



"""simple docstring"""







from __future__ import annotations





class   UpperCamelCase	:




	"""simple docstring"""






	def __init__(     self				,UpperCAmelCase_ = 0   ):
			_lowercase				: List[Any]     =   value
			_lowercase				: Node | None     =   None
			_lowercase				: Node | None     =   None







def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase      ):
		if treea is None:
				return treea
		if treea is None:
				return treea

		_lowercase				: List[str]     =   treea.value + treea.value
		_lowercase				: Optional[int]     =   merge_two_binary_trees(treea.left   ,				treea.left      )
		_lowercase				: List[str]     =   merge_two_binary_trees(treea.right   ,				treea.right      )
		return treea


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase      ):
		if root:
				print(root.value      )
				print_preorder(root.left      )
				print_preorder(root.right      )


if __name__ == "__main__":
		UpperCAmelCase:  Optional[int]			     =				Node(1)
		UpperCAmelCase:  Optional[int]			     =				Node(2)
		UpperCAmelCase:  Any			     =				Node(3)
		UpperCAmelCase:  Union[str, Any]			     =				Node(4)

		UpperCAmelCase:  Union[str, Any]			     =				Node(2)
		UpperCAmelCase:  List[Any]			     =				Node(4)
		UpperCAmelCase:  Dict			     =				Node(6)
		UpperCAmelCase:  Any			     =				Node(9)
		UpperCAmelCase:  Union[str, Any]			     =				Node(5)

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







import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


a					:				Optional[int]       =      False

@nightly
@require_torch_gpu
class   lowerCamelCase						(	unittest.TestCase  ):

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Optional[int]							=   VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''       )
        # remove text_unet
        pipe.remove_unused_weights()
        pipe.to(a__       )
        pipe.set_progress_bar_config(disable=a__       )

        snake_case :			Union[str, Any]							=   load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''       )

        snake_case :			str							=   torch.manual_seed(0       )
        snake_case :			Tuple							=   pipe(
            prompt='''first prompt'''						,    image=a__						,    text_to_image_strength=0.75						,    generator=a__						,    guidance_scale=7.5						,    num_inference_steps=2						,    output_type='''numpy'''						,    ).images

        with tempfile.TemporaryDirectory() as tmpdirname:
         pipe.save_pretrained(a__       )
         snake_case :			str							=   VersatileDiffusionDualGuidedPipeline.from_pretrained(a__       )

        pipe.to(a__       )
        pipe.set_progress_bar_config(disable=a__       )

        snake_case :			Optional[Any]							=   generator.manual_seed(0       )
        snake_case :			List[str]							=   pipe(
            prompt='''first prompt'''						,    image=a__						,    text_to_image_strength=0.75						,    generator=a__						,    guidance_scale=7.5						,    num_inference_steps=2						,    output_type='''numpy'''						,    ).images

        assert np.abs(image - new_image       ).sum() < 1e-5, "Models don't have the same forward pass"



       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Tuple							=   VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''       )
        pipe.remove_unused_weights()
        pipe.to(a__       )
        pipe.set_progress_bar_config(disable=a__       )

        snake_case :			int							=   '''cyberpunk 2077'''
        snake_case :			Dict							=   load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''       )
        snake_case :			Optional[int]							=   torch.manual_seed(0       )
        snake_case :			Union[str, Any]							=   pipe(
            prompt=a__						,    image=a__						,    text_to_image_strength=0.75						,    generator=a__						,    guidance_scale=7.5						,    num_inference_steps=50						,    output_type='''numpy'''						,    ).images

        snake_case :			str							=   image[0, 2_53:2_56, 2_53:2_56, -1]

        assert image.shape == (1, 5_12, 5_12, 3)
        snake_case :			int							=   np.array([0.0787, 0.0849, 0.0826, 0.0812, 0.0807, 0.0795, 0.0818, 0.0798, 0.0779]       )

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






