
'''simple docstring'''







import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__lowerCAmelCase         =    logging.get_logger(__name__)

__lowerCAmelCase         =    {"""vocab_file""": """sentencepiece.bpe.model"""}

__lowerCAmelCase         =    {
    """vocab_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model""",
    }
}

__lowerCAmelCase         =    {
    """camembert-base""": 5_1_2,
}

__lowerCAmelCase         =    """▁"""







class UpperCAmelCase__	( lowercase__	):





  """simple docstring"""

  __UpperCAmelCase				:				Tuple   =					VOCAB_FILES_NAMES
  __UpperCAmelCase				:				Tuple   =					PRETRAINED_VOCAB_FILES_MAP
  __UpperCAmelCase				:				Union[str, Any]   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __UpperCAmelCase				:				Optional[int]   =					['''input_ids''', '''attention_mask''']


  def __init__(		self      :     Tuple							,_a      :     int							,_a      :     List[Any]="<s>"							,_a      :     Union[str, Any]="</s>"							,_a      :     Dict="</s>"							,_a      :     List[str]="<s>"							,_a      :     Union[str, Any]="<unk>"							,_a      :     str="<pad>"							,_a      :     List[str]="<mask>"							,_a      :     List[Any]=["<s>NOTUSED", "</s>NOTUSED"]							,_a      :     Optional[Dict[str, Any]] = None							,**_a      :     List[Any]							,):


     '''simple docstring'''






     _a				:	Optional[Any]			=   AddedToken(_a							,lstrip=_a							,rstrip=_a       ) if isinstance(_a							,_a       ) else mask_token

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

     super().__init__(
         bos_token=_a							,eos_token=_a							,unk_token=_a							,sep_token=_a							,cls_token=_a							,pad_token=_a							,mask_token=_a							,additional_special_tokens=_a							,sp_model_kwargs=self.sp_model_kwargs							,**_a							,)
     _a				:	List[str]			=   spm.SentencePieceProcessor(**self.sp_model_kwargs       )
     self.sp_model.Load(str(_a       )       )
     _a				:	Any			=   vocab_file
     # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
     # sentencepiece vocabulary (this is the case for <s> and </s>
     _a				:	Union[str, Any]			=   {'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
     _a				:	List[str]			=   len(self.fairseq_tokens_to_ids       )
     _a				:	int			=   len(self.sp_model       ) + len(self.fairseq_tokens_to_ids       )
     _a				:	str			=   {v: k for k, v in self.fairseq_tokens_to_ids.items()}


  def   __lowercase		(		self      :     Any							,_a      :     List[int]							,_a      :     Optional[List[int]] = None       ):


     '''simple docstring'''






     if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
     _a				:	Optional[int]			=   [self.cls_token_id]
     _a				:	Union[str, Any]			=   [self.sep_token_id]
     return cls + token_ids_a + sep + sep + token_ids_a + sep


  def   __lowercase		(		self      :     List[str]							,_a      :     List[int]							,_a      :     Optional[List[int]] = None							,_a      :     bool = False       ):


     '''simple docstring'''






     if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=_a							,token_ids_a=_a							,already_has_special_tokens=_a       )

     if token_ids_a is None:
        return [1] + ([0] * len(_a       )) + [1]
     return [1] + ([0] * len(_a       )) + [1, 1] + ([0] * len(_a       )) + [1]


  def   __lowercase		(		self      :     Any							,_a      :     List[int]							,_a      :     Optional[List[int]] = None       ):


     '''simple docstring'''






     _a				:	Optional[int]			=   [self.sep_token_id]
     _a				:	List[Any]			=   [self.cls_token_id]

     if token_ids_a is None:
        return len(cls + token_ids_a + sep       ) * [0]
     return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]


  @property
  def   __lowercase		(		self      :     str       ):


     '''simple docstring'''






     return len(self.fairseq_tokens_to_ids       ) + len(self.sp_model       )


  def   __lowercase		(		self      :     Dict       ):


     '''simple docstring'''






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


  def   __lowercase		(		self      :     Tuple							,_a      :     str       ):


     '''simple docstring'''






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


  def   __lowercase		(		self      :     Any							,_a      :     str       ):


     '''simple docstring'''






     if token in self.fairseq_tokens_to_ids:
        return self.fairseq_tokens_to_ids[token]
     elif self.sp_model.PieceToId(_a       ) == 0:
        # Convert sentence piece unk token to fairseq unk token index
        return self.unk_token_id
     return self.fairseq_offset + self.sp_model.PieceToId(_a       )


  def   __lowercase		(		self      :     List[Any]							,_a      :     Optional[Any]       ):


     '''simple docstring'''






     if index in self.fairseq_ids_to_tokens:
        return self.fairseq_ids_to_tokens[index]
     return self.sp_model.IdToPiece(index - self.fairseq_offset       )


  def   __lowercase		(		self      :     Union[str, Any]							,_a      :     List[Any]       ):


     '''simple docstring'''






     _a				:	str			=   []
     _a				:	int			=   ''
     _a				:	Dict			=   False
     for token in tokens:
        # make sure that special tokens are not decoded using sentencepiece model
        if token in self.all_special_tokens:
           if not prev_is_special:
              out_string += " "
           out_string += self.sp_model.decode(_a       ) + token
           _a				:	Optional[Any]			=   True
           _a				:	Dict			=   []
        else:
           current_sub_tokens.append(_a       )
           _a				:	str			=   False
     out_string += self.sp_model.decode(_a       )
     return out_string.strip()


  def __getstate__(		self      :     Dict       ):


     '''simple docstring'''






     _a				:	Tuple			=   self.__dict__.copy()
     _a				:	Tuple			=   None
     return state


  def __setstate__(		self      :     Dict							,_a      :     List[Any]       ):


     '''simple docstring'''






     _a				:	List[Any]			=   d

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

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


  def   __lowercase		(		self      :     List[Any]							,_a      :     str							,_a      :     Optional[str] = None       ):


     '''simple docstring'''






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

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

     return (out_vocab_file,)





'''simple docstring'''







from __future__ import annotations

from random import choice





def 					UpperCAmelCase_		(__a      :    str ):






   """simple docstring"""

   return choice(__a )





def 					UpperCAmelCase_		(__a      :    list[int] ,	__a      :    int ):






   """simple docstring"""

   _a				:	Dict			=   random_pivot(__a )

   # partition based on pivot
   # linear time
   _a				:	Optional[int]			=   [e for e in lst if e < pivot]
   _a				:	List[str]			=   [e for e in lst if e > pivot]

   # if we get lucky, pivot might be the element we want.
   # we can easily see this:
   # small (elements smaller than k)
   # + pivot (kth element)
   # + big (elements larger than k)
   if len(__a ) == k - 1:
      return pivot
   # pivot is in elements bigger than k
   elif len(__a ) < k - 1:
      return kth_number(__a ,	k - len(__a ) - 1 )
   # pivot is in elements smaller than k
   else:
      return kth_number(__a ,	__a )


if __name__ == "__main__":
  import doctest

  doctest.testmod()



