






import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class   UpperCamelCase			(					snake_case__      ,							unittest.TestCase			):







		"""simple docstring"""



		snake_case			 = RePaintPipeline
		snake_case			 = IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
		snake_case			 = PipelineTesterMixin.required_optional_params - {
		    "latents",
		    "num_images_per_prompt",
		    "callback",
		    "callback_steps",
		}
		snake_case			 = IMAGE_INPAINTING_BATCH_PARAMS
		snake_case			 = False
		def    A(	self		:							Tuple		)     -> Tuple:


						'''simple docstring'''
						torch.manual_seed(0		)
						torch.manual_seed(0		)
						A									=   UNetaDModel(
						    block_out_channels=(3_2, 6_4)    ,layers_per_block=2    ,sample_size=3_2    ,in_channels=3    ,out_channels=3    ,down_block_types=('DownBlock2D', 'AttnDownBlock2D')    ,up_block_types=('AttnUpBlock2D', 'UpBlock2D')    ,)
						A									=   RePaintScheduler()
						A									=   {'unet': unet, 'scheduler': scheduler}
						return components
		def    A(	self		:							Dict    ,_SCREAMING_SNAKE_CASE		:							Optional[Any]    ,_SCREAMING_SNAKE_CASE		:							Optional[int]=0		)     -> Any:


						'''simple docstring'''
						if str(_SCREAMING_SNAKE_CASE		).startswith('mps'		):
										A									=   torch.manual_seed(_SCREAMING_SNAKE_CASE		)
						else:
										A									=   torch.Generator(device=_SCREAMING_SNAKE_CASE		).manual_seed(_SCREAMING_SNAKE_CASE		)
						A									=   np.random.RandomState(_SCREAMING_SNAKE_CASE		).standard_normal((1, 3, 3_2, 3_2)		)
						A									=   torch.from_numpy(_SCREAMING_SNAKE_CASE		).to(device=_SCREAMING_SNAKE_CASE    ,dtype=torch.floataa		)
						A									=   (image > 0).to(device=_SCREAMING_SNAKE_CASE    ,dtype=torch.floataa		)
						A									=   {
						    'image': image,
						    'mask_image': mask,
						    'generator': generator,
						    'num_inference_steps': 5,
						    'eta': 0.0,
						    'jump_length': 2,
						    'jump_n_sample': 2,
						    'output_type': 'numpy',
						}
						return inputs
		def    A(	self		:							Dict		)     -> Dict:


						'''simple docstring'''
						A									=   'cpu'  # ensure determinism for the device-dependent torch.Generator
						A									=   self.get_dummy_components()
						A									=   RePaintPipeline(**_SCREAMING_SNAKE_CASE		)
						A									=   sd_pipe.to(_SCREAMING_SNAKE_CASE		)
						sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

						A									=   self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)
						A									=   sd_pipe(**_SCREAMING_SNAKE_CASE		).images
						A									=   image[0, -3:, -3:, -1]

						assert image.shape == (1, 3_2, 3_2, 3)
						A									=   np.array([1.0000, 0.5426, 0.5497, 0.2200, 1.0000, 1.0000, 0.5623, 1.0000, 0.6274]		)

						assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-3
		@skip_mps
		def    A(	self		:							Optional[int]		)     -> Optional[int]:


						'''simple docstring'''
						return super().test_save_load_local()
		@unittest.skip('non-deterministic pipeline'		)
		def    A(	self		:							int		)     -> str:


						'''simple docstring'''
						return super().test_inference_batch_single_identical()
		@skip_mps
		def    A(	self		:							str		)     -> Optional[Any]:


						'''simple docstring'''
						return super().test_dict_tuple_outputs_equivalent()
		@skip_mps
		def    A(	self		:							Optional[Any]		)     -> Any:


						'''simple docstring'''
						return super().test_save_load_optional_components()







		@skip_mps
		def    A(	self		:							Any		)     -> Union[str, Any]:


						'''simple docstring'''
						return super().test_attention_slicing_forward_pass()


@nightly
@require_torch_gpu
class   UpperCamelCase			(					unittest.TestCase			):







		"""simple docstring"""



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


						'''simple docstring'''
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()







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


						'''simple docstring'''
						A									=   load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
						    'repaint/celeba_hq_256.png'		)
						A									=   load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'		)
						A									=   load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
						    'repaint/celeba_hq_256_result.npy'		)

						A									=   'google/ddpm-ema-celebahq-256'
						A									=   UNetaDModel.from_pretrained(_SCREAMING_SNAKE_CASE		)
						A									=   RePaintScheduler.from_pretrained(_SCREAMING_SNAKE_CASE		)

						A									=   RePaintPipeline(unet=_SCREAMING_SNAKE_CASE    ,scheduler=_SCREAMING_SNAKE_CASE		).to(_SCREAMING_SNAKE_CASE		)
						repaint.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)
						repaint.enable_attention_slicing()

						A									=   torch.manual_seed(0		)
						A									=   repaint(
						    _SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE    ,num_inference_steps=2_5_0    ,eta=0.0    ,jump_length=1_0    ,jump_n_sample=1_0    ,generator=_SCREAMING_SNAKE_CASE    ,output_type='np'    ,)
						A									=   output.images[0]

						assert image.shape == (2_5_6, 2_5_6, 3)
						assert np.abs(expected_image - image		).mean() < 1E-2













def   snake_case		(				UpperCAmelCase						:							set,    UpperCAmelCase						:							set ):
				assert isinstance(UpperCAmelCase,    UpperCAmelCase ), f'The input value of [set_a={set_a}] is not a set'
				assert isinstance(UpperCAmelCase,    UpperCAmelCase ), f'The input value of [set_b={set_b}] is not a set'

				return {a + b for a in set_a for b in set_b}


if __name__ == "__main__":
					from doctest import testmod

					testmod()





