code: 
  
  
  
  
  
  
  class 					snake_case_							:
  
     def __init__(    self:	str   ,       snake_case__:	Optional[int]	)  ->		Union[str, Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	val
         # Number of nodes in left subtree
         _lowercase     =	0
         _lowercase     =	None
         _lowercase     =	None
         _lowercase     =	None
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Any   ,       snake_case__:	Dict	)  ->		Dict:
  
  
         """simple docstring"""
  
  
  
         assert self.left_tree_size == other.left_tree_size, "Unequal Sizes of Blocks"
  
         if self.val < other.val:
             _lowercase     =	self.right
             _lowercase     =	None
             if self.right:
                 _lowercase     =	other
             _lowercase     =	other
             _lowercase     =	self.left_tree_size * 2 + 1
             return self
         else:
             _lowercase     =	other.right
             _lowercase     =	None
             if other.right:
                 _lowercase     =	self
             _lowercase     =	self
             _lowercase     =	other.left_tree_size * 2 + 1
             return other
  
  
  
  
  
  
  class 					snake_case_							:
  
     def __init__(    self:	List[str]   ,       snake_case__:	Tuple=None   ,       snake_case__:	Tuple=None   ,       snake_case__:	Tuple=0	)  ->		Tuple:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	heap_size
         _lowercase     =	bottom_root
         _lowercase     =	min_node
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Any   ,       snake_case__:	Union[str, Any]	)  ->		Tuple:
  
  
         """simple docstring"""
  
  
  
         if other.size == 0:
             return None
         if self.size == 0:
             _lowercase     =	other.size
             _lowercase     =	other.bottom_root
             _lowercase     =	other.min_node
             return None
         # Update size
         _lowercase     =	self.size + other.size
  
         # Update min.node
         if self.min_node.val > other.min_node.val:
             _lowercase     =	other.min_node
         # Merge
  
         # Order roots by left_subtree_size
         _lowercase     =	[]
         _lowercase ,    _lowercase     =	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)	)
                 _lowercase     =	i.parent
             else:
                 combined_roots_list.append((j, False)	)
                 _lowercase     =	j.parent
          # Insert links between them
         for i in range(len(snake_case__	) - 1	):
             if combined_roots_list[i][1] != combined_roots_list[i + 1][1]:
                 _lowercase     =	combined_roots_list[i + 1][0]
                 _lowercase     =	combined_roots_list[i][0]
          # Consecutively merge roots with same left_tree_size
         _lowercase     =	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
                 _lowercase     =	i.left
                 _lowercase     =	i.parent.parent
  
                 # Merging trees
                 _lowercase     =	i.merge_trees(i.parent	)
  
                 # Updating links
                 _lowercase     =	previous_node
                 _lowercase     =	next_node
                 if previous_node:
                     _lowercase     =	i
                 if next_node:
                     _lowercase     =	i
             else:
                 _lowercase     =	i.parent
          # Updating self.bottom_root
         while i.left:
             _lowercase     =	i.left
         _lowercase     =	i
  
         # Update other
         _lowercase     =	self.size
         _lowercase     =	self.bottom_root
         _lowercase     =	self.min_node
  
         # Return the merged heap
         return self
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	List[str]   ,       snake_case__:	Optional[Any]	)  ->		Union[str, Any]:
  
  
         """simple docstring"""
  
  
  
         if self.size == 0:
             _lowercase     =	Node(snake_case__	)
             _lowercase     =	1
             _lowercase     =	self.bottom_root
         else:
             # Create new node
             _lowercase     =	Node(snake_case__	)
  
             # Update size
             self.size += 1
  
             # update min_node
             if val < self.min_node.val:
                 _lowercase     =	new_node
             # Put new_node as a bottom_root in heap
             _lowercase     =	new_node
             _lowercase     =	self.bottom_root
             _lowercase     =	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
                 _lowercase     =	self.bottom_root.parent.parent
  
                 # Merge
                 _lowercase     =	self.bottom_root.merge_trees(self.bottom_root.parent	)
  
                 # Update Links
                 _lowercase     =	next_node
                 _lowercase     =	None
                 if next_node:
                     _lowercase     =	self.bottom_root
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Tuple	)  ->		Tuple:
  
