





"""simple docstring"""


from __future__ import annotations



class        lowercase_			:

							def __init__(     self				,   __SCREAMING_SNAKE_CASE = 0       ):






								'''simple docstring'''

								snake_case__      							=			value
								snake_case__      							=			None
								snake_case__      							=			None





def 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase		)      ->					Node | None:

	'''simple docstring'''






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

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





def 							a__     (					_UpperCAmelCase		)      ->					None:

	'''simple docstring'''






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


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

			a__   :       int   						=			Node(2)
			a__   :       int   						=			Node(4)
			a__   :       Dict   						=			Node(6)
			a__   :       List[str]   						=			Node(9)
			a__   :       List[str]   						=			Node(5)

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








"""simple docstring"""


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class        lowercase_			(       __lowerCAmelCase			,unittest.TestCase							):
       snake_case__			:							Tuple										=       PaintByExamplePipeline
       snake_case__			:							List[str]										=       IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
       snake_case__			:							List[str]										=       IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       snake_case__			:							List[str]										=       frozenset([]							)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        torch.manual_seed(0       )
        snake_case__      							=			UNetaDConditionModel(
            block_out_channels=(32, 64)				,   layers_per_block=2				,   sample_size=32				,   in_channels=9				,   out_channels=4				,   down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")				,   up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")				,   cross_attention_dim=32				,   )
        snake_case__      							=			PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE       )
        torch.manual_seed(0       )
        snake_case__      							=			AutoencoderKL(
            block_out_channels=[32, 64]				,   in_channels=3				,   out_channels=3				,   down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]				,   up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]				,   latent_channels=4				,   )
        torch.manual_seed(0       )
        snake_case__      							=			CLIPVisionConfig(
            hidden_size=32				,   projection_dim=32				,   intermediate_size=37				,   layer_norm_eps=1e-05				,   num_attention_heads=4				,   num_hidden_layers=5				,   image_size=32				,   patch_size=4				,   )
        snake_case__      							=			PaintByExampleImageEncoder(__SCREAMING_SNAKE_CASE				,   proj_size=32       )
        snake_case__      							=			CLIPImageProcessor(crop_size=32				,   size=32       )

        snake_case__      							=			{
            "unet": unet,
            "scheduler": scheduler,
            "vae": vae,
            "image_encoder": image_encoder,
            "safety_checker": None,
            "feature_extractor": feature_extractor,
        }
        return components

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			np.array(image.convert("RGB"       )       )
        snake_case__      							=			image[None].transpose(0				,   3				,   1				,   2       )
        snake_case__      							=			torch.from_numpy(__SCREAMING_SNAKE_CASE       ).to(dtype=torch.floataa       ) / 127.5 - 1.0
        return image

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE="cpu"				,   __SCREAMING_SNAKE_CASE=0       ):






        '''simple docstring'''

        snake_case__      							=			floats_tensor((1, 3, 32, 32)				,   rng=random.Random(__SCREAMING_SNAKE_CASE       )       ).to(__SCREAMING_SNAKE_CASE       )
        snake_case__      							=			image.cpu().permute(0				,   2				,   3				,   1       )[0]
        snake_case__      							=			Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE       )       ).convert("RGB"       ).resize((64, 64)       )
        snake_case__      							=			Image.fromarray(np.uinta(image + 4       )       ).convert("RGB"       ).resize((64, 64)       )
        snake_case__      							=			Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE       )       ).convert("RGB"       ).resize((32, 32)       )

        if str(__SCREAMING_SNAKE_CASE       ).startswith("mps"       ):
         snake_case__      							=			torch.manual_seed(__SCREAMING_SNAKE_CASE       )
        else:
         snake_case__      							=			torch.Generator(device=__SCREAMING_SNAKE_CASE       ).manual_seed(__SCREAMING_SNAKE_CASE       )
        snake_case__      							=			{
            "example_image": example_image,
            "image": init_image,
            "mask_image": mask_image,
            "generator": generator,
            "num_inference_steps": 2,
            "guidance_scale": 6.0,
            "output_type": "numpy",
        }
        return inputs

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.get_dummy_components()

        # make sure here that pndm scheduler skips prk
        snake_case__      							=			PaintByExamplePipeline(**__SCREAMING_SNAKE_CASE       )
        snake_case__      							=			pipe.to("cpu"       )
        pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			self.get_dummy_inputs()
        snake_case__      							=			pipe(**__SCREAMING_SNAKE_CASE       )
        snake_case__      							=			output.images

        snake_case__      							=			image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        snake_case__      							=			np.array([0.4686, 0.5687, 0.4007, 0.5218, 0.5741, 0.4482, 0.4940, 0.4629, 0.4503]       )

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

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			"cpu"
        snake_case__      							=			self.get_dummy_inputs()
        inputs.pop("mask_image"       )
        snake_case__      							=			self.convert_to_pt(inputs.pop("image"       )       )
        snake_case__      							=			image.clamp(0				,   1       ) / 2

        # make sure here that pndm scheduler skips prk
        snake_case__      							=			PaintByExamplePipeline(**self.get_dummy_components()       )
        snake_case__      							=			pipe.to(__SCREAMING_SNAKE_CASE       )
        pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			pipe(image=__SCREAMING_SNAKE_CASE				,   mask_image=mask_image[:, 0]				,   **__SCREAMING_SNAKE_CASE       )
        snake_case__      							=			output.images

        snake_case__      							=			image.cpu().permute(0				,   2				,   3				,   1       )[0]
        snake_case__      							=			mask_image.cpu().permute(0				,   2				,   3				,   1       )[0]

        snake_case__      							=			Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE       )       ).convert("RGB"       )
        snake_case__      							=			Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE       )       ).convert("RGB"       )

        snake_case__      							=			pipe(**self.get_dummy_inputs()       )
        snake_case__      							=			output.images

        assert out_a.shape == (1, 64, 64, 3)
        assert np.abs(out_a.flatten() - out_a.flatten()       ).max() < 5e-2

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        super().test_inference_batch_single_identical(expected_max_diff=3e-3       )



@slow
@require_torch_gpu
class        lowercase_			(       unittest.TestCase							):

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

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

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/paint_by_example/dog_in_bucket.png"       )
        snake_case__      							=			load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/paint_by_example/mask.png"       )
        snake_case__      							=			load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/paint_by_example/panda.jpg"       )

        snake_case__      							=			PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example"       )
        snake_case__      							=			pipe.to(__SCREAMING_SNAKE_CASE       )
        pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			torch.manual_seed(321       )
        snake_case__      							=			pipe(
            image=__SCREAMING_SNAKE_CASE				,   mask_image=__SCREAMING_SNAKE_CASE				,   example_image=__SCREAMING_SNAKE_CASE				,   generator=__SCREAMING_SNAKE_CASE				,   guidance_scale=5.0				,   num_inference_steps=50				,   output_type="np"				,   )

        snake_case__      							=			output.images

        snake_case__      							=			image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        snake_case__      							=			np.array([0.4834, 0.4811, 0.4874, 0.5122, 0.5081, 0.5144, 0.5291, 0.5290, 0.5374]       )

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

