





"""simple docstring"""


import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler



class        lowercase_			(       unittest.TestCase							):
							snake_case__			:							str										=       (ScoreSdeVeScheduler,)
							snake_case__			:							Dict										=       ()

							@property
							def 						_SCREAMING_SNAKE_CASE(     self       ):






								'''simple docstring'''

								snake_case__      							=			4
								snake_case__      							=			3
								snake_case__      							=			8
								snake_case__      							=			8

								snake_case__      							=			torch.rand((batch_size, num_channels, height, width)       )

								return sample

							@property
							def 						_SCREAMING_SNAKE_CASE(     self       ):






								'''simple docstring'''

								snake_case__      							=			4
								snake_case__      							=			3
								snake_case__      							=			8
								snake_case__      							=			8

								snake_case__      							=			batch_size * num_channels * height * width
								snake_case__      							=			torch.arange(__lowerCamelCase       )
								snake_case__      							=			sample.reshape(__lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase       )
								snake_case__      							=			sample / num_elems
								snake_case__      							=			sample.permute(3				,   0				,   1				,   2       )

								return sample

							def 						_SCREAMING_SNAKE_CASE(     self       ):






								'''simple docstring'''

								def model(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   *__SCREAMING_SNAKE_CASE       ):
									return sample * t / (t + 1)

								return model

							def 						_SCREAMING_SNAKE_CASE(     self				,   **__SCREAMING_SNAKE_CASE       ):






								'''simple docstring'''

								snake_case__      							=			{
								    "num_train_timesteps": 2000,
								    "snr": 0.15,
								    "sigma_min": 0.01,
								    "sigma_max": 1348,
								    "sampling_eps": 1e-5,
								}

								config.update(**__lowerCamelCase       )
								return config

							def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE=0				,   **__SCREAMING_SNAKE_CASE       ):






								'''simple docstring'''

								snake_case__      							=			dict(self.forward_default_kwargs       )

								for scheduler_class in self.scheduler_classes:
									snake_case__      							=			self.dummy_sample
									snake_case__      							=			0.1 * sample

									snake_case__      							=			self.get_scheduler_config(**__lowerCamelCase       )
									snake_case__      							=			scheduler_class(**__lowerCamelCase       )

									with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(__lowerCamelCase       )
										snake_case__      							=			scheduler_class.from_pretrained(__lowerCamelCase       )

									snake_case__      							=			scheduler.step_pred(
									    __lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase				,   generator=torch.manual_seed(0       )				,   **__lowerCamelCase       ).prev_sample
									snake_case__      							=			new_scheduler.step_pred(
									    __lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase				,   generator=torch.manual_seed(0       )				,   **__lowerCamelCase       ).prev_sample

									assert torch.sum(torch.abs(output - new_output       )       ) < 1e-5, "Scheduler outputs are not identical"

									snake_case__      							=			scheduler.step_correct(__lowerCamelCase				,   __lowerCamelCase				,   generator=torch.manual_seed(0       )				,   **__lowerCamelCase       ).prev_sample
									snake_case__      							=			new_scheduler.step_correct(
									    __lowerCamelCase				,   __lowerCamelCase				,   generator=torch.manual_seed(0       )				,   **__lowerCamelCase       ).prev_sample

									assert torch.sum(torch.abs(output - new_output       )       ) < 1e-5, "Scheduler correction are not identical"

							def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE=0				,   **__SCREAMING_SNAKE_CASE       ):






