



"""simple docstring"""







import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class        __A       (	A_    ):







		'''simple docstring'''





		lowerCAmelCase			:					str   	=				["image_processor", "tokenizer"]
		lowerCAmelCase			:					str   	=				"CLIPImageProcessor"
		lowerCAmelCase			:					str   	=				("XLMRobertaTokenizer", "XLMRobertaTokenizerFast")







		def __init__(	self						:  int			,_snake_case						:  Union[str, Any]=None			,_snake_case						:  List[str]=None			,**_snake_case						:  int						)					->		Optional[int]:






				"""simple docstring"""



				lowercase__   :    Tuple				=			None
				if "feature_extractor" in kwargs:
						warnings.warn(
						    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
						    ''' instead.'''			,_snake_case			,)
						lowercase__   :    int				=			kwargs.pop('''feature_extractor'''						)

				lowercase__   :    List[Any]				=			image_processor if image_processor is not None else feature_extractor
				if image_processor is None:
						raise ValueError('''You need to specify an `image_processor`.'''						)
				if tokenizer is None:
						raise ValueError('''You need to specify a `tokenizer`.'''						)

				super().__init__(_snake_case			,_snake_case						)







		def __call__(	self						:  Any			,_snake_case						:  List[str]=None			,_snake_case						:  str=None			,_snake_case						:  Optional[Any]=None			,**_snake_case						:  Union[str, Any]						)					->		Optional[int]:






				"""simple docstring"""



				if text is None and images is None:
						raise ValueError('''You have to specify either text or images. Both cannot be none.'''						)

				if text is not None:
						lowercase__   :    Optional[int]				=			self.tokenizer(_snake_case			,return_tensors=_snake_case			,**_snake_case						)

				if images is not None:
						lowercase__   :    Tuple				=			self.image_processor(_snake_case			,return_tensors=_snake_case			,**_snake_case						)

				if text is not None and images is not None:
						lowercase__   :    List[str]				=			image_features.pixel_values
						return encoding
				elif text is not None:
						return encoding
				else:
						return BatchEncoding(data=dict(**_snake_case						)			,tensor_type=_snake_case						)







		def 					UpperCAmelCase    (	self						:  List[Any]			,*_snake_case						:  List[Any]			,**_snake_case						:  Dict						)					->		int:






				"""simple docstring"""



				return self.tokenizer.batch_decode(*_snake_case			,**_snake_case						)







		def 					UpperCAmelCase    (	self						:  Any			,*_snake_case						:  str			,**_snake_case						:  str						)					->		Tuple:






				"""simple docstring"""



				return self.tokenizer.decode(*_snake_case			,**_snake_case						)







		@property
		def 					UpperCAmelCase    (	self						:  Optional[Any]						)					->		List[Any]:






				"""simple docstring"""



				lowercase__   :    int				=			self.tokenizer.model_input_names
				lowercase__   :    Optional[Any]				=			self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)





"""simple docstring"""







import os



def      __UpperCAmelCase      (    )							->      int:
		with open(os.path.dirname(__lowerCamelCase	) + '''/p022_names.txt'''	) as file:
				lowercase__   :    List[Any]				=			str(file.readlines()[0]	)
				lowercase__   :    Dict				=			names.replace('''"'''						,     ''''''	).split(''','''	)

		names.sort()

		lowercase__   :    int				=			0
		lowercase__   :    Optional[Any]				=			0

		for i, name in enumerate(__lowerCamelCase	):
				for letter in name:
						name_score += ord(__lowerCamelCase	) - 64

				total_score += (i + 1) * name_score
				lowercase__   :    List[str]				=			0
		return total_score


if __name__ == "__main__":
							print(solution())
