






from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader


class 							__a							(  __A						):





		'''simple docstring'''



		def __init__(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = True ,    UpperCamelCase__ = None ,    UpperCamelCase__ = False ,    UpperCamelCase__ = None ,    UpperCamelCase__ = True ,    UpperCamelCase__ = "arrow" ,    **UpperCamelCase__ ,    ):
						super().__init__(
						    split=UpperCamelCase__ ,    features=UpperCamelCase__ ,    cache_dir=UpperCamelCase__ ,    keep_in_memory=UpperCamelCase__ ,    streaming=UpperCamelCase__ ,    **UpperCamelCase__ ,    )
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   load_from_cache_file
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   file_format
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   Spark(
						    df=UpperCamelCase__ ,    features=UpperCamelCase__ ,    cache_dir=UpperCamelCase__ ,    working_dir=UpperCamelCase__ ,    **UpperCamelCase__ ,    )






		def   __snake_case			(						self       ):
						if self.streaming:
										return self.builder.as_streaming_dataset(split=self.split       )
						SCREAMING_SNAKE_CASE_   :	str													=   None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
						self.builder.download_and_prepare(
						    download_mode=UpperCamelCase__ ,    file_format=self._file_format ,    )
						return self.builder.as_dataset(split=self.split       )











import importlib
import inspect
import os
import re


# 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
A        =			'src/transformers'


# This is to make sure the transformers module imported is the one in the repo.
A        =			importlib.util.spec_from_file_location(
    'transformers',
    os.path.join(PATH_TO_TRANSFORMERS, '__init__.py'),
    submodule_search_locations=[PATH_TO_TRANSFORMERS],
)
A        =			spec.loader.load_module()

A        =			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)`
A        =			re.compile('\[(.+?)\]\((https://huggingface\.co/.+?)\)')


A        =			{
    'CLIPConfigMixin',
    'DecisionTransformerConfigMixin',
    'EncoderDecoderConfigMixin',
    'RagConfigMixin',
    'SpeechEncoderDecoderConfigMixin',
    'VisionEncoderDecoderConfigMixin',
    'VisionTextDualEncoderConfigMixin',
}



def 					_lowerCamelCase(			):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   []

				for config_class in list(CONFIG_MAPPING.values()      ):
								SCREAMING_SNAKE_CASE_   :	int													=   False

								# source code of `config_class`
								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   inspect.getsource(lowerCAmelCase__      )
								SCREAMING_SNAKE_CASE_   :	List[Any]													=   _re_checkpoint.findall(lowerCAmelCase__      )

								for checkpoint in checkpoints:
												# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
												# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
												SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Tuple													=   checkpoint

												# verify the checkpoint name corresponds to the checkpoint link
												SCREAMING_SNAKE_CASE_   :	List[str]													=   F'''https://huggingface.co/{ckpt_name}'''
												if ckpt_link == ckpt_link_from_name:
																SCREAMING_SNAKE_CASE_   :	Optional[int]													=   True
																break

								SCREAMING_SNAKE_CASE_   :	Tuple													=   config_class.__name__
								if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
												configs_without_checkpoint.append(lowerCAmelCase__      )

				if len(lowerCAmelCase__      ) > 0:
								SCREAMING_SNAKE_CASE_   :	str													=   '\n'.join(sorted(lowerCAmelCase__      )      )
								raise ValueError(F'''The following configurations don\'t contain any valid checkpoint:\n{message}'''      )


if __name__ == "__main__":
				check_config_docstrings_have_checkpoints()