


"""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




"""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()
