code: 
  
  
  '''simple docstring'''
  
  class  UpperCAmelCase :
  
  	def __init__( self :	Optional[Any],					a_ :	Dict					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__ 						=						val
  				# Number of nodes in left subtree
  				UpperCamelCase__ 						=						0
  				UpperCamelCase__ 						=						None
  				UpperCamelCase__ 						=						None
  				UpperCamelCase__ 						=						None
  
  	def 		lowercase_						( self :	List[str],					a_ :	str					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				assert self.left_tree_size == other.left_tree_size, "Unequal Sizes of Blocks"
  
  				if self.val < other.val:
  							UpperCamelCase__ 						=						self.right
  							UpperCamelCase__ 						=						None
  							if self.right:
  										UpperCamelCase__ 						=						other
  							UpperCamelCase__ 						=						other
  							UpperCamelCase__ 						=						self.left_tree_size * 2 + 1
  							return self
  				else:
  							UpperCamelCase__ 						=						other.right
  							UpperCamelCase__ 						=						None
  							if other.right:
  										UpperCamelCase__ 						=						self
  							UpperCamelCase__ 						=						self
  							UpperCamelCase__ 						=						other.left_tree_size * 2 + 1
  							return other
  
  
  
  
  
  
  
  class  UpperCAmelCase :
  
  	def __init__( self :	List[str],					a_ :	List[Any]=None,					a_ :	Any=None,					a_ :	Dict=0					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__ 						=						heap_size
  				UpperCamelCase__ 						=						bottom_root
  				UpperCamelCase__ 						=						min_node
  
  	def 		lowercase_						( self :	Any,					a_ :	Tuple					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if other.size == 0:
  							return None
  				if self.size == 0:
  							UpperCamelCase__ 						=						other.size
  							UpperCamelCase__ 						=						other.bottom_root
  							UpperCamelCase__ 						=						other.min_node
  							return None
  				# Update size
  				UpperCamelCase__ 						=						self.size + other.size
  
  				# Update min.node
  				if self.min_node.val > other.min_node.val:
  							UpperCamelCase__ 						=						other.min_node
  				# Merge
  
  				# Order roots by left_subtree_size
  				UpperCamelCase__ 						=						[]
  				UpperCamelCase__ 						=						self.bottom_root, other.bottom_root
  				while i or j:
  							if i and ((not j) or i.left_tree_size < j.left_tree_size):
  										combined_roots_list.append((i, True)					)
  										UpperCamelCase__ 						=						i.parent
  							else:
  										combined_roots_list.append((j, False)					)
  										UpperCamelCase__ 						=						j.parent
          # Insert links between them
  				for i in range(len(lowercase_					) - 1					):
  							if combined_roots_list[i][1] != combined_roots_list[i + 1][1]:
  										UpperCamelCase__ 						=						combined_roots_list[i + 1][0]
  										UpperCamelCase__ 						=						combined_roots_list[i][0]
          # Consecutively merge roots with same left_tree_size
  				UpperCamelCase__ 						=						combined_roots_list[0][0]
  				while i.parent:
  							if (
  							    (i.left_tree_size == i.parent.left_tree_size) and (not i.parent.parent)
  							) or (
  							    i.left_tree_size == i.parent.left_tree_size
  							    and i.left_tree_size != i.parent.parent.left_tree_size
  							):
  										# Neighbouring Nodes
  										UpperCamelCase__ 						=						i.left
  										UpperCamelCase__ 						=						i.parent.parent
  
  										# Merging trees
  										UpperCamelCase__ 						=						i.merge_trees(i.parent					)
  
  										# Updating links
  										UpperCamelCase__ 						=						previous_node
  										UpperCamelCase__ 						=						next_node
  										if previous_node:
  													UpperCamelCase__ 						=						i
  										if next_node:
  													UpperCamelCase__ 						=						i
  							else:
  										UpperCamelCase__ 						=						i.parent
          # Updating self.bottom_root
  				while i.left:
  							UpperCamelCase__ 						=						i.left
  				UpperCamelCase__ 						=						i
  
