


"""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__       :							Tuple        =  '''src/transformers'''
a__       :							Dict        =  '''docs/source/en/tasks'''




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     ):




     '''simple docstring'''


     with open(SCREAMING_SNAKE_CASE_						,		"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(SCREAMING_SNAKE_CASE_     ):
          start_index += 1
     start_index += 1

     __SCREAMING_SNAKE_CASE		     =       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.
a__       :							int        =  direct_transformers_import(TRANSFORMERS_PATH)

a__       :							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__       :							Optional[int]        =  {
    '''summarization.md''': ('''nllb''',),
    '''translation.md''': ('''nllb''',),
}




def      UpperCAmelCase__ (lowerCAmelCase_     ):




     '''simple docstring'''


     __SCREAMING_SNAKE_CASE		     =       TASK_GUIDE_TO_MODELS[task_guide]
     __SCREAMING_SNAKE_CASE		     =       SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(SCREAMING_SNAKE_CASE_						,		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      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_=False     ):




     '''simple docstring'''


     __SCREAMING_SNAKE_CASE		     =       _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-->"						,		)

     __SCREAMING_SNAKE_CASE		     =       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__":
  a__       :							Optional[int]        =  argparse.ArgumentParser()
  parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
  a__       :							str        =  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 abc import ABC, abstractmethod
from argparse import ArgumentParser




class 				UpperCamelCase_			(				UpperCamelCase):

	"""simple docstring"""





	@staticmethod
	@abstractmethod
	def 						UpperCAmelCase_   (				UpperCAmelCase__    :       ArgumentParser     ) ->			Optional[Any]:
						raise NotImplementedError()




	@abstractmethod
	def 						UpperCAmelCase_   (				self    :       Optional[Any]     ) ->			List[str]:
						raise NotImplementedError()
