import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


a_    					=       logging.get_logger(__name__)

enable_full_determinism()





class 		_lowercase						(					snake_case_						,      snake_case_						,      unittest.TestCase       ):
 lowercase				     =   UNetaDModel
 lowercase				     =   'sample'

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

  """simple docstring"""





  UpperCamelCase_					:						Dict =					4
  UpperCamelCase_					:						str =					3
  UpperCamelCase_					:						str =					(3_2, 3_2)

  UpperCamelCase_					:						Optional[Any] =					floats_tensor((batch_size, num_channels) + sizes			).to(snake_case			)
  UpperCamelCase_					:						List[str] =					torch.tensor([1_0]			).to(snake_case			)

  return {"sample": noise, "timestep": time_step}

 @property
 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]			)   ->						int:

  """simple docstring"""





  return (3, 3_2, 3_2)

 @property
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						Tuple:

  """simple docstring"""





  return (3, 3_2, 3_2)


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

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					{
      'block_out_channels': (3_2, 6_4),
      'down_block_types': ('DownBlock2D', 'AttnDownBlock2D'),
      'up_block_types': ('AttnUpBlock2D', 'UpBlock2D'),
      'attention_head_dim': 3,
      'out_channels': 3,
      'in_channels': 3,
      'layers_per_block': 2,
      'sample_size': 3_2,
  }
  UpperCamelCase_					:						List[Any] =					self.dummy_input
  return init_dict, inputs_dict





class 		_lowercase						(					snake_case_						,      snake_case_						,      unittest.TestCase       ):
 lowercase				     =   UNetaDModel
 lowercase				     =   'sample'

 @property
 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]			)   ->						Optional[int]:

  """simple docstring"""





  UpperCamelCase_					:						Any =					4
  UpperCamelCase_					:						Optional[Any] =					4
  UpperCamelCase_					:						int =					(3_2, 3_2)

  UpperCamelCase_					:						Union[str, Any] =					floats_tensor((batch_size, num_channels) + sizes			).to(snake_case			)
  UpperCamelCase_					:						Optional[Any] =					torch.tensor([1_0]			).to(snake_case			)

  return {"sample": noise, "timestep": time_step}

 @property
 def 						SCREAMING_SNAKE_CASE__   ( self	:							str			)   ->						Optional[Any]:

  """simple docstring"""





  return (4, 3_2, 3_2)

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

  """simple docstring"""





  return (4, 3_2, 3_2)

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

  """simple docstring"""





  UpperCamelCase_					:						str =					{
      'sample_size': 3_2,
      'in_channels': 4,
      'out_channels': 4,
      'layers_per_block': 2,
      'block_out_channels': (3_2, 6_4),
      'attention_head_dim': 3_2,
      'down_block_types': ('DownBlock2D', 'DownBlock2D'),
      'up_block_types': ('UpBlock2D', 'UpBlock2D'),
  }
  UpperCamelCase_					:						Any =					self.dummy_input
  return init_dict, inputs_dict

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

  """simple docstring"""





  UpperCamelCase_, UpperCamelCase_					:						Tuple =					UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'   ,      output_loading_info=snake_case			)

  self.assertIsNotNone(snake_case			)
  self.assertEqual(len(loading_info['missing_keys']			)   ,      0			)

  model.to(snake_case			)
  UpperCamelCase_					:						Optional[Any] =					model(**self.dummy_input			).sample

  assert image is not None, "Make sure output is not None"

 @unittest.skipIf(torch_device != 'cuda'   ,      'This test is supposed to run on GPU'			)
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						Dict:

  """simple docstring"""





  UpperCamelCase_, UpperCamelCase_					:						List[str] =					UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'   ,      output_loading_info=snake_case			)
  model.to(snake_case			)
  UpperCamelCase_					:						int =					model(**self.dummy_input			).sample

  assert image is not None, "Make sure output is not None"

 @unittest.skipIf(torch_device != 'cuda'   ,      'This test is supposed to run on GPU'			)
 def 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						List[str]:

  """simple docstring"""





