





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 :			Tuple     		=    logging.get_logger(__name__)
lowercase :			str     		=    {"""vocab_file""": """spiece.model"""}

lowercase :			int     		=    {
    """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 :			Any     		=    {
    """albert-base-v1""": 5_1_2,
    """albert-large-v1""": 5_1_2,
    """albert-xlarge-v1""": 5_1_2,
    """albert-xxlarge-v1""": 5_1_2,
    """albert-base-v2""": 5_1_2,
    """albert-large-v2""": 5_1_2,
    """albert-xlarge-v2""": 5_1_2,
    """albert-xxlarge-v2""": 5_1_2,
}

lowercase :			int     		=    """▁"""





class 					A__       (				A_      ):




   """simple docstring"""



   __A  :							str			    =    VOCAB_FILES_NAMES
   __A  :							int			    =    PRETRAINED_VOCAB_FILES_MAP
   __A  :							Dict			    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

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







          '''simple docstring'''

          a__		:      Optional[int]					=							(
              AddedToken(lowercase   ,    lstrip=lowercase   ,    rstrip=lowercase   ,    normalized=lowercase)
              if isinstance(lowercase   ,    lowercase)
              else mask_token
          )

          a__		:      Optional[Any]					=							{} if sp_model_kwargs is None else sp_model_kwargs

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

          a__		:      Optional[int]					=							do_lower_case
          a__		:      Any					=							remove_space
          a__		:      Any					=							keep_accents
          a__		:      str					=							vocab_file

          a__		:      List[Any]					=							spm.SentencePieceProcessor(**self.sp_model_kwargs)
          self.sp_model.Load(lowercase)

   @property
   def 				__lowercase						(   self)     ->		int:







          '''simple docstring'''

          return len(self.sp_model)

   def 				__lowercase						(   self)     ->		Optional[int]:







          '''simple docstring'''

          a__		:      Dict					=							{self.convert_ids_to_tokens(lowercase): i for i in range(self.vocab_size)}
          vocab.update(self.added_tokens_encoder)
          return vocab

   def __getstate__(   self)     ->		int:







          '''simple docstring'''

          a__		:      Any					=							self.__dict__.copy()
          a__		:      Any					=							None
          return state

   def __setstate__(   self   ,    lowercase)     ->		Union[str, Any]:







          '''simple docstring'''

          a__		:      Union[str, Any]					=							d

          # for backward compatibility
          if not hasattr(self   ,    'sp_model_kwargs'):
                 a__		:      List[Any]					=							{}

          a__		:      int					=							spm.SentencePieceProcessor(**self.sp_model_kwargs)
          self.sp_model.Load(self.vocab_file)

   def 				__lowercase						(   self   ,    lowercase)     ->		List[Any]:







          '''simple docstring'''

          if self.remove_space:
                 a__		:      Union[str, Any]					=							""" """.join(inputs.strip().split())
          else:
                 a__		:      str					=							inputs
          a__		:      Optional[Any]					=							outputs.replace('``'   ,    '\"').replace('\'\''   ,    '\"')

          if not self.keep_accents:
                 a__		:      Tuple					=							unicodedata.normalize('NFKD'   ,    lowercase)
                 a__		:      List[Any]					=							"""""".join([c for c in outputs if not unicodedata.combining(lowercase)])
          if self.do_lower_case:
                 a__		:      Optional[Any]					=							outputs.lower()

          return outputs

   def 				__lowercase						(   self   ,    lowercase)     ->		List[str]:







          '''simple docstring'''

          a__		:      str					=							self.preprocess_text(lowercase)
          a__		:      Any					=							self.sp_model.encode(lowercase   ,    out_type=lowercase)
          a__		:      str					=							[]
          for piece in pieces:
                 if len(lowercase) > 1 and piece[-1] == str(',') and piece[-2].isdigit():
                        a__		:      str					=							self.sp_model.EncodeAsPieces(piece[:-1].replace(lowercase   ,    ''))
                        if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                               if len(cur_pieces[0]) == 1:
                                      a__		:      int					=							cur_pieces[1:]
                               else:
                                      a__		:      Union[str, Any]					=							cur_pieces[0][1:]
                        cur_pieces.append(piece[-1])
                        new_pieces.extend(lowercase)
                 else:
                        new_pieces.append(lowercase)

          return new_pieces

   def 				__lowercase						(   self   ,    lowercase)     ->		Dict:







