




from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

lowerCAmelCase_						=			TypeVar('KEY')
lowerCAmelCase_						=			TypeVar('VAL')
@dataclass(frozen=_lowerCamelCase      ,       slots=_lowerCamelCase				)
class 						_A (  Generic[KEY, VAL]				):
  _UpperCamelCase       :      KEY
  _UpperCamelCase       :      VAL


class 						_A (  _Item				):






  def __init__(  self		:     List[Any]				)		->   None:






       """simple docstring"""







       super().__init__(_A				,						_A				)






  def __bool__(  self		:     Any				)		->   bool:






       """simple docstring"""







       return False


lowerCAmelCase_						=			_DeletedItem()
class 						_A (  MutableMapping[KEY, VAL]				):






  def __init__(  self		:     List[Any]				,						_A		:     int = 8				,						_A		:     float = 0.75				)		->   None:






       """simple docstring"""







       lowercase :       Any       =			initial_block_size
       lowercase :       list[_Item | None]       =			[None] * initial_block_size
       assert 0.0 < capacity_factor < 1.0
       lowercase :       Dict       =			capacity_factor
       lowercase :       List[str]       =			0






  def  __a      (  self		:     Dict				,						_A		:     KEY				)		->   int:






       """simple docstring"""







       return hash(_A				) % len(self._buckets				)






  def  __a      (  self		:     Optional[Any]				,						_A		:     int				)		->   int:






       """simple docstring"""







       return (ind + 1) % len(self._buckets				)






  def  __a      (  self		:     List[str]				,						_A		:     int				,						_A		:     KEY				,						_A		:     VAL				)		->   bool:






       """simple docstring"""







       lowercase :       Union[str, Any]       =			self._buckets[ind]
       if not stored:
            lowercase :       List[Any]       =			_Item(_A				,						_A				)
            self._len += 1
            return True
       elif stored.key == key:
            lowercase :       Tuple       =			_Item(_A				,						_A				)
            return True
       else:
            return False






  def  __a      (  self		:     List[str]				)		->   bool:






       """simple docstring"""







       lowercase :       Tuple       =			len(self._buckets				) * self._capacity_factor
       return len(self				) >= int(_A				)






  def  __a      (  self		:     Union[str, Any]				)		->   bool:






       """simple docstring"""







       if len(self._buckets				) <= self._initial_block_size:
            return False
       lowercase :       int       =			len(self._buckets				) * self._capacity_factor / 2
       return len(self				) < limit






  def  __a      (  self		:     int				,						_A		:     int				)		->   None:






       """simple docstring"""







       lowercase :       Any       =			self._buckets
       lowercase :       Tuple       =			[None] * new_size
       lowercase :       Optional[int]       =			0
       for item in old_buckets:
            if item:
                 self._add_item(item.key				,						item.val				)






  def  __a      (  self		:     str				)		->   None:






       """simple docstring"""







       self._resize(len(self._buckets				) * 2				)






  def  __a      (  self		:     Any				)		->   None:






       """simple docstring"""







       self._resize(len(self._buckets				) // 2				)






  def  __a      (  self		:     List[str]				,						_A		:     KEY				)		->   Iterator[int]:






       """simple docstring"""







       lowercase :       Union[str, Any]       =			self._get_bucket_index(_A				)
       for _ in range(len(self._buckets				)				):
            yield ind
            lowercase :       Any       =			self._get_next_ind(_A				)






  def  __a      (  self		:     str				,						_A		:     KEY				,						_A		:     VAL				)		->   None:






       """simple docstring"""







       for ind in self._iterate_buckets(_A				):
            if self._try_set(_A				,						_A				,						_A				):
                 break






  def __setitem__(  self		:     Optional[int]				,						_A		:     KEY				,						_A		:     VAL				)		->   None:






       """simple docstring"""







       if self._is_full():
            self._size_up()

       self._add_item(_A				,						_A				)






  def __delitem__(  self		:     Optional[Any]				,						_A		:     KEY				)		->   None:






       """simple docstring"""







       for ind in self._iterate_buckets(_A				):
            lowercase :       int       =			self._buckets[ind]
            if item is None:
                 raise KeyError(_A				)
            if item is _deleted:
                 continue
            if item.key == key:
                 lowercase :       str       =			_deleted
                 self._len -= 1
                 break
       if self._is_sparse():
            self._size_down()






  def __getitem__(  self		:     Union[str, Any]				,						_A		:     KEY				)		->   VAL:






       """simple docstring"""







       for ind in self._iterate_buckets(_A				):
            lowercase :       List[str]       =			self._buckets[ind]
            if item is None:
                 break
            if item is _deleted:
                 continue
            if item.key == key:
                 return item.val
       raise KeyError(_A				)






  def __len__(  self		:     Any				)		->   int:






       """simple docstring"""







       return self._len






  def __iter__(  self		:     str				)		->   Iterator[KEY]:






       """simple docstring"""







       yield from (item.key for item in self._buckets if item)






  def __repr__(  self		:     Union[str, Any]				)		->   str:






       """simple docstring"""







       lowercase :       int       =			''' ,'''.join(
           f"""{item.key}: {item.val}""" for item in self._buckets if item				)
       return f"""HashMap({val_string})"""





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': {
        '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'
        ),
    },
}

lowerCAmelCase_						=			{
    'moussaKam/mbarthez': 10_24,
    'moussaKam/barthez': 10_24,
    'moussaKam/barthez-orangesum-title': 10_24,
}

lowerCAmelCase_						=			'▁'
class 						_A (  _lowerCamelCase				):
  _UpperCamelCase       :      Optional[Any]			=      VOCAB_FILES_NAMES
  _UpperCamelCase       :      Union[str, Any]			=      PRETRAINED_VOCAB_FILES_MAP
  _UpperCamelCase       :      Optional[Any]			=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _UpperCamelCase       :      List[Any]			=      ['''input_ids''', '''attention_mask''']






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






       """simple docstring"""







       lowercase :       List[str]       =			AddedToken(_A				,						lstrip=_A				,						rstrip=_A				) if isinstance(_A				,						_A				) else mask_token

       lowercase :       List[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				,						)

       lowercase :       Any       =			vocab_file
       lowercase :       Any       =			spm.SentencePieceProcessor(**self.sp_model_kwargs				)
       self.sp_model.Load(str(_A				)				)

       lowercase :       Optional[Any]       =			{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

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






  def  __a      (  self		:     Tuple				,						_A		:     List[int]				,						_A		:     Optional[List[int]] = None				)		->   List[int]:






       """simple docstring"""







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






  def  __a      (  self		:     Optional[int]				,						_A		:     List[int]				,						_A		:     Optional[List[int]] = None				,						_A		:     bool = False				)		->   List[int]:






       """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		:     Optional[int]				,						_A		:     List[int]				,						_A		:     Optional[List[int]] = None				)		->   List[int]:






       """simple docstring"""







       lowercase :       List[str]       =			[self.sep_token_id]
       lowercase :       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		:     List[Any]				)		->   Optional[Any]:






       """simple docstring"""







       return len(self.sp_model				)






  def  __a      (  self		:     List[Any]				)		->   int:






       """simple docstring"""







       lowercase :       int       =			{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		:     Union[str, Any]				,						_A		:     str				)		->   List[str]:






       """simple docstring"""







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






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






       """simple docstring"""







       if token in self.fairseq_tokens_to_ids:
            return self.fairseq_tokens_to_ids[token]
       lowercase :       Optional[int]       =			self.sp_model.PieceToId(_A				)

       return spm_id if spm_id else self.unk_token_id






  def  __a      (  self		:     Any				,						_A		:     List[str]				)		->   List[str]:






       """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		:     Any				,						_A		:     Tuple				)		->   Tuple:






       """simple docstring"""







       lowercase :       Dict       =			[]
       lowercase :       Any       =			''''''
       lowercase :       str       =			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
                 lowercase :       int       =			True
                 lowercase :       Optional[Any]       =			[]
            else:
                 current_sub_tokens.append(_A				)
                 lowercase :       List[str]       =			False
       out_string += self.sp_model.decode(_A				)
       return out_string.strip()






  def __getstate__(  self		:     int				)		->   Optional[Any]:






       """simple docstring"""







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






  def __setstate__(  self		:     Optional[int]				,						_A		:     Tuple				)		->   str:






       """simple docstring"""







       lowercase :       List[str]       =			d

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

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






  def  __a      (  self		:     str				,						_A		:     str				,						_A		:     Optional[str] = None				)		->   Tuple[str]:






       """simple docstring"""







       if not os.path.isdir(_A				):
            logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
            return
       lowercase :       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:
                 lowercase :       Optional[Any]       =			self.sp_model.serialized_model_proto()
                 fi.write(_A				)

       return (out_vocab_file,)