
'''simple docstring'''






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







def     UpperCamelCase_      (   A__ :       ndarray ):
  '''simple docstring'''





  return np.dot(_a							,   _a )






class        __snake_case	:







    """simple docstring"""



    def __init__(       self   :  List[str]   ,						*,
        lowerCamelCase   :  Optional[Any] = np.inf   ,						lowerCamelCase   :  List[Any] = "linear"   ,						lowerCamelCase   :  Optional[Any] = 0.0   ,						)       ->  None:
      lowerCAmelCase_      :				Optional[int]      =       regularization
      lowerCAmelCase_      :				List[Any]      =       gamma
      if kernel == "linear":
        lowerCAmelCase_      :				int      =       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"""		)
        lowerCAmelCase_      :				Optional[Any]      =       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:
        lowerCAmelCase_      :				List[Any]      =       F'Unknown kernel: {kernel}'
        raise ValueError(_SCREAMING_SNAKE_CASE		)

    def __lowercase       (       self   :  Optional[Any]   ,						lowerCamelCase   :  Dict   ,						lowerCamelCase   :  Union[str, Any]		)       ->  float:
      return np.dot(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE		)

    def __lowercase       (       self   :  List[str]   ,						lowerCamelCase   :  Dict   ,						lowerCamelCase   :  Any		)       ->  float:
      return np.exp(-(self.gamma * norm_squared(vectora - vectora		))		)

    def __lowercase       (       self   :  Optional[Any]   ,						lowerCamelCase   :  Tuple   ,						lowerCamelCase   :  List[str]		)       ->  None:
      lowerCAmelCase_      :				int      =       observations
      lowerCAmelCase_      :				str      =       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

      (lowerCAmelCase_    )      :				Any      =       np.shape(_SCREAMING_SNAKE_CASE		)

      def to_minimize(lowerCamelCase   :  str		) -> float:
        lowerCAmelCase_      :				Dict      =       0
        (lowerCAmelCase_    )      :				str      =       np.shape(_SCREAMING_SNAKE_CASE		)
        for i in range(_SCREAMING_SNAKE_CASE		):
          for j in range(_SCREAMING_SNAKE_CASE		):
            s += (
                candidate[i]
                * candidate[j]
                * classes[i]
                * classes[j]
                * self.kernel(observations[i]   ,						observations[j]		)
            )
        return 1 / 2 * s - sum(_SCREAMING_SNAKE_CASE		)

      lowerCAmelCase_      :				List[Any]      =       LinearConstraint(_SCREAMING_SNAKE_CASE   ,						0   ,						0		)
      lowerCAmelCase_      :				Union[str, Any]      =       Bounds(0   ,						self.regularization		)

      lowerCAmelCase_      :				Tuple      =       minimize(
          _SCREAMING_SNAKE_CASE   ,						np.ones(_SCREAMING_SNAKE_CASE		)   ,						bounds=_SCREAMING_SNAKE_CASE   ,						constraints=[ly_contraint]		).x
      lowerCAmelCase_      :				Optional[int]      =       l_star

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





    def __lowercase       (       self   :  Union[str, Any]   ,						lowerCamelCase   :  Optional[int]		)       ->  int:
      lowerCAmelCase_      :				List[str]      =       sum(
          self.optimum[n]
          * self.classes[n]
          * self.kernel(self.observations[n]   ,						_SCREAMING_SNAKE_CASE		)
          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 unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    @property
    def __lowercase       (       self   :  str		)       ->  List[Any]:
      torch.manual_seed(0		)
      lowerCAmelCase_      :				Optional[Any]      =       UNetaDModel(
          block_out_channels=(32, 64)   ,						layers_per_block=2   ,						sample_size=32   ,						in_channels=3   ,						out_channels=3   ,						down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")   ,						up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")   ,						)
      return model





    def __lowercase       (       self   :  Tuple		)       ->  Optional[Any]:
      lowerCAmelCase_      :				Optional[int]      =       self.dummy_uncond_unet
      lowerCAmelCase_      :				Tuple      =       PNDMScheduler()

      lowerCAmelCase_      :				List[Any]      =       PNDMPipeline(unet=lowerCamelCase   ,						scheduler=lowerCamelCase		)
      pndm.to(lowerCamelCase		)
      pndm.set_progress_bar_config(disable=lowerCamelCase		)

      lowerCAmelCase_      :				Dict      =       torch.manual_seed(0		)
      lowerCAmelCase_      :				List[Any]      =       pndm(generator=lowerCamelCase   ,						num_inference_steps=20   ,						output_type="""numpy"""		).images

      lowerCAmelCase_      :				str      =       torch.manual_seed(0		)
      lowerCAmelCase_      :				int      =       pndm(generator=lowerCamelCase   ,						num_inference_steps=20   ,						output_type="""numpy"""   ,						return_dict=lowerCamelCase		)[0]

      lowerCAmelCase_      :				Optional[int]      =       image[0, -3:, -3:, -1]
      lowerCAmelCase_      :				Optional[Any]      =       image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      lowerCAmelCase_      :				int      =       np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]		)

      assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2
      assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1E-2







@slow
@require_torch
class        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    def __lowercase       (       self   :  str		)       ->  Tuple:
      lowerCAmelCase_      :				str      =       """google/ddpm-cifar10-32"""

      lowerCAmelCase_      :				Dict      =       UNetaDModel.from_pretrained(lowerCamelCase		)
      lowerCAmelCase_      :				Dict      =       PNDMScheduler()

      lowerCAmelCase_      :				Union[str, Any]      =       PNDMPipeline(unet=lowerCamelCase   ,						scheduler=lowerCamelCase		)
      pndm.to(lowerCamelCase		)
      pndm.set_progress_bar_config(disable=lowerCamelCase		)
      lowerCAmelCase_      :				Any      =       torch.manual_seed(0		)
      lowerCAmelCase_      :				Union[str, Any]      =       pndm(generator=lowerCamelCase   ,						output_type="""numpy"""		).images

      lowerCAmelCase_      :				Optional[int]      =       image[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      lowerCAmelCase_      :				List[Any]      =       np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]		)

      assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2



