
'''simple docstring'''

from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel




class    lowerCAmelCase       (     UpperCamelCase_  ):
 A_				:				Union[str, Any]	=			["""vqvae"""]


 def __init__(    self  : int			,							a__  : AutoencoderKL			,							a__  : UNetaDConditionModel			,							a__  : Mel			,							a__  : Union[DDIMScheduler, DDPMScheduler]			,							):
  '''simple docstring'''

  super().__init__()
  self.register_modules(unet=a__			,							scheduler=a__			,							mel=a__			,							vqvae=a__    )


 def    _A	(    self  : List[Any]    ):
  '''simple docstring'''

  return 50 if isinstance(self.scheduler			,							a__    ) else 1000


 @torch.no_grad()
 def __call__(    self  : Dict			,							a__  : int = 1			,							a__  : str = None			,							a__  : np.ndarray = None			,							a__  : int = 0			,							a__  : int = 0			,							a__  : int = None			,							a__  : torch.Generator = None			,							a__  : float = 0			,							a__  : float = 0			,							a__  : torch.Generator = None			,							a__  : float = 0			,							a__  : torch.Tensor = None			,							a__  : torch.Tensor = None			,							a__  : Optional[int]=True			,							):
  '''simple docstring'''

  lowerCAmelCase__     :   Union[str, Any]     		=	steps or self.get_default_steps()
  self.scheduler.set_timesteps(a__    )
  lowerCAmelCase__     :   Tuple     		=	step_generator or generator
  # For backwards compatibility
  if type(self.unet.config.sample_size    ) == int:
   lowerCAmelCase__     :   Dict     		=	(self.unet.config.sample_size, self.unet.config.sample_size)
  if noise is None:
   lowerCAmelCase__     :   List[str]     		=	randn_tensor(
       (
           batch_size,
           self.unet.config.in_channels,
           self.unet.config.sample_size[0],
           self.unet.config.sample_size[1],
       )			,							generator=a__			,							device=self.device			,							)
  lowerCAmelCase__     :   List[str]     		=	noise
  lowerCAmelCase__     :   int     		=	None

  if audio_file is not None or raw_audio is not None:
   self.mel.load_audio(a__			,							a__    )
   lowerCAmelCase__     :   int     		=	self.mel.audio_slice_to_image(a__    )
   lowerCAmelCase__     :   Optional[int]     		=	np.frombuffer(input_image.tobytes()			,							dtype="uint8"    ).reshape(
       (input_image.height, input_image.width)    )
   lowerCAmelCase__     :   Optional[Any]     		=	(input_image / 255) * 2 - 1
   lowerCAmelCase__     :   Optional[int]     		=	torch.tensor(input_image[np.newaxis, :, :]			,							dtype=torch.float    ).to(self.device    )

   if self.vqvae is not None:
    lowerCAmelCase__     :   Tuple     		=	self.vqvae.encode(torch.unsqueeze(a__			,							0    )    ).latent_dist.sample(
        generator=a__    )[0]
    lowerCAmelCase__     :   Union[str, Any]     		=	self.vqvae.config.scaling_factor * input_images

   if start_step > 0:
    lowerCAmelCase__     :   Tuple     		=	self.scheduler.add_noise(a__			,							a__			,							self.scheduler.timesteps[start_step - 1]    )

   lowerCAmelCase__     :   int     		=	(
       self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
   )
   lowerCAmelCase__     :   List[Any]     		=	int(mask_start_secs * pixels_per_second    )
   lowerCAmelCase__     :   Dict     		=	int(mask_end_secs * pixels_per_second    )
   lowerCAmelCase__     :   int     		=	self.scheduler.add_noise(a__			,							a__			,							torch.tensor(self.scheduler.timesteps[start_step:]    )    )

  for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]    )    ):
   if isinstance(self.unet			,							a__    ):
    lowerCAmelCase__     :   int     		=	self.unet(a__			,							a__			,							a__    )["sample"]
   else:
    lowerCAmelCase__     :   Optional[int]     		=	self.unet(a__			,							a__    )["sample"]

   if isinstance(self.scheduler			,							a__    ):
    lowerCAmelCase__     :   List[str]     		=	self.scheduler.step(
        model_output=a__			,							timestep=a__			,							sample=a__			,							eta=a__			,							generator=a__			,							)["prev_sample"]
   else:
    lowerCAmelCase__     :   int     		=	self.scheduler.step(
        model_output=a__			,							timestep=a__			,							sample=a__			,							generator=a__			,							)["prev_sample"]

   if mask is not None:
    if mask_start > 0:
     lowerCAmelCase__     :   Optional[Any]     		=	mask[:, step, :, :mask_start]
    if mask_end > 0:
     lowerCAmelCase__     :   Optional[Any]     		=	mask[:, step, :, -mask_end:]

