

import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.semantic_stable_diffusion import SemanticStableDiffusionPipeline as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()



class 		a_	(							unittest.TestCase					):



      """simple docstring"""







      def 	_lowerCAmelCase					(				self					:							Optional[Any]    ):
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()
      @property
      def 	_lowerCAmelCase					(				self					:							Any    ):
           SCREAMING_SNAKE_CASE										=1
           SCREAMING_SNAKE_CASE										=3
           SCREAMING_SNAKE_CASE										=(32, 32)

           SCREAMING_SNAKE_CASE										=floats_tensor((batch_size, num_channels) + sizes      ,rng=random.Random(0    )    ).to(snake_case    )
           return image
      @property
      def 	_lowerCAmelCase					(				self					:							Dict    ):
           torch.manual_seed(0    )
           SCREAMING_SNAKE_CASE										=UNetaDConditionModel(
               block_out_channels=(32, 64)      ,layers_per_block=2      ,sample_size=32      ,in_channels=4      ,out_channels=4      ,down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')      ,up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')      ,cross_attention_dim=32      ,)
           return model
      @property
      def 	_lowerCAmelCase					(				self					:							Dict    ):
           torch.manual_seed(0    )
           SCREAMING_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      ,)
           return model
      @property
      def 	_lowerCAmelCase					(				self					:							Optional[int]    ):
           torch.manual_seed(0    )
           SCREAMING_SNAKE_CASE										=CLIPTextConfig(
               bos_token_id=0      ,eos_token_id=2      ,hidden_size=32      ,intermediate_size=37      ,layer_norm_eps=1e-05      ,num_attention_heads=4      ,num_hidden_layers=5      ,pad_token_id=1      ,vocab_size=1000      ,)
           return CLIPTextModel(snake_case    )
      @property
      def 	_lowerCAmelCase					(				self					:							str    ):
           def extract(*snake_case					:							List[Any]      ,**snake_case					:							Optional[int]    ):



                class 		a_	:



                      """simple docstring"""







                      def __init__(				self					:							List[Any]    ):
                           SCREAMING_SNAKE_CASE										=torch.ones([0]    )


                      def 	_lowerCAmelCase					(				self					:							Tuple      ,snake_case					:							Tuple    ):
                           self.pixel_values.to(snake_case    )
                           return self







                return Out()

           return extract
      def 	_lowerCAmelCase					(				self					:							List[Any]    ):
           SCREAMING_SNAKE_CASE										='cpu'  # ensure determinism for the device-dependent torch.Generator
           SCREAMING_SNAKE_CASE										=self.dummy_cond_unet
           SCREAMING_SNAKE_CASE										=DDIMScheduler(
               beta_start=0.00_085      ,beta_end=0.012      ,beta_schedule='scaled_linear'      ,clip_sample=snake_case      ,set_alpha_to_one=snake_case      ,)

           SCREAMING_SNAKE_CASE										=self.dummy_vae
           SCREAMING_SNAKE_CASE										=self.dummy_text_encoder
           SCREAMING_SNAKE_CASE										=CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )

           # make sure here that pndm scheduler skips prk
           SCREAMING_SNAKE_CASE										=StableDiffusionPipeline(
               unet=snake_case      ,scheduler=snake_case      ,vae=snake_case      ,text_encoder=snake_case      ,tokenizer=snake_case      ,safety_checker=snake_case      ,feature_extractor=self.dummy_extractor      ,)
           SCREAMING_SNAKE_CASE										=sd_pipe.to(snake_case    )
           sd_pipe.set_progress_bar_config(disable=snake_case    )

           SCREAMING_SNAKE_CASE										='A painting of a squirrel eating a burger'

           SCREAMING_SNAKE_CASE										=torch.Generator(device=snake_case    ).manual_seed(0    )
           SCREAMING_SNAKE_CASE										=sd_pipe([prompt]      ,generator=snake_case      ,guidance_scale=6.0      ,num_inference_steps=2      ,output_type='np'    )
           SCREAMING_SNAKE_CASE										=output.images

           SCREAMING_SNAKE_CASE										=torch.Generator(device=snake_case    ).manual_seed(0    )
           SCREAMING_SNAKE_CASE										=sd_pipe(
               [prompt]      ,generator=snake_case      ,guidance_scale=6.0      ,num_inference_steps=2      ,output_type='np'      ,return_dict=snake_case      ,)[0]