								'''simple docstring'''

								snake_case__      							=			dict(self.forward_default_kwargs       )
								kwargs.update(__lowerCamelCase       )

								for scheduler_class in self.scheduler_classes:
									snake_case__      							=			self.dummy_sample
									snake_case__      							=			0.1 * sample

									snake_case__      							=			self.get_scheduler_config()
									snake_case__      							=			scheduler_class(**__lowerCamelCase       )

									with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(__lowerCamelCase       )
										snake_case__      							=			scheduler_class.from_pretrained(__lowerCamelCase       )

									snake_case__      							=			scheduler.step_pred(
									    __lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase				,   generator=torch.manual_seed(0       )				,   **__lowerCamelCase       ).prev_sample
									snake_case__      							=			new_scheduler.step_pred(
									    __lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase				,   generator=torch.manual_seed(0       )				,   **__lowerCamelCase       ).prev_sample

									assert torch.sum(torch.abs(output - new_output       )       ) < 1e-5, "Scheduler outputs are not identical"

									snake_case__      							=			scheduler.step_correct(__lowerCamelCase				,   __lowerCamelCase				,   generator=torch.manual_seed(0       )				,   **__lowerCamelCase       ).prev_sample
									snake_case__      							=			new_scheduler.step_correct(
									    __lowerCamelCase				,   __lowerCamelCase				,   generator=torch.manual_seed(0       )				,   **__lowerCamelCase       ).prev_sample

									assert torch.sum(torch.abs(output - new_output       )       ) < 1e-5, "Scheduler correction are not identical"

							def 						_SCREAMING_SNAKE_CASE(     self       ):






								'''simple docstring'''

								for timesteps in [10, 100, 1000]:
									self.check_over_configs(num_train_timesteps=__lowerCamelCase       )

							def 						_SCREAMING_SNAKE_CASE(     self       ):






								'''simple docstring'''

								for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01]				,   [1, 100, 1000]       ):
									self.check_over_configs(sigma_min=__lowerCamelCase				,   sigma_max=__lowerCamelCase       )

							def 						_SCREAMING_SNAKE_CASE(     self       ):






								'''simple docstring'''

								for t in [0.1, 0.5, 0.75]:
									self.check_over_forward(time_step=__lowerCamelCase       )

							def 						_SCREAMING_SNAKE_CASE(     self       ):






								'''simple docstring'''

								snake_case__      							=			dict(self.forward_default_kwargs       )

								snake_case__      							=			self.scheduler_classes[0]
								snake_case__      							=			self.get_scheduler_config()
								snake_case__      							=			scheduler_class(**__lowerCamelCase       )

								snake_case__      							=			3

								snake_case__      							=			self.dummy_model()
								snake_case__      							=			self.dummy_sample_deter

								scheduler.set_sigmas(__lowerCamelCase       )
								scheduler.set_timesteps(__lowerCamelCase       )
								snake_case__      							=			torch.manual_seed(0       )

								for i, t in enumerate(scheduler.timesteps       ):
									snake_case__      							=			scheduler.sigmas[i]

									for _ in range(scheduler.config.correct_steps       ):
										with torch.no_grad():
											snake_case__      							=			model(__lowerCamelCase				,   __lowerCamelCase       )
										snake_case__      							=			scheduler.step_correct(__lowerCamelCase				,   __lowerCamelCase				,   generator=__lowerCamelCase				,   **__lowerCamelCase       ).prev_sample

									with torch.no_grad():
										snake_case__      							=			model(__lowerCamelCase				,   __lowerCamelCase       )

									snake_case__      							=			scheduler.step_pred(__lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase				,   generator=__lowerCamelCase				,   **__lowerCamelCase       )
									snake_case__      							=			output.prev_sample, output.prev_sample_mean

								snake_case__      							=			torch.sum(torch.abs(__lowerCamelCase       )       )
								snake_case__      							=			torch.mean(torch.abs(__lowerCamelCase       )       )

								assert np.isclose(result_sum.item()				,   143_7275_8528.0       )
								assert np.isclose(result_mean.item()				,   1871_4530.0       )

							def 						_SCREAMING_SNAKE_CASE(     self       ):






