

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

def       __snake_case						( __UpperCamelCase	: ndarray    ):



      """simple docstring"""

      return np.dot(__UpperCamelCase				,__UpperCamelCase    )

class   _a					:




      """simple docstring"""

      def __init__(   self							:				Dict						,			*,
          UpperCAmelCase							:				float = np.inf						,			UpperCAmelCase							:				str = "linear"						,			UpperCAmelCase							:				float = 0.0						,			):
            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 		__A	(   self							:				str						,			UpperCAmelCase							:				ndarray						,			UpperCAmelCase							:				ndarray	):
            return np.dot(UpperCAmelCase						,			UpperCAmelCase	)





      def 		__A	(   self							:				Union[str, Any]						,			UpperCAmelCase							:				ndarray						,			UpperCAmelCase							:				ndarray	):
            return np.exp(-(self.gamma * norm_squared(vectora - vectora	))	)





      def 		__A	(   self							:				int						,			UpperCAmelCase							:				list[ndarray]						,			UpperCAmelCase							:				ndarray	):
            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 		__A	(   self							:				Dict						,			UpperCAmelCase							:				ndarray	):
            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 tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest




class   __lowercase						(						__snake_case       ):
	_A       =   (DEISMultistepScheduler,)
	_A       =   (("num_inference_steps", 25),)






	def       _a(self				: Optional[int]      ,	**snake_case				: str    ) ->			Optional[Any]:
							_lowercase       :	str           =   {
							    "num_train_timesteps": 1000,
							    "beta_start": 0.00_01,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							    "solver_order": 2,
							}

							config.update(**snake_case    )
							return config






	def       _a(self				: Optional[int]      ,	snake_case				: Optional[int]=0      ,	**snake_case				: List[Any]    ) ->			Dict:
							_lowercase       :	Dict           =   dict(self.forward_default_kwargs    )
							_lowercase       :	Optional[Any]           =   kwargs.pop("num_inference_steps"      ,	snake_case    )
							_lowercase       :	Optional[int]           =   self.dummy_sample
							_lowercase       :	Tuple           =   0.1 * sample
							_lowercase       :	Optional[Any]           =   [residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
													_lowercase       :	Dict           =   self.get_scheduler_config(**snake_case    )
													_lowercase       :	int           =   scheduler_class(**snake_case    )
													scheduler.set_timesteps(snake_case    )
													# copy over dummy past residuals
													_lowercase       :	Any           =   dummy_past_residuals[: scheduler.config.solver_order]

													with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(snake_case    )
																			_lowercase       :	Any           =   scheduler_class.from_pretrained(snake_case    )
																			new_scheduler.set_timesteps(snake_case    )
																			# copy over dummy past residuals
																			_lowercase       :	int           =   dummy_past_residuals[: new_scheduler.config.solver_order]

													_lowercase      ,    _lowercase       :	Optional[Any]           =   sample, sample
													for t in range(snake_case      ,	time_step + scheduler.config.solver_order + 1    ):
																			_lowercase       :	List[Any]           =   scheduler.step(snake_case      ,	snake_case      ,	snake_case      ,	**snake_case    ).prev_sample
																			_lowercase       :	Union[str, Any]           =   new_scheduler.step(snake_case      ,	snake_case      ,	snake_case      ,	**snake_case    ).prev_sample

																			assert torch.sum(torch.abs(output - new_output    )    ) < 1e-5, "Scheduler outputs are not identical"






	def       _a(self				: str    ) ->			List[str]:
							pass






	def       _a(self				: Optional[Any]      ,	snake_case				: List[Any]=0      ,	**snake_case				: str    ) ->			Union[str, Any]:
							_lowercase       :	List[str]           =   dict(self.forward_default_kwargs    )
							_lowercase       :	Optional[int]           =   kwargs.pop("num_inference_steps"      ,	snake_case    )
							_lowercase       :	Dict           =   self.dummy_sample
							_lowercase       :	Union[str, Any]           =   0.1 * sample
							_lowercase       :	List[str]           =   [residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
													_lowercase       :	List[Any]           =   self.get_scheduler_config()
													_lowercase       :	int           =   scheduler_class(**snake_case    )
													scheduler.set_timesteps(snake_case    )