           SCREAMING_SNAKE_CASE										=image[0, -3:, -3:, -1]
           SCREAMING_SNAKE_CASE										=image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)
           SCREAMING_SNAKE_CASE										=np.array([0.5_753, 0.6_114, 0.5_001, 0.5_034, 0.5_470, 0.4_729, 0.4_971, 0.4_867, 0.4_867]    )

           assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1e-2
      def 	_lowerCAmelCase					(				self					:							Optional[int]    ):
           SCREAMING_SNAKE_CASE										='cpu'  # ensure determinism for the device-dependent torch.Generator
           SCREAMING_SNAKE_CASE										=self.dummy_cond_unet
           SCREAMING_SNAKE_CASE										=PNDMScheduler(skip_prk_steps=snake_case    )
           SCREAMING_SNAKE_CASE										=self.dummy_vae
           SCREAMING_SNAKE_CASE										=self.dummy_text_encoder
           SCREAMING_SNAKE_CASE										=CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )

           # make sure here that pndm scheduler skips prk
           SCREAMING_SNAKE_CASE										=StableDiffusionPipeline(
               unet=snake_case      ,scheduler=snake_case      ,vae=snake_case      ,text_encoder=snake_case      ,tokenizer=snake_case      ,safety_checker=snake_case      ,feature_extractor=self.dummy_extractor      ,)
           SCREAMING_SNAKE_CASE										=sd_pipe.to(snake_case    )
           sd_pipe.set_progress_bar_config(disable=snake_case    )

           SCREAMING_SNAKE_CASE										='A painting of a squirrel eating a burger'
           SCREAMING_SNAKE_CASE										=torch.Generator(device=snake_case    ).manual_seed(0    )
           SCREAMING_SNAKE_CASE										=sd_pipe([prompt]      ,generator=snake_case      ,guidance_scale=6.0      ,num_inference_steps=2      ,output_type='np'    )

           SCREAMING_SNAKE_CASE										=output.images

           SCREAMING_SNAKE_CASE										=torch.Generator(device=snake_case    ).manual_seed(0    )
           SCREAMING_SNAKE_CASE										=sd_pipe(
               [prompt]      ,generator=snake_case      ,guidance_scale=6.0      ,num_inference_steps=2      ,output_type='np'      ,return_dict=snake_case      ,)[0]

           SCREAMING_SNAKE_CASE										=image[0, -3:, -3:, -1]
           SCREAMING_SNAKE_CASE										=image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)
           SCREAMING_SNAKE_CASE										=np.array([0.5_122, 0.5_712, 0.4_825, 0.5_053, 0.5_646, 0.4_769, 0.5_179, 0.4_894, 0.4_994]    )

           assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1e-2
      def 	_lowerCAmelCase					(				self					:							Tuple    ):
           SCREAMING_SNAKE_CASE										=StableDiffusionPipeline.from_pretrained(
               'hf-internal-testing/tiny-stable-diffusion-lms-pipe'      ,safety_checker=snake_case    )
           assert isinstance(snake_case      ,snake_case    )
           assert isinstance(pipe.scheduler      ,snake_case    )
           assert pipe.safety_checker is None

           SCREAMING_SNAKE_CASE										=pipe('example prompt'      ,num_inference_steps=2    ).images[0]
           assert image is not None

           # check that there's no error when saving a pipeline with one of the models being None
           with tempfile.TemporaryDirectory() as tmpdirname:
                pipe.save_pretrained(snake_case    )
                SCREAMING_SNAKE_CASE										=StableDiffusionPipeline.from_pretrained(snake_case    )

           # sanity check that the pipeline still works
           assert pipe.safety_checker is None
           SCREAMING_SNAKE_CASE										=pipe('example prompt'      ,num_inference_steps=2    ).images[0]
           assert image is not None


      @unittest.skipIf(torch_device != 'cuda'      ,'This test requires a GPU'    )
      def 	_lowerCAmelCase					(				self					:							List[str]    ):
           SCREAMING_SNAKE_CASE										=self.dummy_cond_unet
           SCREAMING_SNAKE_CASE										=PNDMScheduler(skip_prk_steps=snake_case    )
           SCREAMING_SNAKE_CASE										=self.dummy_vae
           SCREAMING_SNAKE_CASE										=self.dummy_text_encoder
           SCREAMING_SNAKE_CASE										=CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )

           # put models in fp16
           SCREAMING_SNAKE_CASE										=unet.half()
           SCREAMING_SNAKE_CASE										=vae.half()
           SCREAMING_SNAKE_CASE										=bert.half()

           # make sure here that pndm scheduler skips prk
           SCREAMING_SNAKE_CASE										=StableDiffusionPipeline(
               unet=snake_case      ,scheduler=snake_case      ,vae=snake_case      ,text_encoder=snake_case      ,tokenizer=snake_case      ,safety_checker=snake_case      ,feature_extractor=self.dummy_extractor      ,)
           SCREAMING_SNAKE_CASE										=sd_pipe.to(snake_case    )
           sd_pipe.set_progress_bar_config(disable=snake_case    )

           SCREAMING_SNAKE_CASE										='A painting of a squirrel eating a burger'
           SCREAMING_SNAKE_CASE										=sd_pipe([prompt]      ,num_inference_steps=2      ,output_type='np'    ).images

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







@nightly
@require_torch_gpu
class 		a_	(							unittest.TestCase					):



      """simple docstring"""







      def 	_lowerCAmelCase					(				self					:							List[Any]    ):
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()
      def 	_lowerCAmelCase					(				self					:							int    ):
           SCREAMING_SNAKE_CASE										='cuda'
           SCREAMING_SNAKE_CASE										=StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'    )
           SCREAMING_SNAKE_CASE										=pipe.to(snake_case    )
           pipe.set_progress_bar_config(disable=snake_case    )

           SCREAMING_SNAKE_CASE										='a photo of a cat'
           SCREAMING_SNAKE_CASE										={
               'editing_prompt': ['sunglasses'],
               'reverse_editing_direction': [False],
               'edit_warmup_steps': 10,
               'edit_guidance_scale': 6,
               'edit_threshold': 0.95,
               'edit_momentum_scale': 0.5,
               'edit_mom_beta': 0.6,
           }

           SCREAMING_SNAKE_CASE										=3
           SCREAMING_SNAKE_CASE										=7

           # no sega enabled
           SCREAMING_SNAKE_CASE										=torch.Generator(snake_case    )
           generator.manual_seed(snake_case    )
           SCREAMING_SNAKE_CASE										=pipe(
               [prompt]      ,generator=snake_case      ,guidance_scale=snake_case      ,num_inference_steps=50      ,output_type='np'      ,width=512      ,height=512      ,)

           SCREAMING_SNAKE_CASE										=output.images
           SCREAMING_SNAKE_CASE										=image[0, -3:, -3:, -1]
           SCREAMING_SNAKE_CASE										=[
               0.34_673_113,
               0.38_492_733,
               0.37_597_352,
               0.34_086_335,
               0.35_650_748,
               0.35_579_205,
               0.3_384_763,
               0.34_340_236,
               0.3_573_271,
           ]

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

           assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2

           # with sega enabled
           # generator = torch.manual_seed(seed)
           generator.manual_seed(snake_case    )
           SCREAMING_SNAKE_CASE										=pipe(
               [prompt]      ,generator=snake_case      ,guidance_scale=snake_case      ,num_inference_steps=50      ,output_type='np'      ,width=512      ,height=512      ,**snake_case      ,)

           SCREAMING_SNAKE_CASE										=output.images
           SCREAMING_SNAKE_CASE										=image[0, -3:, -3:, -1]
           SCREAMING_SNAKE_CASE										=[
               0.41_887_826,
               0.37_728_766,
               0.30_138_272,
               0.41_416_335,
               0.41_664_985,
               0.36_283_392,
               0.36_191_246,
               0.43_364_465,
               0.43_001_732,
           ]

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

           assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2
      def 	_lowerCAmelCase					(				self					:							Optional[int]    ):
           SCREAMING_SNAKE_CASE										='cuda'
           SCREAMING_SNAKE_CASE										=StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'    )
           SCREAMING_SNAKE_CASE										=pipe.to(snake_case    )
           pipe.set_progress_bar_config(disable=snake_case    )