  				# Update other
  				UpperCamelCase__ 						=						self.size
  				UpperCamelCase__ 						=						self.bottom_root
  				UpperCamelCase__ 						=						self.min_node
  
  				# Return the merged heap
  				return self
  
  	def 		lowercase_						( self :	Optional[Any],					a_ :	Optional[int]					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if self.size == 0:
  							UpperCamelCase__ 						=						Node(lowercase_					)
  							UpperCamelCase__ 						=						1
  							UpperCamelCase__ 						=						self.bottom_root
  				else:
  							# Create new node
  							UpperCamelCase__ 						=						Node(lowercase_					)
  
  							# Update size
  							self.size += 1
  
  							# update min_node
  							if val < self.min_node.val:
  										UpperCamelCase__ 						=						new_node
  							# Put new_node as a bottom_root in heap
  							UpperCamelCase__ 						=						new_node
  							UpperCamelCase__ 						=						self.bottom_root
  							UpperCamelCase__ 						=						new_node
  
  							# Consecutively merge roots with same left_tree_size
  							while (
  							    self.bottom_root.parent
  							    and self.bottom_root.left_tree_size
  							    == self.bottom_root.parent.left_tree_size
  							):
  										# Next node
  										UpperCamelCase__ 						=						self.bottom_root.parent.parent
  
  										# Merge
  										UpperCamelCase__ 						=						self.bottom_root.merge_trees(self.bottom_root.parent					)
  
  										# Update Links
  										UpperCamelCase__ 						=						next_node
  										UpperCamelCase__ 						=						None
  										if next_node:
  													UpperCamelCase__ 						=						self.bottom_root
  
  	def 		lowercase_						( self :	Optional[int]					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return self.min_node.val
  
  	def 		lowercase_						( self :	List[str]					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return self.size == 0
  
  	def 		lowercase_						( self :	Dict					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__ 						=						self.min_node.val
  
  				# Last element in heap corner case
  				if self.size == 1:
  							# Update size
  							UpperCamelCase__ 						=						0
  
  							# Update bottom root
  							UpperCamelCase__ 						=						None
  
  							# Update min_node
  							UpperCamelCase__ 						=						None
  
  							return min_value
  				# No right subtree corner case
  				# The structure of the tree implies that this should be the bottom root
  				# and there is at least one other root
  				if self.min_node.right is None:
  							# Update size
  							self.size -= 1
  
  							# Update bottom root
  							UpperCamelCase__ 						=						self.bottom_root.parent
  							UpperCamelCase__ 						=						None
  
  							# Update min_node
  							UpperCamelCase__ 						=						self.bottom_root
  							UpperCamelCase__ 						=						self.bottom_root.parent
  							while i:
  										if i.val < self.min_node.val:
  													UpperCamelCase__ 						=						i
  										UpperCamelCase__ 						=						i.parent
  							return min_value
  				# General case
  				# Find the BinomialHeap of the right subtree of min_node
  				UpperCamelCase__ 						=						self.min_node.right
  				UpperCamelCase__ 						=						None
  				UpperCamelCase__ 						=						bottom_of_new
  				UpperCamelCase__ 						=						1
  
  				# Size, min_node and bottom_root
  				while bottom_of_new.left:
  							UpperCamelCase__ 						=						size_of_new * 2 + 1
  							UpperCamelCase__ 						=						bottom_of_new.left
  							if bottom_of_new.val < min_of_new.val:
  										UpperCamelCase__ 						=						bottom_of_new
          # Corner case of single root on top left path
  				if (not self.min_node.left) and (not self.min_node.parent):
  							UpperCamelCase__ 						=						size_of_new
  							UpperCamelCase__ 						=						bottom_of_new
  							UpperCamelCase__ 						=						min_of_new
  							# print("Single root, multiple nodes case")
  							return min_value
  				# Remaining cases
  				# Construct heap of right subtree
  				UpperCamelCase__ 						=						BinomialHeap(
  				    bottom_root=lowercase_,					min_node=lowercase_,					heap_size=lowercase_					)
  
