


"""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     :   Optional[int]		=       logging.get_logger(__name__)


__snake_case     :   Optional[Any]		=       '''▁'''

__snake_case     :   str		=       {'''vocab_file''': '''spiece.model'''}

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

__snake_case     :   Any		=       {
    '''google/reformer-crime-and-punishment''': 524288,
}


class     __lowerCamelCase  (					a__				):

     '''simple docstring'''
     A_       :	List[Any]    =		VOCAB_FILES_NAMES
     A_       :	Any    =		PRETRAINED_VOCAB_FILES_MAP
     A_       :	str    =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A_       :	Optional[Any]    =		['input_ids', 'attention_mask']


     def __init__(      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase="</s>"    ,				__UpperCAmelCase="<unk>"    ,				__UpperCAmelCase=[]    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase    ,				)       ->							None:
          _a    	=				{} 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    ,				)

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


     @property
     def 							_UpperCAmelCase      (      self							)       ->							Union[str, Any]:
          return self.sp_model.get_piece_size()


     def 							_UpperCAmelCase      (      self							)       ->							Dict[str, int]:
          _a    	=				{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							)       ->							Union[str, Any]:
          _a    	=				self.__dict__.copy()
          _a    	=				None
          return state


     def __setstate__(      self    ,				__UpperCAmelCase							)       ->							Optional[Any]:
          _a    	=				d

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

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


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							List[str]:
          return self.sp_model.encode(__UpperCAmelCase    ,				out_type=__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							Any:
          return self.sp_model.piece_to_id(__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							Tuple:
          if index < self.sp_model.get_piece_size():
               _a    	=				self.sp_model.IdToPiece(__UpperCAmelCase							)
          return token


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							List[Any]:
          _a    	=				[]
          _a    	=				''''''
          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
                    _a    	=				[]
               else:
                    current_sub_tokens.append(__UpperCAmelCase							)
          out_string += self.sp_model.decode(__UpperCAmelCase							)
          return out_string.strip()





     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None							)       ->							Tuple[str]:
          if not os.path.isdir(__UpperCAmelCase							):
               logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
               return
          _a    	=				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:
                    _a    	=				self.sp_model.serialized_model_proto()
                    fi.write(__UpperCAmelCase							)

          return (out_vocab_file,)



"""simple docstring"""






def       A_   (   _lowerCAmelCase     :					list,     _lowerCAmelCase     :					list,     _lowerCAmelCase     :					int       ):






     """simple docstring"""

     _a    	=				len(_lowerCAmelCase       )
     _a    	=				[[0] * n for i in range(_lowerCAmelCase       )]
     for i in range(_lowerCAmelCase       ):
          _a    	=				y_points[i]

     for i in range(2,     _lowerCAmelCase       ):
          for j in range(_lowerCAmelCase,     _lowerCAmelCase       ):
               _a    	=				(
                   (xa - x_points[j - i + 1]) * q[j][i - 1]
                   - (xa - x_points[j]) * q[j - 1][i - 1]
               ) / (x_points[j] - x_points[j - i + 1])

     return [q[n - 1][n - 1], q]


if __name__ == "__main__":
   import doctest

   doctest.testmod()