


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    :						Dict     =							logging.get_logger(__name__)


__UpperCamelCase    :						Optional[int]     =							'▁'

__UpperCamelCase    :						List[Any]     =							{'vocab_file': 'spiece.model'}

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

__UpperCamelCase    :						Dict     =							{
    'google/reformer-crime-and-punishment': 524288,
}



class 							_UpperCamelCase (						A						):

   '''simple docstring'''







   a_				:							Any    = VOCAB_FILES_NAMES
   a_				:							Dict    = PRETRAINED_VOCAB_FILES_MAP
   a_				:							Tuple    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   a_				:							Optional[int]    = ["input_ids", "attention_mask"]

   def __init__(  self  : int       ,  _lowerCamelCase  : Dict       ,  _lowerCamelCase  : List[Any]="</s>"       ,  _lowerCamelCase  : List[Any]="<unk>"       ,  _lowerCamelCase  : List[str]=[]       ,  _lowerCamelCase  : Optional[Dict[str, Any]] = None       ,  **_lowerCamelCase  : int       ,  ):






         '''simple docstring'''



         __lowerCamelCase     :      Any       						=						{} if sp_model_kwargs is None else sp_model_kwargs

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

         __lowerCamelCase     :      Any       						=						vocab_file
         __lowerCamelCase     :      Optional[int]       						=						spm.SentencePieceProcessor(**self.sp_model_kwargs							)
         self.sp_model.Load(_lowerCamelCase							)

   @property
   def   _snake_case    (  self  : Optional[Any]							):






         '''simple docstring'''



         return self.sp_model.get_piece_size()

   def   _snake_case    (  self  : Any							):






         '''simple docstring'''



         __lowerCamelCase     :      Any       						=						{self.convert_ids_to_tokens(_lowerCamelCase							): i for i in range(self.vocab_size							)}
         vocab.update(self.added_tokens_encoder							)
         return vocab

   def __getstate__(  self  : Union[str, Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      Dict       						=						self.__dict__.copy()
         __lowerCamelCase     :      str       						=						None
         return state

   def __setstate__(  self  : List[Any]       ,  _lowerCamelCase  : Union[str, Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      Optional[int]       						=						d

         # for backward compatibility
         if not hasattr(self       ,  """sp_model_kwargs"""							):
               __lowerCamelCase     :      Union[str, Any]       						=						{}

         __lowerCamelCase     :      Tuple       						=						spm.SentencePieceProcessor(**self.sp_model_kwargs							)
         self.sp_model.Load(self.vocab_file							)

   def   _snake_case    (  self  : Optional[int]       ,  _lowerCamelCase  : str							):






         '''simple docstring'''



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

   def   _snake_case    (  self  : int       ,  _lowerCamelCase  : Optional[Any]							):






         '''simple docstring'''



         return self.sp_model.piece_to_id(_lowerCamelCase							)

   def   _snake_case    (  self  : Tuple       ,  _lowerCamelCase  : List[str]							):






         '''simple docstring'''



         if index < self.sp_model.get_piece_size():
               __lowerCamelCase     :      Any       						=						self.sp_model.IdToPiece(_lowerCamelCase							)
         return token

   def   _snake_case    (  self  : Optional[int]       ,  _lowerCamelCase  : List[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      Optional[int]       						=						[]
         __lowerCamelCase     :      Dict       						=						""""""
         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(_lowerCamelCase							) + token
                     __lowerCamelCase     :      Optional[int]       						=						[]
               else:
                     current_sub_tokens.append(_lowerCamelCase							)
         out_string += self.sp_model.decode(_lowerCamelCase							)
         return out_string.strip()


   def   _snake_case    (  self  : Any       ,  _lowerCamelCase  : str       ,  _lowerCamelCase  : Optional[str] = None							):






         '''simple docstring'''



         if not os.path.isdir(_lowerCamelCase							):
               logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""							)
               return
         __lowerCamelCase     :      Optional[Any]       						=						os.path.join(
             _lowerCamelCase       ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

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

         return (out_vocab_file,)







def   _UpperCAmelCase       (     UpperCAmelCase      :			list      ):
      """simple docstring"""




      __lowerCamelCase     :      Optional[int]       						=						len(UpperCAmelCase      )
      for _ in range(UpperCAmelCase      ):
            for i in range(_ % 2						,	arr_size - 1						,	2      ):
                  if arr[i + 1] < arr[i]:
                        __lowerCamelCase						, __lowerCamelCase     :      List[str]       						=						arr[i + 1], arr[i]

      return arr


if __name__ == "__main__":
    __UpperCamelCase    :						str     =							list(range(10, 0, -1))
    print(F'''Original: {arr}. Sorted: {odd_even_transposition(arr)}''')