  				# Update size
  				UpperCamelCase__ 						=						self.size - 1 - size_of_new
  
  				# Neighbour nodes
  				UpperCamelCase__ 						=						self.min_node.left
  				UpperCamelCase__ 						=						self.min_node.parent
  
  				# Initialize new bottom_root and min_node
  				UpperCamelCase__ 						=						previous_node or next_node
  				UpperCamelCase__ 						=						next_node
  
  				# Update links of previous_node and search below for new min_node and
  				# bottom_root
  				if previous_node:
  							UpperCamelCase__ 						=						next_node
  
  							# Update bottom_root and search for min_node below
  							UpperCamelCase__ 						=						previous_node
  							UpperCamelCase__ 						=						previous_node
  							while self.bottom_root.left:
  										UpperCamelCase__ 						=						self.bottom_root.left
  										if self.bottom_root.val < self.min_node.val:
  													UpperCamelCase__ 						=						self.bottom_root
  				if next_node:
  							UpperCamelCase__ 						=						previous_node
  
  							# Search for new min_node above min_node
  							UpperCamelCase__ 						=						next_node
  							while i:
  										if i.val < self.min_node.val:
  													UpperCamelCase__ 						=						i
  										UpperCamelCase__ 						=						i.parent
          # Merge heaps
  				self.merge_heaps(lowercase_					)
  
  				return min_value
  
  	def 		lowercase_						( self :	Any					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__ 						=						self.bottom_root
  				while top_root.parent:
  							UpperCamelCase__ 						=						top_root.parent
  				# preorder
  				UpperCamelCase__ 						=						[]
  				self.__traversal(lowercase_,					lowercase_					)
  				return heap_pre_order
  
  	def 		lowercase_						( self :	Any,					a_ :	int,					a_ :	Union[str, Any],					a_ :	List[str]=0					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if curr_node:
  							preorder.append((curr_node.val, level)					)
  							self.__traversal(curr_node.left,					lowercase_,					level + 1					)
  							self.__traversal(curr_node.right,					lowercase_,					level + 1					)
  				else:
  							preorder.append(("#", level)					)
  
  	def __str__( self :	str					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if self.is_empty():
  							return ""
  				UpperCamelCase__ 						=						self.pre_order()
  
  				return "\n".join(("-" * level + str(lowercase_					)) for value, level in preorder_heap					)
  
  
  # Unit Tests
  if __name__ == "__main__":
  							import doctest
  
  							doctest.testmod()
style_context: 
  
  
  '''simple docstring'''
  
  from typing import List
  
  from ...processing_utils import ProcessorMixin
  from ...utils import is_torch_available
  
  
  if is_torch_available():
         import torch
  
  
  
  
  
  
  
  class  UpperCAmelCase (       SCREAMING_SNAKE_CASE__):
   _lowerCamelCase :					Tuple    					=					['image_processor', 'tokenizer']
   _lowerCamelCase :					Tuple    					=					'OneFormerImageProcessor'
   _lowerCamelCase :					List[Any]    					=					('CLIPTokenizer', 'CLIPTokenizerFast')
  
   def __init__( self :	Any,					a_ :	Dict=None,					a_ :	int=None,					a_ :	int = 77,					a_ :	int = 77,					**a_ :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      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`."					)
  
      UpperCamelCase__ 						=						max_seq_length
      UpperCamelCase__ 						=						task_seq_length
  
      super().__init__(a_,					a_					)
  
   def 		lowercase_						( self :	Any,					a_ :	Optional[Any]=None,					a_ :	Optional[int]=77					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if text_list is None:
         raise ValueError("tokens cannot be None."					)
  
      UpperCamelCase__ 						=						self.tokenizer(a_,					padding="max_length",					max_length=a_,					truncation=a_					)
  
      UpperCamelCase__						,		UpperCamelCase__ 						=						tokens["attention_mask"], tokens["input_ids"]
  
      UpperCamelCase__ 						=						[]
      for attn_mask, input_id in zip(a_,					a_					):
         UpperCamelCase__ 						=						torch.tensor(a_					) * torch.tensor(a_					)
         token_inputs.append(token.unsqueeze(0					)					)
  
