


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


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     with open(SCREAMING_SNAKE_CASE       ,					"r"       ,					encoding="utf-8"       ,					newline="\n"							) as f:
          __A	:       Optional[int]      =							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	:       str      =							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.
_UpperCamelCase 							=  direct_transformers_import(TRANSFORMERS_PATH)

_UpperCamelCase 							=  {
    """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`).
_UpperCamelCase 							=  {
    """summarization.md""": ("""nllb""",),
    """translation.md""": ("""nllb""",),
}


def 		_lowercase (SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       int      =							TASK_GUIDE_TO_MODELS[task_guide]
     __A	:       Tuple      =							SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(SCREAMING_SNAKE_CASE       ,					set()							)
     __A	:       Dict      =							{
         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 (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE=False							):




     '''simple docstring'''





     __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	:       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__":
      _UpperCamelCase 							=  argparse.ArgumentParser()
      parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
      _UpperCamelCase 							=  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 functools


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       int      =							len(SCREAMING_SNAKE_CASE							)
     __A	:       int      =							len(SCREAMING_SNAKE_CASE							)

     @functools.cache
     def min_distance(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							) -> int:
          # if first word index is overflow - delete all from the second word
          if indexa >= len_worda:
               return len_worda - indexa
          # if second word index is overflow - delete all from the first word
          if indexa >= len_worda:
               return len_worda - indexa
          __A	:       Optional[int]      =							int(worda[indexa] != worda[indexa]							)  # current letters not identical
          return min(
              1 + min_distance(indexa + 1       ,					SCREAMING_SNAKE_CASE							)       ,					1 + min_distance(SCREAMING_SNAKE_CASE       ,					indexa + 1							)       ,					diff + min_distance(indexa + 1       ,					indexa + 1							)       ,					)

     return min_distance(0       ,					0							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()