  UpperCamelCase_, UpperCamelCase_					:						Union[str, Any] =					UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'   ,      output_loading_info=snake_case			)
  model_accelerate.to(snake_case			)
  model_accelerate.eval()

  UpperCamelCase_					:						Union[str, Any] =					torch.randn(
      1   ,      model_accelerate.config.in_channels   ,      model_accelerate.config.sample_size   ,      model_accelerate.config.sample_size   ,      generator=torch.manual_seed(0			)   ,      )
  UpperCamelCase_					:						int =					noise.to(snake_case			)
  UpperCamelCase_					:						str =					torch.tensor([1_0] * noise.shape[0]			).to(snake_case			)

  UpperCamelCase_					:						Any =					model_accelerate(snake_case   ,      snake_case			)['sample']

  # two models don't need to stay in the device at the same time
  del model_accelerate
  torch.cuda.empty_cache()
  gc.collect()

  UpperCamelCase_, UpperCamelCase_					:						Optional[Any] =					UNetaDModel.from_pretrained(
      'fusing/unet-ldm-dummy-update'   ,      output_loading_info=snake_case   ,      low_cpu_mem_usage=snake_case			)
  model_normal_load.to(snake_case			)
  model_normal_load.eval()
  UpperCamelCase_					:						List[Any] =					model_normal_load(snake_case   ,      snake_case			)['sample']

  assert torch_all_close(snake_case   ,      snake_case   ,      rtol=1e-3			)


 def 						SCREAMING_SNAKE_CASE__   ( self	:							Dict			)   ->						Dict:

  """simple docstring"""





  UpperCamelCase_					:						int =					UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'			)
  model.eval()
  model.to(snake_case			)

  UpperCamelCase_					:						List[Any] =					torch.randn(
      1   ,      model.config.in_channels   ,      model.config.sample_size   ,      model.config.sample_size   ,      generator=torch.manual_seed(0			)   ,      )
  UpperCamelCase_					:						Union[str, Any] =					noise.to(snake_case			)
  UpperCamelCase_					:						Dict =					torch.tensor([1_0] * noise.shape[0]			).to(snake_case			)

  with torch.no_grad():
   UpperCamelCase_					:						Optional[Any] =					model(snake_case   ,      snake_case			).sample

  UpperCamelCase_					:						Union[str, Any] =					output[0, -1, -3:, -3:].flatten().cpu()
  # fmt: off
  UpperCamelCase_					:						Dict =					torch.tensor([-13.3258, -20.1100, -15.9873, -17.6617, -23.0596, -17.9419, -13.3675, -16.1889, -12.3800]			)
  # fmt: on

  self.assertTrue(torch_all_close(snake_case   ,      snake_case   ,      rtol=1e-3			)			)





class 		_lowercase						(					snake_case_						,      snake_case_						,      unittest.TestCase       ):
 lowercase				     =   UNetaDModel
 lowercase				     =   'sample'

 @property
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]   ,      snake_case	:							List[Any]=(3_2, 3_2)			)   ->						str:

  """simple docstring"""





  UpperCamelCase_					:						int =					4
  UpperCamelCase_					:						Tuple =					3

  UpperCamelCase_					:						str =					floats_tensor((batch_size, num_channels) + sizes			).to(snake_case			)
  UpperCamelCase_					:						Dict =					torch.tensor(batch_size * [1_0]			).to(dtype=torch.intaa   ,      device=snake_case			)

  return {"sample": noise, "timestep": time_step}

 @property
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						Any:

  """simple docstring"""





  return (3, 3_2, 3_2)

 @property
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Dict			)   ->						Any:

  """simple docstring"""





  return (3, 3_2, 3_2)

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

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					{
      'block_out_channels': [3_2, 6_4, 6_4, 6_4],
      'in_channels': 3,
      'layers_per_block': 1,
      'out_channels': 3,
      'time_embedding_type': 'fourier',
      'norm_eps': 1e-6,
      'mid_block_scale_factor': math.sqrt(2.0			),
      'norm_num_groups': None,
      'down_block_types': [
          'SkipDownBlock2D',
          'AttnSkipDownBlock2D',
          'SkipDownBlock2D',
          'SkipDownBlock2D',
      ],
      'up_block_types': [
          'SkipUpBlock2D',
          'SkipUpBlock2D',
          'AttnSkipUpBlock2D',
          'SkipUpBlock2D',
      ],
  }
  UpperCamelCase_					:						Union[str, Any] =					self.dummy_input
  return init_dict, inputs_dict