  
         """simple docstring"""
  
  
  
         return self.min_node.val
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	List[Any]	)  ->		int:
  
  
         """simple docstring"""
  
  
  
         return self.size == 0
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Dict	)  ->		Dict:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	self.min_node.val
  
         # Last element in heap corner case
         if self.size == 1:
             # Update size
             _lowercase     =	0
  
             # Update bottom root
             _lowercase     =	None
  
             # Update min_node
             _lowercase     =	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
             _lowercase     =	self.bottom_root.parent
             _lowercase     =	None
  
             # Update min_node
             _lowercase     =	self.bottom_root
             _lowercase     =	self.bottom_root.parent
             while i:
                 if i.val < self.min_node.val:
                     _lowercase     =	i
                 _lowercase     =	i.parent
             return min_value
         # General case
         # Find the BinomialHeap of the right subtree of min_node
         _lowercase     =	self.min_node.right
         _lowercase     =	None
         _lowercase     =	bottom_of_new
         _lowercase     =	1
  
         # Size, min_node and bottom_root
         while bottom_of_new.left:
             _lowercase     =	size_of_new * 2 + 1
             _lowercase     =	bottom_of_new.left
             if bottom_of_new.val < min_of_new.val:
                 _lowercase     =	bottom_of_new
          # Corner case of single root on top left path
         if (not self.min_node.left) and (not self.min_node.parent):
             _lowercase     =	size_of_new
             _lowercase     =	bottom_of_new
             _lowercase     =	min_of_new
             # print("Single root, multiple nodes case")
             return min_value
         # Remaining cases
         # Construct heap of right subtree
         _lowercase     =	BinomialHeap(
             bottom_root=snake_case__   ,       min_node=snake_case__   ,       heap_size=snake_case__	)
  
         # Update size
         _lowercase     =	self.size - 1 - size_of_new
  
         # Neighbour nodes
         _lowercase     =	self.min_node.left
         _lowercase     =	self.min_node.parent
  
         # Initialize new bottom_root and min_node
         _lowercase     =	previous_node or next_node
         _lowercase     =	next_node
  
         # Update links of previous_node and search below for new min_node and
         # bottom_root
         if previous_node:
             _lowercase     =	next_node
  
             # Update bottom_root and search for min_node below
             _lowercase     =	previous_node
             _lowercase     =	previous_node
             while self.bottom_root.left:
                 _lowercase     =	self.bottom_root.left
                 if self.bottom_root.val < self.min_node.val:
                     _lowercase     =	self.bottom_root
         if next_node:
             _lowercase     =	previous_node
  
             # Search for new min_node above min_node
             _lowercase     =	next_node
             while i:
                 if i.val < self.min_node.val:
                     _lowercase     =	i
                 _lowercase     =	i.parent
          # Merge heaps
         self.merge_heaps(snake_case__	)
  
         return min_value
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Dict	)  ->		Optional[Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	self.bottom_root
         while top_root.parent:
             _lowercase     =	top_root.parent
         # preorder
         _lowercase     =	[]
         self.__traversal(snake_case__   ,       snake_case__	)
         return heap_pre_order
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Dict   ,       snake_case__:	Dict   ,       snake_case__:	Optional[Any]   ,       snake_case__:	int=0	)  ->		Dict:
  
  
         """simple docstring"""
  
  
  
         if curr_node:
             preorder.append((curr_node.val, level)	)
             self.__traversal(curr_node.left   ,       snake_case__   ,       level + 1	)
             self.__traversal(curr_node.right   ,       snake_case__   ,       level + 1	)
         else:
             preorder.append(('#', level)	)
  
     def __str__(    self:	List[Any]	)  ->		Optional[int]:
  
  
         """simple docstring"""
  
  
  
         if self.is_empty():
             return ""
         _lowercase     =	self.pre_order()
  
         return "\n".join(('-' * level + str(snake_case__	)) for value, level in preorder_heap	)
  
  
  # Unit Tests
  if __name__ == "__main__":
     import doctest
  
     doctest.testmod()
  
style_context: 
  
  
  
