




'''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   lowerCamelCase_							( UpperCAmelCase_, unittest.TestCase							):




					'''simple docstring'''






					lowercase__					=				PaintByExamplePipeline
					lowercase__					=				IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
					lowercase__					=				IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					lowercase__					=				frozenset([]							)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess






					def        __SCREAMING_SNAKE_CASE			(					self				):
							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-05,						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        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase				):
							_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_27.5 - 1.0
							return image






					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase="cpu",						__UpperCamelCase=0				):
							# 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        __SCREAMING_SNAKE_CASE			(					self				):
							_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([0.46_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        __SCREAMING_SNAKE_CASE			(					self				):
							_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        __SCREAMING_SNAKE_CASE			(					self				):
							super().test_inference_batch_single_identical(expected_max_diff=3E-3				)





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




					'''simple docstring'''






					def        __SCREAMING_SNAKE_CASE			(					self				):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()






					def        __SCREAMING_SNAKE_CASE			(					self				):
							# 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([0.48_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'''







import warnings

from ...utils import logging
from .image_processing_vilt import ViltImageProcessor


_lowerCamelCase							:		Optional[Any]										=						logging.get_logger(__name__)





class   lowerCamelCase_							( UpperCAmelCase_							):




					'''simple docstring'''






					def __init__(					self,						*__UpperCamelCase,						**__UpperCamelCase				):
							warnings.warn(
							    "The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
							    " use ViltImageProcessor instead.",						__UpperCamelCase,						)
							super().__init__(*__UpperCamelCase,						**__UpperCamelCase				)





