
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__snake_case								=  logging.get_logger(__name__)

__snake_case								=  {"""vocab_file""": """spiece.model"""}

__snake_case								=  {
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    }
}

__snake_case								=  {
    """google/bigbird-roberta-base""": 40_96,
    """google/bigbird-roberta-large""": 40_96,
    """google/bigbird-base-trivia-itc""": 40_96,
}




class 						lowercase__  (						_UpperCAmelCase  ):
	A__    :   str				       =VOCAB_FILES_NAMES
	A__    :   str				       =PRETRAINED_VOCAB_FILES_MAP
	A__    :   int				       =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	A__    :   str				       =["""input_ids""", """attention_mask"""]
	A__    :   List[int]				       =[]
	def __init__(			self							:   Optional[Any]       ,   UpperCAmelCase_							:   int       ,   UpperCAmelCase_							:   Dict="<unk>"       ,   UpperCAmelCase_							:   Optional[int]="<s>"       ,   UpperCAmelCase_							:   Any="</s>"       ,   UpperCAmelCase_							:   Optional[Any]="<pad>"       ,   UpperCAmelCase_							:   List[str]="[SEP]"       ,   UpperCAmelCase_							:   List[Any]="[MASK]"       ,   UpperCAmelCase_							:   Optional[int]="[CLS]"       ,   UpperCAmelCase_							:   Optional[Dict[str, Any]] = None       ,   **UpperCAmelCase_							:   Dict       ,   ):
						SCREAMING_SNAKE_CASE__													= AddedToken(UpperCAmelCase_       ,   lstrip=UpperCAmelCase_       ,   rstrip=UpperCAmelCase_							) if isinstance(UpperCAmelCase_       ,   UpperCAmelCase_							) else bos_token
						SCREAMING_SNAKE_CASE__													= AddedToken(UpperCAmelCase_       ,   lstrip=UpperCAmelCase_       ,   rstrip=UpperCAmelCase_							) if isinstance(UpperCAmelCase_       ,   UpperCAmelCase_							) else eos_token
						SCREAMING_SNAKE_CASE__													= AddedToken(UpperCAmelCase_       ,   lstrip=UpperCAmelCase_       ,   rstrip=UpperCAmelCase_							) if isinstance(UpperCAmelCase_       ,   UpperCAmelCase_							) else unk_token
						SCREAMING_SNAKE_CASE__													= AddedToken(UpperCAmelCase_       ,   lstrip=UpperCAmelCase_       ,   rstrip=UpperCAmelCase_							) if isinstance(UpperCAmelCase_       ,   UpperCAmelCase_							) else pad_token
						SCREAMING_SNAKE_CASE__													= AddedToken(UpperCAmelCase_       ,   lstrip=UpperCAmelCase_       ,   rstrip=UpperCAmelCase_							) if isinstance(UpperCAmelCase_       ,   UpperCAmelCase_							) else cls_token
						SCREAMING_SNAKE_CASE__													= AddedToken(UpperCAmelCase_       ,   lstrip=UpperCAmelCase_       ,   rstrip=UpperCAmelCase_							) if isinstance(UpperCAmelCase_       ,   UpperCAmelCase_							) else sep_token

						# Mask token behave like a normal word, i.e. include the space before it
						SCREAMING_SNAKE_CASE__													= AddedToken(UpperCAmelCase_       ,   lstrip=UpperCAmelCase_       ,   rstrip=UpperCAmelCase_							) if isinstance(UpperCAmelCase_       ,   UpperCAmelCase_							) else mask_token

						SCREAMING_SNAKE_CASE__													= {} if sp_model_kwargs is None else sp_model_kwargs

						super().__init__(
						    bos_token=UpperCAmelCase_       ,   eos_token=UpperCAmelCase_       ,   unk_token=UpperCAmelCase_       ,   pad_token=UpperCAmelCase_       ,   sep_token=UpperCAmelCase_       ,   mask_token=UpperCAmelCase_       ,   cls_token=UpperCAmelCase_       ,   sp_model_kwargs=self.sp_model_kwargs       ,   **UpperCAmelCase_       ,   )

