code: 
  
  
  '''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 _snake_case   ( a__    ,   unittest.TestCase   ):
  							"""simple docstring"""
  
  
  
  
  
  							__a												=     PaintByExamplePipeline
  							__a												=     IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
  							__a												=     IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  							__a												=     frozenset([]   )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Dict:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											torch.manual_seed(0	)
  											__lowerCamelCase		:      str											=					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			,				)
  											__lowerCamelCase		:      Any											=					PNDMScheduler(skip_prk_steps=__A	)
  											torch.manual_seed(0	)
  											__lowerCamelCase		:      Dict											=					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	)
  											__lowerCamelCase		:      int											=					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			,				)
  											__lowerCamelCase		:      List[str]											=					PaintByExampleImageEncoder(__A			,				proj_size=32	)
  											__lowerCamelCase		:      List[Any]											=					CLIPImageProcessor(crop_size=32			,				size=32	)
  
  											__lowerCamelCase		:      Tuple											=					{
  											    "unet": unet,
  											    "scheduler": scheduler,
  											    "vae": vae,
  											    "image_encoder": image_encoder,
  											    "safety_checker": None,
  											    "feature_extractor": feature_extractor,
  											}
  											return components
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A	)     ->List[Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      List[Any]											=					np.array(image.convert("RGB"	)	)
  											__lowerCamelCase		:      Dict											=					image[None].transpose(0			,				3			,				1			,				2	)
  											__lowerCamelCase		:      Optional[int]											=					torch.from_numpy(__A	).to(dtype=torch.floataa	) / 127.5 - 1.0
  											return image
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A="cpu"			,				__A=0	)     ->str:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      List[str]											=					floats_tensor((1, 3, 32, 32)			,				rng=random.Random(__A	)	).to(__A	)
  											__lowerCamelCase		:      Union[str, Any]											=					image.cpu().permute(0			,				2			,				3			,				1	)[0]
  											__lowerCamelCase		:      Any											=					Image.fromarray(np.uinta(__A	)	).convert("RGB"	).resize((64, 64)	)
  											__lowerCamelCase		:      int											=					Image.fromarray(np.uinta(image + 4	)	).convert("RGB"	).resize((64, 64)	)
  											__lowerCamelCase		:      List[str]											=					Image.fromarray(np.uinta(__A	)	).convert("RGB"	).resize((32, 32)	)
  
  											if str(__A	).startswith("mps"	):
  															__lowerCamelCase		:      Optional[Any]											=					torch.manual_seed(__A	)
  											else:
  															__lowerCamelCase		:      Optional[Any]											=					torch.Generator(device=__A	).manual_seed(__A	)
  											__lowerCamelCase		:      int											=					{
  											    "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	)     ->Optional[int]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Tuple											=					self.get_dummy_components()
  
  											# make sure here that pndm scheduler skips prk
  											__lowerCamelCase		:      Dict											=					PaintByExamplePipeline(**__A	)
  											__lowerCamelCase		:      Tuple											=					pipe.to("cpu"	)
  											pipe.set_progress_bar_config(disable=__A	)
  
  											__lowerCamelCase		:      Dict											=					self.get_dummy_inputs()
  											__lowerCamelCase		:      Dict											=					pipe(**__A	)
  											__lowerCamelCase		:      Dict											=					output.images
  
  											__lowerCamelCase		:      Dict											=					image[0, -3:, -3:, -1]
  
  											assert image.shape == (1, 64, 64, 3)
  											__lowerCamelCase		:      Dict											=					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	)     ->Dict:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Optional[int]											=					"cpu"
  											__lowerCamelCase		:      List[Any]											=					self.get_dummy_inputs()
  											inputs.pop("mask_image"	)
  											__lowerCamelCase		:      Optional[Any]											=					self.convert_to_pt(inputs.pop("image"	)	)
  											__lowerCamelCase		:      Tuple											=					image.clamp(0			,				1	) / 2
  
  											# make sure here that pndm scheduler skips prk
  											__lowerCamelCase		:      Tuple											=					PaintByExamplePipeline(**self.get_dummy_components()	)
  											__lowerCamelCase		:      Optional[Any]											=					pipe.to(__A	)
  											pipe.set_progress_bar_config(disable=__A	)
  
  											__lowerCamelCase		:      Tuple											=					pipe(image=__A			,				mask_image=mask_image[:, 0]			,				**__A	)
  											__lowerCamelCase		:      List[str]											=					output.images
  
  											__lowerCamelCase		:      Optional[Any]											=					image.cpu().permute(0			,				2			,				3			,				1	)[0]
  											__lowerCamelCase		:      Any											=					mask_image.cpu().permute(0			,				2			,				3			,				1	)[0]
  
  											__lowerCamelCase		:      int											=					Image.fromarray(np.uinta(__A	)	).convert("RGB"	)
  											__lowerCamelCase		:      List[Any]											=					Image.fromarray(np.uinta(__A	)	).convert("RGB"	)
  
  											__lowerCamelCase		:      Optional[Any]											=					pipe(**self.get_dummy_inputs()	)
  											__lowerCamelCase		:      Tuple											=					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	)     ->List[str]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											super().test_inference_batch_single_identical(expected_max_diff=3E-3	)
  
  
  
  
  
  @slow
  @require_torch_gpu
  class _snake_case   ( unittest.TestCase   ):
  							"""simple docstring"""
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Any:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											super().tearDown()
  											gc.collect()
  											torch.cuda.empty_cache()
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Tuple:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      int											=					load_image(
  											    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
  											    "/paint_by_example/dog_in_bucket.png"	)
  											__lowerCamelCase		:      List[str]											=					load_image(
  											    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
  											    "/paint_by_example/mask.png"	)
  											__lowerCamelCase		:      List[Any]											=					load_image(
  											    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
  											    "/paint_by_example/panda.jpg"	)
  
  											__lowerCamelCase		:      List[str]											=					PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example"	)
  											__lowerCamelCase		:      List[str]											=					pipe.to(__A	)
  											pipe.set_progress_bar_config(disable=__A	)
  
  											__lowerCamelCase		:      Dict											=					torch.manual_seed(321	)
  											__lowerCamelCase		:      List[str]											=					pipe(
  											    image=__A			,				mask_image=__A			,				example_image=__A			,				generator=__A			,				guidance_scale=5.0			,				num_inference_steps=50			,				output_type="np"			,				)
  
  											__lowerCamelCase		:      Optional[Any]											=					output.images
  
  											__lowerCamelCase		:      Union[str, Any]											=					image[0, -3:, -3:, -1]
  
  											assert image.shape == (1, 512, 512, 3)
  											__lowerCamelCase		:      Union[str, Any]											=					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
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  
  def 							a_       (						lowercase    )			->str:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				return " ".join(sorted(set(sentence.split()    )    )    )
  
  
  if __name__ == "__main__":
  				import doctest
  
  				doctest.testmod()
  
  
  
  
  
  