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   lowerCamelCase_							( UpperCAmelCase_, unittest.TestCase							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					lowercase__					=				PaintByExamplePipeline
  					lowercase__					=				IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
  					lowercase__					=				IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  					lowercase__					=				frozenset([]							)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							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=__UpperCamelCase				)
  							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(__UpperCamelCase,						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,						__UpperCamelCase				):
  							_snake_case			 = np.array(image.convert("RGB"				)				)
  							_snake_case			 = image[None].transpose(0,						3,						1,						2				)
  							_snake_case			 = torch.from_numpy(__UpperCamelCase				).to(dtype=torch.floataa				) / 1_27.5 - 1.0
  							return image
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase="cpu",						__UpperCamelCase=0				):
  							# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
  							_snake_case			 = floats_tensor((1, 3, 32, 32),						rng=random.Random(__UpperCamelCase				)				).to(__UpperCamelCase				)
  							_snake_case			 = image.cpu().permute(0,						2,						3,						1				)[0]
  							_snake_case			 = Image.fromarray(np.uinta(__UpperCamelCase				)				).convert("RGB"				).resize((64, 64)				)
  							_snake_case			 = Image.fromarray(np.uinta(image + 4				)				).convert("RGB"				).resize((64, 64)				)
  							_snake_case			 = Image.fromarray(np.uinta(__UpperCamelCase				)				).convert("RGB"				).resize((32, 32)				)
  
  							if str(__UpperCamelCase				).startswith("mps"				):
  									_snake_case			 = torch.manual_seed(__UpperCamelCase				)
  							else:
  									_snake_case			 = torch.Generator(device=__UpperCamelCase				).manual_seed(__UpperCamelCase				)
  							_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				):
  							_snake_case			 = self.get_dummy_components()
  
  							# make sure here that pndm scheduler skips prk
  							_snake_case			 = PaintByExamplePipeline(**__UpperCamelCase				)
  							_snake_case			 = pipe.to("cpu"				)
  							pipe.set_progress_bar_config(disable=__UpperCamelCase				)
  
  							_snake_case			 = self.get_dummy_inputs()
  							_snake_case			 = pipe(**__UpperCamelCase				)
  							_snake_case			 = output.images
  
  							_snake_case			 = image[0, -3:, -3:, -1]
  
  							assert image.shape == (1, 64, 64, 3)
  							_snake_case			 = np.array([0.46_86, 0.56_87, 0.40_07, 0.52_18, 0.57_41, 0.44_82, 0.49_40, 0.46_29, 0.45_03]				)
  
  							assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_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(__UpperCamelCase				)
  							pipe.set_progress_bar_config(disable=__UpperCamelCase				)
  
  							_snake_case			 = pipe(image=__UpperCamelCase,						mask_image=mask_image[:, 0],						**__UpperCamelCase				)
  							_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(__UpperCamelCase				)				).convert("RGB"				)
  							_snake_case			 = Image.fromarray(np.uinta(__UpperCamelCase				)				).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				):
  							super().test_inference_batch_single_identical(expected_max_diff=3E-3				)
  
  
  
  
  
  @slow
  @require_torch_gpu
  class   lowerCamelCase_							( unittest.TestCase							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							# clean up the VRAM after each test
  							super().tearDown()
  							gc.collect()
  							torch.cuda.empty_cache()
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							# make sure here that pndm scheduler skips prk
  							_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(__UpperCamelCase				)
  							pipe.set_progress_bar_config(disable=__UpperCamelCase				)
  
  							_snake_case			 = torch.manual_seed(321				)
  							_snake_case			 = pipe(
  							    image=__UpperCamelCase,						mask_image=__UpperCamelCase,						example_image=__UpperCamelCase,						generator=__UpperCamelCase,						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([0.48_34, 0.48_11, 0.48_74, 0.51_22, 0.50_81, 0.51_44, 0.52_91, 0.52_90, 0.53_74]				)
  
  							assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
  
  
  
  
  
  
style_context: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  import warnings
  
  from ...utils import logging
  from .image_processing_vilt import ViltImageProcessor
  
  
  _lowerCamelCase							:		Optional[Any]										=						logging.get_logger(__name__)
  
  
  
  
  
  class   lowerCamelCase_							( UpperCAmelCase_							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					def __init__(					self,						*__UpperCamelCase,						**__UpperCamelCase				):
  							warnings.warn(
  							    "The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
  							    " use ViltImageProcessor instead.",						__UpperCamelCase,						)
  							super().__init__(*__UpperCamelCase,						**__UpperCamelCase				)
  
  
  
  
  
  