code: 
  
  
  """simple docstring"""
  
  
  
  
  import warnings
  
  from ...processing_utils import ProcessorMixin
  from ...tokenization_utils_base import BatchEncoding
  
  
  
  
  
  class __snake_case		(					_SCREAMING_SNAKE_CASE      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     lowerCAmelCase_				:   Tuple     	=			['image_processor', 'tokenizer']
     lowerCAmelCase_				:   Optional[Any]     	=			'VideoMAEImageProcessor'
     lowerCAmelCase_				:   int     	=			('CLIPTokenizer', 'CLIPTokenizerFast')
  
  
  
  
     def __init__(		self							:Dict				,  UpperCamelCase__							:str=None				,  UpperCamelCase__							:Any=None				,  **UpperCamelCase__							:Any       ):
            _a          =				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__				,  )
                   _a          =				kwargs.pop("feature_extractor"       )
  
            _a          =				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__       )
            _a          =				self.image_processor
  
  
  
  
     def __call__(		self							:Dict				,  UpperCamelCase__							:Optional[int]=None				,  UpperCamelCase__							:List[str]=None				,  UpperCamelCase__							:Optional[Any]=None				,  **UpperCamelCase__							:Union[str, Any]       ):
  
            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:
                   _a          =				self.tokenizer(UpperCamelCase__				,  return_tensors=UpperCamelCase__				,  **UpperCamelCase__       )
  
            if videos is not None:
                   _a          =				self.image_processor(UpperCamelCase__				,  return_tensors=UpperCamelCase__				,  **UpperCamelCase__       )
  
            if text is not None and videos is not None:
                   _a          =				image_features.pixel_values
                   return encoding
            elif text is not None:
                   return encoding
            else:
                   return BatchEncoding(data=dict(**UpperCamelCase__       )				,  tensor_type=UpperCamelCase__       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Tuple				,  *UpperCamelCase__							:Tuple				,  **UpperCamelCase__							:Optional[int]       ):
            return self.tokenizer.batch_decode(*UpperCamelCase__				,  **UpperCamelCase__       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:int				,  *UpperCamelCase__							:Optional[int]				,  **UpperCamelCase__							:Any       ):
            return self.tokenizer.decode(*UpperCamelCase__				,  **UpperCamelCase__       )
  
  
  
  
     @property
     def SCREAMING_SNAKE_CASE_      (		self							:Optional[Any]       ):
            return ["input_ids", "attention_mask", "position_ids", "pixel_values"]
  
  
  
  
     @property
     def SCREAMING_SNAKE_CASE_      (		self							:Optional[Any]       ):
            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 SCREAMING_SNAKE_CASE_      (		self							:List[Any]       ):
            warnings.warn(
                "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."				,  UpperCamelCase__				,  )
            return self.image_processor
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  from __future__ import annotations
  
  __SCREAMING_SNAKE_CASE					=						[2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]
  
  
  
  
  
  class __snake_case		:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     def __init__(		self							:Optional[Any]				,  UpperCamelCase__							:int       ):
            _a          =				-1
            _a          =				-1
            _a          =				[-1] * length
            _a          =				None
            _a          =				None
  
  
  
  
     def __repr__(		self							:Optional[Any]       ):
            return f'Node(min_value={self.minn} max_value={self.maxx})'
  
  
  
  
  
  
  def 						__a      (  a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         _a          =				Node(len(a					)					)
         _a   , _a          =				min(a					), max(a					)
         # Leaf node case where the node contains only one unique value
         if root.minn == root.maxx:
                return root
         _a          =				(root.minn + root.maxx) // 2
  
         _a          =				[]
         _a          =				[]
  
         for index, num in enumerate(a					):
                if num <= pivot:
                       left_arr.append(a					)
                else:
                       right_arr.append(a					)
                _a          =				len(a					)
         _a          =				build_tree(a					)
         _a          =				build_tree(a					)
         return root
  
  
  
  def 						__a      (  a,		a,		a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         if index < 0 or node is None:
                return 0
         # Leaf node cases
         if node.minn == node.maxx:
                return index + 1 if node.minn == num else 0
         _a          =				(node.minn + node.maxx) // 2
         if num <= pivot:
                # go the left subtree and map index to the left subtree
                return rank_till_index(node.left,		a,		node.map_left[index] - 1					)
         else:
                # go to the right subtree and map index to the right subtree
                return rank_till_index(node.right,		a,		index - node.map_left[index]					)
  
  
  
  def 						__a      (  a,		a,		a,		a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         if start > end:
                return 0
         _a          =				rank_till_index(a,		a,		a					)
         _a          =				rank_till_index(a,		a,		start - 1					)
         return rank_till_end - rank_before_start
  
  
  
  def 						__a      (  a,		a,		a,		a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         if index > (end - start) or start > end or node is None:
                return -1
         # Leaf node case
         if node.minn == node.maxx:
                return node.minn
         # Number of elements in the left subtree in interval [start, end]
         _a          =				node.map_left[end] - (
             node.map_left[start - 1] if start else 0
         )
         if num_elements_in_left_tree > index:
                return quantile(
                    node.left,		a,		(node.map_left[start - 1] if start else 0),		node.map_left[end] - 1,		)
         else:
                return quantile(
                    node.right,		index - num_elements_in_left_tree,		start - (node.map_left[start - 1] if start else 0),		end - node.map_left[end],		)
  
  
  
  def 						__a      (  a,		a,		a,		a,		a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         if (
             start > end
             or node is None
             or start_num > end_num
             or node.minn > end_num
             or node.maxx < start_num
         ):
                return 0
         if start_num <= node.minn and node.maxx <= end_num:
                return end - start + 1
         _a          =				range_counting(
             node.left,		(node.map_left[start - 1] if start else 0),		node.map_left[end] - 1,		a,		a,		)
         _a          =				range_counting(
             node.right,		start - (node.map_left[start - 1] if start else 0),		end - node.map_left[end],		a,		a,		)
         return left + right
  
  
  if __name__ == "__main__":
     import doctest
  
     doctest.testmod()
  