								'''simple docstring'''

								snake_case__      							=			dict(self.forward_default_kwargs       )

								snake_case__      							=			kwargs.pop("num_inference_steps"				,   __lowerCamelCase       )

								for scheduler_class in self.scheduler_classes:
									snake_case__      							=			self.get_scheduler_config()
									snake_case__      							=			scheduler_class(**__lowerCamelCase       )

									snake_case__      							=			self.dummy_sample
									snake_case__      							=			0.1 * sample

									if num_inference_steps is not None and hasattr(__lowerCamelCase				,   "set_timesteps"       ):
										scheduler.set_timesteps(__lowerCamelCase       )
									elif num_inference_steps is not None and not hasattr(__lowerCamelCase				,   "set_timesteps"       ):
										snake_case__      							=			num_inference_steps

									snake_case__      							=			scheduler.step_pred(__lowerCamelCase				,   0				,   __lowerCamelCase				,   generator=torch.manual_seed(0       )				,   **__lowerCamelCase       ).prev_sample
									snake_case__      							=			scheduler.step_pred(__lowerCamelCase				,   1				,   __lowerCamelCase				,   generator=torch.manual_seed(0       )				,   **__lowerCamelCase       ).prev_sample

									self.assertEqual(output_a.shape				,   sample.shape       )
									self.assertEqual(output_a.shape				,   output_a.shape       )








"""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        lowercase_			(       __lowerCAmelCase			,unittest.TestCase							):
       snake_case__			:							Tuple										=       PaintByExamplePipeline
       snake_case__			:							List[str]										=       IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
       snake_case__			:							List[str]										=       IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       snake_case__			:							List[str]										=       frozenset([]							)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        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=__SCREAMING_SNAKE_CASE       )
        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(__SCREAMING_SNAKE_CASE				,   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				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			np.array(image.convert("RGB"       )       )
        snake_case__      							=			image[None].transpose(0				,   3				,   1				,   2       )
        snake_case__      							=			torch.from_numpy(__SCREAMING_SNAKE_CASE       ).to(dtype=torch.floataa       ) / 127.5 - 1.0
        return image

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE="cpu"				,   __SCREAMING_SNAKE_CASE=0       ):






        '''simple docstring'''

        snake_case__      							=			floats_tensor((1, 3, 32, 32)				,   rng=random.Random(__SCREAMING_SNAKE_CASE       )       ).to(__SCREAMING_SNAKE_CASE       )
        snake_case__      							=			image.cpu().permute(0				,   2				,   3				,   1       )[0]
        snake_case__      							=			Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE       )       ).convert("RGB"       ).resize((64, 64)       )
        snake_case__      							=			Image.fromarray(np.uinta(image + 4       )       ).convert("RGB"       ).resize((64, 64)       )
        snake_case__      							=			Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE       )       ).convert("RGB"       ).resize((32, 32)       )

        if str(__SCREAMING_SNAKE_CASE       ).startswith("mps"       ):
         snake_case__      							=			torch.manual_seed(__SCREAMING_SNAKE_CASE       )
        else:
         snake_case__      							=			torch.Generator(device=__SCREAMING_SNAKE_CASE       ).manual_seed(__SCREAMING_SNAKE_CASE       )
        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       ):






        '''simple docstring'''

        snake_case__      							=			self.get_dummy_components()

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

        snake_case__      							=			self.get_dummy_inputs()
        snake_case__      							=			pipe(**__SCREAMING_SNAKE_CASE       )
        snake_case__      							=			output.images

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

        assert image.shape == (1, 64, 64, 3)
        snake_case__      							=			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       ):






        '''simple docstring'''

        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(__SCREAMING_SNAKE_CASE       )
        pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			pipe(image=__SCREAMING_SNAKE_CASE				,   mask_image=mask_image[:, 0]				,   **__SCREAMING_SNAKE_CASE       )
        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(__SCREAMING_SNAKE_CASE       )       ).convert("RGB"       )
        snake_case__      							=			Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE       )       ).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       ):






        '''simple docstring'''

        super().test_inference_batch_single_identical(expected_max_diff=3e-3       )



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

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        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(__SCREAMING_SNAKE_CASE       )
        pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			torch.manual_seed(321       )
        snake_case__      							=			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"				,   )

        snake_case__      							=			output.images

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

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

