


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


_A			=						logging.get_logger(__name__)

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

_A			=						{
    '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'
        ),
    },
}

_A			=						{
    'moussaKam/mbarthez': 1024,
    'moussaKam/barthez': 1024,
    'moussaKam/barthez-orangesum-title': 1024,
}

_A			=						'▁'






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[int]              =  VOCAB_FILES_NAMES
    UpperCAmelCase__						:		List[Any]              =  PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__						:		Tuple              =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__						:		Optional[int]              =  ["input_ids", "attention_mask"]






    def __init__(      self ,       A_ ,       A_="<s>" ,       A_="</s>" ,       A_="</s>" ,       A_="<s>" ,       A_="<unk>" ,       A_="<pad>" ,       A_="<mask>" ,       A_ = None ,       **A_ ,       )  ->	None:
         # Mask token behave like a normal word, i.e. include the space before it
         __UpperCamelCase   							=AddedToken(A_ ,       lstrip=A_ ,       rstrip=A_   ) if isinstance(A_ ,       A_   ) else mask_token

         __UpperCamelCase   							={} 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_ ,       )

         __UpperCamelCase   							=vocab_file
         __UpperCamelCase   							=spm.SentencePieceProcessor(**self.sp_model_kwargs   )
         self.sp_model.Load(str(A_   )   )

         __UpperCamelCase   							={'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

         __UpperCamelCase   							=len(self.sp_model   ) - 1
         __UpperCamelCase   							={v: k for k, v in self.fairseq_tokens_to_ids.items()}






    def _a (      self ,       A_ ,       A_ = None   )  ->	List[int]:

         if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         __UpperCamelCase   							=[self.cls_token_id]
         __UpperCamelCase   							=[self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep






    def _a (      self ,       A_ ,       A_ = None ,       A_ = False   )  ->	List[int]:
         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 ,       A_ ,       A_ = None   )  ->	List[int]:
         __UpperCamelCase   							=[self.sep_token_id]
         __UpperCamelCase   							=[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   )  ->	Any:
         return len(self.sp_model   )






    def _a (      self   )  ->	Optional[int]:
         __UpperCamelCase   							={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 ,       A_   )  ->	List[str]:
         return self.sp_model.encode(A_ ,       out_type=A_   )






    def _a (      self ,       A_   )  ->	int:
         if token in self.fairseq_tokens_to_ids:
              return self.fairseq_tokens_to_ids[token]
         __UpperCamelCase   							=self.sp_model.PieceToId(A_   )

         return spm_id if spm_id else self.unk_token_id






    def _a (      self ,       A_   )  ->	Dict:
         if index in self.fairseq_ids_to_tokens:
              return self.fairseq_ids_to_tokens[index]
         return self.sp_model.IdToPiece(A_   )






    def _a (      self ,       A_   )  ->	Dict:
         __UpperCamelCase   							=[]
         __UpperCamelCase   							=''
         __UpperCamelCase   							=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
                   __UpperCamelCase   							=True
                   __UpperCamelCase   							=[]
              else:
                   current_sub_tokens.append(A_   )
                   __UpperCamelCase   							=False
         out_string += self.sp_model.decode(A_   )
         return out_string.strip()






    def __getstate__(      self   )  ->	int:
         __UpperCamelCase   							=self.__dict__.copy()
         __UpperCamelCase   							=None
         return state






    def __setstate__(      self ,       A_   )  ->	Union[str, Any]:
         __UpperCamelCase   							=d

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

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






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

         return (out_vocab_file,)









from ....utils import logging


_A			=						logging.get_logger(__name__)






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    def __init__(      self ,       A_ ,       A_=None ,       A_=2048   )  ->	Any:
         __UpperCamelCase   							=config.__dict__
         __UpperCamelCase   							=modal_hidden_size
         if num_labels:
              __UpperCamelCase   							=num_labels





