






'''simple docstring'''





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
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

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class 				lowercase       (							_lowerCamelCase ,       unittest.TestCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							KandinskyVaaControlnetImgaImgPipeline
    lowerCAmelCase__      				=							["image_embeds", "negative_image_embeds", "image", "hint"]
    lowerCAmelCase__      				=							["image_embeds", "negative_image_embeds", "image", "hint"]
    lowerCAmelCase__      				=							[
        "generator",
        "height",
        "width",
        "strength",
        "guidance_scale",
        "num_inference_steps",
        "return_dict",
        "guidance_scale",
        "num_images_per_prompt",
        "output_type",
        "return_dict",
    ]
    lowerCAmelCase__      				=							False

    @property
    def    a__				(					self     :     int     )							->							Optional[int]:

     '''simple docstring'''




     return 32

    @property
    def    a__				(					self     :     Any     )							->							List[Any]:

     '''simple docstring'''




     return 32

    @property
    def    a__				(					self     :     Tuple     )							->							int:

     '''simple docstring'''




     return self.time_input_dim

    @property
    def    a__				(					self     :     List[Any]     )							->							Union[str, Any]:

     '''simple docstring'''




     return self.time_input_dim * 4

    @property
    def    a__				(					self     :     List[Any]     )							->							Dict:

     '''simple docstring'''




     return 100

    @property
    def    a__				(					self     :     List[str]     )							->							List[Any]:

     '''simple docstring'''




     torch.manual_seed(0     )

     lowerCamelCase__  =     {
         "in_channels": 8,
         # Out channels is double in channels because predicts mean and variance
         "out_channels": 8,
         "addition_embed_type": "image_hint",
         "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
         "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
         "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
         "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
         "layers_per_block": 1,
         "encoder_hid_dim": self.text_embedder_hidden_size,
         "encoder_hid_dim_type": "image_proj",
         "cross_attention_dim": self.cross_attention_dim,
         "attention_head_dim": 4,
         "resnet_time_scale_shift": "scale_shift",
         "class_embed_type": None,
     }

     lowerCamelCase__  =     UNetaDConditionModel(**__lowerCamelCase     )
     return model

    @property
    def    a__				(					self     :     List[Any]     )							->							Dict:

     '''simple docstring'''




     return {
         "block_out_channels": [32, 32, 64, 64],
         "down_block_types": [
             "DownEncoderBlock2D",
             "DownEncoderBlock2D",
             "DownEncoderBlock2D",
             "AttnDownEncoderBlock2D",
         ],
         "in_channels": 3,
         "latent_channels": 4,
         "layers_per_block": 1,
         "norm_num_groups": 8,
         "norm_type": "spatial",
         "num_vq_embeddings": 12,
         "out_channels": 3,
         "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
         "vq_embed_dim": 4,
     }

    @property
    def    a__				(					self     :     Dict     )							->							str:

     '''simple docstring'''




     torch.manual_seed(0     )
     lowerCamelCase__  =     VQModel(**self.dummy_movq_kwargs     )
     return model

    def    a__				(					self     :     Dict     )							->							Dict:

     '''simple docstring'''




     lowerCamelCase__  =     self.dummy_unet
     lowerCamelCase__  =     self.dummy_movq

     lowerCamelCase__  =     {
         "num_train_timesteps": 1000,
         "beta_schedule": "linear",
         "beta_start": 0.0_0_0_8_5,
         "beta_end": 0.0_1_2,
         "clip_sample": False,
         "set_alpha_to_one": False,
         "steps_offset": 0,
         "prediction_type": "epsilon",
         "thresholding": False,
     }

     lowerCamelCase__  =     DDIMScheduler(**__lowerCamelCase     )

     lowerCamelCase__  =     {
         "unet": unet,
         "scheduler": scheduler,
         "movq": movq,
     }

     return components

    def    a__				(					self     :     List[Any]				,     __lowerCamelCase     :     Tuple				,     __lowerCamelCase     :     List[Any]=0     )							->							Optional[Any]:

     '''simple docstring'''




     lowerCamelCase__  =     floats_tensor((1, self.text_embedder_hidden_size)				,     rng=random.Random(__lowerCamelCase     )     ).to(__lowerCamelCase     )
     lowerCamelCase__  =     floats_tensor((1, self.text_embedder_hidden_size)				,     rng=random.Random(seed + 1     )     ).to(
         __lowerCamelCase     )
     # create init_image
     lowerCamelCase__  =     floats_tensor((1, 3, 64, 64)				,     rng=random.Random(__lowerCamelCase     )     ).to(__lowerCamelCase     )
     lowerCamelCase__  =     image.cpu().permute(0				,     2				,     3				,     1     )[0]
     lowerCamelCase__  =     Image.fromarray(np.uinta(__lowerCamelCase     )     ).convert("RGB"     ).resize((256, 256)     )
     # create hint
     lowerCamelCase__  =     floats_tensor((1, 3, 64, 64)				,     rng=random.Random(__lowerCamelCase     )     ).to(__lowerCamelCase     )

     if str(__lowerCamelCase     ).startswith("mps"     ):
      lowerCamelCase__  =     torch.manual_seed(__lowerCamelCase     )
     else:
      lowerCamelCase__  =     torch.Generator(device=__lowerCamelCase     ).manual_seed(__lowerCamelCase     )
     lowerCamelCase__  =     {
         "image": init_image,
         "image_embeds": image_embeds,
         "negative_image_embeds": negative_image_embeds,
         "hint": hint,
         "generator": generator,
         "height": 64,
         "width": 64,
         "num_inference_steps": 10,
         "guidance_scale": 7.0,
         "strength": 0.2,
         "output_type": "np",
     }
     return inputs




    def    a__				(					self     :     Dict     )							->							Optional[Any]:

     '''simple docstring'''




     lowerCamelCase__  =     "cpu"

     lowerCamelCase__  =     self.get_dummy_components()

     lowerCamelCase__  =     self.pipeline_class(**__lowerCamelCase     )
     lowerCamelCase__  =     pipe.to(__lowerCamelCase     )

     pipe.set_progress_bar_config(disable=__lowerCamelCase     )

     lowerCamelCase__  =     pipe(**self.get_dummy_inputs(__lowerCamelCase     )     )
     lowerCamelCase__  =     output.images

     lowerCamelCase__  =     pipe(
         **self.get_dummy_inputs(__lowerCamelCase     )				,     return_dict=__lowerCamelCase				,     )[0]

     lowerCamelCase__  =     image[0, -3:, -3:, -1]
     lowerCamelCase__  =     image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)

     lowerCamelCase__  =     np.array(
         [0.5_4_9_8_5_0_3_4, 0.5_5_5_0_9_3_6_5, 0.5_2_5_6_1_5_0_4, 0.5_5_7_0_4_9_4, 0.5_5_9_3_8_1_8, 0.5_2_6_3_9_7_9, 0.5_0_2_8_5_6_4_3, 0.5_0_6_9_8_4_6, 0.5_1_1_9_6_7_3_6]     )
     assert (
         np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2
     ), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
     assert (
         np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1E-2
     ), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''
@slow
@require_torch_gpu
class 				lowercase       (							unittest.TestCase      ):


    '''simple docstring'''

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

     '''simple docstring'''




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




    def    a__				(					self     :     Tuple     )							->							Optional[Any]:

