import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest






class 		a__						(					A__					):
					A	       =							(DPMSolverSinglestepScheduler,)
					A	       =							(('num_inference_steps', 25),)

					def 				__UpperCamelCase   (    self      :       Optional[Any],**_A      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    {
							    "num_train_timesteps": 1000,
							    "beta_start": 0.0001,
							    "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(**_A    )
							return config

					def 				__UpperCamelCase   (    self      :       str,_A      :       Dict=0,**_A      :       Any    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    dict(self.forward_default_kwargs    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    kwargs.pop("num_inference_steps",_A    )
							SCREAMING_SNAKE_CASE_  :		Dict				      =    self.dummy_sample
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    0.1 * sample
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    [residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    self.get_scheduler_config(**_A    )
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    scheduler_class(**_A    )
									scheduler.set_timesteps(_A    )
									# copy over dummy past residuals
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    dummy_past_residuals[: scheduler.config.solver_order]

									with tempfile.TemporaryDirectory() as tmpdirname:
											scheduler.save_config(_A    )
											SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    scheduler_class.from_pretrained(_A    )
											new_scheduler.set_timesteps(_A    )
											# copy over dummy past residuals
											SCREAMING_SNAKE_CASE_  :		List[Any]				      =    dummy_past_residuals[: new_scheduler.config.solver_order]

									SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		str				      =    sample, sample
									for t in range(_A,time_step + scheduler.config.solver_order + 1    ):
											SCREAMING_SNAKE_CASE_  :		str				      =    scheduler.step(_A,_A,_A,**_A    ).prev_sample
											SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    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 				__UpperCamelCase   (    self      :       Optional[int]    ):







							"""simple docstring"""






							pass

					def 				__UpperCamelCase   (    self      :       Any,_A      :       List[Any]=0,**_A      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    dict(self.forward_default_kwargs    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    kwargs.pop("num_inference_steps",_A    )
							SCREAMING_SNAKE_CASE_  :		int				      =    self.dummy_sample
							SCREAMING_SNAKE_CASE_  :		int				      =    0.1 * sample
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    [residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    self.get_scheduler_config()
									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    scheduler_class(**_A    )
									scheduler.set_timesteps(_A    )

									# copy over dummy past residuals (must be after setting timesteps)
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    dummy_past_residuals[: scheduler.config.solver_order]

									with tempfile.TemporaryDirectory() as tmpdirname:
											scheduler.save_config(_A    )
											SCREAMING_SNAKE_CASE_  :		Dict				      =    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)
											SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    dummy_past_residuals[: new_scheduler.config.solver_order]

									SCREAMING_SNAKE_CASE_  :		List[Any]				      =    scheduler.step(_A,_A,_A,**_A    ).prev_sample
									SCREAMING_SNAKE_CASE_  :		Dict				      =    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 				__UpperCamelCase   (    self      :       int,_A      :       Dict=None,**_A      :       List[str]    ):







							"""simple docstring"""






							if scheduler is None:
									SCREAMING_SNAKE_CASE_  :		Dict				      =    self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    self.get_scheduler_config(**_A    )
									SCREAMING_SNAKE_CASE_  :		int				      =    scheduler_class(**_A    )

							SCREAMING_SNAKE_CASE_  :		Tuple				      =    self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE_  :		int				      =    self.get_scheduler_config(**_A    )
							SCREAMING_SNAKE_CASE_  :		str				      =    scheduler_class(**_A    )

							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    10
							SCREAMING_SNAKE_CASE_  :		Any				      =    self.dummy_model()
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    self.dummy_sample_deter
							scheduler.set_timesteps(_A    )

							for i, t in enumerate(scheduler.timesteps    ):
									SCREAMING_SNAKE_CASE_  :		int				      =    model(_A,_A    )
									SCREAMING_SNAKE_CASE_  :		List[Any]				      =    scheduler.step(_A,_A,_A    ).prev_sample

							return sample

					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    DPMSolverSinglestepScheduler(**self.get_scheduler_config()    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    50
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    self.dummy_model()
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    self.dummy_sample_deter
							scheduler.set_timesteps(_A    )

