





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







def 						__lowercase	(  snake_case_    :    List[Any]							,snake_case_    :    str							,snake_case_    :    Optional[Any]		)  ->Union[str, Any]:



    '''simple docstring'''







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

    __A       :		List[Any]							=	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.
a_					=     direct_transformers_import(TRANSFORMERS_PATH)

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







def 						__lowercase	(  snake_case_    :    int		)  ->Optional[Any]:



    '''simple docstring'''







    __A       :		Optional[int]							=	TASK_GUIDE_TO_MODELS[task_guide]
    __A       :		Tuple							=	SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(snake_case_							,set()		)
    __A       :		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 						__lowercase	(  snake_case_    :    int							,snake_case_    :    List[Any]=False		)  ->List[str]:



    '''simple docstring'''







    __A					,			__A					,			__A					,			__A       :		Dict							=	_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       :		Any							=	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__":
       a_					=     argparse.ArgumentParser()
       parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
       a_					=     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_    :    int							,snake_case_    :    int		)  ->int:



    '''simple docstring'''







    return int((input_a, input_a).count(0		) == 0		)







def 						__lowercase	(  )  ->None:



    '''simple docstring'''







    assert and_gate(0							,0		) == 0
    assert and_gate(0							,1		) == 0
    assert and_gate(1							,0		) == 0
    assert and_gate(1							,1		) == 1


if __name__ == "__main__":
       test_and_gate()
       print(and_gate(1, 0))
       print(and_gate(0, 0))
       print(and_gate(0, 1))
       print(and_gate(1, 1))




