

import re
from typing import Callable, List, Optional, Union

import tensorflow as tf


try:
		from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
		from tensorflow.keras.optimizers import Adam


class SCREAMING_SNAKE_CASE	(		tf.keras.optimizers.schedules.LearningRateSchedule				):






			"""simple docstring"""




			def __init__(     self			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase = 1.0			,					__UpperCamelCase = None			,					):

				"""simple docstring"""


				super().__init__()
				snake_case_   		=			initial_learning_rate
				snake_case_   		=			warmup_steps
				snake_case_   		=			power
				snake_case_   		=			decay_schedule_fn
				snake_case_   		=			name


			def __call__(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				with tf.name_scope(self.name or 'WarmUp'				) as name:
					# Implements polynomial warmup. i.e., if global_step < warmup_steps, the
					# learning rate will be `global_step/num_warmup_steps * init_lr`.
					snake_case_   		=			tf.cast(__UpperCamelCase			,					tf.floataa				)
					snake_case_   		=			tf.cast(self.warmup_steps			,					tf.floataa				)
					snake_case_   		=			global_step_float / warmup_steps_float
					snake_case_   		=			self.initial_learning_rate * tf.math.pow(__UpperCamelCase			,					self.power				)
					return tf.cond(
					    global_step_float < warmup_steps_float			,					lambda: warmup_learning_rate			,					lambda: self.decay_schedule_fn(step - self.warmup_steps				)			,					name=__UpperCamelCase			,					)







			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return {
				    "initial_learning_rate": self.initial_learning_rate,
				    "decay_schedule_fn": self.decay_schedule_fn,
				    "warmup_steps": self.warmup_steps,
				    "power": self.power,
				    "name": self.name,
				}







def a(lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__ = 0.0      ,			lowercase__ = 0.9      ,			lowercase__ = 0.999      ,			lowercase__ = 1e-8      ,			lowercase__ = None      ,			lowercase__ = None      ,			lowercase__ = 0.0      ,			lowercase__ = 1.0      ,			lowercase__ = None      ,			):





	'''simple docstring'''
	snake_case_   		=			tf.keras.optimizers.schedules.PolynomialDecay(
	    initial_learning_rate=lowercase__      ,			decay_steps=num_train_steps - num_warmup_steps      ,			end_learning_rate=init_lr * min_lr_ratio      ,			power=lowercase__      ,			)
	if num_warmup_steps:
		snake_case_   		=			WarmUp(
		    initial_learning_rate=lowercase__      ,			decay_schedule_fn=lowercase__      ,			warmup_steps=lowercase__      ,			)
	if weight_decay_rate > 0.0:
		snake_case_   		=			AdamWeightDecay(
		    learning_rate=lowercase__      ,			weight_decay_rate=lowercase__      ,			beta_a=lowercase__      ,			beta_a=lowercase__      ,			epsilon=lowercase__      ,			clipnorm=lowercase__      ,			global_clipnorm=lowercase__      ,			exclude_from_weight_decay=['LayerNorm', 'layer_norm', 'bias']      ,			include_in_weight_decay=lowercase__      ,			)
	else:
		snake_case_   		=			tf.keras.optimizers.Adam(
		    learning_rate=lowercase__      ,			beta_a=lowercase__      ,			beta_a=lowercase__      ,			epsilon=lowercase__      ,			clipnorm=lowercase__      ,			global_clipnorm=lowercase__      ,			)
	# We return the optimizer and the LR scheduler in order to better track the
	# evolution of the LR independently of the optimizer.
	return optimizer, lr_schedule


class SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			def __init__(     self			,					__UpperCamelCase = 0.001			,					__UpperCamelCase = 0.9			,					__UpperCamelCase = 0.999			,					__UpperCamelCase = 1E-7			,					__UpperCamelCase = False			,					__UpperCamelCase = 0.0			,					__UpperCamelCase = None			,					__UpperCamelCase = None			,					__UpperCamelCase = "AdamWeightDecay"			,					**__UpperCamelCase			,					):

				"""simple docstring"""


				super().__init__(__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					**__UpperCamelCase				)
				snake_case_   		=			weight_decay_rate
				snake_case_   		=			include_in_weight_decay
				snake_case_   		=			exclude_from_weight_decay


			@classmethod
			def       __lowerCAmelCase			(     cls			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			{'WarmUp': WarmUp}
				return super(__UpperCamelCase			,					cls				).from_config(__UpperCamelCase			,					custom_objects=__UpperCamelCase				)


