



"""simple docstring"""






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'''





	SCREAMING_SNAKE_CASE					   =			4_2
	SCREAMING_SNAKE_CASE					   =			None
	SCREAMING_SNAKE_CASE					   =			None






def     _lowercase       (						)		->     Node | None:
			__lowerCAmelCase   :						Tuple				= Node(1			)
			__lowerCAmelCase   :						Dict				= Node(2			)
			__lowerCAmelCase   :						str				= Node(3			)
			__lowerCAmelCase   :						str				= Node(4			)
			__lowerCAmelCase   :						Tuple				= Node(5			)
			return tree


def     _lowercase       (						__snake_case			)		->     list[int]:
			return [root.data, *preorder(root.left			), *preorder(root.right			)] if root else []


def     _lowercase       (						__snake_case			)		->     list[int]:
			return postorder(root.left			) + postorder(root.right			) + [root.data] if root else []


def     _lowercase       (						__snake_case			)		->     list[int]:
			return [*inorder(root.left			), root.data, *inorder(root.right			)] if root else []


def     _lowercase       (						__snake_case			)		->     int:
			return (max(height(root.left			)      ,height(root.right			)			) + 1) if root else 0


def     _lowercase       (						__snake_case			)		->     Sequence[Node | None]:
			__lowerCAmelCase   :						list[Any]				= []

			if root is None:
						return output

			__lowerCAmelCase   :						Optional[int]				= deque([root]			)

			while process_queue:
						__lowerCAmelCase   :						Union[str, Any]				= 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     _lowercase       (						__snake_case      ,__snake_case			)		->     Sequence[Node | None]:
			__lowerCAmelCase   :						list[Any]				= []

			def populate_output(__snake_case      ,__snake_case			) -> 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(__snake_case      ,__snake_case			)
			return output


def     _lowercase       (						__snake_case      ,__snake_case			)		->     Sequence[Node | None]:
			__lowerCAmelCase   :						list[Any]				= []

			def populate_output(__snake_case      ,__snake_case			) -> 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(__snake_case      ,__snake_case			)
			return output


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

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

			__lowerCAmelCase   :						Optional[Any]				= 0
			__lowerCAmelCase   :						str				= height(__snake_case			)

			for h in range(1      ,height_tree + 1			):
						if not flag:
									output.append(get_nodes_from_left_to_right(__snake_case      ,__snake_case			)			)
									__lowerCAmelCase   :						Dict				= 1
						else:
									output.append(get_nodes_from_right_to_left(__snake_case      ,__snake_case			)			)
									__lowerCAmelCase   :						Optional[Any]				= 0

			return output


def     _lowercase       (						)		->     None:  # Main function for testing.
			__lowerCAmelCase   :						Tuple				= make_tree()
			print(F"""In-order Traversal: {inorder(__snake_case			)}"""			)
			print(F"""Pre-order Traversal: {preorder(__snake_case			)}"""			)
			print(F"""Post-order Traversal: {postorder(__snake_case			)}"""      ,"\n"			)

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

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

			print("Level-wise order Traversal: "			)

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

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


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()




"""simple docstring"""






import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch



@require_torch
class     A__     :




	'''simple docstring'''





	@property
	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[int])       -> Optional[Any]:

				"""simple docstring"""

				return self.get_dummy_input()

	@property
	def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any])       -> str:

				"""simple docstring"""

				if self.block_type == "down":
							return (4, 32, 16, 16)
				elif self.block_type == "mid":
							return (4, 32, 32, 32)
				elif self.block_type == "up":
							return (4, 32, 64, 64)

				raise ValueError(F"""'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'.""")

