort(ProcessError):\n pass\n\nclass TimeoutError(ProcessError):\n pass\n\nclass AuthenticationError(ProcessError):\n pass\n\n#\n# Base type for contexts. Bound methods of an instance of this type are included in __all__ of __init__.py\n#\n\nclass BaseContext(object):\n\n ProcessError = ProcessError\n BufferTooShort = BufferTooShort\n TimeoutError = TimeoutError\n AuthenticationError = AuthenticationError\n\n current_process = staticmethod(process.current_process)\n parent_process = staticmethod(process.parent_process)\n active_children = staticmethod(process.active_children)\n\n def cpu_count(self):\n '''Returns the number of CPUs in the system'''\n num = os.cpu_count()\n if num is None:\n raise NotImplementedError('cannot determine number of cpus')\n else:\n return num\n\n def Manager(self):\n '''Returns a manager associated with a running server process\n\n The managers methods such as `Lock()`, `Condition()` and `Queue()`\n can be used to create shared objects.\n '''\n from .managers import SyncManager\n m = SyncManager(ctx=self.get_context())\n m.start()\n return m\n\n def Pipe(self, duplex=True):\n '''Returns two connection object connected by a pipe'''\n from .connection import Pipe\n return Pipe(duplex)\n\n def Lock(self):\n '''Returns a non-recursive lock object'''\n from .synchronize import Lock\n return Lock(ctx=self.get_context())\n\n def RLock(self):\n '''Returns a recursive lock object'''\n from .synchronize import RLock\n return RLock(ctx=self.get_context())\n\n def Condition(self, lock=None):\n '''Returns a condition object'''\n from .synchronize import Condition\n return Condition(lock, ctx=self.get_context())\n\n def Semaphore(self, value=1):\n '''Returns a semaphore object'''\n from .synchronize import Semaphore\n return Semaphore(value, ctx=self.get_context())\n\n def BoundedSemaphore(self, value=1):\n '''Returns a bounded semaphore object'''\n from .synchronize import BoundedSemaphore\n return BoundedSemaphore(value, ctx=self.get_context())\n\n def Event(self):\n '''Returns an event object'''\n from .synchronize import Event\n return Event(ctx=self.get_context())\n\n def Barrier(self, parties, action=None, timeout=None):\n '''Returns a barrier object'''\n from .synchronize import Barrier\n return Barrier(parties, action, timeout, ctx=self.get_context())\n\n def Queue(self, maxsize=0):\n '''Returns a queue object'''\n from .queues import Queue\n return Queue(maxsize, ctx=self.get_context())\n\n def JoinableQueue(self, maxsize=0):\n '''Returns a queue object'''\n from .queues import JoinableQueue\n return JoinableQueue(maxsize, ctx=self.get_context())\n\n def SimpleQueue(self):\n '''Returns a queue object'''\n from .queues import SimpleQueue\n return SimpleQueue(ctx=self.get_context())\n\n def Pool(self, processes=None, initializer=None, initargs=(),\n maxtasksperchild=None):\n '''Returns a process pool object'''\n from .pool import Pool\n return Pool(processes, initializer, initargs, maxtasksperchild,\n context=self.get_context())\n\n def RawValue(self, typecode_or_type, *args):\n '''Returns a shared object'''\n from .sharedctypes import RawValue\n return RawValue(typecode_or_type, *args)\n\n def RawArray(self, typecode_or_type, size_or_initializer):\n '''Returns a shared array'''\n from .sharedctypes import RawArray\n return RawArray(typecode_or_type, size_or_initializer)\n\n def Value(self, typecode_or_type, *args, lock=True):\n '''Returns a synchronized shared object'''\n from .sharedctypes import Value\n return Value(typecode_or_type, *args, lock=lock,\n ctx=self.get_context())\n\n def Array(self, typecode_or_type, size_or_initializer, *, lock=True):\n '''Returns a synchronized shared array'''\n from .sharedctypes import Array\n return Array(typecode_or_type, size_or_initializer, lock=lock,\n ctx=self.get_context())\n\n def freeze_support(self):\n '''Check whether this is a fake forked process in a frozen executable.\n If so then run code specified by commandline and exit.\n '''\n if sys.platform == 'win32' and getattr(sys, 'frozen', False):\n from .spawn import freeze_support\n freeze_support()\n\n def get_logger(self):\n '''Return package logger -- if it does not already exist then\n it is created.\n '''\n from .util import get_logger\n return get_logger()\n\n def log_to_stderr(self, level=None):\n '''Turn on logging and add a handler which prints to stderr'''\n from .util import log_to_stderr\n return log_to_stderr(level)\n\n def allow_connection_pickling(self):\n '''Install support for sending connections and sockets\n between processes\n '''\n # This is undocumented. In previous versions of multiprocessing\n # its only effect was to make socket objects inheritable on Windows.\n from . import connection\n\n def set_executable(self, executable):\n '''Sets the path to a python.exe or pythonw.exe binary used to run\n child processes instead of sys.executable when using the 'spawn'\n start method. Useful for people embedding Python.\n '''\n from .spawn import set_executable\n set_executable(executable)\n\n def set_forkserver_preload(self, module_names):\n '''Set list of module names to try to load in forkserver process.\n This is really just a hint.\n '''\n from .forkserver import set_forkserver_preload\n set_forkserver_preload(module_names)\n\n def get_context(self, method=None):\n if method is None:\n return self\n try:\n ctx = _concrete_contexts[method]\n except KeyError:\n raise ValueError('cannot find context for %r' % method) from None\n ctx._check_available()\n return ctx\n\n def get_start_method(self, allow_none=False):\n return self._name\n\n def set_start_method(self, method, force=False):\n raise ValueError('cannot set start method of concrete context')\n\n @property\n def reducer(self):\n '''Controls how objects will be reduced to a form that can be\n shared with other processes.'''\n return globals().get('reduction')\n\n @reducer.setter\n def reducer(self, reduction):\n globals()['reduction'] = reduction\n\n def _check_available(self):\n pass\n\n#\n# Type of default context -- underlying context can be set at most once\n#\n\nclass Process(process.BaseProcess):\n _start_method = None\n @staticmethod\n def _Popen(process_obj):\n return _default_context.get_context().Process._Popen(process_obj)\n\n @staticmethod\n def _after_fork():\n return _default_context.get_context().Process._after_fork()\n\nclass DefaultContext(BaseContext):\n Process = Process\n\n def __init__(self, context):\n self._default_context = context\n self._actual_context = None\n\n def get_context(self, method=None):\n if method is None:\n if self._actual_context is None:\n self._actual_context = self._default_context\n return self._actual_context\n else:\n return super().get_context(method)\n\n def set_start_method(self, method, force=False):\n if self._actual_context is not None and not force:\n raise RuntimeError('context has already been set')\n if method is None and force:\n self._actual_context = None\n return\n self._actual_context = self.get_context(method)\n\n def get_start_method(self, allow_none=False):\n if self._actual_context is None:\n if allow_none:\n return None\n self._actual_context = self._default_context\n return self._actual_context._name\n\n def get_all_start_methods(self):\n \"\"\"Returns a list of the supported start methods, default first.\"\"\"\n if sys.platform == 'win32':\n return ['spawn']\n else:\n methods = ['spawn', 'fork'] if sys.platform == 'darwin' else ['fork', 'spawn']\n if reduction.HAVE_SEND_HANDLE:\n methods.append('forkserver')\n return methods\n\n\n#\n# Context types for fixed start method\n#\n\nif sys.platform != 'win32':\n\n class ForkProcess(process.BaseProcess):\n _start_method = 'fork'\n @staticmethod\n def _Popen(process_obj):\n from .popen_fork import Popen\n return Popen(process_obj)\n\n class SpawnProcess(process.BaseProcess):\n _start_method = 'spawn'\n @staticmethod\n def _Popen(process_obj):\n from .popen_spawn_posix import Popen\n return Popen(process_obj)\n\n @staticmethod\n def _after_fork():\n # process is spawned, nothing to do\n pass\n\n class ForkServerProcess(process.BaseProcess):\n _start_method = 'forkserver'\n @staticmethod\n def _Popen(process_obj):\n from .popen_forkserver import Popen\n return Popen(process_obj)\n\n class ForkContext(BaseContext):\n _name = 'fork'\n Process = ForkProcess\n\n class SpawnContext(BaseContext):\n _name = 'spawn'\n Process = SpawnProcess\n\n class ForkServerContext(BaseContext):\n _name = 'forkserver'\n Process = ForkServerProcess\n def _check_available(self):\n if not reduction.HAVE_SEND_HANDLE:\n raise ValueError('forkserver start method not available')\n\n _concrete_contexts = {\n 'fork': ForkContext(),\n 'spawn': SpawnContext(),\n 'forkserver': ForkServerContext(),\n }\n if sys.platform == 'darwin':\n # bpo-33725: running arbitrary code after fork() is no longer reliable\n # on macOS since macOS 10.14 (Mojave). Use spawn by default instead.\n _default_context = DefaultContext(_concrete_contexts['spawn'])\n else:\n _default_context = DefaultContext(_concrete_contexts['fork'])\n\nelse:\n\n class SpawnProcess(process.BaseProcess):\n _start_method = 'spawn'\n @staticmethod\n def _Popen(process_obj):\n from .popen_spawn_win32 import Popen\n return Popen(process_obj)\n\n @staticmethod\n def _after_fork():\n # process is spawned, nothing to do\n pass\n\n class SpawnContext(BaseContext):\n _name = 'spawn'\n Process = SpawnProcess\n\n _concrete_contexts = {\n 'spawn': SpawnContext(),\n }\n _default_context = DefaultContext(_concrete_contexts['spawn'])\n\n#\n# Force the start method\n#\n\ndef _force_start_method(method):\n _default_context._actual_context = _concrete_contexts[method]\n\n#\n# Check that the current thread is spawning a child process\n#\n\n_tls = threading.local()\n\ndef get_spawning_popen():\n return getattr(_tls, 'spawning_popen', None)\n\ndef set_spawning_popen(popen):\n _tls.spawning_popen = popen\n\ndef assert_spawning(obj):\n if get_spawning_popen() is None:\n raise RuntimeError(\n '%s objects should only be shared between processes'\n ' through inheritance' % type(obj).__name__\n )\n", 377], "/home/gaogaotiantian/programs/viztracer/venv3.13/lib/python3.13/site-packages/_distutils_hack/__init__.py": ["# don't import any costly modules\nimport os\nimport sys\n\nreport_url = (\n \"https://github.com/pypa/setuptools/issues/new?\"\n \"template=distutils-deprecation.yml\"\n)\n\n\ndef warn_distutils_present():\n if 'distutils' not in sys.modules:\n return\n import warnings\n\n warnings.warn(\n \"Distutils was imported before Setuptools, but importing Setuptools \"\n \"also replaces the `distutils` module in `sys.modules`. This may lead \"\n \"to undesirable behaviors or errors. To avoid these issues, avoid \"\n \"using distutils directly, ensure that setuptools is installed in the \"\n \"traditional way (e.g. not an editable install), and/or make sure \"\n \"that setuptools is always imported before distutils.\"\n )\n\n\ndef clear_distutils():\n if 'distutils' not in sys.modules:\n return\n import warnings\n\n warnings.warn(\n \"Setuptools is replacing distutils. Support for replacing \"\n \"an already imported distutils is deprecated. In the future, \"\n \"this condition will fail. \"\n f\"Register concerns at {report_url}\"\n )\n mods = [\n name\n for name in sys.modules\n if name == \"distutils\" or name.startswith(\"distutils.\")\n ]\n for name in mods:\n del sys.modules[name]\n\n\ndef enabled():\n \"\"\"\n Allow selection of distutils by environment variable.\n \"\"\"\n which = os.environ.get('SETUPTOOLS_USE_DISTUTILS', 'local')\n if which == 'stdlib':\n import warnings\n\n warnings.warn(\n \"Reliance on distutils from stdlib is deprecated. Users \"\n \"must rely on setuptools to provide the distutils module. \"\n \"Avoid importing distutils or import setuptools first, \"\n \"and avoid setting SETUPTOOLS_USE_DISTUTILS=stdlib. \"\n f\"Register concerns at {report_url}\"\n )\n return which == 'local'\n\n\ndef ensure_local_distutils():\n import importlib\n\n clear_distutils()\n\n # With the DistutilsMetaFinder in place,\n # perform an import to cause distutils to be\n # loaded from setuptools._distutils. Ref #2906.\n with shim():\n importlib.import_module('distutils')\n\n # check that submodules load as expected\n core = importlib.import_module('distutils.core')\n assert '_distutils' in core.__file__, core.__file__\n assert 'setuptools._distutils.log' not in sys.modules\n\n\ndef do_override():\n \"\"\"\n Ensure that the local copy of distutils is preferred over stdlib.\n\n See https://github.com/pypa/setuptools/issues/417#issuecomment-392298401\n for more motivation.\n \"\"\"\n if enabled():\n warn_distutils_present()\n ensure_local_distutils()\n\n\nclass _TrivialRe:\n def __init__(self, *patterns):\n self._patterns = patterns\n\n def match(self, string):\n return all(pat in string for pat in self._patterns)\n\n\nclass DistutilsMetaFinder:\n def find_spec(self, fullname, path, target=None):\n # optimization: only consider top level modules and those\n # found in the CPython test suite.\n if path is not None and not fullname.startswith('test.'):\n return None\n\n method_name = 'spec_for_{fullname}'.format(**locals())\n method = getattr(self, method_name, lambda: None)\n return method()\n\n def spec_for_distutils(self):\n if self.is_cpython():\n return None\n\n import importlib\n import importlib.abc\n import importlib.util\n\n try:\n mod = importlib.import_module('setuptools._distutils')\n except Exception:\n # There are a couple of cases where setuptools._distutils\n # may not be present:\n # - An older Setuptools without a local distutils is\n # taking precedence. Ref #2957.\n # - Path manipulation during sitecustomize removes\n # setuptools from the path but only after the hook\n # has been loaded. Ref #2980.\n # In either case, fall back to stdlib behavior.\n return None\n\n class DistutilsLoader(importlib.abc.Loader):\n def create_module(self, spec):\n mod.__name__ = 'distutils'\n return mod\n\n def exec_module(self, module):\n pass\n\n return importlib.util.spec_from_loader(\n 'distutils', DistutilsLoader(), origin=mod.__file__\n )\n\n @staticmethod\n def is_cpython():\n \"\"\"\n Suppress supplying distutils for CPython (build and tests).\n Ref #2965 and #3007.\n \"\"\"\n return os.path.isfile('pybuilddir.txt')\n\n def spec_for_pip(self):\n \"\"\"\n Ensure stdlib distutils when running under pip.\n See pypa/pip#8761 for rationale.\n \"\"\"\n if sys.version_info >= (3, 12) or self.pip_imported_during_build():\n return\n clear_distutils()\n self.spec_for_distutils = lambda: None\n\n @classmethod\n def pip_imported_during_build(cls):\n \"\"\"\n Detect if pip is being imported in a build script. Ref #2355.\n \"\"\"\n import traceback\n\n return any(\n cls.frame_file_is_setup(frame) for frame, line in traceback.walk_stack(None)\n )\n\n @staticmethod\n def frame_file_is_setup(frame):\n \"\"\"\n Return True if the indicated frame suggests a setup.py file.\n \"\"\"\n # some frames may not have __file__ (#2940)\n return frame.f_globals.get('__file__', '').endswith('setup.py')\n\n def spec_for_sensitive_tests(self):\n \"\"\"\n Ensure stdlib distutils when running select tests under CPython.\n\n python/cpython#91169\n \"\"\"\n clear_distutils()\n self.spec_for_distutils = lambda: None\n\n sensitive_tests = (\n [\n 'test.test_distutils',\n 'test.test_peg_generator',\n 'test.test_importlib',\n ]\n if sys.version_info < (3, 10)\n else [\n 'test.test_distutils',\n ]\n )\n\n\nfor name in DistutilsMetaFinder.sensitive_tests:\n setattr(\n DistutilsMetaFinder,\n f'spec_for_{name}',\n DistutilsMetaFinder.spec_for_sensitive_tests,\n )\n\n\nDISTUTILS_FINDER = DistutilsMetaFinder()\n\n\ndef add_shim():\n DISTUTILS_FINDER in sys.meta_path or insert_shim()\n\n\nclass shim:\n def __enter__(self) -> None:\n insert_shim()\n\n def __exit__(self, exc: object, value: object, tb: object) -> None:\n _remove_shim()\n\n\ndef insert_shim():\n sys.meta_path.insert(0, DISTUTILS_FINDER)\n\n\ndef _remove_shim():\n try:\n sys.meta_path.remove(DISTUTILS_FINDER)\n except ValueError:\n pass\n\n\nif sys.version_info < (3, 12):\n # DistutilsMetaFinder can only be disabled in Python < 3.12 (PEP 632)\n remove_shim = _remove_shim\n", 240], "/usr/lib/python3.13/heapq.py": ["\"\"\"Heap queue algorithm (a.k.a. priority queue).\n\nHeaps are arrays for which a[k] <= a[2*k+1] and a[k] <= a[2*k+2] for\nall k, counting elements from 0. For the sake of comparison,\nnon-existing elements are considered to be infinite. The interesting\nproperty of a heap is that a[0] is always its smallest element.\n\nUsage:\n\nheap = [] # creates an empty heap\nheappush(heap, item) # pushes a new item on the heap\nitem = heappop(heap) # pops the smallest item from the heap\nitem = heap[0] # smallest item on the heap without popping it\nheapify(x) # transforms list into a heap, in-place, in linear time\nitem = heappushpop(heap, item) # pushes a new item and then returns\n # the smallest item; the heap size is unchanged\nitem = heapreplace(heap, item) # pops and returns smallest item, and adds\n # new item; the heap size is unchanged\n\nOur API differs from textbook heap algorithms as follows:\n\n- We use 0-based indexing. This makes the relationship between the\n index for a node and the indexes for its children slightly less\n obvious, but is more suitable since Python uses 0-based indexing.\n\n- Our heappop() method returns the smallest item, not the largest.\n\nThese two make it possible to view the heap as a regular Python list\nwithout surprises: heap[0] is the smallest item, and heap.sort()\nmaintains the heap invariant!\n\"\"\"\n\n# Original code by Kevin O'Connor, augmented by Tim Peters and Raymond Hettinger\n\n__about__ = \"\"\"Heap queues\n\n[explanation by Fran\u00e7ois Pinard]\n\nHeaps are arrays for which a[k] <= a[2*k+1] and a[k] <= a[2*k+2] for\nall k, counting elements from 0. For the sake of comparison,\nnon-existing elements are considered to be infinite. The interesting\nproperty of a heap is that a[0] is always its smallest element.\n\nThe strange invariant above is meant to be an efficient memory\nrepresentation for a tournament. The numbers below are `k', not a[k]:\n\n 0\n\n 1 2\n\n 3 4 5 6\n\n 7 8 9 10 11 12 13 14\n\n 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30\n\n\nIn the tree above, each cell `k' is topping `2*k+1' and `2*k+2'. In\na usual binary tournament we see in sports, each cell is the winner\nover the two cells it tops, and we can trace the winner down the tree\nto see all opponents s/he had. However, in many computer applications\nof such tournaments, we do not need to trace the history of a winner.\nTo be more memory efficient, when a winner is promoted, we try to\nreplace it by something else at a lower level, and the rule becomes\nthat a cell and the two cells it tops contain three different items,\nbut the top cell \"wins\" over the two topped cells.\n\nIf this heap invariant is protected at all time, index 0 is clearly\nthe overall winner. The simplest algorithmic way to remove it and\nfind the \"next\" winner is to move some loser (let's say cell 30 in the\ndiagram above) into the 0 position, and then percolate this new 0 down\nthe tree, exchanging values, until the invariant is re-established.\nThis is clearly logarithmic on the total number of items in the tree.\nBy iterating over all items, you get an O(n ln n) sort.\n\nA nice feature of this sort is that you can efficiently insert new\nitems while the sort is going on, provided that the inserted items are\nnot \"better\" than the last 0'th element you extracted. This is\nespecially useful in simulation contexts, where the tree holds all\nincoming events, and the \"win\" condition means the smallest scheduled\ntime. When an event schedule other events for execution, they are\nscheduled into the future, so they can easily go into the heap. So, a\nheap is a good structure for implementing schedulers (this is what I\nused for my MIDI sequencer :-).\n\nVarious structures for implementing schedulers have been extensively\nstudied, and heaps are good for this, as they are reasonably speedy,\nthe speed is almost constant, and the worst case is not much different\nthan the average case. However, there are other representations which\nare more efficient overall, yet the worst cases might be terrible.\n\nHeaps are also very useful in big disk sorts. You most probably all\nknow that a big sort implies producing \"runs\" (which are pre-sorted\nsequences, which size is usually related to the amount of CPU memory),\nfollowed by a merging passes for these runs, which merging is often\nvery cleverly organised[1]. It is very important that the initial\nsort produces the longest runs possible. Tournaments are a good way\nto that. If, using all the memory available to hold a tournament, you\nreplace and percolate items that happen to fit the current run, you'll\nproduce runs which are twice the size of the memory for random input,\nand much better for input fuzzily ordered.\n\nMoreover, if you output the 0'th item on disk and get an input which\nmay not fit in the current tournament (because the value \"wins\" over\nthe last output value), it cannot fit in the heap, so the size of the\nheap decreases. The freed memory could be cleverly reused immediately\nfor progressively building a second heap, which grows at exactly the\nsame rate the first heap is melting. When the first heap completely\nvanishes, you switch heaps and start a new run. Clever and quite\neffective!\n\nIn a word, heaps are useful memory structures to know. I use them in\na few applications, and I think it is good to keep a `heap' module\naround. :-)\n\n--------------------\n[1] The disk balancing algorithms which are current, nowadays, are\nmore annoying than clever, and this is a consequence of the seeking\ncapabilities of the disks. On devices which cannot seek, like big\ntape drives, the story was quite different, and one had to be very\nclever to ensure (far in advance) that each tape movement will be the\nmost effective possible (that is, will best participate at\n\"progressing\" the merge). Some tapes were even able to read\nbackwards, and this was also used to avoid the rewinding time.\nBelieve me, real good tape sorts were quite spectacular to watch!\nFrom all times, sorting has always been a Great Art! :-)\n\"\"\"\n\n__all__ = ['heappush', 'heappop', 'heapify', 'heapreplace', 'merge',\n 'nlargest', 'nsmallest', 'heappushpop']\n\ndef heappush(heap, item):\n \"\"\"Push item onto heap, maintaining the heap invariant.\"\"\"\n heap.append(item)\n _siftdown(heap, 0, len(heap)-1)\n\ndef heappop(heap):\n \"\"\"Pop the smallest item off the heap, maintaining the heap invariant.\"\"\"\n lastelt = heap.pop() # raises appropriate IndexError if heap is empty\n if heap:\n returnitem = heap[0]\n heap[0] = lastelt\n _siftup(heap, 0)\n return returnitem\n return lastelt\n\ndef heapreplace(heap, item):\n \"\"\"Pop and return the current smallest value, and add the new item.\n\n This is more efficient than heappop() followed by heappush(), and can be\n more appropriate when using a fixed-size heap. Note that the value\n returned may be larger than item! That constrains reasonable uses of\n this routine unless written as part of a conditional replacement:\n\n if item > heap[0]:\n item = heapreplace(heap, item)\n \"\"\"\n returnitem = heap[0] # raises appropriate IndexError if heap is empty\n heap[0] = item\n _siftup(heap, 0)\n return returnitem\n\ndef heappushpop(heap, item):\n \"\"\"Fast version of a heappush followed by a heappop.\"\"\"\n if heap and heap[0] < item:\n item, heap[0] = heap[0], item\n _siftup(heap, 0)\n return item\n\ndef heapify(x):\n \"\"\"Transform list into a heap, in-place, in O(len(x)) time.\"\"\"\n n = len(x)\n # Transform bottom-up. The largest index there's any point to looking at\n # is the largest with a child index in-range, so must have 2*i + 1 < n,\n # or i < (n-1)/2. If n is even = 2*j, this is (2*j-1)/2 = j-1/2 so\n # j-1 is the largest, which is n//2 - 1. If n is odd = 2*j+1, this is\n # (2*j+1-1)/2 = j so j-1 is the largest, and that's again n//2-1.\n for i in reversed(range(n//2)):\n _siftup(x, i)\n\ndef _heappop_max(heap):\n \"\"\"Maxheap version of a heappop.\"\"\"\n lastelt = heap.pop() # raises appropriate IndexError if heap is empty\n if heap:\n returnitem = heap[0]\n heap[0] = lastelt\n _siftup_max(heap, 0)\n return returnitem\n return lastelt\n\ndef _heapreplace_max(heap, item):\n \"\"\"Maxheap version of a heappop followed by a heappush.\"\"\"\n returnitem = heap[0] # raises appropriate IndexError if heap is empty\n heap[0] = item\n _siftup_max(heap, 0)\n return returnitem\n\ndef _heapify_max(x):\n \"\"\"Transform list into a maxheap, in-place, in O(len(x)) time.\"\"\"\n n = len(x)\n for i in reversed(range(n//2)):\n _siftup_max(x, i)\n\n# 'heap' is a heap at all indices >= startpos, except possibly for pos. pos\n# is the index of a leaf with a possibly out-of-order value. Restore the\n# heap invariant.\ndef _siftdown(heap, startpos, pos):\n newitem = heap[pos]\n # Follow the path to the root, moving parents down until finding a place\n # newitem fits.\n while pos > startpos:\n parentpos = (pos - 1) >> 1\n parent = heap[parentpos]\n if newitem < parent:\n heap[pos] = parent\n pos = parentpos\n continue\n break\n heap[pos] = newitem\n\n# The child indices of heap index pos are already heaps, and we want to make\n# a heap at index pos too. We do this by bubbling the smaller child of\n# pos up (and so on with that child's children, etc) until hitting a leaf,\n# then using _siftdown to move the oddball originally at index pos into place.\n#\n# We *could* break out of the loop as soon as we find a pos where newitem <=\n# both its children, but turns out that's not a good idea, and despite that\n# many books write the algorithm that way. During a heap pop, the last array\n# element is sifted in, and that tends to be large, so that comparing it\n# against values starting from the root usually doesn't pay (= usually doesn't\n# get us out of the loop early). See Knuth, Volume 3, where this is\n# explained and quantified in an exercise.\n#\n# Cutting the # of comparisons is important, since these routines have no\n# way to extract \"the priority\" from an array element, so that intelligence\n# is likely to be hiding in custom comparison methods, or in array elements\n# storing (priority, record) tuples. Comparisons are thus potentially\n# expensive.\n#\n# On random arrays of length 1000, making this change cut the number of\n# comparisons made by heapify() a little, and those made by exhaustive\n# heappop() a lot, in accord with theory. Here are typical results from 3\n# runs (3 just to demonstrate how small the variance is):\n#\n# Compares needed by heapify Compares needed by 1000 heappops\n# -------------------------- --------------------------------\n# 1837 cut to 1663 14996 cut to 8680\n# 1855 cut to 1659 14966 cut to 8678\n# 1847 cut to 1660 15024 cut to 8703\n#\n# Building the heap by using heappush() 1000 times instead required\n# 2198, 2148, and 2219 compares: heapify() is more efficient, when\n# you can use it.\n#\n# The total compares needed by list.sort() on the same lists were 8627,\n# 8627, and 8632 (this should be compared to the sum of heapify() and\n# heappop() compares): list.sort() is (unsurprisingly!) more efficient\n# for sorting.\n\ndef _siftup(heap, pos):\n endpos = len(heap)\n startpos = pos\n newitem = heap[pos]\n # Bubble up the smaller child until hitting a leaf.\n childpos = 2*pos + 1 # leftmost child position\n while childpos < endpos:\n # Set childpos to index of smaller child.\n rightpos = childpos + 1\n if rightpos < endpos and not heap[childpos] < heap[rightpos]:\n childpos = rightpos\n # Move the smaller child up.\n heap[pos] = heap[childpos]\n pos = childpos\n childpos = 2*pos + 1\n # The leaf at pos is empty now. Put newitem there, and bubble it up\n # to its final resting place (by sifting its parents down).\n heap[pos] = newitem\n _siftdown(heap, startpos, pos)\n\ndef _siftdown_max(heap, startpos, pos):\n 'Maxheap variant of _siftdown'\n newitem = heap[pos]\n # Follow the path to the root, moving parents down until finding a place\n # newitem fits.\n while pos > startpos:\n parentpos = (pos - 1) >> 1\n parent = heap[parentpos]\n if parent < newitem:\n heap[pos] = parent\n pos = parentpos\n continue\n break\n heap[pos] = newitem\n\ndef _siftup_max(heap, pos):\n 'Maxheap variant of _siftup'\n endpos = len(heap)\n startpos = pos\n newitem = heap[pos]\n # Bubble up the larger child until hitting a leaf.\n childpos = 2*pos + 1 # leftmost child position\n while childpos < endpos:\n # Set childpos to index of larger child.\n rightpos = childpos + 1\n if rightpos < endpos and not heap[rightpos] < heap[childpos]:\n childpos = rightpos\n # Move the larger child up.\n heap[pos] = heap[childpos]\n pos = childpos\n childpos = 2*pos + 1\n # The leaf at pos is empty now. Put newitem there, and bubble it up\n # to its final resting place (by sifting its parents down).\n heap[pos] = newitem\n _siftdown_max(heap, startpos, pos)\n\ndef merge(*iterables, key=None, reverse=False):\n '''Merge multiple sorted inputs into a single sorted output.\n\n Similar to sorted(itertools.chain(*iterables)) but returns a generator,\n does not pull the data into memory all at once, and assumes that each of\n the input streams is already sorted (smallest to largest).\n\n >>> list(merge([1,3,5,7], [0,2,4,8], [5,10,15,20], [], [25]))\n [0, 1, 2, 3, 4, 5, 5, 7, 8, 10, 15, 20, 25]\n\n If *key* is not None, applies a key function to each element to determine\n its sort order.\n\n >>> list(merge(['dog', 'horse'], ['cat', 'fish', 'kangaroo'], key=len))\n ['dog', 'cat', 'fish', 'horse', 'kangaroo']\n\n '''\n\n h = []\n h_append = h.append\n\n if reverse:\n _heapify = _heapify_max\n _heappop = _heappop_max\n _heapreplace = _heapreplace_max\n direction = -1\n else:\n _heapify = heapify\n _heappop = heappop\n _heapreplace = heapreplace\n direction = 1\n\n if key is None:\n for order, it in enumerate(map(iter, iterables)):\n try:\n next = it.__next__\n h_append([next(), order * direction, next])\n except StopIteration:\n pass\n _heapify(h)\n while len(h) > 1:\n try:\n while True:\n value, order, next = s = h[0]\n yield value\n s[0] = next() # raises StopIteration when exhausted\n _heapreplace(h, s) # restore heap condition\n except StopIteration:\n _heappop(h) # remove empty iterator\n if h:\n # fast case when only a single iterator remains\n value, order, next = h[0]\n yield value\n yield from next.__self__\n return\n\n for order, it in enumerate(map(iter, iterables)):\n try:\n next = it.__next__\n value = next()\n h_append([key(value), order * direction, value, next])\n except StopIteration:\n pass\n _heapify(h)\n while len(h) > 1:\n try:\n while True:\n key_value, order, value, next = s = h[0]\n yield value\n value = next()\n s[0] = key(value)\n s[2] = value\n _heapreplace(h, s)\n except StopIteration:\n _heappop(h)\n if h:\n key_value, order, value, next = h[0]\n yield value\n yield from next.__self__\n\n\n# Algorithm notes for nlargest() and nsmallest()\n# ==============================================\n#\n# Make a single pass over the data while keeping the k most extreme values\n# in a heap. Memory consumption is limited to keeping k values in a list.\n#\n# Measured performance for random inputs:\n#\n# number of comparisons\n# n inputs k-extreme values (average of 5 trials) % more than min()\n# ------------- ---------------- --------------------- -----------------\n# 1,000 100 3,317 231.7%\n# 10,000 100 14,046 40.5%\n# 100,000 100 105,749 5.7%\n# 1,000,000 100 1,007,751 0.8%\n# 10,000,000 100 10,009,401 0.1%\n#\n# Theoretical number of comparisons for k smallest of n random inputs:\n#\n# Step Comparisons Action\n# ---- -------------------------- ---------------------------\n# 1 1.66 * k heapify the first k-inputs\n# 2 n - k compare remaining elements to top of heap\n# 3 k * (1 + lg2(k)) * ln(n/k) replace the topmost value on the heap\n# 4 k * lg2(k) - (k/2) final sort of the k most extreme values\n#\n# Combining and simplifying for a rough estimate gives:\n#\n# comparisons = n + k * (log(k, 2) * log(n/k) + log(k, 2) + log(n/k))\n#\n# Computing the number of comparisons for step 3:\n# -----------------------------------------------\n# * For the i-th new value from the iterable, the probability of being in the\n# k most extreme values is k/i. For example, the probability of the 101st\n# value seen being in the 100 most extreme values is 100/101.\n# * If the value is a new extreme value, the cost of inserting it into the\n# heap is 1 + log(k, 2).\n# * The probability times the cost gives:\n# (k/i) * (1 + log(k, 2))\n# * Summing across the remaining n-k elements gives:\n# sum((k/i) * (1 + log(k, 2)) for i in range(k+1, n+1))\n# * This reduces to:\n# (H(n) - H(k)) * k * (1 + log(k, 2))\n# * Where H(n) is the n-th harmonic number estimated by:\n# gamma = 0.5772156649\n# H(n) = log(n, e) + gamma + 1 / (2 * n)\n# http://en.wikipedia.org/wiki/Harmonic_series_(mathematics)#Rate_of_divergence\n# * Substituting the H(n) formula:\n# comparisons = k * (1 + log(k, 2)) * (log(n/k, e) + (1/n - 1/k) / 2)\n#\n# Worst-case for step 3:\n# ----------------------\n# In the worst case, the input data is reversed sorted so that every new element\n# must be inserted in the heap:\n#\n# comparisons = 1.66 * k + log(k, 2) * (n - k)\n#\n# Alternative Algorithms\n# ----------------------\n# Other algorithms were not used because they:\n# 1) Took much more auxiliary memory,\n# 2) Made multiple passes over the data.\n# 3) Made more comparisons in common cases (small k, large n, semi-random input).\n# See the more detailed comparison of approach at:\n# http://code.activestate.com/recipes/577573-compare-algorithms-for-heapqsmallest\n\ndef nsmallest(n, iterable, key=None):\n \"\"\"Find the n smallest elements in a dataset.\n\n Equivalent to: sorted(iterable, key=key)[:n]\n \"\"\"\n\n # Short-cut for n==1 is to use min()\n if n == 1:\n it = iter(iterable)\n sentinel = object()\n result = min(it, default=sentinel, key=key)\n return [] if result is sentinel else [result]\n\n # When n>=size, it's faster to use sorted()\n try:\n size = len(iterable)\n except (TypeError, AttributeError):\n pass\n else:\n if n >= size:\n return sorted(iterable, key=key)[:n]\n\n # When key is none, use simpler decoration\n if key is None:\n it = iter(iterable)\n # put the range(n) first so that zip() doesn't\n # consume one too many elements from the iterator\n result = [(elem, i) for i, elem in zip(range(n), it)]\n if not result:\n return result\n _heapify_max(result)\n top = result[0][0]\n order = n\n _heapreplace = _heapreplace_max\n for elem in it:\n if elem < top:\n _heapreplace(result, (elem, order))\n top, _order = result[0]\n order += 1\n result.sort()\n return [elem for (elem, order) in result]\n\n # General case, slowest method\n it = iter(iterable)\n result = [(key(elem), i, elem) for i, elem in zip(range(n), it)]\n if not result:\n return result\n _heapify_max(result)\n top = result[0][0]\n order = n\n _heapreplace = _heapreplace_max\n for elem in it:\n k = key(elem)\n if k < top:\n _heapreplace(result, (k, order, elem))\n top, _order, _elem = result[0]\n order += 1\n result.sort()\n return [elem for (k, order, elem) in result]\n\ndef nlargest(n, iterable, key=None):\n \"\"\"Find the n largest elements in a dataset.\n\n Equivalent to: sorted(iterable, key=key, reverse=True)[:n]\n \"\"\"\n\n # Short-cut for n==1 is to use max()\n if n == 1:\n it = iter(iterable)\n sentinel = object()\n result = max(it, default=sentinel, key=key)\n return [] if result is sentinel else [result]\n\n # When n>=size, it's faster to use sorted()\n try:\n size = len(iterable)\n except (TypeError, AttributeError):\n pass\n else:\n if n >= size:\n return sorted(iterable, key=key, reverse=True)[:n]\n\n # When key is none, use simpler decoration\n if key is None:\n it = iter(iterable)\n result = [(elem, i) for i, elem in zip(range(0, -n, -1), it)]\n if not result:\n return result\n heapify(result)\n top = result[0][0]\n order = -n\n _heapreplace = heapreplace\n for elem in it:\n if top < elem:\n _heapreplace(result, (elem, order))\n top, _order = result[0]\n order -= 1\n result.sort(reverse=True)\n return [elem for (elem, order) in result]\n\n # General case, slowest method\n it = iter(iterable)\n result = [(key(elem), i, elem) for i, elem in zip(range(0, -n, -1), it)]\n if not result:\n return result\n heapify(result)\n top = result[0][0]\n order = -n\n _heapreplace = heapreplace\n for elem in it:\n k = key(elem)\n if top < k:\n _heapreplace(result, (k, order, elem))\n top, _order, _elem = result[0]\n order -= 1\n result.sort(reverse=True)\n return [elem for (k, order, elem) in result]\n\n# If available, use C implementation\ntry:\n from _heapq import *\nexcept ImportError:\n pass\ntry:\n from _heapq import _heapreplace_max\nexcept ImportError:\n pass\ntry:\n from _heapq import _heapify_max\nexcept ImportError:\n pass\ntry:\n from _heapq import _heappop_max\nexcept ImportError:\n pass\n\n\nif __name__ == \"__main__\":\n\n import doctest # pragma: no cover\n print(doctest.testmod()) # pragma: no cover\n", 603], "/usr/lib/python3.13/queue.py": ["'''A multi-producer, multi-consumer queue.'''\n\nimport threading\nimport types\nfrom collections import deque\nfrom heapq import heappush, heappop\nfrom time import monotonic as time\ntry:\n from _queue import SimpleQueue\nexcept ImportError:\n SimpleQueue = None\n\n__all__ = [\n 'Empty',\n 'Full',\n 'ShutDown',\n 'Queue',\n 'PriorityQueue',\n 'LifoQueue',\n 'SimpleQueue',\n]\n\n\ntry:\n from _queue import Empty\nexcept ImportError:\n class Empty(Exception):\n 'Exception raised by Queue.get(block=0)/get_nowait().'\n pass\n\nclass Full(Exception):\n 'Exception raised by Queue.put(block=0)/put_nowait().'\n pass\n\n\nclass ShutDown(Exception):\n '''Raised when put/get with shut-down queue.'''\n\n\nclass Queue:\n '''Create a queue object with a given maximum size.\n\n If maxsize is <= 0, the queue size is infinite.\n '''\n\n def __init__(self, maxsize=0):\n self.maxsize = maxsize\n self._init(maxsize)\n\n # mutex must be held whenever the queue is mutating. All methods\n # that acquire mutex must release it before returning. mutex\n # is shared between the three conditions, so acquiring and\n # releasing the conditions also acquires and releases mutex.\n self.mutex = threading.Lock()\n\n # Notify not_empty whenever an item is added to the queue; a\n # thread waiting to get is notified then.\n self.not_empty = threading.Condition(self.mutex)\n\n # Notify not_full whenever an item is removed from the queue;\n # a thread waiting to put is notified then.\n self.not_full = threading.Condition(self.mutex)\n\n # Notify all_tasks_done whenever the number of unfinished tasks\n # drops to zero; thread waiting to join() is notified to resume\n self.all_tasks_done = threading.Condition(self.mutex)\n self.unfinished_tasks = 0\n\n # Queue shutdown state\n self.is_shutdown = False\n\n def task_done(self):\n '''Indicate that a formerly enqueued task is complete.\n\n Used by Queue consumer threads. For each get() used to fetch a task,\n a subsequent call to task_done() tells the queue that the processing\n on the task is complete.\n\n If a join() is currently blocking, it will resume when all items\n have been processed (meaning that a task_done() call was received\n for every item that had been put() into the queue).\n\n shutdown(immediate=True) calls task_done() for each remaining item in\n the queue.\n\n Raises a ValueError if called more times than there were items\n placed in the queue.\n '''\n with self.all_tasks_done:\n unfinished = self.unfinished_tasks - 1\n if unfinished <= 0:\n if unfinished < 0:\n raise ValueError('task_done() called too many times')\n self.all_tasks_done.notify_all()\n self.unfinished_tasks = unfinished\n\n def join(self):\n '''Blocks until all items in the Queue have been gotten and processed.\n\n The count of unfinished tasks goes up whenever an item is added to the\n queue. The count goes down whenever a consumer thread calls task_done()\n to indicate the item was retrieved and all work on it is complete.\n\n When the count of unfinished tasks drops to zero, join() unblocks.\n '''\n with self.all_tasks_done:\n while self.unfinished_tasks:\n self.all_tasks_done.wait()\n\n def qsize(self):\n '''Return the approximate size of the queue (not reliable!).'''\n with self.mutex:\n return self._qsize()\n\n def empty(self):\n '''Return True if the queue is empty, False otherwise (not reliable!).\n\n This method is likely to be removed at some point. Use qsize() == 0\n as a direct substitute, but be aware that either approach risks a race\n condition where a queue can grow before the result of empty() or\n qsize() can be used.\n\n To create code that needs to wait for all queued tasks to be\n completed, the preferred technique is to use the join() method.\n '''\n with self.mutex:\n return not self._qsize()\n\n def full(self):\n '''Return True if the queue is full, False otherwise (not reliable!).\n\n This method is likely to be removed at some point. Use qsize() >= n\n as a direct substitute, but be aware that either approach risks a race\n condition where a queue can shrink before the result of full() or\n qsize() can be used.\n '''\n with self.mutex:\n return 0 < self.maxsize <= self._qsize()\n\n def put(self, item, block=True, timeout=None):\n '''Put an item into the queue.\n\n If optional args 'block' is true and 'timeout' is None (the default),\n block if necessary until a free slot is available. If 'timeout' is\n a non-negative number, it blocks at most 'timeout' seconds and raises\n the Full exception if no free slot was available within that time.\n Otherwise ('block' is false), put an item on the queue if a free slot\n is immediately available, else raise the Full exception ('timeout'\n is ignored in that case).\n\n Raises ShutDown if the queue has been shut down.\n '''\n with self.not_full:\n if self.is_shutdown:\n raise ShutDown\n if self.maxsize > 0:\n if not block:\n if self._qsize() >= self.maxsize:\n raise Full\n elif timeout is None:\n while self._qsize() >= self.maxsize:\n self.not_full.wait()\n if self.is_shutdown:\n raise ShutDown\n elif timeout < 0:\n raise ValueError(\"'timeout' must be a non-negative number\")\n else:\n endtime = time() + timeout\n while self._qsize() >= self.maxsize:\n remaining = endtime - time()\n if remaining <= 0.0:\n raise Full\n self.not_full.wait(remaining)\n if self.is_shutdown:\n raise ShutDown\n self._put(item)\n self.unfinished_tasks += 1\n self.not_empty.notify()\n\n def get(self, block=True, timeout=None):\n '''Remove and return an item from the queue.\n\n If optional args 'block' is true and 'timeout' is None (the default),\n block if necessary until an item is available. If 'timeout' is\n a non-negative number, it blocks at most 'timeout' seconds and raises\n the Empty exception if no item was available within that time.\n Otherwise ('block' is false), return an item if one is immediately\n available, else raise the Empty exception ('timeout' is ignored\n in that case).\n\n Raises ShutDown if the queue has been shut down and is empty,\n or if the queue has been shut down immediately.\n '''\n with self.not_empty:\n if self.is_shutdown and not self._qsize():\n raise ShutDown\n if not block:\n if not self._qsize():\n raise Empty\n elif timeout is None:\n while not self._qsize():\n self.not_empty.wait()\n if self.is_shutdown and not self._qsize():\n raise ShutDown\n elif timeout < 0:\n raise ValueError(\"'timeout' must be a non-negative number\")\n else:\n endtime = time() + timeout\n while not self._qsize():\n remaining = endtime - time()\n if remaining <= 0.0:\n raise Empty\n self.not_empty.wait(remaining)\n if self.is_shutdown and not self._qsize():\n raise ShutDown\n item = self._get()\n self.not_full.notify()\n return item\n\n def put_nowait(self, item):\n '''Put an item into the queue without blocking.\n\n Only enqueue the item if a free slot is immediately available.\n Otherwise raise the Full exception.\n '''\n return self.put(item, block=False)\n\n def get_nowait(self):\n '''Remove and return an item from the queue without blocking.\n\n Only get an item if one is immediately available. Otherwise\n raise the Empty exception.\n '''\n return self.get(block=False)\n\n def shutdown(self, immediate=False):\n '''Shut-down the queue, making queue gets and puts raise ShutDown.\n\n By default, gets will only raise once the queue is empty. Set\n 'immediate' to True to make gets raise immediately instead.\n\n All blocked callers of put() and get() will be unblocked. If\n 'immediate', a task is marked as done for each item remaining in\n the queue, which may unblock callers of join().\n '''\n with self.mutex:\n self.is_shutdown = True\n if immediate:\n while self._qsize():\n self._get()\n if self.unfinished_tasks > 0:\n self.unfinished_tasks -= 1\n # release all blocked threads in `join()`\n self.all_tasks_done.notify_all()\n # All getters need to re-check queue-empty to raise ShutDown\n self.not_empty.notify_all()\n self.not_full.notify_all()\n\n # Override these methods to implement other queue organizations\n # (e.g. stack or priority queue).\n # These will only be called with appropriate locks held\n\n # Initialize the queue representation\n def _init(self, maxsize):\n self.queue = deque()\n\n def _qsize(self):\n return len(self.queue)\n\n # Put a new item in the queue\n def _put(self, item):\n self.queue.append(item)\n\n # Get an item from the queue\n def _get(self):\n return self.queue.popleft()\n\n __class_getitem__ = classmethod(types.GenericAlias)\n\n\nclass PriorityQueue(Queue):\n '''Variant of Queue that retrieves open entries in priority order (lowest first).\n\n Entries are typically tuples of the form: (priority number, data).\n '''\n\n def _init(self, maxsize):\n self.queue = []\n\n def _qsize(self):\n return len(self.queue)\n\n def _put(self, item):\n heappush(self.queue, item)\n\n def _get(self):\n return heappop(self.queue)\n\n\nclass LifoQueue(Queue):\n '''Variant of Queue that retrieves most recently added entries first.'''\n\n def _init(self, maxsize):\n self.queue = []\n\n def _qsize(self):\n return len(self.queue)\n\n def _put(self, item):\n self.queue.append(item)\n\n def _get(self):\n return self.queue.pop()\n\n\nclass _PySimpleQueue:\n '''Simple, unbounded FIFO queue.\n\n This pure Python implementation is not reentrant.\n '''\n # Note: while this pure Python version provides fairness\n # (by using a threading.Semaphore which is itself fair, being based\n # on threading.Condition), fairness is not part of the API contract.\n # This allows the C version to use a different implementation.\n\n def __init__(self):\n self._queue = deque()\n self._count = threading.Semaphore(0)\n\n def put(self, item, block=True, timeout=None):\n '''Put the item on the queue.