code: 
  
  
  import gc
  import unittest
  
  import numpy as np
  import torch
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  
  from diffusers import (
      AutoencoderKL,
      DDIMScheduler,
      EulerAncestralDiscreteScheduler,
      PNDMScheduler,
      StableDiffusionModelEditingPipeline,
      UNetaDConditionModel,
  )
  from diffusers.utils import slow, torch_device
  from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
  
  from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
  from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
  
  
  enable_full_determinism()
  
  
  
  
  
  @skip_mps
  class 					snake_case__  ( __A      ,    __A      ,    __A      ,    unittest.TestCase       ):
  	UpperCAmelCase   :							str         = StableDiffusionModelEditingPipeline
  	UpperCAmelCase   :							Any         = TEXT_TO_IMAGE_PARAMS
  	UpperCAmelCase   :							List[str]         = TEXT_TO_IMAGE_BATCH_PARAMS
  	UpperCAmelCase   :							str         = TEXT_TO_IMAGE_IMAGE_PARAMS
  	UpperCAmelCase   :							Dict         = TEXT_TO_IMAGE_IMAGE_PARAMS
  
  
  
  
  
  	def     A				(  self							)		->			Union[str, Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								torch.manual_seed(0							)
  								a_       :   Tuple			=	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				,    )
  								a_       :   Optional[int]			=	DDIMScheduler()
  								torch.manual_seed(0							)
  								a_       :   Tuple			=	AutoencoderKL(
  								    block_out_channels=[32, 64]				,    in_channels=3				,    out_channels=3				,    down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]				,    up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]				,    latent_channels=4				,    )
  								torch.manual_seed(0							)
  								a_       :   int			=	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				,    )
  								a_       :   List[str]			=	CLIPTextModel(UpperCamelCase_							)
  								a_       :   Any			=	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)
  
  								a_       :   str			=	{
  								    """unet""": unet,
  								    """scheduler""": scheduler,
  								    """vae""": vae,
  								    """text_encoder""": text_encoder,
  								    """tokenizer""": tokenizer,
  								    """safety_checker""": None,
  								    """feature_extractor""": None,
  								}
  								return components
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_=0							)		->			Tuple:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Optional[Any]			=	torch.manual_seed(UpperCamelCase_							)
  								a_       :   List[Any]			=	{
  								    """prompt""": """A field of roses""",
  								    """generator""": generator,
  								    # Setting height and width to None to prevent OOMs on CPU.
  								    """height""": None,
  								    """width""": None,
  								    """num_inference_steps""": 2,
  								    """guidance_scale""": 6.0,
  								    """output_type""": """numpy""",
  								}
  								return inputs
  
  
  
  
  
  	def     A				(  self							)		->			int:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   str			=	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
  								a_       :   List[str]			=	self.get_dummy_components()
  								a_       :   List[Any]			=	StableDiffusionModelEditingPipeline(**UpperCamelCase_							)
  								a_       :   Optional[Any]			=	sd_pipe.to(UpperCamelCase_							)
  								sd_pipe.set_progress_bar_config(disable=UpperCamelCase_							)
  
  								a_       :   List[str]			=	self.get_dummy_inputs(UpperCamelCase_							)
  								a_       :   Any			=	sd_pipe(**UpperCamelCase_							).images
  								a_       :   Union[str, Any]			=	image[0, -3:, -3:, -1]
  								assert image.shape == (1, 64, 64, 3)
  
  								a_       :   Optional[Any]			=	np.array([0.4755, 0.5132, 0.4976, 0.3904, 0.3554, 0.4765, 0.5139, 0.5158, 0.4889]							)
  
  								assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
  
  
  
  
  
  	def     A				(  self							)		->			Optional[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   int			=	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
  								a_       :   Any			=	self.get_dummy_components()
  								a_       :   str			=	StableDiffusionModelEditingPipeline(**UpperCamelCase_							)
  								a_       :   List[str]			=	sd_pipe.to(UpperCamelCase_							)
  								sd_pipe.set_progress_bar_config(disable=UpperCamelCase_							)
  
  								a_       :   Tuple			=	self.get_dummy_inputs(UpperCamelCase_							)
  								a_       :   Optional[int]			=	"""french fries"""
  								a_       :   Optional[Any]			=	sd_pipe(**UpperCamelCase_				,    negative_prompt=UpperCamelCase_							)
  								a_       :   List[str]			=	output.images
  								a_       :   List[Any]			=	image[0, -3:, -3:, -1]
  
  								assert image.shape == (1, 64, 64, 3)
  
  								a_       :   List[str]			=	np.array([0.4992, 0.5101, 0.5004, 0.3949, 0.3604, 0.4735, 0.5216, 0.5204, 0.4913]							)
  
  								assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
  
  
  
  
  
  	def     A				(  self							)		->			str:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   List[Any]			=	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
  								a_       :   str			=	self.get_dummy_components()
  								a_       :   List[str]			=	EulerAncestralDiscreteScheduler(
  								    beta_start=0.0_0085				,    beta_end=0.012				,    beta_schedule="""scaled_linear"""							)
  								a_       :   str			=	StableDiffusionModelEditingPipeline(**UpperCamelCase_							)
  								a_       :   Optional[int]			=	sd_pipe.to(UpperCamelCase_							)
  								sd_pipe.set_progress_bar_config(disable=UpperCamelCase_							)
  
  								a_       :   List[str]			=	self.get_dummy_inputs(UpperCamelCase_							)
  								a_       :   Optional[Any]			=	sd_pipe(**UpperCamelCase_							).images
  								a_       :   int			=	image[0, -3:, -3:, -1]
  
  								assert image.shape == (1, 64, 64, 3)
  
  								a_       :   List[Any]			=	np.array([0.4747, 0.5372, 0.4779, 0.4982, 0.5543, 0.4816, 0.5238, 0.4904, 0.5027]							)
  
  								assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
  
  
  
