code: 
  
  """simple docstring"""
  import json
  from typing import TYPE_CHECKING, List, Optional, Tuple
  
  from tokenizers import pre_tokenizers
  
  from ...tokenization_utils_base import AddedToken, BatchEncoding
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import logging
  from .tokenization_deberta import DebertaTokenizer
  
  
  if TYPE_CHECKING:
      from transformers.pipelines.conversational import Conversation
  
  
  _A						=     logging.get_logger(__name__)
  
  _A						=     {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}
  
  _A						=     {
      """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"""
          ),
      },
  }
  
  _A						=     {
      """microsoft/deberta-base""": 5_12,
      """microsoft/deberta-large""": 5_12,
      """microsoft/deberta-xlarge""": 5_12,
      """microsoft/deberta-base-mnli""": 5_12,
      """microsoft/deberta-large-mnli""": 5_12,
      """microsoft/deberta-xlarge-mnli""": 5_12,
  }
  
  _A						=     {
      """microsoft/deberta-base""": {"""do_lower_case""": False},
      """microsoft/deberta-large""": {"""do_lower_case""": False},
  }
  
  class 			lowerCamelCase  (					lowerCAmelCase__      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    SCREAMING_SNAKE_CASE							=     VOCAB_FILES_NAMES
    SCREAMING_SNAKE_CASE							=     PRETRAINED_VOCAB_FILES_MAP
    SCREAMING_SNAKE_CASE							=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    SCREAMING_SNAKE_CASE							=     ['input_ids', 'attention_mask', 'token_type_ids']
    SCREAMING_SNAKE_CASE							=     DebertaTokenizer
  
  
  
    def __init__(self       ,					_lowerCamelCase=None       ,					_lowerCamelCase=None       ,					_lowerCamelCase=None       ,					_lowerCamelCase="replace"       ,					_lowerCamelCase="[CLS]"       ,					_lowerCamelCase="[SEP]"       ,					_lowerCamelCase="[SEP]"       ,					_lowerCamelCase="[CLS]"       ,					_lowerCamelCase="[UNK]"       ,					_lowerCamelCase="[PAD]"       ,					_lowerCamelCase="[MASK]"       ,					_lowerCamelCase=False       ,					**_lowerCamelCase       ,					):
  
  
          """simple docstring"""
  
          super().__init__(
              _lowerCamelCase       ,					_lowerCamelCase       ,					tokenizer_file=_lowerCamelCase       ,					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       ,					**_lowerCamelCase       ,					)
          UpperCAmelCase__ :       str						=						kwargs.pop("""add_bos_token"""       ,					_lowerCamelCase  )
  
          UpperCAmelCase__ :       Tuple						=						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()  )
          if pre_tok_state.get("""add_prefix_space"""       ,					_lowerCamelCase  ) != add_prefix_space:
                UpperCAmelCase__ :       Optional[int]						=						getattr(_lowerCamelCase       ,					pre_tok_state.pop("""type"""  )  )
                UpperCAmelCase__ :       Dict						=						add_prefix_space
                UpperCAmelCase__ :       int						=						pre_tok_class(**_lowerCamelCase  )
  
          UpperCAmelCase__ :       Dict						=						add_prefix_space
  
  
  
    @property
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          if self._mask_token is None:
                if self.verbose:
                      logger.error("""Using mask_token, but it is not set yet."""  )
                return None
          return str(self._mask_token  )
  
  
  
    @mask_token.setter
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Tuple						=						AddedToken(_lowerCamelCase       ,					lstrip=_lowerCamelCase       ,					rstrip=_lowerCamelCase  ) if isinstance(_lowerCamelCase       ,					_lowerCamelCase  ) else value
          UpperCAmelCase__ :       List[str]						=						value
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase = None  ):
  
  
          """simple docstring"""
  
          if token_ids_a is None:
                return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          UpperCAmelCase__ :       int						=						[self.cls_token_id]
          UpperCAmelCase__ :       Union[str, Any]						=						[self.sep_token_id]
          return cls + token_ids_a + sep + token_ids_a + sep
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase = None  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[Any]						=						[self.sep_token_id]
          UpperCAmelCase__ :       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    _a			(self       ,					*_lowerCamelCase       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Union[str, Any]						=						kwargs.get("""is_split_into_words"""       ,					_lowerCamelCase  )
          assert self.add_prefix_space or not is_split_into_words, (
              F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
              "to use it with pretokenized inputs."
          )
  
          return super()._batch_encode_plus(*_lowerCamelCase       ,					**_lowerCamelCase  )
  
  
  
    def    _a			(self       ,					*_lowerCamelCase       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Tuple						=						kwargs.get("""is_split_into_words"""       ,					_lowerCamelCase  )
  
          assert self.add_prefix_space or not is_split_into_words, (
              F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
              "to use it with pretokenized inputs."
          )
  
          return super()._encode_plus(*_lowerCamelCase       ,					**_lowerCamelCase  )
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase = None  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[str]						=						self._tokenizer.model.save(_lowerCamelCase       ,					name=_lowerCamelCase  )
          return tuple(_lowerCamelCase  )
  
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Tuple						=						[]
          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:
                UpperCAmelCase__ :       Any						=						input_ids[-self.model_max_length :]
          return input_ids
  
  
style_context: 
  
  """simple docstring"""
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_keras_nlp_available,
      is_tensorflow_text_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  _A						=     {
      """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
      """tokenization_gpt2""": ["""GPT2Tokenizer"""],
  }
  
  try:
      if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     ["""GPT2TokenizerFast"""]
  
  try:
      if not is_torch_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     [
          """GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """GPT2DoubleHeadsModel""",
          """GPT2ForQuestionAnswering""",
          """GPT2ForSequenceClassification""",
          """GPT2ForTokenClassification""",
          """GPT2LMHeadModel""",
          """GPT2Model""",
          """GPT2PreTrainedModel""",
          """load_tf_weights_in_gpt2""",
      ]
  
  try:
      if not is_tf_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     [
          """TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFGPT2DoubleHeadsModel""",
          """TFGPT2ForSequenceClassification""",
          """TFGPT2LMHeadModel""",
          """TFGPT2MainLayer""",
          """TFGPT2Model""",
          """TFGPT2PreTrainedModel""",
      ]
  
  try:
      if not is_keras_nlp_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     ["""TFGPT2Tokenizer"""]
  
  try:
      if not is_flax_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     ["""FlaxGPT2LMHeadModel""", """FlaxGPT2Model""", """FlaxGPT2PreTrainedModel"""]
  
  if TYPE_CHECKING:
      from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
      from .tokenization_gpta import GPTaTokenizer
  
      try:
          if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .tokenization_gpta_fast import GPTaTokenizerFast
  
      try:
          if not is_torch_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_gpta import (
              GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTaDoubleHeadsModel,
              GPTaForQuestionAnswering,
              GPTaForSequenceClassification,
              GPTaForTokenClassification,
              GPTaLMHeadModel,
              GPTaModel,
              GPTaPreTrainedModel,
              load_tf_weights_in_gpta,
          )
  
      try:
          if not is_tf_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_tf_gpta import (
              TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFGPTaDoubleHeadsModel,
              TFGPTaForSequenceClassification,
              TFGPTaLMHeadModel,
              TFGPTaMainLayer,
              TFGPTaModel,
              TFGPTaPreTrainedModel,
          )
  
      try:
          if not is_keras_nlp_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .tokenization_gpta_tf import TFGPTaTokenizer
  
      try:
          if not is_flax_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel
  
  else:
      import sys
  
      _A						=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  