

"""simple docstring"""

import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 			_snake_case     (						UpperCamelCase       ):
						__snake_case 			=     (KDPMaAncestralDiscreteScheduler,)
						__snake_case 			=     10







						def        _UpperCamelCase    (self     ,							**UpperCAmelCase_							)			->						Dict:
											__lowercase												=			{
											    '''num_train_timesteps''': 1_1_0_0,
											    '''beta_start''': 0.0_0_0_1,
											    '''beta_end''': 0.0_2,
											    '''beta_schedule''': '''linear''',
											}

											config.update(**UpperCAmelCase_							)
											return config







						def        _UpperCamelCase    (self							)			->						str:
											for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
																self.check_over_configs(num_train_timesteps=UpperCAmelCase_							)







						def        _UpperCamelCase    (self							)			->						str:
											for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]     ,							[0.0_0_0_2, 0.0_0_2, 0.0_2]							):
																self.check_over_configs(beta_start=UpperCAmelCase_     ,							beta_end=UpperCAmelCase_							)







						def        _UpperCamelCase    (self							)			->						int:
											for schedule in ["linear", "scaled_linear"]:
																self.check_over_configs(beta_schedule=UpperCAmelCase_							)







						def        _UpperCamelCase    (self							)			->						str:
											if torch_device == "mps":
																return
											__lowercase												=			self.scheduler_classes[0]
											__lowercase												=			self.get_scheduler_config()
											__lowercase												=			scheduler_class(**UpperCAmelCase_							)

											scheduler.set_timesteps(self.num_inference_steps							)

											__lowercase												=			torch.manual_seed(0							)

											__lowercase												=			self.dummy_model()
											__lowercase												=			self.dummy_sample_deter * scheduler.init_noise_sigma
											__lowercase												=			sample.to(UpperCAmelCase_							)

											for i, t in enumerate(scheduler.timesteps							):
																__lowercase												=			scheduler.scale_model_input(UpperCAmelCase_     ,							UpperCAmelCase_							)

																__lowercase												=			model(UpperCAmelCase_     ,							UpperCAmelCase_							)

																__lowercase												=			scheduler.step(UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							generator=UpperCAmelCase_							)
																__lowercase												=			output.prev_sample

											__lowercase												=			torch.sum(torch.abs(UpperCAmelCase_							)							)
											__lowercase												=			torch.mean(torch.abs(UpperCAmelCase_							)							)

											assert abs(result_sum.item() - 1_3_8_4_9.3_8_7_7							) < 1e-2
											assert abs(result_mean.item() - 1_8.0_3_3_1							) < 5e-3







						def        _UpperCamelCase    (self							)			->						List[Any]:
											for prediction_type in ["epsilon", "v_prediction"]:
																self.check_over_configs(prediction_type=UpperCAmelCase_							)







						def        _UpperCamelCase    (self							)			->						Optional[Any]:
											if torch_device == "mps":
																return
											__lowercase												=			self.scheduler_classes[0]
											__lowercase												=			self.get_scheduler_config(prediction_type='''v_prediction'''							)
											__lowercase												=			scheduler_class(**UpperCAmelCase_							)

											scheduler.set_timesteps(self.num_inference_steps							)

											__lowercase												=			self.dummy_model()
											__lowercase												=			self.dummy_sample_deter * scheduler.init_noise_sigma
											__lowercase												=			sample.to(UpperCAmelCase_							)

											__lowercase												=			torch.manual_seed(0							)

											for i, t in enumerate(scheduler.timesteps							):
																__lowercase												=			scheduler.scale_model_input(UpperCAmelCase_     ,							UpperCAmelCase_							)

																__lowercase												=			model(UpperCAmelCase_     ,							UpperCAmelCase_							)

																__lowercase												=			scheduler.step(UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							generator=UpperCAmelCase_							)
																__lowercase												=			output.prev_sample

											__lowercase												=			torch.sum(torch.abs(UpperCAmelCase_							)							)
											__lowercase												=			torch.mean(torch.abs(UpperCAmelCase_							)							)

											assert abs(result_sum.item() - 3_2_8.9_9_7_0							) < 1e-2
											assert abs(result_mean.item() - 0.4_2_8_4							) < 1e-3







						def        _UpperCamelCase    (self							)			->						Optional[int]:
											if torch_device == "mps":
																return
											__lowercase												=			self.scheduler_classes[0]
											__lowercase												=			self.get_scheduler_config()
											__lowercase												=			scheduler_class(**UpperCAmelCase_							)

											scheduler.set_timesteps(self.num_inference_steps     ,							device=UpperCAmelCase_							)
											__lowercase												=			torch.manual_seed(0							)

											__lowercase												=			self.dummy_model()
											__lowercase												=			self.dummy_sample_deter.to(UpperCAmelCase_							) * scheduler.init_noise_sigma

											for t in scheduler.timesteps:
																__lowercase												=			scheduler.scale_model_input(UpperCAmelCase_     ,							UpperCAmelCase_							)

																__lowercase												=			model(UpperCAmelCase_     ,							UpperCAmelCase_							)

																__lowercase												=			scheduler.step(UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							generator=UpperCAmelCase_							)
																__lowercase												=			output.prev_sample

											__lowercase												=			torch.sum(torch.abs(UpperCAmelCase_							)							)
											__lowercase												=			torch.mean(torch.abs(UpperCAmelCase_							)							)

											assert abs(result_sum.item() - 1_3_8_4_9.3_8_1_8							) < 1e-1
											assert abs(result_mean.item() - 1_8.0_3_3_1							) < 1e-3


"""simple docstring"""

def 				__UpperCamelCase   (    a__: list ,	a__: list ,	a__: list ,	a__: int ,	a__: int			)				->			None:




					"""simple docstring"""



					if target == 0:
										answer.append(path.copy()			)
					else:
										for index in range(a__ ,	len(a__			)			):
															if target >= candidates[index]:
																				path.append(candidates[index]			)
																				backtrack(a__ ,	a__ ,	a__ ,	target - candidates[index] ,	a__			)
																				path.pop(len(a__			) - 1			)





def 				__UpperCamelCase   (    a__: list ,	a__: int			)				->			list:




					"""simple docstring"""



					__lowercase												=			[]  # type: list[int]
					__lowercase												=			[]  # type: list[int]
					backtrack(a__ ,	a__ ,	a__ ,	a__ ,	0			)
					return answer





def 				__UpperCamelCase   (    )				->			None:




					"""simple docstring"""



					print(combination_sum([-8, 2.3, 0] ,	1			)			)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()