  
  
  	def     A				(  self							)		->			Optional[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Dict			=	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
  								a_       :   int			=	self.get_dummy_components()
  								a_       :   Optional[int]			=	PNDMScheduler()
  								a_       :   Union[str, Any]			=	StableDiffusionModelEditingPipeline(**UpperCamelCase_							)
  								a_       :   List[Any]			=	sd_pipe.to(UpperCamelCase_							)
  								sd_pipe.set_progress_bar_config(disable=UpperCamelCase_							)
  
  								a_       :   int			=	self.get_dummy_inputs(UpperCamelCase_							)
  								# the pipeline does not expect pndm so test if it raises error.
  								with self.assertRaises(UpperCamelCase_							):
  															a_       :   Any			=	sd_pipe(**UpperCamelCase_							).images
  
  
  
  
  
  	def     A				(  self							)		->			int:
  								"""simple docstring"""
  
  
  
  
  
  
  								super().test_inference_batch_single_identical(expected_max_diff=5e-3							)
  
  
  
  
  
  	def     A				(  self							)		->			Union[str, Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								super().test_attention_slicing_forward_pass(expected_max_diff=5e-3							)
  
  
  
  
  
  @slow
  @require_torch_gpu
  class 					snake_case__  ( unittest.TestCase       ):
  
  
  
  
  
  	def     A				(  self							)		->			Union[str, Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								super().tearDown()
  								gc.collect()
  								torch.cuda.empty_cache()
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_=0							)		->			Union[str, Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   str			=	torch.manual_seed(UpperCamelCase_							)
  								a_       :   Dict			=	{
  								    """prompt""": """A field of roses""",
  								    """generator""": generator,
  								    """num_inference_steps""": 3,
  								    """guidance_scale""": 7.5,
  								    """output_type""": """numpy""",
  								}
  								return inputs
  
  
  
  
  
  	def     A				(  self							)		->			List[str]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   List[str]			=	"""CompVis/stable-diffusion-v1-4"""
  								a_       :   List[str]			=	StableDiffusionModelEditingPipeline.from_pretrained(UpperCamelCase_				,    safety_checker=UpperCamelCase_							)
  								pipe.to(UpperCamelCase_							)
  								pipe.set_progress_bar_config(disable=UpperCamelCase_							)
  								pipe.enable_attention_slicing()
  
  								a_       :   Union[str, Any]			=	self.get_inputs()
  								a_       :   Dict			=	pipe(**UpperCamelCase_							).images
  								a_       :   str			=	image[0, -3:, -3:, -1].flatten()
  
  								assert image.shape == (1, 512, 512, 3)
  
  								a_       :   Dict			=	np.array(
  								    [0.674_9496, 0.638_6453, 0.5144_3267, 0.6609_4905, 0.6192_1215, 0.549_1332, 0.574_4417, 0.5807_5106, 0.517_4658]							)
  
  								assert np.abs(expected_slice - image_slice							).max() < 1e-2
  
  								# make sure image changes after editing
  								pipe.edit_model("""A pack of roses"""				,    """A pack of blue roses"""							)
  
  								a_       :   Optional[Any]			=	pipe(**UpperCamelCase_							).images
  								a_       :   List[str]			=	image[0, -3:, -3:, -1].flatten()
  
  								assert image.shape == (1, 512, 512, 3)
  
  								assert np.abs(expected_slice - image_slice							).max() > 1e-1
  
  
  
  
  
  	def     A				(  self							)		->			Tuple:
  								"""simple docstring"""
  
  
  
  
  
  
  								torch.cuda.empty_cache()
  								torch.cuda.reset_max_memory_allocated()
  								torch.cuda.reset_peak_memory_stats()
  
  								a_       :   str			=	"""CompVis/stable-diffusion-v1-4"""
  								a_       :   int			=	DDIMScheduler.from_pretrained(UpperCamelCase_				,    subfolder="""scheduler"""							)
  								a_       :   Dict			=	StableDiffusionModelEditingPipeline.from_pretrained(
  								    UpperCamelCase_				,    scheduler=UpperCamelCase_				,    safety_checker=UpperCamelCase_							)
  								a_       :   int			=	pipe.to(UpperCamelCase_							)
  								pipe.set_progress_bar_config(disable=UpperCamelCase_							)
  								pipe.enable_attention_slicing(1							)
  								pipe.enable_sequential_cpu_offload()
  
  								a_       :   List[Any]			=	self.get_inputs()
  								a_       :   Tuple			=	pipe(**UpperCamelCase_							)
  
  								a_       :   int			=	torch.cuda.max_memory_allocated()
  								# make sure that less than 4.4 GB is allocated
  								assert mem_bytes < 4.4 * 10**9
  
  
  
style_context: 
  
  
  from __future__ import annotations
  
  
  
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: list      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							a_       :   Tuple			=	sorted(SCREAMING_SNAKE_CASE_      )
  							a_       :   Tuple			=	len(SCREAMING_SNAKE_CASE_      )
  							a_       :   Tuple			=	length >> 1
  							return (
  							    (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
  							    if length % 2 == 0
  							    else sorted_list[mid_index]
  							)
  
  
  
  
  
  def 						_lowerCamelCase		(							):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							import doctest
  
  							doctest.testmod()
  
  
  if __name__ == "__main__":
  	main()
  
  
  