



'''simple docstring'''

import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest

class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						UpperCAmelCase							:   Any									=		(DEISMultistepScheduler,)
						UpperCAmelCase							:   Optional[Any]									=		(("num_inference_steps", 25),)


						def     __snake_case   ( self     ,	**A_				)     ->     Dict:
							lowerCAmelCase   =     {
							    """num_train_timesteps""": 1000,
							    """beta_start""": 0.0_0_0_1,
							    """beta_end""": 0.0_2,
							    """beta_schedule""": """linear""",
							    """solver_order""": 2,
							}

							config.update(**A_				)
							return config


						def     __snake_case   ( self     ,	A_=0     ,	**A_				)     ->     Optional[int]:
							lowerCAmelCase   =     dict(self.forward_default_kwargs				)
							lowerCAmelCase   =     kwargs.pop("""num_inference_steps"""     ,	A_				)
							lowerCAmelCase   =     self.dummy_sample
							lowerCAmelCase   =     0.1 * sample
							lowerCAmelCase   =     [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

							for scheduler_class in self.scheduler_classes:
								lowerCAmelCase   =     self.get_scheduler_config(**A_				)
								lowerCAmelCase   =     scheduler_class(**A_				)
								scheduler.set_timesteps(A_				)
								# copy over dummy past residuals
								lowerCAmelCase   =     dummy_past_residuals[: scheduler.config.solver_order]

								with tempfile.TemporaryDirectory() as tmpdirname:
									scheduler.save_config(A_				)
									lowerCAmelCase   =     scheduler_class.from_pretrained(A_				)
									new_scheduler.set_timesteps(A_				)
									# copy over dummy past residuals
									lowerCAmelCase   =     dummy_past_residuals[: new_scheduler.config.solver_order]

								lowerCAmelCase,       lowerCAmelCase   =     sample, sample
								for t in range(A_     ,	time_step + scheduler.config.solver_order + 1				):
									lowerCAmelCase   =     scheduler.step(A_     ,	A_     ,	A_     ,	**A_				).prev_sample
									lowerCAmelCase   =     new_scheduler.step(A_     ,	A_     ,	A_     ,	**A_				).prev_sample

									assert torch.sum(torch.abs(output - new_output				)				) < 1e-5, "Scheduler outputs are not identical"


						def     __snake_case   ( self				)     ->     int:
							pass


						def     __snake_case   ( self     ,	A_=0     ,	**A_				)     ->     str:
							lowerCAmelCase   =     dict(self.forward_default_kwargs				)
							lowerCAmelCase   =     kwargs.pop("""num_inference_steps"""     ,	A_				)
							lowerCAmelCase   =     self.dummy_sample
							lowerCAmelCase   =     0.1 * sample
							lowerCAmelCase   =     [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

							for scheduler_class in self.scheduler_classes:
								lowerCAmelCase   =     self.get_scheduler_config()
								lowerCAmelCase   =     scheduler_class(**A_				)
								scheduler.set_timesteps(A_				)

								# copy over dummy past residuals (must be after setting timesteps)
								lowerCAmelCase   =     dummy_past_residuals[: scheduler.config.solver_order]

								with tempfile.TemporaryDirectory() as tmpdirname:
									scheduler.save_config(A_				)
									lowerCAmelCase   =     scheduler_class.from_pretrained(A_				)
									# copy over dummy past residuals
									new_scheduler.set_timesteps(A_				)

									# copy over dummy past residual (must be after setting timesteps)
									lowerCAmelCase   =     dummy_past_residuals[: new_scheduler.config.solver_order]

								lowerCAmelCase   =     scheduler.step(A_     ,	A_     ,	A_     ,	**A_				).prev_sample
								lowerCAmelCase   =     new_scheduler.step(A_     ,	A_     ,	A_     ,	**A_				).prev_sample

								assert torch.sum(torch.abs(output - new_output				)				) < 1e-5, "Scheduler outputs are not identical"


						def     __snake_case   ( self     ,	A_=None     ,	**A_				)     ->     int:
							if scheduler is None:
								lowerCAmelCase   =     self.scheduler_classes[0]
								lowerCAmelCase   =     self.get_scheduler_config(**A_				)
								lowerCAmelCase   =     scheduler_class(**A_				)

							lowerCAmelCase   =     self.scheduler_classes[0]
							lowerCAmelCase   =     self.get_scheduler_config(**A_				)
							lowerCAmelCase   =     scheduler_class(**A_				)

							lowerCAmelCase   =     10
							lowerCAmelCase   =     self.dummy_model()
							lowerCAmelCase   =     self.dummy_sample_deter
							scheduler.set_timesteps(A_				)

							for i, t in enumerate(scheduler.timesteps				):
								lowerCAmelCase   =     model(A_     ,	A_				)
								lowerCAmelCase   =     scheduler.step(A_     ,	A_     ,	A_				).prev_sample

							return sample


						def     __snake_case   ( self				)     ->     List[str]:
							lowerCAmelCase   =     dict(self.forward_default_kwargs				)

							lowerCAmelCase   =     kwargs.pop("""num_inference_steps"""     ,	A_				)

							for scheduler_class in self.scheduler_classes:
								lowerCAmelCase   =     self.get_scheduler_config()
								lowerCAmelCase   =     scheduler_class(**A_				)

								lowerCAmelCase   =     self.dummy_sample
								lowerCAmelCase   =     0.1 * sample

								if num_inference_steps is not None and hasattr(A_     ,	"""set_timesteps"""				):
									scheduler.set_timesteps(A_				)
								elif num_inference_steps is not None and not hasattr(A_     ,	"""set_timesteps"""				):
									lowerCAmelCase   =     num_inference_steps

