code: 
  
  
  
  
  
  from typing import Dict, List, Optional, Tuple
  
  from ...tokenization_utils import AddedToken, PreTrainedTokenizer
  from ...utils import logging
  
  
  UpperCAmelCase			:			Any	  = logging.get_logger(__name__)
  
  
  
  
  
  
  
  class        __lowerCAmelCase (					UpperCamelCase__):
        _lowercase       :      List[Any]        =    ['''input_ids''', '''attention_mask''']
  
  
  
  
  
  
  
        def __init__(					self	,   lowerCAmelCase__="[PAD]"	,   lowerCAmelCase__="[BOS]"	,   lowerCAmelCase__="[EOS]"	,   lowerCAmelCase__="[MASK]"	,   lowerCAmelCase__="[CLS]"	,   lowerCAmelCase__="[SEP]"	,   lowerCAmelCase__=2_0_4_8	,   **lowerCAmelCase__	,   )     ->		None:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Any            =AddedToken(a_	,   lstrip=a_	,   rstrip=a_							) if isinstance(a_	,   a_							) else pad_token
          a__			:      List[str]            =AddedToken(a_	,   lstrip=a_	,   rstrip=a_							) if isinstance(a_	,   a_							) else bos_token
          a__			:      Optional[Any]            =AddedToken(a_	,   lstrip=a_	,   rstrip=a_							) if isinstance(a_	,   a_							) else eos_token
          a__			:      Dict            =AddedToken(a_	,   lstrip=a_	,   rstrip=a_							) if isinstance(a_	,   a_							) else mask_token
          a__			:      Optional[int]            =AddedToken(a_	,   lstrip=a_	,   rstrip=a_							) if isinstance(a_	,   a_							) else cls_token
          a__			:      Any            =AddedToken(a_	,   lstrip=a_	,   rstrip=a_							) if isinstance(a_	,   a_							) else sep_token
  
          super().__init__(
              pad_token=a_	,   bos_token=a_	,   eos_token=a_	,   mask_token=a_	,   cls_token=a_	,   sep_token=a_	,   model_max_length=a_	,   **a_	,   )
  
          a__			:      str            =2**8  # utf is 8 bits
  
          # define special tokens dict
          a__			:      Dict            ={
              self.pad_token: 0,
              self.bos_token: 1,
              self.eos_token: 2,
              self.mask_token: 3,
              self.cls_token: 4,
              self.sep_token: 5,
          }
          a__			:      Tuple            =len(self.special_tokens_encoder							)
          a__			:      str            ={v: k for k, v in self.special_tokens_encoder.items()}
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		Dict[str, int]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Optional[int]            =self.special_tokens_encoder.copy()
          vocab.update(self.added_tokens_encoder							)
          for i in range(self._utf_vocab_size							):
            a__			:      Optional[int]            =chr(a_							)
            a__			:      Tuple            =i + len(self.special_tokens_encoder							)
          return vocab
  
  
  
  
  
  
  
        @property
        def      _lowercase						(					self							)     ->		Union[str, Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          return self._utf_vocab_size + self._num_special_tokens
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__ = None	,   lowerCAmelCase__ = 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_							)
  
          # normal case: some special tokens
          if token_ids_a is None:
            return [1] + [0] * len(a_							) + [1]
          return [1] + ([0] * len(a_							)) + [1] + ([0] * len(a_							)) + [1]
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__ = None							)     ->		List[int]:
  
  
  
          '''simple docstring'''
  
  
  
  
          if token_ids_a is None:
            return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          else:
            return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		List[str]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      List[Any]            =[chr(a_							) for i in text.encode("utf-8"							)]
          return tokens
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		Union[str, Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          if token in self.special_tokens_encoder:
            a__			:      str            =self.special_tokens_encoder[token]
          elif token in self.added_tokens_encoder:
            a__			:      Optional[int]            =self.added_tokens_encoder[token]
          elif len(a_							) != 1:
            a__			:      Union[str, Any]            =self.unk_token_id
          else:
            a__			:      Optional[int]            =ord(a_							) + self._num_special_tokens
          return token_id
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		Union[str, Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          if index in self.special_tokens_decoder:
            a__			:      List[str]            =self.special_tokens_decoder[index]
          elif index in self.added_tokens_decoder:
            a__			:      Optional[int]            =self.added_tokens_decoder[index]
          else:
            a__			:      Dict            =chr(index - self._num_special_tokens							)
          return token
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		Union[str, Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Optional[Any]            =B""
          for token in tokens:
            if token in self.special_tokens_decoder:
              a__			:      int            =self.special_tokens_decoder[token].encode("utf-8"							)
            elif token in self.added_tokens_decoder:
              a__			:      Optional[int]            =self.special_tokens_decoder[token].encode("utf-8"							)
            elif token in self.special_tokens_encoder:
              a__			:      Dict            =token.encode("utf-8"							)
            elif token in self.added_tokens_encoder:
              a__			:      List[Any]            =token.encode("utf-8"							)
            else:
              a__			:      Dict            =bytes([ord(a_							)]							)
            bstring += tok_string
          a__			:      str            =bstring.decode("utf-8"	,   errors="replace"							)
          return string
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__ = None							)     ->		Tuple[str]:
  
  
  
          '''simple docstring'''
  
  
  
  
          return ()
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  import importlib
  import sys
  from argparse import REMAINDER, ArgumentParser
  from pathlib import Path
  
  import torch_xla.distributed.xla_multiprocessing as xmp
  def   _A (	):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__			:      Optional[int]            =ArgumentParser(
        description=(
            "PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"
        )		)
  
    # Optional arguments for the launch helper
    parser.add_argument("--num_cores" , type=SCREAMING_SNAKE_CASE , default=1 , help="Number of TPU cores to use (1 or 8)."		)
  
    # positional
    parser.add_argument(
        "training_script" , type=SCREAMING_SNAKE_CASE , help=(
            "The full path to the single TPU training "
            "program/script to be launched in parallel, "
            "followed by all the arguments for the "
            "training script"
        ) , )
  
    # rest from the training program
    parser.add_argument("training_script_args" , nargs=SCREAMING_SNAKE_CASE		)
  
    return parser.parse_args()
  def   _A (	):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__			:      Optional[Any]            =parse_args()
  
    # Import training_script as a module.
    a__			:      Tuple            =Path(args.training_script		)
    sys.path.append(str(script_fpath.parent.resolve()		)		)
    a__			:      Tuple            =script_fpath.stem
    a__			:      Any            =importlib.import_module(SCREAMING_SNAKE_CASE		)
  
    # Patch sys.argv
    a__			:      List[str]            =[args.training_script] + args.training_script_args + ["--tpu_num_cores", str(args.num_cores		)]
  
    xmp.spawn(mod._mp_fn , args=() , nprocs=args.num_cores		)
  
  
  if __name__ == "__main__":
       main()
  
  
  
  
  
  
  