code: 
  
  
  
  
  
  import json
  import os
  from typing import TYPE_CHECKING, List, Optional, Tuple
  
  import regex as re
  
  from ...tokenization_utils import AddedToken, PreTrainedTokenizer
  from ...utils import logging
  
  
  if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation
  
  UpperCAmelCase			:			Optional[Any]	  = logging.get_logger(__name__)
  
  UpperCAmelCase			:			Optional[int]	  = {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}
  
  UpperCAmelCase			:			Any	  = {
      """vocab_file""": {
          """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json""",
          """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json""",
          """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json""",
          """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json""",
          """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json""",
          """microsoft/deberta-xlarge-mnli""": (
              """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"""
          ),
      },
      """merges_file""": {
          """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt""",
          """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt""",
          """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt""",
          """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt""",
          """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt""",
          """microsoft/deberta-xlarge-mnli""": (
              """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"""
          ),
      },
  }
  
  UpperCAmelCase			:			Dict	  = {
      """microsoft/deberta-base""": 512,
      """microsoft/deberta-large""": 512,
      """microsoft/deberta-xlarge""": 512,
      """microsoft/deberta-base-mnli""": 512,
      """microsoft/deberta-large-mnli""": 512,
      """microsoft/deberta-xlarge-mnli""": 512,
  }
  
  UpperCAmelCase			:			int	  = {
      """microsoft/deberta-base""": {"""do_lower_case""": False},
      """microsoft/deberta-large""": {"""do_lower_case""": False},
  }
  def   _A (	):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__			:      List[str]            =(
        list(range(ord("!"		) , ord("~"		) + 1		)		) + list(range(ord("¡"		) , ord("¬"		) + 1		)		) + list(range(ord("®"		) , ord("ÿ"		) + 1		)		)
    )
    a__			:      List[str]            =bs[:]
    a__			:      Dict            =0
    for b in range(2**8		):
      if b not in bs:
        bs.append(SCREAMING_SNAKE_CASE		)
        cs.append(2**8 + n		)
        n += 1
    a__			:      Tuple            =[chr(SCREAMING_SNAKE_CASE		) for n in cs]
    return dict(zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE		)		)
  def   _A (	SCREAMING_SNAKE_CASE  :     Union[str, Any]		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__			:      str            =set()
    a__			:      str            =word[0]
    for char in word[1:]:
      pairs.add((prev_char, char)		)
      a__			:      Optional[Any]            =char
    return pairs
  
  
  
  
  
  
  
  class        __lowerCAmelCase (					UpperCamelCase__):
        _lowercase       :      Any        =    VOCAB_FILES_NAMES
        _lowercase       :      Union[str, Any]        =    PRETRAINED_VOCAB_FILES_MAP
        _lowercase       :      List[Any]        =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
        _lowercase       :      Any        =    ["""input_ids""", """attention_mask""", """token_type_ids"""]
  
  
  
  
  
  
  
        def __init__(					self	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__="replace"	,   lowerCAmelCase__="[CLS]"	,   lowerCAmelCase__="[SEP]"	,   lowerCAmelCase__="[SEP]"	,   lowerCAmelCase__="[CLS]"	,   lowerCAmelCase__="[UNK]"	,   lowerCAmelCase__="[PAD]"	,   lowerCAmelCase__="[MASK]"	,   lowerCAmelCase__=False	,   lowerCAmelCase__=False	,   **lowerCAmelCase__	,   )     ->		Any:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      List[str]            =AddedToken(lowerCAmelCase__	,   lstrip=lowerCAmelCase__	,   rstrip=lowerCAmelCase__							) if isinstance(lowerCAmelCase__	,   lowerCAmelCase__							) else bos_token
          a__			:      List[str]            =AddedToken(lowerCAmelCase__	,   lstrip=lowerCAmelCase__	,   rstrip=lowerCAmelCase__							) if isinstance(lowerCAmelCase__	,   lowerCAmelCase__							) else eos_token
          a__			:      Union[str, Any]            =AddedToken(lowerCAmelCase__	,   lstrip=lowerCAmelCase__	,   rstrip=lowerCAmelCase__							) if isinstance(lowerCAmelCase__	,   lowerCAmelCase__							) else sep_token
          a__			:      List[str]            =AddedToken(lowerCAmelCase__	,   lstrip=lowerCAmelCase__	,   rstrip=lowerCAmelCase__							) if isinstance(lowerCAmelCase__	,   lowerCAmelCase__							) else cls_token
          a__			:      int            =AddedToken(lowerCAmelCase__	,   lstrip=lowerCAmelCase__	,   rstrip=lowerCAmelCase__							) if isinstance(lowerCAmelCase__	,   lowerCAmelCase__							) else unk_token
          a__			:      Optional[Any]            =AddedToken(lowerCAmelCase__	,   lstrip=lowerCAmelCase__	,   rstrip=lowerCAmelCase__							) if isinstance(lowerCAmelCase__	,   lowerCAmelCase__							) else pad_token
  
