

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
_lowerCamelCase    :       Optional[int]        =							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
_lowerCamelCase    :       Tuple        =							[int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]





class  SCREAMING_SNAKE_CASE					:



   def __init__(							self							:					Optional[Any]						):
     UpperCAmelCase	:							List[Any]						=			WATERMARK_BITS
     UpperCAmelCase	:							str						=			WatermarkEncoder()

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





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

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

     UpperCAmelCase	:							List[Any]						=			[self.encoder.encode(__A,		'''dwtDct'''						) for image in images]

     UpperCAmelCase	:							Dict						=			torch.from_numpy(np.array(__A						)						).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








from ..utils import DummyObject, requires_backends





class  __UpperCAmelCase					(	metaclass=lowerCamelCase__						):
   UpperCamelCase       					=					["""onnx"""]



   def __init__(							self							:					int,		*__A							:					Optional[Any],		**__A							:					Dict						):
     requires_backends(self,		['''onnx''']						)



   @classmethod
   def 	__magic_name__   (							cls							:					Any,		*__A							:					Any,		**__A							:					Dict						):
     requires_backends(cls,		['''onnx''']						)





   @classmethod
   def 	__magic_name__   (							cls							:					Tuple,		*__A							:					List[str],		**__A							:					List[str]						):
     requires_backends(cls,		['''onnx''']						)





