





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
lowercase      :       List[Any]									=  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
lowercase      :       str									=  [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]





class  A__    :




   """simple docstring"""



   def __init__(					self)			->						Union[str, Any]:







          '''simple docstring'''

          a__						:	Any =				WATERMARK_BITS
          a__						:	Any =				WatermarkEncoder()

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

   def 							__lowercase		(					self					,  lowercase)			->						Union[str, Any]:







          '''simple docstring'''

          if images.shape[-1] < 256:
                 return images

          a__						:	List[Any] =				(255 * (images / 2 + 0.5)).cpu().permute(0					,  2					,  3					,  1).float().numpy()

          a__						:	Dict =				[self.encoder.encode(lowercase					,  'dwtDct') for image in images]

          a__						:	str =				torch.from_numpy(np.array(lowercase)).permute(0					,  3					,  1					,  2)

          a__						:	Any =				torch.clamp(2 * (images / 255 - 0.5)					,  min=-1.0					,  max=1.0)
          return images









import math
import random



def A_	(		A__		,      A__ = False				)  ->		float:
       if deriv:
              return value * (1 - value)
       return 1 / (1 + math.exp(-value				))


# Initial Value
lowercase      :       Optional[Any]									=  0.02



def A_	(		A__		,      A__				)  ->		float:
       a__						:	int =				float(2 * (random.randint(1		,      100				)) - 1				)

       for _ in range(A__				):
              # Forward propagation
              a__						:	Union[str, Any] =				sigmoid_function(INITIAL_VALUE * weight				)
              # How much did we miss?
              a__						:	Optional[Any] =				(expected / 100) - layer_a
              # Error delta
              a__						:	Optional[int] =				layer_1_error * sigmoid_function(A__		,      A__				)
              # Update weight
              weight += INITIAL_VALUE * layer_1_delta

       return layer_a * 100


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    lowercase      :       Dict									=  int(input("""Expected value: """))
    lowercase      :       Dict									=  int(input("""Number of propagations: """))
    print(forward_propagation(expected, number_propagations))