 @slow
 def 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						Optional[Any]:

  """simple docstring"""





  UpperCamelCase_, UpperCamelCase_					:						Optional[int] =					UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256'   ,      output_loading_info=snake_case			)
  self.assertIsNotNone(snake_case			)
  self.assertEqual(len(loading_info['missing_keys']			)   ,      0			)

  model.to(snake_case			)
  UpperCamelCase_					:						Union[str, Any] =					self.dummy_input
  UpperCamelCase_					:						Any =					floats_tensor((4, 3) + (2_5_6, 2_5_6)			).to(snake_case			)
  UpperCamelCase_					:						Optional[Any] =					noise
  UpperCamelCase_					:						Optional[Any] =					model(**snake_case			)

  assert image is not None, "Make sure output is not None"

 @slow
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						str:

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256'			)
  model.to(snake_case			)

  UpperCamelCase_					:						Dict =					4
  UpperCamelCase_					:						int =					3
  UpperCamelCase_					:						Optional[Any] =					(2_5_6, 2_5_6)

  UpperCamelCase_					:						str =					torch.ones((batch_size, num_channels) + sizes			).to(snake_case			)
  UpperCamelCase_					:						List[str] =					torch.tensor(batch_size * [1e-4]			).to(snake_case			)

  with torch.no_grad():
   UpperCamelCase_					:						Optional[Any] =					model(snake_case   ,      snake_case			).sample

  UpperCamelCase_					:						Optional[int] =					output[0, -3:, -3:, -1].flatten().cpu()
  # fmt: off
  UpperCamelCase_					:						Optional[int] =					torch.tensor([-4842.8691, -6499.6631, -3800.1953, -7978.2686, -10980.7129, -20028.8535, 8148.2822, 2342.2905, 567.7608]			)
  # fmt: on

  self.assertTrue(torch_all_close(snake_case   ,      snake_case   ,      rtol=1e-2			)			)

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

  """simple docstring"""





  UpperCamelCase_					:						List[Any] =					UNetaDModel.from_pretrained('fusing/ncsnpp-ffhq-ve-dummy-update'			)
  model.to(snake_case			)

  UpperCamelCase_					:						Optional[Any] =					4
  UpperCamelCase_					:						List[Any] =					3
  UpperCamelCase_					:						List[str] =					(3_2, 3_2)

  UpperCamelCase_					:						Union[str, Any] =					torch.ones((batch_size, num_channels) + sizes			).to(snake_case			)
  UpperCamelCase_					:						str =					torch.tensor(batch_size * [1e-4]			).to(snake_case			)

  with torch.no_grad():
   UpperCamelCase_					:						Optional[int] =					model(snake_case   ,      snake_case			).sample

  UpperCamelCase_					:						Any =					output[0, -3:, -3:, -1].flatten().cpu()
  # fmt: off
  UpperCamelCase_					:						List[str] =					torch.tensor([-0.0325, -0.0900, -0.0869, -0.0332, -0.0725, -0.0270, -0.0101, 0.0227, 0.0256]			)
  # fmt: on

  self.assertTrue(torch_all_close(snake_case   ,      snake_case   ,      rtol=1e-2			)			)


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

  """simple docstring"""





  pass




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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]			)   ->						Dict:

