



'''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__						(		_SCREAMING_SNAKE_CASE				,							unittest.TestCase			):
					a   :  Union[str, Any]   =				PaintByExamplePipeline
					a   :  Tuple   =				IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
					a   :  int   =				IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					a   :  str   =				frozenset([]			)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess



					def 							UpperCAmelCase_			(     self  )  ->      Any:
						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=UpperCamelCase  )
						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(UpperCamelCase      ,					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 							UpperCAmelCase_			(     self      ,					UpperCamelCase  )  ->      int:
						__lowerCAmelCase       				=	np.array(image.convert("RGB"  )  )
						__lowerCAmelCase       				=	image[None].transpose(0      ,					3      ,					1      ,					2  )
						__lowerCAmelCase       				=	torch.from_numpy(UpperCamelCase  ).to(dtype=torch.floataa  ) / 127.5 - 1.0
						return image



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase="cpu"      ,					UpperCamelCase=0  )  ->      Optional[Any]:
						# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
						__lowerCAmelCase       				=	floats_tensor((1, 3, 32, 32)      ,					rng=random.Random(UpperCamelCase  )  ).to(UpperCamelCase  )
						__lowerCAmelCase       				=	image.cpu().permute(0      ,					2      ,					3      ,					1  )[0]
						__lowerCAmelCase       				=	Image.fromarray(np.uinta(UpperCamelCase  )  ).convert("RGB"  ).resize((64, 64)  )
						__lowerCAmelCase       				=	Image.fromarray(np.uinta(image + 4  )  ).convert("RGB"  ).resize((64, 64)  )
						__lowerCAmelCase       				=	Image.fromarray(np.uinta(UpperCamelCase  )  ).convert("RGB"  ).resize((32, 32)  )

						if str(UpperCamelCase  ).startswith("mps"  ):
							__lowerCAmelCase       				=	torch.manual_seed(UpperCamelCase  )
						else:
							__lowerCAmelCase       				=	torch.Generator(device=UpperCamelCase  ).manual_seed(UpperCamelCase  )
						__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 							UpperCAmelCase_			(     self  )  ->      List[str]:
						__lowerCAmelCase       				=	self.get_dummy_components()

						# make sure here that pndm scheduler skips prk
						__lowerCAmelCase       				=	PaintByExamplePipeline(**UpperCamelCase  )
						__lowerCAmelCase       				=	pipe.to("cpu"  )
						pipe.set_progress_bar_config(disable=UpperCamelCase  )

						__lowerCAmelCase       				=	self.get_dummy_inputs()
						__lowerCAmelCase       				=	pipe(**UpperCamelCase  )
						__lowerCAmelCase       				=	output.images

						__lowerCAmelCase       				=	image[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						__lowerCAmelCase       				=	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 							UpperCAmelCase_			(     self  )  ->      List[Any]:
						__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(UpperCamelCase  )
						pipe.set_progress_bar_config(disable=UpperCamelCase  )

						__lowerCAmelCase       				=	pipe(image=UpperCamelCase      ,					mask_image=mask_image[:, 0]      ,					**UpperCamelCase  )
						__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(UpperCamelCase  )  ).convert("RGB"  )
						__lowerCAmelCase       				=	Image.fromarray(np.uinta(UpperCamelCase  )  ).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 							UpperCAmelCase_			(     self  )  ->      List[Any]:
						super().test_inference_batch_single_identical(expected_max_diff=3E-3  )






@slow
@require_torch_gpu
class        UpperCAmelCase__						(		unittest.TestCase			):



					def 							UpperCAmelCase_			(     self  )  ->      Optional[Any]:
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()



					def 							UpperCAmelCase_			(     self  )  ->      int:
						# make sure here that pndm scheduler skips prk
						__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(UpperCamelCase  )
						pipe.set_progress_bar_config(disable=UpperCamelCase  )

						__lowerCAmelCase       				=	torch.manual_seed(321  )
						__lowerCAmelCase       				=	pipe(
						    image=UpperCamelCase      ,					mask_image=UpperCamelCase      ,					example_image=UpperCamelCase      ,					generator=UpperCamelCase      ,					guidance_scale=5.0      ,					num_inference_steps=50      ,					output_type="np"      ,					)

						__lowerCAmelCase       				=	output.images

						__lowerCAmelCase       				=	image[0, -3:, -3:, -1]

						assert image.shape == (1, 512, 512, 3)
						__lowerCAmelCase       				=	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



'''simple docstring'''






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
 from .image_encoder import PaintByExampleImageEncoder
 from .pipeline_paint_by_example import PaintByExamplePipeline

