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




class 	__A:

  """simple docstring"""







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

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







  def        UpperCAmelCase_					(self       ,			SCREAMING_SNAKE_CASE_						):

       if images.shape[-1] < 2_56:
            return images

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

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

       UpperCamelCase__				=				torch.from_numpy(np.array(SCREAMING_SNAKE_CASE_						)						).permute(0       ,			3       ,			1       ,			2						)

       UpperCamelCase__				=				torch.clamp(2 * (images / 2_55 - 0.5)       ,			min=-1.0       ,			max=1.0						)
       return images












import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize



def A_	(		A__				)  ->		float:
       return np.dot(A__		,      A__				)





class  A__    :




   """simple docstring"""



   def __init__(					self					,  *,
       lowercase = np.inf					,  lowercase = "linear"					,  lowercase = 0.0					,  )			->						None:







          '''simple docstring'''

          a__						:	Tuple =				regularization
          a__						:	Optional[Any] =				gamma
          if kernel == "linear":
                 a__						:	Optional[Any] =				self.__linear
          elif kernel == "rbf":
                 if self.gamma == 0:
                        raise ValueError('rbf kernel requires gamma')
                 if not isinstance(self.gamma					,  (float, int)):
                        raise ValueError('gamma must be float or int')
                 if not self.gamma > 0:
                        raise ValueError('gamma must be > 0')
                 a__						:	str =				self.__rbf
                 # in the future, there could be a default value like in sklearn
                 # sklear: def_gamma = 1/(n_features * X.var()) (wiki)
                 # previously it was 1/(n_features)
          else:
                 a__						:	Optional[int] =				F'Unknown kernel: {kernel}'
                 raise ValueError(lowercase)

   def 							__lowercase		(					self					,  lowercase					,  lowercase)			->						float:







          '''simple docstring'''

          return np.dot(lowercase					,  lowercase)

   def 							__lowercase		(					self					,  lowercase					,  lowercase)			->						float:







          '''simple docstring'''

          return np.exp(-(self.gamma * norm_squared(vectora - vectora)))

   def 							__lowercase		(					self					,  lowercase					,  lowercase)			->						None:







          '''simple docstring'''

          a__						:	List[str] =				observations
          a__						:	Dict =				classes

          # using Wolfe's Dual to calculate w.
          # Primal problem: minimize 1/2*norm_squared(w)
          #   constraint: yn(w . xn + b) >= 1
          #
          # With l a vector
          # Dual problem: maximize sum_n(ln) -
          #       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
          #   constraint: self.C >= ln >= 0
          #           and sum_n(ln*yn) = 0
          # Then we get w using w = sum_n(ln*yn*xn)
          # At the end we can get b ~= mean(yn - w . xn)
          #
          # Since we use kernels, we only need l_star to calculate b
          # and to classify observations

          ((a__) ,							)						:	Optional[int] =				np.shape(lowercase)

          def to_minimize(lowercase) -> float:
                 a__						:	Tuple =				0
                 ((a__) ,							)						:	Optional[int] =				np.shape(lowercase)
                 for i in range(lowercase):
                        for j in range(lowercase):
                               s += (
                                   candidate[i]
                                   * candidate[j]
                                   * classes[i]
                                   * classes[j]
                                   * self.kernel(observations[i]					,  observations[j])
                               )
                 return 1 / 2 * s - sum(lowercase)

          a__						:	Optional[Any] =				LinearConstraint(lowercase					,  0					,  0)
          a__						:	str =				Bounds(0					,  self.regularization)

          a__						:	List[str] =				minimize(
              lowercase					,  np.ones(lowercase)					,  bounds=lowercase					,  constraints=[ly_contraint]).x
          a__						:	Dict =				l_star

          # calculating mean offset of separation plane to points
          a__						:	int =				0
          for i in range(lowercase):
                 for j in range(lowercase):
                        s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
                            observations[i]					,  observations[j])
          a__						:	List[str] =				s / n

   def 							__lowercase		(					self					,  lowercase)			->						int:







          '''simple docstring'''

          a__						:	int =				sum(
              self.optimum[n]
              * self.classes[n]
              * self.kernel(self.observations[n]					,  lowercase)
              for n in range(len(self.classes)))
          return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
    import doctest

    doctest.testmod()