\n\n The optional 'block' and 'timeout' arguments are ignored, as this method\n never blocks. They are provided for compatibility with the Queue class.\n '''\n self._queue.append(item)\n self._count.release()\n\n def get(self, block=True, timeout=None):\n '''Remove and return an item from the queue.\n\n If optional args 'block' is true and 'timeout' is None (the default),\n block if necessary until an item is available. If 'timeout' is\n a non-negative number, it blocks at most 'timeout' seconds and raises\n the Empty exception if no item was available within that time.\n Otherwise ('block' is false), return an item if one is immediately\n available, else raise the Empty exception ('timeout' is ignored\n in that case).\n '''\n if timeout is not None and timeout < 0:\n raise ValueError(\"'timeout' must be a non-negative number\")\n if not self._count.acquire(block, timeout):\n raise Empty\n return self._queue.popleft()\n\n def put_nowait(self, item):\n '''Put an item into the queue without blocking.\n\n This is exactly equivalent to `put(item, block=False)` and is only provided\n for compatibility with the Queue class.\n '''\n return self.put(item, block=False)\n\n def get_nowait(self):\n '''Remove and return an item from the queue without blocking.\n\n Only get an item if one is immediately available. Otherwise\n raise the Empty exception.\n '''\n return self.get(block=False)\n\n def empty(self):\n '''Return True if the queue is empty, False otherwise (not reliable!).'''\n return len(self._queue) == 0\n\n def qsize(self):\n '''Return the approximate size of the queue (not reliable!).'''\n return len(self._queue)\n\n __class_getitem__ = classmethod(types.GenericAlias)\n\n\nif SimpleQueue is None:\n SimpleQueue = _PySimpleQueue\n", 384], "/usr/lib/python3.13/linecache.py": ["\"\"\"Cache lines from Python source files.\n\nThis is intended to read lines from modules imported -- hence if a filename\nis not found, it will look down the module search path for a file by\nthat name.\n\"\"\"\n\n__all__ = [\"getline\", \"clearcache\", \"checkcache\", \"lazycache\"]\n\n\n# The cache. Maps filenames to either a thunk which will provide source code,\n# or a tuple (size, mtime, lines, fullname) once loaded.\ncache = {}\n\n\ndef clearcache():\n \"\"\"Clear the cache entirely.\"\"\"\n cache.clear()\n\n\ndef getline(filename, lineno, module_globals=None):\n \"\"\"Get a line for a Python source file from the cache.\n Update the cache if it doesn't contain an entry for this file already.\"\"\"\n\n lines = getlines(filename, module_globals)\n if 1 <= lineno <= len(lines):\n return lines[lineno - 1]\n return ''\n\n\ndef getlines(filename, module_globals=None):\n \"\"\"Get the lines for a Python source file from the cache.\n Update the cache if it doesn't contain an entry for this file already.\"\"\"\n\n if filename in cache:\n entry = cache[filename]\n if len(entry) != 1:\n return cache[filename][2]\n\n try:\n return updatecache(filename, module_globals)\n except MemoryError:\n clearcache()\n return []\n\n\ndef checkcache(filename=None):\n \"\"\"Discard cache entries that are out of date.\n (This is not checked upon each call!)\"\"\"\n\n if filename is None:\n filenames = list(cache.keys())\n elif filename in cache:\n filenames = [filename]\n else:\n return\n\n for filename in filenames:\n entry = cache[filename]\n if len(entry) == 1:\n # lazy cache entry, leave it lazy.\n continue\n size, mtime, lines, fullname = entry\n if mtime is None:\n continue # no-op for files loaded via a __loader__\n try:\n # This import can fail if the interpreter is shutting down\n import os\n except ImportError:\n return\n try:\n stat = os.stat(fullname)\n except (OSError, ValueError):\n cache.pop(filename, None)\n continue\n if size != stat.st_size or mtime != stat.st_mtime:\n cache.pop(filename, None)\n\n\ndef updatecache(filename, module_globals=None):\n \"\"\"Update a cache entry and return its list of lines.\n If something's wrong, print a message, discard the cache entry,\n and return an empty list.\"\"\"\n\n # These imports are not at top level because linecache is in the critical\n # path of the interpreter startup and importing os and sys take a lot of time\n # and slows down the startup sequence.\n import os\n import sys\n import tokenize\n\n if filename in cache:\n if len(cache[filename]) != 1:\n cache.pop(filename, None)\n if not filename or (filename.startswith('<') and filename.endswith('>')):\n return []\n\n fullname = filename\n try:\n stat = os.stat(fullname)\n except OSError:\n basename = filename\n\n # Realise a lazy loader based lookup if there is one\n # otherwise try to lookup right now.\n if lazycache(filename, module_globals):\n try:\n data = cache[filename][0]()\n except (ImportError, OSError):\n pass\n else:\n if data is None:\n # No luck, the PEP302 loader cannot find the source\n # for this module.\n return []\n cache[filename] = (\n len(data),\n None,\n [line + '\\n' for line in data.splitlines()],\n fullname\n )\n return cache[filename][2]\n\n # Try looking through the module search path, which is only useful\n # when handling a relative filename.\n if os.path.isabs(filename):\n return []\n\n for dirname in sys.path:\n try:\n fullname = os.path.join(dirname, basename)\n except (TypeError, AttributeError):\n # Not sufficiently string-like to do anything useful with.\n continue\n try:\n stat = os.stat(fullname)\n break\n except (OSError, ValueError):\n pass\n else:\n return []\n except ValueError: # may be raised by os.stat()\n return []\n try:\n with tokenize.open(fullname) as fp:\n lines = fp.readlines()\n except (OSError, UnicodeDecodeError, SyntaxError):\n return []\n if not lines:\n lines = ['\\n']\n elif not lines[-1].endswith('\\n'):\n lines[-1] += '\\n'\n size, mtime = stat.st_size, stat.st_mtime\n cache[filename] = size, mtime, lines, fullname\n return lines\n\n\ndef lazycache(filename, module_globals):\n \"\"\"Seed the cache for filename with module_globals.\n\n The module loader will be asked for the source only when getlines is\n called, not immediately.\n\n If there is an entry in the cache already, it is not altered.\n\n :return: True if a lazy load is registered in the cache,\n otherwise False. To register such a load a module loader with a\n get_source method must be found, the filename must be a cacheable\n filename, and the filename must not be already cached.\n \"\"\"\n if filename in cache:\n if len(cache[filename]) == 1:\n return True\n else:\n return False\n if not filename or (filename.startswith('<') and filename.endswith('>')):\n return False\n # Try for a __loader__, if available\n if module_globals and '__name__' in module_globals:\n spec = module_globals.get('__spec__')\n name = getattr(spec, 'name', None) or module_globals['__name__']\n loader = getattr(spec, 'loader', None)\n if loader is None:\n loader = module_globals.get('__loader__')\n get_source = getattr(loader, 'get_source', None)\n\n if name and get_source:\n def get_lines(name=name, *args, **kwargs):\n return get_source(name, *args, **kwargs)\n cache[filename] = (get_lines,)\n return True\n return False\n\n\ndef _register_code(code, string, name):\n cache[code] = (\n len(string),\n None,\n [line + '\\n' for line in string.splitlines()],\n name)\n", 200], "/usr/lib/python3.13/_colorize.py": ["import io\nimport os\nimport sys\n\nCOLORIZE = True\n\n\nclass ANSIColors:\n BOLD_GREEN = \"\\x1b[1;32m\"\n BOLD_MAGENTA = \"\\x1b[1;35m\"\n BOLD_RED = \"\\x1b[1;31m\"\n GREEN = \"\\x1b[32m\"\n GREY = \"\\x1b[90m\"\n MAGENTA = \"\\x1b[35m\"\n RED = \"\\x1b[31m\"\n RESET = \"\\x1b[0m\"\n YELLOW = \"\\x1b[33m\"\n\n\nNoColors = ANSIColors()\n\nfor attr in dir(NoColors):\n if not attr.startswith(\"__\"):\n setattr(NoColors, attr, \"\")\n\n\ndef get_colors(colorize: bool = False) -> ANSIColors:\n if colorize or can_colorize():\n return ANSIColors()\n else:\n return NoColors\n\n\ndef can_colorize() -> bool:\n if sys.platform == \"win32\":\n try:\n import nt\n\n if not nt._supports_virtual_terminal():\n return False\n except (ImportError, AttributeError):\n return False\n if not sys.flags.ignore_environment:\n if os.environ.get(\"PYTHON_COLORS\") == \"0\":\n return False\n if os.environ.get(\"PYTHON_COLORS\") == \"1\":\n return True\n if \"NO_COLOR\" in os.environ:\n return False\n if not COLORIZE:\n return False\n if not sys.flags.ignore_environment:\n if \"FORCE_COLOR\" in os.environ:\n return True\n if os.environ.get(\"TERM\") == \"dumb\":\n return False\n\n if not hasattr(sys.stderr, \"fileno\"):\n return False\n\n try:\n return os.isatty(sys.stderr.fileno())\n except io.UnsupportedOperation:\n return sys.stderr.isatty()\n", 64], "/usr/lib/python3.13/traceback.py": ["\"\"\"Extract, format and print information about Python stack traces.\"\"\"\n\nimport collections.abc\nimport itertools\nimport linecache\nimport sys\nimport textwrap\nimport warnings\nfrom contextlib import suppress\nimport _colorize\nfrom _colorize import ANSIColors\n\n__all__ = ['extract_stack', 'extract_tb', 'format_exception',\n 'format_exception_only', 'format_list', 'format_stack',\n 'format_tb', 'print_exc', 'format_exc', 'print_exception',\n 'print_last', 'print_stack', 'print_tb', 'clear_frames',\n 'FrameSummary', 'StackSummary', 'TracebackException',\n 'walk_stack', 'walk_tb']\n\n#\n# Formatting and printing lists of traceback lines.\n#\n\n\ndef print_list(extracted_list, file=None):\n \"\"\"Print the list of tuples as returned by extract_tb() or\n extract_stack() as a formatted stack trace to the given file.\"\"\"\n if file is None:\n file = sys.stderr\n for item in StackSummary.from_list(extracted_list).format():\n print(item, file=file, end=\"\")\n\ndef format_list(extracted_list):\n \"\"\"Format a list of tuples or FrameSummary objects for printing.\n\n Given a list of tuples or FrameSummary objects as returned by\n extract_tb() or extract_stack(), return a list of strings ready\n for printing.\n\n Each string in the resulting list corresponds to the item with the\n same index in the argument list. Each string ends in a newline;\n the strings may contain internal newlines as well, for those items\n whose source text line is not None.\n \"\"\"\n return StackSummary.from_list(extracted_list).format()\n\n#\n# Printing and Extracting Tracebacks.\n#\n\ndef print_tb(tb, limit=None, file=None):\n \"\"\"Print up to 'limit' stack trace entries from the traceback 'tb'.\n\n If 'limit' is omitted or None, all entries are printed. If 'file'\n is omitted or None, the output goes to sys.stderr; otherwise\n 'file' should be an open file or file-like object with a write()\n method.\n \"\"\"\n print_list(extract_tb(tb, limit=limit), file=file)\n\ndef format_tb(tb, limit=None):\n \"\"\"A shorthand for 'format_list(extract_tb(tb, limit))'.\"\"\"\n return extract_tb(tb, limit=limit).format()\n\ndef extract_tb(tb, limit=None):\n \"\"\"\n Return a StackSummary object representing a list of\n pre-processed entries from traceback.\n\n This is useful for alternate formatting of stack traces. If\n 'limit' is omitted or None, all entries are extracted. A\n pre-processed stack trace entry is a FrameSummary object\n containing attributes filename, lineno, name, and line\n representing the information that is usually printed for a stack\n trace. The line is a string with leading and trailing\n whitespace stripped; if the source is not available it is None.\n \"\"\"\n return StackSummary._extract_from_extended_frame_gen(\n _walk_tb_with_full_positions(tb), limit=limit)\n\n#\n# Exception formatting and output.\n#\n\n_cause_message = (\n \"\\nThe above exception was the direct cause \"\n \"of the following exception:\\n\\n\")\n\n_context_message = (\n \"\\nDuring handling of the above exception, \"\n \"another exception occurred:\\n\\n\")\n\n\nclass _Sentinel:\n def __repr__(self):\n return \"\"\n\n_sentinel = _Sentinel()\n\ndef _parse_value_tb(exc, value, tb):\n if (value is _sentinel) != (tb is _sentinel):\n raise ValueError(\"Both or neither of value and tb must be given\")\n if value is tb is _sentinel:\n if exc is not None:\n if isinstance(exc, BaseException):\n return exc, exc.__traceback__\n\n raise TypeError(f'Exception expected for value, '\n f'{type(exc).__name__} found')\n else:\n return None, None\n return value, tb\n\n\ndef print_exception(exc, /, value=_sentinel, tb=_sentinel, limit=None, \\\n file=None, chain=True, **kwargs):\n \"\"\"Print exception up to 'limit' stack trace entries from 'tb' to 'file'.\n\n This differs from print_tb() in the following ways: (1) if\n traceback is not None, it prints a header \"Traceback (most recent\n call last):\"; (2) it prints the exception type and value after the\n stack trace; (3) if type is SyntaxError and value has the\n appropriate format, it prints the line where the syntax error\n occurred with a caret on the next line indicating the approximate\n position of the error.\n \"\"\"\n colorize = kwargs.get(\"colorize\", False)\n value, tb = _parse_value_tb(exc, value, tb)\n te = TracebackException(type(value), value, tb, limit=limit, compact=True)\n te.print(file=file, chain=chain, colorize=colorize)\n\n\nBUILTIN_EXCEPTION_LIMIT = object()\n\n\ndef _print_exception_bltin(exc, /):\n file = sys.stderr if sys.stderr is not None else sys.__stderr__\n colorize = _colorize.can_colorize()\n return print_exception(exc, limit=BUILTIN_EXCEPTION_LIMIT, file=file, colorize=colorize)\n\n\ndef format_exception(exc, /, value=_sentinel, tb=_sentinel, limit=None, \\\n chain=True, **kwargs):\n \"\"\"Format a stack trace and the exception information.\n\n The arguments have the same meaning as the corresponding arguments\n to print_exception(). The return value is a list of strings, each\n ending in a newline and some containing internal newlines. When\n these lines are concatenated and printed, exactly the same text is\n printed as does print_exception().\n \"\"\"\n colorize = kwargs.get(\"colorize\", False)\n value, tb = _parse_value_tb(exc, value, tb)\n te = TracebackException(type(value), value, tb, limit=limit, compact=True)\n return list(te.format(chain=chain, colorize=colorize))\n\n\ndef format_exception_only(exc, /, value=_sentinel, *, show_group=False, **kwargs):\n \"\"\"Format the exception part of a traceback.\n\n The return value is a list of strings, each ending in a newline.\n\n The list contains the exception's message, which is\n normally a single string; however, for :exc:`SyntaxError` exceptions, it\n contains several lines that (when printed) display detailed information\n about where the syntax error occurred. Following the message, the list\n contains the exception's ``__notes__``.\n\n When *show_group* is ``True``, and the exception is an instance of\n :exc:`BaseExceptionGroup`, the nested exceptions are included as\n well, recursively, with indentation relative to their nesting depth.\n \"\"\"\n colorize = kwargs.get(\"colorize\", False)\n if value is _sentinel:\n value = exc\n te = TracebackException(type(value), value, None, compact=True)\n return list(te.format_exception_only(show_group=show_group, colorize=colorize))\n\n\n# -- not official API but folk probably use these two functions.\n\ndef _format_final_exc_line(etype, value, *, insert_final_newline=True, colorize=False):\n valuestr = _safe_string(value, 'exception')\n end_char = \"\\n\" if insert_final_newline else \"\"\n if colorize:\n if value is None or not valuestr:\n line = f\"{ANSIColors.BOLD_MAGENTA}{etype}{ANSIColors.RESET}{end_char}\"\n else:\n line = f\"{ANSIColors.BOLD_MAGENTA}{etype}{ANSIColors.RESET}: {ANSIColors.MAGENTA}{valuestr}{ANSIColors.RESET}{end_char}\"\n else:\n if value is None or not valuestr:\n line = f\"{etype}{end_char}\"\n else:\n line = f\"{etype}: {valuestr}{end_char}\"\n return line\n\n\ndef _safe_string(value, what, func=str):\n try:\n return func(value)\n except:\n return f'<{what} {func.__name__}() failed>'\n\n# --\n\ndef print_exc(limit=None, file=None, chain=True):\n \"\"\"Shorthand for 'print_exception(sys.exception(), limit, file, chain)'.\"\"\"\n print_exception(sys.exception(), limit=limit, file=file, chain=chain)\n\ndef format_exc(limit=None, chain=True):\n \"\"\"Like print_exc() but return a string.\"\"\"\n return \"\".join(format_exception(sys.exception(), limit=limit, chain=chain))\n\ndef print_last(limit=None, file=None, chain=True):\n \"\"\"This is a shorthand for 'print_exception(sys.last_exc, limit, file, chain)'.\"\"\"\n if not hasattr(sys, \"last_exc\") and not hasattr(sys, \"last_type\"):\n raise ValueError(\"no last exception\")\n\n if hasattr(sys, \"last_exc\"):\n print_exception(sys.last_exc, limit, file, chain)\n else:\n print_exception(sys.last_type, sys.last_value, sys.last_traceback,\n limit, file, chain)\n\n\n#\n# Printing and Extracting Stacks.\n#\n\ndef print_stack(f=None, limit=None, file=None):\n \"\"\"Print a stack trace from its invocation point.\n\n The optional 'f' argument can be used to specify an alternate\n stack frame at which to start. The optional 'limit' and 'file'\n arguments have the same meaning as for print_exception().\n \"\"\"\n if f is None:\n f = sys._getframe().f_back\n print_list(extract_stack(f, limit=limit), file=file)\n\n\ndef format_stack(f=None, limit=None):\n \"\"\"Shorthand for 'format_list(extract_stack(f, limit))'.\"\"\"\n if f is None:\n f = sys._getframe().f_back\n return format_list(extract_stack(f, limit=limit))\n\n\ndef extract_stack(f=None, limit=None):\n \"\"\"Extract the raw traceback from the current stack frame.\n\n The return value has the same format as for extract_tb(). The\n optional 'f' and 'limit' arguments have the same meaning as for\n print_stack(). Each item in the list is a quadruple (filename,\n line number, function name, text), and the entries are in order\n from oldest to newest stack frame.\n \"\"\"\n if f is None:\n f = sys._getframe().f_back\n stack = StackSummary.extract(walk_stack(f), limit=limit)\n stack.reverse()\n return stack\n\n\ndef clear_frames(tb):\n \"Clear all references to local variables in the frames of a traceback.\"\n while tb is not None:\n try:\n tb.tb_frame.clear()\n except RuntimeError:\n # Ignore the exception raised if the frame is still executing.\n pass\n tb = tb.tb_next\n\n\nclass FrameSummary:\n \"\"\"Information about a single frame from a traceback.\n\n - :attr:`filename` The filename for the frame.\n - :attr:`lineno` The line within filename for the frame that was\n active when the frame was captured.\n - :attr:`name` The name of the function or method that was executing\n when the frame was captured.\n - :attr:`line` The text from the linecache module for the\n of code that was running when the frame was captured.\n - :attr:`locals` Either None if locals were not supplied, or a dict\n mapping the name to the repr() of the variable.\n \"\"\"\n\n __slots__ = ('filename', 'lineno', 'end_lineno', 'colno', 'end_colno',\n 'name', '_lines', '_lines_dedented', 'locals')\n\n def __init__(self, filename, lineno, name, *, lookup_line=True,\n locals=None, line=None,\n end_lineno=None, colno=None, end_colno=None):\n \"\"\"Construct a FrameSummary.\n\n :param lookup_line: If True, `linecache` is consulted for the source\n code line. Otherwise, the line will be looked up when first needed.\n :param locals: If supplied the frame locals, which will be captured as\n object representations.\n :param line: If provided, use this instead of looking up the line in\n the linecache.\n \"\"\"\n self.filename = filename\n self.lineno = lineno\n self.end_lineno = lineno if end_lineno is None else end_lineno\n self.colno = colno\n self.end_colno = end_colno\n self.name = name\n self._lines = line\n self._lines_dedented = None\n if lookup_line:\n self.line\n self.locals = {k: _safe_string(v, 'local', func=repr)\n for k, v in locals.items()} if locals else None\n\n def __eq__(self, other):\n if isinstance(other, FrameSummary):\n return (self.filename == other.filename and\n self.lineno == other.lineno and\n self.name == other.name and\n self.locals == other.locals)\n if isinstance(other, tuple):\n return (self.filename, self.lineno, self.name, self.line) == other\n return NotImplemented\n\n def __getitem__(self, pos):\n return (self.filename, self.lineno, self.name, self.line)[pos]\n\n def __iter__(self):\n return iter([self.filename, self.lineno, self.name, self.line])\n\n def __repr__(self):\n return \"\".format(\n filename=self.filename, lineno=self.lineno, name=self.name)\n\n def __len__(self):\n return 4\n\n def _set_lines(self):\n if (\n self._lines is None\n and self.lineno is not None\n and self.end_lineno is not None\n ):\n lines = []\n for lineno in range(self.lineno, self.end_lineno + 1):\n # treat errors (empty string) and empty lines (newline) as the same\n lines.append(linecache.getline(self.filename, lineno).rstrip())\n self._lines = \"\\n\".join(lines) + \"\\n\"\n\n @property\n def _original_lines(self):\n # Returns the line as-is from the source, without modifying whitespace.\n self._set_lines()\n return self._lines\n\n @property\n def _dedented_lines(self):\n # Returns _original_lines, but dedented\n self._set_lines()\n if self._lines_dedented is None and self._lines is not None:\n self._lines_dedented = textwrap.dedent(self._lines)\n return self._lines_dedented\n\n @property\n def line(self):\n self._set_lines()\n if self._lines is None:\n return None\n # return only the first line, stripped\n return self._lines.partition(\"\\n\")[0].strip()\n\n\ndef walk_stack(f):\n \"\"\"Walk a stack yielding the frame and line number for each frame.\n\n This will follow f.f_back from the given frame. If no frame is given, the\n current stack is used. Usually used with StackSummary.extract.\n \"\"\"\n if f is None:\n f = sys._getframe().f_back.f_back.f_back.f_back\n while f is not None:\n yield f, f.f_lineno\n f = f.f_back\n\n\ndef walk_tb(tb):\n \"\"\"Walk a traceback yielding the frame and line number for each frame.\n\n This will follow tb.tb_next (and thus is in the opposite order to\n walk_stack). Usually used with StackSummary.extract.\n \"\"\"\n while tb is not None:\n yield tb.tb_frame, tb.tb_lineno\n tb = tb.tb_next\n\n\ndef _walk_tb_with_full_positions(tb):\n # Internal version of walk_tb that yields full code positions including\n # end line and column information.\n while tb is not None:\n positions = _get_code_position(tb.tb_frame.f_code, tb.tb_lasti)\n # Yield tb_lineno when co_positions does not have a line number to\n # maintain behavior with walk_tb.\n if positions[0] is None:\n yield tb.tb_frame, (tb.tb_lineno, ) + positions[1:]\n else:\n yield tb.tb_frame, positions\n tb = tb.tb_next\n\n\ndef _get_code_position(code, instruction_index):\n if instruction_index < 0:\n return (None, None, None, None)\n positions_gen = code.co_positions()\n return next(itertools.islice(positions_gen, instruction_index // 2, None))\n\n\n_RECURSIVE_CUTOFF = 3 # Also hardcoded in traceback.c.\n\n\nclass StackSummary(list):\n \"\"\"A list of FrameSummary objects, representing a stack of frames.\"\"\"\n\n @classmethod\n def extract(klass, frame_gen, *, limit=None, lookup_lines=True,\n capture_locals=False):\n \"\"\"Create a StackSummary from a traceback or stack object.\n\n :param frame_gen: A generator that yields (frame, lineno) tuples\n whose summaries are to be included in the stack.\n :param limit: None to include all frames or the number of frames to\n include.\n :param lookup_lines: If True, lookup lines for each frame immediately,\n otherwise lookup is deferred until the frame is rendered.\n :param capture_locals: If True, the local variables from each frame will\n be captured as object representations into the FrameSummary.\n \"\"\"\n def extended_frame_gen():\n for f, lineno in frame_gen:\n yield f, (lineno, None, None, None)\n\n return klass._extract_from_extended_frame_gen(\n extended_frame_gen(), limit=limit, lookup_lines=lookup_lines,\n capture_locals=capture_locals)\n\n @classmethod\n def _extract_from_extended_frame_gen(klass, frame_gen, *, limit=None,\n lookup_lines=True, capture_locals=False):\n # Same as extract but operates on a frame generator that yields\n # (frame, (lineno, end_lineno, colno, end_colno)) in the stack.\n # Only lineno is required, the remaining fields can be None if the\n # information is not available.\n builtin_limit = limit is BUILTIN_EXCEPTION_LIMIT\n if limit is None or builtin_limit:\n limit = getattr(sys, 'tracebacklimit', None)\n if limit is not None and limit < 0:\n limit = 0\n if limit is not None:\n if builtin_limit:\n frame_gen = tuple(frame_gen)\n frame_gen = frame_gen[len(frame_gen) - limit:]\n elif limit >= 0:\n frame_gen = itertools.islice(frame_gen, limit)\n else:\n frame_gen = collections.deque(frame_gen, maxlen=-limit)\n\n result = klass()\n fnames = set()\n for f, (lineno, end_lineno, colno, end_colno) in frame_gen:\n co = f.f_code\n filename = co.co_filename\n name = co.co_name\n fnames.add(filename)\n linecache.lazycache(filename, f.f_globals)\n # Must defer line lookups until we have called checkcache.\n if capture_locals:\n f_locals = f.f_locals\n else:\n f_locals = None\n result.append(FrameSummary(\n filename, lineno, name, lookup_line=False, locals=f_locals,\n end_lineno=end_lineno, colno=colno, end_colno=end_colno))\n for filename in fnames:\n linecache.checkcache(filename)\n\n # If immediate lookup was desired, trigger lookups now.\n if lookup_lines:\n for f in result:\n f.line\n return result\n\n @classmethod\n def from_list(klass, a_list):\n \"\"\"\n Create a StackSummary object from a supplied list of\n FrameSummary objects or old-style list of tuples.\n \"\"\"\n # While doing a fast-path check for isinstance(a_list, StackSummary) is\n # appealing, idlelib.run.cleanup_traceback and other similar code may\n # break this by making arbitrary frames plain tuples, so we need to\n # check on a frame by frame basis.\n result = StackSummary()\n for frame in a_list:\n if isinstance(frame, FrameSummary):\n result.append(frame)\n else:\n filename, lineno, name, line = frame\n result.append(FrameSummary(filename, lineno, name, line=line))\n return result\n\n def format_frame_summary(self, frame_summary, **kwargs):\n \"\"\"Format the lines for a single FrameSummary.\n\n Returns a string representing one frame involved in the stack. This\n gets called for every frame to be printed in the stack summary.\n \"\"\"\n colorize = kwargs.get(\"colorize\", False)\n row = []\n filename = frame_summary.filename\n if frame_summary.filename.startswith(\"-\"):\n filename = \"\"\n if colorize:\n row.append(' File {}\"{}\"{}, line {}{}{}, in {}{}{}\\n'.format(\n ANSIColors.MAGENTA,\n filename,\n ANSIColors.RESET,\n ANSIColors.MAGENTA,\n frame_summary.lineno,\n ANSIColors.RESET,\n ANSIColors.MAGENTA,\n frame_summary.name,\n ANSIColors.RESET,\n )\n )\n else:\n row.append(' File \"{}\", line {}, in {}\\n'.format(\n filename, frame_summary.lineno, frame_summary.name))\n if frame_summary._dedented_lines and frame_summary._dedented_lines.strip():\n if (\n frame_summary.colno is None or\n frame_summary.end_colno is None\n ):\n # only output first line if column information is missing\n row.append(textwrap.indent(frame_summary.line, ' ') + \"\\n\")\n else:\n # get first and last line\n all_lines_original = frame_summary._original_lines.splitlines()\n first_line = all_lines_original[0]\n # assume all_lines_original has enough lines (since we constructed it)\n last_line = all_lines_original[frame_summary.end_lineno - frame_summary.lineno]\n\n # character index of the start/end of the instruction\n start_offset = _byte_offset_to_character_offset(first_line, frame_summary.colno)\n end_offset = _byte_offset_to_character_offset(last_line, frame_summary.end_colno)\n\n all_lines = frame_summary._dedented_lines.splitlines()[\n :frame_summary.end_lineno - frame_summary.lineno + 1\n ]\n\n # adjust start/end offset based on dedent\n dedent_characters = len(first_line) - len(all_lines[0])\n start_offset = max(0, start_offset - dedent_characters)\n end_offset = max(0, end_offset - dedent_characters)\n\n # When showing this on a terminal, some of the non-ASCII characters\n # might be rendered as double-width characters, so we need to take\n # that into account when calculating the length of the line.\n dp_start_offset = _display_width(all_lines[0], offset=start_offset)\n dp_end_offset = _display_width(all_lines[-1], offset=end_offset)\n\n # get exact code segment corresponding to the instruction\n segment = \"\\n\".join(all_lines)\n segment = segment[start_offset:len(segment) - (len(all_lines[-1]) - end_offset)]\n\n # attempt to parse for anchors\n anchors = None\n show_carets = False\n with suppress(Exception):\n anchors = _extract_caret_anchors_from_line_segment(segment)\n show_carets = self._should_show_carets(start_offset, end_offset, all_lines, anchors)\n\n result = []\n\n # only display first line, last line, and lines around anchor start/end\n significant_lines = {0, len(all_lines) - 1}\n\n anchors_left_end_offset = 0\n anchors_right_start_offset = 0\n primary_char = \"^\"\n secondary_char = \"^\"\n if anchors:\n anchors_left_end_offset = anchors.left_end_offset\n anchors_right_start_offset = anchors.right_start_offset\n # computed anchor positions do not take start_offset into account,\n # so account for it here\n if anchors.left_end_lineno == 0:\n anchors_left_end_offset += start_offset\n if anchors.right_start_lineno == 0:\n anchors_right_start_offset += start_offset\n\n # account for display width\n anchors_left_end_offset = _display_width(\n all_lines[anchors.left_end_lineno], offset=anchors_left_end_offset\n )\n anchors_right_start_offset = _display_width(\n all_lines[anchors.right_start_lineno], offset=anchors_right_start_offset\n )\n\n primary_char = anchors.primary_char\n secondary_char = anchors.secondary_char\n significant_lines.update(\n range(anchors.left_end_lineno - 1, anchors.left_end_lineno + 2)\n )\n significant_lines.update(\n range(anchors.right_start_lineno - 1, anchors.right_start_lineno + 2)\n )\n\n # remove bad line numbers\n significant_lines.discard(-1)\n significant_lines.discard(len(all_lines))\n\n def output_line(lineno):\n \"\"\"output all_lines[lineno] along with carets\"\"\"\n result.append(all_lines[lineno] + \"\\n\")\n if not show_carets:\n return\n num_spaces = len(all_lines[lineno]) - len(all_lines[lineno].lstrip())\n carets = []\n num_carets = dp_end_offset if lineno == len(all_lines) - 1 else _display_width(all_lines[lineno])\n # compute caret character for each position\n for col in range(num_carets):\n if col < num_spaces or (lineno == 0 and col < dp_start_offset):\n # before first non-ws char of the line, or before start of instruction\n carets.append(' ')\n elif anchors and (\n lineno > anchors.left_end_lineno or\n (lineno == anchors.left_end_lineno and col >= anchors_left_end_offset)\n ) and (\n lineno < anchors.right_start_lineno or\n (lineno == anchors.right_start_lineno and col < anchors_right_start_offset)\n ):\n # within anchors\n carets.append(secondary_char)\n else:\n carets.append(primary_char)\n if colorize:\n # Replace the previous line with a red version of it only in the parts covered\n # by the carets.\n line = result[-1]\n colorized_line_parts = []\n colorized_carets_parts = []\n\n for color, group in itertools.groupby(itertools.zip_longest(line, carets, fillvalue=\"\"), key=lambda x: x[1]):\n caret_group = list(group)\n if color == \"^\":\n colorized_line_parts.append(ANSIColors.BOLD_RED + \"\".join(char for char, _ in caret_group) + ANSIColors.RESET)\n colorized_carets_parts.append(ANSIColors.BOLD_RED + \"\".join(caret for _, caret in caret_group) + ANSIColors.RESET)\n elif color == \"~\":\n colorized_line_parts.append(ANSIColors.RED + \"\".join(char for char, _ in caret_group) + ANSIColors.RESET)\n colorized_carets_parts.append(ANSIColors.RED + \"\".join(caret for _, caret in caret_group) + ANSIColors.RESET)\n else:\n colorized_line_parts.append(\"\".join(char for char, _ in caret_group))\n colorized_carets_parts.append(\"\".join(caret for _, caret in caret_group))\n\n colorized_line = \"\".join(colorized_line_parts)\n colorized_carets = \"\".join(colorized_carets_parts)\n result[-1] = colorized_line\n result.append(colorized_carets + \"\\n\")\n else:\n result.append(\"\".join(carets) + \"\\n\")\n\n # display significant lines\n sig_lines_list = sorted(significant_lines)\n for i, lineno in enumerate(sig_lines_list):\n if i:\n linediff = lineno - sig_lines_list[i - 1]\n if linediff == 2:\n # 1 line in between - just output it\n output_line(lineno - 1)\n elif linediff > 2:\n # > 1 line in between - abbreviate\n result.append(f\"...<{linediff - 1} lines>...\\n\")\n output_line(lineno)\n\n row.append(\n textwrap.indent(textwrap.dedent(\"\".join(result)), ' ', lambda line: True)\n )\n if frame_summary.locals:\n for name, value in sorted(frame_summary.locals.items()):\n row.append(' {name} = {value}\\n'.format(name=name, value=value))\n\n return ''.join(row)\n\n def _should_show_carets(self, start_offset, end_offset, all_lines, anchors):\n with suppress(SyntaxError, ImportError):\n import ast\n tree = ast.parse('\\n'.join(all_lines))\n if not tree.body:\n return False\n statement = tree.body[0]\n value = None\n def _spawns_full_line(value):\n return (\n value.lineno == 1\n and value.end_lineno == len(all_lines)\n and value.col_offset == start_offset\n and value.end_col_offset == end_offset\n )\n match statement:\n case ast.Return(value=ast.Call()):\n if isinstance(statement.value.func, ast.Name):\n value = statement.value\n case ast.Assign(value=ast.Call()):\n if (\n len(statement.targets) == 1 and\n isinstance(statement.targets[0], ast.Name)\n ):\n value = statement.value\n if value is not None and _spawns_full_line(value):\n return False\n if anchors:\n return True\n if all_lines[0][:start_offset].lstrip() or all_lines[-1][end_offset:].rstrip():\n return True\n return False\n\n def format(self, **kwargs):\n \"\"\"Format the stack ready for printing.\n\n Returns a list of strings ready for printing. Each string in the\n resulting list corresponds to a single frame from the stack.\n Each string ends in a newline; the strings may contain internal\n newlines as well, for those items with source text lines.\n\n For long sequences of the same frame and line, the first few\n repetitions are shown, followed by a summary line stating the exact\n number of further repetitions.\n \"\"\"\n colorize = kwargs.get(\"colorize\", False)\n result = []\n last_file = None\n last_line = None\n last_name = None\n count = 0\n for frame_summary in self:\n formatted_frame = self.format_frame_summary(frame_summary, colorize=colorize)\n if formatted_frame is None:\n continue\n if (last_file is None or last_file != frame_summary.filename or\n last_line is None or last_line != frame_summary.lineno or\n last_name is None or last_name != frame_summary.name):\n if count > _RECURSIVE_CUTOFF:\n count -= _RECURSIVE_CUTOFF\n result.append(\n f' [Previous line repeated {count} more '\n f'time{\"s\" if count > 1 else \"\"}]\\n'\n )\n last_file = frame_summary.filename\n last_line = frame_summary.lineno\n last_name = frame_summary.name\n count = 0\n count += 1\n if count > _RECURSIVE_CUTOFF:\n continue\n result.append(formatted_frame)\n\n if count > _RECURSIVE_CUTOFF:\n count -= _RECURSIVE_CUTOFF\n result.append(\n f' [Previous line repeated {count} more '\n f'time{\"s\" if count > 1 else \"\"}]\\n'\n )\n return result\n\n\ndef _byte_offset_to_character_offset(str, offset):\n as_utf8 = str.encode('utf-8')\n return len(as_utf8[:offset].decode(\"utf-8\", errors=\"replace\"))\n\n\n_Anchors = collections.namedtuple(\n \"_Anchors\",\n [\n \"left_end_lineno\",\n \"left_end_offset\",\n \"right_start_lineno\",\n \"right_start_offset\",\n \"primary_char\",\n \"secondary_char\",\n ],\n defaults=[\"~\", \"^\"]\n)\n\ndef _extract_caret_anchors_from_line_segment(segment):\n \"\"\"\n Given source code `segment` corresponding to a FrameSummary, determine:\n - for binary ops, the location of the binary op\n - for indexing and function calls, the location of the brackets.\n `segment` is expected to be a valid Python expression.\n \"\"\"\n import ast\n\n try:\n # Without parentheses, `segment` is parsed as a statement.\n # Binary ops, subscripts, and calls are expressions, so\n # we can wrap them with parentheses to parse them as\n # (possibly multi-line) expressions.\n # e.g. if we try to highlight the addition in\n # x = (\n # a +\n # b\n # )\n # then we would ast.parse\n # a +\n # b\n # which is not a valid statement because of the newline.\n # Adding brackets makes it a valid expression.\n # (\n # a +\n # b\n # )\n # Line locations will be different than the original,\n # which is taken into account later on.\n tree = ast.parse(f\"(\\n{segment}\\n)\")\n except SyntaxError:\n return None\n\n if len(tree.body) != 1:\n return None\n\n lines = segment.splitlines()\n\n def normalize(lineno, offset):\n \"\"\"Get character index given byte offset\"\"\"\n return _byte_offset_to_character_offset(lines[lineno], offset)\n\n def next_valid_char(lineno, col):\n \"\"\"Gets the next valid character index in `lines`, if\n the current location is not valid. Handles empty lines.\n \"\"\"\n while lineno < len(lines) and col >= len(lines[lineno]):\n col = 0\n lineno += 1\n assert lineno < len(lines) and col < len(lines[lineno])\n return lineno, col\n\n def increment(lineno, col):\n \"\"\"Get the next valid character index in `lines`.\"\"\"\n col += 1\n lineno, col = next_valid_char(lineno, col)\n return lineno, col\n\n def nextline(lineno, col):\n \"\"\"Get the next valid character at least on the next line\"\"\"\n col = 0\n lineno += 1\n lineno, col = next_valid_char(lineno, col)\n return lineno, col\n\n def increment_until(lineno, col, stop):\n \"\"\"Get the next valid non-\"\\\\#\" character that satisfies the `stop` predicate\"\"\"\n while True:\n ch = lines[lineno][col]\n if ch in \"\\\\#\":\n lineno, col = nextline(lineno, col)\n elif not stop(ch):\n lineno, col = increment(lineno, col)\n else:\n break\n return lineno, col\n\n def setup_positions(expr, force_valid=True):\n \"\"\"Get the lineno/col position of the end of `expr`. If `force_valid` is True,\n forces the position to be a valid character (e.g. if the position is beyond the\n end of the line, move to the next line)\n \"\"\"\n # -2 since end_lineno is 1-indexed and because we added an extra\n # bracket + newline to `segment` when calling ast.parse\n lineno = expr.end_lineno - 2\n col = normalize(lineno, expr.end_col_offset)\n return next_valid_char(lineno, col) if force_valid else (lineno, col)\n\n statement = tree.body[0]\n match statement:\n case ast.Expr(expr):\n match expr:\n case ast.BinOp():\n # ast gives these locations for BinOp subexpressions\n # ( left_expr ) + ( right_expr )\n # left^^^^^ right^^^^^\n lineno, col = setup_positions(expr.left)\n\n # First operator character is the first non-space/')' character\n lineno, col = increment_until(lineno, col, lambda x: not x.isspace() and x != ')')\n\n # binary op is 1 or 2 characters long, on the same line,\n # before the right subexpression\n right_col = col + 1\n if (\n right_col < len(lines[lineno])\n and (\n # operator char should not be in the right subexpression\n expr.right.lineno - 2 > lineno or\n right_col < normalize(expr.right.lineno - 2, expr.right.col_offset)\n )\n and not (ch := lines[lineno][right_col]).isspace()\n and ch not in \"\\\\#\"\n ):\n right_col += 1\n\n # right_col can be invalid since it is exclusive\n return _Anchors(lineno, col, lineno, right_col)\n case ast.Subscript():\n # ast gives these locations for value and slice subexpressions\n # ( value_expr ) [ slice_expr ]\n # value^^^^^ slice^^^^^\n # subscript^^^^^^^^^^^^^^^^^^^^\n\n # find left bracket\n left_lineno, left_col = setup_positions(expr.value)\n left_lineno, left_col = increment_until(left_lineno, left_col, lambda x: x == '[')\n # find right bracket (final character of expression)\n right_lineno, right_col = setup_positions(expr, force_valid=False)\n return _Anchors(left_lineno, left_col, right_lineno, right_col)\n case ast.Call():\n # ast gives these locations for function call expressions\n # ( func_expr ) (args, kwargs)\n # func^^^^^\n # call^^^^^^^^^^^^^^^^^^^^^^^^\n\n # find left bracket\n left_lineno, left_col = setup_positions(expr.func)\n left_lineno, left_col = increment_until(left_lineno, left_col, lambda x: x == '(')\n # find right bracket (final character of expression)\n right_lineno, right_col = setup_positions(expr, force_valid=False)\n return _Anchors(left_lineno, left_col, right_lineno, right_col)\n\n return None\n\n_WIDE_CHAR_SPECIFIERS = \"WF\"\n\ndef _display_width(line, offset=None):\n \"\"\"Calculate the extra amount of width space the given source\n code segment might take if it were to be displayed on a fixed\n width output device. Supports wide unicode characters and emojis.\"\"\"\n\n if offset is None:\n offset = len(line)\n\n # Fast track for ASCII-only strings\n if line.isascii():\n return offset\n\n import unicodedata\n\n return sum(\n 2 if unicodedata.east_asian_width(char) in _WIDE_CHAR_SPECIFIERS else 1\n for char in line[:offset]\n )\n\n\n\nclass _ExceptionPrintContext:\n def __init__(self):\n self.seen = set()\n self.exception_group_depth = 0\n self.need_close = False\n\n def indent(self):\n return ' ' * (2 * self.exception_group_depth)\n\n def emit(self, text_gen, margin_char=None):\n if margin_char is None:\n margin_char = '|'\n indent_str = self.indent()\n if self.exception_group_depth:\n indent_str += margin_char + ' '\n\n if isinstance(text_gen, str):\n yield textwrap.indent(text_gen, indent_str, lambda line: True)\n else:\n for text in text_gen:\n yield textwrap.indent(text, indent_str, lambda line: True)\n\n\nclass TracebackException:\n \"\"\"An exception ready for rendering.\n\n The traceback module captures enough attributes from the original exception\n to this intermediary form to ensure that no references are held, while\n still being able to fully print or format it.\n\n max_group_width and max_group_depth control the formatting of exception\n groups. The depth refers to the nesting level of the group, and the width\n refers to the size of a single exception group's exceptions array. The\n formatted output is truncated when either limit is exceeded.\n\n Use `from_exception` to create TracebackException instances from exception\n objects, or the constructor to create TracebackException instances from\n individual components.\n\n - :attr:`__cause__` A TracebackException of the original *__cause__*.\n - :attr:`__context__` A TracebackException of the original *__context__*.\n - :attr:`exceptions` For exception groups - a list of TracebackException\n instances for the nested *exceptions*. ``None`` for other exceptions.\n - :attr:`__suppress_context__` The *__suppress_context__* value from the\n original exception.\n - :attr:`stack` A `StackSummary` representing the traceback.\n - :attr:`exc_type` (deprecated) The class of the original traceback.\n - :attr:`exc_type_str` String display of exc_type\n - :attr:`filename` For syntax errors - the filename where the error\n occurred.\n - :attr:`lineno` For syntax errors - the linenumber where the error\n occurred.\n - :attr:`end_lineno` For syntax errors - the end linenumber where the error\n occurred. Can be `None` if not present.\n - :attr:`text` For syntax errors - the text where the error\n occurred.\n - :attr:`offset` For syntax errors - the offset into the text where the\n error occurred.\n - :attr:`end_offset` For syntax errors - the end offset into the text where\n the error occurred. Can be `None` if not present.\n - :attr:`msg` For syntax errors - the compiler error message.\n \"\"\"\n\n def __init__(self, exc_type, exc_value, exc_traceback, *, limit=None,\n lookup_lines=True, capture_locals=False, compact=False,\n max_group_width=15, max_group_depth=10, save_exc_type=True, _seen=None):\n # NB: we need to accept exc_traceback, exc_value, exc_traceback to\n # permit backwards compat with the existing API, otherwise we\n # need stub thunk objects just to glue it together.\n # Handle loops in __cause__ or __context__.\n is_recursive_call = _seen is not None\n if _seen is None:\n _seen = set()\n _seen.add(id(exc_value))\n\n self.max_group_width = max_group_width\n self.max_group_depth = max_group_depth\n\n self.stack = StackSummary._extract_from_extended_frame_gen(\n _walk_tb_with_full_positions(exc_traceback),\n limit=limit, lookup_lines=lookup_lines,\n capture_locals=capture_locals)\n\n self._exc_type = exc_type if save_exc_type else None\n\n # Capture now to permit freeing resources: only complication is in the\n # unofficial API _format_final_exc_line\n self._str = _safe_string(exc_value, 'exception')\n try:\n self.__notes__ = getattr(exc_value, '__notes__', None)\n except Exception as e:\n self.__notes__ = [\n f'Ignored error getting __notes__: {_safe_string(e, '__notes__', repr)}']\n\n self._is_syntax_error = False\n self._have_exc_type = exc_type is not None\n if exc_type is not None:\n self.exc_type_qualname = exc_type.__qualname__\n self.exc_type_module = exc_type.__module__\n else:\n self.exc_type_qualname = None\n self.exc_type_module = None\n\n if exc_type and issubclass(exc_type, SyntaxError):\n # Handle SyntaxError's specially\n self.filename = exc_value.filename\n lno = exc_value.lineno\n self.lineno = str(lno) if lno is not None else None\n end_lno = exc_value.end_lineno\n self.end_lineno = str(end_lno) if end_lno is not None else None\n self.text = exc_value.text\n self.offset = exc_value.offset\n self.end_offset = exc_value.end_offset\n self.msg = exc_value.msg\n self._is_syntax_error = True\n elif exc_type and issubclass(exc_type, ImportError) and \\\n getattr(exc_value, \"name_from\", None) is not None:\n wrong_name = getattr(exc_value, \"name_from\", None)\n suggestion = _compute_suggestion_error(exc_value, exc_traceback, wrong_name)\n if suggestion:\n self._str += f\". Did you mean: '{suggestion}'?\"\n elif exc_type and issubclass(exc_type, (NameError, AttributeError)) and \\\n getattr(exc_value, \"name\", None) is not None:\n wrong_name = getattr(exc_value, \"name\", None)\n suggestion = _compute_suggestion_error(exc_value, exc_traceback, wrong_name)\n if suggestion:\n self._str += f\". Did you mean: '{suggestion}'?\"\n if issubclass(exc_type, NameError):\n wrong_name = getattr(exc_value, \"name\", None)\n if wrong_name is not None and wrong_name in sys.stdlib_module_names:\n if suggestion:\n self._str += f\" Or did you forget to import '{wrong_name}'?\"\n else:\n self._str += f\". Did you forget to import '{wrong_name}'?\"\n if lookup_lines:\n self._load_lines()\n self.__suppress_context__ = \\\n exc_value.__suppress_context__ if exc_value is not None else False\n\n # Convert __cause__ and __context__ to `TracebackExceptions`s, use a\n # queue to avoid recursion (only the top-level call gets _seen == None)\n if not is_recursive_call:\n queue = [(self, exc_value)]\n while queue:\n te, e = queue.pop()\n if (e and e.