

from __future__ import annotations

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



@dataclass
class     _a						:


					'''simple docstring'''






					UpperCAmelCase__:   int
					UpperCAmelCase__:   Node | None  							=       None
					UpperCAmelCase__:   Node | None  							=       None



def 						UpperCamelCase					()     ->      Node | None:
	A__							:    List[Any]        =		Node(1					)
	A__							:    str        =		Node(2					)
	A__							:    List[Any]        =		Node(3					)
	A__							:    Dict        =		Node(4					)
	A__							:    List[str]        =		Node(5					)
	return tree

def 						UpperCamelCase					(lowercase_: Node | None					)     ->      list[int]:
	return [root.data, *preorder(root.left					), *preorder(root.right					)] if root else []

def 						UpperCamelCase					(lowercase_: Node | None					)     ->      list[int]:
	return postorder(root.left					) + postorder(root.right					) + [root.data] if root else []

def 						UpperCamelCase					(lowercase_: Node | None					)     ->      list[int]:
	return [*inorder(root.left					), root.data, *inorder(root.right					)] if root else []

def 						UpperCamelCase					(lowercase_: Node | None					)     ->      int:
	return (max(height(root.left					)    ,      height(root.right					)					) + 1) if root else 0

def 						UpperCamelCase					(lowercase_: Node | None					)     ->      Sequence[Node | None]:
	A__							:    list[Any]        =		[]

	if root is None:
		return output

	A__							:    Union[str, Any]        =		deque([root]					)

	while process_queue:
		A__							:    int        =		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					(lowercase_: Node | None    ,      lowercase_: int					)     ->      Sequence[Node | None]:
	A__							:    list[Any]        =		[]

	def populate_output(lowercase_: Node | None    ,      lowercase_: 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(lowercase_    ,      lowercase_					)
	return output

def 						UpperCamelCase					(lowercase_: Node | None    ,      lowercase_: int					)     ->      Sequence[Node | None]:
	A__							:    list[Any]        =		[]

	def populate_output(lowercase_: Node | None    ,      lowercase_: 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(lowercase_    ,      lowercase_					)
	return output

def 						UpperCamelCase					(lowercase_: Node | None					)     ->      Sequence[Node | None] | list[Any]:
	if root is None:
		return []

	A__							:    list[Sequence[Node | None]]        =		[]

	A__							:    List[str]        =		0
	A__							:    Any        =		height(lowercase_					)

	for h in range(1    ,      height_tree + 1					):
		if not flag:
			output.append(get_nodes_from_left_to_right(lowercase_    ,      lowercase_					)					)
			A__							:    List[str]        =		1
		else:
			output.append(get_nodes_from_right_to_left(lowercase_    ,      lowercase_					)					)
			A__							:    Any        =		0

	return output

def 						UpperCamelCase					()     ->      None:  # Main function for testing.
	A__							:    Optional[Any]        =		make_tree()
	print(f"""In-order Traversal: {inorder(lowercase_					)}"""					)
	print(f"""Pre-order Traversal: {preorder(lowercase_					)}"""					)
	print(f"""Post-order Traversal: {postorder(lowercase_					)}"""    ,      """\n"""					)

	print(f"""Height of Tree: {height(lowercase_					)}"""    ,      """\n"""					)

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

	print("""Level-wise order Traversal: """					)

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

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


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()






import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()



class     _a						(unittest.TestCase				):


					'''simple docstring'''






					@property
					def   __A			( self			):
						torch.manual_seed(0			)
						A__							:    int        =		UNetaDModel(
						    block_out_channels=(32, 64)    ,       layers_per_block=2    ,       sample_size=32    ,       in_channels=3    ,       out_channels=3    ,       down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")    ,       up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")    ,       )
						return model






					@property
					def   __A			( self			):
						torch.manual_seed(0			)
						A__							:    Dict        =		VQModel(
						    block_out_channels=[32, 64]    ,       in_channels=3    ,       out_channels=3    ,       down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]    ,       up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]    ,       latent_channels=3    ,       )
						return model






					@property
					def   __A			( self			):
						torch.manual_seed(0			)
						A__							:    int        =		CLIPTextConfig(
						    bos_token_id=0    ,       eos_token_id=2    ,       hidden_size=32    ,       intermediate_size=37    ,       layer_norm_eps=1e-05    ,       num_attention_heads=4    ,       num_hidden_layers=5    ,       pad_token_id=1    ,       vocab_size=1000    ,       )
						return CLIPTextModel(A__			)






					def   __A			( self			):
						A__							:    int        =		self.dummy_uncond_unet
						A__							:    Optional[int]        =		DDIMScheduler()
						A__							:    List[Any]        =		self.dummy_vq_model

						A__							:    Optional[int]        =		LDMPipeline(unet=A__    ,       vqvae=A__    ,       scheduler=A__			)
						ldm.to(A__			)
						ldm.set_progress_bar_config(disable=A__			)

						A__							:    Tuple        =		torch.manual_seed(0			)
						A__							:    List[Any]        =		ldm(generator=A__    ,       num_inference_steps=2    ,       output_type="""numpy"""			).images

						A__							:    Optional[int]        =		torch.manual_seed(0			)
						A__							:    List[Any]        =		ldm(generator=A__    ,       num_inference_steps=2    ,       output_type="""numpy"""    ,       return_dict=A__			)[0]

						A__							:    str        =		image[0, -3:, -3:, -1]
						A__							:    Tuple        =		image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						A__							:    Dict        =		np.array([0.8_5_1_2, 0.8_1_8, 0.6_4_1_1, 0.6_8_0_8, 0.4_4_6_5, 0.5_6_1_8, 0.4_6, 0.6_2_3_1, 0.5_1_7_2]			)
						A__							:    List[Any]        =		1e-2 if torch_device != """mps""" else 3e-2

						assert np.abs(image_slice.flatten() - expected_slice			).max() < tolerance
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < tolerance



@slow
@require_torch
class     _a						(unittest.TestCase				):


					'''simple docstring'''






					def   __A			( self			):
						A__							:    Optional[Any]        =		LDMPipeline.from_pretrained("""CompVis/ldm-celebahq-256"""			)
						ldm.to(A__			)
						ldm.set_progress_bar_config(disable=A__			)

						A__							:    Tuple        =		torch.manual_seed(0			)
						A__							:    List[Any]        =		ldm(generator=A__    ,       num_inference_steps=5    ,       output_type="""numpy"""			).images

						A__							:    int        =		image[0, -3:, -3:, -1]

						assert image.shape == (1, 256, 256, 3)
						A__							:    Any        =		np.array([0.4_3_9_9, 0.4_4_9_7_5, 0.4_6_8_2_5, 0.4_7_4, 0.4_3_5_9, 0.4_5_8_1, 0.4_5_0_9_5, 0.4_3_4_1, 0.4_4_4_7]			)
						A__							:    int        =		1e-2 if torch_device != """mps""" else 3e-2

						assert np.abs(image_slice.flatten() - expected_slice			).max() < tolerance



