






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_			      = 'src/transformers'


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

lowerCAmelCase_			      = 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_			      = re.compile(R'\[(.+?)\]\((https://huggingface\.co/.+?)\)')


lowerCAmelCase_			      = {
    'DecisionTransformerConfig',
    'EncoderDecoderConfig',
    'MusicgenConfig',
    'RagConfig',
    'SpeechEncoderDecoderConfig',
    'TimmBackboneConfig',
    'VisionEncoderDecoderConfig',
    'VisionTextDualEncoderConfig',
    'LlamaConfig',
}







def      snake_case		(							UpperCAmelCase	:       Union[str, Any]   ):
				A	 =      None

				# source code of `config_class`
				A	 =      inspect.getsource(UpperCAmelCase   )
				A	 =      _re_checkpoint.findall(UpperCAmelCase   )

				# 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('/'   ):
												A	 =      ckpt_link[:-1]

								# verify the checkpoint name corresponds to the checkpoint link
								A	 =      f'https://huggingface.co/{ckpt_name}'
								if ckpt_link == ckpt_link_from_name:
												A	 =      ckpt_name
												break

				return checkpoint







def      snake_case		(							):
				A	 =      []

				for config_class in list(CONFIG_MAPPING.values()   ):
								# Skip deprecated models
								if "models.deprecated" in config_class.__module__:
												continue
								A	 =      get_checkpoint_from_config_class(UpperCAmelCase   )

								A	 =      config_class.__name__
								if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
												configs_without_checkpoint.append(UpperCAmelCase   )

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


if __name__ == "__main__":
					check_config_docstrings_have_checkpoints()













from ...configuration_utils import PretrainedConfig
class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		snake_case    =   "bert-generation"
		def __init__(  self		:       Tuple						,_SCREAMING_SNAKE_CASE		:       Tuple=5_0_3_5_8						,_SCREAMING_SNAKE_CASE		:       str=1_0_2_4						,_SCREAMING_SNAKE_CASE		:       List[str]=2_4						,_SCREAMING_SNAKE_CASE		:       Optional[Any]=1_6						,_SCREAMING_SNAKE_CASE		:       Dict=4_0_9_6						,_SCREAMING_SNAKE_CASE		:       str="gelu"						,_SCREAMING_SNAKE_CASE		:       str=0.1						,_SCREAMING_SNAKE_CASE		:       Optional[Any]=0.1						,_SCREAMING_SNAKE_CASE		:       Tuple=5_1_2						,_SCREAMING_SNAKE_CASE		:       Optional[Any]=0.02						,_SCREAMING_SNAKE_CASE		:       List[str]=1E-12						,_SCREAMING_SNAKE_CASE		:       Tuple=0						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]=2						,_SCREAMING_SNAKE_CASE		:       Optional[int]=1						,_SCREAMING_SNAKE_CASE		:       str="absolute"						,_SCREAMING_SNAKE_CASE		:       Optional[int]=True						,**_SCREAMING_SNAKE_CASE		:       Optional[int]						,)    ->   Dict:


						'''simple docstring'''
						super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE						,bos_token_id=_SCREAMING_SNAKE_CASE						,eos_token_id=_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE		)

						A	 =      vocab_size
						A	 =      hidden_size
						A	 =      num_hidden_layers
						A	 =      num_attention_heads
						A	 =      hidden_act
						A	 =      intermediate_size
						A	 =      hidden_dropout_prob
						A	 =      attention_probs_dropout_prob
						A	 =      max_position_embeddings
						A	 =      initializer_range
						A	 =      layer_norm_eps
						A	 =      position_embedding_type
						A	 =      use_cache





