


'''simple docstring'''






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_   :      Tuple				    =      {"vocab_file": "sentencepiece.bpe.model"}

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

a_   :      Union[str, Any]				    =      {
    "camembert-base": 5_1_2,
}

a_   :      str				    =      "▁"




class 	a       (       _SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		VOCAB_FILES_NAMES
 _lowerCAmelCase	   =		PRETRAINED_VOCAB_FILES_MAP
 _lowerCAmelCase	   =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCAmelCase	   =		["""input_ids""", """attention_mask"""]
 def __init__(	self ,			__magic_name__ ,			__magic_name__="<s>" ,			__magic_name__="</s>" ,			__magic_name__="</s>" ,			__magic_name__="<s>" ,			__magic_name__="<unk>" ,			__magic_name__="<pad>" ,			__magic_name__="<mask>" ,			__magic_name__=["<s>NOTUSED", "</s>NOTUSED"] ,			__magic_name__ = None ,			**__magic_name__ ,			)   ->							None:
      # Mask token behave like a normal word, i.e. include the space before it
      _a      =							AddedToken(__magic_name__ ,			lstrip=__magic_name__ ,			rstrip=__magic_name__  ) if isinstance(__magic_name__ ,			__magic_name__  ) else mask_token

      _a      =							{} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          bos_token=__magic_name__ ,			eos_token=__magic_name__ ,			unk_token=__magic_name__ ,			sep_token=__magic_name__ ,			cls_token=__magic_name__ ,			pad_token=__magic_name__ ,			mask_token=__magic_name__ ,			additional_special_tokens=__magic_name__ ,			sp_model_kwargs=self.sp_model_kwargs ,			**__magic_name__ ,			)
      _a      =							spm.SentencePieceProcessor(**self.sp_model_kwargs  )
      self.sp_model.Load(str(__magic_name__  )  )
      _a      =							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>
      _a      =							{'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
      _a      =							len(self.fairseq_tokens_to_ids  )
      _a      =							len(self.sp_model  ) + len(self.fairseq_tokens_to_ids  )
      _a      =							{v: k for k, v in self.fairseq_tokens_to_ids.items()}
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = None  )   ->							List[int]:

      if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      _a      =							[self.cls_token_id]
      _a      =							[self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = None ,			__magic_name__ = False  )   ->							List[int]:
      if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=__magic_name__ ,			token_ids_a=__magic_name__ ,			already_has_special_tokens=__magic_name__  )

      if token_ids_a is None:
           return [1] + ([0] * len(__magic_name__  )) + [1]
      return [1] + ([0] * len(__magic_name__  )) + [1, 1] + ([0] * len(__magic_name__  )) + [1]
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = None  )   ->							List[int]:
      _a      =							[self.sep_token_id]
      _a      =							[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 							__UpperCAmelCase						(	self  )   ->							Dict:
      return len(self.fairseq_tokens_to_ids  ) + len(self.sp_model  )
 def 							__UpperCAmelCase						(	self  )   ->							str:
      _a      =							{self.convert_ids_to_tokens(__magic_name__  ): i for i in range(self.vocab_size  )}
      vocab.update(self.added_tokens_encoder  )
      return vocab
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							List[str]:
      return self.sp_model.encode(__magic_name__ ,			out_type=__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							Tuple:
      if token in self.fairseq_tokens_to_ids:
           return self.fairseq_tokens_to_ids[token]
      elif self.sp_model.PieceToId(__magic_name__  ) == 0:
           # Convert sentence piece unk token to fairseq unk token index
           return self.unk_token_id
      return self.fairseq_offset + self.sp_model.PieceToId(__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							Union[str, Any]:
      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 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							Optional[int]:
      _a      =							[]
      _a      =							''
      _a      =							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(__magic_name__  ) + token
                _a      =							True
                _a      =							[]
           else:
                current_sub_tokens.append(__magic_name__  )
                _a      =							False
      out_string += self.sp_model.decode(__magic_name__  )
      return out_string.strip()
 def __getstate__(	self  )   ->							str:
      _a      =							self.__dict__.copy()
      _a      =							None
      return state
 def __setstate__(	self ,			__magic_name__  )   ->							Union[str, Any]:
      _a      =							d

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

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




 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = None  )   ->							Tuple[str]:
      if not os.path.isdir(__magic_name__  ):
           logger.error(f'Vocabulary path ({save_directory}) should be a directory'  )
           return
      _a      =							os.path.join(
          __magic_name__ ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )

      if os.path.abspath(self.vocab_file  ) != os.path.abspath(__magic_name__  ) and os.path.isfile(self.vocab_file  ):
           copyfile(self.vocab_file ,			__magic_name__  )
      elif not os.path.isfile(self.vocab_file  ):
           with open(__magic_name__ ,			'wb'  ) as fi:
                _a      =							self.sp_model.serialized_model_proto()
                fi.write(__magic_name__  )

      return (out_vocab_file,)





'''simple docstring'''






def 		_A			(lowerCAmelCase__					:int						)				->					int:




     '''simple docstring'''



     assert (
         isinstance(lowerCAmelCase__			,  lowerCAmelCase__						) and number_of_steps > 0
     ), f'number_of_steps needs to be positive integer, your input {number_of_steps}'
     if number_of_steps == 1:
          return 1
     _a ,				_a      =							1, 1
     for _ in range(number_of_steps - 1						):
          _a ,				_a      =							current + previous, current
     return current


if __name__ == "__main__":
 import doctest

 doctest.testmod()

