"""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      :		Optional[Any]           =   'src/transformers'
_a      :		Any           =   'docs/source/en/tasks'





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

  _lowerCAmelCase						:       List[Any]  			=     start_index
  while not lines[end_index].startswith(_lowerCamelCase	):
    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      :		Optional[int]           =   {
    '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      :		Tuple           =   {
    'summarization.md': ('nllb',),
    'translation.md': ('nllb',),
}





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

  _lowerCAmelCase						:       Any  			=     get_model_list_for_task(_lowerCamelCase	)

  if current_list != new_list:
    if overwrite:
      with open(os.path.join(_lowerCamelCase			,_lowerCamelCase	)			,"""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      :		List[str]           =   argparse.ArgumentParser()
     parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
     _a      :		Dict           =   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 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Any	)       ->      List[Any]:  # noqa: E741
  _lowerCAmelCase						:       Optional[int]  			=     len(_lowerCamelCase	)
  _lowerCAmelCase						:       str  			=     0
  _lowerCAmelCase						:       Any  			=     [0] * n
  _lowerCAmelCase						:       str  			=     [False] * n
  _lowerCAmelCase						:       str  			=     [False] * n

  def dfs(_lowerCamelCase						:       Tuple			,_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       str	):
    if parent == root:
      out_edge_count += 1
    _lowerCAmelCase						:       Any  			=     True
    _lowerCAmelCase						:       int  			=     at

    for to in l[at]:
      if to == parent:
        pass
      elif not visited[to]:
        _lowerCAmelCase						:       Union[str, Any]  			=     dfs(_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase	)
        _lowerCAmelCase						:       Optional[int]  			=     min(low[at]			,low[to]	)

        # AP found via bridge
        if at < low[to]:
          _lowerCAmelCase						:       int  			=     True
        # AP found via cycle
        if at == low[to]:
          _lowerCAmelCase						:       Tuple  			=     True
      else:
        _lowerCAmelCase						:       Union[str, Any]  			=     min(low[at]			,_lowerCamelCase	)
    return out_edge_count

  for i in range(_lowerCamelCase	):
    if not visited[i]:
      _lowerCAmelCase						:       int  			=     0
      _lowerCAmelCase						:       Dict  			=     dfs(_lowerCamelCase			,_lowerCamelCase			,-1			,_lowerCamelCase	)
      _lowerCAmelCase						:       List[str]  			=     out_edge_count > 1

  for x in range(len(_lowerCamelCase	)	):
    if is_art[x] is True:
      print(_lowerCamelCase	)


# Adjacency list of graph
_a      :		Optional[Any]           =   {
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}






compute_ap(data)




