'''simple docstring'''




import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest

from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError

from .utils import require_pil

class       lowerCamelCase_      (_a       ):
      '''simple docstring'''

      def 	_A      (			self		:							str			):
             _UpperCAmelCase	:    Optional[Any]				 =       pa.array(TypedSequence([1, 2, 3]			)			)
             self.assertEqual(arr.type		,   pa.intaa()			)




      def 	_A      (			self		:							Optional[Any]			):
             with self.assertRaises(lowerCamelCase__			):
                    _UpperCAmelCase	:    Optional[Any]				 =       pa.array(TypedSequence([1, 2, 3]			)		,   type=pa.intaa()			)




      def 	_A      (			self		:							int			):
             with self.assertRaises(lowerCamelCase__			):
                    _UpperCAmelCase	:    List[Any]				 =       pa.array(TypedSequence([1, 2, 3]		,   try_type=Value("bool"			)		,   type=Value("int64"			)			)			)




      def 	_A      (			self		:							List[str]			):
             _UpperCAmelCase	:    Dict				 =       pa.array(TypedSequence([1, 2, 3]		,   type=Value("int32"			)			)			)
             self.assertEqual(arr.type		,   pa.intaa()			)




      def 	_A      (			self		:							Union[str, Any]			):
             with self.assertRaises((TypeError, pa.lib.ArrowInvalid)			):
                    _UpperCAmelCase	:    List[str]				 =       pa.array(TypedSequence(["foo", "bar"]		,   type=Value("int64"			)			)			)




      def 	_A      (			self		:							int			):
             _UpperCAmelCase	:    List[str]				 =       pa.array(TypedSequence([1, 2, 3]		,   try_type=Value("int32"			)			)			)
             self.assertEqual(arr.type		,   pa.intaa()			)




      def 	_A      (			self		:							List[str]			):
             _UpperCAmelCase	:    str				 =       pa.array(TypedSequence(["foo", "bar"]		,   try_type=Value("int64"			)			)			)
             self.assertEqual(arr.type		,   pa.string()			)




      def 	_A      (			self		:							List[str]			):
             _UpperCAmelCase	:    Optional[Any]				 =       pa.array(TypedSequence([[[1, 2, 3]]]		,   type=ArrayaD((1, 3)		,   "int64"			)			)			)
             self.assertEqual(arr.type		,   ArrayaDExtensionType((1, 3)		,   "int64"			)			)




      def 	_A      (			self		:							Optional[int]			):
             with self.assertRaises((TypeError, pa.lib.ArrowInvalid)			):
                    _UpperCAmelCase	:    Union[str, Any]				 =       pa.array(TypedSequence(["foo", "bar"]		,   type=ArrayaD((1, 3)		,   "int64"			)			)			)




      def 	_A      (			self		:							Union[str, Any]			):
             _UpperCAmelCase	:    Any				 =       pa.array(TypedSequence([[[1, 2, 3]]]		,   try_type=ArrayaD((1, 3)		,   "int64"			)			)			)
             self.assertEqual(arr.type		,   ArrayaDExtensionType((1, 3)		,   "int64"			)			)




      def 	_A      (			self		:							Optional[int]			):
             _UpperCAmelCase	:    int				 =       pa.array(TypedSequence(["foo", "bar"]		,   try_type=ArrayaD((1, 3)		,   "int64"			)			)			)
             self.assertEqual(arr.type		,   pa.string()			)





      @require_pil
      def 	_A      (			self		:							Dict			):
             import PIL.Image

             _UpperCAmelCase	:    int				 =       PIL.Image.fromarray(np.arange(10		,   dtype=np.uinta			).reshape(2		,   5			)			)
             with patch(
                 "datasets.arrow_writer.cast_to_python_objects"		,   side_effect=lowerCamelCase__			) as mock_cast_to_python_objects:
                    _UpperCAmelCase	:    Tuple				 =       pa.array(TypedSequence([{"path": None, "bytes": b"image_bytes"}, pil_image]		,   type=Image()			)			)
                    _UpperCAmelCase	:    Union[str, Any]				 =       mock_cast_to_python_objects.call_args_list[-1]
                    self.assertIn("optimize_list_casting"		,   lowerCamelCase__			)
                    self.assertFalse(kwargs["optimize_list_casting"]			)

def       UpperCamelCase_				(   _UpperCAmelCase    : Optional[Any]     ,      _UpperCAmelCase    : Optional[int]       )       ->       int:


       """simple docstring"""







       _UpperCAmelCase	:    Optional[Any]				 =       pa.BufferReader(_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     ,      pa.Buffer       ) else pa.memory_map(_UpperCAmelCase       )
       _UpperCAmelCase	:    str				 =       pa.ipc.open_stream(_UpperCAmelCase       )
       _UpperCAmelCase	:    pa.Table				 =       f.read_all()
       assert len(pa_table.to_batches()       ) == expected_num_chunks
       assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
       del pa_table

@pytest.mark.parametrize("writer_batch_size"     ,      [None, 1, 10]       )
@pytest.mark.parametrize(
    "fields"     ,      [None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]       )
def       UpperCamelCase_				(   _UpperCAmelCase    : Any     ,      _UpperCAmelCase    : Optional[int]       )       ->       List[Any]:


       """simple docstring"""







       _UpperCAmelCase	:    List[str]				 =       pa.BufferOutputStream()
       _UpperCAmelCase	:    Tuple				 =       pa.schema(_UpperCAmelCase       ) if fields else None
       with ArrowWriter(stream=_UpperCAmelCase     ,      schema=_UpperCAmelCase     ,      writer_batch_size=_UpperCAmelCase       ) as writer:
              writer.write({"col_1": "foo", "col_2": 1}       )
              writer.write({"col_1": "bar", "col_2": 2}       )
              _UpperCAmelCase	:    List[Any]				 =       writer.finalize()
       assert num_examples == 2
       assert num_bytes > 0
       if not fields:
              _UpperCAmelCase	:    Optional[int]				 =       {"col_1": pa.string(), "col_2": pa.intaa()}
       assert writer._schema == pa.schema(_UpperCAmelCase     ,      metadata=writer._schema.metadata       )
       _check_output(output.getvalue()     ,      expected_num_chunks=num_examples if writer_batch_size == 1 else 1       )

def       UpperCamelCase_				(   )       ->       int:


       """simple docstring"""







       _UpperCAmelCase	:    Tuple				 =       pa.BufferOutputStream()
       _UpperCAmelCase	:    Any				 =       Features({"labels": ClassLabel(names=["neg", "pos"]       )}       )
       with ArrowWriter(stream=_UpperCAmelCase     ,      features=_UpperCAmelCase       ) as writer:
              writer.write({"labels": 0}       )
              writer.write({"labels": 1}       )
              _UpperCAmelCase	:    List[Any]				 =       writer.finalize()
       assert num_examples == 2
       assert num_bytes > 0
       assert writer._schema == features.arrow_schema
       assert writer._schema.metadata == features.arrow_schema.metadata
       _UpperCAmelCase	:    Dict				 =       pa.BufferReader(output.getvalue()       )
       _UpperCAmelCase	:    Any				 =       pa.ipc.open_stream(_UpperCAmelCase       )
       _UpperCAmelCase	:    pa.Table				 =       f.read_all()
       _UpperCAmelCase	:    Union[str, Any]				 =       pa_table.schema
       assert pa_table.num_rows == 2
       assert schema == features.arrow_schema
       assert schema.metadata == features.arrow_schema.metadata
       assert features == Features.from_arrow_schema(_UpperCAmelCase       )

@pytest.mark.parametrize("writer_batch_size"     ,      [None, 1, 10]       )
def       UpperCamelCase_				(   _UpperCAmelCase    : Tuple       )       ->       Dict:


       """simple docstring"""







       _UpperCAmelCase	:    List[str]				 =       pa.BufferOutputStream()
       with ArrowWriter(
           stream=_UpperCAmelCase     ,      writer_batch_size=_UpperCAmelCase     ,      hash_salt="split_name"     ,      check_duplicates=_UpperCAmelCase     ,      ) as writer:
              with pytest.raises(_UpperCAmelCase       ):
                     writer.write({"col_1": "foo", "col_2": 1}     ,      key=[1, 2]       )
                     _UpperCAmelCase	:    Any				 =       writer.finalize()