__cause__ is not None\n and id(e.__cause__) not in _seen):\n cause = TracebackException(\n type(e.__cause__),\n e.__cause__,\n e.__cause__.__traceback__,\n limit=limit,\n lookup_lines=lookup_lines,\n capture_locals=capture_locals,\n max_group_width=max_group_width,\n max_group_depth=max_group_depth,\n _seen=_seen)\n else:\n cause = None\n\n if compact:\n need_context = (cause is None and\n e is not None and\n not e.__suppress_context__)\n else:\n need_context = True\n if (e and e.__context__ is not None\n and need_context and id(e.__context__) not in _seen):\n context = TracebackException(\n type(e.__context__),\n e.__context__,\n e.__context__.__traceback__,\n limit=limit,\n lookup_lines=lookup_lines,\n capture_locals=capture_locals,\n max_group_width=max_group_width,\n max_group_depth=max_group_depth,\n _seen=_seen)\n else:\n context = None\n\n if e and isinstance(e, BaseExceptionGroup):\n exceptions = []\n for exc in e.exceptions:\n texc = TracebackException(\n type(exc),\n exc,\n exc.__traceback__,\n limit=limit,\n lookup_lines=lookup_lines,\n capture_locals=capture_locals,\n max_group_width=max_group_width,\n max_group_depth=max_group_depth,\n _seen=_seen)\n exceptions.append(texc)\n else:\n exceptions = None\n\n te.__cause__ = cause\n te.__context__ = context\n te.exceptions = exceptions\n if cause:\n queue.append((te.__cause__, e.__cause__))\n if context:\n queue.append((te.__context__, e.__context__))\n if exceptions:\n queue.extend(zip(te.exceptions, e.exceptions))\n\n @classmethod\n def from_exception(cls, exc, *args, **kwargs):\n \"\"\"Create a TracebackException from an exception.\"\"\"\n return cls(type(exc), exc, exc.__traceback__, *args, **kwargs)\n\n @property\n def exc_type(self):\n warnings.warn('Deprecated in 3.13. Use exc_type_str instead.',\n DeprecationWarning, stacklevel=2)\n return self._exc_type\n\n @property\n def exc_type_str(self):\n if not self._have_exc_type:\n return None\n stype = self.exc_type_qualname\n smod = self.exc_type_module\n if smod not in (\"__main__\", \"builtins\"):\n if not isinstance(smod, str):\n smod = \"\"\n stype = smod + '.' + stype\n return stype\n\n def _load_lines(self):\n \"\"\"Private API. force all lines in the stack to be loaded.\"\"\"\n for frame in self.stack:\n frame.line\n\n def __eq__(self, other):\n if isinstance(other, TracebackException):\n return self.__dict__ == other.__dict__\n return NotImplemented\n\n def __str__(self):\n return self._str\n\n def format_exception_only(self, *, show_group=False, _depth=0, **kwargs):\n \"\"\"Format the exception part of the traceback.\n\n The return value is a generator of strings, each ending in a newline.\n\n Generator yields the exception message.\n For :exc:`SyntaxError` exceptions, it\n also yields (before the exception message)\n several lines that (when printed)\n display detailed information about where the syntax error occurred.\n Following the message, generator also yields\n all the exception's ``__notes__``.\n\n When *show_group* is ``True``, and the exception is an instance of\n :exc:`BaseExceptionGroup`, the nested exceptions are included as\n well, recursively, with indentation relative to their nesting depth.\n \"\"\"\n colorize = kwargs.get(\"colorize\", False)\n\n indent = 3 * _depth * ' '\n if not self._have_exc_type:\n yield indent + _format_final_exc_line(None, self._str, colorize=colorize)\n return\n\n stype = self.exc_type_str\n if not self._is_syntax_error:\n if _depth > 0:\n # Nested exceptions needs correct handling of multiline messages.\n formatted = _format_final_exc_line(\n stype, self._str, insert_final_newline=False, colorize=colorize\n ).split('\\n')\n yield from [\n indent + l + '\\n'\n for l in formatted\n ]\n else:\n yield _format_final_exc_line(stype, self._str, colorize=colorize)\n else:\n yield from [indent + l for l in self._format_syntax_error(stype, colorize=colorize)]\n\n if (\n isinstance(self.__notes__, collections.abc.Sequence)\n and not isinstance(self.__notes__, (str, bytes))\n ):\n for note in self.__notes__:\n note = _safe_string(note, 'note')\n yield from [indent + l + '\\n' for l in note.split('\\n')]\n elif self.__notes__ is not None:\n yield indent + \"{}\\n\".format(_safe_string(self.__notes__, '__notes__', func=repr))\n\n if self.exceptions and show_group:\n for ex in self.exceptions:\n yield from ex.format_exception_only(show_group=show_group, _depth=_depth+1, colorize=colorize)\n\n def _format_syntax_error(self, stype, **kwargs):\n \"\"\"Format SyntaxError exceptions (internal helper).\"\"\"\n # Show exactly where the problem was found.\n colorize = kwargs.get(\"colorize\", False)\n filename_suffix = ''\n if self.lineno is not None:\n if colorize:\n yield ' File {}\"{}\"{}, line {}{}{}\\n'.format(\n ANSIColors.MAGENTA,\n self.filename or \"\",\n ANSIColors.RESET,\n ANSIColors.MAGENTA,\n self.lineno,\n ANSIColors.RESET,\n )\n else:\n yield ' File \"{}\", line {}\\n'.format(\n self.filename or \"\", self.lineno)\n elif self.filename is not None:\n filename_suffix = ' ({})'.format(self.filename)\n\n text = self.text\n if text is not None:\n # text = \" foo\\n\"\n # rtext = \" foo\"\n # ltext = \"foo\"\n rtext = text.rstrip('\\n')\n ltext = rtext.lstrip(' \\n\\f')\n spaces = len(rtext) - len(ltext)\n if self.offset is None:\n yield ' {}\\n'.format(ltext)\n else:\n offset = self.offset\n if self.lineno == self.end_lineno:\n end_offset = self.end_offset if self.end_offset not in {None, 0} else offset\n else:\n end_offset = len(rtext) + 1\n\n if self.text and offset > len(self.text):\n offset = len(rtext) + 1\n if self.text and end_offset > len(self.text):\n end_offset = len(rtext) + 1\n if offset >= end_offset or end_offset < 0:\n end_offset = offset + 1\n\n # Convert 1-based column offset to 0-based index into stripped text\n colno = offset - 1 - spaces\n end_colno = end_offset - 1 - spaces\n caretspace = ' '\n if colno >= 0:\n # non-space whitespace (likes tabs) must be kept for alignment\n caretspace = ((c if c.isspace() else ' ') for c in ltext[:colno])\n start_color = end_color = \"\"\n if colorize:\n # colorize from colno to end_colno\n ltext = (\n ltext[:colno] +\n ANSIColors.BOLD_RED + ltext[colno:end_colno] + ANSIColors.RESET +\n ltext[end_colno:]\n )\n start_color = ANSIColors.BOLD_RED\n end_color = ANSIColors.RESET\n yield ' {}\\n'.format(ltext)\n yield ' {}{}{}{}\\n'.format(\n \"\".join(caretspace),\n start_color,\n ('^' * (end_colno - colno)),\n end_color,\n )\n else:\n yield ' {}\\n'.format(ltext)\n msg = self.msg or \"\"\n if colorize:\n yield \"{}{}{}: {}{}{}{}\\n\".format(\n ANSIColors.BOLD_MAGENTA,\n stype,\n ANSIColors.RESET,\n ANSIColors.MAGENTA,\n msg,\n ANSIColors.RESET,\n filename_suffix)\n else:\n yield \"{}: {}{}\\n\".format(stype, msg, filename_suffix)\n\n def format(self, *, chain=True, _ctx=None, **kwargs):\n \"\"\"Format the exception.\n\n If chain is not *True*, *__cause__* and *__context__* will not be formatted.\n\n The return value is a generator of strings, each ending in a newline and\n some containing internal newlines. `print_exception` is a wrapper around\n this method which just prints the lines to a file.\n\n The message indicating which exception occurred is always the last\n string in the output.\n \"\"\"\n colorize = kwargs.get(\"colorize\", False)\n if _ctx is None:\n _ctx = _ExceptionPrintContext()\n\n output = []\n exc = self\n if chain:\n while exc:\n if exc.__cause__ is not None:\n chained_msg = _cause_message\n chained_exc = exc.__cause__\n elif (exc.__context__ is not None and\n not exc.__suppress_context__):\n chained_msg = _context_message\n chained_exc = exc.__context__\n else:\n chained_msg = None\n chained_exc = None\n\n output.append((chained_msg, exc))\n exc = chained_exc\n else:\n output.append((None, exc))\n\n for msg, exc in reversed(output):\n if msg is not None:\n yield from _ctx.emit(msg)\n if exc.exceptions is None:\n if exc.stack:\n yield from _ctx.emit('Traceback (most recent call last):\\n')\n yield from _ctx.emit(exc.stack.format(colorize=colorize))\n yield from _ctx.emit(exc.format_exception_only(colorize=colorize))\n elif _ctx.exception_group_depth > self.max_group_depth:\n # exception group, but depth exceeds limit\n yield from _ctx.emit(\n f\"... (max_group_depth is {self.max_group_depth})\\n\")\n else:\n # format exception group\n is_toplevel = (_ctx.exception_group_depth == 0)\n if is_toplevel:\n _ctx.exception_group_depth += 1\n\n if exc.stack:\n yield from _ctx.emit(\n 'Exception Group Traceback (most recent call last):\\n',\n margin_char = '+' if is_toplevel else None)\n yield from _ctx.emit(exc.stack.format(colorize=colorize))\n\n yield from _ctx.emit(exc.format_exception_only(colorize=colorize))\n num_excs = len(exc.exceptions)\n if num_excs <= self.max_group_width:\n n = num_excs\n else:\n n = self.max_group_width + 1\n _ctx.need_close = False\n for i in range(n):\n last_exc = (i == n-1)\n if last_exc:\n # The closing frame may be added by a recursive call\n _ctx.need_close = True\n\n if self.max_group_width is not None:\n truncated = (i >= self.max_group_width)\n else:\n truncated = False\n title = f'{i+1}' if not truncated else '...'\n yield (_ctx.indent() +\n ('+-' if i==0 else ' ') +\n f'+---------------- {title} ----------------\\n')\n _ctx.exception_group_depth += 1\n if not truncated:\n yield from exc.exceptions[i].format(chain=chain, _ctx=_ctx)\n else:\n remaining = num_excs - self.max_group_width\n plural = 's' if remaining > 1 else ''\n yield from _ctx.emit(\n f\"and {remaining} more exception{plural}\\n\")\n\n if last_exc and _ctx.need_close:\n yield (_ctx.indent() +\n \"+------------------------------------\\n\")\n _ctx.need_close = False\n _ctx.exception_group_depth -= 1\n\n if is_toplevel:\n assert _ctx.exception_group_depth == 1\n _ctx.exception_group_depth = 0\n\n\n def print(self, *, file=None, chain=True, **kwargs):\n \"\"\"Print the result of self.format(chain=chain) to 'file'.\"\"\"\n colorize = kwargs.get(\"colorize\", False)\n if file is None:\n file = sys.stderr\n for line in self.format(chain=chain, colorize=colorize):\n print(line, file=file, end=\"\")\n\n\n_MAX_CANDIDATE_ITEMS = 750\n_MAX_STRING_SIZE = 40\n_MOVE_COST = 2\n_CASE_COST = 1\n\n\ndef _substitution_cost(ch_a, ch_b):\n if ch_a == ch_b:\n return 0\n if ch_a.lower() == ch_b.lower():\n return _CASE_COST\n return _MOVE_COST\n\n\ndef _compute_suggestion_error(exc_value, tb, wrong_name):\n if wrong_name is None or not isinstance(wrong_name, str):\n return None\n if isinstance(exc_value, AttributeError):\n obj = exc_value.obj\n try:\n d = dir(obj)\n hide_underscored = (wrong_name[:1] != '_')\n if hide_underscored and tb is not None:\n while tb.tb_next is not None:\n tb = tb.tb_next\n frame = tb.tb_frame\n if 'self' in frame.f_locals and frame.f_locals['self'] is obj:\n hide_underscored = False\n if hide_underscored:\n d = [x for x in d if x[:1] != '_']\n except Exception:\n return None\n elif isinstance(exc_value, ImportError):\n try:\n mod = __import__(exc_value.name)\n d = dir(mod)\n if wrong_name[:1] != '_':\n d = [x for x in d if x[:1] != '_']\n except Exception:\n return None\n else:\n assert isinstance(exc_value, NameError)\n # find most recent frame\n if tb is None:\n return None\n while tb.tb_next is not None:\n tb = tb.tb_next\n frame = tb.tb_frame\n d = (\n list(frame.f_locals)\n + list(frame.f_globals)\n + list(frame.f_builtins)\n )\n\n # Check first if we are in a method and the instance\n # has the wrong name as attribute\n if 'self' in frame.f_locals:\n self = frame.f_locals['self']\n if hasattr(self, wrong_name):\n return f\"self.{wrong_name}\"\n\n try:\n import _suggestions\n except ImportError:\n pass\n else:\n return _suggestions._generate_suggestions(d, wrong_name)\n\n # Compute closest match\n\n if len(d) > _MAX_CANDIDATE_ITEMS:\n return None\n wrong_name_len = len(wrong_name)\n if wrong_name_len > _MAX_STRING_SIZE:\n return None\n best_distance = wrong_name_len\n suggestion = None\n for possible_name in d:\n if possible_name == wrong_name:\n # A missing attribute is \"found\". Don't suggest it (see GH-88821).\n continue\n # No more than 1/3 of the involved characters should need changed.\n max_distance = (len(possible_name) + wrong_name_len + 3) * _MOVE_COST // 6\n # Don't take matches we've already beaten.\n max_distance = min(max_distance, best_distance - 1)\n current_distance = _levenshtein_distance(wrong_name, possible_name, max_distance)\n if current_distance > max_distance:\n continue\n if not suggestion or current_distance < best_distance:\n suggestion = possible_name\n best_distance = current_distance\n return suggestion\n\n\ndef _levenshtein_distance(a, b, max_cost):\n # A Python implementation of Python/suggestions.c:levenshtein_distance.\n\n # Both strings are the same\n if a == b:\n return 0\n\n # Trim away common affixes\n pre = 0\n while a[pre:] and b[pre:] and a[pre] == b[pre]:\n pre += 1\n a = a[pre:]\n b = b[pre:]\n post = 0\n while a[:post or None] and b[:post or None] and a[post-1] == b[post-1]:\n post -= 1\n a = a[:post or None]\n b = b[:post or None]\n if not a or not b:\n return _MOVE_COST * (len(a) + len(b))\n if len(a) > _MAX_STRING_SIZE or len(b) > _MAX_STRING_SIZE:\n return max_cost + 1\n\n # Prefer shorter buffer\n if len(b) < len(a):\n a, b = b, a\n\n # Quick fail when a match is impossible\n if (len(b) - len(a)) * _MOVE_COST > max_cost:\n return max_cost + 1\n\n # Instead of producing the whole traditional len(a)-by-len(b)\n # matrix, we can update just one row in place.\n # Initialize the buffer row\n row = list(range(_MOVE_COST, _MOVE_COST * (len(a) + 1), _MOVE_COST))\n\n result = 0\n for bindex in range(len(b)):\n bchar = b[bindex]\n distance = result = bindex * _MOVE_COST\n minimum = sys.maxsize\n for index in range(len(a)):\n # 1) Previous distance in this row is cost(b[:b_index], a[:index])\n substitute = distance + _substitution_cost(bchar, a[index])\n # 2) cost(b[:b_index], a[:index+1]) from previous row\n distance = row[index]\n # 3) existing result is cost(b[:b_index+1], a[index])\n\n insert_delete = min(result, distance) + _MOVE_COST\n result = min(insert_delete, substitute)\n\n # cost(b[:b_index+1], a[:index+1])\n row[index] = result\n if result < minimum:\n minimum = result\n if minimum > max_cost:\n # Everything in this row is too big, so bail early.\n return max_cost + 1\n return result\n", 1610], "/usr/lib/python3.13/collections/__init__.py": ["'''This module implements specialized container datatypes providing\nalternatives to Python's general purpose built-in containers, dict,\nlist, set, and tuple.\n\n* namedtuple factory function for creating tuple subclasses with named fields\n* deque list-like container with fast appends and pops on either end\n* ChainMap dict-like class for creating a single view of multiple mappings\n* Counter dict subclass for counting hashable objects\n* OrderedDict dict subclass that remembers the order entries were added\n* defaultdict dict subclass that calls a factory function to supply missing values\n* UserDict wrapper around dictionary objects for easier dict subclassing\n* UserList wrapper around list objects for easier list subclassing\n* UserString wrapper around string objects for easier string subclassing\n\n'''\n\n__all__ = [\n 'ChainMap',\n 'Counter',\n 'OrderedDict',\n 'UserDict',\n 'UserList',\n 'UserString',\n 'defaultdict',\n 'deque',\n 'namedtuple',\n]\n\nimport _collections_abc\nimport sys as _sys\n\nfrom itertools import chain as _chain\nfrom itertools import repeat as _repeat\nfrom itertools import starmap as _starmap\nfrom keyword import iskeyword as _iskeyword\nfrom operator import eq as _eq\nfrom operator import itemgetter as _itemgetter\nfrom reprlib import recursive_repr as _recursive_repr\nfrom _weakref import proxy as _proxy\n\ntry:\n from _collections import deque\nexcept ImportError:\n pass\nelse:\n _collections_abc.MutableSequence.register(deque)\n\ntry:\n from _collections import _deque_iterator\nexcept ImportError:\n pass\n\ntry:\n from _collections import defaultdict\nexcept ImportError:\n pass\n\n\n################################################################################\n### OrderedDict\n################################################################################\n\nclass _OrderedDictKeysView(_collections_abc.KeysView):\n\n def __reversed__(self):\n yield from reversed(self._mapping)\n\nclass _OrderedDictItemsView(_collections_abc.ItemsView):\n\n def __reversed__(self):\n for key in reversed(self._mapping):\n yield (key, self._mapping[key])\n\nclass _OrderedDictValuesView(_collections_abc.ValuesView):\n\n def __reversed__(self):\n for key in reversed(self._mapping):\n yield self._mapping[key]\n\nclass _Link(object):\n __slots__ = 'prev', 'next', 'key', '__weakref__'\n\nclass OrderedDict(dict):\n 'Dictionary that remembers insertion order'\n # An inherited dict maps keys to values.\n # The inherited dict provides __getitem__, __len__, __contains__, and get.\n # The remaining methods are order-aware.\n # Big-O running times for all methods are the same as regular dictionaries.\n\n # The internal self.__map dict maps keys to links in a doubly linked list.\n # The circular doubly linked list starts and ends with a sentinel element.\n # The sentinel element never gets deleted (this simplifies the algorithm).\n # The sentinel is in self.__hardroot with a weakref proxy in self.__root.\n # The prev links are weakref proxies (to prevent circular references).\n # Individual links are kept alive by the hard reference in self.__map.\n # Those hard references disappear when a key is deleted from an OrderedDict.\n\n def __new__(cls, /, *args, **kwds):\n \"Create the ordered dict object and set up the underlying structures.\"\n self = dict.__new__(cls)\n self.__hardroot = _Link()\n self.__root = root = _proxy(self.__hardroot)\n root.prev = root.next = root\n self.__map = {}\n return self\n\n def __init__(self, other=(), /, **kwds):\n '''Initialize an ordered dictionary. The signature is the same as\n regular dictionaries. Keyword argument order is preserved.\n '''\n self.__update(other, **kwds)\n\n def __setitem__(self, key, value,\n dict_setitem=dict.__setitem__, proxy=_proxy, Link=_Link):\n 'od.__setitem__(i, y) <==> od[i]=y'\n # Setting a new item creates a new link at the end of the linked list,\n # and the inherited dictionary is updated with the new key/value pair.\n if key not in self:\n self.__map[key] = link = Link()\n root = self.__root\n last = root.prev\n link.prev, link.next, link.key = last, root, key\n last.next = link\n root.prev = proxy(link)\n dict_setitem(self, key, value)\n\n def __delitem__(self, key, dict_delitem=dict.__delitem__):\n 'od.__delitem__(y) <==> del od[y]'\n # Deleting an existing item uses self.__map to find the link which gets\n # removed by updating the links in the predecessor and successor nodes.\n dict_delitem(self, key)\n link = self.__map.pop(key)\n link_prev = link.prev\n link_next = link.next\n link_prev.next = link_next\n link_next.prev = link_prev\n link.prev = None\n link.next = None\n\n def __iter__(self):\n 'od.__iter__() <==> iter(od)'\n # Traverse the linked list in order.\n root = self.__root\n curr = root.next\n while curr is not root:\n yield curr.key\n curr = curr.next\n\n def __reversed__(self):\n 'od.__reversed__() <==> reversed(od)'\n # Traverse the linked list in reverse order.\n root = self.__root\n curr = root.prev\n while curr is not root:\n yield curr.key\n curr = curr.prev\n\n def clear(self):\n 'od.clear() -> None. Remove all items from od.'\n root = self.__root\n root.prev = root.next = root\n self.__map.clear()\n dict.clear(self)\n\n def popitem(self, last=True):\n '''Remove and return a (key, value) pair from the dictionary.\n\n Pairs are returned in LIFO order if last is true or FIFO order if false.\n '''\n if not self:\n raise KeyError('dictionary is empty')\n root = self.__root\n if last:\n link = root.prev\n link_prev = link.prev\n link_prev.next = root\n root.prev = link_prev\n else:\n link = root.next\n link_next = link.next\n root.next = link_next\n link_next.prev = root\n key = link.key\n del self.__map[key]\n value = dict.pop(self, key)\n return key, value\n\n def move_to_end(self, key, last=True):\n '''Move an existing element to the end (or beginning if last is false).\n\n Raise KeyError if the element does not exist.\n '''\n link = self.__map[key]\n link_prev = link.prev\n link_next = link.next\n soft_link = link_next.prev\n link_prev.next = link_next\n link_next.prev = link_prev\n root = self.__root\n if last:\n last = root.prev\n link.prev = last\n link.next = root\n root.prev = soft_link\n last.next = link\n else:\n first = root.next\n link.prev = root\n link.next = first\n first.prev = soft_link\n root.next = link\n\n def __sizeof__(self):\n sizeof = _sys.getsizeof\n n = len(self) + 1 # number of links including root\n size = sizeof(self.__dict__) # instance dictionary\n size += sizeof(self.__map) * 2 # internal dict and inherited dict\n size += sizeof(self.__hardroot) * n # link objects\n size += sizeof(self.__root) * n # proxy objects\n return size\n\n update = __update = _collections_abc.MutableMapping.update\n\n def keys(self):\n \"D.keys() -> a set-like object providing a view on D's keys\"\n return _OrderedDictKeysView(self)\n\n def items(self):\n \"D.items() -> a set-like object providing a view on D's items\"\n return _OrderedDictItemsView(self)\n\n def values(self):\n \"D.values() -> an object providing a view on D's values\"\n return _OrderedDictValuesView(self)\n\n __ne__ = _collections_abc.MutableMapping.__ne__\n\n __marker = object()\n\n def pop(self, key, default=__marker):\n '''od.pop(k[,d]) -> v, remove specified key and return the corresponding\n value. If key is not found, d is returned if given, otherwise KeyError\n is raised.\n\n '''\n marker = self.__marker\n result = dict.pop(self, key, marker)\n if result is not marker:\n # The same as in __delitem__().\n link = self.__map.pop(key)\n link_prev = link.prev\n link_next = link.next\n link_prev.next = link_next\n link_next.prev = link_prev\n link.prev = None\n link.next = None\n return result\n if default is marker:\n raise KeyError(key)\n return default\n\n def setdefault(self, key, default=None):\n '''Insert key with a value of default if key is not in the dictionary.\n\n Return the value for key if key is in the dictionary, else default.\n '''\n if key in self:\n return self[key]\n self[key] = default\n return default\n\n @_recursive_repr()\n def __repr__(self):\n 'od.__repr__() <==> repr(od)'\n if not self:\n return '%s()' % (self.__class__.__name__,)\n return '%s(%r)' % (self.__class__.__name__, dict(self.items()))\n\n def __reduce__(self):\n 'Return state information for pickling'\n state = self.__getstate__()\n if state:\n if isinstance(state, tuple):\n state, slots = state\n else:\n slots = {}\n state = state.copy()\n slots = slots.copy()\n for k in vars(OrderedDict()):\n state.pop(k, None)\n slots.pop(k, None)\n if slots:\n state = state, slots\n else:\n state = state or None\n return self.__class__, (), state, None, iter(self.items())\n\n def copy(self):\n 'od.copy() -> a shallow copy of od'\n return self.__class__(self)\n\n @classmethod\n def fromkeys(cls, iterable, value=None):\n '''Create a new ordered dictionary with keys from iterable and values set to value.\n '''\n self = cls()\n for key in iterable:\n self[key] = value\n return self\n\n def __eq__(self, other):\n '''od.__eq__(y) <==> od==y. Comparison to another OD is order-sensitive\n while comparison to a regular mapping is order-insensitive.\n\n '''\n if isinstance(other, OrderedDict):\n return dict.__eq__(self, other) and all(map(_eq, self, other))\n return dict.__eq__(self, other)\n\n def __ior__(self, other):\n self.update(other)\n return self\n\n def __or__(self, other):\n if not isinstance(other, dict):\n return NotImplemented\n new = self.__class__(self)\n new.update(other)\n return new\n\n def __ror__(self, other):\n if not isinstance(other, dict):\n return NotImplemented\n new = self.__class__(other)\n new.update(self)\n return new\n\n\ntry:\n from _collections import OrderedDict\nexcept ImportError:\n # Leave the pure Python version in place.\n pass\n\n\n################################################################################\n### namedtuple\n################################################################################\n\ntry:\n from _collections import _tuplegetter\nexcept ImportError:\n _tuplegetter = lambda index, doc: property(_itemgetter(index), doc=doc)\n\ndef namedtuple(typename, field_names, *, rename=False, defaults=None, module=None):\n \"\"\"Returns a new subclass of tuple with named fields.\n\n >>> Point = namedtuple('Point', ['x', 'y'])\n >>> Point.__doc__ # docstring for the new class\n 'Point(x, y)'\n >>> p = Point(11, y=22) # instantiate with positional args or keywords\n >>> p[0] + p[1] # indexable like a plain tuple\n 33\n >>> x, y = p # unpack like a regular tuple\n >>> x, y\n (11, 22)\n >>> p.x + p.y # fields also accessible by name\n 33\n >>> d = p._asdict() # convert to a dictionary\n >>> d['x']\n 11\n >>> Point(**d) # convert from a dictionary\n Point(x=11, y=22)\n >>> p._replace(x=100) # _replace() is like str.replace() but targets named fields\n Point(x=100, y=22)\n\n \"\"\"\n\n # Validate the field names. At the user's option, either generate an error\n # message or automatically replace the field name with a valid name.\n if isinstance(field_names, str):\n field_names = field_names.replace(',', ' ').split()\n field_names = list(map(str, field_names))\n typename = _sys.intern(str(typename))\n\n if rename:\n seen = set()\n for index, name in enumerate(field_names):\n if (not name.isidentifier()\n or _iskeyword(name)\n or name.startswith('_')\n or name in seen):\n field_names[index] = f'_{index}'\n seen.add(name)\n\n for name in [typename] + field_names:\n if type(name) is not str:\n raise TypeError('Type names and field names must be strings')\n if not name.isidentifier():\n raise ValueError('Type names and field names must be valid '\n f'identifiers: {name!r}')\n if _iskeyword(name):\n raise ValueError('Type names and field names cannot be a '\n f'keyword: {name!r}')\n\n seen = set()\n for name in field_names:\n if name.startswith('_') and not rename:\n raise ValueError('Field names cannot start with an underscore: '\n f'{name!r}')\n if name in seen:\n raise ValueError(f'Encountered duplicate field name: {name!r}')\n seen.add(name)\n\n field_defaults = {}\n if defaults is not None:\n defaults = tuple(defaults)\n if len(defaults) > len(field_names):\n raise TypeError('Got more default values than field names')\n field_defaults = dict(reversed(list(zip(reversed(field_names),\n reversed(defaults)))))\n\n # Variables used in the methods and docstrings\n field_names = tuple(map(_sys.intern, field_names))\n num_fields = len(field_names)\n arg_list = ', '.join(field_names)\n if num_fields == 1:\n arg_list += ','\n repr_fmt = '(' + ', '.join(f'{name}=%r' for name in field_names) + ')'\n tuple_new = tuple.__new__\n _dict, _tuple, _len, _map, _zip = dict, tuple, len, map, zip\n\n # Create all the named tuple methods to be added to the class namespace\n\n namespace = {\n '_tuple_new': tuple_new,\n '__builtins__': {},\n '__name__': f'namedtuple_{typename}',\n }\n code = f'lambda _cls, {arg_list}: _tuple_new(_cls, ({arg_list}))'\n __new__ = eval(code, namespace)\n __new__.__name__ = '__new__'\n __new__.__doc__ = f'Create new instance of {typename}({arg_list})'\n if defaults is not None:\n __new__.__defaults__ = defaults\n\n @classmethod\n def _make(cls, iterable):\n result = tuple_new(cls, iterable)\n if _len(result) != num_fields:\n raise TypeError(f'Expected {num_fields} arguments, got {len(result)}')\n return result\n\n _make.__func__.__doc__ = (f'Make a new {typename} object from a sequence '\n 'or iterable')\n\n def _replace(self, /, **kwds):\n result = self._make(_map(kwds.pop, field_names, self))\n if kwds:\n raise TypeError(f'Got unexpected field names: {list(kwds)!r}')\n return result\n\n _replace.__doc__ = (f'Return a new {typename} object replacing specified '\n 'fields with new values')\n\n def __repr__(self):\n 'Return a nicely formatted representation string'\n return self.__class__.__name__ + repr_fmt % self\n\n def _asdict(self):\n 'Return a new dict which maps field names to their values.'\n return _dict(_zip(self._fields, self))\n\n def __getnewargs__(self):\n 'Return self as a plain tuple. Used by copy and pickle.'\n return _tuple(self)\n\n # Modify function metadata to help with introspection and debugging\n for method in (\n __new__,\n _make.__func__,\n _replace,\n __repr__,\n _asdict,\n __getnewargs__,\n ):\n method.__qualname__ = f'{typename}.{method.__name__}'\n\n # Build-up the class namespace dictionary\n # and use type() to build the result class\n class_namespace = {\n '__doc__': f'{typename}({arg_list})',\n '__slots__': (),\n '_fields': field_names,\n '_field_defaults': field_defaults,\n '__new__': __new__,\n '_make': _make,\n '__replace__': _replace,\n '_replace': _replace,\n '__repr__': __repr__,\n '_asdict': _asdict,\n '__getnewargs__': __getnewargs__,\n '__match_args__': field_names,\n }\n for index, name in enumerate(field_names):\n doc = _sys.intern(f'Alias for field number {index}')\n class_namespace[name] = _tuplegetter(index, doc)\n\n result = type(typename, (tuple,), class_namespace)\n\n # For pickling to work, the __module__ variable needs to be set to the frame\n # where the named tuple is created. Bypass this step in environments where\n # sys._getframe is not defined (Jython for example) or sys._getframe is not\n # defined for arguments greater than 0 (IronPython), or where the user has\n # specified a particular module.\n if module is None:\n try:\n module = _sys._getframemodulename(1) or '__main__'\n except AttributeError:\n try:\n module = _sys._getframe(1).f_globals.get('__name__', '__main__')\n except (AttributeError, ValueError):\n pass\n if module is not None:\n result.__module__ = module\n\n return result\n\n\n########################################################################\n### Counter\n########################################################################\n\ndef _count_elements(mapping, iterable):\n 'Tally elements from the iterable.'\n mapping_get = mapping.get\n for elem in iterable:\n mapping[elem] = mapping_get(elem, 0) + 1\n\ntry: # Load C helper function if available\n from _collections import _count_elements\nexcept ImportError:\n pass\n\nclass Counter(dict):\n '''Dict subclass for counting hashable items. Sometimes called a bag\n or multiset. Elements are stored as dictionary keys and their counts\n are stored as dictionary values.\n\n >>> c = Counter('abcdeabcdabcaba') # count elements from a string\n\n >>> c.most_common(3) # three most common elements\n [('a', 5), ('b', 4), ('c', 3)]\n >>> sorted(c) # list all unique elements\n ['a', 'b', 'c', 'd', 'e']\n >>> ''.join(sorted(c.elements())) # list elements with repetitions\n 'aaaaabbbbcccdde'\n >>> sum(c.values()) # total of all counts\n 15\n\n >>> c['a'] # count of letter 'a'\n 5\n >>> for elem in 'shazam': # update counts from an iterable\n ... c[elem] += 1 # by adding 1 to each element's count\n >>> c['a'] # now there are seven 'a'\n 7\n >>> del c['b'] # remove all 'b'\n >>> c['b'] # now there are zero 'b'\n 0\n\n >>> d = Counter('simsalabim') # make another counter\n >>> c.update(d) # add in the second counter\n >>> c['a'] # now there are nine 'a'\n 9\n\n >>> c.clear() # empty the counter\n >>> c\n Counter()\n\n Note: If a count is set to zero or reduced to zero, it will remain\n in the counter until the entry is deleted or the counter is cleared:\n\n >>> c = Counter('aaabbc')\n >>> c['b'] -= 2 # reduce the count of 'b' by two\n >>> c.most_common() # 'b' is still in, but its count is zero\n [('a', 3), ('c', 1), ('b', 0)]\n\n '''\n # References:\n # http://en.wikipedia.org/wiki/Multiset\n # http://www.gnu.org/software/smalltalk/manual-base/html_node/Bag.html\n # http://www.demo2s.com/Tutorial/Cpp/0380__set-multiset/Catalog0380__set-multiset.htm\n # http://code.activestate.com/recipes/259174/\n # Knuth, TAOCP Vol. II section 4.6.3\n\n def __init__(self, iterable=None, /, **kwds):\n '''Create a new, empty Counter object. And if given, count elements\n from an input iterable. Or, initialize the count from another mapping\n of elements to their counts.\n\n >>> c = Counter() # a new, empty counter\n >>> c = Counter('gallahad') # a new counter from an iterable\n >>> c = Counter({'a': 4, 'b': 2}) # a new counter from a mapping\n >>> c = Counter(a=4, b=2) # a new counter from keyword args\n\n '''\n super().__init__()\n self.update(iterable, **kwds)\n\n def __missing__(self, key):\n 'The count of elements not in the Counter is zero.'\n # Needed so that self[missing_item] does not raise KeyError\n return 0\n\n def total(self):\n 'Sum of the counts'\n return sum(self.values())\n\n def most_common(self, n=None):\n '''List the n most common elements and their counts from the most\n common to the least. If n is None, then list all element counts.\n\n >>> Counter('abracadabra').most_common(3)\n [('a', 5), ('b', 2), ('r', 2)]\n\n '''\n # Emulate Bag.sortedByCount from Smalltalk\n if n is None:\n return sorted(self.items(), key=_itemgetter(1), reverse=True)\n\n # Lazy import to speedup Python startup time\n import heapq\n return heapq.nlargest(n, self.items(), key=_itemgetter(1))\n\n def elements(self):\n '''Iterator over elements repeating each as many times as its count.\n\n >>> c = Counter('ABCABC')\n >>> sorted(c.elements())\n ['A', 'A', 'B', 'B', 'C', 'C']\n\n Knuth's example for prime factors of 1836: 2**2 * 3**3 * 17**1\n\n >>> import math\n >>> prime_factors = Counter({2: 2, 3: 3, 17: 1})\n >>> math.prod(prime_factors.elements())\n 1836\n\n Note, if an element's count has been set to zero or is a negative\n number, elements() will ignore it.\n\n '''\n # Emulate Bag.do from Smalltalk and Multiset.begin from C++.\n return _chain.from_iterable(_starmap(_repeat, self.items()))\n\n # Override dict methods where necessary\n\n @classmethod\n def fromkeys(cls, iterable, v=None):\n # There is no equivalent method for counters because the semantics\n # would be ambiguous in cases such as Counter.fromkeys('aaabbc', v=2).\n # Initializing counters to zero values isn't necessary because zero\n # is already the default value for counter lookups. Initializing\n # to one is easily accomplished with Counter(set(iterable)). For\n # more exotic cases, create a dictionary first using a dictionary\n # comprehension or dict.fromkeys().\n raise NotImplementedError(\n 'Counter.fromkeys() is undefined. Use Counter(iterable) instead.')\n\n def update(self, iterable=None, /, **kwds):\n '''Like dict.update() but add counts instead of replacing them.\n\n Source can be an iterable, a dictionary, or another Counter instance.\n\n >>> c = Counter('which')\n >>> c.update('witch') # add elements from another iterable\n >>> d = Counter('watch')\n >>> c.update(d) # add elements from another counter\n >>> c['h'] # four 'h' in which, witch, and watch\n 4\n\n '''\n # The regular dict.update() operation makes no sense here because the\n # replace behavior results in some of the original untouched counts\n # being mixed-in with all of the other counts for a mismash that\n # doesn't have a straight-forward interpretation in most counting\n # contexts. Instead, we implement straight-addition. Both the inputs\n # and outputs are allowed to contain zero and negative counts.\n\n if iterable is not None:\n if isinstance(iterable, _collections_abc.Mapping):\n if self:\n self_get = self.get\n for elem, count in iterable.items():\n self[elem] = count + self_get(elem, 0)\n else:\n # fast path when counter is empty\n super().update(iterable)\n else:\n _count_elements(self, iterable)\n if kwds:\n self.update(kwds)\n\n def subtract(self, iterable=None, /, **kwds):\n '''Like dict.update() but subtracts counts instead of replacing them.\n Counts can be reduced below zero. Both the inputs and outputs are\n allowed to contain zero and negative counts.\n\n Source can be an iterable, a dictionary, or another Counter instance.\n\n >>> c = Counter('which')\n >>> c.subtract('witch') # subtract elements from another iterable\n >>> c.subtract(Counter('watch')) # subtract elements from another counter\n >>> c['h'] # 2 in which, minus 1 in witch, minus 1 in watch\n 0\n >>> c['w'] # 1 in which, minus 1 in witch, minus 1 in watch\n -1\n\n '''\n if iterable is not None:\n self_get = self.get\n if isinstance(iterable, _collections_abc.Mapping):\n for elem, count in iterable.items():\n self[elem] = self_get(elem, 0) - count\n else:\n for elem in iterable:\n self[elem] = self_get(elem, 0) - 1\n if kwds:\n self.subtract(kwds)\n\n def copy(self):\n 'Return a shallow copy.'\n return self.__class__(self)\n\n def __reduce__(self):\n return self.__class__, (dict(self),)\n\n def __delitem__(self, elem):\n 'Like dict.__delitem__() but does not raise KeyError for missing values.'\n if elem in self:\n super().__delitem__(elem)\n\n def __repr__(self):\n if not self:\n return f'{self.__class__.__name__}()'\n try:\n # dict() preserves the ordering returned by most_common()\n d = dict(self.most_common())\n except TypeError:\n # handle case where values are not orderable\n d = dict(self)\n return f'{self.__class__.__name__}({d!r})'\n\n # Multiset-style mathematical operations discussed in:\n # Knuth TAOCP Volume II section 4.6.3 exercise 19\n # and at http://en.wikipedia.org/wiki/Multiset\n #\n # Outputs guaranteed to only include positive counts.\n #\n # To strip negative and zero counts, add-in an empty counter:\n # c += Counter()\n #\n # Results are ordered according to when an element is first\n # encountered in the left operand and then by the order\n # encountered in the right operand.\n #\n # When the multiplicities are all zero or one, multiset operations\n # are guaranteed to be equivalent to the corresponding operations\n # for regular sets.\n # Given counter multisets such as:\n # cp = Counter(a=1, b=0, c=1)\n # cq = Counter(c=1, d=0, e=1)\n # The corresponding regular sets would be:\n # sp = {'a', 'c'}\n # sq = {'c', 'e'}\n # All of the following relations would hold:\n # set(cp + cq) == sp | sq\n # set(cp - cq) == sp - sq\n # set(cp | cq) == sp | sq\n # set(cp & cq) == sp & sq\n # (cp == cq) == (sp == sq)\n # (cp != cq) == (sp != sq)\n # (cp <= cq) == (sp <= sq)\n # (cp < cq) == (sp < sq)\n # (cp >= cq) == (sp >= sq)\n # (cp > cq) == (sp > sq)\n\n def __eq__(self, other):\n 'True if all counts agree. Missing counts are treated as zero.'\n if not isinstance(other, Counter):\n return NotImplemented\n return all(self[e] == other[e] for c in (self, other) for e in c)\n\n def __ne__(self, other):\n 'True if any counts disagree. Missing counts are treated as zero.'\n if not isinstance(other, Counter):\n return NotImplemented\n return not self == other\n\n def __le__(self, other):\n 'True if all counts in self are a subset of those in other.'\n if not isinstance(other, Counter):\n return NotImplemented\n return all(self[e] <= other[e] for c in (self, other) for e in c)\n\n def __lt__(self, other):\n 'True if all counts in self are a proper subset of those in other.'\n if not isinstance(other, Counter):\n return NotImplemented\n return self <= other and self != other\n\n def __ge__(self, other):\n 'True if all counts in self are a superset of those in other.'\n if not isinstance(other, Counter):\n return NotImplemented\n return all(self[e] >= other[e] for c in (self, other) for e in c)\n\n def __gt__(self, other):\n 'True if all counts in self are a proper superset of those in other.'\n if not isinstance(other, Counter):\n return NotImplemented\n return self >= other and self != other\n\n def __add__(self, other):\n '''Add counts from two counters.\n\n >>> Counter('abbb') + Counter('bcc')\n Counter({'b': 4, 'c': 2, 'a': 1})\n\n '''\n if not isinstance(other, Counter):\n return NotImplemented\n result = Counter()\n for elem, count in self.items():\n newcount = count + other[elem]\n if newcount > 0:\n result[elem] = newcount\n for elem, count in other.items():\n if elem not in self and count > 0:\n result[elem] = count\n return result\n\n def __sub__(self, other):\n ''' Subtract count, but keep only results with positive counts.\n\n >>> Counter('abbbc') - Counter('bccd')\n Counter({'b': 2, 'a': 1})\n\n '''\n if not isinstance(other, Counter):\n return NotImplemented\n result = Counter()\n for elem, count in self.items():\n newcount = count - other[elem]\n if newcount > 0:\n result[elem] = newcount\n for elem, count in other.items():\n if elem not in self and count < 0:\n result[elem] = 0 - count\n return result\n\n def __or__(self, other):\n '''Union is the maximum of value in either of the input counters.\n\n >>> Counter('abbb') | Counter('bcc')\n Counter({'b': 3, 'c': 2, 'a': 1})\n\n '''\n if not isinstance(other, Counter):\n return NotImplemented\n result = Counter()\n for elem, count in self.items():\n other_count = other[elem]\n newcount = other_count if count < other_count else count\n if newcount > 0:\n result[elem] = newcount\n for elem, count in other.items():\n if elem not in self and count > 0:\n result[elem] = count\n return result\n\n def __and__(self, other):\n ''' Intersection is the minimum of corresponding counts.\n\n >>> Counter('abbb') & Counter('bcc')\n Counter({'b': 1})\n\n '''\n if not isinstance(other, Counter):\n return NotImplemented\n result = Counter()\n for elem, count in self.items():\n other_count = other[elem]\n newcount = count if count < other_count else other_count\n if newcount > 0:\n result[elem] = newcount\n return result\n\n def __pos__(self):\n 'Adds an empty counter, effectively stripping negative and zero counts'\n result = Counter()\n for elem, count in self.items():\n if count > 0:\n result[elem] = count\n return result\n\n def __neg__(self):\n '''Subtracts from an empty counter. Strips positive and zero counts,\n and flips the sign on negative counts.\n\n '''\n result = Counter()\n for elem, count in self.items():\n if count < 0:\n result[elem] = 0 - count\n return result\n\n def _keep_positive(self):\n '''Internal method to strip elements with a negative or zero count'''\n nonpositive = [elem for elem, count in self.items() if not count > 0]\n for elem in nonpositive:\n del self[elem]\n return self\n\n def __iadd__(self, other):\n '''Inplace add from another counter, keeping only positive counts.\n\n >>> c = Counter('abbb')\n >>> c += Counter('bcc')\n >>> c\n Counter({'b': 4, 'c': 2, 'a': 1})\n\n '''\n for elem, count in other.items():\n self[elem] += count\n return self._keep_positive()\n\n def __isub__(self, other):\n '''Inplace subtract counter, but keep only results with positive counts.\n\n >>> c = Counter('abbbc')\n >>> c -= Counter('bccd')\n >>> c\n Counter({'b': 2, 'a': 1})\n\n '''\n for elem, count in other.items():\n self[elem] -= count\n return self._keep_positive()\n\n def __ior__(self, other):\n '''Inplace union is the maximum of value from either counter.\n\n >>> c = Counter('abbb')\n >>> c |= Counter('bcc')\n >>> c\n Counter({'b': 3, 'c': 2, 'a': 1})\n\n '''\n for elem, other_count in other.items():\n count = self[elem]\n if other_count > count:\n self[elem] = other_count\n return self._keep_positive()\n\n def __iand__(self, other):\n '''Inplace intersection is the minimum of corresponding counts.\n\n >>> c = Counter('abbb')\n >>> c &= Counter('bcc')\n >>> c\n Counter({'b': 1})\n\n '''\n for elem, count in self.items():\n other_count = other[elem]\n if other_count < count:\n self[elem] = other_count\n return self._keep_positive()\n\n\n########################################################################\n### ChainMap\n########################################################################\n\nclass ChainMap(_collections_abc.MutableMapping):\n ''' A ChainMap groups multiple dicts (or other mappings) together\n to create a single, updateable view.\n\n The underlying mappings are stored in a list. That list is public and can\n be accessed or updated using the *maps* attribute. There is no other\n state.\n\n Lookups search the underlying mappings successively until a key is found.\n In contrast, writes, updates, and deletions only operate on the first\n mapping.\n\n '''\n\n def __init__(self, *maps):\n '''Initialize a ChainMap by setting *maps* to the given mappings.\n If no mappings are provided, a single empty dictionary is used.\n\n '''\n self.maps = list(maps) or [{}] # always at least one map\n\n def __missing__(self, key):\n raise KeyError(key)\n\n def __getitem__(self, key):\n for mapping in self.maps:\n try:\n return mapping[key] # can't use 'key in mapping' with defaultdict\n except KeyError:\n pass\n return self.__missing__(key) # support subclasses that define __missing__\n\n def get(self, key, default=None):\n return self[key] if key in self else default\n\n def __len__(self):\n return len(set().union(*self.maps)) # reuses stored hash values if possible\n\n def __iter__(self):\n d = {}\n for mapping in map(dict.fromkeys, reversed(self.maps)):\n d |= mapping # reuses stored hash values if possible\n return iter(d)\n\n def __contains__(self, key):\n return any(key in m for m in self.maps)\n\n def __bool__(self):\n return any(self.maps)\n\n @_recursive_repr()\n def __repr__(self):\n return f'{self.__class__.__name__}({\", \".join(map(repr, self.maps))})'\n\n @classmethod\n def fromkeys(cls, iterable, value=None, /):\n 'Create a new ChainMap with keys from iterable and values set to value.'\n return cls(dict.fromkeys(iterable, value))\n\n def copy(self):\n 'New ChainMap or subclass with a new copy of maps[0] and refs to maps[1:]'\n return self.__class__(self.maps[0].copy(), *self.maps[1:])\n\n __copy__ = copy\n\n def new_child(self, m=None, **kwargs): # like Django's Context.push()\n '''New ChainMap with a new map followed by all previous maps.\n If no map is provided, an empty dict is used.\n Keyword arguments update the map or new empty dict.\n '''\n if m is None:\n m = kwargs\n elif kwargs:\n m.update(kwargs)\n return self.__class__(m, *self.maps)\n\n @property\n def parents(self): # like Django's Context.pop()\n 'New ChainMap from maps[1:].'\n return self.__class__(*self.maps[1:])\n\n def __setitem__(self, key, value):\n self.maps[0][key] = value\n\n def __delitem__(self, key):\n try:\n del self.maps[0][key]\n except KeyError:\n raise KeyError(f'Key not found in the first mapping: {key!r}')\n\n def popitem(self):\n 'Remove and return an item pair from maps[0]. Raise KeyError is maps[0] is empty.'\n try:\n return self.maps[0].popitem()\n except KeyError:\n raise KeyError('No keys found in the first mapping.')\n\n def pop(self, key, *args):\n 'Remove *key* from maps[0] and return its value. Raise KeyError if *key* not in maps[0].'\n try:\n return self.maps[0].pop(key, *args)\n except KeyError:\n raise KeyError(f'Key not found in the first mapping: {key!r}')\n\n def clear(self):\n 'Clear maps[0], leaving maps[1:] intact.'