


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





def   _lowerCAmelCase				(   UpperCamelCase__:     ndarray						) ->   Tuple:





	"""simple docstring"""







	return np.dot(UpperCamelCase__							,					UpperCamelCase__						)






class       _UpperCamelCase    :

				"""simple docstring"""







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

				def      _UpperCAmelCase					(						self				,   a__				,   a__   )	->				float:
					return np.dot(__A				,   __A   )

				def      _UpperCAmelCase					(						self				,   a__				,   a__   )	->				float:
					return np.exp(-(self.gamma * norm_squared(vectora - vectora   ))   )

				def      _UpperCAmelCase					(						self				,   a__				,   a__   )	->				None:
					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(__A   )

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

					A								=				LinearConstraint(__A				,   0				,   0   )
					A								=				Bounds(0				,   self.regularization   )

					A								=				minimize(
					    __A				,   np.ones(__A   )				,   bounds=__A				,   constraints=[ly_contraint]   ).x
					A								=				l_star

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

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


if __name__ == "__main__":
	import doctest

	doctest.testmod()







import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


_lowercase       :      int	=	logging.get_logger(__name__)






class       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				lowerCAmelCase 		= 'upernet'

				def __init__(						self				,   a__=None				,   a__=512				,   a__=0.02				,   a__=[1, 2, 3, 6]				,   a__=True				,   a__=0.4				,   a__=384				,   a__=256				,   a__=1				,   a__=False				,   a__=255				,   **a__				,   )	->				Tuple:
					super().__init__(**a__   )

					if backbone_config is None:
						logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""   )
						A								=				CONFIG_MAPPING["""resnet"""](out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]   )
					elif isinstance(a__				,   a__   ):
						A								=				backbone_config.get("""model_type"""   )
						A								=				CONFIG_MAPPING[backbone_model_type]
						A								=				config_class.from_dict(a__   )

					A								=				backbone_config
					A								=				hidden_size
					A								=				initializer_range
					A								=				pool_scales
					A								=				use_auxiliary_head
					A								=				auxiliary_loss_weight
					A								=				auxiliary_in_channels
					A								=				auxiliary_channels
					A								=				auxiliary_num_convs
					A								=				auxiliary_concat_input
					A								=				loss_ignore_index

				def      _UpperCAmelCase					(						self   )	->				Dict:
					A								=				copy.deepcopy(self.__dict__   )
					A								=				self.backbone_config.to_dict()
					A								=				self.__class__.model_type
					return output



