

import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock

import torch

from ..state import AcceleratorState, PartialState
from ..utils import (
    gather,
    is_bnb_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_mps_available,
    is_safetensors_available,
    is_tensorboard_available,
    is_torch_version,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)




def 					_lowercase     (				a_				:					Any					,a_				:					Optional[Any]=False				)				->	List[str]:




					'''simple docstring'''







					try:
										__magic_name__							=    os.environ[key]
					except KeyError:
										# KEY isn't set, default to `default`.
										__magic_name__							=    default
					else:
										# KEY is set, convert it to True or False.
										try:
															__magic_name__							=    strtobool(a_				)
										except ValueError:
															# More values are supported, but let's keep the message simple.
															raise ValueError(F'If set, {key} must be yes or no.'				)
					return _value


A__						=							parse_flag_from_env("RUN_SLOW", default=False)




def 					_lowercase     (				a_				:					List[Any]				)				->	Tuple:




					'''simple docstring'''







					return unittest.skip('Test was skipped'				)(a_				)




def 					_lowercase     (				a_				:					List[Any]				)				->	Optional[int]:




					'''simple docstring'''







					return unittest.skipUnless(_run_slow_tests					,'test is slow'				)(a_				)




def 					_lowercase     (				a_				:					int				)				->	int:




					'''simple docstring'''







					return unittest.skipUnless(not torch.cuda.is_available()					,'test requires only a CPU'				)(a_				)




def 					_lowercase     (				a_				:					Tuple				)				->	Tuple:




					'''simple docstring'''







					return unittest.skipUnless(torch.cuda.is_available()					,'test requires a GPU'				)(a_				)




def 					_lowercase     (				a_				:					int				)				->	str:




					'''simple docstring'''







					return unittest.skipUnless(is_xpu_available()					,'test requires a XPU'				)(a_				)




def 					_lowercase     (				a_				:					Union[str, Any]				)				->	List[Any]:




					'''simple docstring'''







					return unittest.skipUnless(is_mps_available()					,'test requires a `mps` backend support in `torch`'				)(a_				)




def 					_lowercase     (				a_				:					List[str]				)				->	Tuple:




					'''simple docstring'''







					return unittest.skipUnless(
					    is_transformers_available() and is_datasets_available()					,'test requires the Hugging Face suite'				)(a_				)




def 					_lowercase     (				a_				:					List[str]				)				->	Optional[Any]:




					'''simple docstring'''







					return unittest.skipUnless(is_bnb_available()					,'test requires the bitsandbytes library'				)(a_				)




def 					_lowercase     (				a_				:					List[str]				)				->	Dict:




					'''simple docstring'''







					return unittest.skipUnless(is_tpu_available()					,'test requires TPU'				)(a_				)




def 					_lowercase     (				a_				:					Dict				)				->	Dict:




					'''simple docstring'''







					return unittest.skipUnless(torch.cuda.device_count() == 1					,'test requires a GPU'				)(a_				)




def 					_lowercase     (				a_				:					str				)				->	Dict:




					'''simple docstring'''







					return unittest.skipUnless(torch.xpu.device_count() == 1					,'test requires a XPU'				)(a_				)




def 					_lowercase     (				a_				:					int				)				->	Union[str, Any]:




					'''simple docstring'''







					return unittest.skipUnless(torch.cuda.device_count() > 1					,'test requires multiple GPUs'				)(a_				)




def 					_lowercase     (				a_				:					List[Any]				)				->	Optional[Any]:




					'''simple docstring'''







					return unittest.skipUnless(torch.xpu.device_count() > 1					,'test requires multiple XPUs'				)(a_				)




def 					_lowercase     (				a_				:					Dict				)				->	Union[str, Any]:




					'''simple docstring'''







					return unittest.skipUnless(is_safetensors_available()					,'test requires safetensors'				)(a_				)




def 					_lowercase     (				a_				:					List[Any]				)				->	Union[str, Any]:




					'''simple docstring'''







					return unittest.skipUnless(is_deepspeed_available()					,'test requires DeepSpeed'				)(a_				)




def 					_lowercase     (				a_				:					Optional[Any]				)				->	int:




					'''simple docstring'''







					return unittest.skipUnless(is_torch_version('>='					,'1.12.0'				)					,'test requires torch version >= 1.12.0'				)(a_				)




def 					_lowercase     (				a_				:					Optional[int]=None					,a_				:					Dict=None				)				->	Optional[int]:




					'''simple docstring'''







					if test_case is None:
										return partial(a_					,version=a_				)
					return unittest.skipUnless(is_torch_version('>='					,a_				)					,F'test requires torch version >= {version}'				)(a_				)




def 					_lowercase     (				a_				:					Union[str, Any]				)				->	int:




					'''simple docstring'''







					return unittest.skipUnless(is_tensorboard_available()					,'test requires Tensorboard'				)(a_				)




def 					_lowercase     (				a_				:					str				)				->	Any:




					'''simple docstring'''







					return unittest.skipUnless(is_wandb_available()					,'test requires wandb'				)(a_				)




def 					_lowercase     (				a_				:					Dict				)				->	Union[str, Any]:




					'''simple docstring'''







					return unittest.skipUnless(is_comet_ml_available()					,'test requires comet_ml'				)(a_				)


A__						=							(
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)




def 					_lowercase     (				a_				:					Optional[int]				)				->	Optional[Any]:




					'''simple docstring'''







					return unittest.skipUnless(
					    _atleast_one_tracker_available					,'test requires at least one tracker to be available and for `comet_ml` to not be installed'					,)(a_				)

class 				__UpperCamelCase				(					unittest.TestCase			):
	_lowercase  :   Optional[int]       				=  True







	@classmethod
	def 	_SCREAMING_SNAKE_CASE							(   cls: str      ):

						'''simple docstring'''





						__magic_name__							=    tempfile.mkdtemp()







	@classmethod
	def 	_SCREAMING_SNAKE_CASE							(   cls: List[str]      ):

						'''simple docstring'''





						if os.path.exists(cls.tmpdir      ):
											shutil.rmtree(cls.tmpdir      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Any      ):

						'''simple docstring'''





						if self.clear_on_setup:
											for path in Path(self.tmpdir      ).glob('**/*'      ):
																if path.is_file():
																					path.unlink()
																elif path.is_dir():
																					shutil.rmtree(__UpperCamelCase      )




class 				__UpperCamelCase				(					unittest.TestCase			):







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						super().tearDown()
						# Reset the state of the AcceleratorState singleton.
						AcceleratorState._reset_state()
						PartialState._reset_state()




class 				__UpperCamelCase				(					unittest.TestCase			):







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]  , __UpperCamelCase: Union[mock.Mock, List[mock.Mock]]      ):

						'''simple docstring'''





						__magic_name__							=    mocks if isinstance(__UpperCamelCase  , (tuple, list)      ) else [mocks]
						for m in self.mocks:
											m.start()
											self.addCleanup(m.stop      )




def 					_lowercase     (				a_				:					List[str]				)				->	Dict:




					'''simple docstring'''







					__magic_name__							=    AcceleratorState()
					__magic_name__							=    tensor[None].clone().to(state.device				)
					__magic_name__							=    gather(a_				).cpu()
					__magic_name__							=    tensor[0].cpu()
					for i in range(tensors.shape[0]				):
										if not torch.equal(tensors[i]					,a_				):
															return False
					return True

class 				__UpperCamelCase				:







	def __init__(   self: Optional[Any]  , __UpperCamelCase: str  , __UpperCamelCase: Optional[Any]  , __UpperCamelCase: Optional[int]      ):

						'''simple docstring'''





						__magic_name__							=    returncode
						__magic_name__							=    stdout
						__magic_name__							=    stderr




async def 					_lowercase     (				a_				:					int					,a_				:					Union[str, Any]				)				->	List[Any]:




					'''simple docstring'''







					while True:
										__magic_name__							=    await stream.readline()
										if line:
															callback(a_				)
										else:
															break




async def 					_lowercase     (				a_				:					List[str]					,a_				:					List[Any]=None					,a_				:					List[str]=None					,a_				:					Optional[int]=None					,a_				:					List[str]=False					,a_				:					List[Any]=False				)				->	_RunOutput:




					'''simple docstring'''







					if echo:
										print('\nRunning: '					,' '.join(a_				)				)

					__magic_name__							=    await asyncio.create_subprocess_exec(
					    cmd[0]					,*cmd[1:]					,stdin=a_					,stdout=asyncio.subprocess.PIPE					,stderr=asyncio.subprocess.PIPE					,env=a_					,)

					# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
					# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
					#
					# If it starts hanging, will need to switch to the following code. The problem is that no data
					# will be seen until it's done and if it hangs for example there will be no debug info.
					# out, err = await p.communicate()
					# return _RunOutput(p.returncode, out, err)

					__magic_name__							=    []
					__magic_name__							=    []

					def tee(a_				:					Optional[int]					,a_				:					Dict					,a_				:					Any					,a_				:					int=""				):
										__magic_name__							=    line.decode('utf-8'				).rstrip()
										sink.append(a_				)
										if not quiet:
															print(a_					,a_					,file=a_				)

    # XXX: the timeout doesn't seem to make any difference here
					await asyncio.wait(
					    [
					        asyncio.create_task(_read_stream(p.stdout					,lambda a_				: tee(a_					,a_					,sys.stdout					,label='stdout:'				)				)				),
					        asyncio.create_task(_read_stream(p.stderr					,lambda a_				: tee(a_					,a_					,sys.stderr					,label='stderr:'				)				)				),
					    ]					,timeout=a_					,)
					return _RunOutput(await p.wait()					,a_					,a_				)




def 					_lowercase     (				a_				:					Union[str, Any]					,a_				:					Optional[Any]=None					,a_				:					Optional[Any]=None					,a_				:					Union[str, Any]=1_8_0					,a_				:					List[str]=False					,a_				:					Dict=True				)				->	_RunOutput:




					'''simple docstring'''







					__magic_name__							=    asyncio.get_event_loop()
					__magic_name__							=    loop.run_until_complete(
					    _stream_subprocess(a_					,env=a_					,stdin=a_					,timeout=a_					,quiet=a_					,echo=a_				)				)

					__magic_name__							=    ' '.join(a_				)
					if result.returncode > 0:
										__magic_name__							=    '\n'.join(result.stderr				)
										raise RuntimeError(
										    F'\'{cmd_str}\' failed with returncode {result.returncode}\n\n'
										    F'The combined stderr from workers follows:\n{stderr}'				)

					return result

class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	pass




def 					_lowercase     (				a_				:					List[str]					,a_				:					str=False				)				->	List[Any]:




					'''simple docstring'''







					try:
										__magic_name__							=    subprocess.check_output(a_					,stderr=subprocess.STDOUT				)
										if return_stdout:
															if hasattr(a_					,'decode'				):
																				__magic_name__							=    output.decode('utf-8'				)
															return output
					except subprocess.CalledProcessError as e:
										raise SubprocessCallException(
										    F'Command `{" ".join(a_				)}` failed with the following error:\n\n{e.output.decode()}'				) from e







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 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE  ,						unittest.TestCase			):
	_lowercase  :   Dict       				=  StableDiffusionDiffEditPipeline
	_lowercase  :   Dict       				=  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"height", "width", "image"} | {"image_latents"}
	_lowercase  :   Dict       				=  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"image"} | {"image_latents"}
	_lowercase  :   str       				=  frozenset(
	    []			)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	_lowercase  :   int       				=  frozenset([]			)







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						torch.manual_seed(0      )
						__magic_name__							=    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  , )
						__magic_name__							=    DDIMScheduler(
						    beta_start=0.00085  , beta_end=0.012  , beta_schedule='scaled_linear'  , clip_sample=__UpperCamelCase  , set_alpha_to_one=__UpperCamelCase  , )
						__magic_name__							=    DDIMInverseScheduler(
						    beta_start=0.00085  , beta_end=0.012  , beta_schedule='scaled_linear'  , clip_sample=__UpperCamelCase  , set_alpha_to_zero=__UpperCamelCase  , )
						torch.manual_seed(0      )
						__magic_name__							=    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      )
						__magic_name__							=    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  , )
						__magic_name__							=    CLIPTextModel(__UpperCamelCase      )
						__magic_name__							=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

						__magic_name__							=    {
						    '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 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]  , __UpperCamelCase: Optional[Any]  , __UpperCamelCase: List[Any]=0      ):

						'''simple docstring'''





