





'''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_ : Optional[int]	  =       "src/transformers"
lowerCAmelCase_ : Optional[int]	  =       "docs/source/en/tasks"







def  _lowerCamelCase	(__lowerCamelCase	:  List[Any]       ,	__lowerCamelCase	:  str       ,	__lowerCamelCase	:  Optional[Any]  ) ->     Union[str, Any]:







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

			a__	 =			start_index
			while not lines[end_index].startswith(snake_case_  ):
						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_ : Any	  =       direct_transformers_import(TRANSFORMERS_PATH)

lowerCAmelCase_ : Dict	  =       {
    "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_ : Tuple	  =       {
    "summarization.md": ("nllb",),
    "translation.md": ("nllb",),
}







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

			a__	 =			get_model_list_for_task(snake_case_  )

			if current_list != new_list:
						if overwrite:
									with open(os.path.join(snake_case_       ,	snake_case_  )       ,	"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_ : List[str]	  =       argparse.ArgumentParser()
		parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
		lowerCAmelCase_ : str	  =       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 						__lowercase	(  snake_case_    :    str							,snake_case_    :    str		)  ->float:



    '''simple docstring'''







    def get_matched_characters(snake_case_    :    str							,snake_case_    :    str		) -> str:
        __A       :		Any							=	[]
        __A       :		Any							=	min(len(_stra		)							,len(_stra		)		) // 2
        for i, l in enumerate(_stra		):
            __A       :		Dict							=	int(max(0							,i - limit		)		)
            __A       :		Tuple							=	int(min(i + limit + 1							,len(_stra		)		)		)
            if l in _stra[left:right]:
                matched.append(snake_case_		)
                __A       :		Any							=	F"""{_stra[0:_stra.index(snake_case_		)]} {_stra[_stra.index(snake_case_		) + 1:]}"""

        return "".join(snake_case_		)

    # matching characters
    __A       :		int							=	get_matched_characters(snake_case_							,snake_case_		)
    __A       :		Tuple							=	get_matched_characters(snake_case_							,snake_case_		)
    __A       :		str							=	len(snake_case_		)

    # transposition
    __A       :		Dict							=	(
        len([(ca, ca) for ca, ca in zip(snake_case_							,snake_case_		) if ca != ca]		) // 2
    )

    if not match_count:
        __A       :		List[str]							=	0.0
    else:
        __A       :		Tuple							=	(
            1
            / 3
            * (
                match_count / len(snake_case_		)
                + match_count / len(snake_case_		)
                + (match_count - transpositions) / match_count
            )
        )

    # common prefix up to 4 characters
    __A       :		Tuple							=	0
    for ca, ca in zip(stra[:4]							,stra[:4]		):
        if ca == ca:
            prefix_len += 1
        else:
            break

    return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(jaro_winkler("""hello""", """world"""))