													# copy over dummy past residuals (must be after setting timesteps)
													_lowercase       :	Union[str, Any]           =   dummy_past_residuals[: scheduler.config.solver_order]

													with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(snake_case    )
																			_lowercase       :	str           =   scheduler_class.from_pretrained(snake_case    )
																			# copy over dummy past residuals
																			new_scheduler.set_timesteps(snake_case    )

																			# copy over dummy past residual (must be after setting timesteps)
																			_lowercase       :	int           =   dummy_past_residuals[: new_scheduler.config.solver_order]

													_lowercase       :	Any           =   scheduler.step(snake_case      ,	snake_case      ,	snake_case      ,	**snake_case    ).prev_sample
													_lowercase       :	Dict           =   new_scheduler.step(snake_case      ,	snake_case      ,	snake_case      ,	**snake_case    ).prev_sample

													assert torch.sum(torch.abs(output - new_output    )    ) < 1e-5, "Scheduler outputs are not identical"






	def       _a(self				: Dict      ,	snake_case				: str=None      ,	**snake_case				: Dict    ) ->			Optional[Any]:
							if scheduler is None:
													_lowercase       :	Optional[Any]           =   self.scheduler_classes[0]
													_lowercase       :	Dict           =   self.get_scheduler_config(**snake_case    )
													_lowercase       :	Optional[int]           =   scheduler_class(**snake_case    )

							_lowercase       :	Tuple           =   self.scheduler_classes[0]
							_lowercase       :	str           =   self.get_scheduler_config(**snake_case    )
							_lowercase       :	Tuple           =   scheduler_class(**snake_case    )

							_lowercase       :	Optional[int]           =   10
							_lowercase       :	List[str]           =   self.dummy_model()
							_lowercase       :	List[Any]           =   self.dummy_sample_deter
							scheduler.set_timesteps(snake_case    )

							for i, t in enumerate(scheduler.timesteps    ):
													_lowercase       :	str           =   model(snake_case      ,	snake_case    )
													_lowercase       :	int           =   scheduler.step(snake_case      ,	snake_case      ,	snake_case    ).prev_sample

							return sample






	def       _a(self				: Any    ) ->			Tuple:
							_lowercase       :	Union[str, Any]           =   dict(self.forward_default_kwargs    )

							_lowercase       :	Optional[int]           =   kwargs.pop("num_inference_steps"      ,	snake_case    )

							for scheduler_class in self.scheduler_classes:
													_lowercase       :	List[Any]           =   self.get_scheduler_config()
													_lowercase       :	Optional[Any]           =   scheduler_class(**snake_case    )

													_lowercase       :	Tuple           =   self.dummy_sample
													_lowercase       :	List[Any]           =   0.1 * sample

													if num_inference_steps is not None and hasattr(snake_case      ,	"set_timesteps"    ):
																			scheduler.set_timesteps(snake_case    )
													elif num_inference_steps is not None and not hasattr(snake_case      ,	"set_timesteps"    ):
																			_lowercase       :	str           =   num_inference_steps

													# copy over dummy past residuals (must be done after set_timesteps)
													_lowercase       :	int           =   [residual + 0.2, residual + 0.15, residual + 0.10]
													_lowercase       :	Dict           =   dummy_past_residuals[: scheduler.config.solver_order]

													_lowercase       :	Tuple           =   scheduler.timesteps[5]
													_lowercase       :	Dict           =   scheduler.timesteps[6]

													_lowercase       :	Tuple           =   scheduler.step(snake_case      ,	snake_case      ,	snake_case      ,	**snake_case    ).prev_sample
													_lowercase       :	Union[str, Any]           =   scheduler.step(snake_case      ,	snake_case      ,	snake_case      ,	**snake_case    ).prev_sample

													self.assertEqual(output_a.shape      ,	sample.shape    )
													self.assertEqual(output_a.shape      ,	output_a.shape    )






	def       _a(self				: Optional[int]    ) ->			List[Any]:
							# make sure that iterating over schedulers with same config names gives same results
							# for defaults
							_lowercase       :	Any           =   DEISMultistepScheduler(**self.get_scheduler_config()    )
							_lowercase       :	List[Any]           =   self.full_loop(scheduler=snake_case    )
							_lowercase       :	Tuple           =   torch.mean(torch.abs(snake_case    )    )

							assert abs(result_mean.item() - 0.2_39_16    ) < 1e-3

							_lowercase       :	str           =   DPMSolverSinglestepScheduler.from_config(scheduler.config    )
							_lowercase       :	Optional[int]           =   DPMSolverMultistepScheduler.from_config(scheduler.config    )
							_lowercase       :	List[Any]           =   UniPCMultistepScheduler.from_config(scheduler.config    )
							_lowercase       :	int           =   DEISMultistepScheduler.from_config(scheduler.config    )

