


"""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        __A       (snake_case__  ,						snake_case__  ,						unittest.TestCase):





							'''simple docstring'''

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



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



														"""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=0.00_085						,    beta_end=0.012						,    beta_schedule="""scaled_linear"""						,    clip_sample=UpperCAmelCase_						,    set_alpha_to_one=UpperCAmelCase_						,    )
														snake_case_          =						DDIMInverseScheduler(
														    beta_start=0.00_085						,    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=128						,    )
														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=1_000						,    hidden_act="""gelu"""						,    projection_dim=512						,    )
														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							: Any						,    UpperCAmelCase_							: Optional[int]						,    UpperCAmelCase_							: List[Any]=0    )   ->Dict:



														"""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							: Dict						,    UpperCAmelCase_							: Any						,    UpperCAmelCase_							: Any=0    )   ->Optional[int]:



														"""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							: Tuple						,    UpperCAmelCase_							: List[Any]						,    UpperCAmelCase_							: Any=0    )   ->Any:



														"""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							: Dict    )   ->Optional[Any]:



														"""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							: List[str]    )   ->Optional[Any]:



														"""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							: int    )   ->Any:



														"""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(
														    [0.5_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]						,    )
														snake_case_          =						np.abs(image_slice.flatten() - expected_slice    ).max()
														self.assertLessEqual(UpperCAmelCase_						,    1E-3    )



							def 						lowerCAmelCase    (			self							: Optional[int]    )   ->List[Any]:



														"""simple docstring"""



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



							def 						lowerCAmelCase    (			self							: int    )   ->Tuple:



														"""simple docstring"""



														snake_case_          =						"""cpu"""

														snake_case_          =						self.get_dummy_components()

														snake_case_          =						{"""beta_start""": 0.00_085, """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(
														    [0.5_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]						,    )
														snake_case_          =						np.abs(image_slice.flatten() - expected_slice    ).max()
														self.assertLessEqual(UpperCAmelCase_						,    1E-3    )





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





							'''simple docstring'''

							def 						lowerCAmelCase    (			self							: int    )   ->List[str]:



														"""simple docstring"""



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



							@classmethod
							def 						lowerCAmelCase    (			cls							: Optional[Any]    )   ->Dict:



														"""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((768, 768)    )

														snake_case_          =						raw_image



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



														"""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((768, 768)    )    )
														    / 255
														)
														assert np.abs((expected_image - image).max()    ) < 5E-1



							def 						lowerCAmelCase    (			self							: Any    )   ->Any:



														"""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((768, 768)    )    )
														    / 255
														)
														assert np.abs((expected_image - image).max()    ) < 5E-1







"""simple docstring"""
import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class        __A       (snake_case__  ,						snake_case__  ,						unittest.TestCase):





							'''simple docstring'''

							__lowercase:						List[Any]							 =      CycleDiffusionPipeline
							__lowercase:						int							 =      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
							    """negative_prompt""",
							    """height""",
							    """width""",
							    """negative_prompt_embeds""",
							}
							__lowercase:						str							 =      PipelineTesterMixin.required_optional_params - {"""latents"""}
							__lowercase:						Any							 =      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""source_prompt"""})
							__lowercase:						Any							 =      IMAGE_TO_IMAGE_IMAGE_PARAMS
							__lowercase:						Any							 =      IMAGE_TO_IMAGE_IMAGE_PARAMS



							def 						lowerCAmelCase    (			self							: Dict    )   ->Any:



