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
  
  
  SCREAMING_SNAKE_CASE__    :    List[Any]								=					logging.get_logger(__name__)
  
  SCREAMING_SNAKE_CASE__    :    Dict								=					{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
  
  SCREAMING_SNAKE_CASE__    :    List[str]								=					{
      "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"
          ),
      },
  }
  
  SCREAMING_SNAKE_CASE__    :    int								=					{
      "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,
  }
  
  SCREAMING_SNAKE_CASE__    :    Optional[Any]								=					{
      "microsoft/deberta-base": {"do_lower_case": False},
      "microsoft/deberta-large": {"do_lower_case": False},
  }
  
  
  
  class 			a_					(    SCREAMING_SNAKE_CASE__    ):
  	A     :   List[str]						       =   VOCAB_FILES_NAMES
  	A     :   List[Any]						       =   PRETRAINED_VOCAB_FILES_MAP
  	A     :   Optional[int]						       =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  	A     :   Any						       =   ['''input_ids''', '''attention_mask''', '''token_type_ids''']
  	A     :   Union[str, Any]						       =   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			,    )     ->				List[str]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				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			,    )
  				SCREAMING_SNAKE_CASE_			=	kwargs.pop('add_bos_token'			,    SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
  				if pre_tok_state.get('add_prefix_space'			,    SCREAMING_SNAKE_CASE			) != add_prefix_space:
  							SCREAMING_SNAKE_CASE_			=	getattr(SCREAMING_SNAKE_CASE			,    pre_tok_state.pop('type'			)			)
  							SCREAMING_SNAKE_CASE_			=	add_prefix_space
  							SCREAMING_SNAKE_CASE_			=	pre_tok_class(**SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	add_prefix_space
  
  
  
  
  
  	@property
  	def 	A_(       self			)     ->				str:
  
  
  
  
  				"""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			,    SCREAMING_SNAKE_CASE			)     ->				Tuple:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	AddedToken(SCREAMING_SNAKE_CASE			,    lstrip=SCREAMING_SNAKE_CASE			,    rstrip=SCREAMING_SNAKE_CASE			) if isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			) else value
  				SCREAMING_SNAKE_CASE_			=	value
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			)     ->				List[int]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if token_ids_a is None:
  							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
  				SCREAMING_SNAKE_CASE_			=	[self.cls_token_id]
  				SCREAMING_SNAKE_CASE_			=	[self.sep_token_id]
  				return cls + token_ids_a + sep + token_ids_a + sep
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			)     ->				List[int]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	[self.sep_token_id]
  				SCREAMING_SNAKE_CASE_			=	[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			,    *SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)     ->				BatchEncoding:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	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 	A_(       self			,    *SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)     ->				BatchEncoding:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	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 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			)     ->				Tuple[str]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	self._tokenizer.model.save(SCREAMING_SNAKE_CASE			,    name=SCREAMING_SNAKE_CASE			)
  				return tuple(SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			)     ->				List[int]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	[]
  				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:
  							SCREAMING_SNAKE_CASE_			=	input_ids[-self.model_max_length :]
  				return input_ids
  
style_context: 
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  SCREAMING_SNAKE_CASE__    :    List[Any]								=					{"configuration_detr": ["DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetrConfig", "DetrOnnxConfig"]}
  
  try:
  				if not is_vision_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				SCREAMING_SNAKE_CASE__    :    int								=					["DetrFeatureExtractor"]
  				SCREAMING_SNAKE_CASE__    :    Any								=					["DetrImageProcessor"]
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				SCREAMING_SNAKE_CASE__    :    List[Any]								=					[
  				    "DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
  				    "DetrForObjectDetection",
  				    "DetrForSegmentation",
  				    "DetrModel",
  				    "DetrPreTrainedModel",
  				]
  
  
  if TYPE_CHECKING:
  				from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig
  
  				try:
  								if not is_vision_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .feature_extraction_detr import DetrFeatureExtractor
  								from .image_processing_detr import DetrImageProcessor
  
  				try:
  								if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_detr import (
  								    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    DetrForObjectDetection,
  								    DetrForSegmentation,
  								    DetrModel,
  								    DetrPreTrainedModel,
  								)
  
  else:
  				import sys
  
  				SCREAMING_SNAKE_CASE__    :    Tuple								=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  