           SCREAMING_SNAKE_CASE										='an image of a crowded boulevard, realistic, 4k'
           SCREAMING_SNAKE_CASE										={
               'editing_prompt': 'crowd, crowded, people',
               'reverse_editing_direction': True,
               'edit_warmup_steps': 10,
               'edit_guidance_scale': 8.3,
               'edit_threshold': 0.9,
               'edit_momentum_scale': 0.5,
               'edit_mom_beta': 0.6,
           }

           SCREAMING_SNAKE_CASE										=9
           SCREAMING_SNAKE_CASE										=7

           # no sega enabled
           SCREAMING_SNAKE_CASE										=torch.Generator(snake_case    )
           generator.manual_seed(snake_case    )
           SCREAMING_SNAKE_CASE										=pipe(
               [prompt]      ,generator=snake_case      ,guidance_scale=snake_case      ,num_inference_steps=50      ,output_type='np'      ,width=512      ,height=512      ,)

           SCREAMING_SNAKE_CASE										=output.images
           SCREAMING_SNAKE_CASE										=image[0, -3:, -3:, -1]
           SCREAMING_SNAKE_CASE										=[
               0.43_497_998,
               0.91_814_065,
               0.7_540_739,
               0.55_580_205,
               0.8_467_265,
               0.5_389_691,
               0.62_574_506,
               0.58_897_763,
               0.50_926_757,
           ]

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

           assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2

           # with sega enabled
           # generator = torch.manual_seed(seed)
           generator.manual_seed(snake_case    )
           SCREAMING_SNAKE_CASE										=pipe(
               [prompt]      ,generator=snake_case      ,guidance_scale=snake_case      ,num_inference_steps=50      ,output_type='np'      ,width=512      ,height=512      ,**snake_case      ,)

           SCREAMING_SNAKE_CASE										=output.images
           SCREAMING_SNAKE_CASE										=image[0, -3:, -3:, -1]
           SCREAMING_SNAKE_CASE										=[
               0.3_089_719,
               0.30_500_144,
               0.29_016_042,
               0.30_630_964,
               0.325_687,
               0.29_419_225,
               0.2_908_091,
               0.28_723_598,
               0.27_696_294,
           ]

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

           assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2
      def 	_lowerCAmelCase					(				self					:							Optional[Any]    ):
           SCREAMING_SNAKE_CASE										='cuda'
           SCREAMING_SNAKE_CASE										=StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'    )
           SCREAMING_SNAKE_CASE										=pipe.to(snake_case    )
           pipe.set_progress_bar_config(disable=snake_case    )

           SCREAMING_SNAKE_CASE										='a castle next to a river'
           SCREAMING_SNAKE_CASE										={
               'editing_prompt': ['boat on a river, boat', 'monet, impression, sunrise'],
               'reverse_editing_direction': False,
               'edit_warmup_steps': [15, 18],
               'edit_guidance_scale': 6,
               'edit_threshold': [0.9, 0.8],
               'edit_momentum_scale': 0.5,
               'edit_mom_beta': 0.6,
           }

           SCREAMING_SNAKE_CASE										=48
           SCREAMING_SNAKE_CASE										=7

           # no sega enabled
           SCREAMING_SNAKE_CASE										=torch.Generator(snake_case    )
           generator.manual_seed(snake_case    )
           SCREAMING_SNAKE_CASE										=pipe(
               [prompt]      ,generator=snake_case      ,guidance_scale=snake_case      ,num_inference_steps=50      ,output_type='np'      ,width=512      ,height=512      ,)

           SCREAMING_SNAKE_CASE										=output.images
           SCREAMING_SNAKE_CASE										=image[0, -3:, -3:, -1]
           SCREAMING_SNAKE_CASE										=[
               0.75_163_555,
               0.76_037_145,
               0.61_785,
               0.9_189_673,
               0.8_627_701,
               0.85_189_694,
               0.8_512_813,
               0.87_012_076,
               0.8_312_857,
           ]

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

           assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2

           # with sega enabled
           # generator = torch.manual_seed(seed)
           generator.manual_seed(snake_case    )
           SCREAMING_SNAKE_CASE										=pipe(
               [prompt]      ,generator=snake_case      ,guidance_scale=snake_case      ,num_inference_steps=50      ,output_type='np'      ,width=512      ,height=512      ,**snake_case      ,)

