




UpperCamelCase								=	'0.18.2'

from .configuration_utils import ConfigMixin
from .utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    logging,
)


try:
     if not is_onnx_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_onnx_objects import *  # noqa F403
else:
     from .pipelines import OnnxRuntimeModel

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_pt_objects import *  # noqa F403
else:
     from .models import (
         AutoencoderKL,
         ControlNetModel,
         ModelMixin,
         PriorTransformer,
         TaFilmDecoder,
         TransformeraDModel,
         UNetaDModel,
         UNetaDConditionModel,
         UNetaDModel,
         UNetaDConditionModel,
         VQModel,
     )
     from .optimization import (
         get_constant_schedule,
         get_constant_schedule_with_warmup,
         get_cosine_schedule_with_warmup,
         get_cosine_with_hard_restarts_schedule_with_warmup,
         get_linear_schedule_with_warmup,
         get_polynomial_decay_schedule_with_warmup,
         get_scheduler,
     )
     from .pipelines import (
         AudioPipelineOutput,
         ConsistencyModelPipeline,
         DanceDiffusionPipeline,
         DDIMPipeline,
         DDPMPipeline,
         DiffusionPipeline,
         DiTPipeline,
         ImagePipelineOutput,
         KarrasVePipeline,
         LDMPipeline,
         LDMSuperResolutionPipeline,
         PNDMPipeline,
         RePaintPipeline,
         ScoreSdeVePipeline,
     )
     from .schedulers import (
         CMStochasticIterativeScheduler,
         DDIMInverseScheduler,
         DDIMParallelScheduler,
         DDIMScheduler,
         DDPMParallelScheduler,
         DDPMScheduler,
         DEISMultistepScheduler,
         DPMSolverMultistepInverseScheduler,
         DPMSolverMultistepScheduler,
         DPMSolverSinglestepScheduler,
         EulerAncestralDiscreteScheduler,
         EulerDiscreteScheduler,
         HeunDiscreteScheduler,
         IPNDMScheduler,
         KarrasVeScheduler,
         KDPMaAncestralDiscreteScheduler,
         KDPMaDiscreteScheduler,
         PNDMScheduler,
         RePaintScheduler,
         SchedulerMixin,
         ScoreSdeVeScheduler,
         UnCLIPScheduler,
         UniPCMultistepScheduler,
         VQDiffusionScheduler,
     )
     from .training_utils import EMAModel

try:
     if not (is_torch_available() and is_scipy_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
     from .schedulers import LMSDiscreteScheduler

try:
     if not (is_torch_available() and is_torchsde_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
     from .schedulers import DPMSolverSDEScheduler

