


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    lowercase__  (						_snake_case					,					unittest.TestCase ):





							'''simple docstring'''


							A_							:				Tuple				     =  RePaintPipeline
							A_							:				Optional[int]				     =  IMAGE_INPAINTING_PARAMS - {"""width""", """height""", """guidance_scale"""}
							A_							:				List[Any]				     =  PipelineTesterMixin.required_optional_params - {
							    """latents""",
							    """num_images_per_prompt""",
							    """callback""",
							    """callback_steps""",
							}
							A_							:				Union[str, Any]				     =  IMAGE_INPAINTING_BATCH_PARAMS
							A_							:				Tuple				     =  False






							def UpperCAmelCase_     (   self  ):
											torch.manual_seed(0  )
											torch.manual_seed(0  )
											_SCREAMING_SNAKE_CASE			:	int							=				UNetaDModel(
											    block_out_channels=(32, 64)   , layers_per_block=2   , sample_size=32   , in_channels=3   , out_channels=3   , down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")   , up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")   , )
											_SCREAMING_SNAKE_CASE			:	Any							=				RePaintScheduler()
											_SCREAMING_SNAKE_CASE			:	Any							=				{"""unet""": unet, """scheduler""": scheduler}
											return components






							def UpperCAmelCase_     (   self   , __snake_case   , __snake_case=0  ):
											if str(__snake_case  ).startswith("""mps"""  ):
															_SCREAMING_SNAKE_CASE			:	Optional[int]							=				torch.manual_seed(__snake_case  )
											else:
															_SCREAMING_SNAKE_CASE			:	List[str]							=				torch.Generator(device=__snake_case  ).manual_seed(__snake_case  )
											_SCREAMING_SNAKE_CASE			:	Any							=				np.random.RandomState(__snake_case  ).standard_normal((1, 3, 32, 32)  )
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				torch.from_numpy(__snake_case  ).to(device=__snake_case   , dtype=torch.floataa  )
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				(image > 0).to(device=__snake_case   , dtype=torch.floataa  )
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				{
											    """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 UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				"""cpu"""  # ensure determinism for the device-dependent torch.Generator
											_SCREAMING_SNAKE_CASE			:	List[str]							=				self.get_dummy_components()
											_SCREAMING_SNAKE_CASE			:	Dict							=				RePaintPipeline(**__snake_case  )
											_SCREAMING_SNAKE_CASE			:	Dict							=				sd_pipe.to(__snake_case  )
											sd_pipe.set_progress_bar_config(disable=__snake_case  )

											_SCREAMING_SNAKE_CASE			:	List[str]							=				self.get_dummy_inputs(__snake_case  )
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				sd_pipe(**__snake_case  ).images
											_SCREAMING_SNAKE_CASE			:	List[str]							=				image[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				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 UpperCAmelCase_     (   self  ):
											return super().test_save_load_local()






							@unittest.skip("""non-deterministic pipeline"""  )
							def UpperCAmelCase_     (   self  ):
											return super().test_inference_batch_single_identical()






							@skip_mps
							def UpperCAmelCase_     (   self  ):
											return super().test_dict_tuple_outputs_equivalent()






							@skip_mps
							def UpperCAmelCase_     (   self  ):
											return super().test_save_load_optional_components()






							@skip_mps
							def UpperCAmelCase_     (   self  ):
											return super().test_attention_slicing_forward_pass()





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





							'''simple docstring'''


							def UpperCAmelCase_     (   self  ):
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	int							=				load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
											    """repaint/celeba_hq_256.png"""  )
											_SCREAMING_SNAKE_CASE			:	Dict							=				load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"""  )
											_SCREAMING_SNAKE_CASE			:	Tuple							=				load_numpy(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
											    """repaint/celeba_hq_256_result.npy"""  )

											_SCREAMING_SNAKE_CASE			:	Tuple							=				"""google/ddpm-ema-celebahq-256"""
											_SCREAMING_SNAKE_CASE			:	Dict							=				UNetaDModel.from_pretrained(__snake_case  )
											_SCREAMING_SNAKE_CASE			:	int							=				RePaintScheduler.from_pretrained(__snake_case  )

											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				RePaintPipeline(unet=__snake_case   , scheduler=__snake_case  ).to(__snake_case  )
											repaint.set_progress_bar_config(disable=__snake_case  )
											repaint.enable_attention_slicing()

											_SCREAMING_SNAKE_CASE			:	Dict							=				torch.manual_seed(0  )
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				repaint(
											    __snake_case   , __snake_case   , num_inference_steps=250   , eta=0.0   , jump_length=10   , jump_n_sample=10   , generator=__snake_case   , output_type="""np"""   , )
											_SCREAMING_SNAKE_CASE			:	Tuple							=				output.images[0]

											assert image.shape == (256, 256, 3)
											assert np.abs(expected_image - image  ).mean() < 1e-2








'''simple docstring'''


def       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





				if len(str(SCREAMING_SNAKE_CASE__					)					) == 1 or len(str(SCREAMING_SNAKE_CASE__					)					) == 1:
								return a * b

				_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				max(len(str(SCREAMING_SNAKE_CASE__					)					)							,				len(str(SCREAMING_SNAKE_CASE__					)					)					)
				_SCREAMING_SNAKE_CASE			:	Tuple							=				ma // 2

				_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE			:	Optional[int]							=				divmod(SCREAMING_SNAKE_CASE__							,				10**ma					)
				_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE			:	Optional[int]							=				divmod(SCREAMING_SNAKE_CASE__							,				10**ma					)

				_SCREAMING_SNAKE_CASE			:	Dict							=				karatsuba(SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					)
				_SCREAMING_SNAKE_CASE			:	List[Any]							=				karatsuba((aa + aa)							,				(ba + ba)					)
				_SCREAMING_SNAKE_CASE			:	List[Any]							=				karatsuba(SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					)

				return (z * 10 ** (2 * ma)) + ((y - z - x) * 10 ** (ma)) + (x)







def       snake_case_       (  ):







				"""simple docstring"""





				print(karatsuba(1_5463							,				2_3489					)					)


if __name__ == "__main__":
				main()




