





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 :   Any  			=  logging.get_logger(__name__)

_A :   List[str]  			=  {'vocab_file': 'sentencepiece.bpe.model'}

_A :   List[str]  			=  {
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    }
}

_A :   Dict  			=  {
    'camembert-base': 5_12,
}

_A :   int  			=  '▁'


class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_ ):
       _UpperCAmelCase				:  Tuple			   =   VOCAB_FILES_NAMES
       _UpperCAmelCase				:  str			   =   PRETRAINED_VOCAB_FILES_MAP
       _UpperCAmelCase				:  Any			   =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _UpperCAmelCase				:  Union[str, Any]			   =   ["input_ids", "attention_mask"]







       def __init__(				self					:     Tuple		, A					:     Any		, A					:     Tuple="<s>"		, A					:     List[Any]="</s>"		, A					:     List[Any]="</s>"		, A					:     int="<s>"		, A					:     Optional[int]="<unk>"		, A					:     Optional[Any]="<pad>"		, A					:     Optional[int]="<mask>"		, A					:     Union[str, Any]=["<s>NOTUSED", "</s>NOTUSED"]		, A					:     Optional[Dict[str, Any]] = None		, **A					:     Union[str, Any]		, )       ->None:
          # Mask token behave like a normal word, i.e. include the space before it
          lowerCamelCase__       :						int        =							AddedToken(A		, lstrip=A		, rstrip=A      ) if isinstance(A		, A      ) else mask_token

          lowerCamelCase__       :						Dict        =							{} 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		, )
          lowerCamelCase__       :						Any        =							spm.SentencePieceProcessor(**self.sp_model_kwargs      )
          self.sp_model.Load(str(A      )      )
          lowerCamelCase__       :						List[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>
          lowerCamelCase__       :						List[str]        =							{'''<s>NOTUSED''': 0, '''<pad>''': 1, '''</s>NOTUSED''': 2, '''<unk>''': 3}
          lowerCamelCase__       :						List[str]        =							len(self.fairseq_tokens_to_ids      )
          lowerCamelCase__       :						Optional[Any]        =							len(self.sp_model      ) + len(self.fairseq_tokens_to_ids      )
          lowerCamelCase__       :						str        =							{v: k for k, v in self.fairseq_tokens_to_ids.items()}







       def 						__lowerCamelCase   (				self					:     str		, A					:     List[int]		, A					:     Optional[List[int]] = None      )       ->List[int]:

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







       def 						__lowerCamelCase   (				self					:     Dict		, A					:     List[int]		, A					:     Optional[List[int]] = None		, A					:     bool = 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 						__lowerCamelCase   (				self					:     List[str]		, A					:     List[int]		, A					:     Optional[List[int]] = None      )       ->List[int]:
          lowerCamelCase__       :						str        =							[self.sep_token_id]
          lowerCamelCase__       :						Any        =							[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 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->int:
          return len(self.fairseq_tokens_to_ids      ) + len(self.sp_model      )







       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->Optional[Any]:
          lowerCamelCase__       :						Tuple        =							{self.convert_ids_to_tokens(A      ): i for i in range(self.vocab_size      )}
          vocab.update(self.added_tokens_encoder      )
          return vocab







       def 						__lowerCamelCase   (				self					:     Optional[Any]		, A					:     str      )       ->List[str]:
          return self.sp_model.encode(A		, out_type=A      )







       def 						__lowerCamelCase   (				self					:     str		, A					:     List[str]      )       ->List[str]:
          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 						__lowerCamelCase   (				self					:     Tuple		, A					:     Tuple      )       ->int:
          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 						__lowerCamelCase   (				self					:     Union[str, Any]		, A					:     Union[str, Any]      )       ->List[str]:
          lowerCamelCase__       :						int        =							[]
          lowerCamelCase__       :						Any        =							''''''
          lowerCamelCase__       :						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
                lowerCamelCase__       :						Optional[int]        =							True
                lowerCamelCase__       :						Any        =							[]
             else:
                current_sub_tokens.append(A      )
                lowerCamelCase__       :						str        =							False
          out_string += self.sp_model.decode(A      )
          return out_string.strip()







       def __getstate__(				self					:     Tuple      )       ->Union[str, Any]:
          lowerCamelCase__       :						int        =							self.__dict__.copy()
          lowerCamelCase__       :						Optional[Any]        =							None
          return state







       def __setstate__(				self					:     Optional[Any]		, A					:     int      )       ->str:
          lowerCamelCase__       :						Union[str, Any]        =							d

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

          lowerCamelCase__       :						Tuple        =							spm.SentencePieceProcessor(**self.sp_model_kwargs      )
          self.sp_model.Load(self.vocab_file      )







       def 						__lowerCamelCase   (				self					:     List[Any]		, A					:     str		, A					:     Optional[str] = None      )       ->Tuple[str]:
          if not os.path.isdir(A      ):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory"      )
             return
          lowerCamelCase__       :						List[str]        =							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:
                lowerCamelCase__       :						Optional[Any]        =							self.sp_model.serialized_model_proto()
                fi.write(A      )

          return (out_vocab_file,)










def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    Dict:







   """simple docstring"""
   if b == 0:
      return 1
   if (b % 2) == 0:
      return actual_power(UpperCAmelCase      ,  int(b / 2					)					) * actual_power(UpperCAmelCase      ,  int(b / 2					)					)
   else:
      return a * actual_power(UpperCAmelCase      ,  int(b / 2					)					) * actual_power(UpperCAmelCase      ,  int(b / 2					)					)



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    float:







   """simple docstring"""
   if b < 0:
      return 1 / actual_power(UpperCAmelCase      ,  UpperCAmelCase					)
   return actual_power(UpperCAmelCase      ,  UpperCAmelCase					)


if __name__ == "__main__":
       print(power(-2, -3))