\n self.maps[0].clear()\n\n def __ior__(self, other):\n self.maps[0].update(other)\n return self\n\n def __or__(self, other):\n if not isinstance(other, _collections_abc.Mapping):\n return NotImplemented\n m = self.copy()\n m.maps[0].update(other)\n return m\n\n def __ror__(self, other):\n if not isinstance(other, _collections_abc.Mapping):\n return NotImplemented\n m = dict(other)\n for child in reversed(self.maps):\n m.update(child)\n return self.__class__(m)\n\n\n################################################################################\n### UserDict\n################################################################################\n\nclass UserDict(_collections_abc.MutableMapping):\n\n # Start by filling-out the abstract methods\n def __init__(self, dict=None, /, **kwargs):\n self.data = {}\n if dict is not None:\n self.update(dict)\n if kwargs:\n self.update(kwargs)\n\n def __len__(self):\n return len(self.data)\n\n def __getitem__(self, key):\n if key in self.data:\n return self.data[key]\n if hasattr(self.__class__, \"__missing__\"):\n return self.__class__.__missing__(self, key)\n raise KeyError(key)\n\n def __setitem__(self, key, item):\n self.data[key] = item\n\n def __delitem__(self, key):\n del self.data[key]\n\n def __iter__(self):\n return iter(self.data)\n\n # Modify __contains__ and get() to work like dict\n # does when __missing__ is present.\n def __contains__(self, key):\n return key in self.data\n\n def get(self, key, default=None):\n if key in self:\n return self[key]\n return default\n\n\n # Now, add the methods in dicts but not in MutableMapping\n def __repr__(self):\n return repr(self.data)\n\n def __or__(self, other):\n if isinstance(other, UserDict):\n return self.__class__(self.data | other.data)\n if isinstance(other, dict):\n return self.__class__(self.data | other)\n return NotImplemented\n\n def __ror__(self, other):\n if isinstance(other, UserDict):\n return self.__class__(other.data | self.data)\n if isinstance(other, dict):\n return self.__class__(other | self.data)\n return NotImplemented\n\n def __ior__(self, other):\n if isinstance(other, UserDict):\n self.data |= other.data\n else:\n self.data |= other\n return self\n\n def __copy__(self):\n inst = self.__class__.__new__(self.__class__)\n inst.__dict__.update(self.__dict__)\n # Create a copy and avoid triggering descriptors\n inst.__dict__[\"data\"] = self.__dict__[\"data\"].copy()\n return inst\n\n def copy(self):\n if self.__class__ is UserDict:\n return UserDict(self.data.copy())\n import copy\n data = self.data\n try:\n self.data = {}\n c = copy.copy(self)\n finally:\n self.data = data\n c.update(self)\n return c\n\n @classmethod\n def fromkeys(cls, iterable, value=None):\n d = cls()\n for key in iterable:\n d[key] = value\n return d\n\n\n################################################################################\n### UserList\n################################################################################\n\nclass UserList(_collections_abc.MutableSequence):\n \"\"\"A more or less complete user-defined wrapper around list objects.\"\"\"\n\n def __init__(self, initlist=None):\n self.data = []\n if initlist is not None:\n # XXX should this accept an arbitrary sequence?\n if type(initlist) == type(self.data):\n self.data[:] = initlist\n elif isinstance(initlist, UserList):\n self.data[:] = initlist.data[:]\n else:\n self.data = list(initlist)\n\n def __repr__(self):\n return repr(self.data)\n\n def __lt__(self, other):\n return self.data < self.__cast(other)\n\n def __le__(self, other):\n return self.data <= self.__cast(other)\n\n def __eq__(self, other):\n return self.data == self.__cast(other)\n\n def __gt__(self, other):\n return self.data > self.__cast(other)\n\n def __ge__(self, other):\n return self.data >= self.__cast(other)\n\n def __cast(self, other):\n return other.data if isinstance(other, UserList) else other\n\n def __contains__(self, item):\n return item in self.data\n\n def __len__(self):\n return len(self.data)\n\n def __getitem__(self, i):\n if isinstance(i, slice):\n return self.__class__(self.data[i])\n else:\n return self.data[i]\n\n def __setitem__(self, i, item):\n self.data[i] = item\n\n def __delitem__(self, i):\n del self.data[i]\n\n def __add__(self, other):\n if isinstance(other, UserList):\n return self.__class__(self.data + other.data)\n elif isinstance(other, type(self.data)):\n return self.__class__(self.data + other)\n return self.__class__(self.data + list(other))\n\n def __radd__(self, other):\n if isinstance(other, UserList):\n return self.__class__(other.data + self.data)\n elif isinstance(other, type(self.data)):\n return self.__class__(other + self.data)\n return self.__class__(list(other) + self.data)\n\n def __iadd__(self, other):\n if isinstance(other, UserList):\n self.data += other.data\n elif isinstance(other, type(self.data)):\n self.data += other\n else:\n self.data += list(other)\n return self\n\n def __mul__(self, n):\n return self.__class__(self.data * n)\n\n __rmul__ = __mul__\n\n def __imul__(self, n):\n self.data *= n\n return self\n\n def __copy__(self):\n inst = self.__class__.__new__(self.__class__)\n inst.__dict__.update(self.__dict__)\n # Create a copy and avoid triggering descriptors\n inst.__dict__[\"data\"] = self.__dict__[\"data\"][:]\n return inst\n\n def append(self, item):\n self.data.append(item)\n\n def insert(self, i, item):\n self.data.insert(i, item)\n\n def pop(self, i=-1):\n return self.data.pop(i)\n\n def remove(self, item):\n self.data.remove(item)\n\n def clear(self):\n self.data.clear()\n\n def copy(self):\n return self.__class__(self)\n\n def count(self, item):\n return self.data.count(item)\n\n def index(self, item, *args):\n return self.data.index(item, *args)\n\n def reverse(self):\n self.data.reverse()\n\n def sort(self, /, *args, **kwds):\n self.data.sort(*args, **kwds)\n\n def extend(self, other):\n if isinstance(other, UserList):\n self.data.extend(other.data)\n else:\n self.data.extend(other)\n\n\n################################################################################\n### UserString\n################################################################################\n\nclass UserString(_collections_abc.Sequence):\n\n def __init__(self, seq):\n if isinstance(seq, str):\n self.data = seq\n elif isinstance(seq, UserString):\n self.data = seq.data[:]\n else:\n self.data = str(seq)\n\n def __str__(self):\n return str(self.data)\n\n def __repr__(self):\n return repr(self.data)\n\n def __int__(self):\n return int(self.data)\n\n def __float__(self):\n return float(self.data)\n\n def __complex__(self):\n return complex(self.data)\n\n def __hash__(self):\n return hash(self.data)\n\n def __getnewargs__(self):\n return (self.data[:],)\n\n def __eq__(self, string):\n if isinstance(string, UserString):\n return self.data == string.data\n return self.data == string\n\n def __lt__(self, string):\n if isinstance(string, UserString):\n return self.data < string.data\n return self.data < string\n\n def __le__(self, string):\n if isinstance(string, UserString):\n return self.data <= string.data\n return self.data <= string\n\n def __gt__(self, string):\n if isinstance(string, UserString):\n return self.data > string.data\n return self.data > string\n\n def __ge__(self, string):\n if isinstance(string, UserString):\n return self.data >= string.data\n return self.data >= string\n\n def __contains__(self, char):\n if isinstance(char, UserString):\n char = char.data\n return char in self.data\n\n def __len__(self):\n return len(self.data)\n\n def __getitem__(self, index):\n return self.__class__(self.data[index])\n\n def __add__(self, other):\n if isinstance(other, UserString):\n return self.__class__(self.data + other.data)\n elif isinstance(other, str):\n return self.__class__(self.data + other)\n return self.__class__(self.data + str(other))\n\n def __radd__(self, other):\n if isinstance(other, str):\n return self.__class__(other + self.data)\n return self.__class__(str(other) + self.data)\n\n def __mul__(self, n):\n return self.__class__(self.data * n)\n\n __rmul__ = __mul__\n\n def __mod__(self, args):\n return self.__class__(self.data % args)\n\n def __rmod__(self, template):\n return self.__class__(str(template) % self)\n\n # the following methods are defined in alphabetical order:\n def capitalize(self):\n return self.__class__(self.data.capitalize())\n\n def casefold(self):\n return self.__class__(self.data.casefold())\n\n def center(self, width, *args):\n return self.__class__(self.data.center(width, *args))\n\n def count(self, sub, start=0, end=_sys.maxsize):\n if isinstance(sub, UserString):\n sub = sub.data\n return self.data.count(sub, start, end)\n\n def removeprefix(self, prefix, /):\n if isinstance(prefix, UserString):\n prefix = prefix.data\n return self.__class__(self.data.removeprefix(prefix))\n\n def removesuffix(self, suffix, /):\n if isinstance(suffix, UserString):\n suffix = suffix.data\n return self.__class__(self.data.removesuffix(suffix))\n\n def encode(self, encoding='utf-8', errors='strict'):\n encoding = 'utf-8' if encoding is None else encoding\n errors = 'strict' if errors is None else errors\n return self.data.encode(encoding, errors)\n\n def endswith(self, suffix, start=0, end=_sys.maxsize):\n return self.data.endswith(suffix, start, end)\n\n def expandtabs(self, tabsize=8):\n return self.__class__(self.data.expandtabs(tabsize))\n\n def find(self, sub, start=0, end=_sys.maxsize):\n if isinstance(sub, UserString):\n sub = sub.data\n return self.data.find(sub, start, end)\n\n def format(self, /, *args, **kwds):\n return self.data.format(*args, **kwds)\n\n def format_map(self, mapping):\n return self.data.format_map(mapping)\n\n def index(self, sub, start=0, end=_sys.maxsize):\n return self.data.index(sub, start, end)\n\n def isalpha(self):\n return self.data.isalpha()\n\n def isalnum(self):\n return self.data.isalnum()\n\n def isascii(self):\n return self.data.isascii()\n\n def isdecimal(self):\n return self.data.isdecimal()\n\n def isdigit(self):\n return self.data.isdigit()\n\n def isidentifier(self):\n return self.data.isidentifier()\n\n def islower(self):\n return self.data.islower()\n\n def isnumeric(self):\n return self.data.isnumeric()\n\n def isprintable(self):\n return self.data.isprintable()\n\n def isspace(self):\n return self.data.isspace()\n\n def istitle(self):\n return self.data.istitle()\n\n def isupper(self):\n return self.data.isupper()\n\n def join(self, seq):\n return self.data.join(seq)\n\n def ljust(self, width, *args):\n return self.__class__(self.data.ljust(width, *args))\n\n def lower(self):\n return self.__class__(self.data.lower())\n\n def lstrip(self, chars=None):\n return self.__class__(self.data.lstrip(chars))\n\n maketrans = str.maketrans\n\n def partition(self, sep):\n return self.data.partition(sep)\n\n def replace(self, old, new, maxsplit=-1):\n if isinstance(old, UserString):\n old = old.data\n if isinstance(new, UserString):\n new = new.data\n return self.__class__(self.data.replace(old, new, maxsplit))\n\n def rfind(self, sub, start=0, end=_sys.maxsize):\n if isinstance(sub, UserString):\n sub = sub.data\n return self.data.rfind(sub, start, end)\n\n def rindex(self, sub, start=0, end=_sys.maxsize):\n return self.data.rindex(sub, start, end)\n\n def rjust(self, width, *args):\n return self.__class__(self.data.rjust(width, *args))\n\n def rpartition(self, sep):\n return self.data.rpartition(sep)\n\n def rstrip(self, chars=None):\n return self.__class__(self.data.rstrip(chars))\n\n def split(self, sep=None, maxsplit=-1):\n return self.data.split(sep, maxsplit)\n\n def rsplit(self, sep=None, maxsplit=-1):\n return self.data.rsplit(sep, maxsplit)\n\n def splitlines(self, keepends=False):\n return self.data.splitlines(keepends)\n\n def startswith(self, prefix, start=0, end=_sys.maxsize):\n return self.data.startswith(prefix, start, end)\n\n def strip(self, chars=None):\n return self.__class__(self.data.strip(chars))\n\n def swapcase(self):\n return self.__class__(self.data.swapcase())\n\n def title(self):\n return self.__class__(self.data.title())\n\n def translate(self, *args):\n return self.__class__(self.data.translate(*args))\n\n def upper(self):\n return self.__class__(self.data.upper())\n\n def zfill(self, width):\n return self.__class__(self.data.zfill(width))\n", 1593], "/usr/lib/python3.13/random.py": ["\"\"\"Random variable generators.\n\n bytes\n -----\n uniform bytes (values between 0 and 255)\n\n integers\n --------\n uniform within range\n\n sequences\n ---------\n pick random element\n pick random sample\n pick weighted random sample\n generate random permutation\n\n distributions on the real line:\n ------------------------------\n uniform\n triangular\n normal (Gaussian)\n lognormal\n negative exponential\n gamma\n beta\n pareto\n Weibull\n\n distributions on the circle (angles 0 to 2pi)\n ---------------------------------------------\n circular uniform\n von Mises\n\n discrete distributions\n ----------------------\n binomial\n\n\nGeneral notes on the underlying Mersenne Twister core generator:\n\n* The period is 2**19937-1.\n* It is one of the most extensively tested generators in existence.\n* The random() method is implemented in C, executes in a single Python step,\n and is, therefore, threadsafe.\n\n\"\"\"\n\n# Translated by Guido van Rossum from C source provided by\n# Adrian Baddeley. Adapted by Raymond Hettinger for use with\n# the Mersenne Twister and os.urandom() core generators.\n\nfrom math import log as _log, exp as _exp, pi as _pi, e as _e, ceil as _ceil\nfrom math import sqrt as _sqrt, acos as _acos, cos as _cos, sin as _sin\nfrom math import tau as TWOPI, floor as _floor, isfinite as _isfinite\nfrom math import lgamma as _lgamma, fabs as _fabs, log2 as _log2\nfrom os import urandom as _urandom\nfrom _collections_abc import Sequence as _Sequence\nfrom operator import index as _index\nfrom itertools import accumulate as _accumulate, repeat as _repeat\nfrom bisect import bisect as _bisect\nimport os as _os\nimport _random\n\n__all__ = [\n \"Random\",\n \"SystemRandom\",\n \"betavariate\",\n \"binomialvariate\",\n \"choice\",\n \"choices\",\n \"expovariate\",\n \"gammavariate\",\n \"gauss\",\n \"getrandbits\",\n \"getstate\",\n \"lognormvariate\",\n \"normalvariate\",\n \"paretovariate\",\n \"randbytes\",\n \"randint\",\n \"random\",\n \"randrange\",\n \"sample\",\n \"seed\",\n \"setstate\",\n \"shuffle\",\n \"triangular\",\n \"uniform\",\n \"vonmisesvariate\",\n \"weibullvariate\",\n]\n\nNV_MAGICCONST = 4 * _exp(-0.5) / _sqrt(2.0)\nLOG4 = _log(4.0)\nSG_MAGICCONST = 1.0 + _log(4.5)\nBPF = 53 # Number of bits in a float\nRECIP_BPF = 2 ** -BPF\n_ONE = 1\n_sha512 = None\n\n\nclass Random(_random.Random):\n \"\"\"Random number generator base class used by bound module functions.\n\n Used to instantiate instances of Random to get generators that don't\n share state.\n\n Class Random can also be subclassed if you want to use a different basic\n generator of your own devising: in that case, override the following\n methods: random(), seed(), getstate(), and setstate().\n Optionally, implement a getrandbits() method so that randrange()\n can cover arbitrarily large ranges.\n\n \"\"\"\n\n VERSION = 3 # used by getstate/setstate\n\n def __init__(self, x=None):\n \"\"\"Initialize an instance.\n\n Optional argument x controls seeding, as for Random.seed().\n \"\"\"\n\n self.seed(x)\n self.gauss_next = None\n\n def seed(self, a=None, version=2):\n \"\"\"Initialize internal state from a seed.\n\n The only supported seed types are None, int, float,\n str, bytes, and bytearray.\n\n None or no argument seeds from current time or from an operating\n system specific randomness source if available.\n\n If *a* is an int, all bits are used.\n\n For version 2 (the default), all of the bits are used if *a* is a str,\n bytes, or bytearray. For version 1 (provided for reproducing random\n sequences from older versions of Python), the algorithm for str and\n bytes generates a narrower range of seeds.\n\n \"\"\"\n\n if version == 1 and isinstance(a, (str, bytes)):\n a = a.decode('latin-1') if isinstance(a, bytes) else a\n x = ord(a[0]) << 7 if a else 0\n for c in map(ord, a):\n x = ((1000003 * x) ^ c) & 0xFFFFFFFFFFFFFFFF\n x ^= len(a)\n a = -2 if x == -1 else x\n\n elif version == 2 and isinstance(a, (str, bytes, bytearray)):\n global _sha512\n if _sha512 is None:\n try:\n # hashlib is pretty heavy to load, try lean internal\n # module first\n from _sha2 import sha512 as _sha512\n except ImportError:\n # fallback to official implementation\n from hashlib import sha512 as _sha512\n\n if isinstance(a, str):\n a = a.encode()\n a = int.from_bytes(a + _sha512(a).digest())\n\n elif not isinstance(a, (type(None), int, float, str, bytes, bytearray)):\n raise TypeError('The only supported seed types are:\\n'\n 'None, int, float, str, bytes, and bytearray.')\n\n super().seed(a)\n self.gauss_next = None\n\n def getstate(self):\n \"\"\"Return internal state; can be passed to setstate() later.\"\"\"\n return self.VERSION, super().getstate(), self.gauss_next\n\n def setstate(self, state):\n \"\"\"Restore internal state from object returned by getstate().\"\"\"\n version = state[0]\n if version == 3:\n version, internalstate, self.gauss_next = state\n super().setstate(internalstate)\n elif version == 2:\n version, internalstate, self.gauss_next = state\n # In version 2, the state was saved as signed ints, which causes\n # inconsistencies between 32/64-bit systems. The state is\n # really unsigned 32-bit ints, so we convert negative ints from\n # version 2 to positive longs for version 3.\n try:\n internalstate = tuple(x % (2 ** 32) for x in internalstate)\n except ValueError as e:\n raise TypeError from e\n super().setstate(internalstate)\n else:\n raise ValueError(\"state with version %s passed to \"\n \"Random.setstate() of version %s\" %\n (version, self.VERSION))\n\n\n ## -------------------------------------------------------\n ## ---- Methods below this point do not need to be overridden or extended\n ## ---- when subclassing for the purpose of using a different core generator.\n\n\n ## -------------------- pickle support -------------------\n\n # Issue 17489: Since __reduce__ was defined to fix #759889 this is no\n # longer called; we leave it here because it has been here since random was\n # rewritten back in 2001 and why risk breaking something.\n def __getstate__(self): # for pickle\n return self.getstate()\n\n def __setstate__(self, state): # for pickle\n self.setstate(state)\n\n def __reduce__(self):\n return self.__class__, (), self.getstate()\n\n\n ## ---- internal support method for evenly distributed integers ----\n\n def __init_subclass__(cls, /, **kwargs):\n \"\"\"Control how subclasses generate random integers.\n\n The algorithm a subclass can use depends on the random() and/or\n getrandbits() implementation available to it and determines\n whether it can generate random integers from arbitrarily large\n ranges.\n \"\"\"\n\n for c in cls.__mro__:\n if '_randbelow' in c.__dict__:\n # just inherit it\n break\n if 'getrandbits' in c.__dict__:\n cls._randbelow = cls._randbelow_with_getrandbits\n break\n if 'random' in c.__dict__:\n cls._randbelow = cls._randbelow_without_getrandbits\n break\n\n def _randbelow_with_getrandbits(self, n):\n \"Return a random int in the range [0,n). Defined for n > 0.\"\n\n getrandbits = self.getrandbits\n k = n.bit_length()\n r = getrandbits(k) # 0 <= r < 2**k\n while r >= n:\n r = getrandbits(k)\n return r\n\n def _randbelow_without_getrandbits(self, n, maxsize=1< 0.\n\n The implementation does not use getrandbits, but only random.\n \"\"\"\n\n random = self.random\n if n >= maxsize:\n from warnings import warn\n warn(\"Underlying random() generator does not supply \\n\"\n \"enough bits to choose from a population range this large.\\n\"\n \"To remove the range limitation, add a getrandbits() method.\")\n return _floor(random() * n)\n rem = maxsize % n\n limit = (maxsize - rem) / maxsize # int(limit * maxsize) % n == 0\n r = random()\n while r >= limit:\n r = random()\n return _floor(r * maxsize) % n\n\n _randbelow = _randbelow_with_getrandbits\n\n\n ## --------------------------------------------------------\n ## ---- Methods below this point generate custom distributions\n ## ---- based on the methods defined above. They do not\n ## ---- directly touch the underlying generator and only\n ## ---- access randomness through the methods: random(),\n ## ---- getrandbits(), or _randbelow().\n\n\n ## -------------------- bytes methods ---------------------\n\n def randbytes(self, n):\n \"\"\"Generate n random bytes.\"\"\"\n return self.getrandbits(n * 8).to_bytes(n, 'little')\n\n\n ## -------------------- integer methods -------------------\n\n def randrange(self, start, stop=None, step=_ONE):\n \"\"\"Choose a random item from range(stop) or range(start, stop[, step]).\n\n Roughly equivalent to ``choice(range(start, stop, step))`` but\n supports arbitrarily large ranges and is optimized for common cases.\n\n \"\"\"\n\n # This code is a bit messy to make it fast for the\n # common case while still doing adequate error checking.\n istart = _index(start)\n if stop is None:\n # We don't check for \"step != 1\" because it hasn't been\n # type checked and converted to an integer yet.\n if step is not _ONE:\n raise TypeError(\"Missing a non-None stop argument\")\n if istart > 0:\n return self._randbelow(istart)\n raise ValueError(\"empty range for randrange()\")\n\n # Stop argument supplied.\n istop = _index(stop)\n width = istop - istart\n istep = _index(step)\n # Fast path.\n if istep == 1:\n if width > 0:\n return istart + self._randbelow(width)\n raise ValueError(f\"empty range in randrange({start}, {stop})\")\n\n # Non-unit step argument supplied.\n if istep > 0:\n n = (width + istep - 1) // istep\n elif istep < 0:\n n = (width + istep + 1) // istep\n else:\n raise ValueError(\"zero step for randrange()\")\n if n <= 0:\n raise ValueError(f\"empty range in randrange({start}, {stop}, {step})\")\n return istart + istep * self._randbelow(n)\n\n def randint(self, a, b):\n \"\"\"Return random integer in range [a, b], including both end points.\n \"\"\"\n\n return self.randrange(a, b+1)\n\n\n ## -------------------- sequence methods -------------------\n\n def choice(self, seq):\n \"\"\"Choose a random element from a non-empty sequence.\"\"\"\n\n # As an accommodation for NumPy, we don't use \"if not seq\"\n # because bool(numpy.array()) raises a ValueError.\n if not len(seq):\n raise IndexError('Cannot choose from an empty sequence')\n return seq[self._randbelow(len(seq))]\n\n def shuffle(self, x):\n \"\"\"Shuffle list x in place, and return None.\"\"\"\n\n randbelow = self._randbelow\n for i in reversed(range(1, len(x))):\n # pick an element in x[:i+1] with which to exchange x[i]\n j = randbelow(i + 1)\n x[i], x[j] = x[j], x[i]\n\n def sample(self, population, k, *, counts=None):\n \"\"\"Chooses k unique random elements from a population sequence.\n\n Returns a new list containing elements from the population while\n leaving the original population unchanged. The resulting list is\n in selection order so that all sub-slices will also be valid random\n samples. This allows raffle winners (the sample) to be partitioned\n into grand prize and second place winners (the subslices).\n\n Members of the population need not be hashable or unique. If the\n population contains repeats, then each occurrence is a possible\n selection in the sample.\n\n Repeated elements can be specified one at a time or with the optional\n counts parameter. For example:\n\n sample(['red', 'blue'], counts=[4, 2], k=5)\n\n is equivalent to:\n\n sample(['red', 'red', 'red', 'red', 'blue', 'blue'], k=5)\n\n To choose a sample from a range of integers, use range() for the\n population argument. This is especially fast and space efficient\n for sampling from a large population:\n\n sample(range(10000000), 60)\n\n \"\"\"\n\n # Sampling without replacement entails tracking either potential\n # selections (the pool) in a list or previous selections in a set.\n\n # When the number of selections is small compared to the\n # population, then tracking selections is efficient, requiring\n # only a small set and an occasional reselection. For\n # a larger number of selections, the pool tracking method is\n # preferred since the list takes less space than the\n # set and it doesn't suffer from frequent reselections.\n\n # The number of calls to _randbelow() is kept at or near k, the\n # theoretical minimum. This is important because running time\n # is dominated by _randbelow() and because it extracts the\n # least entropy from the underlying random number generators.\n\n # Memory requirements are kept to the smaller of a k-length\n # set or an n-length list.\n\n # There are other sampling algorithms that do not require\n # auxiliary memory, but they were rejected because they made\n # too many calls to _randbelow(), making them slower and\n # causing them to eat more entropy than necessary.\n\n if not isinstance(population, _Sequence):\n raise TypeError(\"Population must be a sequence. \"\n \"For dicts or sets, use sorted(d).\")\n n = len(population)\n if counts is not None:\n cum_counts = list(_accumulate(counts))\n if len(cum_counts) != n:\n raise ValueError('The number of counts does not match the population')\n total = cum_counts.pop()\n if not isinstance(total, int):\n raise TypeError('Counts must be integers')\n if total <= 0:\n raise ValueError('Total of counts must be greater than zero')\n selections = self.sample(range(total), k=k)\n bisect = _bisect\n return [population[bisect(cum_counts, s)] for s in selections]\n randbelow = self._randbelow\n if not 0 <= k <= n:\n raise ValueError(\"Sample larger than population or is negative\")\n result = [None] * k\n setsize = 21 # size of a small set minus size of an empty list\n if k > 5:\n setsize += 4 ** _ceil(_log(k * 3, 4)) # table size for big sets\n if n <= setsize:\n # An n-length list is smaller than a k-length set.\n # Invariant: non-selected at pool[0 : n-i]\n pool = list(population)\n for i in range(k):\n j = randbelow(n - i)\n result[i] = pool[j]\n pool[j] = pool[n - i - 1] # move non-selected item into vacancy\n else:\n selected = set()\n selected_add = selected.add\n for i in range(k):\n j = randbelow(n)\n while j in selected:\n j = randbelow(n)\n selected_add(j)\n result[i] = population[j]\n return result\n\n def choices(self, population, weights=None, *, cum_weights=None, k=1):\n \"\"\"Return a k sized list of population elements chosen with replacement.\n\n If the relative weights or cumulative weights are not specified,\n the selections are made with equal probability.\n\n \"\"\"\n random = self.random\n n = len(population)\n if cum_weights is None:\n if weights is None:\n floor = _floor\n n += 0.0 # convert to float for a small speed improvement\n return [population[floor(random() * n)] for i in _repeat(None, k)]\n try:\n cum_weights = list(_accumulate(weights))\n except TypeError:\n if not isinstance(weights, int):\n raise\n k = weights\n raise TypeError(\n f'The number of choices must be a keyword argument: {k=}'\n ) from None\n elif weights is not None:\n raise TypeError('Cannot specify both weights and cumulative weights')\n if len(cum_weights) != n:\n raise ValueError('The number of weights does not match the population')\n total = cum_weights[-1] + 0.0 # convert to float\n if total <= 0.0:\n raise ValueError('Total of weights must be greater than zero')\n if not _isfinite(total):\n raise ValueError('Total of weights must be finite')\n bisect = _bisect\n hi = n - 1\n return [population[bisect(cum_weights, random() * total, 0, hi)]\n for i in _repeat(None, k)]\n\n\n ## -------------------- real-valued distributions -------------------\n\n def uniform(self, a, b):\n \"\"\"Get a random number in the range [a, b) or [a, b] depending on rounding.\n\n The mean (expected value) and variance of the random variable are:\n\n E[X] = (a + b) / 2\n Var[X] = (b - a) ** 2 / 12\n\n \"\"\"\n return a + (b - a) * self.random()\n\n def triangular(self, low=0.0, high=1.0, mode=None):\n \"\"\"Triangular distribution.\n\n Continuous distribution bounded by given lower and upper limits,\n and having a given mode value in-between.\n\n http://en.wikipedia.org/wiki/Triangular_distribution\n\n The mean (expected value) and variance of the random variable are:\n\n E[X] = (low + high + mode) / 3\n Var[X] = (low**2 + high**2 + mode**2 - low*high - low*mode - high*mode) / 18\n\n \"\"\"\n u = self.random()\n try:\n c = 0.5 if mode is None else (mode - low) / (high - low)\n except ZeroDivisionError:\n return low\n if u > c:\n u = 1.0 - u\n c = 1.0 - c\n low, high = high, low\n return low + (high - low) * _sqrt(u * c)\n\n def normalvariate(self, mu=0.0, sigma=1.0):\n \"\"\"Normal distribution.\n\n mu is the mean, and sigma is the standard deviation.\n\n \"\"\"\n # Uses Kinderman and Monahan method. Reference: Kinderman,\n # A.J. and Monahan, J.F., \"Computer generation of random\n # variables using the ratio of uniform deviates\", ACM Trans\n # Math Software, 3, (1977), pp257-260.\n\n random = self.random\n while True:\n u1 = random()\n u2 = 1.0 - random()\n z = NV_MAGICCONST * (u1 - 0.5) / u2\n zz = z * z / 4.0\n if zz <= -_log(u2):\n break\n return mu + z * sigma\n\n def gauss(self, mu=0.0, sigma=1.0):\n \"\"\"Gaussian distribution.\n\n mu is the mean, and sigma is the standard deviation. This is\n slightly faster than the normalvariate() function.\n\n Not thread-safe without a lock around calls.\n\n \"\"\"\n # When x and y are two variables from [0, 1), uniformly\n # distributed, then\n #\n # cos(2*pi*x)*sqrt(-2*log(1-y))\n # sin(2*pi*x)*sqrt(-2*log(1-y))\n #\n # are two *independent* variables with normal distribution\n # (mu = 0, sigma = 1).\n # (Lambert Meertens)\n # (corrected version; bug discovered by Mike Miller, fixed by LM)\n\n # Multithreading note: When two threads call this function\n # simultaneously, it is possible that they will receive the\n # same return value. The window is very small though. To\n # avoid this, you have to use a lock around all calls. (I\n # didn't want to slow this down in the serial case by using a\n # lock here.)\n\n random = self.random\n z = self.gauss_next\n self.gauss_next = None\n if z is None:\n x2pi = random() * TWOPI\n g2rad = _sqrt(-2.0 * _log(1.0 - random()))\n z = _cos(x2pi) * g2rad\n self.gauss_next = _sin(x2pi) * g2rad\n\n return mu + z * sigma\n\n def lognormvariate(self, mu, sigma):\n \"\"\"Log normal distribution.\n\n If you take the natural logarithm of this distribution, you'll get a\n normal distribution with mean mu and standard deviation sigma.\n mu can have any value, and sigma must be greater than zero.\n\n \"\"\"\n return _exp(self.normalvariate(mu, sigma))\n\n def expovariate(self, lambd=1.0):\n \"\"\"Exponential distribution.\n\n lambd is 1.0 divided by the desired mean. It should be\n nonzero. (The parameter would be called \"lambda\", but that is\n a reserved word in Python.) Returned values range from 0 to\n positive infinity if lambd is positive, and from negative\n infinity to 0 if lambd is negative.\n\n The mean (expected value) and variance of the random variable are:\n\n E[X] = 1 / lambd\n Var[X] = 1 / lambd ** 2\n\n \"\"\"\n # we use 1-random() instead of random() to preclude the\n # possibility of taking the log of zero.\n\n return -_log(1.0 - self.random()) / lambd\n\n def vonmisesvariate(self, mu, kappa):\n \"\"\"Circular data distribution.\n\n mu is the mean angle, expressed in radians between 0 and 2*pi, and\n kappa is the concentration parameter, which must be greater than or\n equal to zero. If kappa is equal to zero, this distribution reduces\n to a uniform random angle over the range 0 to 2*pi.\n\n \"\"\"\n # Based upon an algorithm published in: Fisher, N.I.,\n # \"Statistical Analysis of Circular Data\", Cambridge\n # University Press, 1993.\n\n # Thanks to Magnus Kessler for a correction to the\n # implementation of step 4.\n\n random = self.random\n if kappa <= 1e-6:\n return TWOPI * random()\n\n s = 0.5 / kappa\n r = s + _sqrt(1.0 + s * s)\n\n while True:\n u1 = random()\n z = _cos(_pi * u1)\n\n d = z / (r + z)\n u2 = random()\n if u2 < 1.0 - d * d or u2 <= (1.0 - d) * _exp(d):\n break\n\n q = 1.0 / r\n f = (q + z) / (1.0 + q * z)\n u3 = random()\n if u3 > 0.5:\n theta = (mu + _acos(f)) % TWOPI\n else:\n theta = (mu - _acos(f)) % TWOPI\n\n return theta\n\n def gammavariate(self, alpha, beta):\n \"\"\"Gamma distribution. Not the gamma function!\n\n Conditions on the parameters are alpha > 0 and beta > 0.\n\n The probability distribution function is:\n\n x ** (alpha - 1) * math.exp(-x / beta)\n pdf(x) = --------------------------------------\n math.gamma(alpha) * beta ** alpha\n\n The mean (expected value) and variance of the random variable are:\n\n E[X] = alpha * beta\n Var[X] = alpha * beta ** 2\n\n \"\"\"\n\n # Warning: a few older sources define the gamma distribution in terms\n # of alpha > -1.0\n if alpha <= 0.0 or beta <= 0.0:\n raise ValueError('gammavariate: alpha and beta must be > 0.0')\n\n random = self.random\n if alpha > 1.0:\n\n # Uses R.C.H. Cheng, \"The generation of Gamma\n # variables with non-integral shape parameters\",\n # Applied Statistics, (1977), 26, No. 1, p71-74\n\n ainv = _sqrt(2.0 * alpha - 1.0)\n bbb = alpha - LOG4\n ccc = alpha + ainv\n\n while True:\n u1 = random()\n if not 1e-7 < u1 < 0.9999999:\n continue\n u2 = 1.0 - random()\n v = _log(u1 / (1.0 - u1)) / ainv\n x = alpha * _exp(v)\n z = u1 * u1 * u2\n r = bbb + ccc * v - x\n if r + SG_MAGICCONST - 4.5 * z >= 0.0 or r >= _log(z):\n return x * beta\n\n elif alpha == 1.0:\n # expovariate(1/beta)\n return -_log(1.0 - random()) * beta\n\n else:\n # alpha is between 0 and 1 (exclusive)\n # Uses ALGORITHM GS of Statistical Computing - Kennedy & Gentle\n while True:\n u = random()\n b = (_e + alpha) / _e\n p = b * u\n if p <= 1.0:\n x = p ** (1.0 / alpha)\n else:\n x = -_log((b - p) / alpha)\n u1 = random()\n if p > 1.0:\n if u1 <= x ** (alpha - 1.0):\n break\n elif u1 <= _exp(-x):\n break\n return x * beta\n\n def betavariate(self, alpha, beta):\n \"\"\"Beta distribution.\n\n Conditions on the parameters are alpha > 0 and beta > 0.\n Returned values range between 0 and 1.\n\n The mean (expected value) and variance of the random variable are:\n\n E[X] = alpha / (alpha + beta)\n Var[X] = alpha * beta / ((alpha + beta)**2 * (alpha + beta + 1))\n\n \"\"\"\n ## See\n ## http://mail.python.org/pipermail/python-bugs-list/2001-January/003752.html\n ## for Ivan Frohne's insightful analysis of why the original implementation:\n ##\n ## def betavariate(self, alpha, beta):\n ## # Discrete Event Simulation in C, pp 87-88.\n ##\n ## y = self.expovariate(alpha)\n ## z = self.expovariate(1.0/beta)\n ## return z/(y+z)\n ##\n ## was dead wrong, and how it probably got that way.\n\n # This version due to Janne Sinkkonen, and matches all the std\n # texts (e.g., Knuth Vol 2 Ed 3 pg 134 \"the beta distribution\").\n y = self.gammavariate(alpha, 1.0)\n if y:\n return y / (y + self.gammavariate(beta, 1.0))\n return 0.0\n\n def paretovariate(self, alpha):\n \"\"\"Pareto distribution. alpha is the shape parameter.\"\"\"\n # Jain, pg. 495\n\n u = 1.0 - self.random()\n return u ** (-1.0 / alpha)\n\n def weibullvariate(self, alpha, beta):\n \"\"\"Weibull distribution.\n\n alpha is the scale parameter and beta is the shape parameter.\n\n \"\"\"\n # Jain, pg. 499; bug fix courtesy Bill Arms\n\n u = 1.0 - self.random()\n return alpha * (-_log(u)) ** (1.0 / beta)\n\n\n ## -------------------- discrete distributions ---------------------\n\n def binomialvariate(self, n=1, p=0.5):\n \"\"\"Binomial random variable.\n\n Gives the number of successes for *n* independent trials\n with the probability of success in each trial being *p*:\n\n sum(random() < p for i in range(n))\n\n Returns an integer in the range: 0 <= X <= n\n\n The mean (expected value) and variance of the random variable are:\n\n E[X] = n * p\n Var[x] = n * p * (1 - p)\n\n \"\"\"\n # Error check inputs and handle edge cases\n if n < 0:\n raise ValueError(\"n must be non-negative\")\n if p <= 0.0 or p >= 1.0:\n if p == 0.0:\n return 0\n if p == 1.0:\n return n\n raise ValueError(\"p must be in the range 0.0 <= p <= 1.0\")\n\n random = self.random\n\n # Fast path for a common case\n if n == 1:\n return _index(random() < p)\n\n # Exploit symmetry to establish: p <= 0.5\n if p > 0.5:\n return n - self.binomialvariate(n, 1.0 - p)\n\n if n * p < 10.0:\n # BG: Geometric method by Devroye with running time of O(np).\n # https://dl.acm.org/doi/pdf/10.1145/42372.42381\n x = y = 0\n c = _log2(1.0 - p)\n if not c:\n return x\n while True:\n y += _floor(_log2(random()) / c) + 1\n if y > n:\n return x\n x += 1\n\n # BTRS: Transformed rejection with squeeze method by Wolfgang H\u00f6rmann\n # https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.47.8407&rep=rep1&type=pdf\n assert n*p >= 10.0 and p <= 0.5\n setup_complete = False\n\n spq = _sqrt(n * p * (1.0 - p)) # Standard deviation of the distribution\n b = 1.15 + 2.53 * spq\n a = -0.0873 + 0.0248 * b + 0.01 * p\n c = n * p + 0.5\n vr = 0.92 - 4.2 / b\n\n while True:\n\n u = random()\n u -= 0.5\n us = 0.5 - _fabs(u)\n k = _floor((2.0 * a / us + b) * u + c)\n if k < 0 or k > n:\n continue\n\n # The early-out \"squeeze\" test substantially reduces\n # the number of acceptance condition evaluations.\n v = random()\n if us >= 0.07 and v <= vr:\n return k\n\n # Acceptance-rejection test.\n # Note, the original paper erroneously omits the call to log(v)\n # when comparing to the log of the rescaled binomial distribution.\n if not setup_complete:\n alpha = (2.83 + 5.1 / b) * spq\n lpq = _log(p / (1.0 - p))\n m = _floor((n + 1) * p) # Mode of the distribution\n h = _lgamma(m + 1) + _lgamma(n - m + 1)\n setup_complete = True # Only needs to be done once\n v *= alpha / (a / (us * us) + b)\n if _log(v) <= h - _lgamma(k + 1) - _lgamma(n - k + 1) + (k - m) * lpq:\n return k\n\n\n## ------------------------------------------------------------------\n## --------------- Operating System Random Source ------------------\n\n\nclass SystemRandom(Random):\n \"\"\"Alternate random number generator using sources provided\n by the operating system (such as /dev/urandom on Unix or\n CryptGenRandom on Windows).\n\n Not available on all systems (see os.urandom() for details).\n\n \"\"\"\n\n def random(self):\n \"\"\"Get the next random number in the range 0.0 <= X < 1.0.\"\"\"\n return (int.from_bytes(_urandom(7)) >> 3) * RECIP_BPF\n\n def getrandbits(self, k):\n \"\"\"getrandbits(k) -> x. Generates an int with k random bits.\"\"\"\n if k < 0:\n raise ValueError('number of bits must be non-negative')\n numbytes = (k + 7) // 8 # bits / 8 and rounded up\n x = int.from_bytes(_urandom(numbytes))\n return x >> (numbytes * 8 - k) # trim excess bits\n\n def randbytes(self, n):\n \"\"\"Generate n random bytes.\"\"\"\n # os.urandom(n) fails with ValueError for n < 0\n # and returns an empty bytes string for n == 0.\n return _urandom(n)\n\n def seed(self, *args, **kwds):\n \"Stub method. Not used for a system random number generator.\"\n return None\n\n def _notimplemented(self, *args, **kwds):\n \"Method should not be called for a system random number generator.\"\n raise NotImplementedError('System entropy source does not have state.')\n getstate = setstate = _notimplemented\n\n\n# ----------------------------------------------------------------------\n# Create one instance, seeded from current time, and export its methods\n# as module-level functions. The functions share state across all uses\n# (both in the user's code and in the Python libraries), but that's fine\n# for most programs and is easier for the casual user than making them\n# instantiate their own Random() instance.\n\n_inst = Random()\nseed = _inst.seed\nrandom = _inst.random\nuniform = _inst.uniform\ntriangular = _inst.triangular\nrandint = _inst.randint\nchoice = _inst.choice\nrandrange = _inst.randrange\nsample = _inst.sample\nshuffle = _inst.shuffle\nchoices = _inst.choices\nnormalvariate = _inst.normalvariate\nlognormvariate = _inst.lognormvariate\nexpovariate = _inst.expovariate\nvonmisesvariate = _inst.vonmisesvariate\ngammavariate = _inst.gammavariate\ngauss = _inst.gauss\nbetavariate = _inst.betavariate\nbinomialvariate = _inst.binomialvariate\nparetovariate = _inst.paretovariate\nweibullvariate = _inst.weibullvariate\ngetstate = _inst.getstate\nsetstate = _inst.setstate\ngetrandbits = _inst.getrandbits\nrandbytes = _inst.randbytes\n\n\n## ------------------------------------------------------\n## ----------------- test program -----------------------\n\ndef _test_generator(n, func, args):\n from statistics import stdev, fmean as mean\n from time import perf_counter\n\n t0 = perf_counter()\n data = [func(*args) for i in _repeat(None, n)]\n t1 = perf_counter()\n\n xbar = mean(data)\n sigma = stdev(data, xbar)\n low = min(data)\n high = max(data)\n\n print(f'{t1 - t0:.3f} sec, {n} times {func.__name__}{args!r}')\n print('avg %g, stddev %g, min %g, max %g\\n' % (xbar, sigma, low, high))\n\n\ndef _test(N=10_000):\n _test_generator(N, random, ())\n _test_generator(N, normalvariate, (0.0, 1.0))\n _test_generator(N, lognormvariate, (0.0, 1.0))\n _test_generator(N, vonmisesvariate, (0.0, 1.0))\n _test_generator(N, binomialvariate, (15, 0.60))\n _test_generator(N, binomialvariate, (100, 0.75))\n _test_generator(N, gammavariate, (0.01, 1.0))\n _test_generator(N, gammavariate, (0.1, 1.0))\n _test_generator(N, gammavariate, (0.1, 2.0))\n _test_generator(N, gammavariate, (0.5, 1.0))\n _test_generator(N, gammavariate, (0.9, 1.0))\n _test_generator(N, gammavariate, (1.0, 1.0))\n _test_generator(N, gammavariate, (2.0, 1.0))\n _test_generator(N, gammavariate, (20.0, 1.0))\n _test_generator(N, gammavariate, (200.0, 1.0))\n _test_generator(N, gauss, (0.0, 1.0))\n _test_generator(N, betavariate, (3.0, 3.0))\n _test_generator(N, triangular, (0.0, 1.0, 1.0 / 3.0))\n\n\n## ------------------------------------------------------\n## ------------------ fork support ---------------------\n\nif hasattr(_os, \"fork\"):\n _os.register_at_fork(after_in_child=_inst.seed)\n\n\n# ------------------------------------------------------\n# -------------- command-line interface ----------------\n\n\ndef _parse_args(arg_list: list[str] | None):\n import argparse\n parser = argparse.ArgumentParser(\n formatter_class=argparse.RawTextHelpFormatter)\n group = parser.add_mutually_exclusive_group()\n group.add_argument(\n \"-c\", \"--choice\", nargs=\"+\",\n help=\"print a random choice\")\n group.add_argument(\n \"-i\", \"--integer\", type=int, metavar=\"N\",\n help=\"print a random integer between 1 and N inclusive\")\n group.add_argument(\n \"-f\", \"--float\", type=float, metavar=\"N\",\n help=\"print a random floating-point number between 0 and N inclusive\")\n group.add_argument(\n \"--test\", type=int, const=10_000, nargs=\"?\",\n help=argparse.SUPPRESS)\n parser.add_argument(\"input\", nargs=\"*\",\n help=\"\"\"\\\nif no options given, output depends on the input\n string or multiple: same as --choice\n integer: same as --integer\n float: same as --float\"\"\")\n args = parser.parse_args(arg_list)\n return args, parser.format_help()\n\n\ndef main(arg_list: list[str] | None = None) -> int | str:\n args, help_text = _parse_args(arg_list)\n\n # Explicit arguments\n if args.choice:\n return choice(args.choice)\n\n if args.integer is not None:\n return randint(1, args.integer)\n\n if args.float is not None:\n return uniform(0, args.float)\n\n if args.test:\n _test(args.test)\n return \"\"\n\n # No explicit argument, select based on input\n if len(args.input) == 1:\n val = args.input[0]\n try:\n # Is it an integer?\n val = int(val)\n return randint(1, val)\n except ValueError:\n try:\n # Is it a float?\n val = float(val)\n return uniform(0, val)\n except ValueError:\n # Split in case of space-separated string: \"a b c\"\n return choice(val.split())\n\n if len(args.input) >= 2:\n return choice(args.input)\n\n return help_text\n\n\nif __name__ == '__main__':\n print(main())\n", 1070], "/usr/lib/python3.13/tempfile.py": ["\"\"\"Temporary files.\n\nThis module provides generic, low- and high-level interfaces for\ncreating temporary files and directories. All of the interfaces\nprovided by this module can be used without fear of race conditions\nexcept for 'mktemp'. 'mktemp' is subject to race conditions and\nshould not be used; it is provided for backward compatibility only.\n\nThe default path names are returned as str. If you supply bytes as\ninput, all return values will be in bytes. Ex:\n\n >>> tempfile.mkstemp()\n (4, '/tmp/tmptpu9nin8')\n >>> tempfile.mkdtemp(suffix=b'')\n b'/tmp/tmppbi8f0hy'\n\nThis module also provides some data items to the user:\n\n TMP_MAX - maximum number of names that will be tried before\n giving up.\n tempdir - If this is set to a string before the first use of\n any routine from this module, it will be considered as\n another candidate location to store temporary files.\n\"\"\"\n\n__all__ = [\n \"NamedTemporaryFile\", \"TemporaryFile\", # high level safe interfaces\n \"SpooledTemporaryFile\", \"TemporaryDirectory\",\n \"mkstemp\", \"mkdtemp\", # low level safe interfaces\n \"mktemp\", # deprecated unsafe interface\n \"TMP_MAX\", \"gettempprefix\", # constants\n \"tempdir\", \"gettempdir\",\n \"gettempprefixb\", \"gettempdirb\",\n ]\n\n\n# Imports.\n\nimport functools as _functools\nimport warnings as _warnings\nimport io as _io\nimport os as _os\ntry:\n import shutil as _shutil\n _rmtree = _shutil.rmtree\nexcept ImportError:\n import sys as _sys\n import stat as _stat\n # version vulnerable to race conditions\n def _rmtree_unsafe(path, onexc):\n try:\n if _os.path.islink(path):\n # symlinks to directories are forbidden, see bug #1669\n raise OSError(\"Cannot call rmtree on a symbolic link\")\n except OSError as e:\n onexc(_os.path.islink, path, e)\n # can't continue even if onexc hook returns\n return\n names = []\n try:\n names = _os.listdir(path)\n except OSError as e:\n onexc(_os.listdir, path, e)\n for name in names:\n fullname = _os.path.join(path, name)\n try:\n mode = _os.lstat(fullname).st_mode\n except OSError:\n mode = 0\n if _stat.S_ISDIR(mode):\n _rmtree_unsafe(fullname, onexc)\n else:\n try:\n _os.unlink(fullname)\n except OSError as e:\n onexc(_os.unlink, fullname, e)\n try:\n _os.rmdir(path)\n except OSError as e:\n onexc(_os.rmdir, path, e)\n\n # Version using fd-based APIs to protect against races\n def _rmtree_safe_fd(topfd, path, onexc):\n names = []\n try:\n names = _os.listdir(topfd)\n except OSError as err:\n err.filename = path\n onexc(_os.listdir, path, err)\n for name in names:\n fullname = _os.path.join(path, name)\n try:\n orig_st = _os.stat(name, dir_fd=topfd, follow_symlinks=False)\n mode = orig_st.st_mode\n except OSError:\n mode = 0\n if _stat.S_ISDIR(mode):\n try:\n dirfd = _os.open(name, _os.O_RDONLY, dir_fd=topfd)\n except OSError as e:\n onexc(_os.open, fullname, e)\n else:\n try:\n if _os.path.samestat(orig_st, _os.fstat(dirfd)):\n _rmtree_safe_fd(dirfd, fullname, onexc)\n try:\n _os.rmdir(name, dir_fd=topfd)\n except OSError as e:\n onexc(_os.rmdir, fullname, e)\n else:\n try:\n # This can only happen if someone replaces\n # a directory with a symlink after the call to\n # stat.S_ISDIR above.