




"""simple docstring"""

def   UpperCamelCase__     (		lowercase__  :     int = 99_8001   ):

		# fetches the next number
		for number in range(n - 1       ,     9999       ,     -1   ):
				snake_case		:						Tuple								=				str(lowercase__   )

				# checks whether 'str_number' is a palindrome.
				if str_number == str_number[::-1]:
						snake_case		:						Union[str, Any]								=				999

						# if 'number' is a product of two 3-digit numbers
						# then number is the answer otherwise fetch next number.
						while divisor != 99:
								if (number % divisor == 0) and (len(str(number // divisor   )   ) == 3.0):
										return number
								divisor -= 1
		raise ValueError("That number is larger than our acceptable range."   )


if __name__ == "__main__":
							print(f'{solution() = }')






"""simple docstring"""

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 			lowerCamelCase__    (  lowerCamelCase_				,  unittest.TestCase			):
			a__ :      Optional[Any]								=     RePaintPipeline
			a__ :      List[Any]								=     IMAGE_INPAINTING_PARAMS - {"""width""", """height""", """guidance_scale"""}
			a__ :      List[Any]								=     PipelineTesterMixin.required_optional_params - {
			    """latents""",
			    """num_images_per_prompt""",
			    """callback""",
			    """callback_steps""",
			}
			a__ :      List[Any]								=     IMAGE_INPAINTING_BATCH_PARAMS
			a__ :      List[str]								=     False


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					torch.manual_seed(0							)
					torch.manual_seed(0							)
					snake_case		:						Union[str, Any]								=				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")				, )
					snake_case		:						Union[str, Any]								=				RePaintScheduler()
					snake_case		:						Optional[Any]								=				{"unet": unet, "scheduler": scheduler}
					return components


			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE=0							):



					"""simple docstring"""







					if str(SCREAMING_SNAKE_CASE							).startswith("mps"							):
							snake_case		:						str								=				torch.manual_seed(SCREAMING_SNAKE_CASE							)
					else:
							snake_case		:						List[Any]								=				torch.Generator(device=SCREAMING_SNAKE_CASE							).manual_seed(SCREAMING_SNAKE_CASE							)
					snake_case		:						Any								=				np.random.RandomState(SCREAMING_SNAKE_CASE							).standard_normal((1, 3, 32, 32)							)
					snake_case		:						Any								=				torch.from_numpy(SCREAMING_SNAKE_CASE							).to(device=SCREAMING_SNAKE_CASE				, dtype=torch.floataa							)
					snake_case		:						List[str]								=				(image > 0).to(device=SCREAMING_SNAKE_CASE				, dtype=torch.floataa							)
					snake_case		:						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 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						List[Any]								=				"cpu"  # ensure determinism for the device-dependent torch.Generator
					snake_case		:						Any								=				self.get_dummy_components()
					snake_case		:						Optional[int]								=				RePaintPipeline(**SCREAMING_SNAKE_CASE							)
					snake_case		:						Optional[Any]								=				sd_pipe.to(SCREAMING_SNAKE_CASE							)
					sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE							)

					snake_case		:						Any								=				self.get_dummy_inputs(SCREAMING_SNAKE_CASE							)
					snake_case		:						Dict								=				sd_pipe(**SCREAMING_SNAKE_CASE							).images
					snake_case		:						Optional[int]								=				image[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					snake_case		:						List[str]								=				np.array([1.00_00, 0.54_26, 0.54_97, 0.22_00, 1.00_00, 1.00_00, 0.56_23, 1.00_00, 0.62_74]							)

					assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-3


			@skip_mps
			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					return super().test_save_load_local()


			@unittest.skip("non-deterministic pipeline"							)
			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					return super().test_inference_batch_single_identical()


			@skip_mps
			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					return super().test_dict_tuple_outputs_equivalent()


			@skip_mps
			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					return super().test_save_load_optional_components()



			@skip_mps
			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					return super().test_attention_slicing_forward_pass()







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


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







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



			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Optional[int]								=				load_image(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
					    "repaint/celeba_hq_256.png"							)
					snake_case		:						Any								=				load_image(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"							)
					snake_case		:						List[Any]								=				load_numpy(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
					    "repaint/celeba_hq_256_result.npy"							)

					snake_case		:						str								=				"google/ddpm-ema-celebahq-256"
					snake_case		:						Optional[Any]								=				UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE							)
					snake_case		:						Optional[int]								=				RePaintScheduler.from_pretrained(SCREAMING_SNAKE_CASE							)

					snake_case		:						str								=				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()

					snake_case		:						Union[str, Any]								=				torch.manual_seed(0							)
					snake_case		:						int								=				repaint(
					    SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, num_inference_steps=250				, eta=0.0				, jump_length=10				, jump_n_sample=10				, generator=SCREAMING_SNAKE_CASE				, output_type="np"				, )
					snake_case		:						List[Any]								=				output.images[0]

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

