





'''simple docstring'''




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






def 	_lowerCAmelCase      (  __snake_case  :			ndarray      )					->					float:
     return np.dot(__snake_case			,							__snake_case      )


class 			SCREAMING_SNAKE_CASE   :


     def __init__(						self						,     *,
         _UpperCAmelCase = np.inf						,     _UpperCAmelCase = "linear"						,     _UpperCAmelCase = 0.0						,     ):

          '''simple docstring'''






          __A						:			Union[str, Any]								=	regularization
          __A						:			Dict								=	gamma
          if kernel == "linear":
               __A						:			Tuple								=	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						:			List[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						:			Dict								=	F'Unknown kernel: {kernel}'
               raise ValueError(_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          return np.dot(_UpperCAmelCase						,     _UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






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


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			Optional[int]								=	observations
          __A						:			Tuple								=	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)			,)						:			Union[str, Any]								=	np.shape(_UpperCAmelCase)

          def to_minimize(_UpperCAmelCase) -> float:
               __A						:			int								=	0
               ((__A)			,)						:			Union[str, Any]								=	np.shape(_UpperCAmelCase)
               for i in range(_UpperCAmelCase):
                    for j in range(_UpperCAmelCase):
                         s += (
                             candidate[i]
                             * candidate[j]
                             * classes[i]
                             * classes[j]
                             * self.kernel(observations[i]						,     observations[j])
                         )
               return 1 / 2 * s - sum(_UpperCAmelCase)

          __A						:			List[str]								=	LinearConstraint(_UpperCAmelCase						,     0						,     0)
          __A						:			Tuple								=	Bounds(0						,     self.regularization)

          __A						:			Optional[int]								=	minimize(
              _UpperCAmelCase						,     np.ones(_UpperCAmelCase)						,     bounds=_UpperCAmelCase						,     constraints=[ly_contraint]).x
          __A						:			str								=	l_star

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


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






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


if __name__ == "__main__":
  import doctest

  doctest.testmod()






'''simple docstring'''




import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('''9.1.0'''):
  lowercase__   :				Dict      =   {
      '''linear''': PIL.Image.Resampling.BILINEAR,
      '''bilinear''': PIL.Image.Resampling.BILINEAR,
      '''bicubic''': PIL.Image.Resampling.BICUBIC,
      '''lanczos''': PIL.Image.Resampling.LANCZOS,
      '''nearest''': PIL.Image.Resampling.NEAREST,
  }
else:
  lowercase__   :				Any      =   {
      '''linear''': PIL.Image.LINEAR,
      '''bilinear''': PIL.Image.BILINEAR,
      '''bicubic''': PIL.Image.BICUBIC,
      '''lanczos''': PIL.Image.LANCZOS,
      '''nearest''': PIL.Image.NEAREST,
  }






def 	_lowerCAmelCase      (  __snake_case  :			Any      )					->					Optional[Any]:
     __A						:			Dict								=	(images / 2 + 0.5).clamp(0			,							1      )
     __A						:			str								=	images.cpu().permute(0			,							2			,							3			,							1      ).float().numpy()
     __A						:			Dict								=	numpy_to_pil(__snake_case      )
     return images







def 	_lowerCAmelCase      (  __snake_case  :			List[Any]      )					->					Optional[Any]:
     if images.ndim == 3:
          __A						:			List[Any]								=	images[None, ...]
     __A						:			List[str]								=	(images * 2_55).round().astype('uint8'      )
     if images.shape[-1] == 1:
          # special case for grayscale (single channel) images
          __A						:			str								=	[Image.fromarray(image.squeeze()			,							mode='L'      ) for image in images]
     else:
          __A						:			str								=	[Image.fromarray(__snake_case      ) for image in images]

     return pil_images