  """simple docstring"""





  torch.manual_seed(0			)
  UpperCamelCase_					:						Dict =					UNetaDConditionModel(
      block_out_channels=(3_2, 6_4)   ,      layers_per_block=2   ,      sample_size=3_2   ,      in_channels=4   ,      out_channels=4   ,      down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')   ,      up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')   ,      cross_attention_dim=3_2   ,      attention_head_dim=(2, 4)   ,      use_linear_projection=snake_case   ,      )
  UpperCamelCase_					:						Optional[int] =					DDIMScheduler(
      beta_start=0.00085   ,      beta_end=0.012   ,      beta_schedule='scaled_linear'   ,      clip_sample=snake_case   ,      set_alpha_to_one=snake_case   ,      )
  UpperCamelCase_					:						Tuple =					DDIMInverseScheduler(
      beta_start=0.00085   ,      beta_end=0.012   ,      beta_schedule='scaled_linear'   ,      clip_sample=snake_case   ,      set_alpha_to_zero=snake_case   ,      )
  torch.manual_seed(0			)
  UpperCamelCase_					:						List[Any] =					AutoencoderKL(
      block_out_channels=[3_2, 6_4]   ,      in_channels=3   ,      out_channels=3   ,      down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']   ,      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']   ,      latent_channels=4   ,      sample_size=1_2_8   ,      )
  torch.manual_seed(0			)
  UpperCamelCase_					:						List[Any] =					CLIPTextConfig(
      bos_token_id=0   ,      eos_token_id=2   ,      hidden_size=3_2   ,      intermediate_size=3_7   ,      layer_norm_eps=1e-05   ,      num_attention_heads=4   ,      num_hidden_layers=5   ,      pad_token_id=1   ,      vocab_size=1_0_0_0   ,      hidden_act='gelu'   ,      projection_dim=5_1_2   ,      )
  UpperCamelCase_					:						List[str] =					CLIPTextModel(snake_case			)
  UpperCamelCase_					:						List[str] =					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

  UpperCamelCase_					:						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 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]   ,      snake_case	:							Dict   ,      snake_case	:							Tuple=0			)   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					floats_tensor((1, 1_6, 1_6)   ,      rng=random.Random(snake_case			)			).to(snake_case			)
  UpperCamelCase_					:						Optional[int] =					floats_tensor((1, 2, 4, 1_6, 1_6)   ,      rng=random.Random(snake_case			)			).to(snake_case			)
  if str(snake_case			).startswith('mps'			):
   UpperCamelCase_					:						int =					torch.manual_seed(snake_case			)
  else:
   UpperCamelCase_					:						Any =					torch.Generator(device=snake_case			).manual_seed(snake_case			)
  UpperCamelCase_					:						Union[str, 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 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]   ,      snake_case	:							Union[str, Any]   ,      snake_case	:							Dict=0			)   ->						str:

  """simple docstring"""





  UpperCamelCase_					:						List[Any] =					floats_tensor((1, 3, 3_2, 3_2)   ,      rng=random.Random(snake_case			)			).to(snake_case			)
  UpperCamelCase_					:						int =					image.cpu().permute(0   ,      2   ,      3   ,      1			)[0]
  UpperCamelCase_					:						Optional[int] =					Image.fromarray(np.uinta(snake_case			)			).convert('RGB'			)
  if str(snake_case			).startswith('mps'			):
   UpperCamelCase_					:						int =					torch.manual_seed(snake_case			)
  else:
   UpperCamelCase_					:						Optional[int] =					torch.Generator(device=snake_case			).manual_seed(snake_case			)
  UpperCamelCase_					:						List[str] =					{
      '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 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]   ,      snake_case	:							str   ,      snake_case	:							Dict=0			)   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						List[Any] =					floats_tensor((1, 3, 3_2, 3_2)   ,      rng=random.Random(snake_case			)			).to(snake_case			)
  UpperCamelCase_					:						Any =					image.cpu().permute(0   ,      2   ,      3   ,      1			)[0]
  UpperCamelCase_					:						List[str] =					Image.fromarray(np.uinta(snake_case			)			).convert('RGB'			)
  if str(snake_case			).startswith('mps'			):
   UpperCamelCase_					:						Any =					torch.manual_seed(snake_case			)
  else:
   UpperCamelCase_					:						int =					torch.Generator(device=snake_case			).manual_seed(snake_case			)
  UpperCamelCase_					:						int =					{
      '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 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]			)   ->						Optional[int]:

  """simple docstring"""





