




import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


__UpperCamelCase   :			Union[str, Any]          = logging.get_logger(__name__)

class 						__lowerCAmelCase      :
							def __init__(		self		:Tuple						, __magic_name__		:Optional[Any]						, __magic_name__		:Tuple							):







								'''simple docstring'''







								a									=				question_encoder
								a									=				generator
								a									=				self.question_encoder
							def  lowerCamelCase__				(		self		:str						, __magic_name__		:Union[str, Any]							):







								'''simple docstring'''







								if os.path.isfile(__magic_name__							):
									raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file'							)
								os.makedirs(__magic_name__						, exist_ok=__magic_name__							)
								a									=				os.path.join(__magic_name__						, """question_encoder_tokenizer"""							)
								a									=				os.path.join(__magic_name__						, """generator_tokenizer"""							)
								self.question_encoder.save_pretrained(__magic_name__							)
								self.generator.save_pretrained(__magic_name__							)
							@classmethod
							def  lowerCamelCase__				(		cls		:Tuple						, __magic_name__		:Union[str, Any]						, **__magic_name__		:int							):







								'''simple docstring'''







								from ..auto.tokenization_auto import AutoTokenizer

								a									=				kwargs.pop("""config"""						, __magic_name__							)

								if config is None:
									a									=				RagConfig.from_pretrained(__magic_name__							)

								a									=				AutoTokenizer.from_pretrained(
								    __magic_name__						, config=config.question_encoder						, subfolder="""question_encoder_tokenizer"""							)
								a									=				AutoTokenizer.from_pretrained(
								    __magic_name__						, config=config.generator						, subfolder="""generator_tokenizer"""							)
								return cls(question_encoder=__magic_name__						, generator=__magic_name__							)
							def __call__(		self		:Union[str, Any]						, *__magic_name__		:Optional[int]						, **__magic_name__		:str							):







								'''simple docstring'''







								return self.current_tokenizer(*__magic_name__						, **__magic_name__							)
							def  lowerCamelCase__				(		self		:Optional[int]						, *__magic_name__		:int						, **__magic_name__		:List[str]							):







								'''simple docstring'''







								return self.generator.batch_decode(*__magic_name__						, **__magic_name__							)
							def  lowerCamelCase__				(		self		:List[str]						, *__magic_name__		:Any						, **__magic_name__		:Tuple							):







								'''simple docstring'''







								return self.generator.decode(*__magic_name__						, **__magic_name__							)
							def  lowerCamelCase__				(		self		:Tuple							):







								'''simple docstring'''







								a									=				self.question_encoder
							def  lowerCamelCase__				(		self		:int							):







								'''simple docstring'''







								a									=				self.generator





							def  lowerCamelCase__				(		self		:Optional[Any]						, __magic_name__		:List[str]						, __magic_name__		:Optional[List[str]] = None						, __magic_name__		:Optional[int] = None						, __magic_name__		:Optional[int] = None						, __magic_name__		:str = "longest"						, __magic_name__		:str = None						, __magic_name__		:bool = True						, **__magic_name__		:Dict						, ):







								'''simple docstring'''







								warnings.warn(
								    """`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the """
								    """regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` """
								    """context manager to prepare your targets. See the documentation of your specific tokenizer for more """
								    """details"""						, __magic_name__						, )
								if max_length is None:
									a									=				self.current_tokenizer.model_max_length
								a									=				self(
								    __magic_name__						, add_special_tokens=__magic_name__						, return_tensors=__magic_name__						, max_length=__magic_name__						, padding=__magic_name__						, truncation=__magic_name__						, **__magic_name__						, )
								if tgt_texts is None:
									return model_inputs
								# Process tgt_texts
								if max_target_length is None:
									a									=				self.current_tokenizer.model_max_length
								a									=				self(
								    text_target=__magic_name__						, add_special_tokens=__magic_name__						, return_tensors=__magic_name__						, padding=__magic_name__						, max_length=__magic_name__						, truncation=__magic_name__						, **__magic_name__						, )
								a									=				labels["""input_ids"""]
								return model_inputs







import os





def 						__A						(      ) ->			Dict:
	with open(os.path.dirname(__lowerCamelCase       ) + """/p022_names.txt"""       ) as file:
		a									=				str(file.readlines()[0]       )
		a									=				names.replace("""\""""				,     """"""       ).split(""","""       )

	names.sort()

	a									=				0
	a									=				0

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

		total_score += (i + 1) * name_score
		a									=				0
	return total_score


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

