



"""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
lowercase  							=					"src/transformers"
lowercase  							=					"docs/source/en/tasks"




def 	UpperCAmelCase     (		A	:						List[Any]     ,				A	:						Tuple     ,				A	:						Tuple       ):

		'''simple docstring'''


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

		_UpperCAmelCase							 =		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.
lowercase  							=					direct_transformers_import(TRANSFORMERS_PATH)

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




def 	UpperCAmelCase     (		A	:						Optional[Any]       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		TASK_GUIDE_TO_MODELS[task_guide]
		_UpperCAmelCase							 =		SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(__lowerCAmelCase     ,				set()       )
		_UpperCAmelCase							 =		{
		    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     (		A	:						Union[str, Any]     ,				A	:						str=False       ):

		'''simple docstring'''


		_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase							 =		_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-->'     ,				)

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





def 	UpperCAmelCase     (		A	:						int       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		abs(A       )
		_UpperCAmelCase							 =		0
		while n > 0:
				res += n % 10
				n //= 10
		return res




def 	UpperCAmelCase     (		A	:						int       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		abs(A       )
		return n if n < 10 else n % 10 + sum_of_digits(n // 10       )




def 	UpperCAmelCase     (		A	:						int       ):

		'''simple docstring'''


		return sum(int(A       ) for c in str(abs(A       )       )       )




def 	UpperCAmelCase     (		):

		'''simple docstring'''


		from collections.abc import Callable
		from timeit import timeit

		def benchmark_a_function(A	:						Callable     ,				A	:						int       ) -> None:
				_UpperCAmelCase							 =		f'{func.__name__}({value})'
				_UpperCAmelCase							 =		timeit(f'__main__.{call}'     ,				setup='import __main__'       )
				print(f'{call:56} = {func(A       )} -- {timing:.4f} seconds'       )

		for value in (26_2144, 1125_8999_0684_2624, 126_7650_6002_2822_9401_4967_0320_5376):
				for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
						benchmark_a_function(A     ,				A       )
				print()


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					benchmark()


