


"""simple docstring"""


import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest
@require_torchsde
class snake_case      (  _UpperCamelCase):
		__UpperCamelCase           =			(DPMSolverSDEScheduler,)
		__UpperCamelCase           =			10







		def  a_ (					self      :       int    ,   **a__      :       Optional[int]		)       ->						Dict:





						'''simple docstring'''







						_A					  =			{
						    "num_train_timesteps": 11_00,
						    "beta_start": 0.0_0_0_1,
						    "beta_end": 0.0_2,
						    "beta_schedule": "linear",
						    "noise_sampler_seed": 0,
						}

						config.update(**a__		)
						return config







		def  a_ (					self      :       Dict		)       ->						Dict:





						'''simple docstring'''







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







		def  a_ (					self      :       Dict		)       ->						Tuple:





						'''simple docstring'''







						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=a__    ,   beta_end=a__		)







		def  a_ (					self      :       Optional[Any]		)       ->						List[Any]:





						'''simple docstring'''







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







		def  a_ (					self      :       Optional[Any]		)       ->						List[Any]:





						'''simple docstring'''







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







		def  a_ (					self      :       Union[str, Any]		)       ->						List[str]:





						'''simple docstring'''







						_A					  =			self.scheduler_classes[0]
						_A					  =			self.get_scheduler_config()
						_A					  =			scheduler_class(**a__		)

						scheduler.set_timesteps(self.num_inference_steps		)

						_A					  =			self.dummy_model()
						_A					  =			self.dummy_sample_deter * scheduler.init_noise_sigma
						_A					  =			sample.to(a__		)

						for i, t in enumerate(scheduler.timesteps		):
										_A					  =			scheduler.scale_model_input(a__    ,   a__		)

										_A					  =			model(a__    ,   a__		)

										_A					  =			scheduler.step(a__    ,   a__    ,   a__		)
										_A					  =			output.prev_sample

						_A					  =			torch.sum(torch.abs(a__		)		)
						_A					  =			torch.mean(torch.abs(a__		)		)

						if torch_device in ["mps"]:
										assert abs(result_sum.item() - 1_6_7.4_7_8_2_1_0_4_4_9_2_1_8_7_5		) < 1E-2
										assert abs(result_mean.item() - 0.2_1_7_8_7_0_5_9_6_4_5_6_5_2_7_7		) < 1E-3
						elif torch_device in ["cuda"]:
										assert abs(result_sum.item() - 1_7_1.5_9_3_5_2_1_1_1_8_1_6_4_0_6		) < 1E-2
										assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_6_8_9_2_2_9_9_6_5_2		) < 1E-3
						else:
										assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2		) < 1E-2
										assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6		) < 1E-3







		def  a_ (					self      :       Optional[int]		)       ->						str:





						'''simple docstring'''







						_A					  =			self.scheduler_classes[0]
						_A					  =			self.get_scheduler_config(prediction_type="v_prediction"		)
						_A					  =			scheduler_class(**a__		)

						scheduler.set_timesteps(self.num_inference_steps		)

						_A					  =			self.dummy_model()
						_A					  =			self.dummy_sample_deter * scheduler.init_noise_sigma
						_A					  =			sample.to(a__		)

						for i, t in enumerate(scheduler.timesteps		):
										_A					  =			scheduler.scale_model_input(a__    ,   a__		)

										_A					  =			model(a__    ,   a__		)

										_A					  =			scheduler.step(a__    ,   a__    ,   a__		)
										_A					  =			output.prev_sample