  
  
  
  from typing import Dict, List, Optional, Union
  
  import numpy as np
  
  from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
  from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
  from ...image_utils import (
      IMAGENET_STANDARD_MEAN,
      IMAGENET_STANDARD_STD,
      ChannelDimension,
      ImageInput,
      PILImageResampling,
      make_list_of_images,
      to_numpy_array,
      valid_images,
  )
  from ...utils import TensorType, is_vision_available, logging
  
  
  if is_vision_available():
     import PIL
  
  
  lowerCamelCase										=  logging.get_logger(__name__)
  
  class 					snake_case_							(       lowercase_      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     __lowerCAmelCase      =						["""pixel_values"""]
  
     def __init__(    self:	int   ,       snake_case__:	bool = True   ,       snake_case__:	Dict[str, int] = None   ,       snake_case__:	PILImageResampling = PIL.Image.NEAREST   ,       snake_case__:	bool = False   ,       snake_case__:	Dict[str, int] = None   ,       snake_case__:	Union[int, float] = 1 / 255   ,       snake_case__:	bool = False   ,       snake_case__:	bool = True   ,       snake_case__:	bool = True   ,       snake_case__:	Optional[Union[float, List[float]]] = None   ,       snake_case__:	Optional[Union[float, List[float]]] = None   ,       snake_case__:	bool = True   ,       **snake_case__:	Optional[Any]   ,       )  ->		None:
  
  
         """simple docstring"""
  
  
  
         super().__init__(**snake_case__	)
         _lowercase     =	size if size is not None else {'height': 346, 'width': 346}
         _lowercase     =	get_size_dict(snake_case__	)
         _lowercase     =	crop_size if crop_size is not None else {'height': 289, 'width': 289}
         _lowercase     =	get_size_dict(snake_case__   ,       param_name='crop_size'	)
  
         _lowercase     =	do_resize
         _lowercase     =	size
         _lowercase     =	resample
         _lowercase     =	do_center_crop
         _lowercase     =	crop_size
         _lowercase     =	do_rescale
         _lowercase     =	rescale_factor
         _lowercase     =	rescale_offset
         _lowercase     =	do_normalize
         _lowercase     =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         _lowercase     =	image_std if image_std is not None else IMAGENET_STANDARD_STD
         _lowercase     =	include_top
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	str   ,       snake_case__:	np.ndarray   ,       snake_case__:	Dict[str, int]   ,       snake_case__:	PILImageResampling = PIL.Image.NEAREST   ,       snake_case__:	Optional[Union[str, ChannelDimension]] = None   ,       **snake_case__:	Optional[int]   ,       )  ->		np.ndarray:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	get_size_dict(snake_case__	)
         if "height" not in size or "width" not in size:
             raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''	)
         return resize(
             snake_case__   ,       size=(size['height'], size['width'])   ,       resample=snake_case__   ,       data_format=snake_case__   ,       **snake_case__	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[Any]   ,       snake_case__:	np.ndarray   ,       snake_case__:	Dict[str, int]   ,       snake_case__:	Optional[Union[str, ChannelDimension]] = None   ,       **snake_case__:	str   ,       )  ->		np.ndarray:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	get_size_dict(snake_case__	)
         if "height" not in size or "width" not in size:
             raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''	)
         return center_crop(snake_case__   ,       size=(size['height'], size['width'])   ,       data_format=snake_case__   ,       **snake_case__	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[Any]   ,       snake_case__:	np.ndarray   ,       snake_case__:	Union[int, float]   ,       snake_case__:	bool = True   ,       snake_case__:	Optional[Union[str, ChannelDimension]] = None   ,       **snake_case__:	List[str]   ,       )  ->		Optional[int]:
  
  
         """simple docstring"""
  
  
  
         if offset:
             _lowercase     =	(image - 1_27.5) * scale
             if data_format is not None:
                 _lowercase     =	to_channel_dimension_format(snake_case__   ,       snake_case__	)
             _lowercase     =	rescaled_image.astype(np.floataa	)
         else:
             _lowercase     =	rescale(snake_case__   ,       scale=snake_case__   ,       data_format=snake_case__   ,       **snake_case__	)
         return rescaled_image
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Dict   ,       snake_case__:	np.ndarray   ,       snake_case__:	Union[float, List[float]]   ,       snake_case__:	Union[float, List[float]]   ,       snake_case__:	Optional[Union[str, ChannelDimension]] = None   ,       **snake_case__:	Any   ,       )  ->		np.ndarray:
  
