


"""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      lowercase			(				A_  ,   A_  ,   A_       )->      Tuple:
  '''simple docstring'''







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

  a						:		int      							=			start_index
  while not lines[end_index].startswith(A__       ):
    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      lowercase			(				A_       )->      Optional[int]:
  '''simple docstring'''







  a						:		Optional[Any]      							=			TASK_GUIDE_TO_MODELS[task_guide]
  a						:		int      							=			SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(A__  ,   set()       )
  a						:		Optional[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			(				A_  ,   A_=False       )->      Optional[int]:
  '''simple docstring'''







  a						:		Dict      							=			_find_text_in_file(
      filename=os.path.join(A__  ,   A__       )  ,   start_prompt="<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->"  ,   end_prompt="<!--End of the generated tip-->"  ,   )

  a						:		List[Any]      							=			get_model_list_for_task(A__       )

  if current_list != new_list:
    if overwrite:
      with open(os.path.join(A__  ,   A__       )  ,   "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)













def A_	(		A__		,      A__				)  ->		str:
       if a < 0 or b < 0:
              raise ValueError('the value of both inputs must be positive'				)

       a__						:	List[str] =				str(bin(A__				)				)[2:]  # remove the leading "0b"
       a__						:	Optional[int] =				str(bin(A__				)				)[2:]  # remove the leading "0b"

       a__						:	List[str] =				max(len(A__				)		,      len(A__				)				)

       return "0b" + "".join(
           str(int(char_a == '1' and char_b == '1'				)				)
           for char_a, char_b in zip(a_binary.zfill(A__				)		,      b_binary.zfill(A__				)				)				)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