						_A					  =			torch.sum(torch.abs(a__		)		)
						_A					  =			torch.mean(torch.abs(a__		)		)

						if torch_device in ["mps"]:
										assert abs(result_sum.item() - 1_2_4.7_7_1_4_9_2_0_0_4_3_9_4_5_3		) < 1E-2
										assert abs(result_mean.item() - 0.1_6_2_2_6_2_8_9_0_1_4_8_1_6_2_8_4		) < 1E-3
						elif torch_device in ["cuda"]:
										assert abs(result_sum.item() - 1_2_8.1_6_6_3_3_6_0_5_9_5_7_0_3		) < 1E-2
										assert abs(result_mean.item() - 0.1_6_6_8_8_3_2_6_0_0_1_1_6_7_2_9_7		) < 1E-3
						else:
										assert abs(result_sum.item() - 1_1_9.8_4_8_7_5_4_8_8_2_8_1_2_5		) < 1E-2
										assert abs(result_mean.item() - 0.1_5_6_0_5_3_0_6_6_2_5_3_6_6_2_1		) < 1E-3







		def  a_ (					self      :       List[str]		)       ->						Any:





						'''simple docstring'''







						_A					  =			self.scheduler_classes[0]
						_A					  =			self.get_scheduler_config()
						_A					  =			scheduler_class(**a__		)

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

						_A					  =			self.dummy_model()
						_A					  =			self.dummy_sample_deter.to(a__		) * scheduler.init_noise_sigma

						for t in scheduler.timesteps:
										_A					  =			scheduler.scale_model_input(a__    ,   a__		)

										_A					  =			model(a__    ,   a__		)

										_A					  =			scheduler.step(a__    ,   a__    ,   a__		)
										_A					  =			output.prev_sample

						_A					  =			torch.sum(torch.abs(a__		)		)
						_A					  =			torch.mean(torch.abs(a__		)		)

						if torch_device in ["mps"]:
										assert abs(result_sum.item() - 1_6_7.4_6_9_5_7_3_9_7_4_6_0_9_3_8		) < 1E-2
										assert abs(result_mean.item() - 0.2_1_8_0_5_9_3_4_6_0_7_9_8_2_6_3_5		) < 1E-3
						elif torch_device in ["cuda"]:
										assert abs(result_sum.item() - 1_7_1.5_9_3_5_3_6_3_7_6_9_5_3_1_2		) < 1E-2
										assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_8_3_8_2_4_1_5_7_7_1		) < 1E-3
						else:
										assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2		) < 1E-2
										assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6		) < 1E-3







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





						'''simple docstring'''







						_A					  =			self.scheduler_classes[0]
						_A					  =			self.get_scheduler_config()
						_A					  =			scheduler_class(**a__    ,   use_karras_sigmas=a__		)

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

						_A					  =			self.dummy_model()
						_A					  =			self.dummy_sample_deter.to(a__		) * scheduler.init_noise_sigma
						_A					  =			sample.to(a__		)

						for t in scheduler.timesteps:
										_A					  =			scheduler.scale_model_input(a__    ,   a__		)

										_A					  =			model(a__    ,   a__		)

										_A					  =			scheduler.step(a__    ,   a__    ,   a__		)
										_A					  =			output.prev_sample

						_A					  =			torch.sum(torch.abs(a__		)		)
						_A					  =			torch.mean(torch.abs(a__		)		)

						if torch_device in ["mps"]:
										assert abs(result_sum.item() - 1_7_6.6_6_9_7_4_1_3_5_7_4_2_1_8_8		) < 1E-2
										assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1		) < 1E-2
						elif torch_device in ["cuda"]:
										assert abs(result_sum.item() - 1_7_7.6_3_6_5_3_5_6_4_4_5_3_1_2_5		) < 1E-2
										assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1		) < 1E-2
						else:
										assert abs(result_sum.item() - 1_7_0.3_1_3_5_2_2_3_3_8_8_6_7_2		) < 1E-2
										assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1		) < 1E-2



"""simple docstring"""


import numpy as np






def      a__ (				__lowercase     ,			__lowercase   )	->	np.ndarray:
				return np.where(vector > 0     ,			__lowercase     ,			(alpha * (np.exp(__lowercase   ) - 1))   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()