





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        __SCREAMING_SNAKE_CASE					:






							@property
							def 				_lowerCamelCase		(						self  ):
														return self.get_dummy_input()






							@property
							def 				_lowerCamelCase		(						self  ):
														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 				_lowerCamelCase		(						self  , __lowerCAmelCase=True  , __lowerCAmelCase=False  , __lowerCAmelCase=False  , __lowerCAmelCase=False  , ):
														UpperCamelCase__				     =			4
														UpperCamelCase__				     =			32
														UpperCamelCase__				     =			(32, 32)

														UpperCamelCase__				     =			torch.manual_seed(0  )
														UpperCamelCase__				     =			torch.device(UpperCamelCase__  )
														UpperCamelCase__				     =			(batch_size, num_channels) + sizes
														UpperCamelCase__				     =			randn_tensor(UpperCamelCase__  , generator=UpperCamelCase__  , device=UpperCamelCase__  )
														UpperCamelCase__				     =			{'''hidden_states''': hidden_states}

														if include_temb:
																					UpperCamelCase__				     =			128
																					UpperCamelCase__				     =			randn_tensor((batch_size, temb_channels)  , generator=UpperCamelCase__  , device=UpperCamelCase__  )

														if include_res_hidden_states_tuple:
																					UpperCamelCase__				     =			torch.manual_seed(1  )
																					UpperCamelCase__				     =			(randn_tensor(UpperCamelCase__  , generator=UpperCamelCase__  , device=UpperCamelCase__  ),)

														if include_encoder_hidden_states:
																					UpperCamelCase__				     =			floats_tensor((batch_size, 32, 32)  ).to(UpperCamelCase__  )

														if include_skip_sample:
																					UpperCamelCase__				     =			randn_tensor(((batch_size, 3) + sizes)  , generator=UpperCamelCase__  , device=UpperCamelCase__  )

														return dummy_input






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			{
														    '''in_channels''': 32,
														    '''out_channels''': 32,
														    '''temb_channels''': 128,
														}
														if self.block_type == "up":
																					UpperCamelCase__				     =			32

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

														UpperCamelCase__				     =			self.dummy_input
														return init_dict, inputs_dict






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			self.prepare_init_args_and_inputs_for_common()
														UpperCamelCase__				     =			self.block_class(**UpperCamelCase__  )
														unet_block.to(UpperCamelCase__  )
														unet_block.eval()

														with torch.no_grad():
																					UpperCamelCase__				     =			unet_block(**UpperCamelCase__  )

														if isinstance(UpperCamelCase__  , UpperCamelCase__  ):
																					UpperCamelCase__				     =			output[0]

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

														UpperCamelCase__				     =			output[0, -1, -3:, -3:]
														UpperCamelCase__				     =			torch.tensor(UpperCamelCase__  ).to(UpperCamelCase__  )
														assert torch_all_close(output_slice.flatten()  , UpperCamelCase__  , atol=5E-3  )







							@unittest.skipIf(torch_device == """mps"""  , """Training is not supported in mps"""  )
							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			self.prepare_init_args_and_inputs_for_common()
														UpperCamelCase__				     =			self.block_class(**UpperCamelCase__  )
														model.to(UpperCamelCase__  )
														model.train()
														UpperCamelCase__				     =			model(**UpperCamelCase__  )

														if isinstance(UpperCamelCase__  , UpperCamelCase__  ):
																					UpperCamelCase__				     =			output[0]

														UpperCamelCase__				     =			torch.device(UpperCamelCase__  )
														UpperCamelCase__				     =			randn_tensor(output.shape  , device=UpperCamelCase__  )
														UpperCamelCase__				     =			torch.nn.functional.mse_loss(UpperCamelCase__  , UpperCamelCase__  )
														loss.backward()











from __future__ import annotations

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

@dataclass
class        __SCREAMING_SNAKE_CASE					:
							snake_case     :      int
							snake_case     :      Node | None						     =       None
							snake_case     :      Node | None						     =       None






def     _UpperCamelCase     ():






							"""simple docstring"""





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





def     _UpperCamelCase     (a__   :Node | None       ):






							"""simple docstring"""





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





def     _UpperCamelCase     (a__   :Node | None       ):






							"""simple docstring"""





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





def     _UpperCamelCase     (a__   :Node | None       ):






							"""simple docstring"""





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





def     _UpperCamelCase     (a__   :Node | None       ):






							"""simple docstring"""





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





def     _UpperCamelCase     (a__   :Node | None       ):






							"""simple docstring"""





							UpperCamelCase__				     =			[]

							if root is None:
														return output

							UpperCamelCase__				     =			deque([root]       )

							while process_queue:
														UpperCamelCase__				     =			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     (a__   :Node | None   ,			a__   :int       ):






							"""simple docstring"""





							UpperCamelCase__				     =			[]

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





def     _UpperCamelCase     (a__   :Node | None   ,			a__   :int       ):






							"""simple docstring"""





							UpperCamelCase__				     =			[]

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





def     _UpperCamelCase     (a__   :Node | None       ):






							"""simple docstring"""





							if root is None:
														return []

							UpperCamelCase__				     =			[]

							UpperCamelCase__				     =			0
							UpperCamelCase__				     =			height(a__       )

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

							return output





def     _UpperCamelCase     ():  # Main function for testing.






							"""simple docstring"""





							UpperCamelCase__				     =			make_tree()
							print(f"""In-order Traversal: {inorder(a__       )}"""       )
							print(f"""Pre-order Traversal: {preorder(a__       )}"""       )
							print(f"""Post-order Traversal: {postorder(a__       )}"""   ,			"""\n"""       )

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

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

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

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

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


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()




