



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
A__  :  List[str]								=					0b10_11_00_11_11_10_11_00_10_01_00_00_01_11_10_11_10_11_00_01_10_01_11_10
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
A__  :  List[str]								=					[int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]



class 				__snake_case						:




							def __init__(       self     :       List[Any]):
									lowerCAmelCase_			:     Tuple									= WATERMARK_BITS
									lowerCAmelCase_			:     int									= WatermarkEncoder()

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







							def    UpperCAmelCase__			(       self     :       Optional[Any]				,				A_     :       torch.FloatTensor):
									# can't encode images that are smaller than 256
									if images.shape[-1] < 2_5_6:
											return images

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

									lowerCAmelCase_			:     Any									= [self.encoder.encode(A_				,				'''dwtDct''') for image in images]

									lowerCAmelCase_			:     Optional[Any]									= torch.from_numpy(np.array(A_)).permute(0				,				3				,				1				,				2)

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









from ..utils import DummyObject, requires_backends


class        _a							(			metaclass=UpperCamelCase__			):
							_lowercase	:			Any        = ['''torch''', '''scipy''']

							def __init__(							self:	int    ,						*UpperCamelCase_:	Any    ,						**UpperCamelCase_:	Optional[Any]							)			->   List[str]:





									"""simple docstring"""







									requires_backends(self    ,						['''torch''', '''scipy''']							)

							@classmethod
							def       lowerCamelCase_ (							cls:	Optional[int]    ,						*UpperCamelCase_:	Any    ,						**UpperCamelCase_:	List[Any]							)			->   int:





									"""simple docstring"""







									requires_backends(cls    ,						['''torch''', '''scipy''']							)


							@classmethod
							def       lowerCamelCase_ (							cls:	Any    ,						*UpperCamelCase_:	Any    ,						**UpperCamelCase_:	Dict							)			->   Optional[int]:





									"""simple docstring"""







									requires_backends(cls    ,						['''torch''', '''scipy''']							)