      UpperCamelCase__ 						=						torch.cat(a_,					dim=0					)
      return token_inputs
  
   def __call__( self :	str,					a_ :	Dict=None,					a_ :	str=None,					a_ :	Tuple=None,					**a_ :	Any					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if task_inputs is None:
         raise ValueError("You have to specify the task_input. Found None."					)
      elif images is None:
         raise ValueError("You have to specify the image. Found None."					)
  
      if not all(task in ["semantic", "instance", "panoptic"] for task in task_inputs					):
         raise ValueError("task_inputs must be semantic, instance, or panoptic."					)
  
      UpperCamelCase__ 						=						self.image_processor(a_,					a_,					a_,					**a_					)
  
      if isinstance(a_,					a_					):
         UpperCamelCase__ 						=						[task_inputs]
  
      if isinstance(a_,					a_					) and all(isinstance(a_,					a_					) for task_input in task_inputs					):
         UpperCamelCase__ 						=						[]
         for task in task_inputs:
            UpperCamelCase__ 						=						f'the task is {task}'
            task_token_inputs.append(a_					)
         UpperCamelCase__ 						=						self._preprocess_text(a_,					max_length=self.task_seq_length					)
      else:
         raise TypeError("Task Inputs should be a string or a list of strings."					)
  
      if hasattr(a_,					"text_inputs"					):
         UpperCamelCase__ 						=						encoded_inputs.text_inputs
  
         UpperCamelCase__ 						=						[]
         for texts in texts_list:
            UpperCamelCase__ 						=						self._preprocess_text(a_,					max_length=self.max_seq_length					)
            text_inputs.append(text_input_list.unsqueeze(0					)					)
  
         UpperCamelCase__ 						=						torch.cat(a_,					dim=0					)
  
      return encoded_inputs
  
   def 		lowercase_						( self :	Union[str, Any],					a_ :	Tuple=None,					a_ :	List[str]=None,					a_ :	Optional[Any]=None,					**a_ :	Tuple					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if task_inputs is None:
         raise ValueError("You have to specify the task_input. Found None."					)
      elif images is None:
         raise ValueError("You have to specify the image. Found None."					)
  
      if not all(task in ["semantic", "instance", "panoptic"] for task in task_inputs					):
         raise ValueError("task_inputs must be semantic, instance, or panoptic."					)
  
      UpperCamelCase__ 						=						self.image_processor.encode_inputs(a_,					a_,					a_,					**a_					)
  
      if isinstance(a_,					a_					):
         UpperCamelCase__ 						=						[task_inputs]
  
      if isinstance(a_,					a_					) and all(isinstance(a_,					a_					) for task_input in task_inputs					):
         UpperCamelCase__ 						=						[]
         for task in task_inputs:
            UpperCamelCase__ 						=						f'the task is {task}'
            task_token_inputs.append(a_					)
         UpperCamelCase__ 						=						self._preprocess_text(a_,					max_length=self.task_seq_length					)
      else:
         raise TypeError("Task Inputs should be a string or a list of strings."					)
  
      if hasattr(a_,					"text_inputs"					):
         UpperCamelCase__ 						=						encoded_inputs.text_inputs
  
         UpperCamelCase__ 						=						[]
         for texts in texts_list:
            UpperCamelCase__ 						=						self._preprocess_text(a_,					max_length=self.max_seq_length					)
            text_inputs.append(text_input_list.unsqueeze(0					)					)
  
         UpperCamelCase__ 						=						torch.cat(a_,					dim=0					)
  
      return encoded_inputs
  
   def 		lowercase_						( self :	Any,					*a_ :	int,					**a_ :	Optional[int]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return self.image_processor.post_process_semantic_segmentation(*a_,					**a_					)
  
   def 		lowercase_						( self :	str,					*a_ :	Tuple,					**a_ :	Optional[int]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return self.image_processor.post_process_instance_segmentation(*a_,					**a_					)
  
   def 		lowercase_						( self :	Dict,					*a_ :	str,					**a_ :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return self.image_processor.post_process_panoptic_segmentation(*a_,					**a_					)