"""simple docstring"""







import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class 		UpperCamelCase      (   _UpperCamelCase						):
				UpperCAmelCase    :				Optional[int]   =		(DDPMParallelScheduler,)



				def  _lowercase					(self    :				Optional[Any]     ,  **_A    :				Dict)   ->     str:
					__snake_case      :							int						=			{
					    'num_train_timesteps': 10_00,
					    'beta_start': 0.0_001,
					    'beta_end': 0.02,
					    'beta_schedule': 'linear',
					    'variance_type': 'fixed_small',
					    'clip_sample': True,
					}

					config.update(**_UpperCAmelCase)
					return config



				def  _lowercase					(self    :				int)   ->     Union[str, Any]:
					for timesteps in [1, 5, 1_00, 10_00]:
						self.check_over_configs(num_train_timesteps=_UpperCAmelCase)



				def  _lowercase					(self    :				Union[str, Any])   ->     List[Any]:
					for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]     ,  [0.002, 0.02, 0.2, 2]):
						self.check_over_configs(beta_start=_UpperCAmelCase     ,  beta_end=_UpperCAmelCase)



				def  _lowercase					(self    :				List[str])   ->     List[Any]:
					for schedule in ["linear", "squaredcos_cap_v2"]:
						self.check_over_configs(beta_schedule=_UpperCAmelCase)



				def  _lowercase					(self    :				Any)   ->     Dict:
					for variance in ["fixed_small", "fixed_large", "other"]:
						self.check_over_configs(variance_type=_UpperCAmelCase)



				def  _lowercase					(self    :				Any)   ->     int:
					for clip_sample in [True, False]:
						self.check_over_configs(clip_sample=_UpperCAmelCase)



				def  _lowercase					(self    :				Tuple)   ->     Optional[int]:
					self.check_over_configs(thresholding=_UpperCAmelCase)
					for threshold in [0.5, 1.0, 2.0]:
						for prediction_type in ["epsilon", "sample", "v_prediction"]:
							self.check_over_configs(
							    thresholding=_UpperCAmelCase     ,  prediction_type=_UpperCAmelCase     ,  sample_max_value=_UpperCAmelCase     ,  )



				def  _lowercase					(self    :				Union[str, Any])   ->     Union[str, Any]:
					for prediction_type in ["epsilon", "sample", "v_prediction"]:
						self.check_over_configs(prediction_type=_UpperCAmelCase)



				def  _lowercase					(self    :				List[str])   ->     Tuple:
					for t in [0, 5_00, 9_99]:
						self.check_over_forward(time_step=_UpperCAmelCase)



				def  _lowercase					(self    :				Dict)   ->     List[Any]:
					__snake_case      :							Union[str, Any]						=			self.scheduler_classes[0]
					__snake_case      :							List[str]						=			self.get_scheduler_config()
					__snake_case      :							Union[str, Any]						=			scheduler_class(**_UpperCAmelCase)

					assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(4_87) - 0.00_979)) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(9_99) - 0.02)) < 1E-5



				def  _lowercase					(self    :				str)   ->     Any:
					__snake_case      :							Tuple						=			self.scheduler_classes[0]
					__snake_case      :							List[Any]						=			self.get_scheduler_config()
					__snake_case      :							str						=			scheduler_class(**_UpperCAmelCase)

					__snake_case      :							Dict						=			len(_UpperCAmelCase)

					__snake_case      :							Any						=			self.dummy_model()
					__snake_case      :							str						=			self.dummy_sample_deter
					__snake_case      :							Dict						=			self.dummy_sample_deter + 0.1
					__snake_case      :							Tuple						=			self.dummy_sample_deter - 0.1

					__snake_case      :							Dict						=			samplea.shape[0]
					__snake_case      :							str						=			torch.stack([samplea, samplea, samplea]     ,  dim=0)
					__snake_case      :							Dict						=			torch.arange(_UpperCAmelCase)[0:3, None].repeat(1     ,  _UpperCAmelCase)

					__snake_case      :							List[Any]						=			model(samples.flatten(0     ,  1)     ,  timesteps.flatten(0     ,  1))
					__snake_case      :							Any						=			scheduler.batch_step_no_noise(_UpperCAmelCase     ,  timesteps.flatten(0     ,  1)     ,  samples.flatten(0     ,  1))

					__snake_case      :							Dict						=			torch.sum(torch.abs(_UpperCAmelCase))
					__snake_case      :							Optional[int]						=			torch.mean(torch.abs(_UpperCAmelCase))

					assert abs(result_sum.item() - 11_53.18_33) < 1E-2
					assert abs(result_mean.item() - 0.5_005) < 1E-3



				def  _lowercase					(self    :				Optional[int])   ->     List[str]:
					__snake_case      :							Union[str, Any]						=			self.scheduler_classes[0]
					__snake_case      :							List[str]						=			self.get_scheduler_config()
					__snake_case      :							Any						=			scheduler_class(**_UpperCAmelCase)

					__snake_case      :							Dict						=			len(_UpperCAmelCase)

					__snake_case      :							Union[str, Any]						=			self.dummy_model()
					__snake_case      :							Union[str, Any]						=			self.dummy_sample_deter
					__snake_case      :							Dict						=			torch.manual_seed(0)

					for t in reversed(range(_UpperCAmelCase)):
						# 1. predict noise residual
						__snake_case      :							Optional[Any]						=			model(_UpperCAmelCase     ,  _UpperCAmelCase)

						# 2. predict previous mean of sample x_t-1
						__snake_case      :							Dict						=			scheduler.step(_UpperCAmelCase     ,  _UpperCAmelCase     ,  _UpperCAmelCase     ,  generator=_UpperCAmelCase).prev_sample

