"""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
lowercase__					       =	'src/transformers'
lowercase__					       =	'docs/source/en/tasks'







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

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

lowercase__					       =	{
    '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`).
lowercase__					       =	{
    'summarization.md': ('nllb',),
    'translation.md': ('nllb',),
}







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->int:
  a__:     int										=	TASK_GUIDE_TO_MODELS[task_guide]
  a__:     Dict										=	SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(_SCREAMING_SNAKE_CASE		,     set()   )
  a__:     Tuple										=	{
      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   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE=False   )	->Optional[int]:
  a__							,    a__							,    a__							,    a__:     int										=	_find_text_in_file(
      filename=os.path.join(_SCREAMING_SNAKE_CASE		,     _SCREAMING_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__:     Optional[Any]										=	get_model_list_for_task(_SCREAMING_SNAKE_CASE   )

  if current_list != new_list:
    if overwrite:
      with open(os.path.join(_SCREAMING_SNAKE_CASE		,     _SCREAMING_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__":
 lowercase__					       =	argparse.ArgumentParser()
 parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
 lowercase__					       =	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"""
import os
from typing import Dict, List, Tuple, TypeVar, Union


lowercase__					       =	TypeVar('T')

lowercase__					       =	Union[List[T], Tuple[T, ...]]
lowercase__					       =	Union[T, List[T], Dict[str, T]]
lowercase__					       =	Union[str, bytes, os.PathLike]


