




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







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	float:



       """simple docstring"""

       return np.dot(lowercase_			,    lowercase_						)


class     UpperCamelCase_   :

    '''simple docstring'''






    def __init__(      self				:     Any ,			*,
        UpperCAmelCase__				:     float = np.inf ,			UpperCAmelCase__				:     str = "linear" ,			UpperCAmelCase__				:     float = 0.0 ,			)						->None:





           '''simple docstring'''

           A__							=   regularization
           A__							=   gamma
           if kernel == "linear":
                  A__							=   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__							=   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__							=   f"""Unknown kernel: {kernel}"""
                  raise ValueError(UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[Any] ,			UpperCAmelCase__				:     ndarray ,			UpperCAmelCase__				:     ndarray)						->float:





           '''simple docstring'''

           return np.dot(UpperCAmelCase__ ,			UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any] ,			UpperCAmelCase__				:     ndarray ,			UpperCAmelCase__				:     ndarray)						->float:





           '''simple docstring'''

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any ,			UpperCAmelCase__				:     list[ndarray] ,			UpperCAmelCase__				:     ndarray)						->None:





           '''simple docstring'''

           A__							=   observations
           A__							=   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__)			,							)							=   np.shape(UpperCAmelCase__)

           def to_minimize(UpperCAmelCase__				:     ndarray) -> float:
                  A__							=   0
                  ((A__)			,							)							=   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__							=   LinearConstraint(UpperCAmelCase__ ,			0 ,			0)
           A__							=   Bounds(0 ,			self.regularization)

           A__							=   minimize(
               UpperCAmelCase__ ,			np.ones(UpperCAmelCase__) ,			bounds=UpperCAmelCase__ ,			constraints=[ly_contraint]).x
           A__							=   l_star

           # calculating mean offset of separation plane to points
           A__							=   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__							=   s / n





    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any] ,			UpperCAmelCase__				:     ndarray)						->int:





           '''simple docstring'''

           A__							=   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()








import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
  import torch

  from transformers.generation import DisjunctiveConstraint


@require_torch
class     UpperCamelCase_   (			unittest.TestCase					):

    '''simple docstring'''






    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict)						->Tuple:





           '''simple docstring'''

           A__							=   [[1, 2, 4], [1, 2, 3, 4]]
           A__							=   DisjunctiveConstraint(UpperCAmelCase__)
           self.assertTrue(isinstance(dc.token_ids ,			UpperCAmelCase__))

           with self.assertRaises(UpperCAmelCase__):
                  DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]))

           with self.assertRaises(UpperCAmelCase__):
                  DisjunctiveConstraint([torch.LongTensor([1, 2, 4]), torch.LongTensor([1, 2, 3, 4, 5])])


    def 	SCREAMING_SNAKE_CASE							(      self				:     int)						->str:





           '''simple docstring'''

           A__							=   [[1, 2], [1, 2, 3, 4]]

           with self.assertRaises(UpperCAmelCase__):
                  DisjunctiveConstraint(UpperCAmelCase__)  # fails here


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->str:





           '''simple docstring'''

           A__							=   [[1, 2, 3], [1, 2, 4]]

           A__							=   DisjunctiveConstraint(UpperCAmelCase__)

           A__			,							A__			,							A__							=   dc.update(1)
           A__							=   stepped is True and completed is False and reset is False
           self.assertTrue(UpperCAmelCase__)
           self.assertTrue(not dc.completed)
           self.assertTrue(dc.current_seq == [1])

           A__			,							A__			,							A__							=   dc.update(2)
           A__							=   stepped is True and completed is False and reset is False
           self.assertTrue(UpperCAmelCase__)
           self.assertTrue(not dc.completed)
           self.assertTrue(dc.current_seq == [1, 2])

           A__			,							A__			,							A__							=   dc.update(3)
           A__							=   stepped is True and completed is True and reset is False
           self.assertTrue(UpperCAmelCase__)
           self.assertTrue(dc.completed)  # Completed!
           self.assertTrue(dc.current_seq == [1, 2, 3])





    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->Tuple:





           '''simple docstring'''

           A__							=   [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

           A__							=   DisjunctiveConstraint(UpperCAmelCase__)

           A__			,							A__			,							A__							=   dc.update(1)
           self.assertTrue(not dc.completed)
           self.assertTrue(dc.current_seq == [1])

           A__			,							A__			,							A__							=   dc.update(2)
           self.assertTrue(not dc.completed)
           self.assertTrue(dc.current_seq == [1, 2])

           A__			,							A__			,							A__							=   dc.update(4)
           self.assertTrue(not dc.completed)
           self.assertTrue(dc.current_seq == [1, 2, 4])

           A__			,							A__			,							A__							=   dc.update(5)
           self.assertTrue(dc.completed)  # Completed!
           self.assertTrue(dc.current_seq == [1, 2, 4, 5])

           dc.reset()

           A__			,							A__			,							A__							=   dc.update(1)
           self.assertTrue(not dc.completed)
           self.assertTrue(dc.remaining() == 3)
           self.assertTrue(dc.current_seq == [1])

           A__			,							A__			,							A__							=   dc.update(2)
           self.assertTrue(not dc.completed)
           self.assertTrue(dc.remaining() == 2)
           self.assertTrue(dc.current_seq == [1, 2])

           A__			,							A__			,							A__							=   dc.update(5)
           self.assertTrue(dc.completed)  # Completed!
           self.assertTrue(dc.remaining() == 0)
           self.assertTrue(dc.current_seq == [1, 2, 5])


