Move file_format parameter from _as_dataset to ReadConfig becau…

…se this is breaking external code that overrides `_as_dataset`.

PiperOrigin-RevId: 704667761

tensorflow_datasets/core/dataset_builder.py

@@ -937,7 +937,6 @@ def as_dataset(
       decoders: TreeDict[decode.partial_decode.DecoderArg] | None = None,
       read_config: read_config_lib.ReadConfig | None = None,
       as_supervised: bool = False,
-      file_format: str | file_adapters.FileFormat | None = None,
   ):
     # pylint: disable=line-too-long
     """Constructs a `tf.data.Dataset`.
@@ -1007,9 +1006,6 @@ def as_dataset(
         a 2-tuple structure `(input, label)` according to
         `builder.info.supervised_keys`. If `False`, the default, the returned
         `tf.data.Dataset` will have a dictionary with all the features.
-      file_format: if the dataset is stored in multiple file formats, then this
-        argument can be used to specify the file format to load. If not
-        specified, the default file format is used.
 
     Returns:
       `tf.data.Dataset`, or if `split=None`, `dict<key: tfds.Split, value:
@@ -1043,7 +1039,6 @@ def as_dataset(
         decoders=decoders,
         read_config=read_config,
         as_supervised=as_supervised,
-        file_format=file_format,
     )
     all_ds = tree.map_structure(build_single_dataset, split)
     return all_ds
@@ -1056,28 +1051,19 @@ def _build_single_dataset(
       decoders: TreeDict[decode.partial_decode.DecoderArg] | None,
       read_config: read_config_lib.ReadConfig,
       as_supervised: bool,
-      file_format: str | file_adapters.FileFormat | None = None,
   ) -> tf.data.Dataset:
     """as_dataset for a single split."""
     wants_full_dataset = batch_size == -1
     if wants_full_dataset:
       batch_size = self.info.splits.total_num_examples or sys.maxsize
 
-    if file_format is not None:
-      file_format = file_adapters.FileFormat.from_value(file_format)
-
     # Build base dataset
-    as_dataset_kwargs = {
-        "split": split,
-        "shuffle_files": shuffle_files,
-        "decoders": decoders,
-        "read_config": read_config,
-    }
-    # Not all dataset builder classes support file_format, so only pass it if
-    # it's supported.
-    if "file_format" in inspect.signature(self._as_dataset).parameters:
-      as_dataset_kwargs["file_format"] = file_format
-    ds = self._as_dataset(**as_dataset_kwargs)
+    ds = self._as_dataset(
+        split=split,
+        shuffle_files=shuffle_files,
+        decoders=decoders,
+        read_config=read_config,
+    )
 
     # Auto-cache small datasets which are small enough to fit in memory.
     if self._should_cache_ds(
@@ -1263,7 +1249,6 @@ def _as_dataset(
       decoders: TreeDict[decode.partial_decode.DecoderArg] | None = None,
       read_config: read_config_lib.ReadConfig | None = None,
       shuffle_files: bool = False,
-      file_format: str | file_adapters.FileFormat | None = None,
   ) -> tf.data.Dataset:
     """Constructs a `tf.data.Dataset`.
 
@@ -1279,9 +1264,6 @@ def _as_dataset(
       read_config: `tfds.ReadConfig`
       shuffle_files: `bool`, whether to shuffle the input files. Optional,
         defaults to `False`.
-      file_format: if the dataset is stored in multiple file formats, then this
-        argument can be used to specify the file format to load. If not
-        specified, the default file format is used.
 
     Returns:
       `tf.data.Dataset`
@@ -1525,14 +1507,16 @@ def _example_specs(self):
       )
     return self.info.features.get_serialized_info()
 
-  def _as_dataset(  # pytype: disable=signature-mismatch  # overriding-parameter-type-checks
+  def _as_dataset(
       self,
       split: splits_lib.Split,
-      decoders: TreeDict[decode.partial_decode.DecoderArg] | None,
-      read_config: read_config_lib.ReadConfig,
-      shuffle_files: bool,
-      file_format: file_adapters.FileFormat | None = None,
+      decoders: TreeDict[decode.partial_decode.DecoderArg] | None = None,
+      read_config: read_config_lib.ReadConfig | None = None,
+      shuffle_files: bool = False,
   ) -> tf.data.Dataset:
+    if read_config is None:
+      read_config = read_config_lib.ReadConfig()
+
     # Partial decoding
     # TODO(epot): Should be moved inside `features.decode_example`
     if isinstance(decoders, decode.PartialDecoding):
@@ -1550,10 +1534,18 @@ def _as_dataset(  # pytype: disable=signature-mismatch  # overriding-parameter-t
           f"Features are not set for dataset {self.name} in {self.data_dir}!"
       )
 
