
'''simple docstring'''






import os
import unicodedata
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


UpperCAmelCase_       :   Tuple   =       logging.get_logger(__name__)
UpperCAmelCase_       :   Union[str, Any]   =       {'vocab_file': 'spiece.model'}

UpperCAmelCase_       :   Union[str, Any]   =       {
    'vocab_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/spiece.model',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/spiece.model',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/spiece.model',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/spiece.model',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model',
    }
}

UpperCAmelCase_       :   int   =       {
    'albert-base-v1': 512,
    'albert-large-v1': 512,
    'albert-xlarge-v1': 512,
    'albert-xxlarge-v1': 512,
    'albert-base-v2': 512,
    'albert-large-v2': 512,
    'albert-xlarge-v2': 512,
    'albert-xxlarge-v2': 512,
}

UpperCAmelCase_       :   Optional[int]   =       '▁'

class  UpperCAmelCase__						(       A ):
    lowerCAmelCase_				    =		VOCAB_FILES_NAMES
    lowerCAmelCase_				    =		PRETRAINED_VOCAB_FILES_MAP
    lowerCAmelCase_				    =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



    def __init__(	self      :       List[Any],__A      :       Any,__A      :       str=True,__A      :       Optional[int]=True,__A      :       List[Any]=False,__A      :       str="[CLS]",__A      :       int="[SEP]",__A      :       Dict="<unk>",__A      :       Union[str, Any]="[SEP]",__A      :       Dict="<pad>",__A      :       Optional[int]="[CLS]",__A      :       Any="[MASK]",__A      :       Optional[Dict[str, Any]] = None,**__A      :       Optional[int],):
          # Mask token behave like a normal word, i.e. include the space before it and
          # is included in the raw text, there should be a match in a non-normalized sentence.
          _lowerCamelCase				:   Dict								=   (
              AddedToken(__A,lstrip=__A,rstrip=__A,normalized=__A     )
              if isinstance(__A,__A     )
              else mask_token
          )

          _lowerCamelCase				:   List[str]								=   {} if sp_model_kwargs is None else sp_model_kwargs

          super().__init__(
              do_lower_case=__A,remove_space=__A,keep_accents=__A,bos_token=__A,eos_token=__A,unk_token=__A,sep_token=__A,pad_token=__A,cls_token=__A,mask_token=__A,sp_model_kwargs=self.sp_model_kwargs,**__A,)

          _lowerCamelCase				:   str								=   do_lower_case
          _lowerCamelCase				:   int								=   remove_space
          _lowerCamelCase				:   List[Any]								=   keep_accents
          _lowerCamelCase				:   int								=   vocab_file

          _lowerCamelCase				:   Any								=   spm.SentencePieceProcessor(**self.sp_model_kwargs     )
          self.sp_model.Load(__A     )



    @property
    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          return len(self.sp_model     )



    def   lowerCamelCase_  (	self      :       int     ):
          _lowerCamelCase				:   Optional[Any]								=   {self.convert_ids_to_tokens(__A     ): i for i in range(self.vocab_size     )}
          vocab.update(self.added_tokens_encoder     )
          return vocab



    def __getstate__(	self      :       Dict     ):
          _lowerCamelCase				:   Optional[int]								=   self.__dict__.copy()
          _lowerCamelCase				:   int								=   None
          return state



    def __setstate__(	self      :       List[Any],__A      :       Dict     ):
          _lowerCamelCase				:   Dict								=   d

          # for backward compatibility
          if not hasattr(self,"sp_model_kwargs"     ):
                _lowerCamelCase				:   List[str]								=   {}

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



    def   lowerCamelCase_  (	self      :       Optional[Any],__A      :       Tuple     ):
          if self.remove_space:
                _lowerCamelCase				:   List[Any]								=   " ".join(inputs.strip().split()     )
          else:
                _lowerCamelCase				:   List[str]								=   inputs
          _lowerCamelCase				:   str								=   outputs.replace("``","\""     ).replace("''","\""     )

          if not self.keep_accents:
                _lowerCamelCase				:   Tuple								=   unicodedata.normalize("NFKD",__A     )
                _lowerCamelCase				:   Optional[Any]								=   "".join([c for c in outputs if not unicodedata.combining(__A     )]     )
          if self.do_lower_case:
                _lowerCamelCase				:   Optional[int]								=   outputs.lower()