	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict ,    _SCREAMING_SNAKE_CASE:	Union[str, Any]=True ,    _SCREAMING_SNAKE_CASE:	Tuple=False ,    _SCREAMING_SNAKE_CASE:	Union[str, Any]=False ,    _SCREAMING_SNAKE_CASE:	Dict=False ,    )       -> Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase   :						Union[str, Any]				= 4
				__lowerCAmelCase   :						List[str]				= 32
				__lowerCAmelCase   :						str				= (32, 32)

				__lowerCAmelCase   :						int				= torch.manual_seed(0)
				__lowerCAmelCase   :						Dict				= torch.device(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						str				= (batch_size, num_channels) + sizes
				__lowerCAmelCase   :						Dict				= randn_tensor(_SCREAMING_SNAKE_CASE ,    generator=_SCREAMING_SNAKE_CASE ,    device=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						str				= {"hidden_states": hidden_states}

				if include_temb:
							__lowerCAmelCase   :						List[str]				= 128
							__lowerCAmelCase   :						Dict				= randn_tensor((batch_size, temb_channels) ,    generator=_SCREAMING_SNAKE_CASE ,    device=_SCREAMING_SNAKE_CASE)

				if include_res_hidden_states_tuple:
							__lowerCAmelCase   :						Dict				= torch.manual_seed(1)
							__lowerCAmelCase   :						int				= (randn_tensor(_SCREAMING_SNAKE_CASE ,    generator=_SCREAMING_SNAKE_CASE ,    device=_SCREAMING_SNAKE_CASE),)

				if include_encoder_hidden_states:
							__lowerCAmelCase   :						int				= floats_tensor((batch_size, 32, 32)).to(_SCREAMING_SNAKE_CASE)

				if include_skip_sample:
							__lowerCAmelCase   :						Optional[Any]				= randn_tensor(((batch_size, 3) + sizes) ,    generator=_SCREAMING_SNAKE_CASE ,    device=_SCREAMING_SNAKE_CASE)

				return dummy_input

	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple)       -> Any:

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[int]				= {
				    "in_channels": 32,
				    "out_channels": 32,
				    "temb_channels": 128,
				}
				if self.block_type == "up":
							__lowerCAmelCase   :						Tuple				= 32

				if self.block_type == "mid":
							init_dict.pop("out_channels")

				__lowerCAmelCase   :						str				= self.dummy_input
				return init_dict, inputs_dict

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str] ,    _SCREAMING_SNAKE_CASE:	List[str])       -> Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase		,			__lowerCAmelCase   :						List[Any]				= self.prepare_init_args_and_inputs_for_common()
				__lowerCAmelCase   :						Dict				= self.block_class(**_SCREAMING_SNAKE_CASE)
				unet_block.to(_SCREAMING_SNAKE_CASE)
				unet_block.eval()

				with torch.no_grad():
							__lowerCAmelCase   :						int				= unet_block(**_SCREAMING_SNAKE_CASE)

				if isinstance(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE):
							__lowerCAmelCase   :						List[Any]				= output[0]

				self.assertEqual(output.shape ,    self.output_shape)

				__lowerCAmelCase   :						str				= output[0, -1, -3:, -3:]
				__lowerCAmelCase   :						List[Any]				= torch.tensor(_SCREAMING_SNAKE_CASE).to(_SCREAMING_SNAKE_CASE)
				assert torch_all_close(output_slice.flatten() ,    _SCREAMING_SNAKE_CASE ,    atol=5e-3)







	@unittest.skipIf(torch_device == "mps" ,    "Training is not supported in mps")
	def 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> Tuple:

				"""simple docstring"""

				__lowerCAmelCase		,			__lowerCAmelCase   :						Optional[Any]				= self.prepare_init_args_and_inputs_for_common()
				__lowerCAmelCase   :						str				= self.block_class(**_SCREAMING_SNAKE_CASE)
				model.to(_SCREAMING_SNAKE_CASE)
				model.train()
				__lowerCAmelCase   :						Union[str, Any]				= model(**_SCREAMING_SNAKE_CASE)

				if isinstance(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE):
							__lowerCAmelCase   :						Union[str, Any]				= output[0]

				__lowerCAmelCase   :						List[Any]				= torch.device(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						int				= randn_tensor(output.shape ,    device=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						int				= torch.nn.functional.mse_loss(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)
				loss.backward()