






'''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
a_					= 0B1011_0011_1110_1100_1001_0000_0111_1011_1011_0001_1001_1110
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
a_					= [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]






class      __SCREAMING_SNAKE_CASE      :




 def __init__(      self	:							Tuple      )	->						Union[str, Any]:
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=WATERMARK_BITS
       SCREAMING_SNAKE_CASE__     :				Tuple									=WatermarkEncoder()

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




 def      __magic_name__	(      self	:							Tuple					,      __lowercase	:							torch.FloatTensor      )	->						int:
       # can't encode images that are smaller than 256
       if images.shape[-1] < 2_56:
             return images

       SCREAMING_SNAKE_CASE__     :				str									=(2_55 * (images / 2 + 0.5)).cpu().permute(0					,      2					,      3					,      1      ).float().numpy()

       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=[self.encoder.encode(__lowercase					,      '''dwtDct'''      ) for image in images]

       SCREAMING_SNAKE_CASE__     :				Optional[int]									=torch.from_numpy(np.array(__lowercase      )      ).permute(0					,      3					,      1					,      2      )

       SCREAMING_SNAKE_CASE__     :				Optional[int]									=torch.clamp(2 * (images / 2_55 - 0.5)					,      min=-1.0					,      max=1.0      )
       return images







'''simple docstring'''
def  _a(  UpperCamelCase__				:						str,	UpperCamelCase__				:						str							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Optional[int]									=len(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Optional[Any]									=len(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Tuple									=[[False for _ in range(m + 1							)] for _ in range(n + 1							)]
      SCREAMING_SNAKE_CASE__     :				List[Any]									=True
      for i in range(UpperCamelCase__							):
            for j in range(m + 1							):
                  if dp[i][j]:
                        if j < m and a[i].upper() == b[j]:
                              SCREAMING_SNAKE_CASE__     :				Optional[int]									=True
                        if a[i].islower():
                              SCREAMING_SNAKE_CASE__     :				List[Any]									=True
      return dp[n][m]


if __name__ == "__main__":
  import doctest

  doctest.testmod()