          # Mask token behave like a normal word, i.e. include the space before it
          a__			:      Dict            =AddedToken(lowerCAmelCase__	,   lstrip=lowerCAmelCase__	,   rstrip=lowerCAmelCase__							) if isinstance(lowerCAmelCase__	,   lowerCAmelCase__							) else mask_token
  
          super().__init__(
              errors=lowerCAmelCase__	,   bos_token=lowerCAmelCase__	,   eos_token=lowerCAmelCase__	,   unk_token=lowerCAmelCase__	,   sep_token=lowerCAmelCase__	,   cls_token=lowerCAmelCase__	,   pad_token=lowerCAmelCase__	,   mask_token=lowerCAmelCase__	,   add_prefix_space=lowerCAmelCase__	,   add_bos_token=lowerCAmelCase__	,   **lowerCAmelCase__	,   )
          a__			:      Optional[int]            =add_bos_token
  
          with open(lowerCAmelCase__	,   encoding="utf-8"							) as vocab_handle:
            a__			:      List[Any]            =json.load(lowerCAmelCase__							)
          a__			:      List[str]            ={v: k for k, v in self.encoder.items()}
          a__			:      List[Any]            =errors  # how to handle errors in decoding
          a__			:      Optional[int]            =bytes_to_unicode()
          a__			:      Dict            ={v: k for k, v in self.byte_encoder.items()}
          with open(lowerCAmelCase__	,   encoding="utf-8"							) as merges_handle:
            a__			:      Any            =merges_handle.read().split("\n"							)[1:-1]
          a__			:      Union[str, Any]            =[tuple(merge.split()							) for merge in bpe_merges]
          a__			:      Optional[Any]            =dict(zip(lowerCAmelCase__	,   range(len(lowerCAmelCase__							)							)							)							)
          a__			:      List[str]            ={}
          a__			:      List[str]            =add_prefix_space
  
          # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
          a__			:      Optional[int]            =re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"							)
  
  
  
  
  
  
  
        @property
        # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.vocab_size
        def      _lowercase						(					self							)     ->		Any:
  
  
  
          '''simple docstring'''
  
  
  
  
          return len(self.encoder							)
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		Optional[int]:
  
  
  
          '''simple docstring'''
  
  
  
  
          return dict(self.encoder	,   **self.added_tokens_encoder							)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		List[str]:
  
  
  
          '''simple docstring'''
  
  
  
  
          if token in self.cache:
            return self.cache[token]
          a__			:      Optional[Any]            =tuple(lowerCAmelCase__							)
          a__			:      str            =get_pairs(lowerCAmelCase__							)
  
          if not pairs:
            return token
  
          while True:
            a__			:      List[str]            =min(lowerCAmelCase__	,   key=lambda lowerCAmelCase__							: self.bpe_ranks.get(lowerCAmelCase__	,   float("inf"							)							)							)
            if bigram not in self.bpe_ranks:
              break
            a__      ,							a__			:      Optional[int]            =bigram
            a__			:      Tuple            =[]
            a__			:      Optional[int]            =0
            while i < len(lowerCAmelCase__							):
              try:
                a__			:      Optional[Any]            =word.index(lowerCAmelCase__	,   lowerCAmelCase__							)
              except ValueError:
                new_word.extend(word[i:]							)
                break
              else:
                new_word.extend(word[i:j]							)
                a__			:      Optional[int]            =j
  
              if word[i] == first and i < len(lowerCAmelCase__							) - 1 and word[i + 1] == second:
                new_word.append(first + second							)
                i += 2
              else:
                new_word.append(word[i]							)
                i += 1
            a__			:      Optional[int]            =tuple(lowerCAmelCase__							)
            a__			:      Any            =new_word
            if len(lowerCAmelCase__							) == 1:
              break
            else:
              a__			:      Optional[int]            =get_pairs(lowerCAmelCase__							)
          a__			:      Union[str, Any]            =" ".join(lowerCAmelCase__							)
          a__			:      Union[str, Any]            =word
          return word
  
