code: 
  
  
  
  
  
  
  """simple docstring"""
  
  
  
  
  
  
  import os
  from shutil import copyfile
  from typing import Optional, Tuple
  
  from ...file_utils import is_sentencepiece_available
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import logging
  
  
  if is_sentencepiece_available():
      from .tokenization_deberta_va import DebertaVaTokenizer
  else:
      A_			  =    None
  
  A_			  =    logging.get_logger(__name__)
  
  A_			  =    {"vocab_file": "spm.model", "tokenizer_file": "tokenizer.json"}
  
  A_			  =    {
      "vocab_file": {
          "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model",
          "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model",
          "microsoft/deberta-v2-xlarge-mnli": (
              "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model"
          ),
          "microsoft/deberta-v2-xxlarge-mnli": (
              "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model"
          ),
      }
  }
  
  A_			  =    {
      "microsoft/deberta-v2-xlarge": 512,
      "microsoft/deberta-v2-xxlarge": 512,
      "microsoft/deberta-v2-xlarge-mnli": 512,
      "microsoft/deberta-v2-xxlarge-mnli": 512,
  }
  
  A_			  =    {
      "microsoft/deberta-v2-xlarge": {"do_lower_case": False},
      "microsoft/deberta-v2-xxlarge": {"do_lower_case": False},
      "microsoft/deberta-v2-xlarge-mnli": {"do_lower_case": False},
      "microsoft/deberta-v2-xxlarge-mnli": {"do_lower_case": False},
  }
  class 						__lowerCAmelCase   (			UpperCAmelCase       ):
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    __lowerCamelCase       :    Union[str, Any]     	=			VOCAB_FILES_NAMES
    __lowerCamelCase       :    List[str]     	=			PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase       :    Optional[Any]     	=			PRETRAINED_INIT_CONFIGURATION
    __lowerCamelCase       :    Union[str, Any]     	=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase       :    Tuple     	=			DebertaVaTokenizer
    def __init__(      self:		Tuple				,			UpperCamelCase_:		Any=None				,			UpperCamelCase_:		Union[str, Any]=None				,			UpperCamelCase_:		Dict=False				,			UpperCamelCase_:		Dict=False				,			UpperCamelCase_:		Union[str, Any]="[CLS]"				,			UpperCamelCase_:		Tuple="[SEP]"				,			UpperCamelCase_:		int="[UNK]"				,			UpperCamelCase_:		Any="[SEP]"				,			UpperCamelCase_:		Tuple="[PAD]"				,			UpperCamelCase_:		Optional[Any]="[CLS]"				,			UpperCamelCase_:		Dict="[MASK]"				,			**UpperCamelCase_:		Dict				,			):
       super().__init__(
           UpperCamelCase_				,			tokenizer_file=UpperCamelCase_				,			do_lower_case=UpperCamelCase_				,			bos_token=UpperCamelCase_				,			eos_token=UpperCamelCase_				,			unk_token=UpperCamelCase_				,			sep_token=UpperCamelCase_				,			pad_token=UpperCamelCase_				,			cls_token=UpperCamelCase_				,			mask_token=UpperCamelCase_				,			split_by_punct=UpperCamelCase_				,			**UpperCamelCase_				,			)
  
       UpperCamelCase_     =do_lower_case
       UpperCamelCase_     =split_by_punct
       UpperCamelCase_     =vocab_file
       UpperCamelCase_     =False if not self.vocab_file else True
    def 					UpperCamelCase__   (      self:		Union[str, Any]				,			UpperCamelCase_:		Any				,			UpperCamelCase_:		Dict=None					):
  
       if token_ids_a is None:
          return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       UpperCamelCase_     =[self.cls_token_id]
       UpperCamelCase_     =[self.sep_token_id]
       return cls + token_ids_a + sep + token_ids_a + sep
    def 					UpperCamelCase__   (      self:		Any				,			UpperCamelCase_:		Any				,			UpperCamelCase_:		str=None				,			UpperCamelCase_:		Tuple=False					):
  
       if already_has_special_tokens:
          return super().get_special_tokens_mask(
              token_ids_a=UpperCamelCase_				,			token_ids_a=UpperCamelCase_				,			already_has_special_tokens=UpperCamelCase_					)
  