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

							SCREAMING_SNAKE_CASE_  :		str				      =    torch.mean(torch.abs(_A    )    )

							assert abs(result_mean.item() - 0.2574    ) < 1E-3

					def 				__UpperCamelCase   (    self      :       Any    ):







							"""simple docstring"""






							for timesteps in [25, 50, 100, 999, 1000]:
									self.check_over_configs(num_train_timesteps=_A    )

					def 				__UpperCamelCase   (    self      :       Optional[int]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    DPMSolverSinglestepScheduler(**self.get_scheduler_config()    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    self.full_loop(scheduler=_A    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    torch.mean(torch.abs(_A    )    )

							assert abs(result_mean.item() - 0.2791    ) < 1E-3

							SCREAMING_SNAKE_CASE_  :		Tuple				      =    DEISMultistepScheduler.from_config(scheduler.config    )
							SCREAMING_SNAKE_CASE_  :		str				      =    DPMSolverMultistepScheduler.from_config(scheduler.config    )
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    UniPCMultistepScheduler.from_config(scheduler.config    )
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    DPMSolverSinglestepScheduler.from_config(scheduler.config    )

							SCREAMING_SNAKE_CASE_  :		int				      =    self.full_loop(scheduler=_A    )
							SCREAMING_SNAKE_CASE_  :		int				      =    torch.mean(torch.abs(_A    )    )

							assert abs(result_mean.item() - 0.2791    ) < 1E-3

					def 				__UpperCamelCase   (    self      :       Union[str, Any]    ):







							"""simple docstring"""






							self.check_over_configs(thresholding=_A    )
							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=_A,prediction_type=_A,sample_max_value=_A,algorithm_type="dpmsolver++",solver_order=_A,solver_type=_A,)

					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






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

					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							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=_A,solver_type=_A,prediction_type=_A,algorithm_type=_A,)
															SCREAMING_SNAKE_CASE_  :		str				      =    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 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							self.check_over_configs(lower_order_final=_A    )
							self.check_over_configs(lower_order_final=_A    )

					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							self.check_over_configs(lambda_min_clipped=-float("inf"    )    )
							self.check_over_configs(lambda_min_clipped=-5.1    )

					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							self.check_over_configs(variance_type=_A    )
							self.check_over_configs(variance_type="learned_range"    )

					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							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 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    self.full_loop()
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    torch.mean(torch.abs(_A    )    )

							assert abs(result_mean.item() - 0.2791    ) < 1E-3

					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    self.full_loop(use_karras_sigmas=_A    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    torch.mean(torch.abs(_A    )    )

							assert abs(result_mean.item() - 0.2248    ) < 1E-3

					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    self.full_loop(prediction_type="v_prediction"    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    torch.mean(torch.abs(_A    )    )

							assert abs(result_mean.item() - 0.1453    ) < 1E-3

					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    self.full_loop(prediction_type="v_prediction",use_karras_sigmas=_A    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    torch.mean(torch.abs(_A    )    )

							assert abs(result_mean.item() - 0.0649    ) < 1E-3






					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    self.get_scheduler_config(thresholding=_A,dynamic_thresholding_ratio=0    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    scheduler_class(**_A    )

							SCREAMING_SNAKE_CASE_  :		Any				      =    10
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self.dummy_model()
							SCREAMING_SNAKE_CASE_  :		Dict				      =    self.dummy_sample_deter.half()
							scheduler.set_timesteps(_A    )

							for i, t in enumerate(scheduler.timesteps    ):
									SCREAMING_SNAKE_CASE_  :		Any				      =    model(_A,_A    )
									SCREAMING_SNAKE_CASE_  :		Dict				      =    scheduler.step(_A,_A,_A    ).prev_sample

							assert sample.dtype == torch.floataa

from functools import lru_cache




@lru_cache
def       _snake_case	(   lowerCAmelCase							:   int	):
		"""simple docstring"""







		if num < 0:
				raise ValueError("Number should not be negative."	)

		return 1 if num in (0, 1) else num * factorial(num - 1	)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