  
  
  
  
  
  
        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]
          a__			:      Union[str, Any]            =[self.cls_token_id]
          a__			:      List[Any]            =[self.sep_token_id]
          return cls + token_ids_a + sep + token_ids_a + sep
  
  
  
  
  
  
  
        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=lowerCAmelCase__	,   token_ids_a=lowerCAmelCase__	,   already_has_special_tokens=lowerCAmelCase__							)
  
          if token_ids_a is None:
            return [1] + ([0] * len(lowerCAmelCase__							)) + [1]
          return [1] + ([0] * len(lowerCAmelCase__							)) + [1] + ([0] * len(lowerCAmelCase__							)) + [1]
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__ = None							)     ->		List[int]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Dict            =[self.sep_token_id]
          a__			:      int            =[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	,   lowerCAmelCase__							)     ->		Optional[int]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Tuple            =[]
          for token in re.findall(self.pat	,   lowerCAmelCase__							):
            a__			:      Any            ="".join(
                self.byte_encoder[b] for b in token.encode("utf-8"							)							)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
            bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCAmelCase__							).split(" "							)							)
          return bpe_tokens
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		int:
  
  
  
          '''simple docstring'''
  
  
  
  
          return self.encoder.get(lowerCAmelCase__	,   self.encoder.get(self.unk_token							)							)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		int:
  
  
  
          '''simple docstring'''
  
  
  
  
          return self.decoder.get(lowerCAmelCase__							)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		str:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Optional[int]            ="".join(lowerCAmelCase__							)
          a__			:      Tuple            =bytearray([self.byte_decoder[c] for c in text]							).decode("utf-8"	,   errors=self.errors							)
          return text
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__ = None							)     ->		Tuple[str]:
  
  
  
          '''simple docstring'''
  
  
  
  
          if not os.path.isdir(lowerCAmelCase__							):
            logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''							)
            return
          a__			:      List[str]            =os.path.join(
              lowerCAmelCase__	,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)
          a__			:      str            =os.path.join(
              lowerCAmelCase__	,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]							)
  
          with open(lowerCAmelCase__	,   "w"	,   encoding="utf-8"							) as f:
            f.write(json.dumps(self.encoder	,   indent=2	,   sort_keys=lowerCAmelCase__	,   ensure_ascii=lowerCAmelCase__							) + "\n"							)
  
          a__			:      List[str]            =0
          with open(lowerCAmelCase__	,   "w"	,   encoding="utf-8"							) as writer:
            writer.write("#version: 0.2\n"							)
            for bpe_tokens, token_index in sorted(self.bpe_ranks.items()	,   key=lambda lowerCAmelCase__							: kv[1]							):
              if index != token_index:
                logger.warning(
                    F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                    " Please check that the tokenizer is not corrupted!"							)
                a__			:      Union[str, Any]            =token_index
              writer.write(" ".join(lowerCAmelCase__							) + "\n"							)
              index += 1
  
          return vocab_file, merge_file
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__=False	,   **lowerCAmelCase__							)     ->		Dict:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      List[Any]            =kwargs.pop("add_prefix_space"	,   self.add_prefix_space							)
          if (is_split_into_words or add_prefix_space) and (len(lowerCAmelCase__							) > 0 and not text[0].isspace()):
            a__			:      Tuple            =" " + text
          return (text, kwargs)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		List[int]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Dict            =[]
          for is_user, text in conversation.iter_texts():
            input_ids.extend(self.encode(lowerCAmelCase__	,   add_special_tokens=lowerCAmelCase__							) + [self.eos_token_id]							)
          if len(lowerCAmelCase__							) > self.model_max_length:
            a__			:      Tuple            =input_ids[-self.model_max_length :]
          return input_ids
  
  
  
  
  
  
  
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()
  
  
  
  
  
  
  