

"""simple docstring"""


import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class     snake_case	(				A_  ):
				SCREAMING_SNAKE_CASE_   :      int         =  ["image_processor", "tokenizer"]
				SCREAMING_SNAKE_CASE_   :      Optional[Any]         =  "FlavaImageProcessor"
				SCREAMING_SNAKE_CASE_   :      Dict         =  ("BertTokenizer", "BertTokenizerFast")

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



						'''simple docstring'''


						__lowerCAmelCase:       Dict          =			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     ,  )
								__lowerCAmelCase:       Dict          =			kwargs.pop("feature_extractor")

						__lowerCAmelCase:       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)
						__lowerCAmelCase:       str          =			self.image_processor

				def __call__(	self							:				Tuple     ,  UpperCamelCase__							:				Optional[ImageInput] = None     ,  UpperCamelCase__							:				Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None     ,  UpperCamelCase__							:				bool = True     ,  UpperCamelCase__							:				Union[bool, str, PaddingStrategy] = False     ,  UpperCamelCase__							:				Union[bool, str, TruncationStrategy] = False     ,  UpperCamelCase__							:				Optional[int] = None     ,  UpperCamelCase__							:				int = 0     ,  UpperCamelCase__							:				Optional[int] = None     ,  UpperCamelCase__							:				Optional[bool] = None     ,  UpperCamelCase__							:				Optional[bool] = None     ,  UpperCamelCase__							:				Optional[bool] = None     ,  UpperCamelCase__							:				Optional[bool] = None     ,  UpperCamelCase__							:				bool = False     ,  UpperCamelCase__							:				bool = False     ,  UpperCamelCase__							:				bool = False     ,  UpperCamelCase__							:				bool = False     ,  UpperCamelCase__							:				bool = True     ,  UpperCamelCase__							:				Optional[Union[str, TensorType]] = None     ,  **UpperCamelCase__							:				Any     ,  )->   Tuple:



						'''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:
								__lowerCAmelCase:       List[str]          =			self.tokenizer(
								    text=_snake_case     ,  add_special_tokens=_snake_case     ,  padding=_snake_case     ,  truncation=_snake_case     ,  max_length=_snake_case     ,  stride=_snake_case     ,  pad_to_multiple_of=_snake_case     ,  return_token_type_ids=_snake_case     ,  return_attention_mask=_snake_case     ,  return_overflowing_tokens=_snake_case     ,  return_special_tokens_mask=_snake_case     ,  return_offsets_mapping=_snake_case     ,  return_length=_snake_case     ,  verbose=_snake_case     ,  return_tensors=_snake_case     ,  **_snake_case     ,  )
						if images is not None:
								__lowerCAmelCase:       List[Any]          =			self.image_processor(
								    _snake_case     ,  return_image_mask=_snake_case     ,  return_codebook_pixels=_snake_case     ,  return_tensors=_snake_case     ,  **_snake_case     ,  )

						if text is not None and images is not None:
								encoding.update(_snake_case)
								return encoding
						elif text is not None:
								return encoding
						else:
								return BatchEncoding(data=dict(**_snake_case)     ,  tensor_type=_snake_case)

				def 							lowercase_     (	self							:				int     ,  *UpperCamelCase__							:				Any     ,  **UpperCamelCase__							:				List[str])->   int:



						'''simple docstring'''


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

				def 							lowercase_     (	self							:				List[str]     ,  *UpperCamelCase__							:				List[Any]     ,  **UpperCamelCase__							:				Any)->   Any:



						'''simple docstring'''


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

				@property
				def 							lowercase_     (	self							:				List[Any])->   int:



						'''simple docstring'''


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

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



						'''simple docstring'''


						warnings.warn(
						    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."     ,  _snake_case     ,  )
						return self.image_processor_class




				@property
				def 							lowercase_     (	self							:				Any)->   Union[str, Any]:



						'''simple docstring'''


						warnings.warn(
						    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."     ,  _snake_case     ,  )
						return self.image_processor







"""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())
