
from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any

@dataclass
class       a		:
							lowercase_		:						int
							lowercase_		:						Node | None   							=  None
							lowercase_		:						Node | None   							=  None






def 		_UpperCAmelCase   (	):






	"""simple docstring"""


	__lowerCAmelCase         =							Node(1  )
	__lowerCAmelCase         =							Node(2  )
	__lowerCAmelCase         =							Node(3  )
	__lowerCAmelCase         =							Node(4  )
	__lowerCAmelCase         =							Node(5  )
	return tree






def 		_UpperCAmelCase   (	UpperCamelCase:     Node | None  ):






	"""simple docstring"""


	return [root.data, *preorder(root.left  ), *preorder(root.right  )] if root else []






def 		_UpperCAmelCase   (	UpperCamelCase:     Node | None  ):






	"""simple docstring"""


	return postorder(root.left  ) + postorder(root.right  ) + [root.data] if root else []






def 		_UpperCAmelCase   (	UpperCamelCase:     Node | None  ):






	"""simple docstring"""


	return [*inorder(root.left  ), root.data, *inorder(root.right  )] if root else []






def 		_UpperCAmelCase   (	UpperCamelCase:     Node | None  ):






	"""simple docstring"""


	return (max(height(root.left  )			,							height(root.right  )  ) + 1) if root else 0






def 		_UpperCAmelCase   (	UpperCamelCase:     Node | None  ):






	"""simple docstring"""


	__lowerCAmelCase         =							[]

	if root is None:
		return output

	__lowerCAmelCase         =							deque([root]  )

	while process_queue:
		__lowerCAmelCase         =							process_queue.popleft()
		output.append(node.data  )

		if node.left:
			process_queue.append(node.left  )
		if node.right:
			process_queue.append(node.right  )
	return output






def 		_UpperCAmelCase   (	UpperCamelCase:     Node | None			,							UpperCamelCase:     int  ):






	"""simple docstring"""


	__lowerCAmelCase         =							[]

	def populate_output(UpperCamelCase:     Node | None			,							UpperCamelCase:     int  ) -> None:
		if not root:
			return
		if level == 1:
			output.append(root.data  )
		elif level > 1:
			populate_output(root.left			,							level - 1  )
			populate_output(root.right			,							level - 1  )

	populate_output(UpperCamelCase			,							UpperCamelCase  )
	return output






def 		_UpperCAmelCase   (	UpperCamelCase:     Node | None			,							UpperCamelCase:     int  ):






	"""simple docstring"""


	__lowerCAmelCase         =							[]

	def populate_output(UpperCamelCase:     Node | None			,							UpperCamelCase:     int  ) -> None:
		if root is None:
			return
		if level == 1:
			output.append(root.data  )
		elif level > 1:
			populate_output(root.right			,							level - 1  )
			populate_output(root.left			,							level - 1  )

	populate_output(UpperCamelCase			,							UpperCamelCase  )
	return output






def 		_UpperCAmelCase   (	UpperCamelCase:     Node | None  ):






	"""simple docstring"""


	if root is None:
		return []

	__lowerCAmelCase         =							[]

	__lowerCAmelCase         =							0
	__lowerCAmelCase         =							height(UpperCamelCase  )

	for h in range(1			,							height_tree + 1  ):
		if not flag:
			output.append(get_nodes_from_left_to_right(UpperCamelCase			,							UpperCamelCase  )  )
			__lowerCAmelCase         =							1
		else:
			output.append(get_nodes_from_right_to_left(UpperCamelCase			,							UpperCamelCase  )  )
			__lowerCAmelCase         =							0

	return output






def 		_UpperCAmelCase   (	):  # Main function for testing.






	"""simple docstring"""


	__lowerCAmelCase         =							make_tree()
	print(F"In-order Traversal: {inorder(UpperCamelCase  )}"  )
	print(F"Pre-order Traversal: {preorder(UpperCamelCase  )}"  )
	print(F"Post-order Traversal: {postorder(UpperCamelCase  )}"			,							"\n"  )

	print(F"Height of Tree: {height(UpperCamelCase  )}"			,							"\n"  )

	print("Complete Level Order Traversal: "  )
	print(level_order(UpperCamelCase  )			,							"\n"  )

	print("Level-wise order Traversal: "  )

	for level in range(1			,							height(UpperCamelCase  ) + 1  ):
		print(F"Level {level}:"			,							get_nodes_from_left_to_right(UpperCamelCase			,							level=UpperCamelCase  )  )

	print("\nZigZag order Traversal: "  )
	print(zigzag(UpperCamelCase  )  )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()








import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


UpperCamelCase_   	=	False

class       a		(   unittest.TestCase					):
							pass

@slow
@require_torch_gpu
class       a		(   unittest.TestCase					):







							def       UpperCAmelCase__					(						self  :  Dict ):
								"""simple docstring"""







								__lowerCAmelCase         =							VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion" )
								pipe.to(snake_case__ )
								pipe.set_progress_bar_config(disable=snake_case__ )

								__lowerCAmelCase         =							load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg" )
								__lowerCAmelCase         =							torch.manual_seed(0 )
								__lowerCAmelCase         =							pipe(
								    image=snake_case__      ,				generator=snake_case__      ,				guidance_scale=7.5      ,				num_inference_steps=50      ,				output_type="numpy"      ,				).images

								__lowerCAmelCase         =							image[0, 253:256, 253:256, -1]

								assert image.shape == (1, 512, 512, 3)
								__lowerCAmelCase         =							np.array([0.0_4_4_1, 0.0_4_6_9, 0.0_5_0_7, 0.0_5_7_5, 0.0_6_3_2, 0.0_6_5_0, 0.0_8_6_5, 0.0_9_0_9, 0.0_9_4_5] )

								assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2






