
from __future__ import annotations


class 							lowercase   :



		def __init__(					self							, A_ = 0			)    ->   None:
				"""simple docstring"""
				UpperCamelCase            =	value
				UpperCamelCase            =	None
				UpperCamelCase            =	None






def  A			(   lowercase       ,			lowercase					)  ->    Node | None:
		'''simple docstring'''

		if treea is None:
				return treea
		if treea is None:
				return treea

		UpperCamelCase            =	treea.value + treea.value
		UpperCamelCase            =	merge_two_binary_trees(treea.left       ,			treea.left					)
		UpperCamelCase            =	merge_two_binary_trees(treea.right       ,			treea.right					)
		return treea


def  A			(   lowercase					)  ->    None:
		'''simple docstring'''

		if root:
				print(root.value					)
				print_preorder(root.left					)
				print_preorder(root.right					)


if __name__ == "__main__":
					_UpperCAmelCase   :					Tuple    =			Node(1)
					_UpperCAmelCase   :					Optional[Any]    =			Node(2)
					_UpperCAmelCase   :					Dict    =			Node(3)
					_UpperCAmelCase   :					str    =			Node(4)

					_UpperCAmelCase   :					Any    =			Node(2)
					_UpperCAmelCase   :					int    =			Node(4)
					_UpperCAmelCase   :					str    =			Node(6)
					_UpperCAmelCase   :					List[str]    =			Node(9)
					_UpperCAmelCase   :					Tuple    =			Node(5)

					print("Tree1 is: ")
					print_preorder(treea)
					print("Tree2 is: ")
					print_preorder(treea)
					_UpperCAmelCase   :					Dict    =			merge_two_binary_trees(treea, treea)
					print("Merged Tree is: ")
					print_preorder(merged_tree)


from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, is_invisible_watermark_available, is_torch_available, is_transformers_available


@dataclass
class 							lowercase   (						_SCREAMING_SNAKE_CASE  ):
		__lowercase			:			Union[List[PIL.Image.Image], np.ndarray]


if is_transformers_available() and is_torch_available() and is_invisible_watermark_available():
					from .pipeline_stable_diffusion_xl import StableDiffusionXLPipeline
					from .pipeline_stable_diffusion_xl_imgaimg import StableDiffusionXLImgaImgPipeline
