





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


lowercase_          =		logging.get_logger(__name__)

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

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

lowercase_          =		{
    """camembert-base""": 512,
}

lowercase_          =		"""▁"""
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   _UpperCamelCase   :		str			 =	VOCAB_FILES_NAMES
   _UpperCamelCase   :		Optional[Any]			 =	PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase   :		str			 =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase   :		int			 =	['input_ids', 'attention_mask']


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





     """simple docstring"""







     lowercase__           =  AddedToken(a      ,					lstrip=a      ,					rstrip=a	) if isinstance(a      ,					a	) else mask_token

     lowercase__           =  {} 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      ,					)
     lowercase__           =  spm.SentencePieceProcessor(**self.sp_model_kwargs	)
     self.sp_model.Load(str(a	)	)
     lowercase__           =  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>
     lowercase__           =  {'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
     lowercase__           =  len(self.fairseq_tokens_to_ids	)
     lowercase__           =  len(self.sp_model	) + len(self.fairseq_tokens_to_ids	)
     lowercase__           =  {v: k for k, v in self.fairseq_tokens_to_ids.items()}


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]      ,					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__           =  [self.cls_token_id]
     lowercase__           =  [self.sep_token_id]
     return cls + token_ids_a + sep + sep + token_ids_a + sep


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]      ,					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 	SCREAMING_SNAKE_CASE_							(   self      :    List[Any]      ,					a      :    List[int]      ,					a      :    Optional[List[int]] = None	)->       List[int]:





     """simple docstring"""







     lowercase__           =  [self.sep_token_id]
     lowercase__           =  [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 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]	)->       Any:





     """simple docstring"""







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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]	)->       List[Any]:





     """simple docstring"""







     lowercase__           =  {self.convert_ids_to_tokens(a	): i for i in range(self.vocab_size	)}
     vocab.update(self.added_tokens_encoder	)
     return vocab


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[Any]      ,					a      :    str	)->       List[str]:





     """simple docstring"""







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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    str      ,					a      :    Optional[Any]	)->       Dict:





     """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 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    Dict	)->       Optional[int]:





     """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 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]      ,					a      :    int	)->       Union[str, Any]:





     """simple docstring"""







     lowercase__           =  []
     lowercase__           =  ''
     lowercase__           =  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__           =  True
         lowercase__           =  []
       else:
         current_sub_tokens.append(a	)
         lowercase__           =  False
     out_string += self.sp_model.decode(a	)
     return out_string.strip()


   def __getstate__(   self      :    Any	)->       Any:





     """simple docstring"""







     lowercase__           =  self.__dict__.copy()
     lowercase__           =  None
     return state


   def __setstate__(   self      :    Dict      ,					a      :    str	)->       List[Any]:





     """simple docstring"""







     lowercase__           =  d

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

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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    int      ,					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__           =  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__           =  self.sp_model.serialized_model_proto()
         fi.write(a	)

     return (out_vocab_file,)









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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_camembert import CamembertTokenizer
else:
      lowercase_          =		None


lowercase_          =		logging.get_logger(__name__)

lowercase_          =		{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

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

lowercase_          =		{
    """camembert-base""": 512,
}

lowercase_          =		"""▁"""
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   _UpperCamelCase   :		int			 =	VOCAB_FILES_NAMES
   _UpperCamelCase   :		Dict			 =	PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase   :		Dict			 =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase   :		Dict			 =	['input_ids', 'attention_mask']
   _UpperCamelCase   :		Optional[int]			 =	CamembertTokenizer


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





     """simple docstring"""







     lowercase__           =  AddedToken(a      ,					lstrip=a      ,					rstrip=a	) if isinstance(a      ,					a	) else mask_token

     super().__init__(
         a      ,					tokenizer_file=a      ,					bos_token=a      ,					eos_token=a      ,					sep_token=a      ,					cls_token=a      ,					unk_token=a      ,					pad_token=a      ,					mask_token=a      ,					additional_special_tokens=a      ,					**a      ,					)

     lowercase__           =  vocab_file
     lowercase__           =  False if not self.vocab_file else True


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					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__           =  [self.cls_token_id]
     lowercase__           =  [self.sep_token_id]
     return cls + token_ids_a + sep + sep + token_ids_a + sep


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    List[int]      ,					a      :    Optional[List[int]] = None	)->       List[int]:





     """simple docstring"""







     lowercase__           =  [self.sep_token_id]
     lowercase__           =  [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]


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]      ,					a      :    str      ,					a      :    Optional[str] = None	)->       Tuple[str]:





     """simple docstring"""







     if not self.can_save_slow_tokenizer:
       raise ValueError(
           'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
           'tokenizer.'	)

     if not os.path.isdir(a	):
       logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""	)
       return
     lowercase__           =  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	):
       copyfile(self.vocab_file      ,					a	)

     return (out_vocab_file,)


