





"""simple docstring"""




from __future__ import annotations





def 	a_    (							_lowercase       ):
						_UpperCamelCase      : List[str]         =   str(_lowercase       )
						return len(_lowercase       ) == 9 and set(_lowercase       ) == set('''123456789'''       )





def 	a_    (							):
						for base_num in range(9999	,						4999	,						-1       ):
												_UpperCamelCase      : List[Any]         =   10_0002 * base_num
												if is_9_pandigital(_lowercase       ):
																		return candidate

						for base_num in range(333	,						99	,						-1       ):
												_UpperCamelCase      : str         =   100_2003 * base_num
												if is_9_pandigital(_lowercase       ):
																		return candidate

						return None


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










"""simple docstring"""




import numpy as np
import torch
from imwatermark import WatermarkEncoder


# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
UpperCamelCase_									=0b1_0_1_1_0_0_1_1_1_1_1_0_1_1_0_0_1_0_0_1_0_0_0_0_0_1_1_1_1_0_1_1_1_0_1_1_0_0_0_1_1_0_0_1_1_1_1_0
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
UpperCamelCase_									=[int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]




class 			_a						:






				def __init__(			self		:   str							)     ->				str:




										'''simple docstring'''



										_UpperCamelCase      : str         =   WATERMARK_BITS
										_UpperCamelCase      : Optional[int]         =   WatermarkEncoder()

										self.encoder.set_watermark('''bits''',			self.watermark							)






				def     snake_case     (			self		:   Dict,			lowerCAmelCase__		:   torch.FloatTensor							)     ->				int:




										'''simple docstring'''



										if images.shape[-1] < 2_5_6:
																return images

										_UpperCamelCase      : Union[str, Any]         =   (2_5_5 * (images / 2 + 0.5)).cpu().permute(0,			2,			3,			1							).float().numpy()

										_UpperCamelCase      : List[str]         =   [self.encoder.encode(lowerCAmelCase__,			'''dwtDct'''							) for image in images]

										_UpperCamelCase      : Dict         =   torch.from_numpy(np.array(lowerCAmelCase__							)							).permute(0,			3,			1,			2							)

										_UpperCamelCase      : Optional[int]         =   torch.clamp(2 * (images / 2_5_5 - 0.5),			min=-1.0,			max=1.0							)
										return images



