


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest

class __snake_case		( __snake_case ):





		"""simple docstring"""







		UpperCamelCase_				      =   (UniPCMultistepScheduler,)
		UpperCamelCase_				      =   (('num_inference_steps', 2_5),)



		def  UpperCAmelCase_							(							self    :   Optional[int]						,**lowerCAmelCase__    :   int      )      ->							Optional[Any]:




								'''simple docstring'''

								lowerCAmelCase_	:     Any          = {
								    "num_train_timesteps": 10_00,
								    "beta_start": 0.0_001,
								    "beta_end": 0.02,
								    "beta_schedule": "linear",
								    "solver_order": 2,
								    "solver_type": "bh2",
								}

								config.update(**__lowerCamelCase      )
								return config



		def  UpperCAmelCase_							(							self    :   Union[str, Any]						,lowerCAmelCase__    :   Optional[Any]=0						,**lowerCAmelCase__    :   Optional[Any]      )      ->							Dict:




								'''simple docstring'''

								lowerCAmelCase_	:     int          = dict(self.forward_default_kwargs      )
								lowerCAmelCase_	:     Optional[int]          = kwargs.pop("num_inference_steps"						,__lowerCamelCase      )
								lowerCAmelCase_	:     Union[str, Any]          = self.dummy_sample
								lowerCAmelCase_	:     Dict          = 0.1 * sample
								lowerCAmelCase_	:     str          = [residual + 0.2, residual + 0.15, residual + 0.10]

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

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

														lowerCAmelCase_    ,					lowerCAmelCase_	:     List[str]          = sample, sample
														for t in range(__lowerCamelCase						,time_step + scheduler.config.solver_order + 1      ):
																				lowerCAmelCase_	:     List[str]          = scheduler.step(__lowerCamelCase						,__lowerCamelCase						,__lowerCamelCase						,**__lowerCamelCase      ).prev_sample
																				lowerCAmelCase_	:     Dict          = 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  UpperCAmelCase_							(							self    :   int						,lowerCAmelCase__    :   Tuple=0						,**lowerCAmelCase__    :   List[Any]      )      ->							Optional[int]:




								'''simple docstring'''

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

								for scheduler_class in self.scheduler_classes:
														lowerCAmelCase_	:     Any          = self.get_scheduler_config()
														lowerCAmelCase_	:     Optional[Any]          = scheduler_class(**__lowerCamelCase      )
														scheduler.set_timesteps(__lowerCamelCase      )

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

														with tempfile.TemporaryDirectory() as tmpdirname:
																				scheduler.save_config(__lowerCamelCase      )
																				lowerCAmelCase_	:     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)
																				lowerCAmelCase_	:     str          = dummy_past_residuals[: new_scheduler.config.solver_order]

														lowerCAmelCase_	:     Optional[Any]          = scheduler.step(__lowerCamelCase						,__lowerCamelCase						,__lowerCamelCase						,**__lowerCamelCase      ).prev_sample
														lowerCAmelCase_	:     Tuple          = 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  UpperCAmelCase_							(							self    :   Any						,lowerCAmelCase__    :   str=None						,**lowerCAmelCase__    :   str      )      ->							Any:




								'''simple docstring'''

								if scheduler is None:
														lowerCAmelCase_	:     Optional[int]          = self.scheduler_classes[0]
														lowerCAmelCase_	:     str          = self.get_scheduler_config(**__lowerCamelCase      )
														lowerCAmelCase_	:     Optional[int]          = scheduler_class(**__lowerCamelCase      )

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

								lowerCAmelCase_	:     Any          = 10
								lowerCAmelCase_	:     Any          = self.dummy_model()
								lowerCAmelCase_	:     Optional[Any]          = self.dummy_sample_deter
								scheduler.set_timesteps(__lowerCamelCase      )

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

								return sample



		def  UpperCAmelCase_							(							self    :   Optional[int]      )      ->							Tuple:




								'''simple docstring'''

								lowerCAmelCase_	:     Optional[Any]          = dict(self.forward_default_kwargs      )

								lowerCAmelCase_	:     Optional[int]          = kwargs.pop("num_inference_steps"						,__lowerCamelCase      )

								for scheduler_class in self.scheduler_classes:
														lowerCAmelCase_	:     Any          = self.get_scheduler_config()
														lowerCAmelCase_	:     Tuple          = scheduler_class(**__lowerCamelCase      )

														lowerCAmelCase_	:     Optional[int]          = self.dummy_sample
														lowerCAmelCase_	:     List[Any]          = 0.1 * sample

														if num_inference_steps is not None and hasattr(__lowerCamelCase						,"set_timesteps"      ):
																				scheduler.set_timesteps(__lowerCamelCase      )
														elif num_inference_steps is not None and not hasattr(__lowerCamelCase						,"set_timesteps"      ):
																				lowerCAmelCase_	:     Optional[int]          = num_inference_steps

														# copy over dummy past residuals (must be done after set_timesteps)
														lowerCAmelCase_	:     Optional[int]          = [residual + 0.2, residual + 0.15, residual + 0.10]
														lowerCAmelCase_	:     Union[str, Any]          = dummy_past_residuals[: scheduler.config.solver_order]

														lowerCAmelCase_	:     Optional[Any]          = scheduler.timesteps[5]
														lowerCAmelCase_	:     Tuple          = scheduler.timesteps[6]

														lowerCAmelCase_	:     Optional[Any]          = scheduler.step(__lowerCamelCase						,__lowerCamelCase						,__lowerCamelCase						,**__lowerCamelCase      ).prev_sample
														lowerCAmelCase_	:     str          = scheduler.step(__lowerCamelCase						,__lowerCamelCase						,__lowerCamelCase						,**__lowerCamelCase      ).prev_sample

