

"""simple docstring"""






import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest

class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_     ):


					'''simple docstring'''





					_a         =							(CMStochasticIterativeScheduler,)
					_a         =							1_0



					def 	snake_case ( self							:						Tuple,						**lowerCamelCase							:						Union[str, Any]  )->      int:
								lowerCamelCase__	:	Union[str, Any]				={
								    '''num_train_timesteps''': 201,
								    '''sigma_min''': 0.002,
								    '''sigma_max''': 80.0,
								}

								config.update(**UpperCAmelCase__  )
								return config



					def 	snake_case ( self							:						int  )->      Dict:
								lowerCamelCase__	:	int				=10

								lowerCamelCase__	:	str				=self.get_scheduler_config()
								lowerCamelCase__	:	Optional[int]				=self.scheduler_classes[0](**UpperCAmelCase__  )

								scheduler.set_timesteps(UpperCAmelCase__  )

								lowerCamelCase__	:	List[str]				=scheduler.timesteps[0]
								lowerCamelCase__	:	List[str]				=scheduler.timesteps[1]

								lowerCamelCase__	:	Optional[int]				=self.dummy_sample
								lowerCamelCase__	:	Optional[int]				=0.1 * sample

								lowerCamelCase__	:	Dict				=scheduler.step(UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__  ).prev_sample
								lowerCamelCase__	:	List[Any]				=scheduler.step(UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__  ).prev_sample

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



					def 	snake_case ( self							:						Tuple  )->      Optional[int]:
								for timesteps in [10, 50, 100, 1000]:
											self.check_over_configs(num_train_timesteps=UpperCAmelCase__  )



					def 	snake_case ( self							:						Dict  )->      List[str]:
								for clip_denoised in [True, False]:
											self.check_over_configs(clip_denoised=UpperCAmelCase__  )



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

								lowerCamelCase__	:	List[Any]				=1
								scheduler.set_timesteps(UpperCAmelCase__  )
								lowerCamelCase__	:	Tuple				=scheduler.timesteps

								lowerCamelCase__	:	List[str]				=torch.manual_seed(0  )

								lowerCamelCase__	:	Dict				=self.dummy_model()
								lowerCamelCase__	:	Dict				=self.dummy_sample_deter * scheduler.init_noise_sigma

								for i, t in enumerate(UpperCAmelCase__  ):
											# 1. scale model input
											lowerCamelCase__	:	List[Any]				=scheduler.scale_model_input(UpperCAmelCase__,						UpperCAmelCase__  )

											# 2. predict noise residual
											lowerCamelCase__	:	int				=model(UpperCAmelCase__,						UpperCAmelCase__  )

											# 3. predict previous sample x_t-1
											lowerCamelCase__	:	Optional[Any]				=scheduler.step(UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__,						generator=UpperCAmelCase__  ).prev_sample

											lowerCamelCase__	:	int				=pred_prev_sample

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

								assert abs(result_sum.item() - 192.7_614  ) < 1E-2
								assert abs(result_mean.item() - 0.2_510  ) < 1E-3



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

								lowerCamelCase__	:	List[Any]				=[106, 0]
								scheduler.set_timesteps(timesteps=UpperCAmelCase__  )
								lowerCamelCase__	:	List[str]				=scheduler.timesteps

								lowerCamelCase__	:	Any				=torch.manual_seed(0  )

								lowerCamelCase__	:	Dict				=self.dummy_model()
								lowerCamelCase__	:	Dict				=self.dummy_sample_deter * scheduler.init_noise_sigma

								for t in timesteps:
											# 1. scale model input
											lowerCamelCase__	:	Optional[int]				=scheduler.scale_model_input(UpperCAmelCase__,						UpperCAmelCase__  )

											# 2. predict noise residual
											lowerCamelCase__	:	Tuple				=model(UpperCAmelCase__,						UpperCAmelCase__  )

											# 3. predict previous sample x_t-1
											lowerCamelCase__	:	Any				=scheduler.step(UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__,						generator=UpperCAmelCase__  ).prev_sample

											lowerCamelCase__	:	Union[str, Any]				=pred_prev_sample

								lowerCamelCase__	:	Optional[int]				=torch.sum(torch.abs(UpperCAmelCase__  )  )
								lowerCamelCase__	:	int				=torch.mean(torch.abs(UpperCAmelCase__  )  )

								assert abs(result_sum.item() - 347.6_357  ) < 1E-2
								assert abs(result_mean.item() - 0.4_527  ) < 1E-3



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

								lowerCamelCase__	:	Dict				=[39, 30, 12, 15, 0]

								with self.assertRaises(UpperCAmelCase__,						msg='''`timesteps` must be in descending order.'''  ):
											scheduler.set_timesteps(timesteps=UpperCAmelCase__  )



					def 	snake_case ( self							:						Any  )->      Optional[int]:
								lowerCamelCase__	:	Any				=self.scheduler_classes[0]
								lowerCamelCase__	:	Union[str, Any]				=self.get_scheduler_config()
								lowerCamelCase__	:	List[Any]				=scheduler_class(**UpperCAmelCase__  )

								lowerCamelCase__	:	Optional[Any]				=[39, 30, 12, 1, 0]
								lowerCamelCase__	:	List[str]				=len(UpperCAmelCase__  )

								with self.assertRaises(UpperCAmelCase__,						msg='''Can only pass one of `num_inference_steps` or `timesteps`.'''  ):
											scheduler.set_timesteps(num_inference_steps=UpperCAmelCase__,						timesteps=UpperCAmelCase__  )




					def 	snake_case ( self							:						Union[str, Any]  )->      Dict:
								lowerCamelCase__	:	Tuple				=self.scheduler_classes[0]
								lowerCamelCase__	:	Optional[Any]				=self.get_scheduler_config()
								lowerCamelCase__	:	Optional[Any]				=scheduler_class(**UpperCAmelCase__  )

								lowerCamelCase__	:	Any				=[scheduler.config.num_train_timesteps]

								with self.assertRaises(
								    UpperCAmelCase__,						msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}''',						):
											scheduler.set_timesteps(timesteps=UpperCAmelCase__  )









"""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) = }')