							_lowercase       :	Union[str, Any]           =   self.full_loop(scheduler=snake_case    )
							_lowercase       :	Dict           =   torch.mean(torch.abs(snake_case    )    )

							assert abs(result_mean.item() - 0.2_39_16    ) < 1e-3






	def       _a(self				: Union[str, Any]    ) ->			Optional[int]:
							for timesteps in [25, 50, 100, 999, 1000]:
													self.check_over_configs(num_train_timesteps=snake_case    )






	def       _a(self				: Optional[Any]    ) ->			List[str]:
							self.check_over_configs(thresholding=snake_case    )
							for order in [1, 2, 3]:
													for solver_type in ["logrho"]:
																			for threshold in [0.5, 1.0, 2.0]:
																									for prediction_type in ["epsilon", "sample"]:
																															self.check_over_configs(
																															    thresholding=snake_case      ,	prediction_type=snake_case      ,	sample_max_value=snake_case      ,	algorithm_type="deis"      ,	solver_order=snake_case      ,	solver_type=snake_case      ,	)






	def       _a(self				: int    ) ->			Tuple:
							for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=snake_case    )






	def       _a(self				: Tuple    ) ->			List[Any]:
							for algorithm_type in ["deis"]:
													for solver_type in ["logrho"]:
																			for order in [1, 2, 3]:
																									for prediction_type in ["epsilon", "sample"]:
																															self.check_over_configs(
																															    solver_order=snake_case      ,	solver_type=snake_case      ,	prediction_type=snake_case      ,	algorithm_type=snake_case      ,	)
																															_lowercase       :	Any           =   self.full_loop(
																															    solver_order=snake_case      ,	solver_type=snake_case      ,	prediction_type=snake_case      ,	algorithm_type=snake_case      ,	)
																															assert not torch.isnan(snake_case    ).any(), "Samples have nan numbers"






	def       _a(self				: int    ) ->			List[str]:
							self.check_over_configs(lower_order_final=snake_case    )
							self.check_over_configs(lower_order_final=snake_case    )






	def       _a(self				: Any    ) ->			List[Any]:
							for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
													self.check_over_forward(num_inference_steps=snake_case      ,	time_step=0    )






	def       _a(self				: Tuple    ) ->			Optional[Any]:
							_lowercase       :	Optional[Any]           =   self.full_loop()
							_lowercase       :	Tuple           =   torch.mean(torch.abs(snake_case    )    )

							assert abs(result_mean.item() - 0.2_39_16    ) < 1e-3






	def       _a(self				: Optional[int]    ) ->			Dict:
							_lowercase       :	int           =   self.full_loop(prediction_type="v_prediction"    )
							_lowercase       :	str           =   torch.mean(torch.abs(snake_case    )    )

							assert abs(result_mean.item() - 0.0_91    ) < 1e-3






	def       _a(self				: int    ) ->			Optional[int]:
							_lowercase       :	int           =   self.scheduler_classes[0]
							_lowercase       :	Tuple           =   self.get_scheduler_config(thresholding=snake_case      ,	dynamic_thresholding_ratio=0    )
							_lowercase       :	int           =   scheduler_class(**snake_case    )

							_lowercase       :	Optional[int]           =   10
							_lowercase       :	Union[str, Any]           =   self.dummy_model()
							_lowercase       :	Dict           =   self.dummy_sample_deter.half()
							scheduler.set_timesteps(snake_case    )

							for i, t in enumerate(scheduler.timesteps    ):
													_lowercase       :	Tuple           =   model(snake_case      ,	snake_case    )
													_lowercase       :	List[str]           =   scheduler.step(snake_case      ,	snake_case      ,	snake_case    ).prev_sample

							assert sample.dtype == torch.floataa