  
         """simple docstring"""
  
  
  
         return normalize(snake_case__   ,       mean=snake_case__   ,       std=snake_case__   ,       data_format=snake_case__   ,       **snake_case__	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[int]   ,       snake_case__:	ImageInput   ,       snake_case__:	bool = None   ,       snake_case__:	Dict[str, int] = None   ,       snake_case__:	Union[str, Any]=None   ,       snake_case__:	bool = None   ,       snake_case__:	Dict[str, int] = None   ,       snake_case__:	bool = None   ,       snake_case__:	float = None   ,       snake_case__:	bool = None   ,       snake_case__:	bool = None   ,       snake_case__:	Optional[Union[float, List[float]]] = None   ,       snake_case__:	Optional[Union[float, List[float]]] = None   ,       snake_case__:	bool = None   ,       snake_case__:	Optional[Union[str, TensorType]] = None   ,       snake_case__:	ChannelDimension = ChannelDimension.FIRST   ,       **snake_case__:	int   ,       )  ->		PIL.Image.Image:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	do_resize if do_resize is not None else self.do_resize
         _lowercase     =	resample if resample is not None else self.resample
         _lowercase     =	do_center_crop if do_center_crop is not None else self.do_center_crop
         _lowercase     =	do_rescale if do_rescale is not None else self.do_rescale
         _lowercase     =	rescale_factor if rescale_factor is not None else self.rescale_factor
         _lowercase     =	rescale_offset if rescale_offset is not None else self.rescale_offset
         _lowercase     =	do_normalize if do_normalize is not None else self.do_normalize
         _lowercase     =	image_mean if image_mean is not None else self.image_mean
         _lowercase     =	image_std if image_std is not None else self.image_std
         _lowercase     =	include_top if include_top is not None else self.include_top
  
         _lowercase     =	size if size is not None else self.size
         _lowercase     =	get_size_dict(snake_case__	)
         _lowercase     =	crop_size if crop_size is not None else self.crop_size
         _lowercase     =	get_size_dict(snake_case__   ,       param_name='crop_size'	)
  
         _lowercase     =	make_list_of_images(snake_case__	)
  
         if not valid_images(snake_case__	):
             raise ValueError(
                 'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                 'torch.Tensor, tf.Tensor or jax.ndarray.'	)
  
         if do_resize and size is None or resample is None:
             raise ValueError('Size and resample must be specified if do_resize is True.'	)
  
         if do_center_crop and crop_size is None:
             raise ValueError('Crop size must be specified if do_center_crop is True.'	)
  
         if do_rescale and rescale_factor is None:
             raise ValueError('Rescale factor must be specified if do_rescale is True.'	)
  
         if do_normalize and (image_mean is None or image_std is None):
             raise ValueError('Image mean and std must be specified if do_normalize is True.'	)
  
         # All transformations expect numpy arrays.
         _lowercase     =	[to_numpy_array(snake_case__	) for image in images]
  
         if do_resize:
             _lowercase     =	[self.resize(image=snake_case__   ,       size=snake_case__   ,       resample=snake_case__	) for image in images]
  
         if do_center_crop:
             _lowercase     =	[self.center_crop(image=snake_case__   ,       size=snake_case__	) for image in images]
  
         if do_rescale:
             _lowercase     =	[self.rescale(image=snake_case__   ,       scale=snake_case__   ,       offset=snake_case__	) for image in images]
  
         if do_normalize:
             _lowercase     =	[self.normalize(image=snake_case__   ,       mean=snake_case__   ,       std=snake_case__	) for image in images]
  
         if include_top:
             _lowercase     =	[self.normalize(image=snake_case__   ,       mean=[0, 0, 0]   ,       std=snake_case__	) for image in images]
  
         _lowercase     =	[to_channel_dimension_format(snake_case__   ,       snake_case__	) for image in images]
  
         _lowercase     =	{'pixel_values': images}
         return BatchFeature(data=snake_case__   ,       tensor_type=snake_case__	)
  