\n raise OSError(\"Cannot call rmtree on a symbolic \"\n \"link\")\n except OSError as e:\n onexc(_os.path.islink, fullname, e)\n finally:\n _os.close(dirfd)\n else:\n try:\n _os.unlink(name, dir_fd=topfd)\n except OSError as e:\n onexc(_os.unlink, fullname, e)\n\n _use_fd_functions = ({_os.open, _os.stat, _os.unlink, _os.rmdir} <=\n _os.supports_dir_fd and\n _os.listdir in _os.supports_fd and\n _os.stat in _os.supports_follow_symlinks)\n\n def _rmtree(path, ignore_errors=False, onexc=None):\n \"\"\"Recursively delete a directory tree.\n\n If ignore_errors is set, errors are ignored; otherwise, if onexc\n is set, it is called to handle the error with arguments (func,\n path, exc_info) where func is platform and implementation dependent;\n path is the argument to that function that caused it to fail; and\n exc_info is a tuple returned by sys.exc_info(). If ignore_errors\n is false and onexc is None, an exception is raised.\n\n \"\"\"\n if ignore_errors:\n def onexc(*args):\n pass\n elif onexc is None:\n def onexc(*args):\n raise\n if _use_fd_functions:\n # While the unsafe rmtree works fine on bytes, the fd based does not.\n if isinstance(path, bytes):\n path = _os.fsdecode(path)\n # Note: To guard against symlink races, we use the standard\n # lstat()/open()/fstat() trick.\n try:\n orig_st = _os.lstat(path)\n except Exception as e:\n onexc(_os.lstat, path, e)\n return\n try:\n fd = _os.open(path, _os.O_RDONLY)\n except Exception as e:\n onexc(_os.lstat, path, e)\n return\n try:\n if _os.path.samestat(orig_st, _os.fstat(fd)):\n _rmtree_safe_fd(fd, path, onexc)\n try:\n _os.rmdir(path)\n except OSError as e:\n onexc(_os.rmdir, path, e)\n else:\n try:\n # symlinks to directories are forbidden, see bug #1669\n raise OSError(\"Cannot call rmtree on a symbolic link\")\n except OSError as e:\n onexc(_os.path.islink, path, e)\n finally:\n _os.close(fd)\n else:\n return _rmtree_unsafe(path, onexc)\n\nimport errno as _errno\nfrom random import Random as _Random\nimport sys as _sys\nimport types as _types\nimport weakref as _weakref\nimport _thread\n_allocate_lock = _thread.allocate_lock\n\n_text_openflags = _os.O_RDWR | _os.O_CREAT | _os.O_EXCL\nif hasattr(_os, 'O_NOFOLLOW'):\n _text_openflags |= _os.O_NOFOLLOW\n\n_bin_openflags = _text_openflags\nif hasattr(_os, 'O_BINARY'):\n _bin_openflags |= _os.O_BINARY\n\nif hasattr(_os, 'TMP_MAX'):\n TMP_MAX = _os.TMP_MAX\nelse:\n TMP_MAX = 10000\n\n# This variable _was_ unused for legacy reasons, see issue 10354.\n# But as of 3.5 we actually use it at runtime so changing it would\n# have a possibly desirable side effect... But we do not want to support\n# that as an API. It is undocumented on purpose. Do not depend on this.\ntemplate = \"tmp\"\n\n# Internal routines.\n\n_once_lock = _allocate_lock()\n\n\ndef _exists(fn):\n try:\n _os.lstat(fn)\n except OSError:\n return False\n else:\n return True\n\n\ndef _infer_return_type(*args):\n \"\"\"Look at the type of all args and divine their implied return type.\"\"\"\n return_type = None\n for arg in args:\n if arg is None:\n continue\n\n if isinstance(arg, _os.PathLike):\n arg = _os.fspath(arg)\n\n if isinstance(arg, bytes):\n if return_type is str:\n raise TypeError(\"Can't mix bytes and non-bytes in \"\n \"path components.\")\n return_type = bytes\n else:\n if return_type is bytes:\n raise TypeError(\"Can't mix bytes and non-bytes in \"\n \"path components.\")\n return_type = str\n if return_type is None:\n if tempdir is None or isinstance(tempdir, str):\n return str # tempfile APIs return a str by default.\n else:\n # we could check for bytes but it'll fail later on anyway\n return bytes\n return return_type\n\n\ndef _sanitize_params(prefix, suffix, dir):\n \"\"\"Common parameter processing for most APIs in this module.\"\"\"\n output_type = _infer_return_type(prefix, suffix, dir)\n if suffix is None:\n suffix = output_type()\n if prefix is None:\n if output_type is str:\n prefix = template\n else:\n prefix = _os.fsencode(template)\n if dir is None:\n if output_type is str:\n dir = gettempdir()\n else:\n dir = gettempdirb()\n return prefix, suffix, dir, output_type\n\n\nclass _RandomNameSequence:\n \"\"\"An instance of _RandomNameSequence generates an endless\n sequence of unpredictable strings which can safely be incorporated\n into file names. Each string is eight characters long. Multiple\n threads can safely use the same instance at the same time.\n\n _RandomNameSequence is an iterator.\"\"\"\n\n characters = \"abcdefghijklmnopqrstuvwxyz0123456789_\"\n\n @property\n def rng(self):\n cur_pid = _os.getpid()\n if cur_pid != getattr(self, '_rng_pid', None):\n self._rng = _Random()\n self._rng_pid = cur_pid\n return self._rng\n\n def __iter__(self):\n return self\n\n def __next__(self):\n return ''.join(self.rng.choices(self.characters, k=8))\n\ndef _candidate_tempdir_list():\n \"\"\"Generate a list of candidate temporary directories which\n _get_default_tempdir will try.\"\"\"\n\n dirlist = []\n\n # First, try the environment.\n for envname in 'TMPDIR', 'TEMP', 'TMP':\n dirname = _os.getenv(envname)\n if dirname: dirlist.append(dirname)\n\n # Failing that, try OS-specific locations.\n if _os.name == 'nt':\n dirlist.extend([ _os.path.expanduser(r'~\\AppData\\Local\\Temp'),\n _os.path.expandvars(r'%SYSTEMROOT%\\Temp'),\n r'c:\\temp', r'c:\\tmp', r'\\temp', r'\\tmp' ])\n else:\n dirlist.extend([ '/tmp', '/var/tmp', '/usr/tmp' ])\n\n # As a last resort, the current directory.\n try:\n dirlist.append(_os.getcwd())\n except (AttributeError, OSError):\n dirlist.append(_os.curdir)\n\n return dirlist\n\ndef _get_default_tempdir():\n \"\"\"Calculate the default directory to use for temporary files.\n This routine should be called exactly once.\n\n We determine whether or not a candidate temp dir is usable by\n trying to create and write to a file in that directory. If this\n is successful, the test file is deleted. To prevent denial of\n service, the name of the test file must be randomized.\"\"\"\n\n namer = _RandomNameSequence()\n dirlist = _candidate_tempdir_list()\n\n for dir in dirlist:\n if dir != _os.curdir:\n dir = _os.path.abspath(dir)\n # Try only a few names per directory.\n for seq in range(100):\n name = next(namer)\n filename = _os.path.join(dir, name)\n try:\n fd = _os.open(filename, _bin_openflags, 0o600)\n try:\n try:\n _os.write(fd, b'blat')\n finally:\n _os.close(fd)\n finally:\n _os.unlink(filename)\n return dir\n except FileExistsError:\n pass\n except PermissionError:\n # This exception is thrown when a directory with the chosen name\n # already exists on windows.\n if (_os.name == 'nt' and _os.path.isdir(dir) and\n _os.access(dir, _os.W_OK)):\n continue\n break # no point trying more names in this directory\n except OSError:\n break # no point trying more names in this directory\n raise FileNotFoundError(_errno.ENOENT,\n \"No usable temporary directory found in %s\" %\n dirlist)\n\n_name_sequence = None\n\ndef _get_candidate_names():\n \"\"\"Common setup sequence for all user-callable interfaces.\"\"\"\n\n global _name_sequence\n if _name_sequence is None:\n _once_lock.acquire()\n try:\n if _name_sequence is None:\n _name_sequence = _RandomNameSequence()\n finally:\n _once_lock.release()\n return _name_sequence\n\n\ndef _mkstemp_inner(dir, pre, suf, flags, output_type):\n \"\"\"Code common to mkstemp, TemporaryFile, and NamedTemporaryFile.\"\"\"\n\n dir = _os.path.abspath(dir)\n names = _get_candidate_names()\n if output_type is bytes:\n names = map(_os.fsencode, names)\n\n for seq in range(TMP_MAX):\n name = next(names)\n file = _os.path.join(dir, pre + name + suf)\n _sys.audit(\"tempfile.mkstemp\", file)\n try:\n fd = _os.open(file, flags, 0o600)\n except FileExistsError:\n continue # try again\n except PermissionError:\n # This exception is thrown when a directory with the chosen name\n # already exists on windows.\n if (_os.name == 'nt' and _os.path.isdir(dir) and\n _os.access(dir, _os.W_OK)):\n continue\n else:\n raise\n return fd, file\n\n raise FileExistsError(_errno.EEXIST,\n \"No usable temporary file name found\")\n\ndef _dont_follow_symlinks(func, path, *args):\n # Pass follow_symlinks=False, unless not supported on this platform.\n if func in _os.supports_follow_symlinks:\n func(path, *args, follow_symlinks=False)\n elif not _os.path.islink(path):\n func(path, *args)\n\ndef _resetperms(path):\n try:\n chflags = _os.chflags\n except AttributeError:\n pass\n else:\n _dont_follow_symlinks(chflags, path, 0)\n _dont_follow_symlinks(_os.chmod, path, 0o700)\n\n\n# User visible interfaces.\n\ndef gettempprefix():\n \"\"\"The default prefix for temporary directories as string.\"\"\"\n return _os.fsdecode(template)\n\ndef gettempprefixb():\n \"\"\"The default prefix for temporary directories as bytes.\"\"\"\n return _os.fsencode(template)\n\ntempdir = None\n\ndef _gettempdir():\n \"\"\"Private accessor for tempfile.tempdir.\"\"\"\n global tempdir\n if tempdir is None:\n _once_lock.acquire()\n try:\n if tempdir is None:\n tempdir = _get_default_tempdir()\n finally:\n _once_lock.release()\n return tempdir\n\ndef gettempdir():\n \"\"\"Returns tempfile.tempdir as str.\"\"\"\n return _os.fsdecode(_gettempdir())\n\ndef gettempdirb():\n \"\"\"Returns tempfile.tempdir as bytes.\"\"\"\n return _os.fsencode(_gettempdir())\n\ndef mkstemp(suffix=None, prefix=None, dir=None, text=False):\n \"\"\"User-callable function to create and return a unique temporary\n file. The return value is a pair (fd, name) where fd is the\n file descriptor returned by os.open, and name is the filename.\n\n If 'suffix' is not None, the file name will end with that suffix,\n otherwise there will be no suffix.\n\n If 'prefix' is not None, the file name will begin with that prefix,\n otherwise a default prefix is used.\n\n If 'dir' is not None, the file will be created in that directory,\n otherwise a default directory is used.\n\n If 'text' is specified and true, the file is opened in text\n mode. Else (the default) the file is opened in binary mode.\n\n If any of 'suffix', 'prefix' and 'dir' are not None, they must be the\n same type. If they are bytes, the returned name will be bytes; str\n otherwise.\n\n The file is readable and writable only by the creating user ID.\n If the operating system uses permission bits to indicate whether a\n file is executable, the file is executable by no one. The file\n descriptor is not inherited by children of this process.\n\n Caller is responsible for deleting the file when done with it.\n \"\"\"\n\n prefix, suffix, dir, output_type = _sanitize_params(prefix, suffix, dir)\n\n if text:\n flags = _text_openflags\n else:\n flags = _bin_openflags\n\n return _mkstemp_inner(dir, prefix, suffix, flags, output_type)\n\n\ndef mkdtemp(suffix=None, prefix=None, dir=None):\n \"\"\"User-callable function to create and return a unique temporary\n directory. The return value is the pathname of the directory.\n\n Arguments are as for mkstemp, except that the 'text' argument is\n not accepted.\n\n The directory is readable, writable, and searchable only by the\n creating user.\n\n Caller is responsible for deleting the directory when done with it.\n \"\"\"\n\n prefix, suffix, dir, output_type = _sanitize_params(prefix, suffix, dir)\n\n names = _get_candidate_names()\n if output_type is bytes:\n names = map(_os.fsencode, names)\n\n for seq in range(TMP_MAX):\n name = next(names)\n file = _os.path.join(dir, prefix + name + suffix)\n _sys.audit(\"tempfile.mkdtemp\", file)\n try:\n _os.mkdir(file, 0o700)\n except FileExistsError:\n continue # try again\n except PermissionError:\n # This exception is thrown when a directory with the chosen name\n # already exists on windows.\n if (_os.name == 'nt' and _os.path.isdir(dir) and\n _os.access(dir, _os.W_OK)):\n continue\n else:\n raise\n return _os.path.abspath(file)\n\n raise FileExistsError(_errno.EEXIST,\n \"No usable temporary directory name found\")\n\ndef mktemp(suffix=\"\", prefix=template, dir=None):\n \"\"\"User-callable function to return a unique temporary file name. The\n file is not created.\n\n Arguments are similar to mkstemp, except that the 'text' argument is\n not accepted, and suffix=None, prefix=None and bytes file names are not\n supported.\n\n THIS FUNCTION IS UNSAFE AND SHOULD NOT BE USED. The file name may\n refer to a file that did not exist at some point, but by the time\n you get around to creating it, someone else may have beaten you to\n the punch.\n \"\"\"\n\n## from warnings import warn as _warn\n## _warn(\"mktemp is a potential security risk to your program\",\n## RuntimeWarning, stacklevel=2)\n\n if dir is None:\n dir = gettempdir()\n\n names = _get_candidate_names()\n for seq in range(TMP_MAX):\n name = next(names)\n file = _os.path.join(dir, prefix + name + suffix)\n if not _exists(file):\n return file\n\n raise FileExistsError(_errno.EEXIST,\n \"No usable temporary filename found\")\n\n\nclass _TemporaryFileCloser:\n \"\"\"A separate object allowing proper closing of a temporary file's\n underlying file object, without adding a __del__ method to the\n temporary file.\"\"\"\n\n cleanup_called = False\n close_called = False\n\n def __init__(self, file, name, delete=True, delete_on_close=True):\n self.file = file\n self.name = name\n self.delete = delete\n self.delete_on_close = delete_on_close\n\n def cleanup(self, windows=(_os.name == 'nt'), unlink=_os.unlink):\n if not self.cleanup_called:\n self.cleanup_called = True\n try:\n if not self.close_called:\n self.close_called = True\n self.file.close()\n finally:\n # Windows provides delete-on-close as a primitive, in which\n # case the file was deleted by self.file.close().\n if self.delete and not (windows and self.delete_on_close):\n try:\n unlink(self.name)\n except FileNotFoundError:\n pass\n\n def close(self):\n if not self.close_called:\n self.close_called = True\n try:\n self.file.close()\n finally:\n if self.delete and self.delete_on_close:\n self.cleanup()\n\n def __del__(self):\n self.cleanup()\n\n\nclass _TemporaryFileWrapper:\n \"\"\"Temporary file wrapper\n\n This class provides a wrapper around files opened for\n temporary use. In particular, it seeks to automatically\n remove the file when it is no longer needed.\n \"\"\"\n\n def __init__(self, file, name, delete=True, delete_on_close=True):\n self.file = file\n self.name = name\n self._closer = _TemporaryFileCloser(file, name, delete,\n delete_on_close)\n\n def __getattr__(self, name):\n # Attribute lookups are delegated to the underlying file\n # and cached for non-numeric results\n # (i.e. methods are cached, closed and friends are not)\n file = self.__dict__['file']\n a = getattr(file, name)\n if hasattr(a, '__call__'):\n func = a\n @_functools.wraps(func)\n def func_wrapper(*args, **kwargs):\n return func(*args, **kwargs)\n # Avoid closing the file as long as the wrapper is alive,\n # see issue #18879.\n func_wrapper._closer = self._closer\n a = func_wrapper\n if not isinstance(a, int):\n setattr(self, name, a)\n return a\n\n # The underlying __enter__ method returns the wrong object\n # (self.file) so override it to return the wrapper\n def __enter__(self):\n self.file.__enter__()\n return self\n\n # Need to trap __exit__ as well to ensure the file gets\n # deleted when used in a with statement\n def __exit__(self, exc, value, tb):\n result = self.file.__exit__(exc, value, tb)\n self._closer.cleanup()\n return result\n\n def close(self):\n \"\"\"\n Close the temporary file, possibly deleting it.\n \"\"\"\n self._closer.close()\n\n # iter() doesn't use __getattr__ to find the __iter__ method\n def __iter__(self):\n # Don't return iter(self.file), but yield from it to avoid closing\n # file as long as it's being used as iterator (see issue #23700). We\n # can't use 'yield from' here because iter(file) returns the file\n # object itself, which has a close method, and thus the file would get\n # closed when the generator is finalized, due to PEP380 semantics.\n for line in self.file:\n yield line\n\ndef NamedTemporaryFile(mode='w+b', buffering=-1, encoding=None,\n newline=None, suffix=None, prefix=None,\n dir=None, delete=True, *, errors=None,\n delete_on_close=True):\n \"\"\"Create and return a temporary file.\n Arguments:\n 'prefix', 'suffix', 'dir' -- as for mkstemp.\n 'mode' -- the mode argument to io.open (default \"w+b\").\n 'buffering' -- the buffer size argument to io.open (default -1).\n 'encoding' -- the encoding argument to io.open (default None)\n 'newline' -- the newline argument to io.open (default None)\n 'delete' -- whether the file is automatically deleted (default True).\n 'delete_on_close' -- if 'delete', whether the file is deleted on close\n (default True) or otherwise either on context manager exit\n (if context manager was used) or on object finalization. .\n 'errors' -- the errors argument to io.open (default None)\n The file is created as mkstemp() would do it.\n\n Returns an object with a file-like interface; the name of the file\n is accessible as its 'name' attribute. The file will be automatically\n deleted when it is closed unless the 'delete' argument is set to False.\n\n On POSIX, NamedTemporaryFiles cannot be automatically deleted if\n the creating process is terminated abruptly with a SIGKILL signal.\n Windows can delete the file even in this case.\n \"\"\"\n\n prefix, suffix, dir, output_type = _sanitize_params(prefix, suffix, dir)\n\n flags = _bin_openflags\n\n # Setting O_TEMPORARY in the flags causes the OS to delete\n # the file when it is closed. This is only supported by Windows.\n if _os.name == 'nt' and delete and delete_on_close:\n flags |= _os.O_TEMPORARY\n\n if \"b\" not in mode:\n encoding = _io.text_encoding(encoding)\n\n name = None\n def opener(*args):\n nonlocal name\n fd, name = _mkstemp_inner(dir, prefix, suffix, flags, output_type)\n return fd\n try:\n file = _io.open(dir, mode, buffering=buffering,\n newline=newline, encoding=encoding, errors=errors,\n opener=opener)\n try:\n raw = getattr(file, 'buffer', file)\n raw = getattr(raw, 'raw', raw)\n raw.name = name\n return _TemporaryFileWrapper(file, name, delete, delete_on_close)\n except:\n file.close()\n raise\n except:\n if name is not None and not (\n _os.name == 'nt' and delete and delete_on_close):\n _os.unlink(name)\n raise\n\nif _os.name != 'posix' or _sys.platform == 'cygwin':\n # On non-POSIX and Cygwin systems, assume that we cannot unlink a file\n # while it is open.\n TemporaryFile = NamedTemporaryFile\n\nelse:\n # Is the O_TMPFILE flag available and does it work?\n # The flag is set to False if os.open(dir, os.O_TMPFILE) raises an\n # IsADirectoryError exception\n _O_TMPFILE_WORKS = hasattr(_os, 'O_TMPFILE')\n\n def TemporaryFile(mode='w+b', buffering=-1, encoding=None,\n newline=None, suffix=None, prefix=None,\n dir=None, *, errors=None):\n \"\"\"Create and return a temporary file.\n Arguments:\n 'prefix', 'suffix', 'dir' -- as for mkstemp.\n 'mode' -- the mode argument to io.open (default \"w+b\").\n 'buffering' -- the buffer size argument to io.open (default -1).\n 'encoding' -- the encoding argument to io.open (default None)\n 'newline' -- the newline argument to io.open (default None)\n 'errors' -- the errors argument to io.open (default None)\n The file is created as mkstemp() would do it.\n\n Returns an object with a file-like interface. The file has no\n name, and will cease to exist when it is closed.\n \"\"\"\n global _O_TMPFILE_WORKS\n\n if \"b\" not in mode:\n encoding = _io.text_encoding(encoding)\n\n prefix, suffix, dir, output_type = _sanitize_params(prefix, suffix, dir)\n\n flags = _bin_openflags\n if _O_TMPFILE_WORKS:\n fd = None\n def opener(*args):\n nonlocal fd\n flags2 = (flags | _os.O_TMPFILE) & ~_os.O_CREAT\n fd = _os.open(dir, flags2, 0o600)\n return fd\n try:\n file = _io.open(dir, mode, buffering=buffering,\n newline=newline, encoding=encoding,\n errors=errors, opener=opener)\n raw = getattr(file, 'buffer', file)\n raw = getattr(raw, 'raw', raw)\n raw.name = fd\n return file\n except IsADirectoryError:\n # Linux kernel older than 3.11 ignores the O_TMPFILE flag:\n # O_TMPFILE is read as O_DIRECTORY. Trying to open a directory\n # with O_RDWR|O_DIRECTORY fails with IsADirectoryError, a\n # directory cannot be open to write. Set flag to False to not\n # try again.\n _O_TMPFILE_WORKS = False\n except OSError:\n # The filesystem of the directory does not support O_TMPFILE.\n # For example, OSError(95, 'Operation not supported').\n #\n # On Linux kernel older than 3.11, trying to open a regular\n # file (or a symbolic link to a regular file) with O_TMPFILE\n # fails with NotADirectoryError, because O_TMPFILE is read as\n # O_DIRECTORY.\n pass\n # Fallback to _mkstemp_inner().\n\n fd = None\n def opener(*args):\n nonlocal fd\n fd, name = _mkstemp_inner(dir, prefix, suffix, flags, output_type)\n try:\n _os.unlink(name)\n except BaseException as e:\n _os.close(fd)\n raise\n return fd\n file = _io.open(dir, mode, buffering=buffering,\n newline=newline, encoding=encoding, errors=errors,\n opener=opener)\n raw = getattr(file, 'buffer', file)\n raw = getattr(raw, 'raw', raw)\n raw.name = fd\n return file\n\nclass SpooledTemporaryFile(_io.IOBase):\n \"\"\"Temporary file wrapper, specialized to switch from BytesIO\n or StringIO to a real file when it exceeds a certain size or\n when a fileno is needed.\n \"\"\"\n _rolled = False\n\n def __init__(self, max_size=0, mode='w+b', buffering=-1,\n encoding=None, newline=None,\n suffix=None, prefix=None, dir=None, *, errors=None):\n if 'b' in mode:\n self._file = _io.BytesIO()\n else:\n encoding = _io.text_encoding(encoding)\n self._file = _io.TextIOWrapper(_io.BytesIO(),\n encoding=encoding, errors=errors,\n newline=newline)\n self._max_size = max_size\n self._rolled = False\n self._TemporaryFileArgs = {'mode': mode, 'buffering': buffering,\n 'suffix': suffix, 'prefix': prefix,\n 'encoding': encoding, 'newline': newline,\n 'dir': dir, 'errors': errors}\n\n __class_getitem__ = classmethod(_types.GenericAlias)\n\n def _check(self, file):\n if self._rolled: return\n max_size = self._max_size\n if max_size and file.tell() > max_size:\n self.rollover()\n\n def rollover(self):\n if self._rolled: return\n file = self._file\n newfile = self._file = TemporaryFile(**self._TemporaryFileArgs)\n del self._TemporaryFileArgs\n\n pos = file.tell()\n if hasattr(newfile, 'buffer'):\n newfile.buffer.write(file.detach().getvalue())\n else:\n newfile.write(file.getvalue())\n newfile.seek(pos, 0)\n\n self._rolled = True\n\n # The method caching trick from NamedTemporaryFile\n # won't work here, because _file may change from a\n # BytesIO/StringIO instance to a real file. So we list\n # all the methods directly.\n\n # Context management protocol\n def __enter__(self):\n if self._file.closed:\n raise ValueError(\"Cannot enter context with closed file\")\n return self\n\n def __exit__(self, exc, value, tb):\n self._file.close()\n\n # file protocol\n def __iter__(self):\n return self._file.__iter__()\n\n def __del__(self):\n if not self.closed:\n _warnings.warn(\n \"Unclosed file {!r}\".format(self),\n ResourceWarning,\n stacklevel=2,\n source=self\n )\n self.close()\n\n def close(self):\n self._file.close()\n\n @property\n def closed(self):\n return self._file.closed\n\n @property\n def encoding(self):\n return self._file.encoding\n\n @property\n def errors(self):\n return self._file.errors\n\n def fileno(self):\n self.rollover()\n return self._file.fileno()\n\n def flush(self):\n self._file.flush()\n\n def isatty(self):\n return self._file.isatty()\n\n @property\n def mode(self):\n try:\n return self._file.mode\n except AttributeError:\n return self._TemporaryFileArgs['mode']\n\n @property\n def name(self):\n try:\n return self._file.name\n except AttributeError:\n return None\n\n @property\n def newlines(self):\n return self._file.newlines\n\n def readable(self):\n return self._file.readable()\n\n def read(self, *args):\n return self._file.read(*args)\n\n def read1(self, *args):\n return self._file.read1(*args)\n\n def readinto(self, b):\n return self._file.readinto(b)\n\n def readinto1(self, b):\n return self._file.readinto1(b)\n\n def readline(self, *args):\n return self._file.readline(*args)\n\n def readlines(self, *args):\n return self._file.readlines(*args)\n\n def seekable(self):\n return self._file.seekable()\n\n def seek(self, *args):\n return self._file.seek(*args)\n\n def tell(self):\n return self._file.tell()\n\n def truncate(self, size=None):\n if size is None:\n return self._file.truncate()\n else:\n if size > self._max_size:\n self.rollover()\n return self._file.truncate(size)\n\n def writable(self):\n return self._file.writable()\n\n def write(self, s):\n file = self._file\n rv = file.write(s)\n self._check(file)\n return rv\n\n def writelines(self, iterable):\n file = self._file\n rv = file.writelines(iterable)\n self._check(file)\n return rv\n\n def detach(self):\n return self._file.detach()\n\n\nclass TemporaryDirectory:\n \"\"\"Create and return a temporary directory. This has the same\n behavior as mkdtemp but can be used as a context manager. For\n example:\n\n with TemporaryDirectory() as tmpdir:\n ...\n\n Upon exiting the context, the directory and everything contained\n in it are removed (unless delete=False is passed or an exception\n is raised during cleanup and ignore_cleanup_errors is not True).\n\n Optional Arguments:\n suffix - A str suffix for the directory name. (see mkdtemp)\n prefix - A str prefix for the directory name. (see mkdtemp)\n dir - A directory to create this temp dir in. (see mkdtemp)\n ignore_cleanup_errors - False; ignore exceptions during cleanup?\n delete - True; whether the directory is automatically deleted.\n \"\"\"\n\n def __init__(self, suffix=None, prefix=None, dir=None,\n ignore_cleanup_errors=False, *, delete=True):\n self.name = mkdtemp(suffix, prefix, dir)\n self._ignore_cleanup_errors = ignore_cleanup_errors\n self._delete = delete\n self._finalizer = _weakref.finalize(\n self, self._cleanup, self.name,\n warn_message=\"Implicitly cleaning up {!r}\".format(self),\n ignore_errors=self._ignore_cleanup_errors, delete=self._delete)\n\n @classmethod\n def _rmtree(cls, name, ignore_errors=False, repeated=False):\n def onexc(func, path, exc):\n if isinstance(exc, PermissionError):\n if repeated and path == name:\n if ignore_errors:\n return\n raise\n\n try:\n if path != name:\n _resetperms(_os.path.dirname(path))\n _resetperms(path)\n\n try:\n _os.unlink(path)\n except IsADirectoryError:\n cls._rmtree(path, ignore_errors=ignore_errors)\n except PermissionError:\n # The PermissionError handler was originally added for\n # FreeBSD in directories, but it seems that it is raised\n # on Windows too.\n # bpo-43153: Calling _rmtree again may\n # raise NotADirectoryError and mask the PermissionError.\n # So we must re-raise the current PermissionError if\n # path is not a directory.\n if not _os.path.isdir(path) or _os.path.isjunction(path):\n if ignore_errors:\n return\n raise\n cls._rmtree(path, ignore_errors=ignore_errors,\n repeated=(path == name))\n except FileNotFoundError:\n pass\n elif isinstance(exc, FileNotFoundError):\n pass\n else:\n if not ignore_errors:\n raise\n\n _rmtree(name, onexc=onexc)\n\n @classmethod\n def _cleanup(cls, name, warn_message, ignore_errors=False, delete=True):\n if delete:\n cls._rmtree(name, ignore_errors=ignore_errors)\n _warnings.warn(warn_message, ResourceWarning)\n\n def __repr__(self):\n return \"<{} {!r}>\".format(self.__class__.__name__, self.name)\n\n def __enter__(self):\n return self.name\n\n def __exit__(self, exc, value, tb):\n if self._delete:\n self.cleanup()\n\n def cleanup(self):\n if self._finalizer.detach() or _os.path.exists(self.name):\n self._rmtree(self.name, ignore_errors=self._ignore_cleanup_errors)\n\n __class_getitem__ = classmethod(_types.GenericAlias)\n", 1091], "/usr/lib/python3.13/weakref.py": ["\"\"\"Weak reference support for Python.\n\nThis module is an implementation of PEP 205:\n\nhttps://peps.python.org/pep-0205/\n\"\"\"\n\n# Naming convention: Variables named \"wr\" are weak reference objects;\n# they are called this instead of \"ref\" to avoid name collisions with\n# the module-global ref() function imported from _weakref.\n\nfrom _weakref import (\n getweakrefcount,\n getweakrefs,\n ref,\n proxy,\n CallableProxyType,\n ProxyType,\n ReferenceType,\n _remove_dead_weakref)\n\nfrom _weakrefset import WeakSet, _IterationGuard\n\nimport _collections_abc # Import after _weakref to avoid circular import.\nimport sys\nimport itertools\n\nProxyTypes = (ProxyType, CallableProxyType)\n\n__all__ = [\"ref\", \"proxy\", \"getweakrefcount\", \"getweakrefs\",\n \"WeakKeyDictionary\", \"ReferenceType\", \"ProxyType\",\n \"CallableProxyType\", \"ProxyTypes\", \"WeakValueDictionary\",\n \"WeakSet\", \"WeakMethod\", \"finalize\"]\n\n\n_collections_abc.MutableSet.register(WeakSet)\n\nclass WeakMethod(ref):\n \"\"\"\n A custom `weakref.ref` subclass which simulates a weak reference to\n a bound method, working around the lifetime problem of bound methods.\n \"\"\"\n\n __slots__ = \"_func_ref\", \"_meth_type\", \"_alive\", \"__weakref__\"\n\n def __new__(cls, meth, callback=None):\n try:\n obj = meth.__self__\n func = meth.__func__\n except AttributeError:\n raise TypeError(\"argument should be a bound method, not {}\"\n .format(type(meth))) from None\n def _cb(arg):\n # The self-weakref trick is needed to avoid creating a reference\n # cycle.\n self = self_wr()\n if self._alive:\n self._alive = False\n if callback is not None:\n callback(self)\n self = ref.__new__(cls, obj, _cb)\n self._func_ref = ref(func, _cb)\n self._meth_type = type(meth)\n self._alive = True\n self_wr = ref(self)\n return self\n\n def __call__(self):\n obj = super().__call__()\n func = self._func_ref()\n if obj is None or func is None:\n return None\n return self._meth_type(func, obj)\n\n def __eq__(self, other):\n if isinstance(other, WeakMethod):\n if not self._alive or not other._alive:\n return self is other\n return ref.__eq__(self, other) and self._func_ref == other._func_ref\n return NotImplemented\n\n def __ne__(self, other):\n if isinstance(other, WeakMethod):\n if not self._alive or not other._alive:\n return self is not other\n return ref.__ne__(self, other) or self._func_ref != other._func_ref\n return NotImplemented\n\n __hash__ = ref.__hash__\n\n\nclass WeakValueDictionary(_collections_abc.MutableMapping):\n \"\"\"Mapping class that references values weakly.\n\n Entries in the dictionary will be discarded when no strong\n reference to the value exists anymore\n \"\"\"\n # We inherit the constructor without worrying about the input\n # dictionary; since it uses our .update() method, we get the right\n # checks (if the other dictionary is a WeakValueDictionary,\n # objects are unwrapped on the way out, and we always wrap on the\n # way in).\n\n def __init__(self, other=(), /, **kw):\n def remove(wr, selfref=ref(self), _atomic_removal=_remove_dead_weakref):\n self = selfref()\n if self is not None:\n if self._iterating:\n self._pending_removals.append(wr.key)\n else:\n # Atomic removal is necessary since this function\n # can be called asynchronously by the GC\n _atomic_removal(self.data, wr.key)\n self._remove = remove\n # A list of keys to be removed\n self._pending_removals = []\n self._iterating = set()\n self.data = {}\n self.update(other, **kw)\n\n def _commit_removals(self, _atomic_removal=_remove_dead_weakref):\n pop = self._pending_removals.pop\n d = self.data\n # We shouldn't encounter any KeyError, because this method should\n # always be called *before* mutating the dict.\n while True:\n try:\n key = pop()\n except IndexError:\n return\n _atomic_removal(d, key)\n\n def __getitem__(self, key):\n if self._pending_removals:\n self._commit_removals()\n o = self.data[key]()\n if o is None:\n raise KeyError(key)\n else:\n return o\n\n def __delitem__(self, key):\n if self._pending_removals:\n self._commit_removals()\n del self.data[key]\n\n def __len__(self):\n if self._pending_removals:\n self._commit_removals()\n return len(self.data)\n\n def __contains__(self, key):\n if self._pending_removals:\n self._commit_removals()\n try:\n o = self.data[key]()\n except KeyError:\n return False\n return o is not None\n\n def __repr__(self):\n return \"<%s at %#x>\" % (self.__class__.__name__, id(self))\n\n def __setitem__(self, key, value):\n if self._pending_removals:\n self._commit_removals()\n self.data[key] = KeyedRef(value, self._remove, key)\n\n def copy(self):\n if self._pending_removals:\n self._commit_removals()\n new = WeakValueDictionary()\n with _IterationGuard(self):\n for key, wr in self.data.items():\n o = wr()\n if o is not None:\n new[key] = o\n return new\n\n __copy__ = copy\n\n def __deepcopy__(self, memo):\n from copy import deepcopy\n if self._pending_removals:\n self._commit_removals()\n new = self.__class__()\n with _IterationGuard(self):\n for key, wr in self.data.items():\n o = wr()\n if o is not None:\n new[deepcopy(key, memo)] = o\n return new\n\n def get(self, key, default=None):\n if self._pending_removals:\n self._commit_removals()\n try:\n wr = self.data[key]\n except KeyError:\n return default\n else:\n o = wr()\n if o is None:\n # This should only happen\n return default\n else:\n return o\n\n def items(self):\n if self._pending_removals:\n self._commit_removals()\n with _IterationGuard(self):\n for k, wr in self.data.items():\n v = wr()\n if v is not None:\n yield k, v\n\n def keys(self):\n if self._pending_removals:\n self._commit_removals()\n with _IterationGuard(self):\n for k, wr in self.data.items():\n if wr() is not None:\n yield k\n\n __iter__ = keys\n\n def itervaluerefs(self):\n \"\"\"Return an iterator that yields the weak references to the values.\n\n The references are not guaranteed to be 'live' at the time\n they are used, so the result of calling the references needs\n to be checked before being used. This can be used to avoid\n creating references that will cause the garbage collector to\n keep the values around longer than needed.\n\n \"\"\"\n if self._pending_removals:\n self._commit_removals()\n with _IterationGuard(self):\n yield from self.data.values()\n\n def values(self):\n if self._pending_removals:\n self._commit_removals()\n with _IterationGuard(self):\n for wr in self.data.values():\n obj = wr()\n if obj is not None:\n yield obj\n\n def popitem(self):\n if self._pending_removals:\n self._commit_removals()\n while True:\n key, wr = self.data.popitem()\n o = wr()\n if o is not None:\n return key, o\n\n def pop(self, key, *args):\n if self._pending_removals:\n self._commit_removals()\n try:\n o = self.data.pop(key)()\n except KeyError:\n o = None\n if o is None:\n if args:\n return args[0]\n else:\n raise KeyError(key)\n else:\n return o\n\n def setdefault(self, key, default=None):\n try:\n o = self.data[key]()\n except KeyError:\n o = None\n if o is None:\n if self._pending_removals:\n self._commit_removals()\n self.data[key] = KeyedRef(default, self._remove, key)\n return default\n else:\n return o\n\n def update(self, other=None, /, **kwargs):\n if self._pending_removals:\n self._commit_removals()\n d = self.data\n if other is not None:\n if not hasattr(other, \"items\"):\n other = dict(other)\n for key, o in other.items():\n d[key] = KeyedRef(o, self._remove, key)\n for key, o in kwargs.items():\n d[key] = KeyedRef(o, self._remove, key)\n\n def valuerefs(self):\n \"\"\"Return a list of weak references to the values.\n\n The references are not guaranteed to be 'live' at the time\n they are used, so the result of calling the references needs\n to be checked before being used. This can be used to avoid\n creating references that will cause the garbage collector to\n keep the values around longer than needed.\n\n \"\"\"\n if self._pending_removals:\n self._commit_removals()\n return list(self.data.values())\n\n def __ior__(self, other):\n self.update(other)\n return self\n\n def __or__(self, other):\n if isinstance(other, _collections_abc.Mapping):\n c = self.copy()\n c.update(other)\n return c\n return NotImplemented\n\n def __ror__(self, other):\n if isinstance(other, _collections_abc.Mapping):\n c = self.__class__()\n c.update(other)\n c.update(self)\n return c\n return NotImplemented\n\n\nclass KeyedRef(ref):\n \"\"\"Specialized reference that includes a key corresponding to the value.\n\n This is used in the WeakValueDictionary to avoid having to create\n a function object for each key stored in the mapping. A shared\n callback object can use the 'key' attribute of a KeyedRef instead\n of getting a reference to the key from an enclosing scope.\n\n \"\"\"\n\n __slots__ = \"key\",\n\n def __new__(type, ob, callback, key):\n self = ref.__new__(type, ob, callback)\n self.key = key\n return self\n\n def __init__(self, ob, callback, key):\n super().__init__(ob, callback)\n\n\nclass WeakKeyDictionary(_collections_abc.MutableMapping):\n \"\"\" Mapping class that references keys weakly.\n\n Entries in the dictionary will be discarded when there is no\n longer a strong reference to the key. This can be used to\n associate additional data with an object owned by other parts of\n an application without adding attributes to those objects. This\n can be especially useful with objects that override attribute\n accesses.\n \"\"\"\n\n def __init__(self, dict=None):\n self.data = {}\n def remove(k, selfref=ref(self)):\n self = selfref()\n if self is not None:\n if self._iterating:\n self._pending_removals.append(k)\n else:\n try:\n del self.data[k]\n except KeyError:\n pass\n self._remove = remove\n # A list of dead weakrefs (keys to be removed)\n self._pending_removals = []\n self._iterating = set()\n self._dirty_len = False\n if dict is not None:\n self.update(dict)\n\n def _commit_removals(self):\n # NOTE: We don't need to call this method before mutating the dict,\n # because a dead weakref never compares equal to a live weakref,\n # even if they happened to refer to equal objects.\n # However, it means keys may already have been removed.\n pop = self._pending_removals.pop\n d = self.data\n while True:\n try:\n key = pop()\n except IndexError:\n return\n\n try:\n del d[key]\n except KeyError:\n pass\n\n def _scrub_removals(self):\n d = self.data\n self._pending_removals = [k for k in self._pending_removals if k in d]\n self._dirty_len = False\n\n def __delitem__(self, key):\n self._dirty_len = True\n del self.data[ref(key)]\n\n def __getitem__(self, key):\n return self.data[ref(key)]\n\n def __len__(self):\n if self._dirty_len and self._pending_removals:\n # self._pending_removals may still contain keys which were\n # explicitly removed, we have to scrub them (see issue #21173).\n self._scrub_removals()\n return len(self.data) - len(self._pending_removals)\n\n def __repr__(self):\n return \"<%s at %#x>\" % (self.__class__.__name__, id(self))\n\n def __setitem__(self, key, value):\n self.data[ref(key, self._remove)] = value\n\n def copy(self):\n new = WeakKeyDictionary()\n with _IterationGuard(self):\n for key, value in self.data.items():\n o = key()\n if o is not None:\n new[o] = value\n return new\n\n __copy__ = copy\n\n def __deepcopy__(self, memo):\n from copy import deepcopy\n new = self.__class__()\n with _IterationGuard(self):\n for key, value in self.data.items():\n o = key()\n if o is not None:\n new[o] = deepcopy(value, memo)\n return new\n\n def get(self, key, default=None):\n return self.data.get(ref(key),default)\n\n def __contains__(self, key):\n try:\n wr = ref(key)\n except TypeError:\n return False\n return wr in self.data\n\n def items(self):\n with _IterationGuard(self):\n for wr, value in self.data.items():\n key = wr()\n if key is not None:\n yield key, value\n\n def keys(self):\n with _IterationGuard(self):\n for wr in self.data:\n obj = wr()\n if obj is not None:\n yield obj\n\n __iter__ = keys\n\n def values(self):\n with _IterationGuard(self):\n for wr, value in self.data.items():\n if wr() is not None:\n yield value\n\n def keyrefs(self):\n \"\"\"Return a list of weak references to the keys.\n\n The references are not guaranteed to be 'live' at the time\n they are used, so the result of calling the references needs\n to be checked before being used. This can be used to avoid\n creating references that will cause the garbage collector to\n keep the keys around longer than needed.\n\n \"\"\"\n return list(self.data)\n\n def popitem(self):\n self._dirty_len = True\n while True:\n key, value = self.data.popitem()\n o = key()\n if o is not None:\n return o, value\n\n def pop(self, key, *args):\n self._dirty_len = True\n return self.data.pop(ref(key), *args)\n\n def setdefault(self, key, default=None):\n return self.data.setdefault(ref(key, self._remove),default)\n\n def update(self, dict=None, /, **kwargs):\n d = self.data\n if dict is not None:\n if not hasattr(dict, \"items\"):\n dict = type({})(dict)\n for key, value in dict.items():\n d[ref(key, self._remove)] = value\n if len(kwargs):\n self.update(kwargs)\n\n def __ior__(self, other):\n self.update(other)\n return self\n\n def __or__(self, other):\n if isinstance(other, _collections_abc.Mapping):\n c = self.copy()\n c.update(other)\n return c\n return NotImplemented\n\n def __ror__(self, other):\n if isinstance(other, _collections_abc.Mapping):\n c = self.__class__()\n c.update(other)\n c.update(self)\n return c\n return NotImplemented\n\n\nclass finalize:\n \"\"\"Class for finalization of weakrefable objects\n\n finalize(obj, func, *args, **kwargs) returns a callable finalizer\n object which will be called when obj is garbage collected. The\n first time the finalizer is called it evaluates func(*arg, **kwargs)\n and returns the result. After this the finalizer is dead, and\n calling it just returns None.\n\n When the program exits any remaining finalizers for which the\n atexit attribute is true will be run in reverse order of creation.\n By default atexit is true.\n \"\"\"\n\n # Finalizer objects don't have any state of their own. They are\n # just used as keys to lookup _Info objects in the registry. This\n # ensures that they cannot be part of a ref-cycle.\n\n __slots__ = ()\n _registry = {}\n _shutdown = False\n _index_iter = itertools.count()\n _dirty = False\n _registered_with_atexit = False\n\n class _Info:\n __slots__ = (\"weakref\", \"func\", \"args\", \"kwargs\", \"atexit\", \"index\")\n\n def __init__(self, obj, func, /, *args, **kwargs):\n if not self._registered_with_atexit:\n # We may register the exit function more than once because\n # of a thread race, but that is harmless\n import atexit\n atexit.register(self._exitfunc)\n finalize._registered_with_atexit = True\n info = self._Info()\n info.weakref = ref(obj, self)\n info.func = func\n info.args = args\n info.kwargs = kwargs or None\n info.atexit = True\n info.index = next(self._index_iter)\n self._registry[self] = info\n finalize._dirty = True\n\n def __call__(self, _=None):\n \"\"\"If alive then mark as dead and return func(*args, **kwargs);\n otherwise return None\"\"\"\n info = self._registry.pop(self, None)\n if info and not self._shutdown:\n return info.func(*info.args, **(info.kwargs or {}))\n\n def detach(self):\n \"\"\"If alive then mark as dead and return (obj, func, args, kwargs);\n otherwise return None\"\"\"\n info = self._registry.get(self)\n obj = info and info.weakref()\n if obj is not None and self._registry.pop(self, None):\n return (obj, info.func, info.args, info.kwargs or {})\n\n def peek(self):\n \"\"\"If alive then return (obj, func, args, kwargs);\n otherwise return None\"\"\"\n info = self._registry.get(self)\n obj = info and info.weakref()\n if obj is not None:\n return (obj, info.func, info.args, info.kwargs or {})\n\n @property\n def alive(self):\n \"\"\"Whether finalizer is alive\"\"\"\n return self in self._registry\n\n @property\n def atexit(self):\n \"\"\"Whether finalizer should be called at exit\"\"\"\n info = self._registry.get(self)\n return bool(info) and info.atexit\n\n @atexit.setter\n def atexit(self, value):\n info = self._registry.get(self)\n if info:\n info.atexit = bool(value)\n\n def __repr__(self):\n info = self._registry.get(self)\n obj = info and info.weakref()\n if obj is None:\n return '<%s object at %#x; dead>' % (type(self).__name__, id(self))\n else:\n return '<%s object at %#x; for %r at %#x>' % \\\n (type(self).__name__, id(self), type(obj).__name__, id(obj))\n\n @classmethod\n def _select_for_exit(cls):\n # Return live finalizers marked for exit, oldest first\n L = [(f,i) for (f,i) in cls._registry.items() if i.atexit]\n L.sort(key=lambda item:item[1].index)\n return [f for (f,i) in L]\n\n @classmethod\n def _exitfunc(cls):\n # At shutdown invoke finalizers for which atexit is true.\n # This is called once all other non-daemonic threads have been\n # joined.\n reenable_gc = False\n try:\n if cls._registry:\n import gc\n if gc.isenabled():\n reenable_gc = True\n gc.disable()\n pending = None\n while True:\n if pending is None or finalize._dirty:\n pending = cls._select_for_exit()\n finalize._dirty = False\n if not pending:\n break\n f = pending.pop()\n try:\n # gc is disabled, so (assuming no daemonic\n # threads) the following is the only line in\n # this function which might trigger creation\n # of a new finalizer\n f()\n except Exception:\n sys.excepthook(*sys.exc_info())\n assert f not in cls._registry\n finally:\n # prevent any more finalizers from executing during shutdown\n finalize._shutdown = True\n if reenable_gc:\n gc.enable()\n", 674], "/usr/lib/python3.13/_weakrefset.py": ["# Access WeakSet through the weakref module.\n# This code is separated-out because it is needed\n# by abc.py to load everything else at startup.\n\nfrom _weakref import ref\nfrom types import GenericAlias\n\n__all__ = ['WeakSet']\n\n\nclass _IterationGuard:\n # This context manager registers itself in the current iterators of the\n # weak container, such as to delay all removals until the context manager\n # exits.\n # This technique should be relatively thread-safe (since sets are).