


"""simple docstring"""






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





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






class      A:

		"""simple docstring"""





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

					"""simple docstring"""



					_UpperCamelCase					:Optional[int]					     =	regularization
					_UpperCamelCase					:Union[str, Any]					     =	gamma
					if kernel == "linear":
								_UpperCamelCase					:List[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'''   )
								_UpperCamelCase					:Optional[int]					     =	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:
								_UpperCamelCase					:List[str]					     =	f"Unknown kernel: {kernel}"
								raise ValueError(SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	float:

					"""simple docstring"""



					return np.dot(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	float:

					"""simple docstring"""



					return np.exp(-(self.gamma * norm_squared(vectora - vectora   ))   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	None:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	observations
					_UpperCamelCase					: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

					((_UpperCamelCase						)     ,      						)					:List[Any]					     =	np.shape(SCREAMING_SNAKE_CASE__   )

					def to_minimize(SCREAMING_SNAKE_CASE__   ) -> float:
								_UpperCamelCase					:Any					     =	0
								((_UpperCamelCase						)     ,      						)					:List[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__   )

					_UpperCamelCase					:Optional[int]					     =	LinearConstraint(SCREAMING_SNAKE_CASE__					,				0					,				0   )
					_UpperCamelCase					:Dict					     =	Bounds(0					,				self.regularization   )

					_UpperCamelCase					:Optional[Any]					     =	minimize(
					    SCREAMING_SNAKE_CASE__					,				np.ones(SCREAMING_SNAKE_CASE__   )					,				bounds=SCREAMING_SNAKE_CASE__					,				constraints=[ly_contraint]   ).x
					_UpperCamelCase					:str					     =	l_star

					# calculating mean offset of separation plane to points
					_UpperCamelCase					:List[str]					     =	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]   )
					_UpperCamelCase					:Dict					     =	s / n





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					: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()



import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class 							lowerCAmelCase__   ( __lowercase						):
						UpperCamelCase_				:			int						=		(KDPMaDiscreteScheduler,)
						UpperCamelCase_				:			Optional[int]						=		10

						def 			A_					(      self							,				**a					)       ->   int:


								'''simple docstring'''

								_UpperCamelCase         =						{
								    """num_train_timesteps""": 11_00,
								    """beta_start""": 0.0001,
								    """beta_end""": 0.02,
								    """beta_schedule""": """linear""",
								}

								config.update(**a					)
								return config

						def 			A_					(      self					)       ->   List[str]:


								'''simple docstring'''

								for timesteps in [10, 50, 1_00, 10_00]:
										self.check_over_configs(num_train_timesteps=a					)

						def 			A_					(      self					)       ->   List[Any]:


								'''simple docstring'''

								for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]							,				[0.0002, 0.002, 0.02]					):
										self.check_over_configs(beta_start=a							,				beta_end=a					)

						def 			A_					(      self					)       ->   Any:


								'''simple docstring'''

								for schedule in ["linear", "scaled_linear"]:
										self.check_over_configs(beta_schedule=a					)

						def 			A_					(      self					)       ->   Optional[int]:


								'''simple docstring'''

								for prediction_type in ["epsilon", "v_prediction"]:
										self.check_over_configs(prediction_type=a					)

						def 			A_					(      self					)       ->   List[Any]:


								'''simple docstring'''

								_UpperCamelCase         =						self.scheduler_classes[0]
								_UpperCamelCase         =						self.get_scheduler_config(prediction_type="""v_prediction"""					)
								_UpperCamelCase         =						scheduler_class(**a					)

								scheduler.set_timesteps(self.num_inference_steps					)

								_UpperCamelCase         =						self.dummy_model()
								_UpperCamelCase         =						self.dummy_sample_deter * scheduler.init_noise_sigma
								_UpperCamelCase         =						sample.to(a					)

								for i, t in enumerate(scheduler.timesteps					):
										_UpperCamelCase         =						scheduler.scale_model_input(a							,				a					)

										_UpperCamelCase         =						model(a							,				a					)

										_UpperCamelCase         =						scheduler.step(a							,				a							,				a					)
										_UpperCamelCase         =						output.prev_sample

								_UpperCamelCase         =						torch.sum(torch.abs(a					)					)
								_UpperCamelCase         =						torch.mean(torch.abs(a					)					)

								if torch_device in ["cpu", "mps"]:
										assert abs(result_sum.item() - 4.6934e-07					) < 1e-2
										assert abs(result_mean.item() - 6.1112e-10					) < 1e-3
								else:
										# CUDA
										assert abs(result_sum.item() - 4.693_4286_5017_0972e-07					) < 1e-2
										assert abs(result_mean.item() - 0.0002					) < 1e-3

						def 			A_					(      self					)       ->   str:


								'''simple docstring'''

								if torch_device == "mps":
										return
								_UpperCamelCase         =						self.scheduler_classes[0]
								_UpperCamelCase         =						self.get_scheduler_config()
								_UpperCamelCase         =						scheduler_class(**a					)

								scheduler.set_timesteps(self.num_inference_steps					)

								_UpperCamelCase         =						self.dummy_model()
								_UpperCamelCase         =						self.dummy_sample_deter * scheduler.init_noise_sigma
								_UpperCamelCase         =						sample.to(a					)

								for i, t in enumerate(scheduler.timesteps					):
										_UpperCamelCase         =						scheduler.scale_model_input(a							,				a					)

										_UpperCamelCase         =						model(a							,				a					)

										_UpperCamelCase         =						scheduler.step(a							,				a							,				a					)
										_UpperCamelCase         =						output.prev_sample

								_UpperCamelCase         =						torch.sum(torch.abs(a					)					)
								_UpperCamelCase         =						torch.mean(torch.abs(a					)					)

								if torch_device in ["cpu", "mps"]:
										assert abs(result_sum.item() - 20.4125					) < 1e-2
										assert abs(result_mean.item() - 0.0266					) < 1e-3
								else:
										# CUDA
										assert abs(result_sum.item() - 20.4125					) < 1e-2
										assert abs(result_mean.item() - 0.0266					) < 1e-3







						def 			A_					(      self					)       ->   str:


								'''simple docstring'''

								if torch_device == "mps":
										return
								_UpperCamelCase         =						self.scheduler_classes[0]
								_UpperCamelCase         =						self.get_scheduler_config()
								_UpperCamelCase         =						scheduler_class(**a					)

								scheduler.set_timesteps(self.num_inference_steps							,				device=a					)

								_UpperCamelCase         =						self.dummy_model()
								_UpperCamelCase         =						self.dummy_sample_deter.to(a					) * scheduler.init_noise_sigma

								for t in scheduler.timesteps:
										_UpperCamelCase         =						scheduler.scale_model_input(a							,				a					)

										_UpperCamelCase         =						model(a							,				a					)

										_UpperCamelCase         =						scheduler.step(a							,				a							,				a					)
										_UpperCamelCase         =						output.prev_sample

								_UpperCamelCase         =						torch.sum(torch.abs(a					)					)
								_UpperCamelCase         =						torch.mean(torch.abs(a					)					)

								if str(a					).startswith("""cpu"""					):
										# The following sum varies between 148 and 156 on mps. Why?
										assert abs(result_sum.item() - 20.4125					) < 1e-2
										assert abs(result_mean.item() - 0.0266					) < 1e-3
								else:
										# CUDA
										assert abs(result_sum.item() - 20.4125					) < 1e-2
										assert abs(result_mean.item() - 0.0266					) < 1e-3