@pytest.mark.parametrize("writer_batch_size"     ,      [None, 2, 10]       )
def       UpperCamelCase_				(   _UpperCAmelCase    : int       )       ->       Union[str, Any]:


       """simple docstring"""







       _UpperCAmelCase	:    int				 =       pa.BufferOutputStream()
       with ArrowWriter(
           stream=_UpperCAmelCase     ,      writer_batch_size=_UpperCAmelCase     ,      hash_salt="split_name"     ,      check_duplicates=_UpperCAmelCase     ,      ) as writer:
              with pytest.raises(_UpperCAmelCase       ):
                     writer.write({"col_1": "foo", "col_2": 1}     ,      key=10       )
                     writer.write({"col_1": "bar", "col_2": 2}     ,      key=10       )
                     _UpperCAmelCase	:    str				 =       writer.finalize()

@pytest.mark.parametrize("writer_batch_size"     ,      [None, 2, 10]       )
def       UpperCamelCase_				(   _UpperCAmelCase    : str       )       ->       Optional[Any]:


       """simple docstring"""







       _UpperCAmelCase	:    Dict				 =       pa.BufferOutputStream()
       with ArrowWriter(
           stream=_UpperCAmelCase     ,      writer_batch_size=_UpperCAmelCase     ,      hash_salt="split_name"     ,      check_duplicates=_UpperCAmelCase     ,      ) as writer:
              writer.write({"col_1": "foo", "col_2": 1}     ,      key=1       )
              writer.write({"col_1": "bar", "col_2": 2}     ,      key=2       )
              _UpperCAmelCase	:    Union[str, Any]				 =       writer.finalize()
       assert num_examples == 2
       assert num_bytes > 0
       _check_output(output.getvalue()     ,      expected_num_chunks=num_examples if writer_batch_size == 1 else 1       )

@pytest.mark.parametrize("writer_batch_size"     ,      [None, 1, 10]       )
@pytest.mark.parametrize(
    "fields"     ,      [None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]       )
def       UpperCamelCase_				(   _UpperCAmelCase    : Dict     ,      _UpperCAmelCase    : Dict       )       ->       Optional[Any]:


       """simple docstring"""







       _UpperCAmelCase	:    int				 =       pa.BufferOutputStream()
       _UpperCAmelCase	:    Optional[Any]				 =       pa.schema(_UpperCAmelCase       ) if fields else None
       with ArrowWriter(stream=_UpperCAmelCase     ,      schema=_UpperCAmelCase     ,      writer_batch_size=_UpperCAmelCase       ) as writer:
              writer.write_batch({"col_1": ["foo", "bar"], "col_2": [1, 2]}       )
              writer.write_batch({"col_1": [], "col_2": []}       )
              _UpperCAmelCase	:    int				 =       writer.finalize()
       assert num_examples == 2
       assert num_bytes > 0
       if not fields:
              _UpperCAmelCase	:    Optional[Any]				 =       {"col_1": pa.string(), "col_2": pa.intaa()}
       assert writer._schema == pa.schema(_UpperCAmelCase     ,      metadata=writer._schema.metadata       )
       _check_output(output.getvalue()     ,      expected_num_chunks=num_examples if writer_batch_size == 1 else 1       )

@pytest.mark.parametrize("writer_batch_size"     ,      [None, 1, 10]       )
@pytest.mark.parametrize(
    "fields"     ,      [None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]       )
def       UpperCamelCase_				(   _UpperCAmelCase    : Tuple     ,      _UpperCAmelCase    : Dict       )       ->       Union[str, Any]:


       """simple docstring"""







       _UpperCAmelCase	:    Tuple				 =       pa.BufferOutputStream()
       _UpperCAmelCase	:    List[str]				 =       pa.schema(_UpperCAmelCase       ) if fields else None
       with ArrowWriter(stream=_UpperCAmelCase     ,      schema=_UpperCAmelCase     ,      writer_batch_size=_UpperCAmelCase       ) as writer:
              writer.write_table(pa.Table.from_pydict({"col_1": ["foo", "bar"], "col_2": [1, 2]}       )       )
              _UpperCAmelCase	:    Any				 =       writer.finalize()
       assert num_examples == 2
       assert num_bytes > 0
       if not fields:
              _UpperCAmelCase	:    str				 =       {"col_1": pa.string(), "col_2": pa.intaa()}
       assert writer._schema == pa.schema(_UpperCAmelCase     ,      metadata=writer._schema.metadata       )
       _check_output(output.getvalue()     ,      expected_num_chunks=num_examples if writer_batch_size == 1 else 1       )