\n\n def __init__(self, weakcontainer):\n # Don't create cycles\n self.weakcontainer = ref(weakcontainer)\n\n def __enter__(self):\n w = self.weakcontainer()\n if w is not None:\n w._iterating.add(self)\n return self\n\n def __exit__(self, e, t, b):\n w = self.weakcontainer()\n if w is not None:\n s = w._iterating\n s.remove(self)\n if not s:\n w._commit_removals()\n\n\nclass WeakSet:\n def __init__(self, data=None):\n self.data = set()\n def _remove(item, selfref=ref(self)):\n self = selfref()\n if self is not None:\n if self._iterating:\n self._pending_removals.append(item)\n else:\n self.data.discard(item)\n self._remove = _remove\n # A list of keys to be removed\n self._pending_removals = []\n self._iterating = set()\n if data is not None:\n self.update(data)\n\n def _commit_removals(self):\n pop = self._pending_removals.pop\n discard = self.data.discard\n while True:\n try:\n item = pop()\n except IndexError:\n return\n discard(item)\n\n def __iter__(self):\n with _IterationGuard(self):\n for itemref in self.data:\n item = itemref()\n if item is not None:\n # Caveat: the iterator will keep a strong reference to\n # `item` until it is resumed or closed.\n yield item\n\n def __len__(self):\n return len(self.data) - len(self._pending_removals)\n\n def __contains__(self, item):\n try:\n wr = ref(item)\n except TypeError:\n return False\n return wr in self.data\n\n def __reduce__(self):\n return self.__class__, (list(self),), self.__getstate__()\n\n def add(self, item):\n if self._pending_removals:\n self._commit_removals()\n self.data.add(ref(item, self._remove))\n\n def clear(self):\n if self._pending_removals:\n self._commit_removals()\n self.data.clear()\n\n def copy(self):\n return self.__class__(self)\n\n def pop(self):\n if self._pending_removals:\n self._commit_removals()\n while True:\n try:\n itemref = self.data.pop()\n except KeyError:\n raise KeyError('pop from empty WeakSet') from None\n item = itemref()\n if item is not None:\n return item\n\n def remove(self, item):\n if self._pending_removals:\n self._commit_removals()\n self.data.remove(ref(item))\n\n def discard(self, item):\n if self._pending_removals:\n self._commit_removals()\n self.data.discard(ref(item))\n\n def update(self, other):\n if self._pending_removals:\n self._commit_removals()\n for element in other:\n self.add(element)\n\n def __ior__(self, other):\n self.update(other)\n return self\n\n def difference(self, other):\n newset = self.copy()\n newset.difference_update(other)\n return newset\n __sub__ = difference\n\n def difference_update(self, other):\n self.__isub__(other)\n def __isub__(self, other):\n if self._pending_removals:\n self._commit_removals()\n if self is other:\n self.data.clear()\n else:\n self.data.difference_update(ref(item) for item in other)\n return self\n\n def intersection(self, other):\n return self.__class__(item for item in other if item in self)\n __and__ = intersection\n\n def intersection_update(self, other):\n self.__iand__(other)\n def __iand__(self, other):\n if self._pending_removals:\n self._commit_removals()\n self.data.intersection_update(ref(item) for item in other)\n return self\n\n def issubset(self, other):\n return self.data.issubset(ref(item) for item in other)\n __le__ = issubset\n\n def __lt__(self, other):\n return self.data < set(map(ref, other))\n\n def issuperset(self, other):\n return self.data.issuperset(ref(item) for item in other)\n __ge__ = issuperset\n\n def __gt__(self, other):\n return self.data > set(map(ref, other))\n\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return NotImplemented\n return self.data == set(map(ref, other))\n\n def symmetric_difference(self, other):\n newset = self.copy()\n newset.symmetric_difference_update(other)\n return newset\n __xor__ = symmetric_difference\n\n def symmetric_difference_update(self, other):\n self.__ixor__(other)\n def __ixor__(self, other):\n if self._pending_removals:\n self._commit_removals()\n if self is other:\n self.data.clear()\n else:\n self.data.symmetric_difference_update(ref(item, self._remove) for item in other)\n return self\n\n def union(self, other):\n return self.__class__(e for s in (self, other) for e in s)\n __or__ = union\n\n def isdisjoint(self, other):\n return len(self.intersection(other)) == 0\n\n def __repr__(self):\n return repr(self.data)\n\n __class_getitem__ = classmethod(GenericAlias)\n", 205], "/usr/lib/python3.13/selectors.py": ["\"\"\"Selectors module.\n\nThis module allows high-level and efficient I/O multiplexing, built upon the\n`select` module primitives.\n\"\"\"\n\n\nfrom abc import ABCMeta, abstractmethod\nfrom collections import namedtuple\nfrom collections.abc import Mapping\nimport math\nimport select\nimport sys\n\n\n# generic events, that must be mapped to implementation-specific ones\nEVENT_READ = (1 << 0)\nEVENT_WRITE = (1 << 1)\n\n\ndef _fileobj_to_fd(fileobj):\n \"\"\"Return a file descriptor from a file object.\n\n Parameters:\n fileobj -- file object or file descriptor\n\n Returns:\n corresponding file descriptor\n\n Raises:\n ValueError if the object is invalid\n \"\"\"\n if isinstance(fileobj, int):\n fd = fileobj\n else:\n try:\n fd = int(fileobj.fileno())\n except (AttributeError, TypeError, ValueError):\n raise ValueError(\"Invalid file object: \"\n \"{!r}\".format(fileobj)) from None\n if fd < 0:\n raise ValueError(\"Invalid file descriptor: {}\".format(fd))\n return fd\n\n\nSelectorKey = namedtuple('SelectorKey', ['fileobj', 'fd', 'events', 'data'])\n\nSelectorKey.__doc__ = \"\"\"SelectorKey(fileobj, fd, events, data)\n\n Object used to associate a file object to its backing\n file descriptor, selected event mask, and attached data.\n\"\"\"\nSelectorKey.fileobj.__doc__ = 'File object registered.'\nSelectorKey.fd.__doc__ = 'Underlying file descriptor.'\nSelectorKey.events.__doc__ = 'Events that must be waited for on this file object.'\nSelectorKey.data.__doc__ = ('''Optional opaque data associated to this file object.\nFor example, this could be used to store a per-client session ID.''')\n\n\nclass _SelectorMapping(Mapping):\n \"\"\"Mapping of file objects to selector keys.\"\"\"\n\n def __init__(self, selector):\n self._selector = selector\n\n def __len__(self):\n return len(self._selector._fd_to_key)\n\n def get(self, fileobj, default=None):\n fd = self._selector._fileobj_lookup(fileobj)\n return self._selector._fd_to_key.get(fd, default)\n\n def __getitem__(self, fileobj):\n fd = self._selector._fileobj_lookup(fileobj)\n key = self._selector._fd_to_key.get(fd)\n if key is None:\n raise KeyError(\"{!r} is not registered\".format(fileobj))\n return key\n\n def __iter__(self):\n return iter(self._selector._fd_to_key)\n\n\nclass BaseSelector(metaclass=ABCMeta):\n \"\"\"Selector abstract base class.\n\n A selector supports registering file objects to be monitored for specific\n I/O events.\n\n A file object is a file descriptor or any object with a `fileno()` method.\n An arbitrary object can be attached to the file object, which can be used\n for example to store context information, a callback, etc.\n\n A selector can use various implementations (select(), poll(), epoll()...)\n depending on the platform. The default `Selector` class uses the most\n efficient implementation on the current platform.\n \"\"\"\n\n @abstractmethod\n def register(self, fileobj, events, data=None):\n \"\"\"Register a file object.\n\n Parameters:\n fileobj -- file object or file descriptor\n events -- events to monitor (bitwise mask of EVENT_READ|EVENT_WRITE)\n data -- attached data\n\n Returns:\n SelectorKey instance\n\n Raises:\n ValueError if events is invalid\n KeyError if fileobj is already registered\n OSError if fileobj is closed or otherwise is unacceptable to\n the underlying system call (if a system call is made)\n\n Note:\n OSError may or may not be raised\n \"\"\"\n raise NotImplementedError\n\n @abstractmethod\n def unregister(self, fileobj):\n \"\"\"Unregister a file object.\n\n Parameters:\n fileobj -- file object or file descriptor\n\n Returns:\n SelectorKey instance\n\n Raises:\n KeyError if fileobj is not registered\n\n Note:\n If fileobj is registered but has since been closed this does\n *not* raise OSError (even if the wrapped syscall does)\n \"\"\"\n raise NotImplementedError\n\n def modify(self, fileobj, events, data=None):\n \"\"\"Change a registered file object monitored events or attached data.\n\n Parameters:\n fileobj -- file object or file descriptor\n events -- events to monitor (bitwise mask of EVENT_READ|EVENT_WRITE)\n data -- attached data\n\n Returns:\n SelectorKey instance\n\n Raises:\n Anything that unregister() or register() raises\n \"\"\"\n self.unregister(fileobj)\n return self.register(fileobj, events, data)\n\n @abstractmethod\n def select(self, timeout=None):\n \"\"\"Perform the actual selection, until some monitored file objects are\n ready or a timeout expires.\n\n Parameters:\n timeout -- if timeout > 0, this specifies the maximum wait time, in\n seconds\n if timeout <= 0, the select() call won't block, and will\n report the currently ready file objects\n if timeout is None, select() will block until a monitored\n file object becomes ready\n\n Returns:\n list of (key, events) for ready file objects\n `events` is a bitwise mask of EVENT_READ|EVENT_WRITE\n \"\"\"\n raise NotImplementedError\n\n def close(self):\n \"\"\"Close the selector.\n\n This must be called to make sure that any underlying resource is freed.\n \"\"\"\n pass\n\n def get_key(self, fileobj):\n \"\"\"Return the key associated to a registered file object.\n\n Returns:\n SelectorKey for this file object\n \"\"\"\n mapping = self.get_map()\n if mapping is None:\n raise RuntimeError('Selector is closed')\n try:\n return mapping[fileobj]\n except KeyError:\n raise KeyError(\"{!r} is not registered\".format(fileobj)) from None\n\n @abstractmethod\n def get_map(self):\n \"\"\"Return a mapping of file objects to selector keys.\"\"\"\n raise NotImplementedError\n\n def __enter__(self):\n return self\n\n def __exit__(self, *args):\n self.close()\n\n\nclass _BaseSelectorImpl(BaseSelector):\n \"\"\"Base selector implementation.\"\"\"\n\n def __init__(self):\n # this maps file descriptors to keys\n self._fd_to_key = {}\n # read-only mapping returned by get_map()\n self._map = _SelectorMapping(self)\n\n def _fileobj_lookup(self, fileobj):\n \"\"\"Return a file descriptor from a file object.\n\n This wraps _fileobj_to_fd() to do an exhaustive search in case\n the object is invalid but we still have it in our map. This\n is used by unregister() so we can unregister an object that\n was previously registered even if it is closed. It is also\n used by _SelectorMapping.\n \"\"\"\n try:\n return _fileobj_to_fd(fileobj)\n except ValueError:\n # Do an exhaustive search.\n for key in self._fd_to_key.values():\n if key.fileobj is fileobj:\n return key.fd\n # Raise ValueError after all.\n raise\n\n def register(self, fileobj, events, data=None):\n if (not events) or (events & ~(EVENT_READ | EVENT_WRITE)):\n raise ValueError(\"Invalid events: {!r}\".format(events))\n\n key = SelectorKey(fileobj, self._fileobj_lookup(fileobj), events, data)\n\n if key.fd in self._fd_to_key:\n raise KeyError(\"{!r} (FD {}) is already registered\"\n .format(fileobj, key.fd))\n\n self._fd_to_key[key.fd] = key\n return key\n\n def unregister(self, fileobj):\n try:\n key = self._fd_to_key.pop(self._fileobj_lookup(fileobj))\n except KeyError:\n raise KeyError(\"{!r} is not registered\".format(fileobj)) from None\n return key\n\n def modify(self, fileobj, events, data=None):\n try:\n key = self._fd_to_key[self._fileobj_lookup(fileobj)]\n except KeyError:\n raise KeyError(\"{!r} is not registered\".format(fileobj)) from None\n if events != key.events:\n self.unregister(fileobj)\n key = self.register(fileobj, events, data)\n elif data != key.data:\n # Use a shortcut to update the data.\n key = key._replace(data=data)\n self._fd_to_key[key.fd] = key\n return key\n\n def close(self):\n self._fd_to_key.clear()\n self._map = None\n\n def get_map(self):\n return self._map\n\n\n\nclass SelectSelector(_BaseSelectorImpl):\n \"\"\"Select-based selector.\"\"\"\n\n def __init__(self):\n super().__init__()\n self._readers = set()\n self._writers = set()\n\n def register(self, fileobj, events, data=None):\n key = super().register(fileobj, events, data)\n if events & EVENT_READ:\n self._readers.add(key.fd)\n if events & EVENT_WRITE:\n self._writers.add(key.fd)\n return key\n\n def unregister(self, fileobj):\n key = super().unregister(fileobj)\n self._readers.discard(key.fd)\n self._writers.discard(key.fd)\n return key\n\n if sys.platform == 'win32':\n def _select(self, r, w, _, timeout=None):\n r, w, x = select.select(r, w, w, timeout)\n return r, w + x, []\n else:\n _select = select.select\n\n def select(self, timeout=None):\n timeout = None if timeout is None else max(timeout, 0)\n ready = []\n try:\n r, w, _ = self._select(self._readers, self._writers, [], timeout)\n except InterruptedError:\n return ready\n r = frozenset(r)\n w = frozenset(w)\n rw = r | w\n fd_to_key_get = self._fd_to_key.get\n for fd in rw:\n key = fd_to_key_get(fd)\n if key:\n events = ((fd in r and EVENT_READ)\n | (fd in w and EVENT_WRITE))\n ready.append((key, events & key.events))\n return ready\n\n\nclass _PollLikeSelector(_BaseSelectorImpl):\n \"\"\"Base class shared between poll, epoll and devpoll selectors.\"\"\"\n _selector_cls = None\n _EVENT_READ = None\n _EVENT_WRITE = None\n\n def __init__(self):\n super().__init__()\n self._selector = self._selector_cls()\n\n def register(self, fileobj, events, data=None):\n key = super().register(fileobj, events, data)\n poller_events = ((events & EVENT_READ and self._EVENT_READ)\n | (events & EVENT_WRITE and self._EVENT_WRITE) )\n try:\n self._selector.register(key.fd, poller_events)\n except:\n super().unregister(fileobj)\n raise\n return key\n\n def unregister(self, fileobj):\n key = super().unregister(fileobj)\n try:\n self._selector.unregister(key.fd)\n except OSError:\n # This can happen if the FD was closed since it\n # was registered.\n pass\n return key\n\n def modify(self, fileobj, events, data=None):\n try:\n key = self._fd_to_key[self._fileobj_lookup(fileobj)]\n except KeyError:\n raise KeyError(f\"{fileobj!r} is not registered\") from None\n\n changed = False\n if events != key.events:\n selector_events = ((events & EVENT_READ and self._EVENT_READ)\n | (events & EVENT_WRITE and self._EVENT_WRITE))\n try:\n self._selector.modify(key.fd, selector_events)\n except:\n super().unregister(fileobj)\n raise\n changed = True\n if data != key.data:\n changed = True\n\n if changed:\n key = key._replace(events=events, data=data)\n self._fd_to_key[key.fd] = key\n return key\n\n def select(self, timeout=None):\n # This is shared between poll() and epoll().\n # epoll() has a different signature and handling of timeout parameter.\n if timeout is None:\n timeout = None\n elif timeout <= 0:\n timeout = 0\n else:\n # poll() has a resolution of 1 millisecond, round away from\n # zero to wait *at least* timeout seconds.\n timeout = math.ceil(timeout * 1e3)\n ready = []\n try:\n fd_event_list = self._selector.poll(timeout)\n except InterruptedError:\n return ready\n\n fd_to_key_get = self._fd_to_key.get\n for fd, event in fd_event_list:\n key = fd_to_key_get(fd)\n if key:\n events = ((event & ~self._EVENT_READ and EVENT_WRITE)\n | (event & ~self._EVENT_WRITE and EVENT_READ))\n ready.append((key, events & key.events))\n return ready\n\n\nif hasattr(select, 'poll'):\n\n class PollSelector(_PollLikeSelector):\n \"\"\"Poll-based selector.\"\"\"\n _selector_cls = select.poll\n _EVENT_READ = select.POLLIN\n _EVENT_WRITE = select.POLLOUT\n\n\nif hasattr(select, 'epoll'):\n\n _NOT_EPOLLIN = ~select.EPOLLIN\n _NOT_EPOLLOUT = ~select.EPOLLOUT\n\n class EpollSelector(_PollLikeSelector):\n \"\"\"Epoll-based selector.\"\"\"\n _selector_cls = select.epoll\n _EVENT_READ = select.EPOLLIN\n _EVENT_WRITE = select.EPOLLOUT\n\n def fileno(self):\n return self._selector.fileno()\n\n def select(self, timeout=None):\n if timeout is None:\n timeout = -1\n elif timeout <= 0:\n timeout = 0\n else:\n # epoll_wait() has a resolution of 1 millisecond, round away\n # from zero to wait *at least* timeout seconds.\n timeout = math.ceil(timeout * 1e3) * 1e-3\n\n # epoll_wait() expects `maxevents` to be greater than zero;\n # we want to make sure that `select()` can be called when no\n # FD is registered.\n max_ev = len(self._fd_to_key) or 1\n\n ready = []\n try:\n fd_event_list = self._selector.poll(timeout, max_ev)\n except InterruptedError:\n return ready\n\n fd_to_key = self._fd_to_key\n for fd, event in fd_event_list:\n key = fd_to_key.get(fd)\n if key:\n events = ((event & _NOT_EPOLLIN and EVENT_WRITE)\n | (event & _NOT_EPOLLOUT and EVENT_READ))\n ready.append((key, events & key.events))\n return ready\n\n def close(self):\n self._selector.close()\n super().close()\n\n\nif hasattr(select, 'devpoll'):\n\n class DevpollSelector(_PollLikeSelector):\n \"\"\"Solaris /dev/poll selector.\"\"\"\n _selector_cls = select.devpoll\n _EVENT_READ = select.POLLIN\n _EVENT_WRITE = select.POLLOUT\n\n def fileno(self):\n return self._selector.fileno()\n\n def close(self):\n self._selector.close()\n super().close()\n\n\nif hasattr(select, 'kqueue'):\n\n class KqueueSelector(_BaseSelectorImpl):\n \"\"\"Kqueue-based selector.\"\"\"\n\n def __init__(self):\n super().__init__()\n self._selector = select.kqueue()\n self._max_events = 0\n\n def fileno(self):\n return self._selector.fileno()\n\n def register(self, fileobj, events, data=None):\n key = super().register(fileobj, events, data)\n try:\n if events & EVENT_READ:\n kev = select.kevent(key.fd, select.KQ_FILTER_READ,\n select.KQ_EV_ADD)\n self._selector.control([kev], 0, 0)\n self._max_events += 1\n if events & EVENT_WRITE:\n kev = select.kevent(key.fd, select.KQ_FILTER_WRITE,\n select.KQ_EV_ADD)\n self._selector.control([kev], 0, 0)\n self._max_events += 1\n except:\n super().unregister(fileobj)\n raise\n return key\n\n def unregister(self, fileobj):\n key = super().unregister(fileobj)\n if key.events & EVENT_READ:\n kev = select.kevent(key.fd, select.KQ_FILTER_READ,\n select.KQ_EV_DELETE)\n self._max_events -= 1\n try:\n self._selector.control([kev], 0, 0)\n except OSError:\n # This can happen if the FD was closed since it\n # was registered.\n pass\n if key.events & EVENT_WRITE:\n kev = select.kevent(key.fd, select.KQ_FILTER_WRITE,\n select.KQ_EV_DELETE)\n self._max_events -= 1\n try:\n self._selector.control([kev], 0, 0)\n except OSError:\n # See comment above.\n pass\n return key\n\n def select(self, timeout=None):\n timeout = None if timeout is None else max(timeout, 0)\n # If max_ev is 0, kqueue will ignore the timeout. For consistent\n # behavior with the other selector classes, we prevent that here\n # (using max). See https://bugs.python.org/issue29255\n max_ev = self._max_events or 1\n ready = []\n try:\n kev_list = self._selector.control(None, max_ev, timeout)\n except InterruptedError:\n return ready\n\n fd_to_key_get = self._fd_to_key.get\n for kev in kev_list:\n fd = kev.ident\n flag = kev.filter\n key = fd_to_key_get(fd)\n if key:\n events = ((flag == select.KQ_FILTER_READ and EVENT_READ)\n | (flag == select.KQ_FILTER_WRITE and EVENT_WRITE))\n ready.append((key, events & key.events))\n return ready\n\n def close(self):\n self._selector.close()\n super().close()\n\n\ndef _can_use(method):\n \"\"\"Check if we can use the selector depending upon the\n operating system. \"\"\"\n # Implementation based upon https://github.com/sethmlarson/selectors2/blob/master/selectors2.py\n selector = getattr(select, method, None)\n if selector is None:\n # select module does not implement method\n return False\n # check if the OS and Kernel actually support the method. Call may fail with\n # OSError: [Errno 38] Function not implemented\n try:\n selector_obj = selector()\n if method == 'poll':\n # check that poll actually works\n selector_obj.poll(0)\n else:\n # close epoll, kqueue, and devpoll fd\n selector_obj.close()\n return True\n except OSError:\n return False\n\n\n# Choose the best implementation, roughly:\n# epoll|kqueue|devpoll > poll > select.\n# select() also can't accept a FD > FD_SETSIZE (usually around 1024)\nif _can_use('kqueue'):\n DefaultSelector = KqueueSelector\nelif _can_use('epoll'):\n DefaultSelector = EpollSelector\nelif _can_use('devpoll'):\n DefaultSelector = DevpollSelector\nelif _can_use('poll'):\n DefaultSelector = PollSelector\nelse:\n DefaultSelector = SelectSelector\n", 603]}, "functions": {"SemLock.__init__.._after_fork (/usr/lib/python3.13/multiprocessing/synchronize.py:71)": ["/usr/lib/python3.13/multiprocessing/synchronize.py", 71], "_run_after_forkers (/usr/lib/python3.13/multiprocessing/util.py:154)": ["/usr/lib/python3.13/multiprocessing/util.py", 154], "BaseProcess._after_fork (/usr/lib/python3.13/multiprocessing/process.py:335)": ["/usr/lib/python3.13/multiprocessing/process.py", 335], "info (/usr/lib/python3.13/multiprocessing/util.py:52)": ["/usr/lib/python3.13/multiprocessing/util.py", 52], "Connection._close (/usr/lib/python3.13/multiprocessing/connection.py:376)": ["/usr/lib/python3.13/multiprocessing/connection.py", 376], "_ConnectionBase.close (/usr/lib/python3.13/multiprocessing/connection.py:174)": ["/usr/lib/python3.13/multiprocessing/connection.py", 174], "SemLock.__enter__ (/usr/lib/python3.13/multiprocessing/synchronize.py:94)": ["/usr/lib/python3.13/multiprocessing/synchronize.py", 94], "_ConnectionBase._check_closed (/usr/lib/python3.13/multiprocessing/connection.py:135)": ["/usr/lib/python3.13/multiprocessing/connection.py", 135], "_ConnectionBase._check_readable (/usr/lib/python3.13/multiprocessing/connection.py:139)": ["/usr/lib/python3.13/multiprocessing/connection.py", 139], "Connection._recv (/usr/lib/python3.13/multiprocessing/connection.py:390)": ["/usr/lib/python3.13/multiprocessing/connection.py", 390], "Connection._recv_bytes (/usr/lib/python3.13/multiprocessing/connection.py:429)": ["/usr/lib/python3.13/multiprocessing/connection.py", 429], "_ConnectionBase.recv_bytes (/usr/lib/python3.13/multiprocessing/connection.py:208)": ["/usr/lib/python3.13/multiprocessing/connection.py", 208], "SemLock.__exit__ (/usr/lib/python3.13/multiprocessing/synchronize.py:97)": ["/usr/lib/python3.13/multiprocessing/synchronize.py", 97], "SimpleQueue.get (/usr/lib/python3.13/multiprocessing/queues.py:383)": ["/usr/lib/python3.13/multiprocessing/queues.py", 383], "f (/home/gaogaotiantian/programs/viztracer/example/src/multi_process_pool.py:5)": ["/home/gaogaotiantian/programs/viztracer/example/src/multi_process_pool.py", 5], "mapstar (/usr/lib/python3.13/multiprocessing/pool.py:47)": ["/usr/lib/python3.13/multiprocessing/pool.py", 47], "ForkingPickler.__init__ (/usr/lib/python3.13/multiprocessing/reduction.py:38)": ["/usr/lib/python3.13/multiprocessing/reduction.py", 38], "ForkingPickler.dumps (/usr/lib/python3.13/multiprocessing/reduction.py:48)": ["/usr/lib/python3.13/multiprocessing/reduction.py", 48], "_ConnectionBase._check_writable (/usr/lib/python3.13/multiprocessing/connection.py:143)": ["/usr/lib/python3.13/multiprocessing/connection.py", 143], "Connection._send (/usr/lib/python3.13/multiprocessing/connection.py:381)": ["/usr/lib/python3.13/multiprocessing/connection.py", 381], "Connection._send_bytes (/usr/lib/python3.13/multiprocessing/connection.py:406)": ["/usr/lib/python3.13/multiprocessing/connection.py", 406], "_ConnectionBase.send_bytes (/usr/lib/python3.13/multiprocessing/connection.py:182)": ["/usr/lib/python3.13/multiprocessing/connection.py", 182], "SimpleQueue.put (/usr/lib/python3.13/multiprocessing/queues.py:389)": ["/usr/lib/python3.13/multiprocessing/queues.py", 389], "debug (/usr/lib/python3.13/multiprocessing/util.py:48)": ["/usr/lib/python3.13/multiprocessing/util.py", 48], "worker (/usr/lib/python3.13/multiprocessing/pool.py:97)": ["/usr/lib/python3.13/multiprocessing/pool.py", 97], "BaseProcess.run (/usr/lib/python3.13/multiprocessing/process.py:103)": ["/usr/lib/python3.13/multiprocessing/process.py", 103], "_shutdown (/usr/lib/python3.13/threading.py:1516)": ["/usr/lib/python3.13/threading.py", 1516], "_flush_std_streams (/usr/lib/python3.13/multiprocessing/util.py:425)": ["/usr/lib/python3.13/multiprocessing/util.py", 425], "BaseProcess._bootstrap (/usr/lib/python3.13/multiprocessing/process.py:290)": ["/usr/lib/python3.13/multiprocessing/process.py", 290], "_run_finalizers.. 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Those original names are not in any imminent danger of\n# being deprecated (even for Py3k),so this module provides them as an\n# alias for the PEP 8 compliant names\n# Note that using the new PEP 8 compliant names facilitates substitution\n# with the multiprocessing module, which doesn't provide the old\n# Java inspired names.\n\n__all__ = ['get_ident', 'active_count', 'Condition', 'current_thread',\n 'enumerate', 'main_thread', 'TIMEOUT_MAX',\n 'Event', 'Lock', 'RLock', 'Semaphore', 'BoundedSemaphore', 'Thread',\n 'Barrier', 'BrokenBarrierError', 'Timer', 'ThreadError',\n 'setprofile', 'settrace', 'local', 'stack_size']\n\n# Rename some stuff so \"from threading import *\" is safe\n_start_new_thread = _thread.start_new_thread\n_allocate_lock = _thread.allocate_lock\n_set_sentinel = _thread._set_sentinel\nget_ident = _thread.get_ident\nThreadError = _thread.error\ntry:\n _CRLock = _thread.RLock\nexcept AttributeError:\n _CRLock = None\nTIMEOUT_MAX = _thread.TIMEOUT_MAX\ndel _thread\n\n\n# Support for profile and trace hooks\n\n_profile_hook = None\n_trace_hook = None\n\ndef setprofile(func):\n \"\"\"Set a profile function for all threads started from the threading module.\n\n The func will be passed to sys.setprofile() for each thread, before its\n run() method is called.\n\n \"\"\"\n global _profile_hook\n _profile_hook = func\n\ndef settrace(func):\n \"\"\"Set a trace function for all threads started from the threading module.\n\n The func will be passed to sys.settrace() for each thread, before its run()\n method is called.\n\n \"\"\"\n global _trace_hook\n _trace_hook = func\n\n# Synchronization classes\n\nLock = _allocate_lock\n\ndef RLock(*args, **kwargs):\n \"\"\"Factory function that returns a new reentrant lock.\n\n A reentrant lock must be released by the thread that acquired it. Once a\n thread has acquired a reentrant lock, the same thread may acquire it again\n without blocking; the thread must release it once for each time it has\n acquired it.\n\n \"\"\"\n if _CRLock is None:\n return _PyRLock(*args, **kwargs)\n return _CRLock(*args, **kwargs)\n\nclass _RLock:\n \"\"\"This class implements reentrant lock objects.\n\n A reentrant lock must be released by the thread that acquired it. Once a\n thread has acquired a reentrant lock, the same thread may acquire it\n again without blocking; the thread must release it once for each time it\n has acquired it.\n\n \"\"\"\n\n def __init__(self):\n self._block = _allocate_lock()\n self._owner = None\n self._count = 0\n\n def __repr__(self):\n owner = self._owner\n try:\n owner = _active[owner].name\n except KeyError:\n pass\n return \"<%s %s.%s object owner=%r count=%d at %s>\" % (\n \"locked\" if self._block.locked() else \"unlocked\",\n self.__class__.__module__,\n self.__class__.__qualname__,\n owner,\n self._count,\n hex(id(self))\n )\n\n def acquire(self, blocking=True, timeout=-1):\n \"\"\"Acquire a lock, blocking or non-blocking.\n\n When invoked without arguments: if this thread already owns the lock,\n increment the recursion level by one, and return immediately. Otherwise,\n if another thread owns the lock, block until the lock is unlocked. Once\n the lock is unlocked (not owned by any thread), then grab ownership, set\n the recursion level to one, and return. If more than one thread is\n blocked waiting until the lock is unlocked, only one at a time will be\n able to grab ownership of the lock. There is no return value in this\n case.\n\n When invoked with the blocking argument set to true, do the same thing\n as when called without arguments, and return true.\n\n When invoked with the blocking argument set to false, do not block. If a\n call without an argument would block, return false immediately;\n otherwise, do the same thing as when called without arguments, and\n return true.\n\n When invoked with the floating-point timeout argument set to a positive\n value, block for at most the number of seconds specified by timeout\n and as long as the lock cannot be acquired. Return true if the lock has\n been acquired, false if the timeout has elapsed.\n\n \"\"\"\n me = get_ident()\n if self._owner == me:\n self._count += 1\n return 1\n rc = self._block.acquire(blocking, timeout)\n if rc:\n self._owner = me\n self._count = 1\n return rc\n\n __enter__ = acquire\n\n def release(self):\n \"\"\"Release a lock, decrementing the recursion level.\n\n If after the decrement it is zero, reset the lock to unlocked (not owned\n by any thread), and if any other threads are blocked waiting for the\n lock to become unlocked, allow exactly one of them to proceed. If after\n the decrement the recursion level is still nonzero, the lock remains\n locked and owned by the calling thread.\n\n Only call this method when the calling thread owns the lock. A\n RuntimeError is raised if this method is called when the lock is\n unlocked.\n\n There is no return value.\n\n \"\"\"\n if self._owner != get_ident():\n raise RuntimeError(\"cannot release un-acquired lock\")\n self._count = count = self._count - 1\n if not count:\n self._owner = None\n self._block.release()\n\n def __exit__(self, t, v, tb):\n self.release()\n\n # Internal methods used by condition variables\n\n def _acquire_restore(self, state):\n self._block.acquire()\n self._count, self._owner = state\n\n def _release_save(self):\n if self._count == 0:\n raise RuntimeError(\"cannot release un-acquired lock\")\n count = self._count\n self._count = 0\n owner = self._owner\n self._owner = None\n self._block.release()\n return (count, owner)\n\n def _is_owned(self):\n return self._owner == get_ident()\n\n_PyRLock = _RLock\n\n\nclass Condition:\n \"\"\"Class that implements a condition variable.\n\n A condition variable allows one or more threads to wait until they are\n notified by another thread.\n\n If the lock argument is given and not None, it must be a Lock or RLock\n object, and it is used as the underlying lock. Otherwise, a new RLock object\n is created and used as the underlying lock.\n\n \"\"\"\n\n def __init__(self, lock=None):\n if lock is None:\n lock = RLock()\n self._lock = lock\n # Export the lock's acquire() and release() methods\n self.acquire = lock.acquire\n self.release = lock.release\n # If the lock defines _release_save() and/or _acquire_restore(),\n # these override the default implementations (which just call\n # release() and acquire() on the lock). Ditto for _is_owned().\n try:\n self._release_save = lock._release_save\n except AttributeError:\n pass\n try:\n self._acquire_restore = lock._acquire_restore\n except AttributeError:\n pass\n try:\n self._is_owned = lock._is_owned\n except AttributeError:\n pass\n self._waiters = _deque()\n\n def __enter__(self):\n return self._lock.__enter__()\n\n def __exit__(self, *args):\n return self._lock.__exit__(*args)\n\n def __repr__(self):\n return \"\" % (self._lock, len(self._waiters))\n\n def _release_save(self):\n self._lock.release() # No state to save\n\n def _acquire_restore(self, x):\n self._lock.acquire() # Ignore saved state\n\n def _is_owned(self):\n # Return True if lock is owned by current_thread.\n # This method is called only if _lock doesn't have _is_owned().\n if self._lock.acquire(0):\n self._lock.release()\n return False\n else:\n return True\n\n def wait(self, timeout=None):\n \"\"\"Wait until notified or until a timeout occurs.\n\n If the calling thread has not acquired the lock when this method is\n called, a RuntimeError is raised.\n\n This method releases the underlying lock, and then blocks until it is\n awakened by a notify() or notify_all() call for the same condition\n variable in another thread, or until the optional timeout occurs. Once\n awakened or timed out, it re-acquires the lock and returns.\n\n When the timeout argument is present and not None, it should be a\n floating point number specifying a timeout for the operation in seconds\n (or fractions thereof).\n\n When the underlying lock is an RLock, it is not released using its\n release() method, since this may not actually unlock the lock when it\n was acquired multiple times recursively. Instead, an internal interface\n of the RLock class is used, which really unlocks it even when it has\n been recursively acquired several times. Another internal interface is\n then used to restore the recursion level when the lock is reacquired.\n\n \"\"\"\n if not self._is_owned():\n raise RuntimeError(\"cannot wait on un-acquired lock\")\n waiter = _allocate_lock()\n waiter.acquire()\n self._waiters.append(waiter)\n saved_state = self._release_save()\n gotit = False\n try: # restore state no matter what (e.g., KeyboardInterrupt)\n if timeout is None:\n waiter.acquire()\n gotit = True\n else:\n if timeout > 0:\n gotit = waiter.acquire(True, timeout)\n else:\n gotit = waiter.acquire(False)\n return gotit\n finally:\n self._acquire_restore(saved_state)\n if not gotit:\n try:\n self._waiters.remove(waiter)\n except ValueError:\n pass\n\n def wait_for(self, predicate, timeout=None):\n \"\"\"Wait until a condition evaluates to True.\n\n predicate should be a callable which result will be interpreted as a\n boolean value. A timeout may be provided giving the maximum time to\n wait.\n\n \"\"\"\n endtime = None\n waittime = timeout\n result = predicate()\n while not result:\n if waittime is not None:\n if endtime is None:\n endtime = _time() + waittime\n else:\n waittime = endtime - _time()\n if waittime <= 0:\n break\n self.wait(waittime)\n result = predicate()\n return result\n\n def notify(self, n=1):\n \"\"\"Wake up one or more threads waiting on this condition, if any.\n\n If the calling thread has not acquired the lock when this method is\n called, a RuntimeError is raised.\n\n This method wakes up at most n of the threads waiting for the condition\n variable; it is a no-op if no threads are waiting.\n\n \"\"\"\n if not self._is_owned():\n raise RuntimeError(\"cannot notify on un-acquired lock\")\n all_waiters = self._waiters\n waiters_to_notify = _deque(_islice(all_waiters, n))\n if not waiters_to_notify:\n return\n for waiter in waiters_to_notify:\n waiter.release()\n try:\n all_waiters.remove(waiter)\n except ValueError:\n pass\n\n def notify_all(self):\n \"\"\"Wake up all threads waiting on this condition.\n\n If the calling thread has not acquired the lock when this method\n is called, a RuntimeError is raised.\n\n \"\"\"\n self.notify(len(self._waiters))\n\n notifyAll = notify_all\n\n\nclass Semaphore:\n \"\"\"This class implements semaphore objects.\n\n Semaphores manage a counter representing the number of release() calls minus\n the number of acquire() calls, plus an initial value. The acquire() method\n blocks if necessary until it can return without making the counter\n negative. If not given, value defaults to 1.\n\n \"\"\"\n\n # After Tim Peters' semaphore class, but not quite the same (no maximum)\n\n def __init__(self, value=1):\n if value < 0:\n raise ValueError(\"semaphore initial value must be >= 0\")\n self._cond = Condition(Lock())\n self._value = value\n\n def acquire(self, blocking=True, timeout=None):\n \"\"\"Acquire a semaphore, decrementing the internal counter by one.\n\n When invoked without arguments: if the internal counter is larger than\n zero on entry, decrement it by one and return immediately. If it is zero\n on entry, block, waiting until some other thread has called release() to\n make it larger than zero. This is done with proper interlocking so that\n if multiple acquire() calls are blocked, release() will wake exactly one\n of them up. The implementation may pick one at random, so the order in\n which blocked threads are awakened should not be relied on. There is no\n return value in this case.\n\n When invoked with blocking set to true, do the same thing as when called\n without arguments, and return true.\n\n When invoked with blocking set to false, do not block. If a call without\n an argument would block, return false immediately; otherwise, do the\n same thing as when called without arguments, and return true.\n\n When invoked with a timeout other than None, it will block for at\n most timeout seconds. If acquire does not complete successfully in\n that interval, return false. Return true otherwise.\n\n \"\"\"\n if not blocking and timeout is not None:\n raise ValueError(\"can't specify timeout for non-blocking acquire\")\n rc = False\n endtime = None\n with self._cond:\n while self._value == 0:\n if not blocking:\n break\n if timeout is not None:\n if endtime is None:\n endtime = _time() + timeout\n else:\n timeout = endtime - _time()\n if timeout <= 0:\n break\n self._cond.wait(timeout)\n else:\n self._value -= 1\n rc = True\n return rc\n\n __enter__ = acquire\n\n def release(self):\n \"\"\"Release a semaphore, incrementing the internal counter by one.\n\n When the counter is zero on entry and another thread is waiting for it\n to become larger than zero again, wake up that thread.\n\n \"\"\"\n with self._cond:\n self._value += 1\n self._cond.notify()\n\n def __exit__(self, t, v, tb):\n self.release()\n\n\nclass BoundedSemaphore(Semaphore):\n \"\"\"Implements a bounded semaphore.\n\n A bounded semaphore checks to make sure its current value doesn't exceed its\n initial value. If it does, ValueError is raised. In most situations\n semaphores are used to guard resources with limited capacity.\n\n If the semaphore is released too many times it's a sign of a bug. If not\n given, value defaults to 1.\n\n Like regular semaphores, bounded semaphores manage a counter representing\n the number of release() calls minus the number of acquire() calls, plus an\n initial value. The acquire() method blocks if necessary until it can return\n without making the counter negative. If not given, value defaults to 1.\n\n \"\"\"\n\n def __init__(self, value=1):\n Semaphore.__init__(self, value)\n self._initial_value = value\n\n def release(self):\n \"\"\"Release a semaphore, incrementing the internal counter by one.\n\n When the counter is zero on entry and another thread is waiting for it\n to become larger than zero again, wake up that thread.\n\n If the number of releases exceeds the number of acquires,\n raise a ValueError.\n\n \"\"\"\n with self._cond:\n if self._value >= self._initial_value:\n raise ValueError(\"Semaphore released too many times\")\n self._value += 1\n self._cond.notify()\n\n\nclass Event:\n \"\"\"Class implementing event objects.\n\n Events manage a flag that can be set to true with the set() method and reset\n to false with the clear() method. The wait() method blocks until the flag is\n true. The flag is initially false.\n\n \"\"\"\n\n # After Tim Peters' event class (without is_posted())\n\n def __init__(self):\n self._cond = Condition(Lock())\n self._flag = False\n\n def _reset_internal_locks(self):\n # private! called by Thread._reset_internal_locks by _after_fork()\n self._cond.__init__(Lock())\n\n def is_set(self):\n \"\"\"Return true if and only if the internal flag is true.\"\"\"\n return self._flag\n\n isSet = is_set\n\n def set(self):\n \"\"\"Set the internal flag to true.\n\n All threads waiting for it to become true are awakened. Threads\n that call wait() once the flag is true will not block at all.\n\n \"\"\"\n with self._cond:\n self._flag = True\n self._cond.notify_all()\n\n def clear(self):\n \"\"\"Reset the internal flag to false.\n\n Subsequently, threads calling wait() will block until set() is called to\n set the internal flag to true again.\n\n \"\"\"\n with self._cond:\n self._flag = False\n\n def wait(self, timeout=None):\n \"\"\"Block until the internal flag is true.\n\n If the internal flag is true on entry, return immediately. Otherwise,\n block until another thread calls set() to set the flag to true, or until\n the optional timeout occurs.\n\n When the timeout argument is present and not None, it should be a\n floating point number specifying a timeout for the operation in seconds\n (or fractions thereof).\n\n This method returns the internal flag on exit, so it will always return\n True except if a timeout is given and the operation times out.\n\n \"\"\"\n with self._cond:\n signaled = self._flag\n if not signaled:\n signaled = self._cond.wait(timeout)\n return signaled\n\n\n# A barrier class. Inspired in part by the pthread_barrier_* api and\n# the CyclicBarrier class from Java. See\n# http://sourceware.org/pthreads-win32/manual/pthread_barrier_init.html and\n# http://java.sun.com/j2se/1.5.0/docs/api/java/util/concurrent/\n# CyclicBarrier.html\n# for information.\n# We maintain two main states, 'filling' and 'draining' enabling the barrier\n# to be cyclic. Threads are not allowed into it until it has fully drained\n# since the previous cycle. In addition, a 'resetting' state exists which is\n# similar to 'draining' except that threads leave with a BrokenBarrierError,\n# and a 'broken' state in which all threads get the exception.\nclass Barrier:\n \"\"\"Implements a Barrier.\n\n Useful for synchronizing a fixed number of threads at known synchronization\n points. Threads block on 'wait()' and are simultaneously once they have all\n made that call.\n\n \"\"\"\n\n def __init__(self, parties, action=None, timeout=None):\n \"\"\"Create a barrier, initialised to 'parties' threads.\n\n 'action' is a callable which, when supplied, will be called by one of\n the threads after they have all entered the barrier and just prior to\n releasing them all. If a 'timeout' is provided, it is uses as the\n default for all subsequent 'wait()' calls.\n\n \"\"\"\n self._cond = Condition(Lock())\n self._action = action\n self._timeout = timeout\n self._parties = parties\n self._state = 0 #0 filling, 1, draining, -1 resetting, -2 broken\n self._count = 0\n\n def wait(self, timeout=None):\n \"\"\"Wait for the barrier.\n\n When the specified number of threads have started waiting, they are all\n simultaneously awoken. If an 'action' was provided for the barrier, one\n of the threads will have executed that callback prior to returning.\n Returns an individual index number from 0 to 'parties-1'.\n\n \"\"\"\n if timeout is None:\n timeout = self._timeout\n with self._cond:\n self._enter() # Block while the barrier drains.\n index = self._count\n self._count += 1\n try:\n if index + 1 == self._parties:\n # We release the barrier\n self._release()\n else:\n # We wait until someone releases us\n self._wait(timeout)\n return index\n finally:\n self._count -= 1\n # Wake up any threads waiting for barrier to drain.\n self._exit()\n\n # Block until the barrier is ready for us, or raise an exception\n # if it is broken.\n def _enter(self):\n while self._state in (-1, 1):\n # It is draining or resetting, wait until done\n self._cond.wait()\n #see if the barrier is in a broken state\n if self._state < 0:\n raise BrokenBarrierError\n assert self._state == 0\n\n # Optionally run the 'action' and release the threads waiting\n # in the barrier.\n def _release(self):\n try:\n if self._action:\n self._action()\n # enter draining state\n self._state = 1\n self._cond.notify_all()\n except:\n #an exception during the _action handler. Break and reraise\n self._break()\n raise\n\n # Wait in the barrier until we are released. Raise an exception\n # if the barrier is reset or broken.\n def _wait(self, timeout):\n if not self._cond.wait_for(lambda : self._state != 0, timeout):\n #timed out. Break the barrier\n self._break()\n raise BrokenBarrierError\n if self._state < 0:\n raise BrokenBarrierError\n assert self._state == 1\n\n # If we are the last thread to exit the barrier, signal any threads\n # waiting for the barrier to drain.\n def _exit(self):\n if self._count == 0:\n if self._state in (-1, 1):\n #resetting or draining\n self._state = 0\n self._cond.notify_all()\n\n def reset(self):\n \"\"\"Reset the barrier to the initial state.\n\n Any threads currently waiting will get the BrokenBarrier exception\n raised.\n\n \"\"\"\n with self._cond:\n if self._count > 0:\n if self._state == 0:\n #reset the barrier, waking up threads\n self._state = -1\n elif self._state == -2:\n #was broken, set it to reset state\n #which clears when the last thread exits\n self._state = -1\n else:\n self._state = 0\n self._cond.notify_all()\n\n def abort(self):\n \"\"\"Place the barrier into a 'broken' state.\n\n Useful in case of error. Any currently waiting threads and threads\n attempting to 'wait()' will have BrokenBarrierError raised.\n\n \"\"\"\n with self._cond:\n self._break()\n\n def _break(self):\n # An internal error was detected. The barrier is set to\n # a broken state all parties awakened.\n self._state = -2\n self._cond.notify_all()\n\n @property\n def parties(self):\n \"\"\"Return the number of threads required to trip the barrier.\"\"\"\n return self._parties\n\n @property\n def n_waiting(self):\n \"\"\"Return the number of threads currently waiting at the barrier.\"\"\"\n # We don't need synchronization here since this is an ephemeral result\n # anyway. It returns the correct value in the steady state.\n if self._state == 0:\n return self._count\n return 0\n\n @property\n def broken(self):\n \"\"\"Return True if the barrier is in a broken state.\"\"\"\n return self._state == -2\n\n# exception raised by the Barrier class\nclass BrokenBarrierError(RuntimeError):\n pass\n\n\n# Helper to generate new thread names\n_counter = _count().__next__\n_counter() # Consume 0 so first non-main thread has id 1.\ndef _newname(template=\"Thread-%d\"):\n return template % _counter()\n\n# Active thread administration\n_active_limbo_lock = _allocate_lock()\n_active = {} # maps thread id to Thread object\n_limbo = {}\n_dangling = WeakSet()\n\n# Main class for threads\n\nclass Thread:\n \"\"\"A class that represents a thread of control.\n\n This class can be safely subclassed in a limited fashion. There are two ways\n to specify the activity: by passing a callable object to the constructor, or\n by overriding the run() method in a subclass.\n\n \"\"\"\n\n _initialized = False\n # Need to store a reference to sys.exc_info for printing\n # out exceptions when a thread tries to use a global var. during interp.\n # shutdown and thus raises an exception about trying to perform some\n # operation on/with a NoneType\n _exc_info = _sys.exc_info\n # Keep sys.exc_clear too to clear the exception just before\n # allowing .join() to return.\n #XXX __exc_clear = _sys.exc_clear\n\n def __init__(self, group=None, target=None, name=None,\n args=(), kwargs=None, *, daemon=None):\n \"\"\"This constructor should always be called with keyword arguments. Arguments are:\n\n *group* should be None; reserved for future extension when a ThreadGroup\n class is implemented.\n\n *target* is the callable object to be invoked by the run()\n method. Defaults to None, meaning nothing is called.\n\n *name* is the thread name. By default, a unique name is constructed of\n the form \"Thread-N\" where N is a small decimal number.\n\n *args* is the argument tuple for the target invocation. Defaults to ().