





"""simple docstring"""




import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class        __UpperCamelCase				(						a__						):
							lowerCamelCase   :					Any      					=(DPMSolverSinglestepScheduler,)
							lowerCamelCase   :					str      					=(("""num_inference_steps""", 25),)






							def     __a   (   self      ,					**lowerCAmelCase__      )	-> str:
								a      :		List[Any]	=				{
								    "num_train_timesteps": 1000,
								    "beta_start": 0.0_001,
								    "beta_end": 0.02,
								    "beta_schedule": "linear",
								    "solver_order": 2,
								    "prediction_type": "epsilon",
								    "thresholding": False,
								    "sample_max_value": 1.0,
								    "algorithm_type": "dpmsolver++",
								    "solver_type": "midpoint",
								    "lambda_min_clipped": -float("inf"      ),
								    "variance_type": None,
								}

								config.update(**lowerCAmelCase__      )
								return config






							def     __a   (   self      ,					lowerCAmelCase__=0      ,					**lowerCAmelCase__      )	-> Union[str, Any]:
								a      :		Any	=				dict(self.forward_default_kwargs      )
								a      :		Dict	=				kwargs.pop("num_inference_steps"      ,					lowerCAmelCase__      )
								a      :		List[str]	=				self.dummy_sample
								a      :		List[Any]	=				0.1 * sample
								a      :		int	=				[residual + 0.2, residual + 0.15, residual + 0.10]

								for scheduler_class in self.scheduler_classes:
									a      :		Tuple	=				self.get_scheduler_config(**lowerCAmelCase__      )
									a      :		Dict	=				scheduler_class(**lowerCAmelCase__      )
									scheduler.set_timesteps(lowerCAmelCase__      )
									# copy over dummy past residuals
									a      :		Optional[Any]	=				dummy_past_residuals[: scheduler.config.solver_order]

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

									a,       a      :		Union[str, Any]	=				sample, sample
									for t in range(lowerCAmelCase__      ,					time_step + scheduler.config.solver_order + 1      ):
										a      :		List[str]	=				scheduler.step(lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					**lowerCAmelCase__      ).prev_sample
										a      :		Optional[Any]	=				new_scheduler.step(lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					**lowerCAmelCase__      ).prev_sample

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






							def     __a   (   self      )	-> Tuple:
								pass






							def     __a   (   self      ,					lowerCAmelCase__=0      ,					**lowerCAmelCase__      )	-> Tuple:
								a      :		Union[str, Any]	=				dict(self.forward_default_kwargs      )
								a      :		int	=				kwargs.pop("num_inference_steps"      ,					lowerCAmelCase__      )
								a      :		List[str]	=				self.dummy_sample
								a      :		Optional[int]	=				0.1 * sample
								a      :		Tuple	=				[residual + 0.2, residual + 0.15, residual + 0.10]

								for scheduler_class in self.scheduler_classes:
									a      :		Dict	=				self.get_scheduler_config()
									a      :		str	=				scheduler_class(**lowerCAmelCase__      )
									scheduler.set_timesteps(lowerCAmelCase__      )

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

									with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(lowerCAmelCase__      )
										a      :		List[str]	=				scheduler_class.from_pretrained(lowerCAmelCase__      )
										# copy over dummy past residuals
										new_scheduler.set_timesteps(lowerCAmelCase__      )

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

									a      :		List[str]	=				scheduler.step(lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					**lowerCAmelCase__      ).prev_sample
									a      :		Optional[int]	=				new_scheduler.step(lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					**lowerCAmelCase__      ).prev_sample

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






							def     __a   (   self      ,					lowerCAmelCase__=None      ,					**lowerCAmelCase__      )	-> Dict:
								if scheduler is None:
									a      :		Dict	=				self.scheduler_classes[0]
									a      :		int	=				self.get_scheduler_config(**lowerCAmelCase__      )
									a      :		Tuple	=				scheduler_class(**lowerCAmelCase__      )

								a      :		List[Any]	=				self.scheduler_classes[0]
								a      :		Optional[Any]	=				self.get_scheduler_config(**lowerCAmelCase__      )
								a      :		List[Any]	=				scheduler_class(**lowerCAmelCase__      )

								a      :		int	=				10
								a      :		str	=				self.dummy_model()
								a      :		Union[str, Any]	=				self.dummy_sample_deter
								scheduler.set_timesteps(lowerCAmelCase__      )

								for i, t in enumerate(scheduler.timesteps      ):
									a      :		Optional[int]	=				model(lowerCAmelCase__      ,					lowerCAmelCase__      )
									a      :		Tuple	=				scheduler.step(lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ).prev_sample

								return sample






							def     __a   (   self      )	-> str:
								a      :		Union[str, Any]	=				DPMSolverSinglestepScheduler(**self.get_scheduler_config()      )
								a      :		Optional[int]	=				50
								a      :		List[str]	=				self.dummy_model()
								a      :		Union[str, Any]	=				self.dummy_sample_deter
								scheduler.set_timesteps(lowerCAmelCase__      )

