





import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class 							__SCREAMING_SNAKE_CASE       (  _a							):
							snake_case  :   Dict      =							(EulerDiscreteScheduler,)
							snake_case  :   List[Any]      =							10






							def 					_lowerCamelCase		(    self	,			**__lowerCAmelCase	):
														UpperCamelCase__					  =      {
														    """num_train_timesteps""": 1100,
														    """beta_start""": 0.0001,
														    """beta_end""": 0.02,
														    """beta_schedule""": """linear""",
														}

														config.update(**__lowerCAmelCase	)
														return config






							def 					_lowerCamelCase		(    self	):
														for timesteps in [10, 50, 100, 1000]:
																					self.check_over_configs(num_train_timesteps=__lowerCAmelCase	)






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






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






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      self.scheduler_classes[0]
														UpperCamelCase__					  =      self.get_scheduler_config()
														UpperCamelCase__					  =      scheduler_class(**__lowerCAmelCase	)

														scheduler.set_timesteps(self.num_inference_steps	)

														UpperCamelCase__					  =      torch.manual_seed(0	)

														UpperCamelCase__					  =      self.dummy_model()
														UpperCamelCase__					  =      self.dummy_sample_deter * scheduler.init_noise_sigma
														UpperCamelCase__					  =      sample.to(__lowerCAmelCase	)

														for i, t in enumerate(scheduler.timesteps	):
																					UpperCamelCase__					  =      scheduler.scale_model_input(__lowerCAmelCase	,			__lowerCAmelCase	)

																					UpperCamelCase__					  =      model(__lowerCAmelCase	,			__lowerCAmelCase	)

																					UpperCamelCase__					  =      scheduler.step(__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			generator=__lowerCAmelCase	)
																					UpperCamelCase__					  =      output.prev_sample

														UpperCamelCase__					  =      torch.sum(torch.abs(__lowerCAmelCase	)	)
														UpperCamelCase__					  =      torch.mean(torch.abs(__lowerCAmelCase	)	)

														assert abs(result_sum.item() - 10.0807	) < 1E-2
														assert abs(result_mean.item() - 0.0131	) < 1E-3






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      self.scheduler_classes[0]
														UpperCamelCase__					  =      self.get_scheduler_config(prediction_type="""v_prediction"""	)
														UpperCamelCase__					  =      scheduler_class(**__lowerCAmelCase	)

														scheduler.set_timesteps(self.num_inference_steps	)

														UpperCamelCase__					  =      torch.manual_seed(0	)

														UpperCamelCase__					  =      self.dummy_model()
														UpperCamelCase__					  =      self.dummy_sample_deter * scheduler.init_noise_sigma
														UpperCamelCase__					  =      sample.to(__lowerCAmelCase	)

														for i, t in enumerate(scheduler.timesteps	):
																					UpperCamelCase__					  =      scheduler.scale_model_input(__lowerCAmelCase	,			__lowerCAmelCase	)

																					UpperCamelCase__					  =      model(__lowerCAmelCase	,			__lowerCAmelCase	)

																					UpperCamelCase__					  =      scheduler.step(__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			generator=__lowerCAmelCase	)
																					UpperCamelCase__					  =      output.prev_sample

														UpperCamelCase__					  =      torch.sum(torch.abs(__lowerCAmelCase	)	)
														UpperCamelCase__					  =      torch.mean(torch.abs(__lowerCAmelCase	)	)

														assert abs(result_sum.item() - 0.0002	) < 1E-2
														assert abs(result_mean.item() - 2.2676E-06	) < 1E-3






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      self.scheduler_classes[0]
														UpperCamelCase__					  =      self.get_scheduler_config()
														UpperCamelCase__					  =      scheduler_class(**__lowerCAmelCase	)

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

														UpperCamelCase__					  =      torch.manual_seed(0	)

														UpperCamelCase__					  =      self.dummy_model()
														UpperCamelCase__					  =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
														UpperCamelCase__					  =      sample.to(__lowerCAmelCase	)

														for t in scheduler.timesteps:
																					UpperCamelCase__					  =      scheduler.scale_model_input(__lowerCAmelCase	,			__lowerCAmelCase	)

																					UpperCamelCase__					  =      model(__lowerCAmelCase	,			__lowerCAmelCase	)

																					UpperCamelCase__					  =      scheduler.step(__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			generator=__lowerCAmelCase	)
																					UpperCamelCase__					  =      output.prev_sample

														UpperCamelCase__					  =      torch.sum(torch.abs(__lowerCAmelCase	)	)
														UpperCamelCase__					  =      torch.mean(torch.abs(__lowerCAmelCase	)	)

														assert abs(result_sum.item() - 10.0807	) < 1E-2
														assert abs(result_mean.item() - 0.0131	) < 1E-3







							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      self.scheduler_classes[0]
														UpperCamelCase__					  =      self.get_scheduler_config()
														UpperCamelCase__					  =      scheduler_class(**__lowerCAmelCase	,			use_karras_sigmas=__lowerCAmelCase	)

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

														UpperCamelCase__					  =      torch.manual_seed(0	)

														UpperCamelCase__					  =      self.dummy_model()
														UpperCamelCase__					  =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
														UpperCamelCase__					  =      sample.to(__lowerCAmelCase	)

														for t in scheduler.timesteps:
																					UpperCamelCase__					  =      scheduler.scale_model_input(__lowerCAmelCase	,			__lowerCAmelCase	)

																					UpperCamelCase__					  =      model(__lowerCAmelCase	,			__lowerCAmelCase	)

																					UpperCamelCase__					  =      scheduler.step(__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			generator=__lowerCAmelCase	)
																					UpperCamelCase__					  =      output.prev_sample

														UpperCamelCase__					  =      torch.sum(torch.abs(__lowerCAmelCase	)	)
														UpperCamelCase__					  =      torch.mean(torch.abs(__lowerCAmelCase	)	)

														assert abs(result_sum.item() - 124.52_2994_9951_1719	) < 1E-2
														assert abs(result_mean.item() - 0.1_6213_9326_3339_9963	) < 1E-3











from __future__ import annotations

from math import pi





def      _UpperCamelCase  (a__ :float ,      a__ :float ,      a__ :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()