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

  UpperCamelCase_					:						int =					self.get_dummy_components()
  UpperCamelCase_					:						str =					self.pipeline_class(**snake_case			)
  pipe.to(snake_case			)
  pipe.set_progress_bar_config(disable=snake_case			)

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

  UpperCamelCase_					:						Any =					self.get_dummy_inputs(snake_case			)
  UpperCamelCase_					:						Any =					pipe(**snake_case			)[0]

  with tempfile.TemporaryDirectory() as tmpdir:
   pipe.save_pretrained(snake_case			)
   UpperCamelCase_					:						int =					self.pipeline_class.from_pretrained(snake_case			)
   pipe_loaded.to(snake_case			)
   pipe_loaded.set_progress_bar_config(disable=snake_case			)

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

  UpperCamelCase_					:						Dict =					self.get_dummy_inputs(snake_case			)
  UpperCamelCase_					:						int =					pipe_loaded(**snake_case			)[0]

  UpperCamelCase_					:						int =					np.abs(output - output_loaded			).max()
  self.assertLess(snake_case   ,      1e-4			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							str			)   ->						Any:

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					'cpu'

  UpperCamelCase_					:						Any =					self.get_dummy_components()
  UpperCamelCase_					:						int =					self.pipeline_class(**snake_case			)
  pipe.to(snake_case			)
  pipe.set_progress_bar_config(disable=snake_case			)

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

  self.assertEqual(mask.shape   ,      (1, 1_6, 1_6)			)
  UpperCamelCase_					:						Tuple =					np.array([0] * 9			)
  UpperCamelCase_					:						int =					np.abs(mask_slice.flatten() - expected_slice			).max()
  self.assertLessEqual(snake_case   ,      1e-3			)
  self.assertEqual(mask[0, -3, -4]   ,      0			)

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

  """simple docstring"""





  UpperCamelCase_					:						Dict =					'cpu'

  UpperCamelCase_					:						List[str] =					self.get_dummy_components()
  UpperCamelCase_					:						List[Any] =					self.pipeline_class(**snake_case			)
  pipe.to(snake_case			)
  pipe.set_progress_bar_config(disable=snake_case			)

  UpperCamelCase_					:						Optional[int] =					self.get_dummy_inversion_inputs(snake_case			)
  UpperCamelCase_					:						Optional[Any] =					pipe.invert(**snake_case			).images
  UpperCamelCase_					:						Tuple =					image[0, -1, -3:, -3:]

  self.assertEqual(image.shape   ,      (2, 3_2, 3_2, 3)			)
  UpperCamelCase_					:						str =					np.array(
      [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]   ,      )
  UpperCamelCase_					:						Any =					np.abs(image_slice.flatten() - expected_slice			).max()
  self.assertLessEqual(snake_case   ,      1e-3			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						Any:

  """simple docstring"""





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


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

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					'cpu'

  UpperCamelCase_					:						Tuple =					self.get_dummy_components()

  UpperCamelCase_					:						int =					{'beta_start': 0.00085, 'beta_end': 0.012, 'beta_schedule': 'scaled_linear'}
  UpperCamelCase_					:						str =					DPMSolverMultistepScheduler(**snake_case			)
  UpperCamelCase_					:						Union[str, Any] =					DPMSolverMultistepInverseScheduler(**snake_case			)

  UpperCamelCase_					:						Optional[int] =					self.pipeline_class(**snake_case			)
  pipe.to(snake_case			)
  pipe.set_progress_bar_config(disable=snake_case			)

  UpperCamelCase_					:						Any =					self.get_dummy_inversion_inputs(snake_case			)
  UpperCamelCase_					:						Union[str, Any] =					pipe.invert(**snake_case			).images
  UpperCamelCase_					:						Any =					image[0, -1, -3:, -3:]

  self.assertEqual(image.shape   ,      (2, 3_2, 3_2, 3)			)
  UpperCamelCase_					:						int =					np.array(
      [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]   ,      )
  UpperCamelCase_					:						Union[str, Any] =					np.abs(image_slice.flatten() - expected_slice			).max()
  self.assertLessEqual(snake_case   ,      1e-3			)