@pytest.mark.parametrize("writer_batch_size"     ,      [None, 1, 10]       )
@pytest.mark.parametrize(
    "fields"     ,      [None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]       )
def       UpperCamelCase_				(   _UpperCAmelCase    : Any     ,      _UpperCAmelCase    : List[Any]       )       ->       Optional[int]:


       """simple docstring"""







       _UpperCAmelCase	:    List[str]				 =       pa.BufferOutputStream()
       _UpperCAmelCase	:    List[Any]				 =       pa.schema(_UpperCAmelCase       ) if fields else None
       with ArrowWriter(stream=_UpperCAmelCase     ,      schema=_UpperCAmelCase     ,      writer_batch_size=_UpperCAmelCase       ) as writer:
              writer.write_row(pa.Table.from_pydict({"col_1": ["foo"], "col_2": [1]}       )       )
              writer.write_row(pa.Table.from_pydict({"col_1": ["bar"], "col_2": [2]}       )       )
              _UpperCAmelCase	:    Any				 =       writer.finalize()
       assert num_examples == 2
       assert num_bytes > 0
       if not fields:
              _UpperCAmelCase	:    Dict				 =       {"col_1": pa.string(), "col_2": pa.intaa()}
       assert writer._schema == pa.schema(_UpperCAmelCase     ,      metadata=writer._schema.metadata       )
       _check_output(output.getvalue()     ,      expected_num_chunks=num_examples if writer_batch_size == 1 else 1       )

def       UpperCamelCase_				(   )       ->       Any:


       """simple docstring"""







       with tempfile.TemporaryDirectory() as tmp_dir:
              _UpperCAmelCase	:    List[str]				 =       {"col_1": pa.string(), "col_2": pa.intaa()}
              _UpperCAmelCase	:    str				 =       os.path.join(_UpperCAmelCase     ,      "test.arrow"       )
              with ArrowWriter(path=_UpperCAmelCase     ,      schema=pa.schema(_UpperCAmelCase       )       ) as writer:
                     writer.write_batch({"col_1": ["foo", "bar"], "col_2": [1, 2]}       )
                     _UpperCAmelCase	:    Union[str, Any]				 =       writer.finalize()
              assert num_examples == 2
              assert num_bytes > 0
              assert writer._schema == pa.schema(_UpperCAmelCase     ,      metadata=writer._schema.metadata       )
              _check_output(_UpperCAmelCase     ,      1       )

def       UpperCamelCase_				(   _UpperCAmelCase    : int       )       ->       Tuple:


       """simple docstring"""







       if pa.types.is_list(_UpperCAmelCase       ):
              return get_base_dtype(arr_type.value_type       )
       else:
              return arr_type

def       UpperCamelCase_				(   _UpperCAmelCase    : Optional[int]     ,      _UpperCAmelCase    : Dict       )       ->       int:


       """simple docstring"""







       if isinstance(lst[0]     ,      _UpperCAmelCase       ):
              change_first_primitive_element_in_list(lst[0]     ,      _UpperCAmelCase       )
       else:
              _UpperCAmelCase	:    Dict				 =       value

@pytest.mark.parametrize("optimized_int_type, expected_dtype"     ,      [(None, pa.intaa()), (Value("int32"       ), pa.intaa())]       )
@pytest.mark.parametrize("sequence"     ,      [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]       )
def       UpperCamelCase_				(   _UpperCAmelCase    : Optional[Any]     ,      _UpperCAmelCase    : Dict     ,      _UpperCAmelCase    : Dict       )       ->       Optional[int]:


       """simple docstring"""







       _UpperCAmelCase	:    str				 =       pa.array(TypedSequence(_UpperCAmelCase     ,      optimized_int_type=_UpperCAmelCase       )       )
       assert get_base_dtype(arr.type       ) == expected_dtype