  if self.vqvae is not None:
   # 0.18215 was scaling factor used in training to ensure unit variance
   lowerCAmelCase__     :   Tuple     		=	1 / self.vqvae.config.scaling_factor * images
   lowerCAmelCase__     :   str     		=	self.vqvae.decode(a__    )["sample"]

  lowerCAmelCase__     :   int     		=	(images / 2 + 0.5).clamp(0			,							1    )
  lowerCAmelCase__     :   Union[str, Any]     		=	images.cpu().permute(0			,							2			,							3			,							1    ).numpy()
  lowerCAmelCase__     :   Optional[Any]     		=	(images * 255).round().astype("uint8"    )
  lowerCAmelCase__     :   int     		=	list(
      (Image.fromarray(_[:, :, 0]    ) for _ in images)
      if images.shape[3] == 1
      else (Image.fromarray(a__			,							mode="RGB"    ).convert("L"    ) for _ in images)    )

  lowerCAmelCase__     :   str     		=	[self.mel.image_to_audio(a__    ) for _ in images]
  if not return_dict:
   return images, (self.mel.get_sample_rate(), audios)

  return BaseOutput(**AudioPipelineOutput(np.array(a__    )[:, np.newaxis, :]    )			,							**ImagePipelineOutput(a__    )    )


 @torch.no_grad()
 def    _A	(    self  : Optional[Any]			,							a__  : List[Image.Image]			,							a__  : int = 50    ):
  '''simple docstring'''

  assert isinstance(self.scheduler			,							a__    )
  self.scheduler.set_timesteps(a__    )
  lowerCAmelCase__     :   int     		=	np.array(
      [np.frombuffer(image.tobytes()			,							dtype="uint8"    ).reshape((1, image.height, image.width)    ) for image in images]    )
  lowerCAmelCase__     :   Tuple     		=	(sample / 255) * 2 - 1
  lowerCAmelCase__     :   Union[str, Any]     		=	torch.Tensor(a__    ).to(self.device    )

  for t in self.progress_bar(torch.flip(self.scheduler.timesteps			,							(0,)    )    ):
   lowerCAmelCase__     :   List[Any]     		=	t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
   lowerCAmelCase__     :   int     		=	self.scheduler.alphas_cumprod[t]
   lowerCAmelCase__     :   List[Any]     		=	(
       self.scheduler.alphas_cumprod[prev_timestep]
       if prev_timestep >= 0
       else self.scheduler.final_alpha_cumprod
   )
   lowerCAmelCase__     :   Dict     		=	1 - alpha_prod_t
   lowerCAmelCase__     :   int     		=	self.unet(a__			,							a__    )["sample"]
   lowerCAmelCase__     :   List[Any]     		=	(1 - alpha_prod_t_prev) ** 0.5 * model_output
   lowerCAmelCase__     :   List[Any]     		=	(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
   lowerCAmelCase__     :   Any     		=	sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

  return sample


 @staticmethod
 def    _A	(    a__  : torch.Tensor			,							a__  : torch.Tensor			,							a__  : float    ):
  '''simple docstring'''

  lowerCAmelCase__     :   int     		=	acos(torch.dot(torch.flatten(a__    )			,							torch.flatten(a__    )    ) / torch.norm(a__    ) / torch.norm(a__    )    )
  return sin((1 - alpha) * theta    ) * xa / sin(a__    ) + sin(alpha * theta    ) * xa / sin(a__    )







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


 def    _A	(    self  : Tuple    ):
  '''simple docstring'''

  torch.manual_seed(0    )
  lowerCAmelCase__     :   str     		=	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=a__			,							)
  lowerCAmelCase__     :   str     		=	DDIMScheduler(
      beta_start=0.00085			,							beta_end=0.012			,							beta_schedule="scaled_linear"			,							clip_sample=a__			,							set_alpha_to_one=a__			,							)
  lowerCAmelCase__     :   str     		=	DDIMInverseScheduler(
      beta_start=0.00085			,							beta_end=0.012			,							beta_schedule="scaled_linear"			,							clip_sample=a__			,							set_alpha_to_zero=a__			,							)
  torch.manual_seed(0    )
  lowerCAmelCase__     :   int     		=	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__     :   Dict     		=	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__     :   Tuple     		=	CLIPTextModel(a__    )
  lowerCAmelCase__     :   int     		=	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"    )