														"""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						,    )
														snake_case_          =						DDIMScheduler(
														    beta_start=0.00_085						,    beta_end=0.012						,    beta_schedule="""scaled_linear"""						,    num_train_timesteps=1_000						,    clip_sample=UpperCAmelCase_						,    set_alpha_to_one=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						,    )
														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=1_000						,    )
														snake_case_          =						CLIPTextModel(UpperCAmelCase_    )
														snake_case_          =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

														snake_case_          =						{
														    """unet""": unet,
														    """scheduler""": scheduler,
														    """vae""": vae,
														    """text_encoder""": text_encoder,
														    """tokenizer""": tokenizer,
														    """safety_checker""": None,
														    """feature_extractor""": None,
														}
														return components



							def 						lowerCAmelCase    (			self							: int						,    UpperCAmelCase_							: str						,    UpperCAmelCase_							: List[str]=0    )   ->str:



														"""simple docstring"""



														snake_case_          =						floats_tensor((1, 3, 32, 32)						,    rng=random.Random(UpperCAmelCase_    )    ).to(UpperCAmelCase_    )
														snake_case_          =						image / 2 + 0.5
														if str(UpperCAmelCase_    ).startswith("""mps"""    ):
																					snake_case_          =						torch.manual_seed(UpperCAmelCase_    )
														else:
																					snake_case_          =						torch.Generator(device=UpperCAmelCase_    ).manual_seed(UpperCAmelCase_    )
														snake_case_          =						{
														    """prompt""": """An astronaut riding an elephant""",
														    """source_prompt""": """An astronaut riding a horse""",
														    """image""": image,
														    """generator""": generator,
														    """num_inference_steps""": 2,
														    """eta""": 0.1,
														    """strength""": 0.8,
														    """guidance_scale""": 3,
														    """source_guidance_scale""": 1,
														    """output_type""": """numpy""",
														}
														return inputs



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



														"""simple docstring"""



														snake_case_          =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator

														snake_case_          =						self.get_dummy_components()
														snake_case_          =						CycleDiffusionPipeline(**UpperCAmelCase_    )
														snake_case_          =						pipe.to(UpperCAmelCase_    )
														pipe.set_progress_bar_config(disable=UpperCAmelCase_    )

														snake_case_          =						self.get_dummy_inputs(UpperCAmelCase_    )
														snake_case_          =						pipe(**UpperCAmelCase_    )
														snake_case_          =						output.images

														snake_case_          =						images[0, -3:, -3:, -1]

														assert images.shape == (1, 32, 32, 3)
														snake_case_          =						np.array([0.4_459, 0.4_943, 0.4_544, 0.6_643, 0.5_474, 0.4_327, 0.5_701, 0.5_959, 0.5_179]    )

														assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2



							@unittest.skipIf(torch_device != """cuda"""						,    """This test requires a GPU"""    )
							def 						lowerCAmelCase    (			self							: Union[str, Any]    )   ->str:



														"""simple docstring"""



														snake_case_          =						self.get_dummy_components()
														for name, module in components.items():
																					if hasattr(UpperCAmelCase_						,    """half"""    ):
																												snake_case_          =						module.half()
														snake_case_          =						CycleDiffusionPipeline(**UpperCAmelCase_    )
														snake_case_          =						pipe.to(UpperCAmelCase_    )
														pipe.set_progress_bar_config(disable=UpperCAmelCase_    )

														snake_case_          =						self.get_dummy_inputs(UpperCAmelCase_    )
														snake_case_          =						pipe(**UpperCAmelCase_    )
														snake_case_          =						output.images

														snake_case_          =						images[0, -3:, -3:, -1]

														assert images.shape == (1, 32, 32, 3)
														snake_case_          =						np.array([0.3_506, 0.4_543, 0.446, 0.4_575, 0.5_195, 0.4_155, 0.5_273, 0.518, 0.4_116]    )

														assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2



							@skip_mps
							def 						lowerCAmelCase    (			self							: Tuple    )   ->Optional[int]:



														"""simple docstring"""



														return super().test_save_load_local()



							@unittest.skip("""non-deterministic pipeline"""    )
							def 						lowerCAmelCase    (			self							: int    )   ->Optional[int]:



														"""simple docstring"""



														return super().test_inference_batch_single_identical()



