



"""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


_lowerCAmelCase    :Any     =					logging.get_logger(__name__)

_lowerCAmelCase    :Optional[Any]     =					{'vocab_file': 'sentencepiece.bpe.model'}

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

_lowerCAmelCase    :int     =					{
    'camembert-base': 512,
}

_lowerCAmelCase    :List[str]     =					'▁'
class 							_UpperCAmelCase							( a      ):





     '''simple docstring'''



     a__									=VOCAB_FILES_NAMES
     a__									=PRETRAINED_VOCAB_FILES_MAP
     a__									=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     a__									=['''input_ids''', '''attention_mask''']





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

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





     def  __lowerCAmelCase	(					self       ,	A       ,	A = None		)							->					List[int]:

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





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





     def  __lowerCAmelCase	(					self		)							->					Tuple:
         _UpperCAmelCase							:  Union[str, Any]											=    {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       ,	A		)							->					List[str]:
         return self.sp_model.encode(A       ,	out_type=A		)





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





     def __getstate__(					self		)							->					Tuple:
         _UpperCAmelCase							:  str											=    self.__dict__.copy()
         _UpperCAmelCase							:  int											=    None
         return state





     def __setstate__(					self       ,	A		)							->					int:
         _UpperCAmelCase							:  List[str]											=    d

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

         _UpperCAmelCase							:  Union[str, Any]											=    spm.SentencePieceProcessor(**self.sp_model_kwargs		)
         self.sp_model.Load(self.vocab_file		)





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

         return (out_vocab_file,)







"""simple docstring"""
def  lowerCamelCase_						(UpperCamelCase__		:		int							):
    if not isinstance(UpperCamelCase__     ,       UpperCamelCase__							) or number < 0:
        raise ValueError('''Input must be a non-negative integer'''							)

    _UpperCAmelCase							:  str											=    0
    while number:
        # This way we arrive at next set bit (next 1) instead of looping
        # through each bit and checking for 1s hence the
        # loop won't run 32 times it will only run the number of `1` times
        number &= number - 1
        count += 1
    return count


if __name__ == "__main__":
  import doctest

  doctest.testmod()