           SCREAMING_SNAKE_CASE										=output.images
           SCREAMING_SNAKE_CASE										=image[0, -3:, -3:, -1]
           SCREAMING_SNAKE_CASE										=[
               0.73_553_365,
               0.7_537_271,
               0.74_341_905,
               0.66_480_356,
               0.6_472_925,
               0.63_039_416,
               0.64_812_905,
               0.6_749_717,
               0.6_517_102,
           ]

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

           assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2


      def 	_lowerCAmelCase					(				self					:							Optional[Any]    ):
           SCREAMING_SNAKE_CASE										='cuda'
           SCREAMING_SNAKE_CASE										=StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'      ,torch_dtype=torch.floataa    )
           SCREAMING_SNAKE_CASE										=pipe.to(snake_case    )
           pipe.set_progress_bar_config(disable=snake_case    )

           SCREAMING_SNAKE_CASE										='a photo of a cat'
           SCREAMING_SNAKE_CASE										={
               'editing_prompt': ['sunglasses'],
               'reverse_editing_direction': [False],
               'edit_warmup_steps': 10,
               'edit_guidance_scale': 6,
               'edit_threshold': 0.95,
               'edit_momentum_scale': 0.5,
               'edit_mom_beta': 0.6,
           }

           SCREAMING_SNAKE_CASE										=3
           SCREAMING_SNAKE_CASE										=7

           # no sega enabled
           SCREAMING_SNAKE_CASE										=torch.Generator(snake_case    )
           generator.manual_seed(snake_case    )
           SCREAMING_SNAKE_CASE										=pipe(
               [prompt]      ,generator=snake_case      ,guidance_scale=snake_case      ,num_inference_steps=50      ,output_type='np'      ,width=512      ,height=512      ,)

           SCREAMING_SNAKE_CASE										=output.images
           SCREAMING_SNAKE_CASE										=image[0, -3:, -3:, -1]
           SCREAMING_SNAKE_CASE										=[
               0.34_887_695,
               0.3_876_953,
               0.375,
               0.34_423_828,
               0.3_581_543,
               0.35_717_773,
               0.3_383_789,
               0.34_570_312,
               0.359_375,
           ]

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

           assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2

           # with sega enabled
           # generator = torch.manual_seed(seed)
           generator.manual_seed(snake_case    )
           SCREAMING_SNAKE_CASE										=pipe(
               [prompt]      ,generator=snake_case      ,guidance_scale=snake_case      ,num_inference_steps=50      ,output_type='np'      ,width=512      ,height=512      ,**snake_case      ,)

           SCREAMING_SNAKE_CASE										=output.images
           SCREAMING_SNAKE_CASE										=image[0, -3:, -3:, -1]
           SCREAMING_SNAKE_CASE										=[
               0.42_285_156,
               0.36_914_062,
               0.29_077_148,
               0.42_041_016,
               0.41_918_945,
               0.35_498_047,
               0.3_618_164,
               0.4_423_828,
               0.43_115_234,
           ]

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

           assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2




import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class 		a_	(							lowerCamelCase_			,      unittest.TestCase					):



      """simple docstring"""







