



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






def 			snake_case_    ( A_   :						Union[str, Any],							A_   :						List[Any],							A_   :						Optional[int]		):


       '''simple docstring'''





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

       _lowerCamelCase		:							Dict            =					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.
lowerCAmelCase__              =							direct_transformers_import(TRANSFORMERS_PATH)

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






def 			snake_case_    ( A_   :						str		):


       '''simple docstring'''





       _lowerCamelCase		:							str            =					TASK_GUIDE_TO_MODELS[task_guide]
       _lowerCamelCase		:							Optional[int]            =					SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(a__,							set()		)
       _lowerCamelCase		:							int            =					{
           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 			snake_case_    ( A_   :						Any,							A_   :						int=False		):


       '''simple docstring'''





       _lowerCamelCase     ,       _lowerCamelCase     ,       _lowerCamelCase     ,       _lowerCamelCase		:							Optional[int]            =					_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-->''',							)

       _lowerCamelCase		:							str            =					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__":
   lowerCAmelCase__              =							argparse.ArgumentParser()
   parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
   lowerCAmelCase__              =							parser.parse_args()

   for task_guide in TASK_GUIDE_TO_MODELS.keys():
      check_model_list_for_task(task_guide, args.fix_and_overwrite)









def      _UpperCamelCase  (a__ :int = 1000							):






							"""simple docstring"""





							UpperCamelCase__					  =      2**power
							UpperCamelCase__					  =      0
							while n:
														UpperCamelCase__  ,  UpperCamelCase__					  =      r + n % 10, n // 10
							return r


if __name__ == "__main__":
					print(solution(int(str(input()).strip())))




