



"""simple docstring"""





import argparse
import os

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_task_guides.py
lowerCAmelCase_     =	'src/transformers'
lowerCAmelCase_     =	'docs/source/en/tasks'



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Tuple:





							with open(_lowerCamelCase     ,	"""r"""     ,	encoding="""utf-8"""     ,	newline="""\n"""  ) as f:
														_SCREAMING_SNAKE_CASE		:						Dict			  =    f.readlines()
							# Find the start prompt.
							_SCREAMING_SNAKE_CASE		:						str			  =    0
							while not lines[start_index].startswith(_lowerCamelCase  ):
														start_index += 1
							start_index += 1

							_SCREAMING_SNAKE_CASE		:						List[str]			  =    start_index
							while not lines[end_index].startswith(_lowerCamelCase  ):
														end_index += 1
							end_index -= 1

							while len(lines[start_index]  ) <= 1:
														start_index += 1
							while len(lines[end_index]  ) <= 1:
														end_index -= 1
							end_index += 1
							return "".join(lines[start_index:end_index]  ), start_index, end_index, lines


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

lowerCAmelCase_     =	{
    'asr.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
    'audio_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
    'language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    'image_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
    'masked_language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
    'multiple_choice.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
    'object_detection.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
    'question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
    'semantic_segmentation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
    'sequence_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
    'summarization.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    'token_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
    'translation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    'video_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
    'document_question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
    'monocular_depth_estimation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}

# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
lowerCAmelCase_     =	{
    'summarization.md': ('nllb',),
    'translation.md': ('nllb',),
}



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    List[str]:
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    TASK_GUIDE_TO_MODELS[task_guide]
							_SCREAMING_SNAKE_CASE		:						str			  =    SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(_lowerCamelCase     ,	set()  )
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    {
							    code: name
							    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
							    if (code in model_maping_names or code in special_model_types)
							}
							return ", ".join([F"""[{name}](../model_doc/{code})""" for code, name in model_names.items()]  ) + "\n"



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=False  )->    Any:
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    _find_text_in_file(
							    filename=os.path.join(_lowerCamelCase     ,	_lowerCamelCase  )     ,	start_prompt="""<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->"""     ,	end_prompt="""<!--End of the generated tip-->"""     ,	)

							_SCREAMING_SNAKE_CASE		:						str			  =    get_model_list_for_task(_lowerCamelCase  )

							if current_list != new_list:
														if overwrite:
																					with open(os.path.join(_lowerCamelCase     ,	_lowerCamelCase  )     ,	"""w"""     ,	encoding="""utf-8"""     ,	newline="""\n"""  ) as f:
																												f.writelines(lines[:start_index] + [new_list] + lines[end_index:]  )
														else:
																					raise ValueError(
																					    F"""The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`"""
																					    """ to fix this."""  )


if __name__ == "__main__":
			lowerCAmelCase_     =	argparse.ArgumentParser()
			parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
			lowerCAmelCase_     =	parser.parse_args()

			for task_guide in TASK_GUIDE_TO_MODELS.keys():
						check_model_list_for_task(task_guide, args.fix_and_overwrite)



"""simple docstring"""





def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    int:

							if not isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ):
														raise TypeError("""only integers accepted as input"""  )
							else:
														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    str(abs(__SCREAMING_SNAKE_CASE  )  )
														_SCREAMING_SNAKE_CASE		:						List[str]			  =    [list(__SCREAMING_SNAKE_CASE  ) for char in range(len(__SCREAMING_SNAKE_CASE  )  )]
														for index in range(len(__SCREAMING_SNAKE_CASE  )  ):
																					num_transpositions[index].pop(__SCREAMING_SNAKE_CASE  )
														return max(
														    int("""""".join(list(__SCREAMING_SNAKE_CASE  )  )  ) for transposition in num_transpositions  )


if __name__ == "__main__":
			__import__('''doctest''').testmod()


