

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_     :    Dict     		=   "src/transformers"
a_     :    Tuple     		=   "docs/source/en/tasks"


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Dict		,				_UpperCamelCase		:	List[str]		,				_UpperCamelCase		:	List[Any]						)		->				List[Any]:
 '''simple docstring'''


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

 SCREAMING_SNAKE_CASE		    =      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_     :    Tuple     		=   direct_transformers_import(TRANSFORMERS_PATH)

a_     :    Any     		=   {
    "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_     :    List[str]     		=   {
    "summarization.md": ("nllb",),
    "translation.md": ("nllb",),
}


def 							__lowerCAmelCase     (							_UpperCamelCase		:	List[Any]						)		->				Union[str, Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      TASK_GUIDE_TO_MODELS[task_guide]
 SCREAMING_SNAKE_CASE		    =      SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(_UpperCamelCase		,				set()						)
 SCREAMING_SNAKE_CASE		    =      {
     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 							__lowerCAmelCase     (							_UpperCamelCase		:	List[str]		,				_UpperCamelCase		:	Optional[Any]=False						)		->				int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      _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-->'		,				)

 SCREAMING_SNAKE_CASE		    =      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_     :    int     		=   argparse.ArgumentParser()
      parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
      a_     :    Any     		=   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       snake_case   (	snake_case      : list					,		snake_case      : list					,		snake_case      : int					,		snake_case      : int					,		snake_case      : int			) ->  int:


    """simple docstring"""


    if index == number_of_items:
        return 0
    lowerCAmelCase 						=							0
    lowerCAmelCase 						=							0
    lowerCAmelCase 						=							knapsack(snake_case					,		snake_case					,		snake_case					,		snake_case					,		index + 1			)
    if weights[index] <= max_weight:
        lowerCAmelCase 						=							values[index] + knapsack(
            snake_case					,		snake_case					,		snake_case					,		max_weight - weights[index]					,		index + 1			)
    return max(snake_case					,		snake_case			)


if __name__ == "__main__":
     import doctest

     doctest.testmod()