     '''simple docstring'''




     lowerCamelCase__  =     load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
         "/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"     )

     lowerCamelCase__  =     load_image(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"     )
     lowerCamelCase__  =     init_image.resize((512, 512)     )

     lowerCamelCase__  =     load_image(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
         "/kandinskyv22/hint_image_cat.png"     )
     lowerCamelCase__  =     torch.from_numpy(np.array(__lowerCamelCase     )     ).float() / 2_5_5.0
     lowerCamelCase__  =     hint.permute(2				,     0				,     1     ).unsqueeze(0     )

     lowerCamelCase__  =     "A robot, 4k photo"

     lowerCamelCase__  =     KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
         "kandinsky-community/kandinsky-2-2-prior"				,     torch_dtype=torch.floataa     )
     pipe_prior.to(__lowerCamelCase     )

     lowerCamelCase__  =     KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
         "kandinsky-community/kandinsky-2-2-controlnet-depth"				,     torch_dtype=torch.floataa     )
     lowerCamelCase__  =     pipeline.to(__lowerCamelCase     )

     pipeline.set_progress_bar_config(disable=__lowerCamelCase     )

     lowerCamelCase__  =     torch.Generator(device="cpu"     ).manual_seed(0     )

     lowerCamelCase__  ,	lowerCamelCase__  =     pipe_prior(
         __lowerCamelCase				,     image=__lowerCamelCase				,     strength=0.8_5				,     generator=__lowerCamelCase				,     negative_prompt=""				,     ).to_tuple()

     lowerCamelCase__  =     pipeline(
         image=__lowerCamelCase				,     image_embeds=__lowerCamelCase				,     negative_image_embeds=__lowerCamelCase				,     hint=__lowerCamelCase				,     generator=__lowerCamelCase				,     num_inference_steps=100				,     height=512				,     width=512				,     strength=0.5				,     output_type="np"				,     )

     lowerCamelCase__  =     output.images[0]

     assert image.shape == (512, 512, 3)

     assert_mean_pixel_difference(__lowerCamelCase				,     __lowerCamelCase     )









'''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 				lowercase       (							_lowerCamelCase ,       _lowerCamelCase ,       unittest.TestCase      ):


    '''simple docstring'''

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

    def    a__				(					self     :     int     )							->							Dict:

     '''simple docstring'''




     torch.manual_seed(0     )
     lowerCamelCase__  =     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=__lowerCamelCase				,     )
     lowerCamelCase__  =     DDIMScheduler(
         beta_start=0.0_0_0_8_5				,     beta_end=0.0_1_2				,     beta_schedule="scaled_linear"				,     clip_sample=__lowerCamelCase				,     set_alpha_to_one=__lowerCamelCase				,     )
     lowerCamelCase__  =     DDIMInverseScheduler(
         beta_start=0.0_0_0_8_5				,     beta_end=0.0_1_2				,     beta_schedule="scaled_linear"				,     clip_sample=__lowerCamelCase				,     set_alpha_to_zero=__lowerCamelCase				,     )
     torch.manual_seed(0     )
     lowerCamelCase__  =     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     )
     lowerCamelCase__  =     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				,     hidden_act="gelu"				,     projection_dim=512				,     )
     lowerCamelCase__  =     CLIPTextModel(__lowerCamelCase     )
     lowerCamelCase__  =     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

     lowerCamelCase__  =     {
         "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    a__				(					self     :     List[Any]				,     __lowerCamelCase     :     Optional[Any]				,     __lowerCamelCase     :     Any=0     )							->							Tuple:

     '''simple docstring'''




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

     '''simple docstring'''




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

     '''simple docstring'''




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

     '''simple docstring'''




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

     lowerCamelCase__  =     self.get_dummy_components()
     lowerCamelCase__  =     self.pipeline_class(**__lowerCamelCase     )
     pipe.to(__lowerCamelCase     )
     pipe.set_progress_bar_config(disable=__lowerCamelCase     )

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

     lowerCamelCase__  =     self.get_dummy_inputs(__lowerCamelCase     )
     lowerCamelCase__  =     pipe(**__lowerCamelCase     )[0]

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

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

     lowerCamelCase__  =     self.get_dummy_inputs(__lowerCamelCase     )
     lowerCamelCase__  =     pipe_loaded(**__lowerCamelCase     )[0]

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

    def    a__				(					self     :     List[str]     )							->							List[Any]:

     '''simple docstring'''




     lowerCamelCase__  =     "cpu"

     lowerCamelCase__  =     self.get_dummy_components()
     lowerCamelCase__  =     self.pipeline_class(**__lowerCamelCase     )
     pipe.to(__lowerCamelCase     )
     pipe.set_progress_bar_config(disable=__lowerCamelCase     )

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

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

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

     '''simple docstring'''




     lowerCamelCase__  =     "cpu"

     lowerCamelCase__  =     self.get_dummy_components()
     lowerCamelCase__  =     self.pipeline_class(**__lowerCamelCase     )
     pipe.to(__lowerCamelCase     )
     pipe.set_progress_bar_config(disable=__lowerCamelCase     )

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

     self.assertEqual(image.shape				,     (2, 32, 32, 3)     )
     lowerCamelCase__  =     np.array(
         [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9]				,     )
     lowerCamelCase__  =     np.abs(image_slice.flatten() - expected_slice     ).max()
     self.assertLessEqual(__lowerCamelCase				,     1E-3     )

    def    a__				(					self     :     Tuple     )							->							Tuple:

     '''simple docstring'''




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




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

     '''simple docstring'''




     lowerCamelCase__  =     "cpu"

     lowerCamelCase__  =     self.get_dummy_components()

     lowerCamelCase__  =     {"beta_start": 0.0_0_0_8_5, "beta_end": 0.0_1_2, "beta_schedule": "scaled_linear"}
     lowerCamelCase__  =     DPMSolverMultistepScheduler(**__lowerCamelCase     )
     lowerCamelCase__  =     DPMSolverMultistepInverseScheduler(**__lowerCamelCase     )

     lowerCamelCase__  =     self.pipeline_class(**__lowerCamelCase     )
     pipe.to(__lowerCamelCase     )
     pipe.set_progress_bar_config(disable=__lowerCamelCase     )

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

     self.assertEqual(image.shape				,     (2, 32, 32, 3)     )
     lowerCamelCase__  =     np.array(
         [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9]				,     )
     lowerCamelCase__  =     np.abs(image_slice.flatten() - expected_slice     ).max()
     self.assertLessEqual(__lowerCamelCase				,     1E-3     )
@require_torch_gpu
@slow
class 				lowercase       (							unittest.TestCase      ):


    '''simple docstring'''

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

     '''simple docstring'''




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

    @classmethod
    def    a__				(					cls     :     int     )							->							List[Any]:

     '''simple docstring'''




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

     lowerCamelCase__  =     raw_image.convert("RGB"     ).resize((768, 768)     )

     lowerCamelCase__  =     raw_image

    def    a__				(					self     :     Optional[Any]     )							->							str:

     '''simple docstring'''




     lowerCamelCase__  =     torch.manual_seed(0     )

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

     lowerCamelCase__  =     "a bowl of fruit"
     lowerCamelCase__  =     "a bowl of pears"

     lowerCamelCase__  =     pipe.generate_mask(
         image=self.raw_image				,     source_prompt=__lowerCamelCase				,     target_prompt=__lowerCamelCase				,     generator=__lowerCamelCase				,     )

     lowerCamelCase__  =     pipe.invert(
         prompt=__lowerCamelCase				,     image=self.raw_image				,     inpaint_strength=0.7				,     generator=__lowerCamelCase     ).latents

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

     lowerCamelCase__  =     (
         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    a__				(					self     :     List[str]     )							->							Dict:

     '''simple docstring'''




     lowerCamelCase__  =     torch.manual_seed(0     )

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

     lowerCamelCase__  =     "a bowl of fruit"
     lowerCamelCase__  =     "a bowl of pears"

     lowerCamelCase__  =     pipe.generate_mask(
         image=self.raw_image				,     source_prompt=__lowerCamelCase				,     target_prompt=__lowerCamelCase				,     generator=__lowerCamelCase				,     )

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

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

     lowerCamelCase__  =     (
         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