								# copy over dummy past residuals (must be done after set_timesteps)
								lowerCAmelCase   =     [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
								lowerCAmelCase   =     dummy_past_residuals[: scheduler.config.solver_order]

								lowerCAmelCase   =     scheduler.timesteps[5]
								lowerCAmelCase   =     scheduler.timesteps[6]

								lowerCAmelCase   =     scheduler.step(A_     ,	A_     ,	A_     ,	**A_				).prev_sample
								lowerCAmelCase   =     scheduler.step(A_     ,	A_     ,	A_     ,	**A_				).prev_sample

								self.assertEqual(output_a.shape     ,	sample.shape				)
								self.assertEqual(output_a.shape     ,	output_a.shape				)


						def     __snake_case   ( self				)     ->     int:
							# make sure that iterating over schedulers with same config names gives same results
							# for defaults
							lowerCAmelCase   =     DEISMultistepScheduler(**self.get_scheduler_config()				)
							lowerCAmelCase   =     self.full_loop(scheduler=A_				)
							lowerCAmelCase   =     torch.mean(torch.abs(A_				)				)

							assert abs(result_mean.item() - 0.2_3_9_1_6				) < 1e-3

							lowerCAmelCase   =     DPMSolverSinglestepScheduler.from_config(scheduler.config				)
							lowerCAmelCase   =     DPMSolverMultistepScheduler.from_config(scheduler.config				)
							lowerCAmelCase   =     UniPCMultistepScheduler.from_config(scheduler.config				)
							lowerCAmelCase   =     DEISMultistepScheduler.from_config(scheduler.config				)

							lowerCAmelCase   =     self.full_loop(scheduler=A_				)
							lowerCAmelCase   =     torch.mean(torch.abs(A_				)				)

							assert abs(result_mean.item() - 0.2_3_9_1_6				) < 1e-3


						def     __snake_case   ( self				)     ->     int:
							for timesteps in [25, 50, 100, 999, 1000]:
								self.check_over_configs(num_train_timesteps=A_				)


						def     __snake_case   ( self				)     ->     str:
							self.check_over_configs(thresholding=A_				)
							for order in [1, 2, 3]:
								for solver_type in ["logrho"]:
									for threshold in [0.5, 1.0, 2.0]:
										for prediction_type in ["epsilon", "sample"]:
											self.check_over_configs(
											    thresholding=A_     ,	prediction_type=A_     ,	sample_max_value=A_     ,	algorithm_type="""deis"""     ,	solver_order=A_     ,	solver_type=A_     ,	)


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


						def     __snake_case   ( self				)     ->     List[Any]:
							for algorithm_type in ["deis"]:
								for solver_type in ["logrho"]:
									for order in [1, 2, 3]:
										for prediction_type in ["epsilon", "sample"]:
											self.check_over_configs(
											    solver_order=A_     ,	solver_type=A_     ,	prediction_type=A_     ,	algorithm_type=A_     ,	)
											lowerCAmelCase   =     self.full_loop(
											    solver_order=A_     ,	solver_type=A_     ,	prediction_type=A_     ,	algorithm_type=A_     ,	)
											assert not torch.isnan(A_				).any(), "Samples have nan numbers"


						def     __snake_case   ( self				)     ->     str:
							self.check_over_configs(lower_order_final=A_				)
							self.check_over_configs(lower_order_final=A_				)


						def     __snake_case   ( self				)     ->     int:
							for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
								self.check_over_forward(num_inference_steps=A_     ,	time_step=0				)


						def     __snake_case   ( self				)     ->     Dict:
							lowerCAmelCase   =     self.full_loop()
							lowerCAmelCase   =     torch.mean(torch.abs(A_				)				)

							assert abs(result_mean.item() - 0.2_3_9_1_6				) < 1e-3


						def     __snake_case   ( self				)     ->     Tuple:
							lowerCAmelCase   =     self.full_loop(prediction_type="""v_prediction"""				)
							lowerCAmelCase   =     torch.mean(torch.abs(A_				)				)

							assert abs(result_mean.item() - 0.0_9_1				) < 1e-3




						def     __snake_case   ( self				)     ->     Union[str, Any]:
							lowerCAmelCase   =     self.scheduler_classes[0]
							lowerCAmelCase   =     self.get_scheduler_config(thresholding=A_     ,	dynamic_thresholding_ratio=0				)
							lowerCAmelCase   =     scheduler_class(**A_				)

							lowerCAmelCase   =     10
							lowerCAmelCase   =     self.dummy_model()
							lowerCAmelCase   =     self.dummy_sample_deter.half()
							scheduler.set_timesteps(A_				)

							for i, t in enumerate(scheduler.timesteps				):
								lowerCAmelCase   =     model(A_     ,	A_				)
								lowerCAmelCase   =     scheduler.step(A_     ,	A_     ,	A_				).prev_sample

							assert sample.dtype == torch.floataa




'''simple docstring'''

import cmath
import math






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						float							,		_SCREAMING_SNAKE_CASE					:						float							,		_SCREAMING_SNAKE_CASE					:						float							,		_SCREAMING_SNAKE_CASE					:						float							)      ->						complex:







	"""simple docstring"""
	lowerCAmelCase   =     math.radians(_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     math.radians(_SCREAMING_SNAKE_CASE							)

	# Convert voltage and current to rectangular form
	lowerCAmelCase   =     cmath.rect(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     cmath.rect(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)

	# Calculate apparent power
	return voltage_rect * current_rect


if __name__ == "__main__":
				import doctest

				doctest.testmod()