          return outputs



    def   lowerCamelCase_  (	self      :       str,__A      :       str     ):
          _lowerCamelCase				:   Dict								=   self.preprocess_text(__A     )
          _lowerCamelCase				:   Optional[Any]								=   self.sp_model.encode(__A,out_type=__A     )
          _lowerCamelCase				:   Union[str, Any]								=   []
          for piece in pieces:
                if len(__A     ) > 1 and piece[-1] == str(","     ) and piece[-2].isdigit():
                      _lowerCamelCase				:   List[str]								=   self.sp_model.EncodeAsPieces(piece[:-1].replace(__A,""     )     )
                      if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                            if len(cur_pieces[0]     ) == 1:
                                  _lowerCamelCase				:   Union[str, Any]								=   cur_pieces[1:]
                            else:
                                  _lowerCamelCase				:   Dict								=   cur_pieces[0][1:]
                      cur_pieces.append(piece[-1]     )
                      new_pieces.extend(__A     )
                else:
                      new_pieces.append(__A     )

          return new_pieces



    def   lowerCamelCase_  (	self      :       int,__A      :       Union[str, Any]     ):
          return self.sp_model.PieceToId(__A     )



    def   lowerCamelCase_  (	self      :       Union[str, Any],__A      :       str     ):
          return self.sp_model.IdToPiece(__A     )



    def   lowerCamelCase_  (	self      :       List[str],__A      :       Tuple     ):
          _lowerCamelCase				:   Union[str, Any]								=   []
          _lowerCamelCase				:   List[Any]								=   ""
          _lowerCamelCase				:   List[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				:   Any								=   True
                      _lowerCamelCase				:   List[Any]								=   []
                else:
                      current_sub_tokens.append(__A     )
                      _lowerCamelCase				:   Optional[Any]								=   False
          out_string += self.sp_model.decode(__A     )
          return out_string.strip()



    def   lowerCamelCase_  (	self      :       Dict,__A      :       List[int],__A      :       Optional[List[int]] = None     ):
          _lowerCamelCase				:   Any								=   [self.sep_token_id]
          _lowerCamelCase				:   List[Any]								=   [self.cls_token_id]
          if token_ids_a is None:
                return cls + token_ids_a + sep
          return cls + token_ids_a + sep + token_ids_a + sep



    def   lowerCamelCase_  (	self      :       List[Any],__A      :       List[int],__A      :       Optional[List[int]] = None,__A      :       bool = False     ):

          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 not None:
                return [1] + ([0] * len(__A     )) + [1] + ([0] * len(__A     )) + [1]
          return [1] + ([0] * len(__A     )) + [1]



    def   lowerCamelCase_  (	self      :       str,__A      :       List[int],__A      :       Optional[List[int]] = None     ):
          _lowerCamelCase				:   Any								=   [self.sep_token_id]
          _lowerCamelCase				:   List[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     ) * [0] + len(token_ids_a + sep     ) * [1]



    def   lowerCamelCase_  (	self      :       Optional[Any],__A      :       str,__A      :       Optional[str] = None     ):
          if not os.path.isdir(__A     ):
                logger.error(f'Vocabulary path ({save_directory}) should be a directory'     )
                return
          _lowerCamelCase				:   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				:   List[str]								=   self.sp_model.serialized_model_proto()
                      fi.write(__A     )

          return (out_vocab_file,)

'''simple docstring'''






def 		A_       (	_lowerCAmelCase   :    int							):



      """simple docstring"""




      if isinstance(_lowerCAmelCase				,	_lowerCAmelCase							):
            raise TypeError("'float' object cannot be interpreted as an integer"							)
      if isinstance(_lowerCAmelCase				,	_lowerCAmelCase							):
            raise TypeError("'str' object cannot be interpreted as an integer"							)

      if num == 0:
            return "0b0"

      _lowerCamelCase				:   Tuple								=   False

      if num < 0:
            _lowerCamelCase				:   List[Any]								=   True
            _lowerCamelCase				:   int								=   -num

      _lowerCamelCase				:   list[int]								=   []
      while num > 0:
            binary.insert(0				,	num % 2							)
            num >>= 1

      if negative:
            return "-0b" + "".join(str(_lowerCAmelCase							) for e in binary							)

      return "0b" + "".join(str(_lowerCAmelCase							) for e in binary							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()