      __UpperCAmelCase      		=				RePaintPipeline
      __UpperCAmelCase      		=				IMAGE_INPAINTING_PARAMS - {'width', 'height', 'guidance_scale'}
      __UpperCAmelCase      		=				PipelineTesterMixin.required_optional_params - {
          'latents',
          'num_images_per_prompt',
          'callback',
          'callback_steps',
      }
      __UpperCAmelCase      		=				IMAGE_INPAINTING_BATCH_PARAMS
      __UpperCAmelCase      		=				False
      def 	_lowerCAmelCase					(				self					:							Tuple    ):
           torch.manual_seed(0    )
           torch.manual_seed(0    )
           SCREAMING_SNAKE_CASE										=UNetaDModel(
               block_out_channels=(32, 64)      ,layers_per_block=2      ,sample_size=32      ,in_channels=3      ,out_channels=3      ,down_block_types=('DownBlock2D', 'AttnDownBlock2D')      ,up_block_types=('AttnUpBlock2D', 'UpBlock2D')      ,)
           SCREAMING_SNAKE_CASE										=RePaintScheduler()
           SCREAMING_SNAKE_CASE										={'unet': unet, 'scheduler': scheduler}
           return components
      def 	_lowerCAmelCase					(				self					:							Tuple      ,snake_case					:							Optional[int]      ,snake_case					:							Dict=0    ):
           if str(snake_case    ).startswith('mps'    ):
                SCREAMING_SNAKE_CASE										=torch.manual_seed(snake_case    )
           else:
                SCREAMING_SNAKE_CASE										=torch.Generator(device=snake_case    ).manual_seed(snake_case    )
           SCREAMING_SNAKE_CASE										=np.random.RandomState(snake_case    ).standard_normal((1, 3, 32, 32)    )
           SCREAMING_SNAKE_CASE										=torch.from_numpy(snake_case    ).to(device=snake_case      ,dtype=torch.floataa    )
           SCREAMING_SNAKE_CASE										=(image > 0).to(device=snake_case      ,dtype=torch.floataa    )
           SCREAMING_SNAKE_CASE										={
               'image': image,
               'mask_image': mask,
               'generator': generator,
               'num_inference_steps': 5,
               'eta': 0.0,
               'jump_length': 2,
               'jump_n_sample': 2,
               'output_type': 'numpy',
           }
           return inputs
      def 	_lowerCAmelCase					(				self					:							Optional[Any]    ):
           SCREAMING_SNAKE_CASE										='cpu'  # ensure determinism for the device-dependent torch.Generator
           SCREAMING_SNAKE_CASE										=self.get_dummy_components()
           SCREAMING_SNAKE_CASE										=RePaintPipeline(**snake_case    )
           SCREAMING_SNAKE_CASE										=sd_pipe.to(snake_case    )
           sd_pipe.set_progress_bar_config(disable=snake_case    )

           SCREAMING_SNAKE_CASE										=self.get_dummy_inputs(snake_case    )
           SCREAMING_SNAKE_CASE										=sd_pipe(**snake_case    ).images
           SCREAMING_SNAKE_CASE										=image[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           SCREAMING_SNAKE_CASE										=np.array([1.0_000, 0.5_426, 0.5_497, 0.2_200, 1.0_000, 1.0_000, 0.5_623, 1.0_000, 0.6_274]    )

           assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-3
      @skip_mps
      def 	_lowerCAmelCase					(				self					:							Optional[int]    ):
           return super().test_save_load_local()
      @unittest.skip('non-deterministic pipeline'    )
      def 	_lowerCAmelCase					(				self					:							Tuple    ):
           return super().test_inference_batch_single_identical()
      @skip_mps
      def 	_lowerCAmelCase					(				self					:							Optional[Any]    ):
           return super().test_dict_tuple_outputs_equivalent()
      @skip_mps
      def 	_lowerCAmelCase					(				self					:							Optional[Any]    ):
           return super().test_save_load_optional_components()


      @skip_mps
      def 	_lowerCAmelCase					(				self					:							Any    ):
           return super().test_attention_slicing_forward_pass()







@nightly
@require_torch_gpu
class 		a_	(							unittest.TestCase					):



      """simple docstring"""







      def 	_lowerCAmelCase					(				self					:							Union[str, Any]    ):
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()


      def 	_lowerCAmelCase					(				self					:							Tuple    ):
           SCREAMING_SNAKE_CASE										=load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
               'repaint/celeba_hq_256.png'    )
           SCREAMING_SNAKE_CASE										=load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'    )
           SCREAMING_SNAKE_CASE										=load_numpy(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
               'repaint/celeba_hq_256_result.npy'    )

           SCREAMING_SNAKE_CASE										='google/ddpm-ema-celebahq-256'
           SCREAMING_SNAKE_CASE										=UNetaDModel.from_pretrained(snake_case    )
           SCREAMING_SNAKE_CASE										=RePaintScheduler.from_pretrained(snake_case    )

           SCREAMING_SNAKE_CASE										=RePaintPipeline(unet=snake_case      ,scheduler=snake_case    ).to(snake_case    )
           repaint.set_progress_bar_config(disable=snake_case    )
           repaint.enable_attention_slicing()

           SCREAMING_SNAKE_CASE										=torch.manual_seed(0    )
           SCREAMING_SNAKE_CASE										=repaint(
               snake_case      ,snake_case      ,num_inference_steps=250      ,eta=0.0      ,jump_length=10      ,jump_n_sample=10      ,generator=snake_case      ,output_type='np'      ,)
           SCREAMING_SNAKE_CASE										=output.images[0]

           assert image.shape == (256, 256, 3)
           assert np.abs(expected_image - image    ).mean() < 1e-2

