






"""simple docstring"""







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_ (       a_   ,					a_   ,					unittest.TestCase  ):
  _A       :					Dict				=	StableDiffusionDiffEditPipeline
  _A       :					Optional[Any]				=	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'height', 'width', 'image'} | {'image_latents'}
  _A       :					Union[str, Any]				=	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'image'} | {'image_latents'}
  _A       :					Optional[int]				=	frozenset(
      []  )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
  _A       :					List[str]				=	frozenset([]  )



  def 		UpperCamelCase_				(      self				)			->			Dict:



         """simple docstring"""





         torch.manual_seed(0				)
         UpperCAmelCase	    =			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=snake_case__ ,		)
         UpperCAmelCase	    =			DDIMScheduler(
             beta_start=0.00_085 ,		beta_end=0.012 ,		beta_schedule="""scaled_linear""" ,		clip_sample=snake_case__ ,		set_alpha_to_one=snake_case__ ,		)
         UpperCAmelCase	    =			DDIMInverseScheduler(
             beta_start=0.00_085 ,		beta_end=0.012 ,		beta_schedule="""scaled_linear""" ,		clip_sample=snake_case__ ,		set_alpha_to_zero=snake_case__ ,		)
         torch.manual_seed(0				)
         UpperCAmelCase	    =			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				)
         UpperCAmelCase	    =			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 ,		)
         UpperCAmelCase	    =			CLIPTextModel(snake_case__				)
         UpperCAmelCase	    =			CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

         UpperCAmelCase	    =			{
             """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 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__=0				)			->			str:



         """simple docstring"""





         UpperCAmelCase	    =			floats_tensor((1, 16, 16) ,		rng=random.Random(snake_case__				)				).to(snake_case__				)
         UpperCAmelCase	    =			floats_tensor((1, 2, 4, 16, 16) ,		rng=random.Random(snake_case__				)				).to(snake_case__				)
         if str(snake_case__				).startswith("""mps"""				):
                UpperCAmelCase	    =			torch.manual_seed(snake_case__				)
         else:
                UpperCAmelCase	    =			torch.Generator(device=snake_case__				).manual_seed(snake_case__				)
         UpperCAmelCase	    =			{
             """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 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__=0				)			->			Optional[int]:



         """simple docstring"""





         UpperCAmelCase	    =			floats_tensor((1, 3, 32, 32) ,		rng=random.Random(snake_case__				)				).to(snake_case__				)
         UpperCAmelCase	    =			image.cpu().permute(0 ,		2 ,		3 ,		1				)[0]
         UpperCAmelCase	    =			Image.fromarray(np.uinta(snake_case__				)				).convert("""RGB"""				)
         if str(snake_case__				).startswith("""mps"""				):
                UpperCAmelCase	    =			torch.manual_seed(snake_case__				)
         else:
                UpperCAmelCase	    =			torch.Generator(device=snake_case__				).manual_seed(snake_case__				)
         UpperCAmelCase	    =			{
             """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 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__=0				)			->			Dict:



         """simple docstring"""





         UpperCAmelCase	    =			floats_tensor((1, 3, 32, 32) ,		rng=random.Random(snake_case__				)				).to(snake_case__				)
         UpperCAmelCase	    =			image.cpu().permute(0 ,		2 ,		3 ,		1				)[0]
         UpperCAmelCase	    =			Image.fromarray(np.uinta(snake_case__				)				).convert("""RGB"""				)
         if str(snake_case__				).startswith("""mps"""				):
                UpperCAmelCase	    =			torch.manual_seed(snake_case__				)
         else:
                UpperCAmelCase	    =			torch.Generator(device=snake_case__				).manual_seed(snake_case__				)
         UpperCAmelCase	    =			{
             """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 		UpperCamelCase_				(      self				)			->			Optional[Any]:



         """simple docstring"""





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

         UpperCAmelCase	    =			self.get_dummy_components()
         UpperCAmelCase	    =			self.pipeline_class(**snake_case__				)
         pipe.to(snake_case__				)
         pipe.set_progress_bar_config(disable=snake_case__				)

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

         UpperCAmelCase	    =			self.get_dummy_inputs(snake_case__				)
         UpperCAmelCase	    =			pipe(**snake_case__				)[0]

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

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

         UpperCAmelCase	    =			self.get_dummy_inputs(snake_case__				)
         UpperCAmelCase	    =			pipe_loaded(**snake_case__				)[0]