+    file_format = (
+        read_config.file_format
+        or self.info.file_format
+        or file_adapters.DEFAULT_FILE_FORMAT
+    )
+    if file_format is not None:
+      file_format = file_adapters.FileFormat.from_value(file_format)
+
     reader = reader_lib.Reader(
         self.data_dir,
         example_specs=example_specs,
-        file_format=file_format or self.info.file_format,
+        file_format=file_format,
     )
     decode_fn = functools.partial(features.decode_example, decoders=decoders)
     return reader.read(

tensorflow_datasets/core/load.py

@@ -20,7 +20,6 @@
 from collections.abc import Iterable, Iterator, Mapping, Sequence
 import dataclasses
 import difflib
-import inspect
 import posixpath
 import re
 import textwrap
@@ -671,9 +670,10 @@ def load(
   as_dataset_kwargs.setdefault('batch_size', batch_size)
   as_dataset_kwargs.setdefault('decoders', decoders)
   as_dataset_kwargs.setdefault('shuffle_files', shuffle_files)
+  if file_format is not None:
+    read_config = read_config or read_config_lib.ReadConfig()
+    read_config = read_config.replace(file_format=file_format)
   as_dataset_kwargs.setdefault('read_config', read_config)
-  if 'file_format' in inspect.signature(dbuilder.as_dataset).parameters:
-    as_dataset_kwargs.setdefault('file_format', file_format)
 
   ds = dbuilder.as_dataset(**as_dataset_kwargs)
   if with_info:

tensorflow_datasets/core/utils/read_config.py

@@ -17,9 +17,11 @@
 
 from __future__ import annotations
 
+from collections.abc import Sequence
 import dataclasses
-from typing import Callable, Optional, Sequence, Union, cast
+from typing import Callable, cast
 
+from tensorflow_datasets.core import file_adapters
 from tensorflow_datasets.core.utils import shard_utils
 from tensorflow_datasets.core.utils.lazy_imports_utils import tensorflow as tf
 
@@ -91,36 +93,45 @@ class ReadConfig:
       False if input files have been tempered with and they don't mind missing
       records or have too many of them.
     override_buffer_size: number of bytes to pass to file readers for buffering.
+    file_format: if the dataset is stored in multiple file formats, then this
+      argument can be used to specify the file format to load. If not specified,
+      the default file format is used.
   """
   # pyformat: enable
 
   # General tf.data.Dataset parametters
-  options: Optional[tf.data.Options] = None
+  options: tf.data.Options | None = None
   try_autocache: bool = True
   repeat_filenames: bool = False
   add_tfds_id: bool = False
   # tf.data.Dataset.shuffle parameters
-  shuffle_seed: Optional[int] = None
-  shuffle_reshuffle_each_iteration: Optional[bool] = None
+  shuffle_seed: int | None = None
+  shuffle_reshuffle_each_iteration: bool | None = None
   # Interleave parameters
   # Ideally, we should switch interleave values to None to dynamically set
   # those value depending on the user system. However, this would make the
   # generation order non-deterministic accross machines.
-  interleave_cycle_length: Union[Optional[int], _MISSING] = MISSING
-  interleave_block_length: Optional[int] = 16
-  input_context: Optional[tf.distribute.InputContext] = None
-  experimental_interleave_sort_fn: Optional[InterleaveSortFn] = None
+  interleave_cycle_length: int | None | _MISSING = MISSING
+  interleave_block_length: int | None = 16
+  input_context: tf.distribute.InputContext | None = None
+  experimental_interleave_sort_fn: InterleaveSortFn | None = None
   skip_prefetch: bool = False
-  num_parallel_calls_for_decode: Optional[int] = None
+  num_parallel_calls_for_decode: int | None = None
   # Cast to an `int`. `__post_init__` will ensure the type invariant.
-  num_parallel_calls_for_interleave_files: Optional[int] = cast(int, MISSING)
+  num_parallel_calls_for_interleave_files: int | None = cast(int, MISSING)
   enable_ordering_guard: bool = True
   assert_cardinality: bool = True
-  override_buffer_size: Optional[int] = None
+  override_buffer_size: int | None = None
+  file_format: str | file_adapters.FileFormat | None = None
 
   def __post_init__(self):
     self.options = self.options or tf.data.Options()
     if self.num_parallel_calls_for_decode is None:
       self.num_parallel_calls_for_decode = tf.data.AUTOTUNE
     if self.num_parallel_calls_for_interleave_files == MISSING:
       self.num_parallel_calls_for_interleave_files = tf.data.AUTOTUNE
+    if isinstance(self.file_format, str):
+      self.file_format = file_adapters.FileFormat.from_value(self.file_format)
+
+  def replace(self, **kwargs) -> ReadConfig:
+    return dataclasses.replace(self, **kwargs)