


'''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_					:    Tuple          =    logging.get_logger(__name__)

a_					:    str          =    {"vocab_file": "sentencepiece.bpe.model"}

a_					:    Union[str, Any]          =    {
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    }
}

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

a_					:    List[Any]          =    "▁"




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						)					->							Optional[int]:
      return len(self.fairseq_tokens_to_ids						) + len(self.sp_model						)
 def 							__UpperCAmelCase		( self						)					->							Any:
      _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__						)					->							List[str]:
      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__						)					->							List[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__						)					->							Union[str, Any]:
      _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						)					->							Union[str, Any]:
      _a			      =							self.__dict__.copy()
      _a			      =							None
      return state
 def __setstate__( self      ,				__magic_name__						)					->							Optional[int]:
      _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'''






from typing import Any






def 	_A				(lowerCAmelCase__  :list		)   ->  list[Any]:




     '''simple docstring'''



     if not input_list:
          return []
     _a			      =							[input_list.count(lowerCAmelCase__		) for value in input_list]
     _a			      =							max(lowerCAmelCase__		)  # Gets the maximum count in the input list.
     # Gets values of modes
     return sorted({input_list[i] for i, value in enumerate(lowerCAmelCase__		) if value == y}		)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