          '''simple docstring'''

          return self.sp_model.PieceToId(lowercase)

   def 				__lowercase						(   self   ,    lowercase)     ->		Union[str, Any]:







          '''simple docstring'''

          return self.sp_model.IdToPiece(lowercase)

   def 				__lowercase						(   self   ,    lowercase)     ->		List[str]:







          '''simple docstring'''

          a__		:      Union[str, Any]					=							[]
          a__		:      Tuple					=							""""""
          a__		:      Tuple					=							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(lowercase) + token
                        a__		:      List[str]					=							True
                        a__		:      int					=							[]
                 else:
                        current_sub_tokens.append(lowercase)
                        a__		:      Optional[Any]					=							False
          out_string += self.sp_model.decode(lowercase)
          return out_string.strip()

   def 				__lowercase						(   self   ,    lowercase   ,    lowercase = None)     ->		List[int]:







          '''simple docstring'''

          a__		:      List[Any]					=							[self.sep_token_id]
          a__		:      List[str]					=							[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 				__lowercase						(   self   ,    lowercase   ,    lowercase = None   ,    lowercase = False)     ->		List[int]:







          '''simple docstring'''

          if already_has_special_tokens:
                 return super().get_special_tokens_mask(
                     token_ids_a=lowercase   ,    token_ids_a=lowercase   ,    already_has_special_tokens=lowercase)

          if token_ids_a is not None:
                 return [1] + ([0] * len(lowercase)) + [1] + ([0] * len(lowercase)) + [1]
          return [1] + ([0] * len(lowercase)) + [1]

   def 				__lowercase						(   self   ,    lowercase   ,    lowercase = None)     ->		List[int]:







          '''simple docstring'''

          a__		:      Optional[int]					=							[self.sep_token_id]
          a__		:      List[str]					=							[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 				__lowercase						(   self   ,    lowercase   ,    lowercase = None)     ->		Tuple[str]:







          '''simple docstring'''

          if not os.path.isdir(lowercase):
                 logger.error(F'Vocabulary path ({save_directory}) should be a directory')
                 return
          a__		:      Optional[int]					=							os.path.join(
              lowercase   ,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

          if os.path.abspath(self.vocab_file) != os.path.abspath(lowercase) and os.path.isfile(self.vocab_file):
                 copyfile(self.vocab_file   ,    lowercase)
          elif not os.path.isfile(self.vocab_file):
                 with open(lowercase   ,    'wb') as fi:
                        a__		:      Optional[int]					=							self.sp_model.serialized_model_proto()
                        fi.write(lowercase)

          return (out_vocab_file,)






"""simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments

def 				_snake_case      (   ):




   """simple docstring"""







   _lowerCamelCase	:					Any   =		HfArgumentParser(__snake_case   )
   _lowerCamelCase	:					int   =		parser.parse_args_into_dataclasses()[0]
   _lowerCamelCase	:					Dict   =		TensorFlowBenchmark(args=__snake_case   )
   try:
      _lowerCamelCase	:					Optional[int]   =		parser.parse_args_into_dataclasses()[0]
   except ValueError as e:
      _lowerCamelCase	:					Union[str, Any]   =		"""Arg --no_{0} is no longer used, please use --no-{0} instead."""
      _lowerCamelCase	:					List[str]   =		""" """.join(str(__snake_case   ).split(""" """   )[:-1]   )
      _lowerCamelCase	:					Dict   =		""""""
      _lowerCamelCase	:					List[Any]   =		eval(str(__snake_case   ).split(""" """   )[-1]   )
      _lowerCamelCase	:					Tuple   =		[]
      for arg in depreciated_args:
         # arg[2:] removes '--'
         if arg[2:] in TensorFlowBenchmark.deprecated_args:
            # arg[5:] removes '--no_'
            full_error_msg += arg_error_msg.format(arg[5:]   )
         else:
            wrong_args.append(__snake_case   )
      if len(__snake_case   ) > 0:
         _lowerCamelCase	:					Tuple   =		full_error_msg + begin_error_msg + str(__snake_case   )
      raise ValueError(__snake_case   )
   benchmark.run()


if __name__ == "__main__":
 main()

