


def 				__lowerCamelCase						( UpperCAmelCase_						:		list						):




      """simple docstring"""





      if len(UpperCAmelCase_						) <= 1:
            return [tuple(UpperCAmelCase_						)]

      a						:Tuple 							=       []

      def generate(UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		list						):
            a						:str 							=       [0] * n
            res.append(tuple(UpperCAmelCase_						)						)

            a						:Optional[Any] 							=       0
            while i < n:
                  if c[i] < i:
                        if i % 2 == 0:
                              a							,				a						:str 							=       arr[i], arr[0]
                        else:
                              a							,				a						:List[str] 							=       arr[i], arr[c[i]]
                        res.append(tuple(UpperCAmelCase_						)						)
                        c[i] += 1
                        a						:int 							=       0
                  else:
                        a						:List[str] 							=       0
                        i += 1

      generate(len(UpperCAmelCase_						) ,  UpperCAmelCase_						)
      return res


if __name__ == "__main__":
     snake_case				:    Any       				=     input('''Enter numbers separated by a comma:\n''').strip()
     snake_case				:    str       				=     [int(item) for item in user_input.split(''',''')]
     print(heaps(arr))






"""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
__lowerCAmelCase    :       Union[str, Any]					     =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
__lowerCAmelCase    :       Optional[int]					     =[int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]





class  _A						:

      def __init__(						self						):







            """simple docstring"""



            lowercase														=	WATERMARK_BITS
            lowercase														=	WatermarkEncoder()

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







      def 				A__       (						self		,       __lowerCAmelCase						):







            """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(__lowerCAmelCase		,       """dwtDct"""						) for image in images]

            lowercase														=	torch.from_numpy(np.array(__lowerCAmelCase						)						).permute(0		,       3		,       1		,       2						)

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



