


"""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"""
def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE = 0			)      ->			list:
							snake_case_          =						length or len(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						False
							for i in range(length - 1			):
														if list_data[i] > list_data[i + 1]:
																					snake_case_    ,  snake_case_          =						list_data[i + 1], list_data[i]
																					snake_case_          =						True

							return list_data if not swapped else bubble_sort(_SCREAMING_SNAKE_CASE			,	length - 1			)


if __name__ == "__main__":
				import doctest

				doctest.testmod()