         UpperCAmelCase	    =			np.abs(output - output_loaded				).max()
         self.assertLess(snake_case__ ,		1e-4				)



  def 		UpperCamelCase_				(      self				)			->			List[str]:



         """simple docstring"""





         UpperCAmelCase	    =			"""cpu"""

         UpperCAmelCase	    =			self.get_dummy_components()
         UpperCAmelCase	    =			self.pipeline_class(**snake_case__				)
         pipe.to(snake_case__				)
         pipe.set_progress_bar_config(disable=snake_case__				)

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

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



  def 		UpperCamelCase_				(      self				)			->			List[Any]:



         """simple docstring"""





         UpperCAmelCase	    =			"""cpu"""

         UpperCAmelCase	    =			self.get_dummy_components()
         UpperCAmelCase	    =			self.pipeline_class(**snake_case__				)
         pipe.to(snake_case__				)
         pipe.set_progress_bar_config(disable=snake_case__				)

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

         self.assertEqual(image.shape ,		(2, 32, 32, 3)				)
         UpperCAmelCase	    =			np.array(
             [0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799] ,		)
         UpperCAmelCase	    =			np.abs(image_slice.flatten() - expected_slice				).max()
         self.assertLessEqual(snake_case__ ,		1e-3				)



  def 		UpperCamelCase_				(      self				)			->			Any:



         """simple docstring"""





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



  def 		UpperCamelCase_				(      self				)			->			Optional[Any]:



         """simple docstring"""





         UpperCAmelCase	    =			"""cpu"""

         UpperCAmelCase	    =			self.get_dummy_components()

         UpperCAmelCase	    =			{"""beta_start""": 0.00_085, """beta_end""": 0.012, """beta_schedule""": """scaled_linear"""}
         UpperCAmelCase	    =			DPMSolverMultistepScheduler(**snake_case__				)
         UpperCAmelCase	    =			DPMSolverMultistepInverseScheduler(**snake_case__				)

         UpperCAmelCase	    =			self.pipeline_class(**snake_case__				)
         pipe.to(snake_case__				)
         pipe.set_progress_bar_config(disable=snake_case__				)

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

         self.assertEqual(image.shape ,		(2, 32, 32, 3)				)
         UpperCAmelCase	    =			np.array(
             [0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799] ,		)
         UpperCAmelCase	    =			np.abs(image_slice.flatten() - expected_slice				).max()
         self.assertLessEqual(snake_case__ ,		1e-3				)





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



  def 		UpperCamelCase_				(      self				)			->			Optional[int]:



         """simple docstring"""





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



  @classmethod
  def 		UpperCamelCase_				(      cls				)			->			Any:



         """simple docstring"""





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

         UpperCAmelCase	    =			raw_image.convert("""RGB"""				).resize((7_68, 7_68)				)

         UpperCAmelCase	    =			raw_image



  def 		UpperCamelCase_				(      self				)			->			Union[str, Any]:



         """simple docstring"""





         UpperCAmelCase	    =			torch.manual_seed(0				)

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

         UpperCAmelCase	    =			"""a bowl of fruit"""
         UpperCAmelCase	    =			"""a bowl of pears"""

         UpperCAmelCase	    =			pipe.generate_mask(
             image=self.raw_image ,		source_prompt=snake_case__ ,		target_prompt=snake_case__ ,		generator=snake_case__ ,		)

         UpperCAmelCase	    =			pipe.invert(
             prompt=snake_case__ ,		image=self.raw_image ,		inpaint_strength=0.7 ,		generator=snake_case__				).latents

         UpperCAmelCase	    =			pipe(
             prompt=snake_case__ ,		mask_image=snake_case__ ,		image_latents=snake_case__ ,		generator=snake_case__ ,		negative_prompt=snake_case__ ,		inpaint_strength=0.7 ,		output_type="""numpy""" ,		).images[0]

         UpperCAmelCase	    =			(
             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 		UpperCamelCase_				(      self				)			->			str:



         """simple docstring"""





         UpperCAmelCase	    =			torch.manual_seed(0				)

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

         UpperCAmelCase	    =			"""a bowl of fruit"""
         UpperCAmelCase	    =			"""a bowl of pears"""

         UpperCAmelCase	    =			pipe.generate_mask(
             image=self.raw_image ,		source_prompt=snake_case__ ,		target_prompt=snake_case__ ,		generator=snake_case__ ,		)

