code: 
  
  
  
  
  
  
  """simple docstring"""
  
  
  
  
  
  
  from collections import defaultdict
  
  
  
  
  def 						_UpperCamelCase			(  A = 5			):
     UpperCamelCase_     =defaultdict(A			)
     UpperCamelCase_     =0
  
     while True:
        UpperCamelCase_     =get_digits(A			)
        freqs[digits].append(A			)
  
        if len(freqs[digits]			) == max_base:
           UpperCamelCase_     =freqs[digits][0] ** 3
           return base
  
        num += 1
  
  
  
  
  def 						_UpperCamelCase			(  A			):
     return "".join(sorted(str(num**3			)			)			)
  
  
  if __name__ == "__main__":
      print(f'''{solution() = }''')
  
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
  