						__snake_case      :							int						=			pred_prev_sample

					__snake_case      :							Tuple						=			torch.sum(torch.abs(_UpperCAmelCase))
					__snake_case      :							Optional[int]						=			torch.mean(torch.abs(_UpperCAmelCase))

					assert abs(result_sum.item() - 2_58.96_06) < 1E-2
					assert abs(result_mean.item() - 0.3_372) < 1E-3



				def  _lowercase					(self    :				str)   ->     str:
					__snake_case      :							List[Any]						=			self.scheduler_classes[0]
					__snake_case      :							Any						=			self.get_scheduler_config(prediction_type='v_prediction')
					__snake_case      :							Dict						=			scheduler_class(**_UpperCAmelCase)

					__snake_case      :							Tuple						=			len(_UpperCAmelCase)

					__snake_case      :							int						=			self.dummy_model()
					__snake_case      :							str						=			self.dummy_sample_deter
					__snake_case      :							Union[str, Any]						=			torch.manual_seed(0)

					for t in reversed(range(_UpperCAmelCase)):
						# 1. predict noise residual
						__snake_case      :							List[str]						=			model(_UpperCAmelCase     ,  _UpperCAmelCase)

						# 2. predict previous mean of sample x_t-1
						__snake_case      :							List[Any]						=			scheduler.step(_UpperCAmelCase     ,  _UpperCAmelCase     ,  _UpperCAmelCase     ,  generator=_UpperCAmelCase).prev_sample

						__snake_case      :							Optional[Any]						=			pred_prev_sample

					__snake_case      :							Optional[Any]						=			torch.sum(torch.abs(_UpperCAmelCase))
					__snake_case      :							Dict						=			torch.mean(torch.abs(_UpperCAmelCase))

					assert abs(result_sum.item() - 2_02.02_96) < 1E-2
					assert abs(result_mean.item() - 0.2_631) < 1E-3



				def  _lowercase					(self    :				int)   ->     List[str]:
					__snake_case      :							Optional[Any]						=			self.scheduler_classes[0]
					__snake_case      :							Dict						=			self.get_scheduler_config()
					__snake_case      :							Dict						=			scheduler_class(**_UpperCAmelCase)

					__snake_case      :							Dict						=			[1_00, 87, 50, 1, 0]

					scheduler.set_timesteps(timesteps=_UpperCAmelCase)

					__snake_case      :							Dict						=			scheduler.timesteps

					for i, timestep in enumerate(_UpperCAmelCase):
						if i == len(_UpperCAmelCase) - 1:
							__snake_case      :							Union[str, Any]						=			-1
						else:
							__snake_case      :							List[str]						=			timesteps[i + 1]

						__snake_case      :							str						=			scheduler.previous_timestep(_UpperCAmelCase)
						__snake_case      :							Optional[int]						=			prev_t.item()

						self.assertEqual(_UpperCAmelCase     ,  _UpperCAmelCase)



				def  _lowercase					(self    :				Optional[Any])   ->     Tuple:
					__snake_case      :							str						=			self.scheduler_classes[0]
					__snake_case      :							Tuple						=			self.get_scheduler_config()
					__snake_case      :							Optional[int]						=			scheduler_class(**_UpperCAmelCase)

					__snake_case      :							str						=			[1_00, 87, 50, 51, 0]

					with self.assertRaises(_UpperCAmelCase     ,  msg='`custom_timesteps` must be in descending order.'):
						scheduler.set_timesteps(timesteps=_UpperCAmelCase)



				def  _lowercase					(self    :				Union[str, Any])   ->     int:
					__snake_case      :							Optional[int]						=			self.scheduler_classes[0]
					__snake_case      :							int						=			self.get_scheduler_config()
					__snake_case      :							Dict						=			scheduler_class(**_UpperCAmelCase)

					__snake_case      :							List[str]						=			[1_00, 87, 50, 1, 0]
					__snake_case      :							Optional[Any]						=			len(_UpperCAmelCase)

					with self.assertRaises(_UpperCAmelCase     ,  msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.'):
						scheduler.set_timesteps(num_inference_steps=_UpperCAmelCase     ,  timesteps=_UpperCAmelCase)






				def  _lowercase					(self    :				Any)   ->     Tuple:
					__snake_case      :							Tuple						=			self.scheduler_classes[0]
					__snake_case      :							Optional[int]						=			self.get_scheduler_config()
					__snake_case      :							str						=			scheduler_class(**_UpperCAmelCase)

					__snake_case      :							str						=			[scheduler.config.num_train_timesteps]

					with self.assertRaises(
					    _UpperCAmelCase     ,  msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'     ,  ):
						scheduler.set_timesteps(timesteps=_UpperCAmelCase)






"""simple docstring"""







from __future__ import annotations

from math import pow, sqrt
def   __UpperCAmelCase    (   UpperCAmelCase_   :    float      ,							UpperCAmelCase_   :    float      ,							UpperCAmelCase_   :    float  )							->      dict[str, float]:

	'''simple docstring'''





	if (resistance, reactance, impedance).count(0  ) != 1:
		raise ValueError('One and only one argument must be 0'  )
	if resistance == 0:
		return {"resistance": sqrt(pow(UpperCAmelCase_      ,							2  ) - pow(UpperCAmelCase_      ,							2  )  )}
	elif reactance == 0:
		return {"reactance": sqrt(pow(UpperCAmelCase_      ,							2  ) - pow(UpperCAmelCase_      ,							2  )  )}
	elif impedance == 0:
		return {"impedance": sqrt(pow(UpperCAmelCase_      ,							2  ) + pow(UpperCAmelCase_      ,							2  )  )}
	else:
		raise ValueError('Exactly one argument must be 0'  )


if __name__ == "__main__":
	import doctest

	doctest.testmod()