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

         UpperCAmelCase	    =			pipe(
             prompt=snake_case__ ,		mask_image=snake_case__ ,		image_latents=snake_case__ ,		generator=snake_case__ ,		negative_prompt=snake_case__ ,		inpaint_strength=0.7 ,		num_inference_steps=25 ,		output_type="""numpy""" ,		).images[0]

         UpperCAmelCase	    =			(
             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








"""simple docstring"""







import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np





class   UpperCamelCase_ :



  def 		UpperCamelCase_				(      self				)			->			Any:



         """simple docstring"""





         torch.manual_seed(0				)
         UpperCAmelCase	    =			TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5"""				)

         torch.manual_seed(0				)
         UpperCAmelCase	    =			AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5"""				)

         torch.manual_seed(0				)
         UpperCAmelCase	    =			UNetaDConditionModel(
             sample_size=32 ,		layers_per_block=1 ,		block_out_channels=[32, 64] ,		down_block_types=[
                 """ResnetDownsampleBlock2D""",
                 """SimpleCrossAttnDownBlock2D""",
             ] ,		mid_block_type="""UNetMidBlock2DSimpleCrossAttn""" ,		up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""] ,		in_channels=3 ,		out_channels=6 ,		cross_attention_dim=32 ,		encoder_hid_dim=32 ,		attention_head_dim=8 ,		addition_embed_type="""text""" ,		addition_embed_type_num_heads=2 ,		cross_attention_norm="""group_norm""" ,		resnet_time_scale_shift="""scale_shift""" ,		act_fn="""gelu""" ,		)
         unet.set_attn_processor(AttnAddedKVProcessor()				)  # For reproducibility tests

         torch.manual_seed(0				)
         UpperCAmelCase	    =			DDPMScheduler(
             num_train_timesteps=10_00 ,		beta_schedule="""squaredcos_cap_v2""" ,		beta_start=0.0_001 ,		beta_end=0.02 ,		thresholding=snake_case__ ,		dynamic_thresholding_ratio=0.95 ,		sample_max_value=1.0 ,		prediction_type="""epsilon""" ,		variance_type="""learned_range""" ,		)

         torch.manual_seed(0				)
         UpperCAmelCase	    =			IFWatermarker()

         return {
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "unet": unet,
             "scheduler": scheduler,
             "watermarker": watermarker,
             "safety_checker": None,
             "feature_extractor": None,
         }



  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         torch.manual_seed(0				)
         UpperCAmelCase	    =			TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5"""				)

         torch.manual_seed(0				)
         UpperCAmelCase	    =			AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5"""				)

         torch.manual_seed(0				)
         UpperCAmelCase	    =			UNetaDConditionModel(
             sample_size=32 ,		layers_per_block=[1, 2] ,		block_out_channels=[32, 64] ,		down_block_types=[
                 """ResnetDownsampleBlock2D""",
                 """SimpleCrossAttnDownBlock2D""",
             ] ,		mid_block_type="""UNetMidBlock2DSimpleCrossAttn""" ,		up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""] ,		in_channels=6 ,		out_channels=6 ,		cross_attention_dim=32 ,		encoder_hid_dim=32 ,		attention_head_dim=8 ,		addition_embed_type="""text""" ,		addition_embed_type_num_heads=2 ,		cross_attention_norm="""group_norm""" ,		resnet_time_scale_shift="""scale_shift""" ,		act_fn="""gelu""" ,		class_embed_type="""timestep""" ,		mid_block_scale_factor=1.414 ,		time_embedding_act_fn="""gelu""" ,		time_embedding_dim=32 ,		)
         unet.set_attn_processor(AttnAddedKVProcessor()				)  # For reproducibility tests

         torch.manual_seed(0				)
         UpperCAmelCase	    =			DDPMScheduler(
             num_train_timesteps=10_00 ,		beta_schedule="""squaredcos_cap_v2""" ,		beta_start=0.0_001 ,		beta_end=0.02 ,		thresholding=snake_case__ ,		dynamic_thresholding_ratio=0.95 ,		sample_max_value=1.0 ,		prediction_type="""epsilon""" ,		variance_type="""learned_range""" ,		)

         torch.manual_seed(0				)
         UpperCAmelCase	    =			DDPMScheduler(
             num_train_timesteps=10_00 ,		beta_schedule="""squaredcos_cap_v2""" ,		beta_start=0.0_001 ,		beta_end=0.02 ,		)

         torch.manual_seed(0				)
         UpperCAmelCase	    =			IFWatermarker()

         return {
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "unet": unet,
             "scheduler": scheduler,
             "image_noising_scheduler": image_noising_scheduler,
             "watermarker": watermarker,
             "safety_checker": None,
             "feature_extractor": None,
         }



  def 		UpperCamelCase_				(      self				)			->			Tuple:



         """simple docstring"""





         UpperCAmelCase	    =			self.get_dummy_components()
         UpperCAmelCase	    =			self.pipeline_class(**snake_case__				)
         pipe.to(snake_case__				)
         pipe.set_progress_bar_config(disable=snake_case__				)

         UpperCAmelCase	    =			self.get_dummy_inputs(snake_case__				)

         UpperCAmelCase	    =			inputs["""prompt"""]
         UpperCAmelCase	    =			inputs["""generator"""]
         UpperCAmelCase	    =			inputs["""num_inference_steps"""]
         UpperCAmelCase	    =			inputs["""output_type"""]

         if "image" in inputs:
                UpperCAmelCase	    =			inputs["""image"""]
         else:
                UpperCAmelCase	    =			None

         if "mask_image" in inputs:
                UpperCAmelCase	    =			inputs["""mask_image"""]
         else:
                UpperCAmelCase	    =			None

         if "original_image" in inputs:
                UpperCAmelCase	    =			inputs["""original_image"""]
         else:
                UpperCAmelCase	    =			None

         UpperCAmelCase    ,       UpperCAmelCase	    =			pipe.encode_prompt(snake_case__				)

         # inputs with prompt converted to embeddings
         UpperCAmelCase	    =			{
             """prompt_embeds""": prompt_embeds,
             """negative_prompt_embeds""": negative_prompt_embeds,
             """generator""": generator,
             """num_inference_steps""": num_inference_steps,
             """output_type""": output_type,
         }

         if image is not None:
                UpperCAmelCase	    =			image

         if mask_image is not None:
                UpperCAmelCase	    =			mask_image

         if original_image is not None:
                UpperCAmelCase	    =			original_image

         # set all optional components to None
         for optional_component in pipe._optional_components:
                setattr(snake_case__ ,		snake_case__ ,		snake_case__				)

         UpperCAmelCase	    =			pipe(**snake_case__				)[0]

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

         pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()				)  # For reproducibility tests

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

         UpperCAmelCase	    =			self.get_dummy_inputs(snake_case__				)

         UpperCAmelCase	    =			inputs["""generator"""]
         UpperCAmelCase	    =			inputs["""num_inference_steps"""]
         UpperCAmelCase	    =			inputs["""output_type"""]

         # inputs with prompt converted to embeddings
         UpperCAmelCase	    =			{
             """prompt_embeds""": prompt_embeds,
             """negative_prompt_embeds""": negative_prompt_embeds,
             """generator""": generator,
             """num_inference_steps""": num_inference_steps,
             """output_type""": output_type,
         }

         if image is not None:
                UpperCAmelCase	    =			image

         if mask_image is not None:
                UpperCAmelCase	    =			mask_image

         if original_image is not None:
                UpperCAmelCase	    =			original_image

         UpperCAmelCase	    =			pipe_loaded(**snake_case__				)[0]

         UpperCAmelCase	    =			np.abs(to_np(snake_case__				) - to_np(snake_case__				)				).max()
         self.assertLess(snake_case__ ,		1e-4				)



  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         UpperCAmelCase	    =			self.get_dummy_components()
         UpperCAmelCase	    =			self.pipeline_class(**snake_case__				)
         pipe.to(snake_case__				)
         pipe.set_progress_bar_config(disable=snake_case__				)

         UpperCAmelCase	    =			self.get_dummy_inputs(snake_case__				)
         UpperCAmelCase	    =			pipe(**snake_case__				)[0]

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

         pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()				)  # For reproducibility tests

         UpperCAmelCase	    =			self.get_dummy_inputs(snake_case__				)
         UpperCAmelCase	    =			pipe_loaded(**snake_case__				)[0]

         UpperCAmelCase	    =			np.abs(to_np(snake_case__				) - to_np(snake_case__				)				).max()
         self.assertLess(snake_case__ ,		1e-4				)
