






'''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 		SCREAMING_SNAKE_CASE__   (     lowercase_		,		unittest.TestCase    ):
		_UpperCAmelCase         =PaintByExamplePipeline
		_UpperCAmelCase         =IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
		_UpperCAmelCase         =IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		_UpperCAmelCase         =frozenset([]    )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess



		def _lowerCAmelCase     (    self:    Optional[Any])  ->Union[str, Any]:






								'''simple docstring'''


								torch.manual_seed(0)
								a_				     =   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				,   )
								a_				     =   PNDMScheduler(skip_prk_steps=a)
								torch.manual_seed(0)
								a_				     =   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)
								a_				     =   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				,   )
								a_				     =   PaintByExampleImageEncoder(a				,   proj_size=32)
								a_				     =   CLIPImageProcessor(crop_size=32				,   size=32)

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



		def _lowerCAmelCase     (    self:    Any				,   a:    Dict)  ->Optional[Any]:






								'''simple docstring'''


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



		def _lowerCAmelCase     (    self:    str				,   a:    List[str]="cpu"				,   a:    int=0)  ->Any:






								'''simple docstring'''


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

								if str(a).startswith("mps"):
														a_				     =   torch.manual_seed(a)
								else:
														a_				     =   torch.Generator(device=a).manual_seed(a)
								a_				     =   {
								    "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 _lowerCAmelCase     (    self:    Dict)  ->int:






								'''simple docstring'''


								a_				     =   self.get_dummy_components()

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

								a_				     =   self.get_dummy_inputs()
								a_				     =   pipe(**a)
								a_				     =   output.images

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

								assert image.shape == (1, 64, 64, 3)
								a_				     =   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 _lowerCAmelCase     (    self:    Dict)  ->Any:






								'''simple docstring'''


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

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

								a_				     =   pipe(image=a				,   mask_image=mask_image[:, 0]				,   **a)
								a_				     =   output.images

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

								a_				     =   Image.fromarray(np.uinta(a)).convert("RGB")
								a_				     =   Image.fromarray(np.uinta(a)).convert("RGB")

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

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




		def _lowerCAmelCase     (    self:    Optional[int])  ->Any:






								'''simple docstring'''


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





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



		def _lowerCAmelCase     (    self:    str)  ->List[str]:






								'''simple docstring'''


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




		def _lowerCAmelCase     (    self:    List[str])  ->Any:






								'''simple docstring'''


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

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

								a_				     =   torch.manual_seed(3_21)
								a_				     =   pipe(
								    image=a				,   mask_image=a				,   example_image=a				,   generator=a				,   guidance_scale=5.0				,   num_inference_steps=50				,   output_type="np"				,   )

								a_				     =   output.images

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

								assert image.shape == (1, 5_12, 5_12, 3)
								a_				     =   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











'''simple docstring'''





from __future__ import annotations





class 		SCREAMING_SNAKE_CASE__   :



		def __init__(    self:    List[str]				,   a:    int = 0)  ->None:






								'''simple docstring'''


								a_				     =   value
								a_				     =   None
								a_				     =   None





def 			__UpperCAmelCase				(lowercase__	,lowercase__				)		->				Node | None:

						'''simple docstring'''


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

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



def 			__UpperCAmelCase				(lowercase__				)		->				None:

						'''simple docstring'''


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


if __name__ == "__main__":
							a_      = Node(1)
							a_      = Node(2)
							a_      = Node(3)
							a_      = Node(4)

							a_      = Node(2)
							a_      = Node(4)
							a_      = Node(6)
							a_      = Node(9)
							a_      = Node(5)

							print('Tree1 is: ')
							print_preorder(treea)
							print('Tree2 is: ')
							print_preorder(treea)
							a_      = merge_two_binary_trees(treea, treea)
							print('Merged Tree is: ')
							print_preorder(merged_tree)