							@skip_mps
							def 						lowerCAmelCase    (			self							: Tuple    )   ->List[Any]:



														"""simple docstring"""



														return super().test_dict_tuple_outputs_equivalent()



							@skip_mps
							def 						lowerCAmelCase    (			self							: Any    )   ->Tuple:



														"""simple docstring"""



														return super().test_save_load_optional_components()



							@skip_mps
							def 						lowerCAmelCase    (			self							: List[Any]    )   ->str:



														"""simple docstring"""



														return super().test_attention_slicing_forward_pass()





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





							'''simple docstring'''

							def 						lowerCAmelCase    (			self							: List[Any]    )   ->Optional[int]:



														"""simple docstring"""



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



							def 						lowerCAmelCase    (			self							: Optional[int]    )   ->str:



														"""simple docstring"""



														snake_case_          =						load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
														    """/cycle-diffusion/black_colored_car.png"""    )
														snake_case_          =						load_numpy(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy"""    )
														snake_case_          =						init_image.resize((512, 512)    )

														snake_case_          =						"""CompVis/stable-diffusion-v1-4"""
														snake_case_          =						DDIMScheduler.from_pretrained(UpperCAmelCase_						,    subfolder="""scheduler"""    )
														snake_case_          =						CycleDiffusionPipeline.from_pretrained(
														    UpperCAmelCase_						,    scheduler=UpperCAmelCase_						,    safety_checker=UpperCAmelCase_						,    torch_dtype=torch.floataa						,    revision="""fp16"""    )

														pipe.to(UpperCAmelCase_    )
														pipe.set_progress_bar_config(disable=UpperCAmelCase_    )
														pipe.enable_attention_slicing()

														snake_case_          =						"""A black colored car"""
														snake_case_          =						"""A blue colored car"""

														snake_case_          =						torch.manual_seed(0    )
														snake_case_          =						pipe(
														    prompt=UpperCAmelCase_						,    source_prompt=UpperCAmelCase_						,    image=UpperCAmelCase_						,    num_inference_steps=100						,    eta=0.1						,    strength=0.85						,    guidance_scale=3						,    source_guidance_scale=1						,    generator=UpperCAmelCase_						,    output_type="""np"""						,    )
														snake_case_          =						output.images

														# the values aren't exactly equal, but the images look the same visually
														assert np.abs(image - expected_image    ).max() < 5E-1



							def 						lowerCAmelCase    (			self							: Tuple    )   ->List[str]:



														"""simple docstring"""



														snake_case_          =						load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
														    """/cycle-diffusion/black_colored_car.png"""    )
														snake_case_          =						load_numpy(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy"""    )
														snake_case_          =						init_image.resize((512, 512)    )

														snake_case_          =						"""CompVis/stable-diffusion-v1-4"""
														snake_case_          =						DDIMScheduler.from_pretrained(UpperCAmelCase_						,    subfolder="""scheduler"""    )
														snake_case_          =						CycleDiffusionPipeline.from_pretrained(UpperCAmelCase_						,    scheduler=UpperCAmelCase_						,    safety_checker=UpperCAmelCase_    )

														pipe.to(UpperCAmelCase_    )
														pipe.set_progress_bar_config(disable=UpperCAmelCase_    )
														pipe.enable_attention_slicing()

														snake_case_          =						"""A black colored car"""
														snake_case_          =						"""A blue colored car"""

														snake_case_          =						torch.manual_seed(0    )
														snake_case_          =						pipe(
														    prompt=UpperCAmelCase_						,    source_prompt=UpperCAmelCase_						,    image=UpperCAmelCase_						,    num_inference_steps=100						,    eta=0.1						,    strength=0.85						,    guidance_scale=3						,    source_guidance_scale=1						,    generator=UpperCAmelCase_						,    output_type="""np"""						,    )
														snake_case_          =						output.images

														assert np.abs(image - expected_image    ).max() < 2E-2



