def 							_lowerCamelCase(				__snake_case	)  ->   list:
		if len(__snake_case	) <= 1:
				return data
		else:
				return [
				    *quick_sort([e for e in data[1:] if e <= data[0]]	),
				    data[0],
				    *quick_sort([e for e in data[1:] if e > data[0]]	),
				]


if __name__ == "__main__":
		import doctest

		doctest.testmod()






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 					UpperCamelCase	(     snake_case__			,      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 							UpperCamelCase_						( self :					Tuple						):


							"""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=_lowerCAmelCase						)
							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(_lowerCAmelCase      ,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 							UpperCamelCase_						( self :					Union[str, Any]      ,_lowerCAmelCase :					Any						):


							"""simple docstring"""




							__snake_case		       =     np.array(image.convert("RGB"						)						)
							__snake_case		       =     image[None].transpose(0      ,3      ,1      ,2						)
							__snake_case		       =     torch.from_numpy(_lowerCAmelCase						).to(dtype=torch.floataa						) / 1_2_7.5 - 1.0
							return image




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


							"""simple docstring"""




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

							if str(_lowerCAmelCase						).startswith("mps"						):
									__snake_case		       =     torch.manual_seed(_lowerCAmelCase						)
							else:
									__snake_case		       =     torch.Generator(device=_lowerCAmelCase						).manual_seed(_lowerCAmelCase						)
							__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 							UpperCamelCase_						( self :					int						):


							"""simple docstring"""




							__snake_case		       =     self.get_dummy_components()

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

							__snake_case		       =     self.get_dummy_inputs()
							__snake_case		       =     pipe(**_lowerCAmelCase						)
							__snake_case		       =     output.images

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

							assert image.shape == (1, 64, 64, 3)
							__snake_case		       =     np.array([0.4_6_8_6, 0.5_6_8_7, 0.4_0_0_7, 0.5_2_1_8, 0.5_7_4_1, 0.4_4_8_2, 0.4_9_4_0, 0.4_6_2_9, 0.4_5_0_3]						)

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




					def 							UpperCamelCase_						( self :					int						):


							"""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(_lowerCAmelCase						)
							pipe.set_progress_bar_config(disable=_lowerCAmelCase						)

							__snake_case		       =     pipe(image=_lowerCAmelCase      ,mask_image=mask_image[:, 0]      ,**_lowerCAmelCase						)
							__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(_lowerCAmelCase						)						).convert("RGB"						)
							__snake_case		       =     Image.fromarray(np.uinta(_lowerCAmelCase						)						).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 							UpperCamelCase_						( self :					Union[str, Any]						):


							"""simple docstring"""




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




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




					def 							UpperCamelCase_						( self :					Any						):


							"""simple docstring"""




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







					def 							UpperCamelCase_						( self :					Dict						):


							"""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(_lowerCAmelCase						)
							pipe.set_progress_bar_config(disable=_lowerCAmelCase						)

							__snake_case		       =     torch.manual_seed(321						)
							__snake_case		       =     pipe(
							    image=_lowerCAmelCase      ,mask_image=_lowerCAmelCase      ,example_image=_lowerCAmelCase      ,generator=_lowerCAmelCase      ,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([0.4_8_3_4, 0.4_8_1_1, 0.4_8_7_4, 0.5_1_2_2, 0.5_0_8_1, 0.5_1_4_4, 0.5_2_9_1, 0.5_2_9_0, 0.5_3_7_4]						)

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





