





"""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
UpperCamelCase_									="""src/transformers"""
UpperCamelCase_									="""docs/source/en/tasks"""





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase       ):







						with open(UpperCamelCase__	,						'''r'''	,						encoding='''utf-8'''	,						newline='''\n'''       ) as f:
												_UpperCamelCase      : Optional[Any]         =   f.readlines()
						# Find the start prompt.
						_UpperCamelCase      : int         =   0
						while not lines[start_index].startswith(UpperCamelCase__       ):
												start_index += 1
						start_index += 1

						_UpperCamelCase      : int         =   start_index
						while not lines[end_index].startswith(UpperCamelCase__       ):
												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.
UpperCamelCase_									=direct_transformers_import(TRANSFORMERS_PATH)

UpperCamelCase_									={
    """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`).
UpperCamelCase_									={
    """summarization.md""": ("""nllb""",),
    """translation.md""": ("""nllb""",),
}





def 	a_    (							_lowercase       ):
						_UpperCamelCase      : str         =   TASK_GUIDE_TO_MODELS[task_guide]
						_UpperCamelCase      : Any         =   SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(UpperCamelCase__	,						set()       )
						_UpperCamelCase      : Union[str, Any]         =   {
						    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 	a_    (							_lowercase	,						_lowercase=False       ):
						_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase      : Optional[int]         =   _find_text_in_file(
						    filename=os.path.join(UpperCamelCase__	,						UpperCamelCase__       )	,						start_prompt='''<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->'''	,						end_prompt='''<!--End of the generated tip-->'''	,						)

						_UpperCamelCase      : List[str]         =   get_model_list_for_task(UpperCamelCase__       )

						if current_list != new_list:
												if overwrite:
																		with open(os.path.join(UpperCamelCase__	,						UpperCamelCase__       )	,						'''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__":
						UpperCamelCase_									=argparse.ArgumentParser()
						parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
						UpperCamelCase_									=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"""




from functools import lru_cache





@lru_cache
def 	a_    (							_lowercase       ):
						if num < 0:
												raise ValueError('''Number should not be negative.'''       )

						return 1 if num in (0, 1) else num * factorial(num - 1       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()



