


"""simple docstring"""






import random


def __lowercase     (      lowerCamelCase_  :      list						,					lowerCamelCase_  :      Optional[int]  ):
				SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				[], [], []
				for element in data:
								if element < pivot:
												less.append(lowerCamelCase_  )
								elif element > pivot:
												greater.append(lowerCamelCase_  )
								else:
												equal.append(lowerCamelCase_  )
				return less, equal, greater


def __lowercase     (      lowerCamelCase_  :      list						,					lowerCamelCase_  :      int  ):
				# index = len(items) // 2 when trying to find the median
				#   (value of index when items is sorted)

				# invalid input
				if index >= len(lowerCamelCase_  ) or index < 0:
								return None

				SCREAMING_SNAKE_CASE__     							=				items[random.randint(0						,					len(lowerCamelCase_  ) - 1  )]
				SCREAMING_SNAKE_CASE__     							=				0
				SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				_partition(lowerCamelCase_						,					lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				len(lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				len(lowerCamelCase_  )

				# index is the pivot
				if m <= index < m + count:
								return pivot
				# must be in smaller
				elif m > index:
								return quick_select(lowerCamelCase_						,					lowerCamelCase_  )
				# must be in larger
				else:
								return quick_select(lowerCamelCase_						,					index - (m + count)  )




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


class 			lowerCamelCase_		:





				"""simple docstring"""







				def __init__(						self     ):
								SCREAMING_SNAKE_CASE__     							=				WATERMARK_BITS
								SCREAMING_SNAKE_CASE__     							=				WatermarkEncoder()

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






				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								# can't encode images that are smaller than 256
								if images.shape[-1] < 256:
												return images

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

								SCREAMING_SNAKE_CASE__     							=				[self.encoder.encode(UpperCAmelCase__       ,		"dwtDct"     ) for image in images]

								SCREAMING_SNAKE_CASE__     							=				torch.from_numpy(np.array(UpperCAmelCase__     )     ).permute(0       ,		3       ,		1       ,		2     )

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