@pytest.mark.parametrize(
    "col, expected_dtype"     ,      [
        ("attention_mask", pa.inta()),
        ("special_tokens_mask", pa.inta()),
        ("token_type_ids", pa.inta()),
        ("input_ids", pa.intaa()),
        ("other", pa.intaa()),
    ]     ,      )
@pytest.mark.parametrize("sequence"     ,      [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]       )
def       UpperCamelCase_				(   _UpperCAmelCase    : str     ,      _UpperCAmelCase    : int     ,      _UpperCAmelCase    : str       )       ->       int:


       """simple docstring"""







       _UpperCAmelCase	:    List[Any]				 =       pa.array(OptimizedTypedSequence(_UpperCAmelCase     ,      col=_UpperCAmelCase       )       )
       assert get_base_dtype(arr.type       ) == expected_dtype

       # not in range
       if col != "other":
              # avoids errors due to in-place modifications
              _UpperCAmelCase	:    Dict				 =       copy.deepcopy(_UpperCAmelCase       )
              _UpperCAmelCase	:    List[Any]				 =       np.iinfo(expected_dtype.to_pandas_dtype()       ).max + 1
              change_first_primitive_element_in_list(_UpperCAmelCase     ,      _UpperCAmelCase       )
              _UpperCAmelCase	:    Tuple				 =       pa.array(OptimizedTypedSequence(_UpperCAmelCase     ,      col=_UpperCAmelCase       )       )
              assert get_base_dtype(arr.type       ) == pa.intaa()

@pytest.mark.parametrize("raise_exception"     ,      [False, True]       )
def       UpperCamelCase_				(   _UpperCAmelCase    : Dict     ,      _UpperCAmelCase    : List[Any]       )       ->       Tuple:


       """simple docstring"""







       _UpperCAmelCase	:    List[Any]				 =       str(tmp_path / "dataset-train.arrow"       )
       try:
              with ArrowWriter(path=_UpperCAmelCase       ) as writer:
                     if raise_exception:
                            raise pa.lib.ArrowInvalid()
                     else:
                            writer.stream.close()
       except pa.lib.ArrowInvalid:
              pass
       finally:
              assert writer.stream.closed

def       UpperCamelCase_				(   _UpperCAmelCase    : int       )       ->       Dict:


       """simple docstring"""







       _UpperCAmelCase	:    List[Any]				 =       "mock://dataset-train.arrow"
       with ArrowWriter(path=_UpperCAmelCase     ,      storage_options=mockfs.storage_options       ) as writer:
              assert isinstance(writer._fs     ,      type(_UpperCAmelCase       )       )
              assert writer._fs.storage_options == mockfs.storage_options
              writer.write({"col_1": "foo", "col_2": 1}       )
              writer.write({"col_1": "bar", "col_2": 2}       )
              _UpperCAmelCase	:    List[Any]				 =       writer.finalize()
       assert num_examples == 2
       assert num_bytes > 0
       assert mockfs.exists(_UpperCAmelCase       )

def       UpperCamelCase_				(   )       ->       Optional[int]:


       """simple docstring"""







       _UpperCAmelCase	:    Dict				 =       pa.BufferOutputStream()
       with ParquetWriter(stream=_UpperCAmelCase       ) as writer:
              writer.write({"col_1": "foo", "col_2": 1}       )
              writer.write({"col_1": "bar", "col_2": 2}       )
              _UpperCAmelCase	:    str				 =       writer.finalize()
       assert num_examples == 2
       assert num_bytes > 0
       _UpperCAmelCase	:    str				 =       pa.BufferReader(output.getvalue()       )
       _UpperCAmelCase	:    pa.Table				 =       pq.read_table(_UpperCAmelCase       )
       assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}

@require_pil
@pytest.mark.parametrize("embed_local_files"     ,      [False, True]       )
def       UpperCamelCase_				(   _UpperCAmelCase    : List[Any]     ,      _UpperCAmelCase    : List[Any]       )       ->       int:


       """simple docstring"""







       import PIL.Image

       _UpperCAmelCase	:    List[str]				 =       str(tmp_path / "test_image_rgb.jpg"       )
       PIL.Image.fromarray(np.zeros((5, 5)     ,      dtype=np.uinta       )       ).save(_UpperCAmelCase     ,      format="png"       )
       _UpperCAmelCase	:    List[Any]				 =       pa.BufferOutputStream()
       with ParquetWriter(
           stream=_UpperCAmelCase     ,      features=Features({"image": Image()}       )     ,      embed_local_files=_UpperCAmelCase       ) as writer:
              writer.write({"image": image_path}       )
              writer.finalize()
       _UpperCAmelCase	:    Optional[int]				 =       pa.BufferReader(output.getvalue()       )
       _UpperCAmelCase	:    pa.Table				 =       pq.read_table(_UpperCAmelCase       )
       _UpperCAmelCase	:    Dict				 =       pa_table.to_pydict()
       if embed_local_files:
              assert isinstance(out["image"][0]["path"]     ,      _UpperCAmelCase       )
              with open(_UpperCAmelCase     ,      "rb"       ) as f:
                     assert out["image"][0]["bytes"] == f.read()
       else:
              assert out["image"][0]["path"] == image_path
              assert out["image"][0]["bytes"] is None


def       UpperCamelCase_				(   )       ->       Any:


       """simple docstring"""







       _UpperCAmelCase	:    List[str]				 =       pa.schema([pa.field("col_1"     ,      pa.string()     ,      nullable=_UpperCAmelCase       )]       )
       _UpperCAmelCase	:    Optional[Any]				 =       pa.BufferOutputStream()
       with ArrowWriter(stream=_UpperCAmelCase       ) as writer:
              writer._build_writer(inferred_schema=_UpperCAmelCase       )
              assert writer._schema == pa.schema([pa.field("col_1"     ,      pa.string()       )]       )




import inspect
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_lowerCAmelCase  :      int			 ="""src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
_lowerCAmelCase  :      Dict			 =direct_transformers_import(PATH_TO_TRANSFORMERS)

_lowerCAmelCase  :      List[str]			 =transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
_lowerCAmelCase  :      int			 =re.compile(r"""\[(.+?)\]\((https://huggingface\.co/.+?)\)""")


_lowerCAmelCase  :      Union[str, Any]			 ={
    """DecisionTransformerConfig""",
    """EncoderDecoderConfig""",
    """MusicgenConfig""",
    """RagConfig""",
    """SpeechEncoderDecoderConfig""",
    """TimmBackboneConfig""",
    """VisionEncoderDecoderConfig""",
    """VisionTextDualEncoderConfig""",
    """LlamaConfig""",
}



def 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	str									= None

	# source code of `config_class`
	UpperCAmelCase__:	Optional[int]									= inspect.getsource(SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	List[Any]									= _re_checkpoint.findall(SCREAMING_SNAKE_CASE      )

	# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
	# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
	for ckpt_name, ckpt_link in checkpoints:
		# allow the link to end with `/`
		if ckpt_link.endswith("/"      ):
			UpperCAmelCase__:	Any									= ckpt_link[:-1]

		# verify the checkpoint name corresponds to the checkpoint link
		UpperCAmelCase__:	int									= f"https://huggingface.co/{ckpt_name}"
		if ckpt_link == ckpt_link_from_name:
			UpperCAmelCase__:	Optional[int]									= ckpt_name
			break

	return checkpoint



def 			_A	(      ):
	UpperCAmelCase__:	List[str]									= []

	for config_class in list(CONFIG_MAPPING.values()      ):
		# Skip deprecated models
		if "models.deprecated" in config_class.__module__:
			continue
		UpperCAmelCase__:	Optional[Any]									= get_checkpoint_from_config_class(SCREAMING_SNAKE_CASE      )

		UpperCAmelCase__:	List[str]									= config_class.__name__
		if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
			configs_without_checkpoint.append(SCREAMING_SNAKE_CASE      )

	if len(SCREAMING_SNAKE_CASE      ) > 0:
		UpperCAmelCase__:	Dict									= "\n".join(sorted(SCREAMING_SNAKE_CASE      )      )
		raise ValueError(f"The following configurations don't contain any valid checkpoint:\n{message}"      )


if __name__ == "__main__":
					check_config_docstrings_have_checkpoints()