


'''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


__magic_name__			=      logging.get_logger(__name__)

__magic_name__			=      {'vocab_file': 'sentencepiece.bpe.model'}

__magic_name__			=      {
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
}

__magic_name__			=      {
    'moussaKam/mbarthez': 1_024,
    'moussaKam/barthez': 1_024,
    'moussaKam/barthez-orangesum-title': 1_024,
}

__magic_name__			=      '▁'



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







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






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

        '''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	,sp_model_kwargs=self.sp_model_kwargs	,**_a	,)

        A_						:  Any          =				vocab_file
        A_						:  Optional[int]          =				spm.SentencePieceProcessor(**self.sp_model_kwargs )
        self.sp_model.Load(str(_a ) )

        A_						:  str          =				{"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

        A_						:  Optional[Any]          =				len(self.sp_model ) - 1
        A_						:  Optional[int]          =				{v: k for k, v in self.fairseq_tokens_to_ids.items()}






  def        _a							(							self :       Optional[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_						:  Tuple          =				[self.cls_token_id]
        A_						:  List[str]          =				[self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep






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

        '''simple docstring'''




        A_						:  str          =				[self.sep_token_id]
        A_						:  List[str]          =				[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        _a							(							self :       Optional[int] ):

        '''simple docstring'''




        return len(self.sp_model )






  def        _a							(							self :       Any ):

        '''simple docstring'''




        A_						:  Tuple          =				{self.convert_ids_to_tokens(_a ): i for i in range(self.vocab_size )}
        vocab.update(self.added_tokens_encoder )
        return vocab






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

        '''simple docstring'''




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






  def        _a							(							self :       Dict	,_a :       List[str] ):

        '''simple docstring'''




        if token in self.fairseq_tokens_to_ids:
              return self.fairseq_tokens_to_ids[token]
        A_						:  str          =				self.sp_model.PieceToId(_a )

        return spm_id if spm_id else self.unk_token_id






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

        '''simple docstring'''




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






  def        _a							(							self :       Dict	,_a :       Optional[Any] ):

        '''simple docstring'''




        A_						:  Optional[Any]          =				[]
        A_						:  int          =				""""""
        A_						:  int          =				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_						:  int          =				True
                    A_						:  Optional[int]          =				[]
              else:
                    current_sub_tokens.append(_a )
                    A_						:  Optional[Any]          =				False
        out_string += self.sp_model.decode(_a )
        return out_string.strip()






  def __getstate__(							self :       List[str] ):

        '''simple docstring'''




        A_						:  str          =				self.__dict__.copy()
        A_						:  Any          =				None
        return state






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

        '''simple docstring'''




        A_						:  Tuple          =				d

        # for backward compatibility
        if not hasattr(self	,"""sp_model_kwargs""" ):
              A_						:  Optional[int]          =				{}

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






  def        _a							(							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_						:  Dict          =				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_						:  int          =				self.sp_model.serialized_model_proto()
                    fi.write(_a )

        return (out_vocab_file,)




'''simple docstring'''


import baseaa




def      lowerCamelCase					(      lowerCamelCase  :      str):
      return baseaa.aaaencode(string.encode("""utf-8"""))




def      lowerCamelCase					(      lowerCamelCase  :      bytes):
      return baseaa.aaadecode(lowerCamelCase).decode("""utf-8""")


if __name__ == "__main__":
     import doctest

     doctest.testmod()
