





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             =							logging.get_logger(__name__)


_UpperCAmelCase             =							'▁'

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

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

_UpperCAmelCase             =							{
    'google/reformer-crime-and-punishment': 52_4288,
}






class        snake_case_    (		__lowercase						):
  A_      							=   VOCAB_FILES_NAMES
  A_      							=   PRETRAINED_VOCAB_FILES_MAP
  A_      							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  A_      							=   ['input_ids', 'attention_mask']






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






   '''simple docstring'''

   __lowerCAmelCase					:	Union[str, Any]      =       {} 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       ,  )

   __lowerCAmelCase					:	Dict      =       vocab_file
   __lowerCAmelCase					:	List[Any]      =       spm.SentencePieceProcessor(**self.sp_model_kwargs      )
   self.sp_model.Load(_snake_case      )






  @property
  def 				UpperCAmelCase__						(							self			:					List[Any]      )->List[Any]:






   '''simple docstring'''

   return self.sp_model.get_piece_size()






  def 				UpperCAmelCase__						(							self			:					Optional[int]      )->Dict[str, int]:






   '''simple docstring'''

   __lowerCAmelCase					:	List[str]      =       {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			:					str      )->Union[str, Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	List[Any]      =       self.__dict__.copy()
   __lowerCAmelCase					:	Dict      =       None
   return state






  def __setstate__(							self			:					Any       ,  _snake_case			:					Tuple      )->Union[str, Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[Any]      =       d

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

   __lowerCAmelCase					:	str      =       spm.SentencePieceProcessor(**self.sp_model_kwargs      )
   self.sp_model.Load(self.vocab_file      )






  def 				UpperCAmelCase__						(							self			:					Optional[int]       ,  _snake_case			:					str      )->List[str]:






   '''simple docstring'''

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






  def 				UpperCAmelCase__						(							self			:					Optional[Any]       ,  _snake_case			:					List[str]      )->Optional[int]:






   '''simple docstring'''

   return self.sp_model.piece_to_id(_snake_case      )






  def 				UpperCAmelCase__						(							self			:					Tuple       ,  _snake_case			:					Dict      )->str:






   '''simple docstring'''

   if index < self.sp_model.get_piece_size():
    __lowerCAmelCase					:	int      =       self.sp_model.IdToPiece(_snake_case      )
   return token






  def 				UpperCAmelCase__						(							self			:					Optional[int]       ,  _snake_case			:					int      )->Any:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[Any]      =       []
   __lowerCAmelCase					:	int      =       """"""
   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
     __lowerCAmelCase					:	List[Any]      =       []
    else:
     current_sub_tokens.append(_snake_case      )
   out_string += self.sp_model.decode(_snake_case      )
   return out_string.strip()






  def 				UpperCAmelCase__						(							self			:					Any       ,  _snake_case			:					str       ,  _snake_case			:					Optional[str] = None      )->Tuple[str]:






   '''simple docstring'''

   if not os.path.isdir(_snake_case      ):
    logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''      )
    return
   __lowerCAmelCase					:	Union[str, Any]      =       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:
     __lowerCAmelCase					:	Tuple      =       self.sp_model.serialized_model_proto()
     fi.write(_snake_case      )

   return (out_vocab_file,)






import random






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list   ,   SCREAMING_SNAKE_CASE		:Dict   )      ->   tuple:
 __lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase					:	List[str]      =       [], [], []
 for element in data:
  if element < pivot:
   less.append(SCREAMING_SNAKE_CASE   )
  elif element > pivot:
   greater.append(SCREAMING_SNAKE_CASE   )
  else:
   equal.append(SCREAMING_SNAKE_CASE   )
 return less, equal, greater







def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list   ,   SCREAMING_SNAKE_CASE		:int   )      ->   Dict:
 # index = len(items) // 2 when trying to find the median
 #   (value of index when items is sorted)

 # invalid input
 if index >= len(SCREAMING_SNAKE_CASE   ) or index < 0:
  return None

 __lowerCAmelCase					:	Union[str, Any]      =       items[random.randint(0   ,   len(SCREAMING_SNAKE_CASE   ) - 1   )]
 __lowerCAmelCase					:	int      =       0
 __lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase					:	Tuple      =       _partition(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	Optional[int]      =       len(SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	List[str]      =       len(SCREAMING_SNAKE_CASE   )

 # index is the pivot
 if m <= index < m + count:
  return pivot
 # must be in smaller
 elif m > index:
  return quick_select(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 # must be in larger
 else:
  return quick_select(SCREAMING_SNAKE_CASE   ,   index - (m + count)   )