



'''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


lowercase_									=    logging.get_logger(__name__)
lowercase_									=    {"vocab_file": "spiece.model"}

lowercase_									=    {
    "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",
    }
}

lowercase_									=    {
    "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,
}

lowercase_									=    "▁"






class __A     (    A		):



 '''simple docstring'''







 __lowerCamelCase       :			List[Any]		=			VOCAB_FILES_NAMES
 __lowerCamelCase       :			str		=			PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase       :			List[str]		=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



 def __init__(self					,  A					,  A=True					,  A=True					,  A=False					,  A="[CLS]"					,  A="[SEP]"					,  A="<unk>"					,  A="[SEP]"					,  A="<pad>"					,  A="[CLS]"					,  A="[MASK]"					,  A = None					,  **A					,  )       ->	None:


      """simple docstring"""



      _a           =    (
          AddedToken(A					,  lstrip=A					,  rstrip=A					,  normalized=A			)
          if isinstance(A					,  A			)
          else mask_token
      )

      _a           =    {} 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					,  )

      _a           =    do_lower_case
      _a           =    remove_space
      _a           =    keep_accents
      _a           =    vocab_file

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



 @property
 def    a__    (self			)       ->	Tuple:


      """simple docstring"""



      return len(self.sp_model			)



 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    {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			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    self.__dict__.copy()
      _a           =    None
      return state



 def __setstate__(self					,  A			)       ->	Tuple:


      """simple docstring"""



      _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    a__    (self					,  A			)       ->	Tuple:


      """simple docstring"""



      if self.remove_space:
           _a           =    ''' '''.join(inputs.strip().split()			)
      else:
           _a           =    inputs
      _a           =    outputs.replace('''``'''					,  '''"'''			).replace('''\'\''''					,  '''"'''			)

      if not self.keep_accents:
           _a           =    unicodedata.normalize('''NFKD'''					,  A			)
           _a           =    ''''''.join([c for c in outputs if not unicodedata.combining(A			)]			)
      if self.do_lower_case:
           _a           =    outputs.lower()

      return outputs



 def    a__    (self					,  A			)       ->	List[str]:


      """simple docstring"""



      _a           =    self.preprocess_text(A			)
      _a           =    self.sp_model.encode(A					,  out_type=A			)
      _a           =    []
      for piece in pieces:
           if len(A			) > 1 and piece[-1] == str(''','''			) and piece[-2].isdigit():
                _a           =    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:
                          _a           =    cur_pieces[1:]
                     else:
                          _a           =    cur_pieces[0][1:]
                cur_pieces.append(piece[-1]			)
                new_pieces.extend(A			)
           else:
                new_pieces.append(A			)

      return new_pieces



 def    a__    (self					,  A			)       ->	Dict:


      """simple docstring"""



      return self.sp_model.PieceToId(A			)



 def    a__    (self					,  A			)       ->	Tuple:


      """simple docstring"""



      return self.sp_model.IdToPiece(A			)



 def    a__    (self					,  A			)       ->	Union[str, Any]:


      """simple docstring"""



      _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(A			) + token
                _a           =    True
                _a           =    []
           else:
                current_sub_tokens.append(A			)
                _a           =    False
      out_string += self.sp_model.decode(A			)
      return out_string.strip()



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


      """simple docstring"""



      _a           =    [self.sep_token_id]
      _a           =    [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    a__    (self					,  A					,  A = None					,  A = False			)       ->	List[int]:


      """simple docstring"""



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


      """simple docstring"""



      _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			) * [0] + len(token_ids_a + sep			) * [1]



 def    a__    (self					,  A					,  A = None			)       ->	Tuple[str]:


      """simple docstring"""



      if not os.path.isdir(A			):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''			)
           return
      _a           =    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:
                _a           =    self.sp_model.serialized_model_proto()
                fi.write(A			)

      return (out_vocab_file,)








'''simple docstring'''





def lowerCAmelCase    (__A    ,      __A):



     """simple docstring"""



     if digit_amount > 0:
          return round(number - int(__A)    ,      __A)
     return number - int(__A)


if __name__ == "__main__":
  print(decimal_isolate(1.53, 0))
  print(decimal_isolate(35.345, 1))
  print(decimal_isolate(35.345, 2))
  print(decimal_isolate(35.345, 3))
  print(decimal_isolate(-14.789, 3))
  print(decimal_isolate(0, 2))
  print(decimal_isolate(-14.123, 1))
  print(decimal_isolate(-14.123, 2))
  print(decimal_isolate(-14.123, 3))



