




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__ :   List[Any]						=							'''src/transformers'''
UpperCamelCase__ :   Tuple						=							'''docs/source/en/tasks'''





def        __UpperCAmelCase						(							lowerCamelCase_ :     Union[str, Any]  ,							lowerCamelCase_ :     List[Any]  ,							lowerCamelCase_ :     List[str]  )       ->  str:







			"""simple docstring"""





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

			SCREAMING_SNAKE_CASE_   : Any					  =      start_index
			while not lines[end_index].startswith(lowerCamelCase_  ):
						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__ :   Dict						=							direct_transformers_import(TRANSFORMERS_PATH)

UpperCamelCase__ :   str						=							{
    '''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__ :   List[str]						=							{
    '''summarization.md''': ('''nllb''',),
    '''translation.md''': ('''nllb''',),
}





def        __UpperCAmelCase						(							lowerCamelCase_ :     Union[str, Any]  )       ->  List[Any]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      TASK_GUIDE_TO_MODELS[task_guide]
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(lowerCamelCase_  ,							set()  )
			SCREAMING_SNAKE_CASE_   : 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        __UpperCAmelCase						(							lowerCamelCase_ :     Tuple  ,							lowerCamelCase_ :     Union[str, Any]=False  )       ->  Union[str, Any]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Optional[int]					  =      _find_text_in_file(
			    filename=os.path.join(lowerCamelCase_  ,							lowerCamelCase_  )  ,							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_   : Optional[int]					  =      get_model_list_for_task(lowerCamelCase_  )

			if current_list != new_list:
						if overwrite:
									with open(os.path.join(lowerCamelCase_  ,							lowerCamelCase_  )  ,							'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__ :   Optional[int]						=							argparse.ArgumentParser()
						parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
						UpperCamelCase__ :   Optional[int]						=							parser.parse_args()

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






from math import factorial, radians







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = 18		,				SCREAMING_SNAKE_CASE = 10   ):
      '''simple docstring'''







      A_							       =		angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)

      # Converting from degrees to radians
      A_							       =		radians(SCREAMING_SNAKE_CASE   )

      A_							       =		angle_in_radians
      A_							       =		3
      A_							       =		-1

      for _ in range(SCREAMING_SNAKE_CASE   ):
            result += (b * (angle_in_radians**a)) / factorial(SCREAMING_SNAKE_CASE   )

            A_							       =		-b  # One positive term and the next will be negative and so on...
            a += 2  # Increased by 2 for every term.

      return round(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
      __import__("""doctest""").testmod()