  lowerCAmelCase__     :   List[Any]     		=	{
      "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  : Dict			,							a__  : List[Any]			,							a__  : str=0    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Union[str, Any]     		=	floats_tensor((1, 16, 16)			,							rng=random.Random(a__    )    ).to(a__    )
  lowerCAmelCase__     :   Optional[int]     		=	floats_tensor((1, 2, 4, 16, 16)			,							rng=random.Random(a__    )    ).to(a__    )
  if str(a__    ).startswith("mps"    ):
   lowerCAmelCase__     :   Any     		=	torch.manual_seed(a__    )
  else:
   lowerCAmelCase__     :   int     		=	torch.Generator(device=a__    ).manual_seed(a__    )
  lowerCAmelCase__     :   Optional[Any]     		=	{
      "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  : Union[str, Any]			,							a__  : Union[str, Any]			,							a__  : Tuple=0    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Any     		=	floats_tensor((1, 3, 32, 32)			,							rng=random.Random(a__    )    ).to(a__    )
  lowerCAmelCase__     :   Optional[Any]     		=	image.cpu().permute(0			,							2			,							3			,							1    )[0]
  lowerCAmelCase__     :   Tuple     		=	Image.fromarray(np.uinta(a__    )    ).convert("RGB"    )
  if str(a__    ).startswith("mps"    ):
   lowerCAmelCase__     :   str     		=	torch.manual_seed(a__    )
  else:
   lowerCAmelCase__     :   List[str]     		=	torch.Generator(device=a__    ).manual_seed(a__    )
  lowerCAmelCase__     :   Dict     		=	{
      "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  : Tuple			,							a__  : Any			,							a__  : Optional[Any]=0    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[Any]     		=	floats_tensor((1, 3, 32, 32)			,							rng=random.Random(a__    )    ).to(a__    )
  lowerCAmelCase__     :   List[str]     		=	image.cpu().permute(0			,							2			,							3			,							1    )[0]
  lowerCAmelCase__     :   Tuple     		=	Image.fromarray(np.uinta(a__    )    ).convert("RGB"    )
  if str(a__    ).startswith("mps"    ):
   lowerCAmelCase__     :   Dict     		=	torch.manual_seed(a__    )
  else:
   lowerCAmelCase__     :   Tuple     		=	torch.Generator(device=a__    ).manual_seed(a__    )
  lowerCAmelCase__     :   Dict     		=	{
      "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  : List[str]    ):
  '''simple docstring'''

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

  lowerCAmelCase__     :   int     		=	self.get_dummy_components()
  lowerCAmelCase__     :   Optional[Any]     		=	self.pipeline_class(**a__    )
  pipe.to(a__    )
  pipe.set_progress_bar_config(disable=a__    )

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

  lowerCAmelCase__     :   str     		=	self.get_dummy_inputs(a__    )
  lowerCAmelCase__     :   List[Any]     		=	pipe(**a__    )[0]

  with tempfile.TemporaryDirectory() as tmpdir:
   pipe.save_pretrained(a__    )
   lowerCAmelCase__     :   Tuple     		=	self.pipeline_class.from_pretrained(a__    )
   pipe_loaded.to(a__    )
   pipe_loaded.set_progress_bar_config(disable=a__    )

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

  lowerCAmelCase__     :   Union[str, Any]     		=	self.get_dummy_inputs(a__    )
  lowerCAmelCase__     :   int     		=	pipe_loaded(**a__    )[0]

  lowerCAmelCase__     :   Dict     		=	np.abs(output - output_loaded    ).max()
  self.assertLess(a__			,							1e-4    )


 def    _A	(    self  : str    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Union[str, Any]     		=	"cpu"

  lowerCAmelCase__     :   Any     		=	self.get_dummy_components()
  lowerCAmelCase__     :   List[Any]     		=	self.pipeline_class(**a__    )
  pipe.to(a__    )
  pipe.set_progress_bar_config(disable=a__    )

  lowerCAmelCase__     :   Union[str, Any]     		=	self.get_dummy_mask_inputs(a__    )
  lowerCAmelCase__     :   List[str]     		=	pipe.generate_mask(**a__    )
  lowerCAmelCase__     :   Optional[Any]     		=	mask[0, -3:, -3:]

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


 def    _A	(    self  : List[str]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   int     		=	"cpu"

  lowerCAmelCase__     :   Union[str, Any]     		=	self.get_dummy_components()
  lowerCAmelCase__     :   List[Any]     		=	self.pipeline_class(**a__    )
  pipe.to(a__    )
  pipe.set_progress_bar_config(disable=a__    )

  lowerCAmelCase__     :   List[str]     		=	self.get_dummy_inversion_inputs(a__    )
  lowerCAmelCase__     :   Optional[int]     		=	pipe.invert(**a__    ).images
  lowerCAmelCase__     :   Dict     		=	image[0, -1, -3:, -3:]

  self.assertEqual(image.shape			,							(2, 32, 32, 3)    )
  lowerCAmelCase__     :   Tuple     		=	np.array(
      [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]			,							)
  lowerCAmelCase__     :   Union[str, Any]     		=	np.abs(image_slice.flatten() - expected_slice    ).max()
  self.assertLessEqual(a__			,							1e-3    )


 def    _A	(    self  : str    ):
  '''simple docstring'''