       if token_ids_a is not None:
          return [1] + ([0] * len(UpperCamelCase_					)) + [1] + ([0] * len(UpperCamelCase_					)) + [1]
       return [1] + ([0] * len(UpperCamelCase_					)) + [1]
    def 					UpperCamelCase__   (      self:		List[Any]				,			UpperCamelCase_:		List[Any]				,			UpperCamelCase_:		Dict=None					):
       UpperCamelCase_     =[self.sep_token_id]
       UpperCamelCase_     =[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 					UpperCamelCase__   (      self:		List[str]				,			UpperCamelCase_:		str				,			UpperCamelCase_:		Optional[str] = None					):
       if not self.can_save_slow_tokenizer:
          raise ValueError(
              "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
              "tokenizer."					)
  
       if not os.path.isdir(UpperCamelCase_					):
          logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""					)
          return
       UpperCamelCase_     =os.path.join(
           UpperCamelCase_				,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]					)
  
       if os.path.abspath(self.vocab_file					) != os.path.abspath(UpperCamelCase_					):
          copyfile(self.vocab_file				,			UpperCamelCase_					)
  
       return (out_vocab_file,)
  
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  
  
  
  
  
  
  import warnings
  
  from ...processing_utils import ProcessorMixin
  from ...tokenization_utils_base import BatchEncoding
  class 						__lowerCAmelCase   (			UpperCAmelCase       ):
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    __lowerCamelCase       :    Optional[Any]     	=			["image_processor", "tokenizer"]
    __lowerCamelCase       :    List[str]     	=			"VideoMAEImageProcessor"
    __lowerCamelCase       :    Dict     	=			("CLIPTokenizer", "CLIPTokenizerFast")
    def __init__(      self:		str				,			UpperCamelCase_:		Dict=None				,			UpperCamelCase_:		str=None				,			**UpperCamelCase_:		str					):
       UpperCamelCase_     =None
       if "feature_extractor" in kwargs:
          warnings.warn(
              "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
              " instead."				,			UpperCamelCase_				,			)
          UpperCamelCase_     =kwargs.pop("feature_extractor"					)
  
       UpperCamelCase_     =image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
          raise ValueError("You need to specify an `image_processor`."					)
       if tokenizer is None:
          raise ValueError("You need to specify a `tokenizer`."					)
  
       super().__init__(UpperCamelCase_				,			UpperCamelCase_					)
       UpperCamelCase_     =self.image_processor
    def __call__(      self:		Tuple				,			UpperCamelCase_:		str=None				,			UpperCamelCase_:		str=None				,			UpperCamelCase_:		Optional[Any]=None				,			**UpperCamelCase_:		int					):
  
       if text is None and videos is None:
          raise ValueError("You have to specify either text or videos. Both cannot be none."					)
  
       if text is not None:
          UpperCamelCase_     =self.tokenizer(UpperCamelCase_				,			return_tensors=UpperCamelCase_				,			**UpperCamelCase_					)
  
       if videos is not None:
          UpperCamelCase_     =self.image_processor(UpperCamelCase_				,			return_tensors=UpperCamelCase_				,			**UpperCamelCase_					)
  
       if text is not None and videos is not None:
          UpperCamelCase_     =image_features.pixel_values
          return encoding
       elif text is not None:
          return encoding
       else:
          return BatchEncoding(data=dict(**UpperCamelCase_					)				,			tensor_type=UpperCamelCase_					)
    def 					UpperCamelCase__   (      self:		Any				,			*UpperCamelCase_:		List[str]				,			**UpperCamelCase_:		Optional[int]					):
       return self.tokenizer.batch_decode(*UpperCamelCase_				,			**UpperCamelCase_					)
    def 					UpperCamelCase__   (      self:		Optional[Any]				,			*UpperCamelCase_:		List[str]				,			**UpperCamelCase_:		Tuple					):
       return self.tokenizer.decode(*UpperCamelCase_				,			**UpperCamelCase_					)
    @property
    def 					UpperCamelCase__   (      self:		List[Any]					):
       return ["input_ids", "attention_mask", "position_ids", "pixel_values"]
    @property
    def 					UpperCamelCase__   (      self:		str					):
       warnings.warn(
           "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."				,			UpperCamelCase_				,			)
       return self.image_processor_class
  
  
  
  
  
    @property
    def 					UpperCamelCase__   (      self:		Optional[Any]					):
       warnings.warn(
           "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."				,			UpperCamelCase_				,			)
       return self.image_processor
  