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

				"""simple docstring"""


				super(__UpperCamelCase			,					self				)._prepare_local(__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase				)
				snake_case_   		=			tf.constant(
				    self.weight_decay_rate			,					name='adam_weight_decay_rate'				)


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

				"""simple docstring"""


				snake_case_   		=			self._do_use_weight_decay(var.name				)
				if do_decay:
					return var.assign_sub(
					    learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]['weight_decay_rate']			,					use_locking=self._use_locking			,					)
				return tf.no_op()


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase=None			,					**__UpperCamelCase				):

				"""simple docstring"""


				snake_case_		,							snake_case_   		=			list(zip(*__UpperCamelCase				)				)
				return super(__UpperCamelCase			,					self				).apply_gradients(zip(__UpperCamelCase			,					__UpperCamelCase				)			,					name=__UpperCamelCase			,					**__UpperCamelCase				)


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

				"""simple docstring"""


				if apply_state is None:
					return self._decayed_lr_t[var_dtype], {}

				snake_case_   		=			apply_state or {}
				snake_case_   		=			apply_state.get((var_device, var_dtype)				)
				if coefficients is None:
					snake_case_   		=			self._fallback_apply_state(__UpperCamelCase			,					__UpperCamelCase				)
					snake_case_   		=			coefficients

				return coefficients["lr_t"], {"apply_state": apply_state}


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase=None				):

				"""simple docstring"""


				snake_case_		,							snake_case_   		=			self._get_lr(var.device			,					var.dtype.base_dtype			,					__UpperCamelCase				)
				snake_case_   		=			self._decay_weights_op(__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase				)
				with tf.control_dependencies([decay]				):
					return super(__UpperCamelCase			,					self				)._resource_apply_dense(__UpperCamelCase			,					__UpperCamelCase			,					**__UpperCamelCase				)


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase=None				):

				"""simple docstring"""


				snake_case_		,							snake_case_   		=			self._get_lr(var.device			,					var.dtype.base_dtype			,					__UpperCamelCase				)
				snake_case_   		=			self._decay_weights_op(__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase				)
				with tf.control_dependencies([decay]				):
					return super(__UpperCamelCase			,					self				)._resource_apply_sparse(__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					**__UpperCamelCase				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			super().get_config()
				config.update({'weight_decay_rate': self.weight_decay_rate}				)
				return config







			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				if self.weight_decay_rate == 0:
					return False

				if self._include_in_weight_decay:
					for r in self._include_in_weight_decay:
						if re.search(__UpperCamelCase			,					__UpperCamelCase				) is not None:
							return True

				if self._exclude_from_weight_decay:
					for r in self._exclude_from_weight_decay:
						if re.search(__UpperCamelCase			,					__UpperCamelCase				) is not None:
							return False
				return True







class SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			def __init__(     self				):

				"""simple docstring"""


				snake_case_   		=			[]
				snake_case_   		=			None


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				if self._accum_steps is None:
					snake_case_   		=			tf.Variable(
					    tf.constant(0			,					dtype=tf.intaa				)			,					trainable=__UpperCamelCase			,					synchronization=tf.VariableSynchronization.ON_READ			,					aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA			,					)

				return self._accum_steps.value()


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				if not self._gradients:
					raise ValueError('The accumulator should be called first to initialize the gradients'				)
				return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]


			def __call__(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				if not self._gradients:
					snake_case_   		=			self.step  # Create the step variable.
					self._gradients.extend(
					    [
					        tf.Variable(
					            tf.zeros_like(__UpperCamelCase				)			,					trainable=__UpperCamelCase			,					synchronization=tf.VariableSynchronization.ON_READ			,					aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA			,					)
					        if gradient is not None
					        else gradient
					        for gradient in gradients
					    ]				)
				if len(__UpperCamelCase				) != len(self._gradients				):
					raise ValueError(f"""Expected {len(self._gradients				)} gradients, but got {len(__UpperCamelCase				)}"""				)

				for accum_gradient, gradient in zip(self._gradients			,					__UpperCamelCase				):
					if accum_gradient is not None and gradient is not None:
						accum_gradient.assign_add(__UpperCamelCase				)

				self._accum_steps.assign_add(1				)







			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				if not self._gradients:
					return
				self._accum_steps.assign(0				)
				for gradient in self._gradients:
					if gradient is not None:
						gradient.assign(tf.zeros_like(__UpperCamelCase				)				)



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