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


 def    _A	(    self  : Optional[int]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Optional[int]     		=	"cpu"

  lowerCAmelCase__     :   Optional[int]     		=	self.get_dummy_components()

  lowerCAmelCase__     :   List[Any]     		=	{"beta_start": 0.00085, "beta_end": 0.012, "beta_schedule": "scaled_linear"}
  lowerCAmelCase__     :   List[Any]     		=	DPMSolverMultistepScheduler(**a__    )
  lowerCAmelCase__     :   List[Any]     		=	DPMSolverMultistepInverseScheduler(**a__    )

  lowerCAmelCase__     :   Any     		=	self.pipeline_class(**a__    )
  pipe.to(a__    )
  pipe.set_progress_bar_config(disable=a__    )

  lowerCAmelCase__     :   Dict     		=	self.get_dummy_inversion_inputs(a__    )
  lowerCAmelCase__     :   Optional[Any]     		=	pipe.invert(**a__    ).images
  lowerCAmelCase__     :   Any     		=	image[0, -1, -3:, -3:]

  self.assertEqual(image.shape			,							(2, 32, 32, 3)    )
  lowerCAmelCase__     :   Dict     		=	np.array(
      [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]			,							)
  lowerCAmelCase__     :   int     		=	np.abs(image_slice.flatten() - expected_slice    ).max()
  self.assertLessEqual(a__			,							1e-3    )




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


 def    _A	(    self  : int    ):
  '''simple docstring'''

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


 @classmethod
 def    _A	(    cls  : Dict    ):
  '''simple docstring'''

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

  lowerCAmelCase__     :   Dict     		=	raw_image.convert("RGB"    ).resize((768, 768)    )

  lowerCAmelCase__     :   str     		=	raw_image


 def    _A	(    self  : Dict    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[str]     		=	torch.manual_seed(0    )

  lowerCAmelCase__     :   str     		=	StableDiffusionDiffEditPipeline.from_pretrained(
      "stabilityai/stable-diffusion-2-1"			,							safety_checker=a__			,							torch_dtype=torch.floataa    )
  lowerCAmelCase__     :   Optional[Any]     		=	DDIMScheduler.from_config(pipe.scheduler.config    )
  lowerCAmelCase__     :   int     		=	DDIMInverseScheduler.from_config(pipe.scheduler.config    )
  pipe.enable_model_cpu_offload()
  pipe.set_progress_bar_config(disable=a__    )

  lowerCAmelCase__     :   Optional[Any]     		=	"a bowl of fruit"
  lowerCAmelCase__     :   Union[str, Any]     		=	"a bowl of pears"

  lowerCAmelCase__     :   Optional[Any]     		=	pipe.generate_mask(
      image=self.raw_image			,							source_prompt=a__			,							target_prompt=a__			,							generator=a__			,							)

  lowerCAmelCase__     :   str     		=	pipe.invert(
      prompt=a__			,							image=self.raw_image			,							inpaint_strength=0.7			,							generator=a__    ).latents

  lowerCAmelCase__     :   Tuple     		=	pipe(
      prompt=a__			,							mask_image=a__			,							image_latents=a__			,							generator=a__			,							negative_prompt=a__			,							inpaint_strength=0.7			,							output_type="numpy"			,							).images[0]

  lowerCAmelCase__     :   List[str]     		=	(
      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  : Optional[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Tuple     		=	torch.manual_seed(0    )

  lowerCAmelCase__     :   str     		=	StableDiffusionDiffEditPipeline.from_pretrained(
      "stabilityai/stable-diffusion-2-1"			,							safety_checker=a__			,							torch_dtype=torch.floataa    )
  lowerCAmelCase__     :   int     		=	DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
  lowerCAmelCase__     :   Any     		=	DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config    )
  pipe.enable_model_cpu_offload()
  pipe.set_progress_bar_config(disable=a__    )

  lowerCAmelCase__     :   Any     		=	"a bowl of fruit"
  lowerCAmelCase__     :   Optional[int]     		=	"a bowl of pears"

  lowerCAmelCase__     :   Optional[Any]     		=	pipe.generate_mask(
      image=self.raw_image			,							source_prompt=a__			,							target_prompt=a__			,							generator=a__			,							)

  lowerCAmelCase__     :   Optional[int]     		=	pipe.invert(
      prompt=a__			,							image=self.raw_image			,							inpaint_strength=0.7			,							generator=a__			,							num_inference_steps=25			,							).latents

  lowerCAmelCase__     :   Any     		=	pipe(
      prompt=a__			,							mask_image=a__			,							image_latents=a__			,							generator=a__			,							negative_prompt=a__			,							inpaint_strength=0.7			,							num_inference_steps=25			,							output_type="numpy"			,							).images[0]

  lowerCAmelCase__     :   Optional[int]     		=	(
      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





