






'''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
SCREAMING_SNAKE_CASE							=						'src/transformers'
SCREAMING_SNAKE_CASE							=						'docs/source/en/tasks'


def 				lowercase_					(     __A			:							Union[str, Any]       ,   __A			:							str       ,   __A			:							Any      )							->   Tuple:



 """simple docstring"""




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

 lowercase       : List[Any]						=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.
SCREAMING_SNAKE_CASE							=						direct_transformers_import(TRANSFORMERS_PATH)

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


def 				lowercase_					(     __A			:							Optional[int]      )							->   str:



 """simple docstring"""




 lowercase       : Dict						=TASK_GUIDE_TO_MODELS[task_guide]
 lowercase       : Tuple						=SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(__A       ,   set()      )
 lowercase       : 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			:							Optional[int]       ,   __A			:							Union[str, Any]=False      )							->   str:



 """simple docstring"""




 lowercase	,		lowercase	,		lowercase	,		lowercase       : List[str]						=_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-->'''       ,   )

 lowercase       : Dict						=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__":
      SCREAMING_SNAKE_CASE							=						argparse.ArgumentParser()
      parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
      SCREAMING_SNAKE_CASE							=						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"""



from .imports import is_tqdm_available


if is_tqdm_available():
  from tqdm.auto import tqdm as _tqdm

from ..state import PartialState







def     __lowerCAmelCase		(		lowercase    :    bool = True		,				*lowercase    :    str		,				**lowercase    :    List[Any]					)      ->		List[Any]:


    """simple docstring"""





    if not is_tqdm_available():
        raise ImportError("Accelerate's `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`."					)
    snake_case       : Dict    =       False
    if main_process_only:
        snake_case       : int    =       PartialState().local_process_index == 0
    return _tqdm(*lowercase		,				**lowercase		,				disable=lowercase					)