						SCREAMING_SNAKE_CASE__													= vocab_file

						SCREAMING_SNAKE_CASE__													= spm.SentencePieceProcessor(**self.sp_model_kwargs							)
						self.sp_model.Load(UpperCAmelCase_							)
	@property
	def      A_				(			self							:   Optional[Any]							):
						return self.sp_model.get_piece_size()
	def      A_				(			self							:   Optional[int]							):
						SCREAMING_SNAKE_CASE__													= {self.convert_ids_to_tokens(UpperCAmelCase_							): i for i in range(self.vocab_size							)}
						vocab.update(self.added_tokens_encoder							)
						return vocab
	def __getstate__(			self							:   Any							):
						SCREAMING_SNAKE_CASE__													= self.__dict__.copy()
						SCREAMING_SNAKE_CASE__													= None
						return state
	def __setstate__(			self							:   Dict       ,   UpperCAmelCase_							:   Union[str, Any]							):
						SCREAMING_SNAKE_CASE__													= d

						# for backward compatibility
						if not hasattr(self       ,   'sp_model_kwargs'							):
											SCREAMING_SNAKE_CASE__													= {}

						SCREAMING_SNAKE_CASE__													= spm.SentencePieceProcessor(**self.sp_model_kwargs							)
						self.sp_model.Load(self.vocab_file							)
	def      A_				(			self							:   List[Any]       ,   UpperCAmelCase_							:   str							):
						return self.sp_model.encode(UpperCAmelCase_       ,   out_type=UpperCAmelCase_							)
	def      A_				(			self							:   List[Any]       ,   UpperCAmelCase_							:   Any							):
						return self.sp_model.piece_to_id(UpperCAmelCase_							)
	def      A_				(			self							:   int       ,   UpperCAmelCase_							:   Dict							):
						SCREAMING_SNAKE_CASE__													= self.sp_model.IdToPiece(UpperCAmelCase_							)
						return token
	def      A_				(			self							:   Dict       ,   UpperCAmelCase_							:   Dict							):
						SCREAMING_SNAKE_CASE__													= []
						SCREAMING_SNAKE_CASE__													= ''
						SCREAMING_SNAKE_CASE__													= False
						for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
																if not prev_is_special:
																					out_string += " "
																out_string += self.sp_model.decode(UpperCAmelCase_							) + token
																SCREAMING_SNAKE_CASE__													= True
																SCREAMING_SNAKE_CASE__													= []
											else:
																current_sub_tokens.append(UpperCAmelCase_							)
																SCREAMING_SNAKE_CASE__													= False
						out_string += self.sp_model.decode(UpperCAmelCase_							)
						return out_string.strip()
	def      A_				(			self							:   Union[str, Any]       ,   UpperCAmelCase_							:   List[int]       ,   UpperCAmelCase_							:   bool = False       ,   UpperCAmelCase_							:   bool = None       ,   UpperCAmelCase_							:   bool = True       ,   **UpperCAmelCase_							:   Dict       ,   ):
						SCREAMING_SNAKE_CASE__													= kwargs.pop('use_source_tokenizer'       ,   UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= self.convert_ids_to_tokens(UpperCAmelCase_       ,   skip_special_tokens=UpperCAmelCase_							)

						# To avoid mixing byte-level and unicode for byte-level BPT
						# we need to build string separately for added tokens and byte-level tokens
						# cf. https://github.com/huggingface/transformers/issues/1133
						SCREAMING_SNAKE_CASE__													= []
						SCREAMING_SNAKE_CASE__													= []
						for token in filtered_tokens:
											if skip_special_tokens and token in self.all_special_ids:
																continue
											if token in self.added_tokens_encoder:
																if current_sub_text:
																					sub_texts.append(self.convert_tokens_to_string(UpperCAmelCase_							)							)
																					SCREAMING_SNAKE_CASE__													= []
																sub_texts.append(UpperCAmelCase_							)
											else:
																current_sub_text.append(UpperCAmelCase_							)
						if current_sub_text:
											sub_texts.append(self.convert_tokens_to_string(UpperCAmelCase_							)							)

