

"""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
__UpperCamelCase  :      Optional[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
__UpperCamelCase  :      int							      =		[int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]







class 				SCREAMING_SNAKE_CASE					:






					"""simple docstring"""



					def __init__(       self			:				Union[str, Any]		):
						lowerCAmelCase__	:						Tuple  				=	WATERMARK_BITS
						lowerCAmelCase__	:						Dict  				=	WatermarkEncoder()

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







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

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

						lowerCAmelCase__	:						List[str]  				=	[self.encoder.encode(_lowerCamelCase   ,'''dwtDct'''		) for image in images]

						lowerCAmelCase__	:						Any  				=	torch.from_numpy(np.array(_lowerCamelCase		)		).permute(0   ,3   ,1   ,2		)

						lowerCAmelCase__	:						List[Any]  				=	torch.clamp(2 * (images / 2_5_5 - 0.5)   ,min=-1.0   ,max=1.0		)
						return images












"""simple docstring"""


from __future__ import annotations


class 					snake_case		:


   '''simple docstring'''

   def __init__(	self :     int,       _lowerCamelCase :     List[Any]=None  ):

     '''simple docstring'''
     __A									= data
     __A									= None






   def __repr__(	self :     Union[str, Any]  ):

     '''simple docstring'''
     __A									= []
     __A									= self
     while temp:
       string_rep.append(f'{temp.data}'  )
       __A									= temp.next
     return "->".join(_lowerCamelCase  )





def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  if not elements_list:
    raise Exception('''The Elements List is empty''' )

  __A									= __A									= Node(elements_list[0] )
  for i in range(1		,       len(__UpperCamelCase ) ):
    __A									= Node(elements_list[i] )
    __A									= current.next
  return head



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  if head_node is not None and isinstance(__UpperCamelCase		,       __UpperCamelCase ):
    print_reverse(head_node.next )
    print(head_node.data )



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  from doctest import testmod

  testmod()

  __A									= make_linked_list([1_4, 5_2, 1_4, 1_2, 4_3] )
  print('''Linked List:''' )
  print(__UpperCamelCase )
  print('''Elements in Reverse:''' )
  print_reverse(__UpperCamelCase )


if __name__ == "__main__":
       main()




