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		(					_SCREAMING_SNAKE_CASE				,       unittest.TestCase      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     lowerCAmelCase_				:   List[Any]     	=			PaintByExamplePipeline
     lowerCAmelCase_				:   str     	=			IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
     lowerCAmelCase_				:   int     	=			IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     lowerCAmelCase_				:   Tuple     	=			frozenset([]      )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:str       ):
            torch.manual_seed(0       )
            _a          =				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				,  )
            _a          =				PNDMScheduler(skip_prk_steps=UpperCamelCase__       )
            torch.manual_seed(0       )
            _a          =				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       )
            _a          =				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				,  )
            _a          =				PaintByExampleImageEncoder(UpperCamelCase__				,  proj_size=32       )
            _a          =				CLIPImageProcessor(crop_size=32				,  size=32       )
  
            _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							:Optional[int]				,  UpperCamelCase__							:Tuple       ):
            _a          =				np.array(image.convert("RGB"       )       )
            _a          =				image[None].transpose(0				,  3				,  1				,  2       )
            _a          =				torch.from_numpy(UpperCamelCase__       ).to(dtype=torch.floataa       ) / 127.5 - 1.0
            return image
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Tuple				,  UpperCamelCase__							:int="cpu"				,  UpperCamelCase__							: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, 32, 32)				,  rng=random.Random(UpperCamelCase__       )       ).to(UpperCamelCase__       )
            _a          =				image.cpu().permute(0				,  2				,  3				,  1       )[0]
            _a          =				Image.fromarray(np.uinta(UpperCamelCase__       )       ).convert("RGB"       ).resize((64, 64)       )
            _a          =				Image.fromarray(np.uinta(image + 4       )       ).convert("RGB"       ).resize((64, 64)       )
            _a          =				Image.fromarray(np.uinta(UpperCamelCase__       )       ).convert("RGB"       ).resize((32, 32)       )
  
            if str(UpperCamelCase__       ).startswith("mps"       ):
                   _a          =				torch.manual_seed(UpperCamelCase__       )
            else:
                   _a          =				torch.Generator(device=UpperCamelCase__       ).manual_seed(UpperCamelCase__       )
            _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							:int       ):
            _a          =				self.get_dummy_components()
  
            # make sure here that pndm scheduler skips prk
            _a          =				PaintByExamplePipeline(**UpperCamelCase__       )
            _a          =				pipe.to("cpu"       )
            pipe.set_progress_bar_config(disable=UpperCamelCase__       )
  
            _a          =				self.get_dummy_inputs()
            _a          =				pipe(**UpperCamelCase__       )
            _a          =				output.images
  
            _a          =				image[0, -3:, -3:, -1]
  
            assert image.shape == (1, 64, 64, 3)
            _a          =				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							:List[str]       ):
            _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(UpperCamelCase__       )
            pipe.set_progress_bar_config(disable=UpperCamelCase__       )
  
            _a          =				pipe(image=UpperCamelCase__				,  mask_image=mask_image[:, 0]				,  **UpperCamelCase__       )
            _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(UpperCamelCase__       )       ).convert("RGB"       )
            _a          =				Image.fromarray(np.uinta(UpperCamelCase__       )       ).convert("RGB"       )
  
            _a          =				pipe(**self.get_dummy_inputs()       )
            _a          =				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							:Dict       ):
            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							:List[Any]       ):
            # clean up the VRAM after each test
            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:List[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(UpperCamelCase__       )
            pipe.set_progress_bar_config(disable=UpperCamelCase__       )
  
            _a          =				torch.manual_seed(321       )
            _a          =				pipe(
                image=UpperCamelCase__				,  mask_image=UpperCamelCase__				,  example_image=UpperCamelCase__				,  generator=UpperCamelCase__				,  guidance_scale=5.0				,  num_inference_steps=50				,  output_type="np"				,  )
  
            _a          =				output.images
  
            _a          =				image[0, -3:, -3:, -1]
  
            assert image.shape == (1, 512, 512, 3)
            _a          =				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 __future__ import annotations
  
  from collections.abc import Iterator
  from typing import Any
  
  
  
  
  
  class __snake_case		:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     def __init__(		self							:List[Any]				,  UpperCamelCase__							:Any       ):
            _a          =				data
            _a          =				None
  
  
  
  
     def __str__(		self							:Any       ):
            return f'{self.data}'
  
  
  
  
  
  
  class __snake_case		:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     def __init__(		self							:Any       ):
            _a          =				None
            _a          =				None
  
  
  
  
     def __iter__(		self							:List[str]       ):
            _a          =				self.front
            while node:
                   yield node.data
                   _a          =				node.next
  
  
  
  
     def __len__(		self							:List[str]       ):
            return len(tuple(iter(self       )       )       )
  
  
  
  
     def __str__(		self							:Union[str, Any]       ):
            return " <- ".join(str(UpperCamelCase__       ) for item in self       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Any       ):
            return len(self       ) == 0
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:List[str]				,  UpperCamelCase__							:Any       ):
            _a          =				Node(UpperCamelCase__       )
            if self.is_empty():
                   _a          =				_a          =				node
            else:
                   assert isinstance(self.rear				,  UpperCamelCase__       )
                   _a          =				node
                   _a          =				node
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Tuple       ):
            if self.is_empty():
                   raise IndexError("dequeue from empty queue"       )
            assert isinstance(self.front				,  UpperCamelCase__       )
            _a          =				self.front
            _a          =				self.front.next
            if self.front is None:
                   _a          =				None
            return node.data
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:int       ):
            _a          =				_a          =				None
  
  
  if __name__ == "__main__":
     from doctest import testmod
  
     testmod()
  