\n\n *kwargs* is a dictionary of keyword arguments for the target\n invocation. Defaults to {}.\n\n If a subclass overrides the constructor, it must make sure to invoke\n the base class constructor (Thread.__init__()) before doing anything\n else to the thread.\n\n \"\"\"\n assert group is None, \"group argument must be None for now\"\n if kwargs is None:\n kwargs = {}\n self._target = target\n self._name = str(name or _newname())\n self._args = args\n self._kwargs = kwargs\n if daemon is not None:\n self._daemonic = daemon\n else:\n self._daemonic = current_thread().daemon\n self._ident = None\n self._tstate_lock = None\n self._started = Event()\n self._is_stopped = False\n self._initialized = True\n # sys.stderr is not stored in the class like\n # sys.exc_info since it can be changed between instances\n self._stderr = _sys.stderr\n # For debugging and _after_fork()\n _dangling.add(self)\n\n def _reset_internal_locks(self, is_alive):\n # private! Called by _after_fork() to reset our internal locks as\n # they may be in an invalid state leading to a deadlock or crash.\n self._started._reset_internal_locks()\n if is_alive:\n self._set_tstate_lock()\n else:\n # The thread isn't alive after fork: it doesn't have a tstate\n # anymore.\n self._is_stopped = True\n self._tstate_lock = None\n\n def __repr__(self):\n assert self._initialized, \"Thread.__init__() was not called\"\n status = \"initial\"\n if self._started.is_set():\n status = \"started\"\n self.is_alive() # easy way to get ._is_stopped set when appropriate\n if self._is_stopped:\n status = \"stopped\"\n if self._daemonic:\n status += \" daemon\"\n if self._ident is not None:\n status += \" %s\" % self._ident\n return \"<%s(%s, %s)>\" % (self.__class__.__name__, self._name, status)\n\n def start(self):\n \"\"\"Start the thread's activity.\n\n It must be called at most once per thread object. It arranges for the\n object's run() method to be invoked in a separate thread of control.\n\n This method will raise a RuntimeError if called more than once on the\n same thread object.\n\n \"\"\"\n if not self._initialized:\n raise RuntimeError(\"thread.__init__() not called\")\n\n if self._started.is_set():\n raise RuntimeError(\"threads can only be started once\")\n with _active_limbo_lock:\n _limbo[self] = self\n try:\n _start_new_thread(self._bootstrap, ())\n except Exception:\n with _active_limbo_lock:\n del _limbo[self]\n raise\n self._started.wait()\n\n def run(self):\n \"\"\"Method representing the thread's activity.\n\n You may override this method in a subclass. The standard run() method\n invokes the callable object passed to the object's constructor as the\n target argument, if any, with sequential and keyword arguments taken\n from the args and kwargs arguments, respectively.\n\n \"\"\"\n try:\n if self._target:\n self._target(*self._args, **self._kwargs)\n finally:\n # Avoid a refcycle if the thread is running a function with\n # an argument that has a member that points to the thread.\n del self._target, self._args, self._kwargs\n\n def _bootstrap(self):\n # Wrapper around the real bootstrap code that ignores\n # exceptions during interpreter cleanup. Those typically\n # happen when a daemon thread wakes up at an unfortunate\n # moment, finds the world around it destroyed, and raises some\n # random exception *** while trying to report the exception in\n # _bootstrap_inner() below ***. Those random exceptions\n # don't help anybody, and they confuse users, so we suppress\n # them. We suppress them only when it appears that the world\n # indeed has already been destroyed, so that exceptions in\n # _bootstrap_inner() during normal business hours are properly\n # reported. Also, we only suppress them for daemonic threads;\n # if a non-daemonic encounters this, something else is wrong.\n try:\n self._bootstrap_inner()\n except:\n if self._daemonic and _sys is None:\n return\n raise\n\n def _set_ident(self):\n self._ident = get_ident()\n\n def _set_tstate_lock(self):\n \"\"\"\n Set a lock object which will be released by the interpreter when\n the underlying thread state (see pystate.h) gets deleted.\n \"\"\"\n self._tstate_lock = _set_sentinel()\n self._tstate_lock.acquire()\n\n def _bootstrap_inner(self):\n try:\n self._set_ident()\n self._set_tstate_lock()\n self._started.set()\n with _active_limbo_lock:\n _active[self._ident] = self\n del _limbo[self]\n\n if _trace_hook:\n _sys.settrace(_trace_hook)\n if _profile_hook:\n _sys.setprofile(_profile_hook)\n\n try:\n self.run()\n except SystemExit:\n pass\n except:\n # If sys.stderr is no more (most likely from interpreter\n # shutdown) use self._stderr. Otherwise still use sys (as in\n # _sys) in case sys.stderr was redefined since the creation of\n # self.\n if _sys and _sys.stderr is not None:\n print(\"Exception in thread %s:\\n%s\" %\n (self.name, _format_exc()), file=_sys.stderr)\n elif self._stderr is not None:\n # Do the best job possible w/o a huge amt. of code to\n # approximate a traceback (code ideas from\n # Lib/traceback.py)\n exc_type, exc_value, exc_tb = self._exc_info()\n try:\n print((\n \"Exception in thread \" + self.name +\n \" (most likely raised during interpreter shutdown):\"), file=self._stderr)\n print((\n \"Traceback (most recent call last):\"), file=self._stderr)\n while exc_tb:\n print((\n ' File \"%s\", line %s, in %s' %\n (exc_tb.tb_frame.f_code.co_filename,\n exc_tb.tb_lineno,\n exc_tb.tb_frame.f_code.co_name)), file=self._stderr)\n exc_tb = exc_tb.tb_next\n print((\"%s: %s\" % (exc_type, exc_value)), file=self._stderr)\n # Make sure that exc_tb gets deleted since it is a memory\n # hog; deleting everything else is just for thoroughness\n finally:\n del exc_type, exc_value, exc_tb\n finally:\n # Prevent a race in\n # test_threading.test_no_refcycle_through_target when\n # the exception keeps the target alive past when we\n # assert that it's dead.\n #XXX self._exc_clear()\n pass\n finally:\n with _active_limbo_lock:\n try:\n # We don't call self._delete() because it also\n # grabs _active_limbo_lock.\n del _active[get_ident()]\n except:\n pass\n\n def _stop(self):\n # After calling ._stop(), .is_alive() returns False and .join() returns\n # immediately. ._tstate_lock must be released before calling ._stop().\n #\n # Normal case: C code at the end of the thread's life\n # (release_sentinel in _threadmodule.c) releases ._tstate_lock, and\n # that's detected by our ._wait_for_tstate_lock(), called by .join()\n # and .is_alive(). Any number of threads _may_ call ._stop()\n # simultaneously (for example, if multiple threads are blocked in\n # .join() calls), and they're not serialized. That's harmless -\n # they'll just make redundant rebindings of ._is_stopped and\n # ._tstate_lock. Obscure: we rebind ._tstate_lock last so that the\n # \"assert self._is_stopped\" in ._wait_for_tstate_lock() always works\n # (the assert is executed only if ._tstate_lock is None).\n #\n # Special case: _main_thread releases ._tstate_lock via this\n # module's _shutdown() function.\n lock = self._tstate_lock\n if lock is not None:\n assert not lock.locked()\n self._is_stopped = True\n self._tstate_lock = None\n\n def _delete(self):\n \"Remove current thread from the dict of currently running threads.\"\n\n # Notes about running with _dummy_thread:\n #\n # Must take care to not raise an exception if _dummy_thread is being\n # used (and thus this module is being used as an instance of\n # dummy_threading). _dummy_thread.get_ident() always returns -1 since\n # there is only one thread if _dummy_thread is being used. Thus\n # len(_active) is always <= 1 here, and any Thread instance created\n # overwrites the (if any) thread currently registered in _active.\n #\n # An instance of _MainThread is always created by 'threading'. This\n # gets overwritten the instant an instance of Thread is created; both\n # threads return -1 from _dummy_thread.get_ident() and thus have the\n # same key in the dict. So when the _MainThread instance created by\n # 'threading' tries to clean itself up when atexit calls this method\n # it gets a KeyError if another Thread instance was created.\n #\n # This all means that KeyError from trying to delete something from\n # _active if dummy_threading is being used is a red herring. But\n # since it isn't if dummy_threading is *not* being used then don't\n # hide the exception.\n\n try:\n with _active_limbo_lock:\n del _active[get_ident()]\n # There must not be any python code between the previous line\n # and after the lock is released. Otherwise a tracing function\n # could try to acquire the lock again in the same thread, (in\n # current_thread()), and would block.\n except KeyError:\n if 'dummy_threading' not in _sys.modules:\n raise\n\n def join(self, timeout=None):\n \"\"\"Wait until the thread terminates.\n\n This blocks the calling thread until the thread whose join() method is\n called terminates -- either normally or through an unhandled exception\n or until the optional timeout occurs.\n\n When the timeout argument is present and not None, it should be a\n floating point number specifying a timeout for the operation in seconds\n (or fractions thereof). As join() always returns None, you must call\n isAlive() after join() to decide whether a timeout happened -- if the\n thread is still alive, the join() call timed out.\n\n When the timeout argument is not present or None, the operation will\n block until the thread terminates.\n\n A thread can be join()ed many times.\n\n join() raises a RuntimeError if an attempt is made to join the current\n thread as that would cause a deadlock. It is also an error to join() a\n thread before it has been started and attempts to do so raises the same\n exception.\n\n \"\"\"\n if not self._initialized:\n raise RuntimeError(\"Thread.__init__() not called\")\n if not self._started.is_set():\n raise RuntimeError(\"cannot join thread before it is started\")\n if self is current_thread():\n raise RuntimeError(\"cannot join current thread\")\n\n if timeout is None:\n self._wait_for_tstate_lock()\n else:\n # the behavior of a negative timeout isn't documented, but\n # historically .join(timeout=x) for x<0 has acted as if timeout=0\n self._wait_for_tstate_lock(timeout=max(timeout, 0))\n\n def _wait_for_tstate_lock(self, block=True, timeout=-1):\n # Issue #18808: wait for the thread state to be gone.\n # At the end of the thread's life, after all knowledge of the thread\n # is removed from C data structures, C code releases our _tstate_lock.\n # This method passes its arguments to _tstate_lock.acquire().\n # If the lock is acquired, the C code is done, and self._stop() is\n # called. That sets ._is_stopped to True, and ._tstate_lock to None.\n lock = self._tstate_lock\n if lock is None: # already determined that the C code is done\n assert self._is_stopped\n elif lock.acquire(block, timeout):\n lock.release()\n self._stop()\n\n @property\n def name(self):\n \"\"\"A string used for identification purposes only.\n\n It has no semantics. Multiple threads may be given the same name. The\n initial name is set by the constructor.\n\n \"\"\"\n assert self._initialized, \"Thread.__init__() not called\"\n return self._name\n\n @name.setter\n def name(self, name):\n assert self._initialized, \"Thread.__init__() not called\"\n self._name = str(name)\n\n @property\n def ident(self):\n \"\"\"Thread identifier of this thread or None if it has not been started.\n\n This is a nonzero integer. See the get_ident() function. Thread\n identifiers may be recycled when a thread exits and another thread is\n created. The identifier is available even after the thread has exited.\n\n \"\"\"\n assert self._initialized, \"Thread.__init__() not called\"\n return self._ident\n\n def is_alive(self):\n \"\"\"Return whether the thread is alive.\n\n This method returns True just before the run() method starts until just\n after the run() method terminates. The module function enumerate()\n returns a list of all alive threads.\n\n \"\"\"\n assert self._initialized, \"Thread.__init__() not called\"\n if self._is_stopped or not self._started.is_set():\n return False\n self._wait_for_tstate_lock(False)\n return not self._is_stopped\n\n isAlive = is_alive\n\n @property\n def daemon(self):\n \"\"\"A boolean value indicating whether this thread is a daemon thread.\n\n This must be set before start() is called, otherwise RuntimeError is\n raised. Its initial value is inherited from the creating thread; the\n main thread is not a daemon thread and therefore all threads created in\n the main thread default to daemon = False.\n\n The entire Python program exits when no alive non-daemon threads are\n left.\n\n \"\"\"\n assert self._initialized, \"Thread.__init__() not called\"\n return self._daemonic\n\n @daemon.setter\n def daemon(self, daemonic):\n if not self._initialized:\n raise RuntimeError(\"Thread.__init__() not called\")\n if self._started.is_set():\n raise RuntimeError(\"cannot set daemon status of active thread\")\n self._daemonic = daemonic\n\n def isDaemon(self):\n return self.daemon\n\n def setDaemon(self, daemonic):\n self.daemon = daemonic\n\n def getName(self):\n return self.name\n\n def setName(self, name):\n self.name = name\n\n# The timer class was contributed by Itamar Shtull-Trauring\n\nclass Timer(Thread):\n \"\"\"Call a function after a specified number of seconds:\n\n t = Timer(30.0, f, args=None, kwargs=None)\n t.start()\n t.cancel() # stop the timer's action if it's still waiting\n\n \"\"\"\n\n def __init__(self, interval, function, args=None, kwargs=None):\n Thread.__init__(self)\n self.interval = interval\n self.function = function\n self.args = args if args is not None else []\n self.kwargs = kwargs if kwargs is not None else {}\n self.finished = Event()\n\n def cancel(self):\n \"\"\"Stop the timer if it hasn't finished yet.\"\"\"\n self.finished.set()\n\n def run(self):\n self.finished.wait(self.interval)\n if not self.finished.is_set():\n self.function(*self.args, **self.kwargs)\n self.finished.set()\n\n\n# Special thread class to represent the main thread\n\nclass _MainThread(Thread):\n\n def __init__(self):\n Thread.__init__(self, name=\"MainThread\", daemon=False)\n self._set_tstate_lock()\n self._started.set()\n self._set_ident()\n with _active_limbo_lock:\n _active[self._ident] = self\n\n\n# Dummy thread class to represent threads not started here.\n# These aren't garbage collected when they die, nor can they be waited for.\n# If they invoke anything in threading.py that calls current_thread(), they\n# leave an entry in the _active dict forever after.\n# Their purpose is to return *something* from current_thread().\n# They are marked as daemon threads so we won't wait for them\n# when we exit (conform previous semantics).\n\nclass _DummyThread(Thread):\n\n def __init__(self):\n Thread.__init__(self, name=_newname(\"Dummy-%d\"), daemon=True)\n\n self._started.set()\n self._set_ident()\n with _active_limbo_lock:\n _active[self._ident] = self\n\n def _stop(self):\n pass\n\n def is_alive(self):\n assert not self._is_stopped and self._started.is_set()\n return True\n\n def join(self, timeout=None):\n assert False, \"cannot join a dummy thread\"\n\n\n# Global API functions\n\ndef current_thread():\n \"\"\"Return the current Thread object, corresponding to the caller's thread of control.\n\n If the caller's thread of control was not created through the threading\n module, a dummy thread object with limited functionality is returned.\n\n \"\"\"\n try:\n return _active[get_ident()]\n except KeyError:\n return _DummyThread()\n\ncurrentThread = current_thread\n\ndef active_count():\n \"\"\"Return the number of Thread objects currently alive.\n\n The returned count is equal to the length of the list returned by\n enumerate().\n\n \"\"\"\n with _active_limbo_lock:\n return len(_active) + len(_limbo)\n\nactiveCount = active_count\n\ndef _enumerate():\n # Same as enumerate(), but without the lock. Internal use only.\n return list(_active.values()) + list(_limbo.values())\n\ndef enumerate():\n \"\"\"Return a list of all Thread objects currently alive.\n\n The list includes daemonic threads, dummy thread objects created by\n current_thread(), and the main thread. It excludes terminated threads and\n threads that have not yet been started.\n\n \"\"\"\n with _active_limbo_lock:\n return list(_active.values()) + list(_limbo.values())\n\nfrom _thread import stack_size\n\n# Create the main thread object,\n# and make it available for the interpreter\n# (Py_Main) as threading._shutdown.\n\n_main_thread = _MainThread()\n\ndef _shutdown():\n # Obscure: other threads may be waiting to join _main_thread. That's\n # dubious, but some code does it. We can't wait for C code to release\n # the main thread's tstate_lock - that won't happen until the interpreter\n # is nearly dead. So we release it here. Note that just calling _stop()\n # isn't enough: other threads may already be waiting on _tstate_lock.\n tlock = _main_thread._tstate_lock\n # The main thread isn't finished yet, so its thread state lock can't have\n # been released.\n assert tlock is not None\n assert tlock.locked()\n tlock.release()\n _main_thread._stop()\n t = _pickSomeNonDaemonThread()\n while t:\n t.join()\n t = _pickSomeNonDaemonThread()\n\ndef _pickSomeNonDaemonThread():\n for t in enumerate():\n if not t.daemon and t.is_alive():\n return t\n return None\n\ndef main_thread():\n \"\"\"Return the main thread object.\n\n In normal conditions, the main thread is the thread from which the\n Python interpreter was started.\n \"\"\"\n return _main_thread\n\n# get thread-local implementation, either from the thread\n# module, or from the python fallback\n\ntry:\n from _thread import _local as local\nexcept ImportError:\n from _threading_local import local\n\n\ndef _after_fork():\n # This function is called by Python/ceval.c:PyEval_ReInitThreads which\n # is called from PyOS_AfterFork. Here we cleanup threading module state\n # that should not exist after a fork.\n\n # Reset _active_limbo_lock, in case we forked while the lock was held\n # by another (non-forked) thread. http://bugs.python.org/issue874900\n global _active_limbo_lock, _main_thread\n _active_limbo_lock = _allocate_lock()\n\n # fork() only copied the current thread; clear references to others.\n new_active = {}\n current = current_thread()\n _main_thread = current\n with _active_limbo_lock:\n # Dangling thread instances must still have their locks reset,\n # because someone may join() them.\n threads = set(_enumerate())\n threads.update(_dangling)\n for thread in threads:\n # Any lock/condition variable may be currently locked or in an\n # invalid state, so we reinitialize them.\n if thread is current:\n # There is only one active thread. We reset the ident to\n # its new value since it can have changed.\n thread._reset_internal_locks(True)\n ident = get_ident()\n thread._ident = ident\n new_active[ident] = thread\n else:\n # All the others are already stopped.\n thread._reset_internal_locks(False)\n thread._stop()\n\n _limbo.clear()\n _active.clear()\n _active.update(new_active)\n assert len(_active) == 1\n",1357],"/usr/lib/python3.6/_weakrefset.py":["# Access WeakSet through the weakref module.\n# This code is separated-out because it is needed\n# by abc.py to load everything else at startup.\n\nfrom _weakref import ref\n\n__all__ = ['WeakSet']\n\n\nclass _IterationGuard:\n # This context manager registers itself in the current iterators of the\n # weak container, such as to delay all removals until the context manager\n # exits.\n # This technique should be relatively thread-safe (since sets are).\n\n def __init__(self, weakcontainer):\n # Don't create cycles\n self.weakcontainer = ref(weakcontainer)\n\n def __enter__(self):\n w = self.weakcontainer()\n if w is not None:\n w._iterating.add(self)\n return self\n\n def __exit__(self, e, t, b):\n w = self.weakcontainer()\n if w is not None:\n s = w._iterating\n s.remove(self)\n if not s:\n w._commit_removals()\n\n\nclass WeakSet:\n def __init__(self, data=None):\n self.data = set()\n def _remove(item, selfref=ref(self)):\n self = selfref()\n if self is not None:\n if self._iterating:\n self._pending_removals.append(item)\n else:\n self.data.discard(item)\n self._remove = _remove\n # A list of keys to be removed\n self._pending_removals = []\n self._iterating = set()\n if data is not None:\n self.update(data)\n\n def _commit_removals(self):\n l = self._pending_removals\n discard = self.data.discard\n while l:\n discard(l.pop())\n\n def __iter__(self):\n with _IterationGuard(self):\n for itemref in self.data:\n item = itemref()\n if item is not None:\n # Caveat: the iterator will keep a strong reference to\n # `item` until it is resumed or closed.\n yield item\n\n def __len__(self):\n return len(self.data) - len(self._pending_removals)\n\n def __contains__(self, item):\n try:\n wr = ref(item)\n except TypeError:\n return False\n return wr in self.data\n\n def __reduce__(self):\n return (self.__class__, (list(self),),\n getattr(self, '__dict__', None))\n\n def add(self, item):\n if self._pending_removals:\n self._commit_removals()\n self.data.add(ref(item, self._remove))\n\n def clear(self):\n if self._pending_removals:\n self._commit_removals()\n self.data.clear()\n\n def copy(self):\n return self.__class__(self)\n\n def pop(self):\n if self._pending_removals:\n self._commit_removals()\n while True:\n try:\n itemref = self.data.pop()\n except KeyError:\n raise KeyError('pop from empty WeakSet')\n item = itemref()\n if item is not None:\n return item\n\n def remove(self, item):\n if self._pending_removals:\n self._commit_removals()\n self.data.remove(ref(item))\n\n def discard(self, item):\n if self._pending_removals:\n self._commit_removals()\n self.data.discard(ref(item))\n\n def update(self, other):\n if self._pending_removals:\n self._commit_removals()\n for element in other:\n self.add(element)\n\n def __ior__(self, other):\n self.update(other)\n return self\n\n def difference(self, other):\n newset = self.copy()\n newset.difference_update(other)\n return newset\n __sub__ = difference\n\n def difference_update(self, other):\n self.__isub__(other)\n def __isub__(self, other):\n if self._pending_removals:\n self._commit_removals()\n if self is other:\n self.data.clear()\n else:\n self.data.difference_update(ref(item) for item in other)\n return self\n\n def intersection(self, other):\n return self.__class__(item for item in other if item in self)\n __and__ = intersection\n\n def intersection_update(self, other):\n self.__iand__(other)\n def __iand__(self, other):\n if self._pending_removals:\n self._commit_removals()\n self.data.intersection_update(ref(item) for item in other)\n return self\n\n def issubset(self, other):\n return self.data.issubset(ref(item) for item in other)\n __le__ = issubset\n\n def __lt__(self, other):\n return self.data < set(ref(item) for item in other)\n\n def issuperset(self, other):\n return self.data.issuperset(ref(item) for item in other)\n __ge__ = issuperset\n\n def __gt__(self, other):\n return self.data > set(ref(item) for item in other)\n\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return NotImplemented\n return self.data == set(ref(item) for item in other)\n\n def symmetric_difference(self, other):\n newset = self.copy()\n newset.symmetric_difference_update(other)\n return newset\n __xor__ = symmetric_difference\n\n def symmetric_difference_update(self, other):\n self.__ixor__(other)\n def __ixor__(self, other):\n if self._pending_removals:\n self._commit_removals()\n if self is other:\n self.data.clear()\n else:\n self.data.symmetric_difference_update(ref(item, self._remove) for item in other)\n return self\n\n def union(self, other):\n return self.__class__(e for s in (self, other) for e in s)\n __or__ = union\n\n def isdisjoint(self, other):\n return len(self.intersection(other)) == 0\n",196],"example/src/multithread.py":["import os\nimport threading\nimport time\nfrom viztracer import VizTracer\n\n\ndef fib(n):\n if n < 2:\n return 1\n time.sleep(0.0000001)\n return fib(n - 1) + fib(n - 2)\n\n\nclass MyThread(threading.Thread):\n def run(self):\n fib(7)\n\n\nwith VizTracer(output_file=os.path.join(os.path.dirname(__file__), \"../\", \"json/multithread.json\"),\n file_info=True) as _:\n thread1 = MyThread()\n thread2 = MyThread()\n thread3 = MyThread()\n thread4 = MyThread()\n\n thread1.start()\n thread2.start()\n thread3.start()\n thread4.start()\n\n threads = [thread1, thread2, thread3, thread4]\n\n for thread in threads:\n thread.join()\n",34]},"functions":{"_newname (/usr/lib/python3.6/threading.py:727)":["/usr/lib/python3.6/threading.py",727],"current_thread (/usr/lib/python3.6/threading.py:1230)":["/usr/lib/python3.6/threading.py",1230],"daemon (/usr/lib/python3.6/threading.py:1120)":["/usr/lib/python3.6/threading.py",1120],"__init__ (/usr/lib/python3.6/threading.py:215)":["/usr/lib/python3.6/threading.py",215],"__init__ (/usr/lib/python3.6/threading.py:498)":["/usr/lib/python3.6/threading.py",498],"add (/usr/lib/python3.6/_weakrefset.py:81)":["/usr/lib/python3.6/_weakrefset.py",81],"__init__ (/usr/lib/python3.6/threading.py:757)":["/usr/lib/python3.6/threading.py",757],"is_set (/usr/lib/python3.6/threading.py:506)":["/usr/lib/python3.6/threading.py",506],"__enter__ (/usr/lib/python3.6/threading.py:239)":["/usr/lib/python3.6/threading.py",239],"_is_owned (/usr/lib/python3.6/threading.py:254)":["/usr/lib/python3.6/threading.py",254],"_release_save (/usr/lib/python3.6/threading.py:248)":["/usr/lib/python3.6/threading.py",248],"_acquire_restore (/usr/lib/python3.6/threading.py:251)":["/usr/lib/python3.6/threading.py",251],"wait 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"current_thread (/usr/lib/python3.13/threading.py:1427)", "cat": "FEE"}, {"pid": 30322, "tid": 30322, "ts": 45578181768.682, "ph": "X", "dur": 0.2736689703813838, "name": "_thread._ThreadHandle.join", "cat": "FEE"}, {"pid": 30322, "tid": 30322, "ts": 45578181768.084, "ph": "X", "dur": 0.9346745346521318, "name": "Thread.join (/usr/lib/python3.13/threading.py:1056)", "cat": "FEE"}], "viztracer_metadata": {"version": "0.17.1", "overflow": false, "baseTimeNanoseconds": 1731943920418642201}, "file_info": {"files": {"/usr/lib/python3.13/threading.py": ["\"\"\"Thread module emulating a subset of Java's threading model.\"\"\"\n\nimport os as _os\nimport sys as _sys\nimport _thread\nimport warnings\n\nfrom time import monotonic as _time\nfrom _weakrefset import WeakSet\nfrom itertools import count as _count\ntry:\n from _collections import deque as _deque\nexcept ImportError:\n from collections import deque as _deque\n\n# Note regarding PEP 8 compliant names\n# This threading model was originally inspired by Java, and inherited\n# the convention of camelCase function and method names from that\n# language. Those original names are not in any imminent danger of\n# being deprecated (even for Py3k),so this module provides them as an\n# alias for the PEP 8 compliant names\n# Note that using the new PEP 8 compliant names facilitates substitution\n# with the multiprocessing module, which doesn't provide the old\n# Java inspired names.\n\n__all__ = ['get_ident', 'active_count', 'Condition', 'current_thread',\n 'enumerate', 'main_thread', 'TIMEOUT_MAX',\n 'Event', 'Lock', 'RLock', 'Semaphore', 'BoundedSemaphore', 'Thread',\n 'Barrier', 'BrokenBarrierError', 'Timer', 'ThreadError',\n 'setprofile', 'settrace', 'local', 'stack_size',\n 'excepthook', 'ExceptHookArgs', 'gettrace', 'getprofile',\n 'setprofile_all_threads','settrace_all_threads']\n\n# Rename some stuff so \"from threading import *\" is safe\n_start_joinable_thread = _thread.start_joinable_thread\n_daemon_threads_allowed = _thread.daemon_threads_allowed\n_allocate_lock = _thread.allocate_lock\n_LockType = _thread.LockType\n_thread_shutdown = _thread._shutdown\n_make_thread_handle = _thread._make_thread_handle\n_ThreadHandle = _thread._ThreadHandle\nget_ident = _thread.get_ident\n_get_main_thread_ident = _thread._get_main_thread_ident\n_is_main_interpreter = _thread._is_main_interpreter\ntry:\n get_native_id = _thread.get_native_id\n _HAVE_THREAD_NATIVE_ID = True\n __all__.append('get_native_id')\nexcept AttributeError:\n _HAVE_THREAD_NATIVE_ID = False\nThreadError = _thread.error\ntry:\n _CRLock = _thread.RLock\nexcept AttributeError:\n _CRLock = None\nTIMEOUT_MAX = _thread.TIMEOUT_MAX\ndel _thread\n\n# get thread-local implementation, either from the thread\n# module, or from the python fallback\n\ntry:\n from _thread import _local as local\nexcept ImportError:\n from _threading_local import local\n\n# Support for profile and trace hooks\n\n_profile_hook = None\n_trace_hook = None\n\ndef setprofile(func):\n \"\"\"Set a profile function for all threads started from the threading module.\n\n The func will be passed to sys.setprofile() for each thread, before its\n run() method is called.\n \"\"\"\n global _profile_hook\n _profile_hook = func\n\ndef setprofile_all_threads(func):\n \"\"\"Set a profile function for all threads started from the threading module\n and all Python threads that are currently executing.\n\n The func will be passed to sys.setprofile() for each thread, before its\n run() method is called.\n \"\"\"\n setprofile(func)\n _sys._setprofileallthreads(func)\n\ndef getprofile():\n \"\"\"Get the profiler function as set by threading.setprofile().\"\"\"\n return _profile_hook\n\ndef settrace(func):\n \"\"\"Set a trace function for all threads started from the threading module.\n\n The func will be passed to sys.settrace() for each thread, before its run()\n method is called.\n \"\"\"\n global _trace_hook\n _trace_hook = func\n\ndef settrace_all_threads(func):\n \"\"\"Set a trace function for all threads started from the threading module\n and all Python threads that are currently executing.\n\n The func will be passed to sys.settrace() for each thread, before its run()\n method is called.\n \"\"\"\n settrace(func)\n _sys._settraceallthreads(func)\n\ndef gettrace():\n \"\"\"Get the trace function as set by threading.settrace().\"\"\"\n return _trace_hook\n\n# Synchronization classes\n\nLock = _LockType\n\ndef RLock(*args, **kwargs):\n \"\"\"Factory function that returns a new reentrant lock.\n\n A reentrant lock must be released by the thread that acquired it. Once a\n thread has acquired a reentrant lock, the same thread may acquire it again\n without blocking; the thread must release it once for each time it has\n acquired it.\n\n \"\"\"\n if args or kwargs:\n warnings.warn(\n 'Passing arguments to RLock is deprecated and will be removed in 3.15',\n DeprecationWarning,\n stacklevel=2,\n )\n if _CRLock is None:\n return _PyRLock(*args, **kwargs)\n return _CRLock(*args, **kwargs)\n\nclass _RLock:\n \"\"\"This class implements reentrant lock objects.\n\n A reentrant lock must be released by the thread that acquired it. Once a\n thread has acquired a reentrant lock, the same thread may acquire it\n again without blocking; the thread must release it once for each time it\n has acquired it.\n\n \"\"\"\n\n def __init__(self):\n self._block = _allocate_lock()\n self._owner = None\n self._count = 0\n\n def __repr__(self):\n owner = self._owner\n try:\n owner = _active[owner].name\n except KeyError:\n pass\n return \"<%s %s.%s object owner=%r count=%d at %s>\" % (\n \"locked\" if self._block.locked() else \"unlocked\",\n self.__class__.__module__,\n self.__class__.__qualname__,\n owner,\n self._count,\n hex(id(self))\n )\n\n def _at_fork_reinit(self):\n self._block._at_fork_reinit()\n self._owner = None\n self._count = 0\n\n def acquire(self, blocking=True, timeout=-1):\n \"\"\"Acquire a lock, blocking or non-blocking.\n\n When invoked without arguments: if this thread already owns the lock,\n increment the recursion level by one, and return immediately. Otherwise,\n if another thread owns the lock, block until the lock is unlocked. Once\n the lock is unlocked (not owned by any thread), then grab ownership, set\n the recursion level to one, and return. If more than one thread is\n blocked waiting until the lock is unlocked, only one at a time will be\n able to grab ownership of the lock. There is no return value in this\n case.\n\n When invoked with the blocking argument set to true, do the same thing\n as when called without arguments, and return true.\n\n When invoked with the blocking argument set to false, do not block. If a\n call without an argument would block, return false immediately;\n otherwise, do the same thing as when called without arguments, and\n return true.\n\n When invoked with the floating-point timeout argument set to a positive\n value, block for at most the number of seconds specified by timeout\n and as long as the lock cannot be acquired. Return true if the lock has\n been acquired, false if the timeout has elapsed.\n\n \"\"\"\n me = get_ident()\n if self._owner == me:\n self._count += 1\n return 1\n rc = self._block.acquire(blocking, timeout)\n if rc:\n self._owner = me\n self._count = 1\n return rc\n\n __enter__ = acquire\n\n def release(self):\n \"\"\"Release a lock, decrementing the recursion level.\n\n If after the decrement it is zero, reset the lock to unlocked (not owned\n by any thread), and if any other threads are blocked waiting for the\n lock to become unlocked, allow exactly one of them to proceed. If after\n the decrement the recursion level is still nonzero, the lock remains\n locked and owned by the calling thread.\n\n Only call this method when the calling thread owns the lock. A\n RuntimeError is raised if this method is called when the lock is\n unlocked.\n\n There is no return value.\n\n \"\"\"\n if self._owner != get_ident():\n raise RuntimeError(\"cannot release un-acquired lock\")\n self._count = count = self._count - 1\n if not count:\n self._owner = None\n self._block.release()\n\n def __exit__(self, t, v, tb):\n self.release()\n\n # Internal methods used by condition variables\n\n def _acquire_restore(self, state):\n self._block.acquire()\n self._count, self._owner = state\n\n def _release_save(self):\n if self._count == 0:\n raise RuntimeError(\"cannot release un-acquired lock\")\n count = self._count\n self._count = 0\n owner = self._owner\n self._owner = None\n self._block.release()\n return (count, owner)\n\n def _is_owned(self):\n return self._owner == get_ident()\n\n # Internal method used for reentrancy checks\n\n def _recursion_count(self):\n if self._owner != get_ident():\n return 0\n return self._count\n\n_PyRLock = _RLock\n\n\nclass Condition:\n \"\"\"Class that implements a condition variable.\n\n A condition variable allows one or more threads to wait until they are\n notified by another thread.\n\n If the lock argument is given and not None, it must be a Lock or RLock\n object, and it is used as the underlying lock. Otherwise, a new RLock object\n is created and used as the underlying lock.\n\n \"\"\"\n\n def __init__(self, lock=None):\n if lock is None:\n lock = RLock()\n self._lock = lock\n # Export the lock's acquire() and release() methods\n self.acquire = lock.acquire\n self.release = lock.release\n # If the lock defines _release_save() and/or _acquire_restore(),\n # these override the default implementations (which just call\n # release() and acquire() on the lock). Ditto for _is_owned().\n if hasattr(lock, '_release_save'):\n self._release_save = lock._release_save\n if hasattr(lock, '_acquire_restore'):\n self._acquire_restore = lock._acquire_restore\n if hasattr(lock, '_is_owned'):\n self._is_owned = lock._is_owned\n self._waiters = _deque()\n\n def _at_fork_reinit(self):\n self._lock._at_fork_reinit()\n self._waiters.clear()\n\n def __enter__(self):\n return self._lock.__enter__()\n\n def __exit__(self, *args):\n return self._lock.__exit__(*args)\n\n def __repr__(self):\n return \"\" % (self._lock, len(self._waiters))\n\n def _release_save(self):\n self._lock.release() # No state to save\n\n def _acquire_restore(self, x):\n self._lock.acquire() # Ignore saved state\n\n def _is_owned(self):\n # Return True if lock is owned by current_thread.\n # This method is called only if _lock doesn't have _is_owned().\n if self._lock.acquire(False):\n self._lock.release()\n return False\n else:\n return True\n\n def wait(self, timeout=None):\n \"\"\"Wait until notified or until a timeout occurs.\n\n If the calling thread has not acquired the lock when this method is\n called, a RuntimeError is raised.\n\n This method releases the underlying lock, and then blocks until it is\n awakened by a notify() or notify_all() call for the same condition\n variable in another thread, or until the optional timeout occurs. Once\n awakened or timed out, it re-acquires the lock and returns.\n\n When the timeout argument is present and not None, it should be a\n floating-point number specifying a timeout for the operation in seconds\n (or fractions thereof).\n\n When the underlying lock is an RLock, it is not released using its\n release() method, since this may not actually unlock the lock when it\n was acquired multiple times recursively. Instead, an internal interface\n of the RLock class is used, which really unlocks it even when it has\n been recursively acquired several times. Another internal interface is\n then used to restore the recursion level when the lock is reacquired.\n\n \"\"\"\n if not self._is_owned():\n raise RuntimeError(\"cannot wait on un-acquired lock\")\n waiter = _allocate_lock()\n waiter.acquire()\n self._waiters.append(waiter)\n saved_state = self._release_save()\n gotit = False\n try: # restore state no matter what (e.g., KeyboardInterrupt)\n if timeout is None:\n waiter.acquire()\n gotit = True\n else:\n if timeout > 0:\n gotit = waiter.acquire(True, timeout)\n else:\n gotit = waiter.acquire(False)\n return gotit\n finally:\n self._acquire_restore(saved_state)\n if not gotit:\n try:\n self._waiters.remove(waiter)\n except ValueError:\n pass\n\n def wait_for(self, predicate, timeout=None):\n \"\"\"Wait until a condition evaluates to True.\n\n predicate should be a callable which result will be interpreted as a\n boolean value. A timeout may be provided giving the maximum time to\n wait.\n\n \"\"\"\n endtime = None\n waittime = timeout\n result = predicate()\n while not result:\n if waittime is not None:\n if endtime is None:\n endtime = _time() + waittime\n else:\n waittime = endtime - _time()\n if waittime <= 0:\n break\n self.wait(waittime)\n result = predicate()\n return result\n\n def notify(self, n=1):\n \"\"\"Wake up one or more threads waiting on this condition, if any.\n\n If the calling thread has not acquired the lock when this method is\n called, a RuntimeError is raised.\n\n This method wakes up at most n of the threads waiting for the condition\n variable; it is a no-op if no threads are waiting.\n\n \"\"\"\n if not self._is_owned():\n raise RuntimeError(\"cannot notify on un-acquired lock\")\n waiters = self._waiters\n while waiters and n > 0:\n waiter = waiters[0]\n try:\n waiter.release()\n except RuntimeError:\n # gh-92530: The previous call of notify() released the lock,\n # but was interrupted before removing it from the queue.\n # It can happen if a signal handler raises an exception,\n # like CTRL+C which raises KeyboardInterrupt.\n pass\n else:\n n -= 1\n try:\n waiters.remove(waiter)\n except ValueError:\n pass\n\n def notify_all(self):\n \"\"\"Wake up all threads waiting on this condition.\n\n If the calling thread has not acquired the lock when this method\n is called, a RuntimeError is raised.\n\n \"\"\"\n self.notify(len(self._waiters))\n\n def notifyAll(self):\n \"\"\"Wake up all threads waiting on this condition.\n\n This method is deprecated, use notify_all() instead.\n\n \"\"\"\n import warnings\n warnings.warn('notifyAll() is deprecated, use notify_all() instead',\n DeprecationWarning, stacklevel=2)\n self.notify_all()\n\n\nclass Semaphore:\n \"\"\"This class implements semaphore objects.\n\n Semaphores manage a counter representing the number of release() calls minus\n the number of acquire() calls, plus an initial value. The acquire() method\n blocks if necessary until it can return without making the counter\n negative. If not given, value defaults to 1.\n\n \"\"\"\n\n # After Tim Peters' semaphore class, but not quite the same (no maximum)\n\n def __init__(self, value=1):\n if value < 0:\n raise ValueError(\"semaphore initial value must be >= 0\")\n self._cond = Condition(Lock())\n self._value = value\n\n def __repr__(self):\n cls = self.__class__\n return (f\"<{cls.__module__}.{cls.__qualname__} at {id(self):#x}:\"\n f\" value={self._value}>\")\n\n def acquire(self, blocking=True, timeout=None):\n \"\"\"Acquire a semaphore, decrementing the internal counter by one.\n\n When invoked without arguments: if the internal counter is larger than\n zero on entry, decrement it by one and return immediately. If it is zero\n on entry, block, waiting until some other thread has called release() to\n make it larger than zero. This is done with proper interlocking so that\n if multiple acquire() calls are blocked, release() will wake exactly one\n of them up. The implementation may pick one at random, so the order in\n which blocked threads are awakened should not be relied on. There is no\n return value in this case.\n\n When invoked with blocking set to true, do the same thing as when called\n without arguments, and return true.\n\n When invoked with blocking set to false, do not block. If a call without\n an argument would block, return false immediately; otherwise, do the\n same thing as when called without arguments, and return true.\n\n When invoked with a timeout other than None, it will block for at\n most timeout seconds. If acquire does not complete successfully in\n that interval, return false. Return true otherwise.\n\n \"\"\"\n if not blocking and timeout is not None:\n raise ValueError(\"can't specify timeout for non-blocking acquire\")\n rc = False\n endtime = None\n with self._cond:\n while self._value == 0:\n if not blocking:\n break\n if timeout is not None:\n if endtime is None:\n endtime = _time() + timeout\n else:\n timeout = endtime - _time()\n if timeout <= 0:\n break\n self._cond.wait(timeout)\n else:\n self._value -= 1\n rc = True\n return rc\n\n __enter__ = acquire\n\n def release(self, n=1):\n \"\"\"Release a semaphore, incrementing the internal counter by one or more.\n\n When the counter is zero on entry and another thread is waiting for it\n to become larger than zero again, wake up that thread.\n\n \"\"\"\n if n < 1:\n raise ValueError('n must be one or more')\n with self._cond:\n self._value += n\n self._cond.notify(n)\n\n def __exit__(self, t, v, tb):\n self.release()\n\n\nclass BoundedSemaphore(Semaphore):\n \"\"\"Implements a bounded semaphore.\n\n A bounded semaphore checks to make sure its current value doesn't exceed its\n initial value. If it does, ValueError is raised. In most situations\n semaphores are used to guard resources with limited capacity.\n\n If the semaphore is released too many times it's a sign of a bug. If not\n given, value defaults to 1.\n\n Like regular semaphores, bounded semaphores manage a counter representing\n the number of release() calls minus the number of acquire() calls, plus an\n initial value. The acquire() method blocks if necessary until it can return\n without making the counter negative. If not given, value defaults to 1.\n\n \"\"\"\n\n def __init__(self, value=1):\n super().__init__(value)\n self._initial_value = value\n\n def __repr__(self):\n cls = self.__class__\n return (f\"<{cls.__module__}.{cls.__qualname__} at {id(self):#x}:\"\n f\" value={self._value}/{self._initial_value}>\")\n\n def release(self, n=1):\n \"\"\"Release a semaphore, incrementing the internal counter by one or more.\n\n When the counter is zero on entry and another thread is waiting for it\n to become larger than zero again, wake up that thread.\n\n If the number of releases exceeds the number of acquires,\n raise a ValueError.\n\n \"\"\"\n if n < 1:\n raise ValueError('n must be one or more')\n with self._cond:\n if self._value + n > self._initial_value:\n raise ValueError(\"Semaphore released too many times\")\n self._value += n\n self._cond.notify(n)\n\n\nclass Event:\n \"\"\"Class implementing event objects.\n\n Events manage a flag that can be set to true with the set() method and reset\n to false with the clear() method. The wait() method blocks until the flag is\n true. The flag is initially false.\n\n \"\"\"\n\n # After Tim Peters' event class (without is_posted())\n\n def __init__(self):\n self._cond = Condition(Lock())\n self._flag = False\n\n def __repr__(self):\n cls = self.__class__\n status = 'set' if self._flag else 'unset'\n return f\"<{cls.__module__}.{cls.__qualname__} at {id(self):#x}: {status}>\"\n\n def _at_fork_reinit(self):\n # Private method called by Thread._after_fork()\n self._cond._at_fork_reinit()\n\n def is_set(self):\n \"\"\"Return true if and only if the internal flag is true.\"\"\"\n return self._flag\n\n def isSet(self):\n \"\"\"Return true if and only if the internal flag is true.\n\n This method is deprecated, use is_set() instead.\n\n \"\"\"\n import warnings\n warnings.warn('isSet() is deprecated, use is_set() instead',\n DeprecationWarning, stacklevel=2)\n return self.is_set()\n\n def set(self):\n \"\"\"Set the internal flag to true.\n\n All threads waiting for it to become true are awakened. Threads\n that call wait() once the flag is true will not block at all.\n\n \"\"\"\n with self._cond:\n self._flag = True\n self._cond.notify_all()\n\n def clear(self):\n \"\"\"Reset the internal flag to false.\n\n Subsequently, threads calling wait() will block until set() is called to\n set the internal flag to true again.\n\n \"\"\"\n with self._cond:\n self._flag = False\n\n def wait(self, timeout=None):\n \"\"\"Block until the internal flag is true.\n\n If the internal flag is true on entry, return immediately. Otherwise,\n block until another thread calls set() to set the flag to true, or until\n the optional timeout occurs.\n\n When the timeout argument is present and not None, it should be a\n floating-point number specifying a timeout for the operation in seconds\n (or fractions thereof).\n\n This method returns the internal flag on exit, so it will always return\n True except if a timeout is given and the operation times out.\n\n \"\"\"\n with self._cond:\n signaled = self._flag\n if not signaled:\n signaled = self._cond.wait(timeout)\n return signaled\n\n\n# A barrier class. Inspired in part by the pthread_barrier_* api and\n# the CyclicBarrier class from Java. See\n# http://sourceware.org/pthreads-win32/manual/pthread_barrier_init.html and\n# http://java.sun.com/j2se/1.5.0/docs/api/java/util/concurrent/\n# CyclicBarrier.html\n# for information.