code: 
  
  
  
  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
  
  
  __UpperCAmelCase      = logging.get_logger(__name__)
  
  __UpperCAmelCase      = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
  
  __UpperCAmelCase      = {
      '''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      = {
      '''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      = {
      '''microsoft/deberta-base''': {'''do_lower_case''': False},
      '''microsoft/deberta-large''': {'''do_lower_case''': False},
  }
  
  
  
  class        lowerCAmelCase_	(						a__							):
         UpperCAmelCase__       :      List[Any]       =						VOCAB_FILES_NAMES
         UpperCAmelCase__       :      int       =						PRETRAINED_VOCAB_FILES_MAP
         UpperCAmelCase__       :      Dict       =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
         UpperCAmelCase__       :      List[Any]       =						["input_ids", "attention_mask", "token_type_ids"]
         UpperCAmelCase__       :      int       =						DebertaTokenizer
         def __init__(    self,    SCREAMING_SNAKE_CASE_=None,    SCREAMING_SNAKE_CASE_=None,    SCREAMING_SNAKE_CASE_=None,    SCREAMING_SNAKE_CASE_="replace",    SCREAMING_SNAKE_CASE_="[CLS]",    SCREAMING_SNAKE_CASE_="[SEP]",    SCREAMING_SNAKE_CASE_="[SEP]",    SCREAMING_SNAKE_CASE_="[CLS]",    SCREAMING_SNAKE_CASE_="[UNK]",    SCREAMING_SNAKE_CASE_="[PAD]",    SCREAMING_SNAKE_CASE_="[MASK]",    SCREAMING_SNAKE_CASE_=False,    **SCREAMING_SNAKE_CASE_,    )  ->	Union[str, Any]:
            super().__init__(
                SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    tokenizer_file=SCREAMING_SNAKE_CASE_,    errors=SCREAMING_SNAKE_CASE_,    bos_token=SCREAMING_SNAKE_CASE_,    eos_token=SCREAMING_SNAKE_CASE_,    unk_token=SCREAMING_SNAKE_CASE_,    sep_token=SCREAMING_SNAKE_CASE_,    cls_token=SCREAMING_SNAKE_CASE_,    pad_token=SCREAMING_SNAKE_CASE_,    mask_token=SCREAMING_SNAKE_CASE_,    add_prefix_space=SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_,    )
            UpperCamelCase					:						Optional[Any]					=			kwargs.pop('add_bos_token',    SCREAMING_SNAKE_CASE_ )
  
            UpperCamelCase					:						Any					=			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
            if pre_tok_state.get('add_prefix_space',    SCREAMING_SNAKE_CASE_ ) != add_prefix_space:
               UpperCamelCase					:						str					=			getattr(SCREAMING_SNAKE_CASE_,    pre_tok_state.pop('type' ) )
               UpperCamelCase					:						List[str]					=			add_prefix_space
               UpperCamelCase					:						int					=			pre_tok_class(**SCREAMING_SNAKE_CASE_ )
  
            UpperCamelCase					:						str					=			add_prefix_space
         @property
         def       snake_case_ (    self )  ->	str:
            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       snake_case_ (    self,    SCREAMING_SNAKE_CASE_ )  ->	Optional[Any]:
            UpperCamelCase					:						List[Any]					=			AddedToken(SCREAMING_SNAKE_CASE_,    lstrip=SCREAMING_SNAKE_CASE_,    rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ ) else value
            UpperCamelCase					:						Optional[Any]					=			value
         def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ = None )  ->	List[int]:
            if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
            UpperCamelCase					:						Dict					=			[self.cls_token_id]
            UpperCamelCase					:						Optional[int]					=			[self.sep_token_id]
            return cls + token_ids_a + sep + token_ids_a + sep
         def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ = None )  ->	List[int]:
            UpperCamelCase					:						Dict					=			[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       snake_case_ (    self,    *SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_ )  ->	BatchEncoding:
            UpperCamelCase					:						Optional[int]					=			kwargs.get('is_split_into_words',    SCREAMING_SNAKE_CASE_ )
            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(*SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_ )
         def       snake_case_ (    self,    *SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_ )  ->	BatchEncoding:
            UpperCamelCase					:						List[str]					=			kwargs.get('is_split_into_words',    SCREAMING_SNAKE_CASE_ )
  
            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(*SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_ )
         def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ = None )  ->	Tuple[str]:
            UpperCamelCase					:						Tuple					=			self._tokenizer.model.save(SCREAMING_SNAKE_CASE_,    name=SCREAMING_SNAKE_CASE_ )
            return tuple(SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
         def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_ )  ->	List[int]:
            UpperCamelCase					:						List[Any]					=			[]
            for is_user, text in conversation.iter_texts():
               input_ids.extend(self.encode(SCREAMING_SNAKE_CASE_,    add_special_tokens=SCREAMING_SNAKE_CASE_ ) + [self.eos_token_id] )
  
            if len(SCREAMING_SNAKE_CASE_ ) > self.model_max_length:
               UpperCamelCase					:						List[str]					=			input_ids[-self.model_max_length :]
            return input_ids
  
  
  
  
style_context: 
  
  
  
  from typing import Optional, Tuple
  
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import logging
  from .tokenization_openai import OpenAIGPTTokenizer
  
  
  __UpperCAmelCase      = logging.get_logger(__name__)
  
  __UpperCAmelCase      = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
  
  __UpperCAmelCase      = {
      '''vocab_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/vocab.json'''},
      '''merges_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/merges.txt'''},
      '''tokenizer_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/tokenizer.json'''},
  }
  
  __UpperCAmelCase      = {
      '''openai-gpt''': 512,
  }
  
  
  
  class        lowerCAmelCase_	(						a__							):
         UpperCAmelCase__       :      Union[str, Any]       =						VOCAB_FILES_NAMES
         UpperCAmelCase__       :      int       =						PRETRAINED_VOCAB_FILES_MAP
         UpperCAmelCase__       :      Optional[int]       =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
         UpperCAmelCase__       :      Optional[int]       =						["input_ids", "attention_mask"]
         UpperCAmelCase__       :      Tuple       =						OpenAIGPTTokenizer
         def __init__(    self,    SCREAMING_SNAKE_CASE_=None,    SCREAMING_SNAKE_CASE_=None,    SCREAMING_SNAKE_CASE_=None,    SCREAMING_SNAKE_CASE_="<unk>",    **SCREAMING_SNAKE_CASE_ )  ->	List[str]:
            super().__init__(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    tokenizer_file=SCREAMING_SNAKE_CASE_,    unk_token=SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_ )
         @property
         def       snake_case_ (    self )  ->	Dict:
            return True
  
  
  
  
  
  
         def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ = None )  ->	Tuple[str]:
            UpperCamelCase					:						List[Any]					=			self._tokenizer.model.save(SCREAMING_SNAKE_CASE_,    name=SCREAMING_SNAKE_CASE_ )
            return tuple(SCREAMING_SNAKE_CASE_ )
  
  
  
  