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



		def  UpperCAmelCase_							(							self    :   int      )      ->							Optional[int]:




								'''simple docstring'''

								lowerCAmelCase_	:     Dict          = UniPCMultistepScheduler(**self.get_scheduler_config()      )
								lowerCAmelCase_	:     str          = self.full_loop(scheduler=__lowerCamelCase      )
								lowerCAmelCase_	:     str          = torch.mean(torch.abs(__lowerCamelCase      )      )

								assert abs(result_mean.item() - 0.2_464      ) < 1e-3

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

								lowerCAmelCase_	:     str          = self.full_loop(scheduler=__lowerCamelCase      )
								lowerCAmelCase_	:     List[Any]          = torch.mean(torch.abs(__lowerCamelCase      )      )

								assert abs(result_mean.item() - 0.2_464      ) < 1e-3



		def  UpperCAmelCase_							(							self    :   Any      )      ->							str:




								'''simple docstring'''

								for timesteps in [25, 50, 1_00, 9_99, 10_00]:
														self.check_over_configs(num_train_timesteps=__lowerCamelCase      )



		def  UpperCAmelCase_							(							self    :   List[str]      )      ->							Optional[Any]:




								'''simple docstring'''

								self.check_over_configs(thresholding=__lowerCamelCase      )
								for order in [1, 2, 3]:
														for solver_type in ["bh1", "bh2"]:
																				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						,solver_order=__lowerCamelCase						,solver_type=__lowerCamelCase						,)



		def  UpperCAmelCase_							(							self    :   Optional[int]      )      ->							str:




								'''simple docstring'''

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



		def  UpperCAmelCase_							(							self    :   Optional[Any]      )      ->							Tuple:




								'''simple docstring'''

								for solver_type in ["bh1", "bh2"]:
														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						,)
																										lowerCAmelCase_	:     Dict          = self.full_loop(
																										    solver_order=__lowerCamelCase						,solver_type=__lowerCamelCase						,prediction_type=__lowerCamelCase						,)
																										assert not torch.isnan(__lowerCamelCase      ).any(), "Samples have nan numbers"



		def  UpperCAmelCase_							(							self    :   Tuple      )      ->							int:




								'''simple docstring'''

								self.check_over_configs(lower_order_final=__lowerCamelCase      )
								self.check_over_configs(lower_order_final=__lowerCamelCase      )



		def  UpperCAmelCase_							(							self    :   List[str]      )      ->							Tuple:




								'''simple docstring'''

								for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
														self.check_over_forward(num_inference_steps=__lowerCamelCase						,time_step=0      )



		def  UpperCAmelCase_							(							self    :   Tuple      )      ->							List[str]:




								'''simple docstring'''

								lowerCAmelCase_	:     Optional[int]          = self.full_loop()
								lowerCAmelCase_	:     int          = torch.mean(torch.abs(__lowerCamelCase      )      )

								assert abs(result_mean.item() - 0.2_464      ) < 1e-3



		def  UpperCAmelCase_							(							self    :   Union[str, Any]      )      ->							int:




								'''simple docstring'''

								lowerCAmelCase_	:     str          = self.full_loop(prediction_type="v_prediction"      )
								lowerCAmelCase_	:     int          = torch.mean(torch.abs(__lowerCamelCase      )      )

								assert abs(result_mean.item() - 0.1_014      ) < 1e-3



		def  UpperCAmelCase_							(							self    :   Dict      )      ->							str:




								'''simple docstring'''

								lowerCAmelCase_	:     Tuple          = self.scheduler_classes[0]
								lowerCAmelCase_	:     int          = self.get_scheduler_config(thresholding=__lowerCamelCase						,dynamic_thresholding_ratio=0      )
								lowerCAmelCase_	:     Optional[int]          = scheduler_class(**__lowerCamelCase      )

								lowerCAmelCase_	:     int          = 10
								lowerCAmelCase_	:     Dict          = self.dummy_model()
								lowerCAmelCase_	:     Tuple          = self.dummy_sample_deter.half()
								scheduler.set_timesteps(__lowerCamelCase      )

								for i, t in enumerate(scheduler.timesteps      ):
														lowerCAmelCase_	:     Dict          = model(__lowerCamelCase						,__lowerCamelCase      )
														lowerCAmelCase_	:     Optional[Any]          = scheduler.step(__lowerCamelCase						,__lowerCamelCase						,__lowerCamelCase      ).prev_sample

								assert sample.dtype == torch.floataa



		def  UpperCAmelCase_							(							self    :   Optional[int]						,**lowerCAmelCase__    :   List[str]      )      ->							List[Any]:




								'''simple docstring'''

								for scheduler_class in self.scheduler_classes:
														lowerCAmelCase_	:     Optional[Any]          = self.get_scheduler_config(**__lowerCamelCase      )
														lowerCAmelCase_	:     List[str]          = scheduler_class(**__lowerCamelCase      )

														scheduler.set_timesteps(scheduler.config.num_train_timesteps      )
														assert len(scheduler.timesteps.unique()      ) == scheduler.num_inference_steps





def 	__a     (      A__			:  int       ):
    if not isinstance(A__			,  A__       ):
        raise ValueError("Input must be an integer"       )
    if input_num <= 0:
        raise ValueError("Input must be positive"       )
    return sum(
        divisor for divisor in range(1			,  input_num // 2 + 1       ) if input_num % divisor == 0       )


if __name__ == "__main__":
    import doctest

    doctest.testmod()