\n# We maintain two main states, 'filling' and 'draining' enabling the barrier\n# to be cyclic. Threads are not allowed into it until it has fully drained\n# since the previous cycle. In addition, a 'resetting' state exists which is\n# similar to 'draining' except that threads leave with a BrokenBarrierError,\n# and a 'broken' state in which all threads get the exception.\nclass Barrier:\n \"\"\"Implements a Barrier.\n\n Useful for synchronizing a fixed number of threads at known synchronization\n points. Threads block on 'wait()' and are simultaneously awoken once they\n have all made that call.\n\n \"\"\"\n\n def __init__(self, parties, action=None, timeout=None):\n \"\"\"Create a barrier, initialised to 'parties' threads.\n\n 'action' is a callable which, when supplied, will be called by one of\n the threads after they have all entered the barrier and just prior to\n releasing them all. If a 'timeout' is provided, it is used as the\n default for all subsequent 'wait()' calls.\n\n \"\"\"\n if parties < 1:\n raise ValueError(\"parties must be > 0\")\n self._cond = Condition(Lock())\n self._action = action\n self._timeout = timeout\n self._parties = parties\n self._state = 0 # 0 filling, 1 draining, -1 resetting, -2 broken\n self._count = 0\n\n def __repr__(self):\n cls = self.__class__\n if self.broken:\n return f\"<{cls.__module__}.{cls.__qualname__} at {id(self):#x}: broken>\"\n return (f\"<{cls.__module__}.{cls.__qualname__} at {id(self):#x}:\"\n f\" waiters={self.n_waiting}/{self.parties}>\")\n\n def wait(self, timeout=None):\n \"\"\"Wait for the barrier.\n\n When the specified number of threads have started waiting, they are all\n simultaneously awoken. If an 'action' was provided for the barrier, one\n of the threads will have executed that callback prior to returning.\n Returns an individual index number from 0 to 'parties-1'.\n\n \"\"\"\n if timeout is None:\n timeout = self._timeout\n with self._cond:\n self._enter() # Block while the barrier drains.\n index = self._count\n self._count += 1\n try:\n if index + 1 == self._parties:\n # We release the barrier\n self._release()\n else:\n # We wait until someone releases us\n self._wait(timeout)\n return index\n finally:\n self._count -= 1\n # Wake up any threads waiting for barrier to drain.\n self._exit()\n\n # Block until the barrier is ready for us, or raise an exception\n # if it is broken.\n def _enter(self):\n while self._state in (-1, 1):\n # It is draining or resetting, wait until done\n self._cond.wait()\n #see if the barrier is in a broken state\n if self._state < 0:\n raise BrokenBarrierError\n assert self._state == 0\n\n # Optionally run the 'action' and release the threads waiting\n # in the barrier.\n def _release(self):\n try:\n if self._action:\n self._action()\n # enter draining state\n self._state = 1\n self._cond.notify_all()\n except:\n #an exception during the _action handler. Break and reraise\n self._break()\n raise\n\n # Wait in the barrier until we are released. Raise an exception\n # if the barrier is reset or broken.\n def _wait(self, timeout):\n if not self._cond.wait_for(lambda : self._state != 0, timeout):\n #timed out. Break the barrier\n self._break()\n raise BrokenBarrierError\n if self._state < 0:\n raise BrokenBarrierError\n assert self._state == 1\n\n # If we are the last thread to exit the barrier, signal any threads\n # waiting for the barrier to drain.\n def _exit(self):\n if self._count == 0:\n if self._state in (-1, 1):\n #resetting or draining\n self._state = 0\n self._cond.notify_all()\n\n def reset(self):\n \"\"\"Reset the barrier to the initial state.\n\n Any threads currently waiting will get the BrokenBarrier exception\n raised.\n\n \"\"\"\n with self._cond:\n if self._count > 0:\n if self._state == 0:\n #reset the barrier, waking up threads\n self._state = -1\n elif self._state == -2:\n #was broken, set it to reset state\n #which clears when the last thread exits\n self._state = -1\n else:\n self._state = 0\n self._cond.notify_all()\n\n def abort(self):\n \"\"\"Place the barrier into a 'broken' state.\n\n Useful in case of error. Any currently waiting threads and threads\n attempting to 'wait()' will have BrokenBarrierError raised.\n\n \"\"\"\n with self._cond:\n self._break()\n\n def _break(self):\n # An internal error was detected. The barrier is set to\n # a broken state all parties awakened.\n self._state = -2\n self._cond.notify_all()\n\n @property\n def parties(self):\n \"\"\"Return the number of threads required to trip the barrier.\"\"\"\n return self._parties\n\n @property\n def n_waiting(self):\n \"\"\"Return the number of threads currently waiting at the barrier.\"\"\"\n # We don't need synchronization here since this is an ephemeral result\n # anyway. It returns the correct value in the steady state.\n if self._state == 0:\n return self._count\n return 0\n\n @property\n def broken(self):\n \"\"\"Return True if the barrier is in a broken state.\"\"\"\n return self._state == -2\n\n# exception raised by the Barrier class\nclass BrokenBarrierError(RuntimeError):\n pass\n\n\n# Helper to generate new thread names\n_counter = _count(1).__next__\ndef _newname(name_template):\n return name_template % _counter()\n\n# Active thread administration.\n#\n# bpo-44422: Use a reentrant lock to allow reentrant calls to functions like\n# threading.enumerate().\n_active_limbo_lock = RLock()\n_active = {} # maps thread id to Thread object\n_limbo = {}\n_dangling = WeakSet()\n\n\n# Main class for threads\n\nclass Thread:\n \"\"\"A class that represents a thread of control.\n\n This class can be safely subclassed in a limited fashion. There are two ways\n to specify the activity: by passing a callable object to the constructor, or\n by overriding the run() method in a subclass.\n\n \"\"\"\n\n _initialized = False\n\n def __init__(self, group=None, target=None, name=None,\n args=(), kwargs=None, *, daemon=None):\n \"\"\"This constructor should always be called with keyword arguments. Arguments are:\n\n *group* should be None; reserved for future extension when a ThreadGroup\n class is implemented.\n\n *target* is the callable object to be invoked by the run()\n method. Defaults to None, meaning nothing is called.\n\n *name* is the thread name. By default, a unique name is constructed of\n the form \"Thread-N\" where N is a small decimal number.\n\n *args* is a list or tuple of arguments for the target invocation. Defaults to ().\n\n *kwargs* is a dictionary of keyword arguments for the target\n invocation. Defaults to {}.\n\n If a subclass overrides the constructor, it must make sure to invoke\n the base class constructor (Thread.__init__()) before doing anything\n else to the thread.\n\n \"\"\"\n assert group is None, \"group argument must be None for now\"\n if kwargs is None:\n kwargs = {}\n if name:\n name = str(name)\n else:\n name = _newname(\"Thread-%d\")\n if target is not None:\n try:\n target_name = target.__name__\n name += f\" ({target_name})\"\n except AttributeError:\n pass\n\n self._target = target\n self._name = name\n self._args = args\n self._kwargs = kwargs\n if daemon is not None:\n if daemon and not _daemon_threads_allowed():\n raise RuntimeError('daemon threads are disabled in this (sub)interpreter')\n self._daemonic = daemon\n else:\n self._daemonic = current_thread().daemon\n self._ident = None\n if _HAVE_THREAD_NATIVE_ID:\n self._native_id = None\n self._handle = _ThreadHandle()\n self._started = Event()\n self._initialized = True\n # Copy of sys.stderr used by self._invoke_excepthook()\n self._stderr = _sys.stderr\n self._invoke_excepthook = _make_invoke_excepthook()\n # For debugging and _after_fork()\n _dangling.add(self)\n\n def _after_fork(self, new_ident=None):\n # Private! Called by threading._after_fork().\n self._started._at_fork_reinit()\n if new_ident is not None:\n # This thread is alive.\n self._ident = new_ident\n assert self._handle.ident == new_ident\n else:\n # Otherwise, the thread is dead, Jim. _PyThread_AfterFork()\n # already marked our handle done.\n pass\n\n def __repr__(self):\n assert self._initialized, \"Thread.__init__() was not called\"\n status = \"initial\"\n if self._started.is_set():\n status = \"started\"\n if self._handle.is_done():\n status = \"stopped\"\n if self._daemonic:\n status += \" daemon\"\n if self._ident is not None:\n status += \" %s\" % self._ident\n return \"<%s(%s, %s)>\" % (self.__class__.__name__, self._name, status)\n\n def start(self):\n \"\"\"Start the thread's activity.\n\n It must be called at most once per thread object. It arranges for the\n object's run() method to be invoked in a separate thread of control.\n\n This method will raise a RuntimeError if called more than once on the\n same thread object.\n\n \"\"\"\n if not self._initialized:\n raise RuntimeError(\"thread.__init__() not called\")\n\n if self._started.is_set():\n raise RuntimeError(\"threads can only be started once\")\n\n with _active_limbo_lock:\n _limbo[self] = self\n try:\n # Start joinable thread\n _start_joinable_thread(self._bootstrap, handle=self._handle,\n daemon=self.daemon)\n except Exception:\n with _active_limbo_lock:\n del _limbo[self]\n raise\n self._started.wait() # Will set ident and native_id\n\n def run(self):\n \"\"\"Method representing the thread's activity.\n\n You may override this method in a subclass. The standard run() method\n invokes the callable object passed to the object's constructor as the\n target argument, if any, with sequential and keyword arguments taken\n from the args and kwargs arguments, respectively.\n\n \"\"\"\n try:\n if self._target is not None:\n self._target(*self._args, **self._kwargs)\n finally:\n # Avoid a refcycle if the thread is running a function with\n # an argument that has a member that points to the thread.\n del self._target, self._args, self._kwargs\n\n def _bootstrap(self):\n # Wrapper around the real bootstrap code that ignores\n # exceptions during interpreter cleanup. Those typically\n # happen when a daemon thread wakes up at an unfortunate\n # moment, finds the world around it destroyed, and raises some\n # random exception *** while trying to report the exception in\n # _bootstrap_inner() below ***. Those random exceptions\n # don't help anybody, and they confuse users, so we suppress\n # them. We suppress them only when it appears that the world\n # indeed has already been destroyed, so that exceptions in\n # _bootstrap_inner() during normal business hours are properly\n # reported. Also, we only suppress them for daemonic threads;\n # if a non-daemonic encounters this, something else is wrong.\n try:\n self._bootstrap_inner()\n except:\n if self._daemonic and _sys is None:\n return\n raise\n\n def _set_ident(self):\n self._ident = get_ident()\n\n if _HAVE_THREAD_NATIVE_ID:\n def _set_native_id(self):\n self._native_id = get_native_id()\n\n def _bootstrap_inner(self):\n try:\n self._set_ident()\n if _HAVE_THREAD_NATIVE_ID:\n self._set_native_id()\n self._started.set()\n with _active_limbo_lock:\n _active[self._ident] = self\n del _limbo[self]\n\n if _trace_hook:\n _sys.settrace(_trace_hook)\n if _profile_hook:\n _sys.setprofile(_profile_hook)\n\n try:\n self.run()\n except:\n self._invoke_excepthook(self)\n finally:\n self._delete()\n\n def _delete(self):\n \"Remove current thread from the dict of currently running threads.\"\n with _active_limbo_lock:\n del _active[get_ident()]\n # There must not be any python code between the previous line\n # and after the lock is released. Otherwise a tracing function\n # could try to acquire the lock again in the same thread, (in\n # current_thread()), and would block.\n\n def join(self, timeout=None):\n \"\"\"Wait until the thread terminates.\n\n This blocks the calling thread until the thread whose join() method is\n called terminates -- either normally or through an unhandled exception\n or until the optional timeout occurs.\n\n When the timeout argument is present and not None, it should be a\n floating-point number specifying a timeout for the operation in seconds\n (or fractions thereof). As join() always returns None, you must call\n is_alive() after join() to decide whether a timeout happened -- if the\n thread is still alive, the join() call timed out.\n\n When the timeout argument is not present or None, the operation will\n block until the thread terminates.\n\n A thread can be join()ed many times.\n\n join() raises a RuntimeError if an attempt is made to join the current\n thread as that would cause a deadlock. It is also an error to join() a\n thread before it has been started and attempts to do so raises the same\n exception.\n\n \"\"\"\n if not self._initialized:\n raise RuntimeError(\"Thread.__init__() not called\")\n if not self._started.is_set():\n raise RuntimeError(\"cannot join thread before it is started\")\n if self is current_thread():\n raise RuntimeError(\"cannot join current thread\")\n\n # the behavior of a negative timeout isn't documented, but\n # historically .join(timeout=x) for x<0 has acted as if timeout=0\n if timeout is not None:\n timeout = max(timeout, 0)\n\n self._handle.join(timeout)\n\n @property\n def name(self):\n \"\"\"A string used for identification purposes only.\n\n It has no semantics. Multiple threads may be given the same name. The\n initial name is set by the constructor.\n\n \"\"\"\n assert self._initialized, \"Thread.__init__() not called\"\n return self._name\n\n @name.setter\n def name(self, name):\n assert self._initialized, \"Thread.__init__() not called\"\n self._name = str(name)\n\n @property\n def ident(self):\n \"\"\"Thread identifier of this thread or None if it has not been started.\n\n This is a nonzero integer. See the get_ident() function. Thread\n identifiers may be recycled when a thread exits and another thread is\n created. The identifier is available even after the thread has exited.\n\n \"\"\"\n assert self._initialized, \"Thread.__init__() not called\"\n return self._ident\n\n if _HAVE_THREAD_NATIVE_ID:\n @property\n def native_id(self):\n \"\"\"Native integral thread ID of this thread, or None if it has not been started.\n\n This is a non-negative integer. See the get_native_id() function.\n This represents the Thread ID as reported by the kernel.\n\n \"\"\"\n assert self._initialized, \"Thread.__init__() not called\"\n return self._native_id\n\n def is_alive(self):\n \"\"\"Return whether the thread is alive.\n\n This method returns True just before the run() method starts until just\n after the run() method terminates. See also the module function\n enumerate().\n\n \"\"\"\n assert self._initialized, \"Thread.__init__() not called\"\n return self._started.is_set() and not self._handle.is_done()\n\n @property\n def daemon(self):\n \"\"\"A boolean value indicating whether this thread is a daemon thread.\n\n This must be set before start() is called, otherwise RuntimeError is\n raised. Its initial value is inherited from the creating thread; the\n main thread is not a daemon thread and therefore all threads created in\n the main thread default to daemon = False.\n\n The entire Python program exits when only daemon threads are left.\n\n \"\"\"\n assert self._initialized, \"Thread.__init__() not called\"\n return self._daemonic\n\n @daemon.setter\n def daemon(self, daemonic):\n if not self._initialized:\n raise RuntimeError(\"Thread.__init__() not called\")\n if daemonic and not _daemon_threads_allowed():\n raise RuntimeError('daemon threads are disabled in this interpreter')\n if self._started.is_set():\n raise RuntimeError(\"cannot set daemon status of active thread\")\n self._daemonic = daemonic\n\n def isDaemon(self):\n \"\"\"Return whether this thread is a daemon.\n\n This method is deprecated, use the daemon attribute instead.\n\n \"\"\"\n import warnings\n warnings.warn('isDaemon() is deprecated, get the daemon attribute instead',\n DeprecationWarning, stacklevel=2)\n return self.daemon\n\n def setDaemon(self, daemonic):\n \"\"\"Set whether this thread is a daemon.\n\n This method is deprecated, use the .daemon property instead.\n\n \"\"\"\n import warnings\n warnings.warn('setDaemon() is deprecated, set the daemon attribute instead',\n DeprecationWarning, stacklevel=2)\n self.daemon = daemonic\n\n def getName(self):\n \"\"\"Return a string used for identification purposes only.\n\n This method is deprecated, use the name attribute instead.\n\n \"\"\"\n import warnings\n warnings.warn('getName() is deprecated, get the name attribute instead',\n DeprecationWarning, stacklevel=2)\n return self.name\n\n def setName(self, name):\n \"\"\"Set the name string for this thread.\n\n This method is deprecated, use the name attribute instead.\n\n \"\"\"\n import warnings\n warnings.warn('setName() is deprecated, set the name attribute instead',\n DeprecationWarning, stacklevel=2)\n self.name = name\n\n\ntry:\n from _thread import (_excepthook as excepthook,\n _ExceptHookArgs as ExceptHookArgs)\nexcept ImportError:\n # Simple Python implementation if _thread._excepthook() is not available\n from traceback import print_exception as _print_exception\n from collections import namedtuple\n\n _ExceptHookArgs = namedtuple(\n 'ExceptHookArgs',\n 'exc_type exc_value exc_traceback thread')\n\n def ExceptHookArgs(args):\n return _ExceptHookArgs(*args)\n\n def excepthook(args, /):\n \"\"\"\n Handle uncaught Thread.run() exception.\n \"\"\"\n if args.exc_type == SystemExit:\n # silently ignore SystemExit\n return\n\n if _sys is not None and _sys.stderr is not None:\n stderr = _sys.stderr\n elif args.thread is not None:\n stderr = args.thread._stderr\n if stderr is None:\n # do nothing if sys.stderr is None and sys.stderr was None\n # when the thread was created\n return\n else:\n # do nothing if sys.stderr is None and args.thread is None\n return\n\n if args.thread is not None:\n name = args.thread.name\n else:\n name = get_ident()\n print(f\"Exception in thread {name}:\",\n file=stderr, flush=True)\n _print_exception(args.exc_type, args.exc_value, args.exc_traceback,\n file=stderr)\n stderr.flush()\n\n\n# Original value of threading.excepthook\n__excepthook__ = excepthook\n\n\ndef _make_invoke_excepthook():\n # Create a local namespace to ensure that variables remain alive\n # when _invoke_excepthook() is called, even if it is called late during\n # Python shutdown. It is mostly needed for daemon threads.\n\n old_excepthook = excepthook\n old_sys_excepthook = _sys.excepthook\n if old_excepthook is None:\n raise RuntimeError(\"threading.excepthook is None\")\n if old_sys_excepthook is None:\n raise RuntimeError(\"sys.excepthook is None\")\n\n sys_exc_info = _sys.exc_info\n local_print = print\n local_sys = _sys\n\n def invoke_excepthook(thread):\n global excepthook\n try:\n hook = excepthook\n if hook is None:\n hook = old_excepthook\n\n args = ExceptHookArgs([*sys_exc_info(), thread])\n\n hook(args)\n except Exception as exc:\n exc.__suppress_context__ = True\n del exc\n\n if local_sys is not None and local_sys.stderr is not None:\n stderr = local_sys.stderr\n else:\n stderr = thread._stderr\n\n local_print(\"Exception in threading.excepthook:\",\n file=stderr, flush=True)\n\n if local_sys is not None and local_sys.excepthook is not None:\n sys_excepthook = local_sys.excepthook\n else:\n sys_excepthook = old_sys_excepthook\n\n sys_excepthook(*sys_exc_info())\n finally:\n # Break reference cycle (exception stored in a variable)\n args = None\n\n return invoke_excepthook\n\n\n# The timer class was contributed by Itamar Shtull-Trauring\n\nclass Timer(Thread):\n \"\"\"Call a function after a specified number of seconds:\n\n t = Timer(30.0, f, args=None, kwargs=None)\n t.start()\n t.cancel() # stop the timer's action if it's still waiting\n\n \"\"\"\n\n def __init__(self, interval, function, args=None, kwargs=None):\n Thread.__init__(self)\n self.interval = interval\n self.function = function\n self.args = args if args is not None else []\n self.kwargs = kwargs if kwargs is not None else {}\n self.finished = Event()\n\n def cancel(self):\n \"\"\"Stop the timer if it hasn't finished yet.\"\"\"\n self.finished.set()\n\n def run(self):\n self.finished.wait(self.interval)\n if not self.finished.is_set():\n self.function(*self.args, **self.kwargs)\n self.finished.set()\n\n\n# Special thread class to represent the main thread\n\nclass _MainThread(Thread):\n\n def __init__(self):\n Thread.__init__(self, name=\"MainThread\", daemon=False)\n self._started.set()\n self._ident = _get_main_thread_ident()\n self._handle = _make_thread_handle(self._ident)\n if _HAVE_THREAD_NATIVE_ID:\n self._set_native_id()\n with _active_limbo_lock:\n _active[self._ident] = self\n\n\n# Helper thread-local instance to detect when a _DummyThread\n# is collected. Not a part of the public API.\n_thread_local_info = local()\n\n\nclass _DeleteDummyThreadOnDel:\n '''\n Helper class to remove a dummy thread from threading._active on __del__.\n '''\n\n def __init__(self, dummy_thread):\n self._dummy_thread = dummy_thread\n self._tident = dummy_thread.ident\n # Put the thread on a thread local variable so that when\n # the related thread finishes this instance is collected.\n #\n # Note: no other references to this instance may be created.\n # If any client code creates a reference to this instance,\n # the related _DummyThread will be kept forever!\n _thread_local_info._track_dummy_thread_ref = self\n\n def __del__(self):\n with _active_limbo_lock:\n if _active.get(self._tident) is self._dummy_thread:\n _active.pop(self._tident, None)\n\n\n# Dummy thread class to represent threads not started here.\n# These should be added to `_active` and removed automatically\n# when they die, although they can't be waited for.\n# Their purpose is to return *something* from current_thread().\n# They are marked as daemon threads so we won't wait for them\n# when we exit (conform previous semantics).\n\nclass _DummyThread(Thread):\n\n def __init__(self):\n Thread.__init__(self, name=_newname(\"Dummy-%d\"),\n daemon=_daemon_threads_allowed())\n self._started.set()\n self._set_ident()\n self._handle = _make_thread_handle(self._ident)\n if _HAVE_THREAD_NATIVE_ID:\n self._set_native_id()\n with _active_limbo_lock:\n _active[self._ident] = self\n _DeleteDummyThreadOnDel(self)\n\n def is_alive(self):\n if not self._handle.is_done() and self._started.is_set():\n return True\n raise RuntimeError(\"thread is not alive\")\n\n def join(self, timeout=None):\n raise RuntimeError(\"cannot join a dummy thread\")\n\n def _after_fork(self, new_ident=None):\n if new_ident is not None:\n self.__class__ = _MainThread\n self._name = 'MainThread'\n self._daemonic = False\n Thread._after_fork(self, new_ident=new_ident)\n\n\n# Global API functions\n\ndef current_thread():\n \"\"\"Return the current Thread object, corresponding to the caller's thread of control.\n\n If the caller's thread of control was not created through the threading\n module, a dummy thread object with limited functionality is returned.\n\n \"\"\"\n try:\n return _active[get_ident()]\n except KeyError:\n return _DummyThread()\n\ndef currentThread():\n \"\"\"Return the current Thread object, corresponding to the caller's thread of control.\n\n This function is deprecated, use current_thread() instead.\n\n \"\"\"\n import warnings\n warnings.warn('currentThread() is deprecated, use current_thread() instead',\n DeprecationWarning, stacklevel=2)\n return current_thread()\n\ndef active_count():\n \"\"\"Return the number of Thread objects currently alive.\n\n The returned count is equal to the length of the list returned by\n enumerate().\n\n \"\"\"\n # NOTE: if the logic in here ever changes, update Modules/posixmodule.c\n # warn_about_fork_with_threads() to match.\n with _active_limbo_lock:\n return len(_active) + len(_limbo)\n\ndef activeCount():\n \"\"\"Return the number of Thread objects currently alive.\n\n This function is deprecated, use active_count() instead.\n\n \"\"\"\n import warnings\n warnings.warn('activeCount() is deprecated, use active_count() instead',\n DeprecationWarning, stacklevel=2)\n return active_count()\n\ndef _enumerate():\n # Same as enumerate(), but without the lock. Internal use only.\n return list(_active.values()) + list(_limbo.values())\n\ndef enumerate():\n \"\"\"Return a list of all Thread objects currently alive.\n\n The list includes daemonic threads, dummy thread objects created by\n current_thread(), and the main thread. It excludes terminated threads and\n threads that have not yet been started.\n\n \"\"\"\n with _active_limbo_lock:\n return list(_active.values()) + list(_limbo.values())\n\n\n_threading_atexits = []\n_SHUTTING_DOWN = False\n\ndef _register_atexit(func, *arg, **kwargs):\n \"\"\"CPython internal: register *func* to be called before joining threads.\n\n The registered *func* is called with its arguments just before all\n non-daemon threads are joined in `_shutdown()`. It provides a similar\n purpose to `atexit.register()`, but its functions are called prior to\n threading shutdown instead of interpreter shutdown.\n\n For similarity to atexit, the registered functions are called in reverse.\n \"\"\"\n if _SHUTTING_DOWN:\n raise RuntimeError(\"can't register atexit after shutdown\")\n\n _threading_atexits.append(lambda: func(*arg, **kwargs))\n\n\nfrom _thread import stack_size\n\n# Create the main thread object,\n# and make it available for the interpreter\n# (Py_Main) as threading._shutdown.\n\n_main_thread = _MainThread()\n\ndef _shutdown():\n \"\"\"\n Wait until the Python thread state of all non-daemon threads get deleted.\n \"\"\"\n # Obscure: other threads may be waiting to join _main_thread. That's\n # dubious, but some code does it. We can't wait for it to be marked as done\n # normally - that won't happen until the interpreter is nearly dead. So\n # mark it done here.\n if _main_thread._handle.is_done() and _is_main_interpreter():\n # _shutdown() was already called\n return\n\n global _SHUTTING_DOWN\n _SHUTTING_DOWN = True\n\n # Call registered threading atexit functions before threads are joined.\n # Order is reversed, similar to atexit.\n for atexit_call in reversed(_threading_atexits):\n atexit_call()\n\n if _is_main_interpreter():\n _main_thread._handle._set_done()\n\n # Wait for all non-daemon threads to exit.\n _thread_shutdown()\n\n\ndef main_thread():\n \"\"\"Return the main thread object.\n\n In normal conditions, the main thread is the thread from which the\n Python interpreter was started.\n \"\"\"\n # XXX Figure this out for subinterpreters. (See gh-75698.)\n return _main_thread\n\n\ndef _after_fork():\n \"\"\"\n Cleanup threading module state that should not exist after a fork.\n \"\"\"\n # Reset _active_limbo_lock, in case we forked while the lock was held\n # by another (non-forked) thread. http://bugs.python.org/issue874900\n global _active_limbo_lock, _main_thread\n _active_limbo_lock = RLock()\n\n # fork() only copied the current thread; clear references to others.\n new_active = {}\n\n try:\n current = _active[get_ident()]\n except KeyError:\n # fork() was called in a thread which was not spawned\n # by threading.Thread. For example, a thread spawned\n # by thread.start_new_thread().\n current = _MainThread()\n\n _main_thread = current\n\n with _active_limbo_lock:\n # Dangling thread instances must still have their locks reset,\n # because someone may join() them.\n threads = set(_enumerate())\n threads.update(_dangling)\n for thread in threads:\n # Any lock/condition variable may be currently locked or in an\n # invalid state, so we reinitialize them.\n if thread is current:\n # This is the one and only active thread.\n ident = get_ident()\n thread._after_fork(new_ident=ident)\n new_active[ident] = thread\n else:\n # All the others are already stopped.\n thread._after_fork()\n\n _limbo.clear()\n _active.clear()\n _active.update(new_active)\n assert len(_active) == 1\n\n\nif hasattr(_os, \"register_at_fork\"):\n _os.register_at_fork(after_in_child=_after_fork)\n", 1599], "/usr/lib/python3.13/_weakrefset.py": ["# Access WeakSet through the weakref module.\n# This code is separated-out because it is needed\n# by abc.py to load everything else at startup.\n\nfrom _weakref import ref\nfrom types import GenericAlias\n\n__all__ = ['WeakSet']\n\n\nclass _IterationGuard:\n # This context manager registers itself in the current iterators of the\n # weak container, such as to delay all removals until the context manager\n # exits.\n # This technique should be relatively thread-safe (since sets are).\n\n def __init__(self, weakcontainer):\n # Don't create cycles\n self.weakcontainer = ref(weakcontainer)\n\n def __enter__(self):\n w = self.weakcontainer()\n if w is not None:\n w._iterating.add(self)\n return self\n\n def __exit__(self, e, t, b):\n w = self.weakcontainer()\n if w is not None:\n s = w._iterating\n s.remove(self)\n if not s:\n w._commit_removals()\n\n\nclass WeakSet:\n def __init__(self, data=None):\n self.data = set()\n def _remove(item, selfref=ref(self)):\n self = selfref()\n if self is not None:\n if self._iterating:\n self._pending_removals.append(item)\n else:\n self.data.discard(item)\n self._remove = _remove\n # A list of keys to be removed\n self._pending_removals = []\n self._iterating = set()\n if data is not None:\n self.update(data)\n\n def _commit_removals(self):\n pop = self._pending_removals.pop\n discard = self.data.discard\n while True:\n try:\n item = pop()\n except IndexError:\n return\n discard(item)\n\n def __iter__(self):\n with _IterationGuard(self):\n for itemref in self.data:\n item = itemref()\n if item is not None:\n # Caveat: the iterator will keep a strong reference to\n # `item` until it is resumed or closed.\n yield item\n\n def __len__(self):\n return len(self.data) - len(self._pending_removals)\n\n def __contains__(self, item):\n try:\n wr = ref(item)\n except TypeError:\n return False\n return wr in self.data\n\n def __reduce__(self):\n return self.__class__, (list(self),), self.__getstate__()\n\n def add(self, item):\n if self._pending_removals:\n self._commit_removals()\n self.data.add(ref(item, self._remove))\n\n def clear(self):\n if self._pending_removals:\n self._commit_removals()\n self.data.clear()\n\n def copy(self):\n return self.__class__(self)\n\n def pop(self):\n if self._pending_removals:\n self._commit_removals()\n while True:\n try:\n itemref = self.data.pop()\n except KeyError:\n raise KeyError('pop from empty WeakSet') from None\n item = itemref()\n if item is not None:\n return item\n\n def remove(self, item):\n if self._pending_removals:\n self._commit_removals()\n self.data.remove(ref(item))\n\n def discard(self, item):\n if self._pending_removals:\n self._commit_removals()\n self.data.discard(ref(item))\n\n def update(self, other):\n if self._pending_removals:\n self._commit_removals()\n for element in other:\n self.add(element)\n\n def __ior__(self, other):\n self.update(other)\n return self\n\n def difference(self, other):\n newset = self.copy()\n newset.difference_update(other)\n return newset\n __sub__ = difference\n\n def difference_update(self, other):\n self.__isub__(other)\n def __isub__(self, other):\n if self._pending_removals:\n self._commit_removals()\n if self is other:\n self.data.clear()\n else:\n self.data.difference_update(ref(item) for item in other)\n return self\n\n def intersection(self, other):\n return self.__class__(item for item in other if item in self)\n __and__ = intersection\n\n def intersection_update(self, other):\n self.__iand__(other)\n def __iand__(self, other):\n if self._pending_removals:\n self._commit_removals()\n self.data.intersection_update(ref(item) for item in other)\n return self\n\n def issubset(self, other):\n return self.data.issubset(ref(item) for item in other)\n __le__ = issubset\n\n def __lt__(self, other):\n return self.data < set(map(ref, other))\n\n def issuperset(self, other):\n return self.data.issuperset(ref(item) for item in other)\n __ge__ = issuperset\n\n def __gt__(self, other):\n return self.data > set(map(ref, other))\n\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return NotImplemented\n return self.data == set(map(ref, other))\n\n def symmetric_difference(self, other):\n newset = self.copy()\n newset.symmetric_difference_update(other)\n return newset\n __xor__ = symmetric_difference\n\n def symmetric_difference_update(self, other):\n self.__ixor__(other)\n def __ixor__(self, other):\n if self._pending_removals:\n self._commit_removals()\n if self is other:\n self.data.clear()\n else:\n self.data.symmetric_difference_update(ref(item, self._remove) for item in other)\n return self\n\n def union(self, other):\n return self.__class__(e for s in (self, other) for e in s)\n __or__ = union\n\n def isdisjoint(self, other):\n return len(self.intersection(other)) == 0\n\n def __repr__(self):\n return repr(self.data)\n\n __class_getitem__ = classmethod(GenericAlias)\n", 205], "/home/gaogaotiantian/programs/viztracer/example/src/multithread.py": ["import os\nimport threading\nimport time\n\nfrom viztracer import VizTracer\n\n\ndef fib(n):\n if n < 2:\n return 1\n time.sleep(0.0000001)\n return fib(n - 1) + fib(n - 2)\n\n\nclass MyThread(threading.Thread):\n def run(self):\n fib(7)\n\n\nwith VizTracer(output_file=os.path.join(os.path.dirname(__file__), \"../\", \"json/multithread.json\"),\n file_info=True) as _:\n thread1 = MyThread()\n thread2 = MyThread()\n thread3 = MyThread()\n thread4 = MyThread()\n\n thread1.start()\n thread2.start()\n thread3.start()\n thread4.start()\n\n threads = [thread1, thread2, thread3, thread4]\n\n for thread in threads:\n thread.join()\n", 35]}, "functions": {"_newname (/usr/lib/python3.13/threading.py:843)": ["/usr/lib/python3.13/threading.py", 843], "current_thread (/usr/lib/python3.13/threading.py:1427)": ["/usr/lib/python3.13/threading.py", 1427], "Thread.daemon (/usr/lib/python3.13/threading.py:1145)": ["/usr/lib/python3.13/threading.py", 1145], "Condition.__init__ (/usr/lib/python3.13/threading.py:281)": ["/usr/lib/python3.13/threading.py", 281], "Event.__init__ (/usr/lib/python3.13/threading.py:592)": ["/usr/lib/python3.13/threading.py", 592], "_make_invoke_excepthook (/usr/lib/python3.13/threading.py:1265)": ["/usr/lib/python3.13/threading.py", 1265], "WeakSet.add (/usr/lib/python3.13/_weakrefset.py:85)": ["/usr/lib/python3.13/_weakrefset.py", 85], "Thread.__init__ (/usr/lib/python3.13/threading.py:869)": ["/usr/lib/python3.13/threading.py", 869], "Event.is_set (/usr/lib/python3.13/threading.py:605)": ["/usr/lib/python3.13/threading.py", 605], "Condition.__enter__ (/usr/lib/python3.13/threading.py:303)": ["/usr/lib/python3.13/threading.py", 303], "Condition._is_owned (/usr/lib/python3.13/threading.py:318)": ["/usr/lib/python3.13/threading.py", 318], "Condition._release_save (/usr/lib/python3.13/threading.py:312)": ["/usr/lib/python3.13/threading.py", 312], "Thread._set_ident (/usr/lib/python3.13/threading.py:1018)": ["/usr/lib/python3.13/threading.py", 1018], "Thread._set_native_id (/usr/lib/python3.13/threading.py:1022)": ["/usr/lib/python3.13/threading.py", 1022], "Condition.notify (/usr/lib/python3.13/threading.py:398)": ["/usr/lib/python3.13/threading.py", 398], "Condition.notify_all (/usr/lib/python3.13/threading.py:428)": ["/usr/lib/python3.13/threading.py", 428], "Condition.__exit__ (/usr/lib/python3.13/threading.py:306)": ["/usr/lib/python3.13/threading.py", 306], "Event.set (/usr/lib/python3.13/threading.py:620)": ["/usr/lib/python3.13/threading.py", 620], "Condition._acquire_restore (/usr/lib/python3.13/threading.py:315)": ["/usr/lib/python3.13/threading.py", 315], "Condition.wait (/usr/lib/python3.13/threading.py:327)": ["/usr/lib/python3.13/threading.py", 327], "Event.wait (/usr/lib/python3.13/threading.py:641)": ["/usr/lib/python3.13/threading.py", 641], "Thread.start (/usr/lib/python3.13/threading.py:953)": ["/usr/lib/python3.13/threading.py", 953], "fib (/home/gaogaotiantian/programs/viztracer/example/src/multithread.py:8)": ["/home/gaogaotiantian/programs/viztracer/example/src/multithread.py", 8], "MyThread.run (/home/gaogaotiantian/programs/viztracer/example/src/multithread.py:16)": ["/home/gaogaotiantian/programs/viztracer/example/src/multithread.py", 16], "Thread._delete (/usr/lib/python3.13/threading.py:1047)": ["/usr/lib/python3.13/threading.py", 1047], "Thread._bootstrap_inner (/usr/lib/python3.13/threading.py:1025)": ["/usr/lib/python3.13/threading.py", 1025], "Thread._bootstrap (/usr/lib/python3.13/threading.py:998)": ["/usr/lib/python3.13/threading.py", 998], "_DeleteDummyThreadOnDel.__del__ (/usr/lib/python3.13/threading.py:1382)": ["/usr/lib/python3.13/threading.py", 1382], "WeakSet.__init__.._remove (/usr/lib/python3.13/_weakrefset.py:39)": ["/usr/lib/python3.13/_weakrefset.py", 39], "Thread.join (/usr/lib/python3.13/threading.py:1056)": ["/usr/lib/python3.13/threading.py", 1056]}}} \ No newline at end of file diff --git a/example/src/gradient_descent.py b/example/src/gradient_descent.py index 01c6391d..80bce39c 100644 --- a/example/src/gradient_descent.py +++ b/example/src/gradient_descent.py @@ -5,7 +5,8 @@ import numpy -from viztracer import VizCounter, VizTracer +from viztracer import VizTracer +from viztracer.vizcounter import VizCounter # List of input, output pairs train_data = ( diff --git a/example/src/logging_integration.py b/example/src/logging_integration.py index 06aa43c5..17f8c0b8 100644 --- a/example/src/logging_integration.py +++ b/example/src/logging_integration.py @@ -1,6 +1,7 @@ import logging -from viztracer import VizLoggingHandler, get_tracer +from viztracer import get_tracer +from viztracer.vizlogging import VizLoggingHandler def fib(n): diff --git a/pyproject.toml b/pyproject.toml index a6b3fb74..f79c5fff 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -36,8 +36,8 @@ Documentation = "https://viztracer.readthedocs.io" full = ["orjson"] [project.scripts] -viztracer = "viztracer:main" -vizviewer = "viztracer:viewer_main" +viztracer = "viztracer.main:main" +vizviewer = "viztracer.viewer:viewer_main" [tool.setuptools.dynamic] version = {attr = "viztracer.__version__"} diff --git a/src/viztracer/__init__.py b/src/viztracer/__init__.py index ae2c82e7..78982357 100644 --- a/src/viztracer/__init__.py +++ b/src/viztracer/__init__.py @@ -6,24 +6,14 @@ from .cellmagic import load_ipython_extension from .decorator import ignore_function, log_sparse, trace_and_save -from .main import main -from .viewer import viewer_main -from .vizcounter import VizCounter -from .vizlogging import VizLoggingHandler -from .vizobject import VizObject from .viztracer import VizTracer, get_tracer __all__ = [ "__version__", - "main", - "viewer_main", "VizTracer", "ignore_function", "trace_and_save", "log_sparse", "get_tracer", - "VizCounter", - "VizObject", - "VizLoggingHandler", "load_ipython_extension", ] diff --git a/src/viztracer/main.py b/src/viztracer/main.py index a2c6e252..7cbf972c 100644 --- a/src/viztracer/main.py +++ b/src/viztracer/main.py @@ -5,7 +5,6 @@ import atexit import base64 import builtins -import configparser import io import json import multiprocessing.util # type: ignore @@ -23,7 +22,6 @@ from typing import Any, Optional, Union from . import __version__ -from .attach_process.add_code_to_python_process import run_python_code # type: ignore from .code_monkey import CodeMonkey from .patch import install_all_hooks from .report_builder import ReportBuilder @@ -166,6 +164,7 @@ def create_parser(self) -> argparse.ArgumentParser: def load_config_file(self, filename: str = ".viztracerrc") -> argparse.Namespace: ret = argparse.Namespace() if os.path.exists(filename): + import configparser cfg_parser = configparser.ConfigParser() cfg_parser.read(filename) if "default" not in cfg_parser: @@ -531,6 +530,8 @@ def _check_attach_availability(self) -> tuple[bool, Optional[str]]: return True, None def attach(self) -> VizProcedureResult: + from .attach_process.add_code_to_python_process import run_python_code # type: ignore + pid = self.options.attach interval = self.options.t @@ -570,6 +571,8 @@ def attach(self) -> VizProcedureResult: return True, None def uninstall(self) -> VizProcedureResult: + from .attach_process.add_code_to_python_process import run_python_code # type: ignore + pid = self.options.uninstall success, err_msg = self._check_attach_availability() diff --git a/src/viztracer/modules/snaptrace.c b/src/viztracer/modules/snaptrace.c index 4658bd22..02639e9e 100644 --- a/src/viztracer/modules/snaptrace.c +++ b/src/viztracer/modules/snaptrace.c @@ -34,7 +34,7 @@ PyObject* trio_lowlevel_module = NULL; PyObject* sys_module = NULL; PyObject* sys_monitoring_missing = NULL; -static PyObject* curr_task_getters[2] = {0}; +PyObject* curr_task_getters[2] = {0}; // ============================================================================= // Utility function @@ -2142,11 +2142,6 @@ PyInit_snaptrace(void) threading_module = PyImport_ImportModule("threading"); multiprocessing_module = PyImport_ImportModule("multiprocessing"); - asyncio_module = PyImport_ImportModule("asyncio"); - asyncio_tasks_module = PyImport_AddModule("asyncio.tasks"); - if (PyObject_HasAttrString(asyncio_tasks_module, "current_task")) { - curr_task_getters[0] = PyObject_GetAttrString(asyncio_tasks_module, "current_task"); - } if ((trio_module = PyImport_ImportModule("trio"))) { trio_lowlevel_module = PyImport_AddModule("trio.lowlevel"); curr_task_getters[1] = PyObject_GetAttrString(trio_lowlevel_module, "current_task"); diff --git a/src/viztracer/modules/snaptrace_member.c b/src/viztracer/modules/snaptrace_member.c index 5e331dc9..67403098 100644 --- a/src/viztracer/modules/snaptrace_member.c +++ b/src/viztracer/modules/snaptrace_member.c @@ -1,6 +1,13 @@ +// Licensed under the Apache License: http://www.apache.org/licenses/LICENSE-2.0 +// For details: https://github.com/gaogaotiantian/viztracer/blob/master/NOTICE.txt + #include "pythoncapi_compat.h" #include "snaptrace.h" +extern PyObject* asyncio_module; +extern PyObject* asyncio_tasks_module; +extern PyObject* curr_task_getters[2]; + // ================================================================ // Tracer members // ================================================================ @@ -368,6 +375,14 @@ Tracer_log_async_setter(TracerObject* self, PyObject* value, void* closure) } if (value == Py_True) { + // Lazy import asyncio because it's slow + if (asyncio_module == NULL) { + asyncio_module = PyImport_ImportModule("asyncio"); + asyncio_tasks_module = PyImport_AddModule("asyncio.tasks"); + if (PyObject_HasAttrString(asyncio_tasks_module, "current_task")) { + curr_task_getters[0] = PyObject_GetAttrString(asyncio_tasks_module, "current_task"); + } + } SET_FLAG(self->check_flags, SNAPTRACE_LOG_ASYNC); } else { UNSET_FLAG(self->check_flags, SNAPTRACE_LOG_ASYNC); diff --git a/src/viztracer/util.py b/src/viztracer/util.py index 26ee6aeb..df5f2b1b 100644 --- a/src/viztracer/util.py +++ b/src/viztracer/util.py @@ -7,7 +7,6 @@ import os import re import sys -import ctypes from typing import Union # Windows macros @@ -137,6 +136,7 @@ def pid_exists(pid): raise ValueError('invalid PID 0') if sys.platform == "win32": # Windows + import ctypes kernel32 = ctypes.windll.kernel32 process = kernel32.OpenProcess(PROCESS_QUERY_LIMITED_INFORMATION, 0, pid) diff --git a/src/viztracer/viztracer.py b/src/viztracer/viztracer.py index 28bc506e..150d80dc 100644 --- a/src/viztracer/viztracer.py +++ b/src/viztracer/viztracer.py @@ -12,8 +12,6 @@ from typing import Any, Callable, Literal, Optional, Sequence, Union from viztracer.snaptrace import Tracer -import objprint # type: ignore - from . import __version__ from .report_builder import ReportBuilder from .vizevent import VizEvent @@ -80,6 +78,7 @@ def __init__(self, self.exclude_files = exclude_files[:] + [os.path.abspath(f) for f in exclude_files if not f.startswith("/")] if log_func_with_objprint: + import objprint # type: ignore if log_func_repr: raise ValueError("log_func_repr and log_func_with_objprint can't be both set") log_func_repr = objprint.objstr @@ -200,6 +199,7 @@ def log_var(self, name: str, var: Any, cond: bool = True) -> None: if isinstance(var, (float, int)): self.add_counter(name, {"value": var}) else: + import objprint # type: ignore self.add_instant(name, args={"object": objprint.objstr(var, color=False)}, scope="t") def log_event(self, event_name: str) -> VizEvent: diff --git a/tests/data/vdb_basic.py b/tests/data/vdb_basic.py index da5a92f2..de3f5231 100644 --- a/tests/data/vdb_basic.py +++ b/tests/data/vdb_basic.py @@ -1,4 +1,6 @@ -from viztracer import VizCounter, VizObject, VizTracer +from viztracer import VizTracer +from viztracer.vizcounter import VizCounter +from viztracer.vizobject import VizObject def h(a): diff --git a/tests/test_logging.py b/tests/test_logging.py index 67d87f65..fcf120f6 100644 --- a/tests/test_logging.py +++ b/tests/test_logging.py @@ -3,7 +3,8 @@ import logging -from viztracer import VizLoggingHandler, VizTracer +from viztracer import VizTracer +from viztracer.vizlogging import VizLoggingHandler from .base_tmpl import BaseTmpl diff --git a/tests/test_regression.py b/tests/test_regression.py index 08eaf998..ee7a3209 100644 --- a/tests/test_regression.py +++ b/tests/test_regression.py @@ -367,7 +367,9 @@ def check_func(data): issue285_code = """ import threading -from viztracer import get_tracer, VizCounter, VizObject +from viztracer import get_tracer +from viztracer.vizcounter import VizCounter +from viztracer.vizobject import VizObject def fib(n): diff --git a/tests/test_vcompressor.py b/tests/test_vcompressor.py index 9e91ede5..68b30f92 100644 --- a/tests/test_vcompressor.py +++ b/tests/test_vcompressor.py @@ -337,7 +337,8 @@ def assertThreadOrProcessEqual(self, first: list, second: list): import threading import time import sys -from viztracer import VizTracer, VizCounter +from viztracer import VizTracer +from viztracer.vizcounter import VizCounter tracer = VizTracer() tracer.start() @@ -396,7 +397,8 @@ def call_self(n): test_non_frequent_events = """ import threading -from viztracer import VizTracer, VizObject +from viztracer import VizTracer +from viztracer.vizobject import VizObject tracer = VizTracer() tracer.start() diff --git a/tests/test_vizcounter.py b/tests/test_vizcounter.py index 32988106..d04abd9d 100644 --- a/tests/test_vizcounter.py +++ b/tests/test_vizcounter.py @@ -1,7 +1,8 @@ # Licensed under the Apache License: http://www.apache.org/licenses/LICENSE-2.0 # For details: https://github.com/gaogaotiantian/viztracer/blob/master/NOTICE.txt -from viztracer import VizCounter, VizTracer +from viztracer import VizTracer +from viztracer.vizcounter import VizCounter from .base_tmpl import BaseTmpl diff --git a/tests/test_vizobject.py b/tests/test_vizobject.py index 19d293d7..30c664cd 100644 --- a/tests/test_vizobject.py +++ b/tests/test_vizobject.py @@ -1,7 +1,8 @@ # Licensed under the Apache License: http://www.apache.org/licenses/LICENSE-2.0 # For details: https://github.com/gaogaotiantian/viztracer/blob/master/NOTICE.txt -from viztracer import VizObject, VizTracer +from viztracer import VizTracer +from viztracer.vizobject import VizObject from .base_tmpl import BaseTmpl