
'''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


__snake_case:  Any												=			logging.get_logger(__name__)


__snake_case:  Dict												=			'''▁'''

__snake_case:  Optional[Any]												=			{'''vocab_file''': '''spiece.model'''}

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

__snake_case:  Optional[int]												=			{
    '''google/reformer-crime-and-punishment''': 52_42_88,
}


class  _UpperCAmelCase ( __UpperCAmelCase    ):

     """simple docstring"""

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


     def __init__(      self							,		lowerCAmelCase_							,		lowerCAmelCase_="</s>"							,		lowerCAmelCase_="<unk>"							,		lowerCAmelCase_=[]							,		lowerCAmelCase_ = None							,		**lowerCAmelCase_							,		):

       '''simple docstring'''

       a_      :	Dict      =							{} if sp_model_kwargs is None else sp_model_kwargs

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

       a_      :	Union[str, Any]      =							vocab_file
       a_      :	Dict      =							spm.SentencePieceProcessor(**self.sp_model_kwargs )
       self.sp_model.Load(__SCREAMING_SNAKE_CASE )


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return self.sp_model.get_piece_size()


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[Any]      =							{self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE ): i for i in range(self.vocab_size )}
       vocab.update(self.added_tokens_encoder )
       return vocab


     def __getstate__(      self ):

       '''simple docstring'''

       a_      :	Tuple      =							self.__dict__.copy()
       a_      :	Optional[int]      =							None
       return state


     def __setstate__(      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Tuple      =							d

       # for backward compatibility
       if not hasattr(self							,		"""sp_model_kwargs""" ):
         a_      :	Optional[int]      =							{}

       a_      :	Dict      =							spm.SentencePieceProcessor(**self.sp_model_kwargs )
       self.sp_model.Load(self.vocab_file )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       return self.sp_model.encode(__SCREAMING_SNAKE_CASE							,		out_type=__SCREAMING_SNAKE_CASE )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       return self.sp_model.piece_to_id(__SCREAMING_SNAKE_CASE )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       if index < self.sp_model.get_piece_size():
         a_      :	Union[str, Any]      =							self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE )
       return token


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	int      =							[]
       a_      :	Tuple      =							""""""
       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(__SCREAMING_SNAKE_CASE ) + token
           a_      :	Tuple      =							[]
         else:
           current_sub_tokens.append(__SCREAMING_SNAKE_CASE )
       out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE )
       return out_string.strip()






     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ = None ):

       '''simple docstring'''

       if not os.path.isdir(__SCREAMING_SNAKE_CASE ):
         logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
         return
       a_      :	int      =							os.path.join(
           __SCREAMING_SNAKE_CASE							,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )

       if os.path.abspath(self.vocab_file ) != os.path.abspath(__SCREAMING_SNAKE_CASE ) and os.path.isfile(self.vocab_file ):
         copyfile(self.vocab_file							,		__SCREAMING_SNAKE_CASE )
       elif not os.path.isfile(self.vocab_file ):
         with open(__SCREAMING_SNAKE_CASE							,		"""wb""" ) as fi:
           a_      :	List[Any]      =							self.sp_model.serialized_model_proto()
           fi.write(__SCREAMING_SNAKE_CASE )

       return (out_vocab_file,)




'''simple docstring'''

from __future__ import annotations






def 	_snake_case						(      A_       :    int   ):
  """simple docstring"""







  a_      :	Optional[Any]      =							2
  a_      :	int      =							[]
  while i * i <= n:
    if n % i:
      i += 1
    else:
      n //= i
      factors.append(A_   )
  if n > 1:
    factors.append(A_   )
  return factors


if __name__ == "__main__":
    import doctest

    doctest.testmod()


