






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     :							Union[str, Any]    =logging.get_logger(__name__)

lowerCamelCase     :							List[Any]    ={'''vocab_file''': '''sentencepiece.bpe.model'''}

lowerCamelCase     :							Optional[Any]    ={
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    }
}

lowerCamelCase     :							Optional[Any]    ={
    '''camembert-base''': 512,
}

lowerCamelCase     :							List[str]    ='''▁'''





class       __a  (   A__	):
 _lowerCAmelCase	:	str				=			VOCAB_FILES_NAMES
 _lowerCAmelCase	:	str				=			PRETRAINED_VOCAB_FILES_MAP
 _lowerCAmelCase	:	List[str]				=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCAmelCase	:	Dict				=			['''input_ids''', '''attention_mask''']







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


      '''simple docstring'''






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

      UpperCamelCase__      :       str	    =  {} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          bos_token=SCREAMING_SNAKE_CASE							,       eos_token=SCREAMING_SNAKE_CASE							,       unk_token=SCREAMING_SNAKE_CASE							,       sep_token=SCREAMING_SNAKE_CASE							,       cls_token=SCREAMING_SNAKE_CASE							,       pad_token=SCREAMING_SNAKE_CASE							,       mask_token=SCREAMING_SNAKE_CASE							,       additional_special_tokens=SCREAMING_SNAKE_CASE							,       sp_model_kwargs=self.sp_model_kwargs							,       **SCREAMING_SNAKE_CASE							,       )
      UpperCamelCase__      :       Union[str, Any]	    =  spm.SentencePieceProcessor(**self.sp_model_kwargs						)
      self.sp_model.Load(str(SCREAMING_SNAKE_CASE						)						)
      UpperCamelCase__      :       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>
      UpperCamelCase__      :       Optional[int]	    =  {"<s>NOTUSED": 0, "<pad>": 1, "</s>NOTUSED": 2, "<unk>": 3}
      UpperCamelCase__      :       List[Any]	    =  len(self.fairseq_tokens_to_ids						)
      UpperCamelCase__      :       Any	    =  len(self.sp_model						) + len(self.fairseq_tokens_to_ids						)
      UpperCamelCase__      :       Optional[Any]	    =  {v: k for k, v in self.fairseq_tokens_to_ids.items()}







 def  __lowercase (		self :       List[str]							,       SCREAMING_SNAKE_CASE :       List[int]							,       SCREAMING_SNAKE_CASE :       Optional[List[int]] = None						):


      '''simple docstring'''






      if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      UpperCamelCase__      :       Dict	    =  [self.cls_token_id]
      UpperCamelCase__      :       List[Any]	    =  [self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep







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


      '''simple docstring'''






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

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







 def  __lowercase (		self :       Dict							,       SCREAMING_SNAKE_CASE :       List[int]							,       SCREAMING_SNAKE_CASE :       Optional[List[int]] = None						):


      '''simple docstring'''






      UpperCamelCase__      :       Dict	    =  [self.sep_token_id]
      UpperCamelCase__      :       Tuple	    =  [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 :       int						):


      '''simple docstring'''






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







 def  __lowercase (		self :       Optional[Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       str	    =  {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE						): i for i in range(self.vocab_size						)}
      vocab.update(self.added_tokens_encoder						)
      return vocab







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


      '''simple docstring'''






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







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


      '''simple docstring'''






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







 def  __lowercase (		self :       Optional[Any]							,       SCREAMING_SNAKE_CASE :       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  __lowercase (		self :       Dict							,       SCREAMING_SNAKE_CASE :       int						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[int]	    =  []
      UpperCamelCase__      :       List[Any]	    =  ""
      UpperCamelCase__      :       List[Any]	    =  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(SCREAMING_SNAKE_CASE						) + token
                UpperCamelCase__      :       Any	    =  True
                UpperCamelCase__      :       Tuple	    =  []
           else:
                current_sub_tokens.append(SCREAMING_SNAKE_CASE						)
                UpperCamelCase__      :       Dict	    =  False
      out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE						)
      return out_string.strip()







 def __getstate__(		self :       str						):


      '''simple docstring'''






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







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


      '''simple docstring'''






      UpperCamelCase__      :       Optional[Any]	    =  d

      # for backward compatibility
      if not hasattr(self							,       "sp_model_kwargs"						):
           UpperCamelCase__      :       int	    =  {}

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







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


      '''simple docstring'''






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

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

      return (out_vocab_file,)







from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate





@dataclass(frozen=A__	)
class       __a  (   A__	):
 _lowerCAmelCase	:	str				=			field(default='''language-modeling'''	,       metadata={'''include_in_asdict_even_if_is_default''': True}	)

 _lowerCAmelCase	:	ClassVar[Features]				=			Features({'''text''': Value('''string'''	)}	)
 _lowerCAmelCase	:	ClassVar[Features]				=			Features({}	)
 _lowerCAmelCase	:	str				=			"text"







 @property
 def  __lowercase (		self :       str						):


      '''simple docstring'''






      return {self.text_column: "text"}