


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__			:      List[Any]								='src/transformers'
lowerCAmelCase__			:      Optional[int]								='docs/source/en/tasks'






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

			SCREAMING_SNAKE_CASE_		:     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.
lowerCAmelCase__			:      List[Any]								=direct_transformers_import(TRANSFORMERS_PATH)

lowerCAmelCase__			:      Dict								={
    '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__			:      Optional[Any]								={
    'summarization.md': ('nllb',),
    'translation.md': ('nllb',),
}






def 						a__			(				A__				):
			SCREAMING_SNAKE_CASE_		:     str					=					TASK_GUIDE_TO_MODELS[task_guide]
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(A__,      set()				)
			SCREAMING_SNAKE_CASE_		:     List[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 						a__			(				A__,      A__=False				):
			SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Any					=					_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-->',      )

			SCREAMING_SNAKE_CASE_		:     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__			:      Any								=argparse.ArgumentParser()
				parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
				lowerCAmelCase__			:      Optional[Any]								=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 __future__ import annotations







def a_     (    __UpperCAmelCase					)				->					list[int]:





			"""simple docstring"""







			snake_case:    Tuple      							=[True] * limit
			snake_case:    Optional[int]      							=False
			snake_case:    Union[str, Any]      							=False
			snake_case:    List[Any]      							=True

			for i in range(3				,							int(limit**0.5 + 1					)				,							2					):
						snake_case:    str      							=i * 2
						while index < limit:
									snake_case:    List[Any]      							=False
									snake_case:    List[str]      							=index + i

			snake_case:    Union[str, Any]      							=[2]

			for i in range(3				,							__UpperCAmelCase				,							2					):
						if is_prime[i]:
									primes.append(__UpperCAmelCase					)

			return primes







def a_     (    __UpperCAmelCase = 1_00_00_00					)				->					int:





			"""simple docstring"""







			snake_case:    str      							=prime_sieve(__UpperCAmelCase					)
			snake_case:    str      							=0
			snake_case:    str      							=0

			for i in range(len(__UpperCAmelCase					)					):
						for j in range(i + length				,							len(__UpperCAmelCase					)					):
									snake_case:    Tuple      							=sum(primes[i:j]					)
									if sol >= ceiling:
												break

									if sol in primes:
												snake_case:    List[str]      							=j - i
												snake_case:    Optional[int]      							=sol

			return largest


if __name__ == "__main__":
	print(F"""{solution() = }""")