try:
     if not (is_torch_available() and is_transformers_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
     from .pipelines import (
         AltDiffusionImgaImgPipeline,
         AltDiffusionPipeline,
         AudioLDMPipeline,
         CycleDiffusionPipeline,
         IFImgaImgPipeline,
         IFImgaImgSuperResolutionPipeline,
         IFInpaintingPipeline,
         IFInpaintingSuperResolutionPipeline,
         IFPipeline,
         IFSuperResolutionPipeline,
         ImageTextPipelineOutput,
         KandinskyImgaImgPipeline,
         KandinskyInpaintPipeline,
         KandinskyPipeline,
         KandinskyPriorPipeline,
         KandinskyVaaControlnetImgaImgPipeline,
         KandinskyVaaControlnetPipeline,
         KandinskyVaaImgaImgPipeline,
         KandinskyVaaInpaintPipeline,
         KandinskyVaaPipeline,
         KandinskyVaaPriorEmbaEmbPipeline,
         KandinskyVaaPriorPipeline,
         LDMTextToImagePipeline,
         PaintByExamplePipeline,
         SemanticStableDiffusionPipeline,
         ShapEImgaImgPipeline,
         ShapEPipeline,
         StableDiffusionAttendAndExcitePipeline,
         StableDiffusionControlNetImgaImgPipeline,
         StableDiffusionControlNetInpaintPipeline,
         StableDiffusionControlNetPipeline,
         StableDiffusionDepthaImgPipeline,
         StableDiffusionDiffEditPipeline,
         StableDiffusionImageVariationPipeline,
         StableDiffusionImgaImgPipeline,
         StableDiffusionInpaintPipeline,
         StableDiffusionInpaintPipelineLegacy,
         StableDiffusionInstructPixaPixPipeline,
         StableDiffusionLatentUpscalePipeline,
         StableDiffusionLDMaDPipeline,
         StableDiffusionModelEditingPipeline,
         StableDiffusionPanoramaPipeline,
         StableDiffusionParadigmsPipeline,
         StableDiffusionPipeline,
         StableDiffusionPipelineSafe,
         StableDiffusionPixaPixZeroPipeline,
         StableDiffusionSAGPipeline,
         StableDiffusionUpscalePipeline,
         StableUnCLIPImgaImgPipeline,
         StableUnCLIPPipeline,
         TextToVideoSDPipeline,
         TextToVideoZeroPipeline,
         UnCLIPImageVariationPipeline,
         UnCLIPPipeline,
         UniDiffuserModel,
         UniDiffuserPipeline,
         UniDiffuserTextDecoder,
         VersatileDiffusionDualGuidedPipeline,
         VersatileDiffusionImageVariationPipeline,
         VersatileDiffusionPipeline,
         VersatileDiffusionTextToImagePipeline,
         VideoToVideoSDPipeline,
         VQDiffusionPipeline,
     )

try:
     if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
     from .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
     if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
     from .pipelines import StableDiffusionKDiffusionPipeline

try:
     if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
     from .pipelines import (
         OnnxStableDiffusionImgaImgPipeline,
         OnnxStableDiffusionInpaintPipeline,
         OnnxStableDiffusionInpaintPipelineLegacy,
         OnnxStableDiffusionPipeline,
         OnnxStableDiffusionUpscalePipeline,
         StableDiffusionOnnxPipeline,
     )

try:
     if not (is_torch_available() and is_librosa_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
     from .pipelines import AudioDiffusionPipeline, Mel

try:
     if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
     from .pipelines import SpectrogramDiffusionPipeline

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_flax_objects import *  # noqa F403
else:
     from .models.controlnet_flax import FlaxControlNetModel
     from .models.modeling_flax_utils import FlaxModelMixin
     from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
     from .models.vae_flax import FlaxAutoencoderKL
     from .pipelines import FlaxDiffusionPipeline
     from .schedulers import (
         FlaxDDIMScheduler,
         FlaxDDPMScheduler,
         FlaxDPMSolverMultistepScheduler,
         FlaxKarrasVeScheduler,
         FlaxLMSDiscreteScheduler,
         FlaxPNDMScheduler,
         FlaxSchedulerMixin,
         FlaxScoreSdeVeScheduler,
     )


try:
     if not (is_flax_available() and is_transformers_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
     from .pipelines import (
         FlaxStableDiffusionControlNetPipeline,
         FlaxStableDiffusionImgaImgPipeline,
         FlaxStableDiffusionInpaintPipeline,
         FlaxStableDiffusionPipeline,
     )

try:
     if not (is_note_seq_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_note_seq_objects import *  # noqa F403
else:
     from .pipelines import MidiProcessor



import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class SCREAMING_SNAKE_CASE	(		__snake_case				,    __snake_case				,    unittest.TestCase				):






			"""simple docstring"""