								# make sure that the first t is uneven
								for i, t in enumerate(scheduler.timesteps[3:]      ):
									a      :		Any	=				model(lowerCAmelCase__      ,					lowerCAmelCase__      )
									a      :		Union[str, Any]	=				scheduler.step(lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ).prev_sample

								a      :		Union[str, Any]	=				torch.mean(torch.abs(lowerCAmelCase__      )      )

								assert abs(result_mean.item() - 0.2_574      ) < 1E-3






							def     __a   (   self      )	-> Union[str, Any]:
								for timesteps in [25, 50, 100, 999, 1000]:
									self.check_over_configs(num_train_timesteps=lowerCAmelCase__      )






							def     __a   (   self      )	-> Optional[int]:
								# make sure that iterating over schedulers with same config names gives same results
								# for defaults
								a      :		Dict	=				DPMSolverSinglestepScheduler(**self.get_scheduler_config()      )
								a      :		List[Any]	=				self.full_loop(scheduler=lowerCAmelCase__      )
								a      :		int	=				torch.mean(torch.abs(lowerCAmelCase__      )      )

								assert abs(result_mean.item() - 0.2_791      ) < 1E-3

								a      :		List[Any]	=				DEISMultistepScheduler.from_config(scheduler.config      )
								a      :		List[str]	=				DPMSolverMultistepScheduler.from_config(scheduler.config      )
								a      :		int	=				UniPCMultistepScheduler.from_config(scheduler.config      )
								a      :		Any	=				DPMSolverSinglestepScheduler.from_config(scheduler.config      )

								a      :		List[Any]	=				self.full_loop(scheduler=lowerCAmelCase__      )
								a      :		int	=				torch.mean(torch.abs(lowerCAmelCase__      )      )

								assert abs(result_mean.item() - 0.2_791      ) < 1E-3






							def     __a   (   self      )	-> Optional[int]:
								self.check_over_configs(thresholding=lowerCAmelCase__      )
								for order in [1, 2, 3]:
									for solver_type in ["midpoint", "heun"]:
										for threshold in [0.5, 1.0, 2.0]:
											for prediction_type in ["epsilon", "sample"]:
												self.check_over_configs(
												    thresholding=lowerCAmelCase__      ,					prediction_type=lowerCAmelCase__      ,					sample_max_value=lowerCAmelCase__      ,					algorithm_type="dpmsolver++"      ,					solver_order=lowerCAmelCase__      ,					solver_type=lowerCAmelCase__      ,					)






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






							def     __a   (   self      )	-> str:
								for algorithm_type in ["dpmsolver", "dpmsolver++"]:
									for solver_type in ["midpoint", "heun"]:
										for order in [1, 2, 3]:
											for prediction_type in ["epsilon", "sample"]:
												self.check_over_configs(
												    solver_order=lowerCAmelCase__      ,					solver_type=lowerCAmelCase__      ,					prediction_type=lowerCAmelCase__      ,					algorithm_type=lowerCAmelCase__      ,					)
												a      :		Optional[Any]	=				self.full_loop(
												    solver_order=lowerCAmelCase__      ,					solver_type=lowerCAmelCase__      ,					prediction_type=lowerCAmelCase__      ,					algorithm_type=lowerCAmelCase__      ,					)
												assert not torch.isnan(lowerCAmelCase__      ).any(), "Samples have nan numbers"






							def     __a   (   self      )	-> Any:
								self.check_over_configs(lower_order_final=lowerCAmelCase__      )
								self.check_over_configs(lower_order_final=lowerCAmelCase__      )






							def     __a   (   self      )	-> List[Any]:
								self.check_over_configs(lambda_min_clipped=-float("inf"      )      )
								self.check_over_configs(lambda_min_clipped=-5.1      )






							def     __a   (   self      )	-> Tuple:
								self.check_over_configs(variance_type=lowerCAmelCase__      )
								self.check_over_configs(variance_type="learned_range"      )






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






							def     __a   (   self      )	-> List[str]:
								a      :		Any	=				self.full_loop()
								a      :		Union[str, Any]	=				torch.mean(torch.abs(lowerCAmelCase__      )      )

								assert abs(result_mean.item() - 0.2_791      ) < 1E-3






							def     __a   (   self      )	-> Any:
								a      :		Optional[int]	=				self.full_loop(use_karras_sigmas=lowerCAmelCase__      )
								a      :		int	=				torch.mean(torch.abs(lowerCAmelCase__      )      )

								assert abs(result_mean.item() - 0.2_248      ) < 1E-3






							def     __a   (   self      )	-> Optional[int]:
								a      :		Optional[Any]	=				self.full_loop(prediction_type="v_prediction"      )
								a      :		Optional[int]	=				torch.mean(torch.abs(lowerCAmelCase__      )      )

								assert abs(result_mean.item() - 0.1_453      ) < 1E-3






							def     __a   (   self      )	-> Optional[int]:
								a      :		List[str]	=				self.full_loop(prediction_type="v_prediction"      ,					use_karras_sigmas=lowerCAmelCase__      )
								a      :		int	=				torch.mean(torch.abs(lowerCAmelCase__      )      )

								assert abs(result_mean.item() - 0.0_649      ) < 1E-3






							def     __a   (   self      )	-> Any:
								a      :		int	=				self.scheduler_classes[0]
								a      :		Optional[Any]	=				self.get_scheduler_config(thresholding=lowerCAmelCase__      ,					dynamic_thresholding_ratio=0      )
								a      :		Tuple	=				scheduler_class(**lowerCAmelCase__      )

								a      :		Optional[int]	=				10
								a      :		List[Any]	=				self.dummy_model()
								a      :		List[str]	=				self.dummy_sample_deter.half()
								scheduler.set_timesteps(lowerCAmelCase__      )

								for i, t in enumerate(scheduler.timesteps      ):
									a      :		Any	=				model(lowerCAmelCase__      ,					lowerCAmelCase__      )
									a      :		Any	=				scheduler.step(lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ).prev_sample

								assert sample.dtype == torch.floataa









"""simple docstring"""




def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    float    ,       _lowercase						:    float					) ->float:






	'''simple docstring'''






	return price * (1 + tax_rate)


if __name__ == "__main__":
		print(F'''{price_plus_tax(100, 0.25) = }''')
		print(F'''{price_plus_tax(125.50, 0.05) = }''')


