code: 
  
  
  import warnings
  
  from ...processing_utils import ProcessorMixin
  from ...tokenization_utils_base import BatchEncoding
  
  
  
  
  
  class    A					(     A_				):
  							UpperCamelCase_     :							Optional[int] =['''image_processor''', '''tokenizer''']
  							UpperCamelCase_     :							Optional[Any] ='''VideoMAEImageProcessor'''
  							UpperCamelCase_     :							Dict =('''CLIPTokenizer''', '''CLIPTokenizerFast''')
  
  
  							def __init__(self   ,       lowerCAmelCase=None   ,       lowerCAmelCase=None   ,       **lowerCAmelCase      ):
  									__lowercase=    None
  									if "feature_extractor" in kwargs:
  											warnings.warn(
  											    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
  											    ' instead.'   ,       lowerCAmelCase   ,       )
  											__lowercase=    kwargs.pop('feature_extractor'      )
  
  									__lowercase=    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__(lowerCAmelCase   ,       lowerCAmelCase      )
  									__lowercase=    self.image_processor
  
  
  							def __call__(self   ,       lowerCAmelCase=None   ,       lowerCAmelCase=None   ,       lowerCAmelCase=None   ,       **lowerCAmelCase      ):
  
  									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:
  											__lowercase=    self.tokenizer(lowerCAmelCase   ,       return_tensors=lowerCAmelCase   ,       **lowerCAmelCase      )
  
  									if videos is not None:
  											__lowercase=    self.image_processor(lowerCAmelCase   ,       return_tensors=lowerCAmelCase   ,       **lowerCAmelCase      )
  
  									if text is not None and videos is not None:
  											__lowercase=    image_features.pixel_values
  											return encoding
  									elif text is not None:
  											return encoding
  									else:
  											return BatchEncoding(data=dict(**lowerCAmelCase      )   ,       tensor_type=lowerCAmelCase      )
  
  
  							def     _A (self   ,       *lowerCAmelCase   ,       **lowerCAmelCase      ):
  									return self.tokenizer.batch_decode(*lowerCAmelCase   ,       **lowerCAmelCase      )
  
  
  							def     _A (self   ,       *lowerCAmelCase   ,       **lowerCAmelCase      ):
  									return self.tokenizer.decode(*lowerCAmelCase   ,       **lowerCAmelCase      )
  
  
  							@property
  							def     _A (self      ):
  									return ["input_ids", "attention_mask", "position_ids", "pixel_values"]
  
  
  							@property
  							def     _A (self      ):
  									warnings.warn(
  									    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'   ,       lowerCAmelCase   ,       )
  									return self.image_processor_class
  
  
  
  
  
  
  
  							@property
  							def     _A (self      ):
  									warnings.warn(
  									    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'   ,       lowerCAmelCase   ,       )
  									return self.image_processor
  
  
  
  
  
  
  
style_context: 
  
  
  from __future__ import annotations
  
  from enum import Enum
  
  
  
  
  
  class    A					(     A_				):
  							UpperCamelCase_     :							List[Any] =24
  							UpperCamelCase_     :							List[Any] =21
  							UpperCamelCase_     :							int =18
  							UpperCamelCase_     :							Optional[int] =15
  							UpperCamelCase_     :							Tuple =12
  							UpperCamelCase_     :							List[str] =9
  							UpperCamelCase_     :							Any =6
  							UpperCamelCase_     :							List[str] =3
  							UpperCamelCase_     :							str =2
  							UpperCamelCase_     :							str =1
  							UpperCamelCase_     :							Optional[int] =-1
  							UpperCamelCase_     :							List[Any] =-2
  							UpperCamelCase_     :							str =-3
  							UpperCamelCase_     :							Tuple =-6
  							UpperCamelCase_     :							Tuple =-9
  							UpperCamelCase_     :							Optional[int] =-12
  							UpperCamelCase_     :							str =-15
  							UpperCamelCase_     :							Optional[Any] =-18
  							UpperCamelCase_     :							Any =-21
  							UpperCamelCase_     :							List[str] =-24
  
  
  
  
  
  class    A					(     A_				):
  							UpperCamelCase_     :							str =8
  							UpperCamelCase_     :							Optional[Any] =7
  							UpperCamelCase_     :							Tuple =6
  							UpperCamelCase_     :							List[Any] =5
  							UpperCamelCase_     :							Optional[Any] =4
  							UpperCamelCase_     :							Union[str, Any] =3
  							UpperCamelCase_     :							Union[str, Any] =2
  							UpperCamelCase_     :							int =1
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__     ,				lowercase__     ,				lowercase__     ,				)		->			float:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		if isinstance(lowercase__     ,				lowercase__			):
  				__lowercase=    SIUnit[known_prefix.lower()]
  		if isinstance(lowercase__     ,				lowercase__			):
  				__lowercase=    SIUnit[unknown_prefix.lower()]
  		__lowercase=    known_amount * (
  		    1_0 ** (known_prefix.value - unknown_prefix.value)
  		)
  		return unknown_amount
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__     ,				lowercase__     ,				lowercase__     ,				)		->			float:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		if isinstance(lowercase__     ,				lowercase__			):
  				__lowercase=    BinaryUnit[known_prefix.lower()]
  		if isinstance(lowercase__     ,				lowercase__			):
  				__lowercase=    BinaryUnit[unknown_prefix.lower()]
  		__lowercase=    known_amount * (
  		    2 ** ((known_prefix.value - unknown_prefix.value) * 1_0)
  		)
  		return unknown_amount
  
  
  if __name__ == "__main__":
  	import doctest
  
  	doctest.testmod()
  
  
  
  
  
  
  