			__A    	=	StableDiffusionDiffEditPipeline
			__A    	=	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""height""", """width""", """image"""} | {"""image_latents"""}
			__A    	=	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"""image"""} | {"""image_latents"""}
			__A    	=	frozenset(
			    []				)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
			__A    	=	frozenset([]				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				torch.manual_seed(0				)
				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			,					attention_head_dim=(2, 4)			,					use_linear_projection=__UpperCamelCase			,					)
				snake_case_   		=			DDIMScheduler(
				    beta_start=0.0_0085			,					beta_end=0.012			,					beta_schedule='scaled_linear'			,					clip_sample=__UpperCamelCase			,					set_alpha_to_one=__UpperCamelCase			,					)
				snake_case_   		=			DDIMInverseScheduler(
				    beta_start=0.0_0085			,					beta_end=0.012			,					beta_schedule='scaled_linear'			,					clip_sample=__UpperCamelCase			,					set_alpha_to_zero=__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			,					sample_size=1_28			,					)
				torch.manual_seed(0				)
				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=10_00			,					hidden_act='gelu'			,					projection_dim=5_12			,					)
				snake_case_   		=			CLIPTextModel(__UpperCamelCase				)
				snake_case_   		=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)

				snake_case_   		=			{
				    'unet': unet,
				    'scheduler': scheduler,
				    'inverse_scheduler': inverse_scheduler,
				    'vae': vae,
				    'text_encoder': text_encoder,
				    'tokenizer': tokenizer,
				    'safety_checker': None,
				    'feature_extractor': None,
				}

				return components


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase=0				):

				"""simple docstring"""


				snake_case_   		=			floats_tensor((1, 16, 16)			,					rng=random.Random(__UpperCamelCase				)				).to(__UpperCamelCase				)
				snake_case_   		=			floats_tensor((1, 2, 4, 16, 16)			,					rng=random.Random(__UpperCamelCase				)				).to(__UpperCamelCase				)
				if str(__UpperCamelCase				).startswith('mps'				):
					snake_case_   		=			torch.manual_seed(__UpperCamelCase				)
				else:
					snake_case_   		=			torch.Generator(device=__UpperCamelCase				).manual_seed(__UpperCamelCase				)
				snake_case_   		=			{
				    'prompt': 'a dog and a newt',
				    'mask_image': mask,
				    'image_latents': latents,
				    'generator': generator,
				    'num_inference_steps': 2,
				    'inpaint_strength': 1.0,
				    'guidance_scale': 6.0,
				    'output_type': 'numpy',
				}

				return inputs


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase=0				):

				"""simple docstring"""


				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'				)
				if str(__UpperCamelCase				).startswith('mps'				):
					snake_case_   		=			torch.manual_seed(__UpperCamelCase				)
				else:
					snake_case_   		=			torch.Generator(device=__UpperCamelCase				).manual_seed(__UpperCamelCase				)
				snake_case_   		=			{
				    'image': image,
				    'source_prompt': 'a cat and a frog',
				    'target_prompt': 'a dog and a newt',
				    'generator': generator,
				    'num_inference_steps': 2,
				    'num_maps_per_mask': 2,
				    'mask_encode_strength': 1.0,
				    'guidance_scale': 6.0,
				    'output_type': 'numpy',
				}

				return inputs


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase=0				):

				"""simple docstring"""


				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'				)
				if str(__UpperCamelCase				).startswith('mps'				):
					snake_case_   		=			torch.manual_seed(__UpperCamelCase				)
				else:
					snake_case_   		=			torch.Generator(device=__UpperCamelCase				).manual_seed(__UpperCamelCase				)
				snake_case_   		=			{
				    'image': image,
				    'prompt': 'a cat and a frog',
				    'generator': generator,
				    'num_inference_steps': 2,
				    'inpaint_strength': 1.0,
				    'guidance_scale': 6.0,
				    'decode_latents': True,
				    'output_type': 'numpy',
				}
				return inputs


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				if not hasattr(self.pipeline_class			,					'_optional_components'				):
					return

				snake_case_   		=			self.get_dummy_components()
				snake_case_   		=			self.pipeline_class(**__UpperCamelCase				)
				pipe.to(__UpperCamelCase				)
				pipe.set_progress_bar_config(disable=__UpperCamelCase				)

