
'''simple docstring'''



from __future__ import annotations
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     list[int]:




   """simple docstring"""
   return [ord(SCREAMING_SNAKE_CASE_						) - 96 for elem in plain]
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     str:




   """simple docstring"""
   return "".join(chr(elem + 96						) for elem in encoded						)
def    lowerCAmelCase_	(				)     ->     None:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				encode(input("""-> """						).strip().lower()						)
   print("""Encoded: """    ,      SCREAMING_SNAKE_CASE_						)
   print("""Decoded:"""    ,      decode(SCREAMING_SNAKE_CASE_						)						)


if __name__ == "__main__":
     main()








'''simple docstring'''



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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCamelCase__      :		List[Any]       							=  logging.get_logger(__name__)


UpperCamelCase__      :		Any       							=  "▁"

UpperCamelCase__      :		Any       							=  {"vocab_file": "spiece.model"}

UpperCamelCase__      :		int       							=  {
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    }
}

UpperCamelCase__      :		Optional[int]       							=  {
    "google/reformer-crime-and-punishment": 524_288,
}

class    _a				(_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE      =  VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE      =  PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE      =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE      =  ['input_ids', 'attention_mask']






       def __init__(							self       ,							A__       ,							A__="</s>"       ,							A__="<unk>"       ,							A__=[]       ,							A__ = None       ,							**A__       ,							)    ->			None:
          _SCREAMING_SNAKE_CASE      =				{} if sp_model_kwargs is None else sp_model_kwargs

          super().__init__(
              eos_token=A__       ,							unk_token=A__       ,							additional_special_tokens=A__       ,							sp_model_kwargs=self.sp_model_kwargs       ,							**A__       ,							)

          _SCREAMING_SNAKE_CASE      =				vocab_file
          _SCREAMING_SNAKE_CASE      =				spm.SentencePieceProcessor(**self.sp_model_kwargs      )
          self.sp_model.Load(A__      )






       @property
       def        UpperCamelCase				(							self      )    ->			Any:
          return self.sp_model.get_piece_size()






       def        UpperCamelCase				(							self      )    ->			Dict[str, int]:
          _SCREAMING_SNAKE_CASE      =				{self.convert_ids_to_tokens(A__      ): i for i in range(self.vocab_size      )}
          vocab.update(self.added_tokens_encoder      )
          return vocab






       def __getstate__(							self      )    ->			int:
          _SCREAMING_SNAKE_CASE      =				self.__dict__.copy()
          _SCREAMING_SNAKE_CASE      =				None
          return state






       def __setstate__(							self       ,							A__      )    ->			str:
          _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        UpperCamelCase				(							self       ,							A__      )    ->			List[str]:
          return self.sp_model.encode(A__       ,							out_type=A__      )






       def        UpperCamelCase				(							self       ,							A__      )    ->			Union[str, Any]:
          return self.sp_model.piece_to_id(A__      )






       def        UpperCamelCase				(							self       ,							A__      )    ->			List[Any]:
          if index < self.sp_model.get_piece_size():
             _SCREAMING_SNAKE_CASE      =				self.sp_model.IdToPiece(A__      )
          return token






       def        UpperCamelCase				(							self       ,							A__      )    ->			str:
          _SCREAMING_SNAKE_CASE      =				[]
          _SCREAMING_SNAKE_CASE      =				""""""
          for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                out_string += self.sp_model.decode(A__      ) + token
                _SCREAMING_SNAKE_CASE      =				[]
             else:
                current_sub_tokens.append(A__      )
          out_string += self.sp_model.decode(A__      )
          return out_string.strip()






       def        UpperCamelCase				(							self       ,							A__       ,							A__ = None      )    ->			Tuple[str]:
          if not os.path.isdir(A__      ):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory"      )
             return
          _SCREAMING_SNAKE_CASE      =				os.path.join(
              A__       ,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]      )

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

          return (out_vocab_file,)






