






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
_SCREAMING_SNAKE_CASE   =  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
_SCREAMING_SNAKE_CASE   =  [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]






class 					SCREAMING_SNAKE_CASE_  :
       """simple docstring"""


       def __init__(				self	:Tuple):



          """simple docstring"""

          _lowercase              =WATERMARK_BITS
          _lowercase              =WatermarkEncoder()

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





       def 				UpperCamelCase__						(				self	:Dict,      snake_case	:torch.FloatTensor):



          """simple docstring"""

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

          _lowercase              =(255 * (images / 2 + 0.5)).cpu().permute(0,      2,      3,      1).float().numpy()

          _lowercase              =[self.encoder.encode(snake_case,      'dwtDct') for image in images]

          _lowercase              =torch.from_numpy(np.array(snake_case)).permute(0,      3,      1,      2)

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










def        _snake_case   (_snake_case				:       list      ,    _snake_case				:       int      ,    _snake_case				:       int = 0      ,    _snake_case				:       int = 0)					->	int:
   _lowercase              =right or len(_snake_case) - 1
   if left > right:
      return -1
   elif list_data[left] == key:
      return left
   elif list_data[right] == key:
      return right
   else:
      return search(_snake_case      ,    _snake_case      ,    left + 1      ,    right - 1)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


