






'''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


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


a_						:       int      			=				"""▁"""

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

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

a_						:       List[str]      			=				{
    """google/reformer-crime-and-punishment""": 524288,
}





class   snake_case					(					lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						VOCAB_FILES_NAMES
  _lowerCamelCase						=						PRETRAINED_VOCAB_FILES_MAP
  _lowerCamelCase						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowerCamelCase						=						["input_ids", "attention_mask"]






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







   """simple docstring"""
   lowerCamelCase_  				=						{} if sp_model_kwargs is None else sp_model_kwargs

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

   lowerCamelCase_  				=						vocab_file
   lowerCamelCase_  				=						spm.SentencePieceProcessor(**self.sp_model_kwargs  )
   self.sp_model.Load(UpperCamelCase  )






  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   return self.sp_model.get_piece_size()






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						{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  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.__dict__.copy()
   lowerCamelCase_  				=						None
   return state






  def __setstate__(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						d

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

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






  def 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   return self.sp_model.encode(UpperCamelCase    ,						out_type=UpperCamelCase  )






  def 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   return self.sp_model.piece_to_id(UpperCamelCase  )






  def 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   if index < self.sp_model.get_piece_size():
    lowerCamelCase_  				=						self.sp_model.IdToPiece(UpperCamelCase  )
   return token






  def 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						[]
   lowerCamelCase_  				=						""
   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(UpperCamelCase  ) + token
     lowerCamelCase_  				=						[]
    else:
     current_sub_tokens.append(UpperCamelCase  )
   out_string += self.sp_model.decode(UpperCamelCase  )
   return out_string.strip()







  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase = None  ):







   """simple docstring"""
   if not os.path.isdir(UpperCamelCase  ):
    logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
    return
   lowerCamelCase_  				=						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:
     lowerCamelCase_  				=						self.sp_model.serialized_model_proto()
     fi.write(UpperCamelCase  )

   return (out_vocab_file,)







'''simple docstring'''

def __snake_case      (   UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		int	):
 return "\n".join(
     F'''{number} * {i} = {number * i}''' for i in range(1      ,       number_of_terms + 1	)	)


if __name__ == "__main__":
     print(multiplication_table(number=5, number_of_terms=10))