				# set all optional components to None and update pipeline config accordingly
				for optional_component in pipe._optional_components:
					setattr(__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase				)
				pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}				)

				snake_case_   		=			self.get_dummy_inputs(__UpperCamelCase				)
				snake_case_   		=			pipe(**__UpperCamelCase				)[0]

				with tempfile.TemporaryDirectory() as tmpdir:
					pipe.save_pretrained(__UpperCamelCase				)
					snake_case_   		=			self.pipeline_class.from_pretrained(__UpperCamelCase				)
					pipe_loaded.to(__UpperCamelCase				)
					pipe_loaded.set_progress_bar_config(disable=__UpperCamelCase				)

				for optional_component in pipe._optional_components:
					self.assertTrue(
					    getattr(__UpperCamelCase			,					__UpperCamelCase				) is None			,					f"""`{optional_component}` did not stay set to None after loading."""			,					)

				snake_case_   		=			self.get_dummy_inputs(__UpperCamelCase				)
				snake_case_   		=			pipe_loaded(**__UpperCamelCase				)[0]

				snake_case_   		=			np.abs(output - output_loaded				).max()
				self.assertLess(__UpperCamelCase			,					1E-4				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			'cpu'

				snake_case_   		=			self.get_dummy_components()
				snake_case_   		=			self.pipeline_class(**__UpperCamelCase				)
				pipe.to(__UpperCamelCase				)
				pipe.set_progress_bar_config(disable=__UpperCamelCase				)

				snake_case_   		=			self.get_dummy_mask_inputs(__UpperCamelCase				)
				snake_case_   		=			pipe.generate_mask(**__UpperCamelCase				)
				snake_case_   		=			mask[0, -3:, -3:]

				self.assertEqual(mask.shape			,					(1, 16, 16)				)
				snake_case_   		=			np.array([0] * 9				)
				snake_case_   		=			np.abs(mask_slice.flatten() - expected_slice				).max()
				self.assertLessEqual(__UpperCamelCase			,					1E-3				)
				self.assertEqual(mask[0, -3, -4]			,					0				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			'cpu'

				snake_case_   		=			self.get_dummy_components()
				snake_case_   		=			self.pipeline_class(**__UpperCamelCase				)
				pipe.to(__UpperCamelCase				)
				pipe.set_progress_bar_config(disable=__UpperCamelCase				)

				snake_case_   		=			self.get_dummy_inversion_inputs(__UpperCamelCase				)
				snake_case_   		=			pipe.invert(**__UpperCamelCase				).images
				snake_case_   		=			image[0, -1, -3:, -3:]

				self.assertEqual(image.shape			,					(2, 32, 32, 3)				)
				snake_case_   		=			np.array(
				    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799]			,					)
				snake_case_   		=			np.abs(image_slice.flatten() - expected_slice				).max()
				self.assertLessEqual(__UpperCamelCase			,					1E-3				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				super().test_inference_batch_single_identical(expected_max_diff=5E-3				)







			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			'cpu'

				snake_case_   		=			self.get_dummy_components()

				snake_case_   		=			{'beta_start': 0.0_0085, 'beta_end': 0.012, 'beta_schedule': 'scaled_linear'}
				snake_case_   		=			DPMSolverMultistepScheduler(**__UpperCamelCase				)
				snake_case_   		=			DPMSolverMultistepInverseScheduler(**__UpperCamelCase				)

				snake_case_   		=			self.pipeline_class(**__UpperCamelCase				)
				pipe.to(__UpperCamelCase				)
				pipe.set_progress_bar_config(disable=__UpperCamelCase				)

				snake_case_   		=			self.get_dummy_inversion_inputs(__UpperCamelCase				)
				snake_case_   		=			pipe.invert(**__UpperCamelCase				).images
				snake_case_   		=			image[0, -1, -3:, -3:]

				self.assertEqual(image.shape			,					(2, 32, 32, 3)				)
				snake_case_   		=			np.array(
				    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799]			,					)
				snake_case_   		=			np.abs(image_slice.flatten() - expected_slice				).max()
				self.assertLessEqual(__UpperCamelCase			,					1E-3				)