						__magic_name__							=    floats_tensor((1, 16, 16)  , rng=random.Random(__UpperCamelCase      )      ).to(__UpperCamelCase      )
						__magic_name__							=    floats_tensor((1, 2, 4, 16, 16)  , rng=random.Random(__UpperCamelCase      )      ).to(__UpperCamelCase      )
						if str(__UpperCamelCase      ).startswith('mps'      ):
											__magic_name__							=    torch.manual_seed(__UpperCamelCase      )
						else:
											__magic_name__							=    torch.Generator(device=__UpperCamelCase      ).manual_seed(__UpperCamelCase      )
						__magic_name__							=    {
						    '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 	_SCREAMING_SNAKE_CASE							(   self: str  , __UpperCamelCase: str  , __UpperCamelCase: Optional[int]=0      ):

						'''simple docstring'''





						__magic_name__							=    floats_tensor((1, 3, 32, 32)  , rng=random.Random(__UpperCamelCase      )      ).to(__UpperCamelCase      )
						__magic_name__							=    image.cpu().permute(0  , 2  , 3  , 1      )[0]
						__magic_name__							=    Image.fromarray(np.uinta(__UpperCamelCase      )      ).convert('RGB'      )
						if str(__UpperCamelCase      ).startswith('mps'      ):
											__magic_name__							=    torch.manual_seed(__UpperCamelCase      )
						else:
											__magic_name__							=    torch.Generator(device=__UpperCamelCase      ).manual_seed(__UpperCamelCase      )
						__magic_name__							=    {
						    '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 	_SCREAMING_SNAKE_CASE							(   self: List[Any]  , __UpperCamelCase: Optional[int]  , __UpperCamelCase: Optional[int]=0      ):

						'''simple docstring'''





						__magic_name__							=    floats_tensor((1, 3, 32, 32)  , rng=random.Random(__UpperCamelCase      )      ).to(__UpperCamelCase      )
						__magic_name__							=    image.cpu().permute(0  , 2  , 3  , 1      )[0]
						__magic_name__							=    Image.fromarray(np.uinta(__UpperCamelCase      )      ).convert('RGB'      )
						if str(__UpperCamelCase      ).startswith('mps'      ):
											__magic_name__							=    torch.manual_seed(__UpperCamelCase      )
						else:
											__magic_name__							=    torch.Generator(device=__UpperCamelCase      ).manual_seed(__UpperCamelCase      )
						__magic_name__							=    {
						    '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 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]      ):

						'''simple docstring'''





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

						__magic_name__							=    self.get_dummy_components()
						__magic_name__							=    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}      )

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

						with tempfile.TemporaryDirectory() as tmpdir:
											pipe.save_pretrained(__UpperCamelCase      )
											__magic_name__							=    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.'  , )

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

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







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						__magic_name__							=    'cpu'

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

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

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







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]      ):

						'''simple docstring'''





						__magic_name__							=    'cpu'

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

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

						self.assertEqual(image.shape  , (2, 32, 32, 3)      )
						__magic_name__							=    np.array(
						    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]  , )
						__magic_name__							=    np.abs(image_slice.flatten() - expected_slice      ).max()
						self.assertLessEqual(__UpperCamelCase  , 1E-3      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





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







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						__magic_name__							=    'cpu'

						__magic_name__							=    self.get_dummy_components()

						__magic_name__							=    {'beta_start': 0.00085, 'beta_end': 0.012, 'beta_schedule': 'scaled_linear'}
						__magic_name__							=    DPMSolverMultistepScheduler(**__UpperCamelCase      )
						__magic_name__							=    DPMSolverMultistepInverseScheduler(**__UpperCamelCase      )

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

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

						self.assertEqual(image.shape  , (2, 32, 32, 3)      )
						__magic_name__							=    np.array(
						    [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]  , )
						__magic_name__							=    np.abs(image_slice.flatten() - expected_slice      ).max()
						self.assertLessEqual(__UpperCamelCase  , 1E-3      )




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







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





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







	@classmethod
	def 	_SCREAMING_SNAKE_CASE							(   cls: List[str]      ):

						'''simple docstring'''





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

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

						__magic_name__							=    raw_image







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						__magic_name__							=    torch.manual_seed(0      )

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

						__magic_name__							=    'a bowl of fruit'
						__magic_name__							=    'a bowl of pears'

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

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

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

						__magic_name__							=    (
						    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 	_SCREAMING_SNAKE_CASE							(   self: int      ):

						'''simple docstring'''





						__magic_name__							=    torch.manual_seed(0      )

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

						__magic_name__							=    'a bowl of fruit'
						__magic_name__							=    'a bowl of pears'

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

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

						__magic_name__							=    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]

						__magic_name__							=    (
						    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




