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'''
  
  
  
  
  from __future__ import annotations
  
  from collections import deque
  from enum import Enum
  from math import atana, degrees
  from sys import maxsize
  
  
  
  
  
  class _snake_case   ( a__   ):
  							"""simple docstring"""
  
  
  
  
  
  							__a												=     1
  							__a												=     2
  							__a												=     3
  
  
  
  
  
  
  
  							def __repr__(   self	)     ->Tuple:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											return f'{self.__class__.__name__}.{self.name}'
  
  
  
  
  
  def 							a_       (						lowercase							,						lowercase							,						lowercase    )			->float:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				__lowerCamelCase						,					__lowerCamelCase		:      str											=					point
  				return degrees(atana(y - miny							,						x - minx    )    )
  
  
  
  
  
  def 							a_       (						lowercase							,						lowercase							,						lowercase    )			->Direction:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				__lowerCamelCase						,					__lowerCamelCase		:      List[str]											=					starting
  				__lowerCamelCase						,					__lowerCamelCase		:      Any											=					via
  				__lowerCamelCase						,					__lowerCamelCase		:      str											=					target
  				__lowerCamelCase		:      str											=					degrees(atana(ya - ya							,						xa - xa    )    )
  				via_angle %= 360
  				__lowerCamelCase		:      List[Any]											=					degrees(atana(ya - ya							,						xa - xa    )    )
  				target_angle %= 360
  				# t-
  				#  \ \
  				#   \ v
  				#    \|
  				#     s
  				# via_angle is always lower than target_angle, if direction is left.
  				# If they are same, it means they are on a same line of convex hull.
  				if target_angle > via_angle:
  								return Direction.left
  				elif target_angle == via_angle:
  								return Direction.straight
  				else:
  								return Direction.right
  
  
  
  
  
  def 							a_       (						lowercase    )			->list[tuple[int, int]]:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				if len(lowercase    ) <= 2:
  								# There is no convex hull
  								raise ValueError("graham_scan: argument must contain more than 3 points."    )
  				if len(lowercase    ) == 3:
  								return points
  				# find the lowest and the most left point
  				__lowerCamelCase		:      Optional[Any]											=					0
  				__lowerCamelCase						,					__lowerCamelCase		:      List[Any]											=					maxsize, maxsize
  				for i, point in enumerate(lowercase    ):
  								__lowerCamelCase		:      Dict											=					point[0]
  								__lowerCamelCase		:      Optional[int]											=					point[1]
  								if y < miny:
  												__lowerCamelCase		:      Dict											=					y
  												__lowerCamelCase		:      int											=					x
  												__lowerCamelCase		:      List[Any]											=					i
  								if y == miny and x < minx:
  												__lowerCamelCase		:      Optional[int]											=					x
  												__lowerCamelCase		:      Any											=					i
  
      # remove the lowest and the most left point from points for preparing for sort
  				points.pop(lowercase    )
  
  				__lowerCamelCase		:      Optional[Any]											=					sorted(lowercase							,						key=lambda lowercase    : angle_comparer(lowercase							,						lowercase							,						lowercase    )    )
  				# This insert actually costs complexity,
  				# and you should instead add (minx, miny) into stack later.
  				# I'm using insert just for easy understanding.
  				sorted_points.insert(0							,						(minx, miny)    )
  
  				__lowerCamelCase		:      deque[tuple[int, int]]											=					deque()
  				stack.append(sorted_points[0]    )
  				stack.append(sorted_points[1]    )
  				stack.append(sorted_points[2]    )
  				# In any ways, the first 3 points line are towards left.
  				# Because we sort them the angle from minx, miny.
  				__lowerCamelCase		:      str											=					Direction.left
  
  				for i in range(3							,						len(lowercase    )    ):
  								while True:
  												__lowerCamelCase		:      List[Any]											=					stack[-2]
  												__lowerCamelCase		:      str											=					stack[-1]
  												__lowerCamelCase		:      int											=					sorted_points[i]
  												__lowerCamelCase		:      List[Any]											=					check_direction(lowercase							,						lowercase							,						lowercase    )
  
  												if next_direction == Direction.left:
  																__lowerCamelCase		:      Optional[int]											=					Direction.left
  																break
  												if next_direction == Direction.straight:
  																if current_direction == Direction.left:
  																				# We keep current_direction as left.
  																				# Because if the straight line keeps as straight,
  																				# we want to know if this straight line is towards left.
  																				break
  																elif current_direction == Direction.right:
  																				# If the straight line is towards right,
  																				# every previous points on those straigh line is not convex hull.
  																				stack.pop()
  												if next_direction == Direction.right:
  																stack.pop()
  								stack.append(sorted_points[i]    )
  				return list(lowercase    )
  
  
  
  
  
  