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






			"""simple docstring"""




			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


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


			@classmethod
			def       __lowerCAmelCase			(     cls				):

				"""simple docstring"""


				snake_case_   		=			load_image(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'				)

				snake_case_   		=			raw_image.convert('RGB'				).resize((7_68, 7_68)				)

				snake_case_   		=			raw_image


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			torch.manual_seed(0				)

				snake_case_   		=			StableDiffusionDiffEditPipeline.from_pretrained(
				    'stabilityai/stable-diffusion-2-1'			,					safety_checker=__UpperCamelCase			,					torch_dtype=torch.floataa				)
				snake_case_   		=			DDIMScheduler.from_config(pipe.scheduler.config				)
				snake_case_   		=			DDIMInverseScheduler.from_config(pipe.scheduler.config				)
				pipe.enable_model_cpu_offload()
				pipe.set_progress_bar_config(disable=__UpperCamelCase				)

				snake_case_   		=			'a bowl of fruit'
				snake_case_   		=			'a bowl of pears'

				snake_case_   		=			pipe.generate_mask(
				    image=self.raw_image			,					source_prompt=__UpperCamelCase			,					target_prompt=__UpperCamelCase			,					generator=__UpperCamelCase			,					)

				snake_case_   		=			pipe.invert(
				    prompt=__UpperCamelCase			,					image=self.raw_image			,					inpaint_strength=0.7			,					generator=__UpperCamelCase				).latents

				snake_case_   		=			pipe(
				    prompt=__UpperCamelCase			,					mask_image=__UpperCamelCase			,					image_latents=__UpperCamelCase			,					generator=__UpperCamelCase			,					negative_prompt=__UpperCamelCase			,					inpaint_strength=0.7			,					output_type='numpy'			,					).images[0]

				snake_case_   		=			(
				    np.array(
				        load_image(
				            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				            '/diffedit/pears.png'				).resize((7_68, 7_68)				)				)
				    / 2_55
				)
				assert np.abs((expected_image - image).max()				) < 5E-1







			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			torch.manual_seed(0				)

				snake_case_   		=			StableDiffusionDiffEditPipeline.from_pretrained(
				    'stabilityai/stable-diffusion-2-1'			,					safety_checker=__UpperCamelCase			,					torch_dtype=torch.floataa				)
				snake_case_   		=			DPMSolverMultistepScheduler.from_config(pipe.scheduler.config				)
				snake_case_   		=			DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config				)
				pipe.enable_model_cpu_offload()
				pipe.set_progress_bar_config(disable=__UpperCamelCase				)

				snake_case_   		=			'a bowl of fruit'
				snake_case_   		=			'a bowl of pears'

				snake_case_   		=			pipe.generate_mask(
				    image=self.raw_image			,					source_prompt=__UpperCamelCase			,					target_prompt=__UpperCamelCase			,					generator=__UpperCamelCase			,					)

				snake_case_   		=			pipe.invert(
				    prompt=__UpperCamelCase			,					image=self.raw_image			,					inpaint_strength=0.7			,					generator=__UpperCamelCase			,					num_inference_steps=25			,					).latents

				snake_case_   		=			pipe(
				    prompt=__UpperCamelCase			,					mask_image=__UpperCamelCase			,					image_latents=__UpperCamelCase			,					generator=__UpperCamelCase			,					negative_prompt=__UpperCamelCase			,					inpaint_strength=0.7			,					num_inference_steps=25			,					output_type='numpy'			,					).images[0]

				snake_case_   		=			(
				    np.array(
				        load_image(
				            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				            '/diffedit/pears.png'				).resize((7_68, 7_68)				)				)
				    / 2_55
				)
				assert np.abs((expected_image - image).max()				) < 5E-1
