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      __lowercase					(    _A    ):
						lowercase					 =     (DPMSolverSDEScheduler,)
						lowercase					 =     10

						def        __a		(	self				:      Any							,							**__lowerCamelCase				:      Dict   )							->		Tuple:


									'''simple docstring'''

									lowercase							=		{
									    '''num_train_timesteps''': 11_00,
									    '''beta_start''': 0.0001,
									    '''beta_end''': 0.02,
									    '''beta_schedule''': '''linear''',
									    '''noise_sampler_seed''': 0,
									}

									config.update(**__lowerCamelCase   )
									return config

						def        __a		(	self				:      Tuple   )							->		Dict:


									'''simple docstring'''

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

						def        __a		(	self				:      List[str]   )							->		Dict:


									'''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=__lowerCamelCase							,							beta_end=__lowerCamelCase   )

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


									'''simple docstring'''

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

						def        __a		(	self				:      Tuple   )							->		int:


									'''simple docstring'''

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

						def        __a		(	self				:      Any   )							->		Optional[int]:


									'''simple docstring'''

									lowercase							=		self.scheduler_classes[0]
									lowercase							=		self.get_scheduler_config()
									lowercase							=		scheduler_class(**__lowerCamelCase   )

									scheduler.set_timesteps(self.num_inference_steps   )

									lowercase							=		self.dummy_model()
									lowercase							=		self.dummy_sample_deter * scheduler.init_noise_sigma
									lowercase							=		sample.to(__lowerCamelCase   )

									for i, t in enumerate(scheduler.timesteps   ):
												lowercase							=		scheduler.scale_model_input(__lowerCamelCase							,							__lowerCamelCase   )

												lowercase							=		model(__lowerCamelCase							,							__lowerCamelCase   )

												lowercase							=		scheduler.step(__lowerCamelCase							,							__lowerCamelCase							,							__lowerCamelCase   )
												lowercase							=		output.prev_sample

									lowercase							=		torch.sum(torch.abs(__lowerCamelCase   )   )
									lowercase							=		torch.mean(torch.abs(__lowerCamelCase   )   )

									if torch_device in ["mps"]:
												assert abs(result_sum.item() - 167.47_8210_4492_1875   ) < 1E-2
												assert abs(result_mean.item() - 0.2178_7059_6456_5277   ) < 1E-3
									elif torch_device in ["cuda"]:
												assert abs(result_sum.item() - 171.59_3521_1181_6406   ) < 1E-2
												assert abs(result_mean.item() - 0.2_2342_9068_9229_9652   ) < 1E-3
									else:
												assert abs(result_sum.item() - 162.52_3834_2285_1562   ) < 1E-2
												assert abs(result_mean.item() - 0.211_6195_7085_1326   ) < 1E-3

						def        __a		(	self				:      int   )							->		Any:


									'''simple docstring'''

									lowercase							=		self.scheduler_classes[0]
									lowercase							=		self.get_scheduler_config(prediction_type='''v_prediction'''   )
									lowercase							=		scheduler_class(**__lowerCamelCase   )

									scheduler.set_timesteps(self.num_inference_steps   )

									lowercase							=		self.dummy_model()
									lowercase							=		self.dummy_sample_deter * scheduler.init_noise_sigma
									lowercase							=		sample.to(__lowerCamelCase   )

									for i, t in enumerate(scheduler.timesteps   ):
												lowercase							=		scheduler.scale_model_input(__lowerCamelCase							,							__lowerCamelCase   )

												lowercase							=		model(__lowerCamelCase							,							__lowerCamelCase   )

												lowercase							=		scheduler.step(__lowerCamelCase							,							__lowerCamelCase							,							__lowerCamelCase   )
												lowercase							=		output.prev_sample

									lowercase							=		torch.sum(torch.abs(__lowerCamelCase   )   )
									lowercase							=		torch.mean(torch.abs(__lowerCamelCase   )   )

									if torch_device in ["mps"]:
												assert abs(result_sum.item() - 124.77_1492_0043_9453   ) < 1E-2
												assert abs(result_mean.item() - 0.1_6226_2890_1481_6284   ) < 1E-3
									elif torch_device in ["cuda"]:
												assert abs(result_sum.item() - 128.1_6633_6059_5703   ) < 1E-2
												assert abs(result_mean.item() - 0.1_6688_3260_0116_7297   ) < 1E-3
									else:
												assert abs(result_sum.item() - 119.8_4875_4882_8125   ) < 1E-2
												assert abs(result_mean.item() - 0.1560_5306_6253_6621   ) < 1E-3

						def        __a		(	self				:      List[str]   )							->		Tuple:


									'''simple docstring'''

									lowercase							=		self.scheduler_classes[0]
									lowercase							=		self.get_scheduler_config()
									lowercase							=		scheduler_class(**__lowerCamelCase   )

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

									lowercase							=		self.dummy_model()
									lowercase							=		self.dummy_sample_deter.to(__lowerCamelCase   ) * scheduler.init_noise_sigma

									for t in scheduler.timesteps:
												lowercase							=		scheduler.scale_model_input(__lowerCamelCase							,							__lowerCamelCase   )

												lowercase							=		model(__lowerCamelCase							,							__lowerCamelCase   )

												lowercase							=		scheduler.step(__lowerCamelCase							,							__lowerCamelCase							,							__lowerCamelCase   )
												lowercase							=		output.prev_sample

									lowercase							=		torch.sum(torch.abs(__lowerCamelCase   )   )
									lowercase							=		torch.mean(torch.abs(__lowerCamelCase   )   )

									if torch_device in ["mps"]:
												assert abs(result_sum.item() - 167.46_9573_9746_0938   ) < 1E-2
												assert abs(result_mean.item() - 0.2_1805_9346_0798_2635   ) < 1E-3
									elif torch_device in ["cuda"]:
												assert abs(result_sum.item() - 171.59_3536_3769_5312   ) < 1E-2
												assert abs(result_mean.item() - 0.2_2342_9083_8241_5771   ) < 1E-3
									else:
												assert abs(result_sum.item() - 162.52_3834_2285_1562   ) < 1E-2
												assert abs(result_mean.item() - 0.211_6195_7085_1326   ) < 1E-3



						def        __a		(	self				:      Union[str, Any]   )							->		int:


									'''simple docstring'''

									lowercase							=		self.scheduler_classes[0]
									lowercase							=		self.get_scheduler_config()
									lowercase							=		scheduler_class(**__lowerCamelCase							,							use_karras_sigmas=__lowerCamelCase   )

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

									lowercase							=		self.dummy_model()
									lowercase							=		self.dummy_sample_deter.to(__lowerCamelCase   ) * scheduler.init_noise_sigma
									lowercase							=		sample.to(__lowerCamelCase   )

									for t in scheduler.timesteps:
												lowercase							=		scheduler.scale_model_input(__lowerCamelCase							,							__lowerCamelCase   )

												lowercase							=		model(__lowerCamelCase							,							__lowerCamelCase   )

												lowercase							=		scheduler.step(__lowerCamelCase							,							__lowerCamelCase							,							__lowerCamelCase   )
												lowercase							=		output.prev_sample

									lowercase							=		torch.sum(torch.abs(__lowerCamelCase   )   )
									lowercase							=		torch.mean(torch.abs(__lowerCamelCase   )   )

									if torch_device in ["mps"]:
												assert abs(result_sum.item() - 176.66_9741_3574_2188   ) < 1E-2
												assert abs(result_mean.item() - 0.2_3003_8727_3098_1811   ) < 1E-2
									elif torch_device in ["cuda"]:
												assert abs(result_sum.item() - 177.63_6535_6445_3125   ) < 1E-2
												assert abs(result_mean.item() - 0.2_3003_8727_3098_1811   ) < 1E-2
									else:
												assert abs(result_sum.item() - 170.3_1352_2338_8672   ) < 1E-2
												assert abs(result_mean.item() - 0.2_3003_8727_3098_1811   ) < 1E-2






from __future__ import annotations

from math import pi






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       dict[str, float]:



			"""simple docstring"""







			if (inductance, frequency, reactance).count(0       ) != 1:
						raise ValueError('''One and only one argument must be 0'''       )
			if inductance < 0:
						raise ValueError('''Inductance cannot be negative'''       )
			if frequency < 0:
						raise ValueError('''Frequency cannot be negative'''       )
			if reactance < 0:
						raise ValueError('''Inductive reactance cannot be negative'''       )
			if inductance == 0:
						return {"inductance": reactance / (2 * pi * frequency)}
			elif frequency == 0:
						return {"frequency": reactance / (2 * pi * inductance)}
			elif reactance == 0:
						return {"reactance": 2 * pi * frequency * inductance}
			else:
						raise ValueError('''Exactly one argument must be 0'''       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()





