



"""simple docstring"""






import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest

class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_     ):


					'''simple docstring'''





					_a         =							(UnCLIPScheduler,)



					def 	snake_case ( self							:						Tuple,						**lowerCamelCase							:						Union[str, Any]  )->      int:
								lowerCamelCase__	:	Tuple				={
								    '''num_train_timesteps''': 1000,
								    '''variance_type''': '''fixed_small_log''',
								    '''clip_sample''': True,
								    '''clip_sample_range''': 1.0,
								    '''prediction_type''': '''epsilon''',
								}

								config.update(**lowerCamelCase  )
								return config



					def 	snake_case ( self							:						Optional[Any]  )->      List[str]:
								for timesteps in [1, 5, 100, 1000]:
											self.check_over_configs(num_train_timesteps=lowerCamelCase  )



					def 	snake_case ( self							:						str  )->      Tuple:
								for variance in ["fixed_small_log", "learned_range"]:
											self.check_over_configs(variance_type=lowerCamelCase  )



					def 	snake_case ( self							:						List[Any]  )->      Dict:
								for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=lowerCamelCase  )



					def 	snake_case ( self							:						Any  )->      Union[str, Any]:
								for clip_sample_range in [1, 5, 10, 20]:
											self.check_over_configs(clip_sample_range=lowerCamelCase  )



					def 	snake_case ( self							:						int  )->      int:
								for prediction_type in ["epsilon", "sample"]:
											self.check_over_configs(prediction_type=lowerCamelCase  )



					def 	snake_case ( self							:						List[str]  )->      Optional[Any]:
								for time_step in [0, 500, 999]:
											for prev_timestep in [None, 5, 100, 250, 500, 750]:
														if prev_timestep is not None and prev_timestep >= time_step:
																	continue

														self.check_over_forward(time_step=lowerCamelCase,						prev_timestep=lowerCamelCase  )



					def 	snake_case ( self							:						Optional[Any]  )->      List[str]:
								lowerCamelCase__	:	Dict				=self.scheduler_classes[0]
								lowerCamelCase__	:	Tuple				=self.get_scheduler_config(variance_type='''fixed_small_log'''  )
								lowerCamelCase__	:	Optional[Any]				=scheduler_class(**lowerCamelCase  )

								assert torch.sum(torch.abs(scheduler._get_variance(0  ) - 1.0000E-10  )  ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(487  ) - 0.0_549_625  )  ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(999  ) - 0.9_994_987  )  ) < 1E-5



					def 	snake_case ( self							:						Union[str, Any]  )->      Union[str, Any]:
								lowerCamelCase__	:	Tuple				=self.scheduler_classes[0]
								lowerCamelCase__	:	Optional[int]				=self.get_scheduler_config(variance_type='''learned_range'''  )
								lowerCamelCase__	:	Optional[int]				=scheduler_class(**lowerCamelCase  )

								lowerCamelCase__	:	Union[str, Any]				=0.5

								assert scheduler._get_variance(1,						predicted_variance=lowerCamelCase  ) - -10.1_712_790 < 1E-5
								assert scheduler._get_variance(487,						predicted_variance=lowerCamelCase  ) - -5.7_998_052 < 1E-5
								assert scheduler._get_variance(999,						predicted_variance=lowerCamelCase  ) - -0.0_010_011 < 1E-5



					def 	snake_case ( self							:						int  )->      List[Any]:
								lowerCamelCase__	:	Tuple				=self.scheduler_classes[0]
								lowerCamelCase__	:	int				=self.get_scheduler_config()
								lowerCamelCase__	:	int				=scheduler_class(**lowerCamelCase  )

								lowerCamelCase__	:	Union[str, Any]				=scheduler.timesteps

								lowerCamelCase__	:	List[str]				=self.dummy_model()
								lowerCamelCase__	:	List[str]				=self.dummy_sample_deter
								lowerCamelCase__	:	List[Any]				=torch.manual_seed(0  )

								for i, t in enumerate(lowerCamelCase  ):
											# 1. predict noise residual
											lowerCamelCase__	:	List[str]				=model(lowerCamelCase,						lowerCamelCase  )

											# 2. predict previous mean of sample x_t-1
											lowerCamelCase__	:	Tuple				=scheduler.step(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						generator=lowerCamelCase  ).prev_sample

											lowerCamelCase__	:	str				=pred_prev_sample

								lowerCamelCase__	:	List[str]				=torch.sum(torch.abs(lowerCamelCase  )  )
								lowerCamelCase__	:	Union[str, Any]				=torch.mean(torch.abs(lowerCamelCase  )  )

								assert abs(result_sum.item() - 252.2_682_495  ) < 1E-2
								assert abs(result_mean.item() - 0.3_284_743  ) < 1E-3



					def 	snake_case ( self							:						List[str]  )->      Optional[Any]:
								lowerCamelCase__	:	List[str]				=self.scheduler_classes[0]
								lowerCamelCase__	:	Optional[Any]				=self.get_scheduler_config()
								lowerCamelCase__	:	Dict				=scheduler_class(**lowerCamelCase  )

								scheduler.set_timesteps(25  )

								lowerCamelCase__	:	Any				=scheduler.timesteps

								lowerCamelCase__	:	Optional[int]				=self.dummy_model()
								lowerCamelCase__	:	Dict				=self.dummy_sample_deter
								lowerCamelCase__	:	List[Any]				=torch.manual_seed(0  )

								for i, t in enumerate(lowerCamelCase  ):
											# 1. predict noise residual
											lowerCamelCase__	:	List[Any]				=model(lowerCamelCase,						lowerCamelCase  )

											if i + 1 == timesteps.shape[0]:
														lowerCamelCase__	:	Tuple				=None
											else:
														lowerCamelCase__	:	str				=timesteps[i + 1]

											# 2. predict previous mean of sample x_t-1
											lowerCamelCase__	:	Union[str, Any]				=scheduler.step(
											    lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						prev_timestep=lowerCamelCase,						generator=lowerCamelCase  ).prev_sample

											lowerCamelCase__	:	List[str]				=pred_prev_sample

								lowerCamelCase__	:	List[str]				=torch.sum(torch.abs(lowerCamelCase  )  )
								lowerCamelCase__	:	int				=torch.mean(torch.abs(lowerCamelCase  )  )

								assert abs(result_sum.item() - 258.2_044_983  ) < 1E-2
								assert abs(result_mean.item() - 0.3_362_038  ) < 1E-3



					def 	snake_case ( self							:						int  )->      Optional[int]:
								pass




					def 	snake_case ( self							:						str  )->      str:
								pass









"""simple docstring"""






def   snake_case__     (	__lowerCamelCase       :					int = 10							,	__lowerCamelCase       :					int = 22			):
			"""simple docstring"""
			lowerCamelCase__	:	Optional[Any]				=range(1							,	__lowerCamelCase			)
			lowerCamelCase__	:	str				=range(1							,	__lowerCamelCase			)
			return sum(
			    1 for power in powers for base in bases if len(str(base**power			)			) == power			)


if __name__ == "__main__":
		print(f'{solution(1_0, 2_2) = }')