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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						Any:

  """simple docstring"""





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

 @classmethod
 def 						SCREAMING_SNAKE_CASE__   ( cls	:							Any			)   ->						Any:

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					load_image(
      'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'			)

  UpperCamelCase_					:						Any =					raw_image.convert('RGB'			).resize((7_6_8, 7_6_8)			)

  UpperCamelCase_					:						Optional[Any] =					raw_image

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

  """simple docstring"""





  UpperCamelCase_					:						Dict =					torch.manual_seed(0			)

  UpperCamelCase_					:						List[str] =					StableDiffusionDiffEditPipeline.from_pretrained(
      'stabilityai/stable-diffusion-2-1'   ,      safety_checker=snake_case   ,      torch_dtype=torch.floataa			)
  UpperCamelCase_					:						int =					DDIMScheduler.from_config(pipe.scheduler.config			)
  UpperCamelCase_					:						Tuple =					DDIMInverseScheduler.from_config(pipe.scheduler.config			)
  pipe.enable_model_cpu_offload()
  pipe.set_progress_bar_config(disable=snake_case			)

  UpperCamelCase_					:						Optional[int] =					'a bowl of fruit'
  UpperCamelCase_					:						int =					'a bowl of pears'

  UpperCamelCase_					:						Any =					pipe.generate_mask(
      image=self.raw_image   ,      source_prompt=snake_case   ,      target_prompt=snake_case   ,      generator=snake_case   ,      )

  UpperCamelCase_					:						int =					pipe.invert(
      prompt=snake_case   ,      image=self.raw_image   ,      inpaint_strength=0.7   ,      generator=snake_case			).latents

  UpperCamelCase_					:						Optional[int] =					pipe(
      prompt=snake_case   ,      mask_image=snake_case   ,      image_latents=snake_case   ,      generator=snake_case   ,      negative_prompt=snake_case   ,      inpaint_strength=0.7   ,      output_type='numpy'   ,      ).images[0]

  UpperCamelCase_					:						Any =					(
      np.array(
          load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/diffedit/pears.png'			).resize((7_6_8, 7_6_8)			)			)
      / 2_5_5
  )
  assert np.abs((expected_image - image).max()			) < 5e-1


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

  """simple docstring"""





  UpperCamelCase_					:						Union[str, Any] =					torch.manual_seed(0			)

  UpperCamelCase_					:						List[Any] =					StableDiffusionDiffEditPipeline.from_pretrained(
      'stabilityai/stable-diffusion-2-1'   ,      safety_checker=snake_case   ,      torch_dtype=torch.floataa			)
  UpperCamelCase_					:						Optional[int] =					DPMSolverMultistepScheduler.from_config(pipe.scheduler.config			)
  UpperCamelCase_					:						Union[str, Any] =					DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config			)
  pipe.enable_model_cpu_offload()
  pipe.set_progress_bar_config(disable=snake_case			)

  UpperCamelCase_					:						str =					'a bowl of fruit'
  UpperCamelCase_					:						int =					'a bowl of pears'

  UpperCamelCase_					:						Union[str, Any] =					pipe.generate_mask(
      image=self.raw_image   ,      source_prompt=snake_case   ,      target_prompt=snake_case   ,      generator=snake_case   ,      )

  UpperCamelCase_					:						Any =					pipe.invert(
      prompt=snake_case   ,      image=self.raw_image   ,      inpaint_strength=0.7   ,      generator=snake_case   ,      num_inference_steps=2_5   ,      ).latents

  UpperCamelCase_					:						int =					pipe(
      prompt=snake_case   ,      mask_image=snake_case   ,      image_latents=snake_case   ,      generator=snake_case   ,      negative_prompt=snake_case   ,      inpaint_strength=0.7   ,      num_inference_steps=2_5   ,      output_type='numpy'   ,      ).images[0]

  UpperCamelCase_					:						str =					(
      np.array(
          load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/diffedit/pears.png'			).resize((7_6_8, 7_6_8)			)			)
      / 2_5_5
  )
  assert np.abs((expected_image - image).max()			) < 5e-1