						# Mimic the behavior of the Rust tokenizer:
						# No space before [MASK] and [SEP]
						if spaces_between_special_tokens:
											SCREAMING_SNAKE_CASE__													= re.sub(r' (\[(MASK|SEP)\])'       ,   r'\1'       ,   ' '.join(UpperCAmelCase_							)							)
						else:
											SCREAMING_SNAKE_CASE__													= ''.join(UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= (
						    clean_up_tokenization_spaces
						    if clean_up_tokenization_spaces is not None
						    else self.clean_up_tokenization_spaces
						)
						if clean_up_tokenization_spaces:
											SCREAMING_SNAKE_CASE__													= self.clean_up_tokenization(UpperCAmelCase_							)
											return clean_text
						else:
											return text
	def      A_				(			self							:   List[str]       ,   UpperCAmelCase_							:   str       ,   UpperCAmelCase_							:   Optional[str] = None							):
						if not os.path.isdir(UpperCAmelCase_							):
											logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
											return
						SCREAMING_SNAKE_CASE__													= os.path.join(
						    UpperCAmelCase_       ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']							)

						if os.path.abspath(self.vocab_file							) != os.path.abspath(UpperCAmelCase_							) and os.path.isfile(self.vocab_file							):
											copyfile(self.vocab_file       ,   UpperCAmelCase_							)
						elif not os.path.isfile(self.vocab_file							):
											with open(UpperCAmelCase_       ,   'wb'							) as fi:
																SCREAMING_SNAKE_CASE__													= self.sp_model.serialized_model_proto()
																fi.write(UpperCAmelCase_							)

						return (out_vocab_file,)
	def      A_				(			self							:   Any       ,   UpperCAmelCase_							:   List[int]       ,   UpperCAmelCase_							:   Optional[List[int]] = None							):
						if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						SCREAMING_SNAKE_CASE__													= [self.cls_token_id]
						SCREAMING_SNAKE_CASE__													= [self.sep_token_id]
						return cls + token_ids_a + sep + token_ids_a + sep
	def      A_				(			self							:   Any       ,   UpperCAmelCase_							:   List[int]       ,   UpperCAmelCase_							:   Optional[List[int]] = None       ,   UpperCAmelCase_							:   bool = False							):
						if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=UpperCAmelCase_       ,   token_ids_a=UpperCAmelCase_       ,   already_has_special_tokens=UpperCAmelCase_							)

						if token_ids_a is None:
											return [1] + ([0] * len(UpperCAmelCase_							)) + [1]
						return [1] + ([0] * len(UpperCAmelCase_							)) + [1] + ([0] * len(UpperCAmelCase_							)) + [1]





	def      A_				(			self							:   int       ,   UpperCAmelCase_							:   List[int]       ,   UpperCAmelCase_							:   Optional[List[int]] = None							):
						SCREAMING_SNAKE_CASE__													= [self.sep_token_id]
						SCREAMING_SNAKE_CASE__													= [self.cls_token_id]
						if token_ids_a is None:
											return len(cls + token_ids_a + sep							) * [0]
						return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]







import os






def 							_lowercase							(     )		->		List[str]:
					'''simple docstring'''


					with open(os.path.dirname(UpperCamelCase_		) + '/p022_names.txt'		) as file:
										SCREAMING_SNAKE_CASE__													= str(file.readlines()[0]		)
										SCREAMING_SNAKE_CASE__													= names.replace('"'	,	''		).split(','		)

					names.sort()

					SCREAMING_SNAKE_CASE__													= 0
					SCREAMING_SNAKE_CASE__													= 0

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

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


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





