

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





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

	__UpperCAmelCase      		= start_index
	while not lines[end_index].startswith(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.
_lowercase   :	Union[str, Any]												=						direct_transformers_import(TRANSFORMERS_PATH)

_lowercase   :	List[Any]												=						{
    '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   :	int												=						{
    'summarization.md': ('nllb',),
    'translation.md': ('nllb',),
}





def       lowercase__						(       snake_case_   :str    ):
	__UpperCAmelCase      		= TASK_GUIDE_TO_MODELS[task_guide]
	__UpperCAmelCase      		= SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(snake_case_			,		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       lowercase__						(       snake_case_   :List[str]			,		snake_case_   :Optional[Any]=False    ):
	__UpperCAmelCase		,							__UpperCAmelCase		,							__UpperCAmelCase		,							__UpperCAmelCase      		= _find_text_in_file(
	    filename=os.path.join(snake_case_			,		snake_case_    )			,		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(snake_case_    )

	if current_list != new_list:
		if overwrite:
			with open(os.path.join(snake_case_			,		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__":
				_lowercase   :	List[str]												=						argparse.ArgumentParser()
				parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
				_lowercase   :	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 statistics import mean, stdev

def 					lowerCamelCase_ ( __UpperCamelCase					:       list							,    __UpperCamelCase					:       int = 3		)					->	list:







     """simple docstring"""
     _A				=							min(__UpperCamelCase		)
     _A				=							max(__UpperCamelCase		)
     # normalize data
     return [round((x - x_min) / (x_max - x_min)							,    __UpperCamelCase		) for x in data]

def 					lowerCamelCase_ ( __UpperCamelCase					:       list							,    __UpperCamelCase					:       int = 3		)					->	list:







     """simple docstring"""
     _A				=							mean(__UpperCamelCase		)
     _A				=							stdev(__UpperCamelCase		)
     # standardize data
     return [round((x - mu) / (sigma)							,    __UpperCamelCase		) for x in data]





