

"""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 			__UpperCamelCase (   _A     ,		unittest.TestCase						):
		SCREAMING_SNAKE_CASE								=    PaintByExamplePipeline
		SCREAMING_SNAKE_CASE								=    IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
		SCREAMING_SNAKE_CASE								=    IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		SCREAMING_SNAKE_CASE								=    frozenset([]						)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess


		def 					SCREAMING_SNAKE_CASE__   (self :		Union[str, Any]):
							torch.manual_seed(0)
							A			=					UNetaDConditionModel(
							    block_out_channels=(3_2, 6_4)     ,     layers_per_block=2     ,     sample_size=3_2     ,     in_channels=9     ,     out_channels=4     ,     down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")     ,     up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")     ,     cross_attention_dim=3_2     ,     )
							A			=					PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE)
							torch.manual_seed(0)
							A			=					AutoencoderKL(
							    block_out_channels=[3_2, 6_4]     ,     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=3_2     ,     projection_dim=3_2     ,     intermediate_size=3_7     ,     layer_norm_eps=1E-05     ,     num_attention_heads=4     ,     num_hidden_layers=5     ,     image_size=3_2     ,     patch_size=4     ,     )
							A			=					PaintByExampleImageEncoder(__SCREAMING_SNAKE_CASE     ,     proj_size=3_2)
							A			=					CLIPImageProcessor(crop_size=3_2     ,     size=3_2)

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


		def 					SCREAMING_SNAKE_CASE__   (self :		List[str]     ,     __SCREAMING_SNAKE_CASE :		Any):
							A			=					np.array(image.convert("RGB"))
							A			=					image[None].transpose(0     ,     3     ,     1     ,     2)
							A			=					torch.from_numpy(__SCREAMING_SNAKE_CASE).to(dtype=torch.floataa) / 1_2_7.5 - 1.0
							return image


		def 					SCREAMING_SNAKE_CASE__   (self :		Union[str, Any]     ,     __SCREAMING_SNAKE_CASE :		Tuple="cpu"     ,     __SCREAMING_SNAKE_CASE :		List[Any]=0):
							# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
							A			=					floats_tensor((1, 3, 3_2, 3_2)     ,     rng=random.Random(__SCREAMING_SNAKE_CASE)).to(__SCREAMING_SNAKE_CASE)
							A			=					image.cpu().permute(0     ,     2     ,     3     ,     1)[0]
							A			=					Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE)).convert("RGB").resize((6_4, 6_4))
							A			=					Image.fromarray(np.uinta(image + 4)).convert("RGB").resize((6_4, 6_4))
							A			=					Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE)).convert("RGB").resize((3_2, 3_2))

							if str(__SCREAMING_SNAKE_CASE).startswith("mps"):
												A			=					torch.manual_seed(__SCREAMING_SNAKE_CASE)
							else:
												A			=					torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(__SCREAMING_SNAKE_CASE)
							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 					SCREAMING_SNAKE_CASE__   (self :		Dict):
							A			=					self.get_dummy_components()

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

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

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

							assert image.shape == (1, 6_4, 6_4, 3)
							A			=					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 					SCREAMING_SNAKE_CASE__   (self :		int):
							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(__SCREAMING_SNAKE_CASE)
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

							A			=					pipe(image=__SCREAMING_SNAKE_CASE     ,     mask_image=mask_image[:, 0]     ,     **__SCREAMING_SNAKE_CASE)
							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(__SCREAMING_SNAKE_CASE)).convert("RGB")
							A			=					Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE)).convert("RGB")

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

							assert out_a.shape == (1, 6_4, 6_4, 3)
							assert np.abs(out_a.flatten() - out_a.flatten()).max() < 5E-2


		def 					SCREAMING_SNAKE_CASE__   (self :		Tuple):
							super().test_inference_batch_single_identical(expected_max_diff=3E-3)





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


		def 					SCREAMING_SNAKE_CASE__   (self :		List[Any]):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()


		def 					SCREAMING_SNAKE_CASE__   (self :		str):
							# make sure here that pndm scheduler skips prk
							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(__SCREAMING_SNAKE_CASE)
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

							A			=					torch.manual_seed(3_2_1)
							A			=					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=5_0     ,     output_type="np"     ,     )

							A			=					output.images

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

							assert image.shape == (1, 5_1_2, 5_1_2, 3)
							A			=					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








"""simple docstring"""




def    __SCREAMING_SNAKE_CASE						(						lowercase__ = 1_000      ):
					"""simple docstring"""
					return sum(i for i in range(lowercase__      ) if i % 3 == 0 or i % 5 == 0      )


if __name__ == "__main__":
	print(f"""{solution() = }""")





