



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_    :  int						  =		'src/transformers'
a_    :  Any						  =		'docs/source/en/tasks'

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



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

      lowerCamelCase             =		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.
a_    :  Union[str, Any]						  =		direct_transformers_import(TRANSFORMERS_PATH)

a_    :  Tuple						  =		{
    '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_    :  Union[str, Any]						  =		{
    'summarization.md': ('nllb',),
    'translation.md': ('nllb',),
}

def      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		TASK_GUIDE_TO_MODELS[task_guide]
      lowerCamelCase             =		SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(UpperCAmelCase__ , set()			)
      lowerCamelCase             =		{
          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(						UpperCAmelCase__ , UpperCAmelCase__=False			):


      """simple docstring"""



      lowerCamelCase      ,				lowerCamelCase      ,				lowerCamelCase      ,				lowerCamelCase             =		_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-->" , )

      lowerCamelCase             =		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__":
     a_    :  Dict						  =		argparse.ArgumentParser()
     parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
     a_    :  int						  =		parser.parse_args()

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




from ..utils import DummyObject, requires_backends





class  lowerCamelCase__	(    metaclass=UpperCAmelCase_):
     """simple docstring"""



     _A      =      ['transformers', 'torch', 'note_seq']







     def __init__(self					,      *__a					,      **__a     ):






           '''simple docstring'''



           requires_backends(self					,      ["transformers", "torch", "note_seq"]     )







     @classmethod
     def     _a   (cls					,      *__a					,      **__a     ):






           '''simple docstring'''



           requires_backends(cls					,      ["transformers", "torch", "note_seq"]     )







     @classmethod
     def     _a   (cls					,      *__a					,      **__a     ):






           '''simple docstring'''



           requires_backends(cls					,      ["transformers", "torch", "note_seq"]     )