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    (				__magic_name__					,					unittest.TestCase       ):
  	__lowerCAmelCase						=     PaintByExamplePipeline
  	__lowerCAmelCase						=     IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
  	__lowerCAmelCase						=     IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  	__lowerCAmelCase						=     frozenset([]       )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Union[str, Any]:
  		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-0_5			,					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      _snake_case      (		self			,					UpperCamelCase_		)			->		Optional[int]:
  		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_2_7.5 - 1.0
  		return image
  
  
  
  
  
  	def      _snake_case      (		self			,					UpperCamelCase_="cpu"			,					UpperCamelCase_=0		)			->		List[str]:
  		# 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      _snake_case      (		self		)			->		int:
  		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([0.4_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      _snake_case      (		self		)			->		int:
  		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      _snake_case      (		self		)			->		Tuple:
  		super().test_inference_batch_single_identical(expected_max_diff=3E-3		)
  
  
  
  
  
  
  @slow
  @require_torch_gpu
  class 							__snake_case    (				unittest.TestCase       ):
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Optional[int]:
  		# clean up the VRAM after each test
  		super().tearDown()
  		gc.collect()
  		torch.cuda.empty_cache()
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Tuple:
  		# 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([0.4_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
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_       )      ->int:
  	return 1 if input_a == 0 else 0
  
  
  
  
  def 	__lowerCamelCase (  )      ->None:
  	assert not_gate(0       ) == 1
  	assert not_gate(1       ) == 0
  
  
  if __name__ == "__main__":
  			print(not_gate(0))
  			print(not_gate(1))
  
  
  
  