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 					_UpperCAmelCase							(		lowerCAmelCase_			,      unittest.TestCase     ):
  		a     :     str     						=PaintByExamplePipeline
  		a     :     List[Any]     						=IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
  		a     :     List[str]     						=IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  		a     :     int     						=frozenset([]     )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					torch.manual_seed(0    )
  					__lowerCAmelCase      		=       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      		=       PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE    )
  					torch.manual_seed(0    )
  					__lowerCAmelCase      		=       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      		=       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      		=       PaintByExampleImageEncoder(__SCREAMING_SNAKE_CASE,proj_size=32    )
  					__lowerCAmelCase      		=       CLIPImageProcessor(crop_size=32,size=32    )
  
  					__lowerCAmelCase      		=       {
  					    """unet""": unet,
  					    """scheduler""": scheduler,
  					    """vae""": vae,
  					    """image_encoder""": image_encoder,
  					    """safety_checker""": None,
  					    """feature_extractor""": feature_extractor,
  					}
  					return components
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       np.array(image.convert("""RGB"""    )    )
  					__lowerCAmelCase      		=       image[None].transpose(0,3,1,2    )
  					__lowerCAmelCase      		=       torch.from_numpy(__SCREAMING_SNAKE_CASE    ).to(dtype=torch.floataa    ) / 127.5 - 1.0
  					return image
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE="cpu",__SCREAMING_SNAKE_CASE=0    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       floats_tensor((1, 3, 32, 32),rng=random.Random(__SCREAMING_SNAKE_CASE    )    ).to(__SCREAMING_SNAKE_CASE    )
  					__lowerCAmelCase      		=       image.cpu().permute(0,2,3,1    )[0]
  					__lowerCAmelCase      		=       Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE    )    ).convert("""RGB"""    ).resize((64, 64)    )
  					__lowerCAmelCase      		=       Image.fromarray(np.uinta(image + 4    )    ).convert("""RGB"""    ).resize((64, 64)    )
  					__lowerCAmelCase      		=       Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE    )    ).convert("""RGB"""    ).resize((32, 32)    )
  
  					if str(__SCREAMING_SNAKE_CASE    ).startswith("""mps"""    ):
  								__lowerCAmelCase      		=       torch.manual_seed(__SCREAMING_SNAKE_CASE    )
  					else:
  								__lowerCAmelCase      		=       torch.Generator(device=__SCREAMING_SNAKE_CASE    ).manual_seed(__SCREAMING_SNAKE_CASE    )
  					__lowerCAmelCase      		=       {
  					    """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 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       self.get_dummy_components()
  
  					# make sure here that pndm scheduler skips prk
  					__lowerCAmelCase      		=       PaintByExamplePipeline(**__SCREAMING_SNAKE_CASE    )
  					__lowerCAmelCase      		=       pipe.to("""cpu"""    )
  					pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE    )
  
  					__lowerCAmelCase      		=       self.get_dummy_inputs()
  					__lowerCAmelCase      		=       pipe(**__SCREAMING_SNAKE_CASE    )
  					__lowerCAmelCase      		=       output.images
  
  					__lowerCAmelCase      		=       image[0, -3:, -3:, -1]
  
  					assert image.shape == (1, 64, 64, 3)
  					__lowerCAmelCase      		=       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 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       """cpu"""
  					__lowerCAmelCase      		=       self.get_dummy_inputs()
  					inputs.pop("""mask_image"""    )
  					__lowerCAmelCase      		=       self.convert_to_pt(inputs.pop("""image"""    )    )
  					__lowerCAmelCase      		=       image.clamp(0,1    ) / 2
  
  					# make sure here that pndm scheduler skips prk
  					__lowerCAmelCase      		=       PaintByExamplePipeline(**self.get_dummy_components()    )
  					__lowerCAmelCase      		=       pipe.to(__SCREAMING_SNAKE_CASE    )
  					pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE    )
  
  					__lowerCAmelCase      		=       pipe(image=__SCREAMING_SNAKE_CASE,mask_image=mask_image[:, 0],**__SCREAMING_SNAKE_CASE    )
  					__lowerCAmelCase      		=       output.images
  
  					__lowerCAmelCase      		=       image.cpu().permute(0,2,3,1    )[0]
  					__lowerCAmelCase      		=       mask_image.cpu().permute(0,2,3,1    )[0]
  
  					__lowerCAmelCase      		=       Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE    )    ).convert("""RGB"""    )
  					__lowerCAmelCase      		=       Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE    )    ).convert("""RGB"""    )
  
  					__lowerCAmelCase      		=       pipe(**self.get_dummy_inputs()    )
  					__lowerCAmelCase      		=       output.images
  
  					assert out_a.shape == (1, 64, 64, 3)
  					assert np.abs(out_a.flatten() - out_a.flatten()    ).max() < 5e-2
  
  
  
  
  
  
  
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					super().test_inference_batch_single_identical(expected_max_diff=3e-3    )
  
  
  
  
  
  @slow
  @require_torch_gpu
  class 					_UpperCAmelCase							(		unittest.TestCase     ):
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					super().tearDown()
  					gc.collect()
  					torch.cuda.empty_cache()
  
  
  
  
  
  
  
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       load_image(
  					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
  					    """/paint_by_example/dog_in_bucket.png"""    )
  					__lowerCAmelCase      		=       load_image(
  					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
  					    """/paint_by_example/mask.png"""    )
  					__lowerCAmelCase      		=       load_image(
  					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
  					    """/paint_by_example/panda.jpg"""    )
  
  					__lowerCAmelCase      		=       PaintByExamplePipeline.from_pretrained("""Fantasy-Studio/Paint-by-Example"""    )
  					__lowerCAmelCase      		=       pipe.to(__SCREAMING_SNAKE_CASE    )
  					pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE    )
  
  					__lowerCAmelCase      		=       torch.manual_seed(3_21    )
  					__lowerCAmelCase      		=       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=50,output_type="""np""",)
  
  					__lowerCAmelCase      		=       output.images
  
  					__lowerCAmelCase      		=       image[0, -3:, -3:, -1]
  
  					assert image.shape == (1, 5_12, 5_12, 3)
  					__lowerCAmelCase      		=       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'''
  
  
  
  
  
  
  
  from cva import destroyAllWindows, imread, imshow, waitKey
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase      ,			lowercase			)      ->      int:
  			__lowerCAmelCase		, __lowerCAmelCase      		=       img.shape[0], img.shape[1]
  
  			def to_grayscale(lowercase      ,			lowercase      ,			lowercase			):
  						return 0.21_26 * red + 0.5_87 * green + 0.1_14 * blue
  
  			def normalize(lowercase			):
  						return min(lowercase      ,			255			)
  
  			for i in range(lowercase			):
  						for j in range(lowercase			):
  									__lowerCAmelCase      		=       int(to_grayscale(*img[i][j]			)			)
  									__lowerCAmelCase      		=       [
  									    normalize(lowercase			),
  									    normalize(greyscale + factor			),
  									    normalize(greyscale + 2 * factor			),
  									]
  
  			return img
  
  
  if __name__ == "__main__":
  	# read original image
  	_a      :				Any											=					{
  	    percentage: imread("""image_data/lena.jpg""", 1) for percentage in (1_0, 2_0, 3_0, 4_0)
  	}
  
  	for percentage, img in images.items():
  		make_sepia(img, percentage)
  
  	for percentage, img in images.items():
  		imshow(f'Original image with sepia (factor: {percentage})', img)
  
  	waitKey(0)
  	destroyAllWindows()
  
  