added a function

claude/script.py

@@ -32,6 +32,22 @@ def starter_code(key):
     with open("diff.txt", '+rb') as f:
         # intelligent regex 
         content = f.open()
+        fns_without_docstring = dict()
+        contains_docstring = False
+        for line in content:
+            if line.contains("+def "):
+                in_func = True
+                func_name = line.split('+def ')[1].split('(')[0]
+                # regex to check if there exists a docstring
+            if line == "+":
+                if in_func and not contains_docstring:
+                    fns_without_docstring[func_name] = generate_docstring(filename, func_name, key)
+                in_func = False
+                contains_docstring = False
+                func_name = ""
+            if line.contains('"""'):
+                contains_docstring = True
+
         # changed function names here
     # changed file names
     with open("~/files.json", 'r') as f:

dummy-files/test.py

@@ -1,95 +1 @@
-# global
-from numbers import Number
-import numpy as np
-from typing import Union, Optional, List, Sequence, Tuple
-
-import jax.dlpack
-import jax.numpy as jnp
-import jax._src as _src
-import jaxlib.xla_extension
-
-# local
-import ivy
-from ivy import as_native_dtype
-from ivy.functional.backends.jax import JaxArray
-from ivy.functional.ivy.creation import (
-    _asarray_to_native_arrays_and_back,
-    _asarray_infer_device,
-    _asarray_infer_dtype,
-    _asarray_handle_nestable,
-    NestedSequence,
-    SupportsBufferProtocol,
-    _asarray_inputs_to_native_shapes,
-)
-
-
-# Array API Standard #
-# ------------------ #
-
-@_asarray_to_native_arrays_and_back
-@_asarray_infer_device
-@_asarray_handle_nestable
-@_asarray_inputs_to_native_shapes
-@_asarray_infer_dtype
-def asarray(
-    obj: Union[
-        JaxArray,
-        bool,
-        int,
-        float,
-        tuple,
-        NestedSequence,
-        SupportsBufferProtocol,
-        np.ndarray,
-    ],
-    /,
-    *,
-    copy: Optional[bool] = None,
-    dtype: Optional[jnp.dtype] = None,
-    device: jaxlib.xla_extension.Device = None,
-    out: Optional[JaxArray] = None,
-) -> JaxArray:
-    ivy.utils.assertions._check_jax_x64_flag(dtype)
-    if copy is True:
-        return jnp.array(obj, dtype=dtype, copy=True)
-    else:
-        return jnp.asarray(obj, dtype=dtype)
-
-def arange(
-    start: float,
-    /,
-    stop: Optional[float] = None,
-    step: float = 1,
-    *,
-    dtype: Optional[jnp.dtype] = None,
-    device: jaxlib.xla_extension.Device = None,
-    out: Optional[JaxArray] = None,
-) -> JaxArray:
-    if dtype:
-        dtype = as_native_dtype(dtype)
-        ivy.utils.assertions._check_jax_x64_flag(dtype.name)
-    res = jnp.arange(start, stop, step, dtype=dtype)
-    if not dtype:
-        if res.dtype == jnp.float64:
-            return res.astype(jnp.float32)
-        elif res.dtype == jnp.int64:
-            return res.astype(jnp.int32)
-    return res
-
-def meshgrid(
-    *arrays: JaxArray,
-    sparse: bool = False,
-    indexing: str = "xy",
-    out: Optional[JaxArray] = None,
-) -> List[JaxArray]:
-    return jnp.meshgrid(*arrays, sparse=sparse, indexing=indexing)
-
-def ones_like(
-    x: JaxArray,
-    /,
-    *,
-    dtype: jnp.dtype,
-    device: jaxlib.xla_extension.Device = None,
-    out: Optional[JaxArray] = None,
-) -> JaxArray:
-    return jnp.ones_like(x, dtype=dtype)
+1234