




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


_lowerCamelCase      			=						logging.get_logger(__name__)


_lowerCamelCase      			=						'▁'

_lowerCamelCase      			=						{'vocab_file': 'spiece.model'}

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

_lowerCamelCase      			=						{
    'google/reformer-crime-and-punishment': 52_42_88,
}



class       a					(       _A      ):

     '''simple docstring'''





     lowerCAmelCase    :	Union[str, Any]       		=  VOCAB_FILES_NAMES
     lowerCAmelCase    :	List[Any]       		=  PRETRAINED_VOCAB_FILES_MAP
     lowerCAmelCase    :	List[Any]       		=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCAmelCase    :	Optional[int]       		=  ['input_ids', 'attention_mask']
     def __init__(     self       :      Dict ,   __snake_case       :      List[str] ,   __snake_case       :      str="</s>" ,   __snake_case       :      int="<unk>" ,   __snake_case       :      str=[] ,   __snake_case       :      Optional[Dict[str, Any]] = None ,   **__snake_case       :      Tuple ,   ):
         UpperCAmelCase_										=					{} if sp_model_kwargs is None else sp_model_kwargs

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

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

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

         UpperCAmelCase_										=					spm.SentencePieceProcessor(**self.sp_model_kwargs    )
         self.sp_model.Load(self.vocab_file    )
     def   lowerCamelCase_       (     self       :      Union[str, Any] ,   __snake_case       :      str    ):
         return self.sp_model.encode(__snake_case ,   out_type=__snake_case    )
     def   lowerCamelCase_       (     self       :      Optional[int] ,   __snake_case       :      Optional[int]    ):
         return self.sp_model.piece_to_id(__snake_case    )
     def   lowerCamelCase_       (     self       :      List[str] ,   __snake_case       :      str    ):
         if index < self.sp_model.get_piece_size():
             UpperCAmelCase_										=					self.sp_model.IdToPiece(__snake_case    )
         return token
     def   lowerCamelCase_       (     self       :      Any ,   __snake_case       :      Union[str, Any]    ):
         UpperCAmelCase_										=					[]
         UpperCAmelCase_										=					''''''
         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(__snake_case    ) + token
                 UpperCAmelCase_										=					[]
             else:
                 current_sub_tokens.append(__snake_case    )
         out_string += self.sp_model.decode(__snake_case    )
         return out_string.strip()




     def   lowerCamelCase_       (     self       :      Union[str, Any] ,   __snake_case       :      str ,   __snake_case       :      Optional[str] = None    ):
         if not os.path.isdir(__snake_case    ):
             logger.error(F'Vocabulary path ({save_directory}) should be a directory'    )
             return
         UpperCAmelCase_										=					os.path.join(
             __snake_case ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )

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

         return (out_vocab_file,)











import baseaa




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						str				)  ->							bytes:
    return baseaa.baaencode(string.encode('''utf-8'''				)				)




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						bytes				)  ->							str:
    return baseaa.baadecode(__UpperCamelCase				).decode('''utf-8'''				)


if __name__ == "__main__":
 _lowerCamelCase      			=						'Hello World!'
 _lowerCamelCase      			=						baseaa_encode(test)
 print(encoded)

 _lowerCamelCase      			=						baseaa_decode(encoded)
 print(decoded)





