





"""simple docstring"""




import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest


class 			__UpperCamelCase							(		a__		):
							lowerCamelCase  :   Dict    =(CMStochasticIterativeScheduler,)
							lowerCamelCase  :   Union[str, Any]    =10






							def        __a  (  self ,   **lowerCAmelCase__ )      ->					Optional[int]:
								a :						Tuple      =							{
								    "num_train_timesteps": 201,
								    "sigma_min": 0.002,
								    "sigma_max": 80.0,
								}

								config.update(**lowerCAmelCase__ )
								return config






							def        __a  (  self )      ->					Tuple:
								a :						Any      =							10

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

								scheduler.set_timesteps(lowerCAmelCase__ )

								a :						Any      =							scheduler.timesteps[0]
								a :						Dict      =							scheduler.timesteps[1]

								a :						Union[str, Any]      =							self.dummy_sample
								a :						str      =							0.1 * sample

								a :						Optional[Any]      =							scheduler.step(lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ).prev_sample
								a :						Union[str, Any]      =							scheduler.step(lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ).prev_sample

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






							def        __a  (  self )      ->					str:
								for timesteps in [10, 50, 100, 1000]:
									self.check_over_configs(num_train_timesteps=lowerCAmelCase__ )






							def        __a  (  self )      ->					int:
								for clip_denoised in [True, False]:
									self.check_over_configs(clip_denoised=lowerCAmelCase__ )






							def        __a  (  self )      ->					Dict:
								a :						List[Any]      =							self.scheduler_classes[0]
								a :						List[Any]      =							self.get_scheduler_config()
								a :						Tuple      =							scheduler_class(**lowerCAmelCase__ )

								a :						List[Any]      =							1
								scheduler.set_timesteps(lowerCAmelCase__ )
								a :						int      =							scheduler.timesteps

								a :						Optional[Any]      =							torch.manual_seed(0 )

								a :						List[Any]      =							self.dummy_model()
								a :						Tuple      =							self.dummy_sample_deter * scheduler.init_noise_sigma

								for i, t in enumerate(lowerCAmelCase__ ):
									# 1. scale model input
									a :						int      =							scheduler.scale_model_input(lowerCAmelCase__ ,   lowerCAmelCase__ )

									# 2. predict noise residual
									a :						Any      =							model(lowerCAmelCase__ ,   lowerCAmelCase__ )

									# 3. predict previous sample x_t-1
									a :						Tuple      =							scheduler.step(lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   generator=lowerCAmelCase__ ).prev_sample

									a :						List[str]      =							pred_prev_sample

								a :						Tuple      =							torch.sum(torch.abs(lowerCAmelCase__ ) )
								a :						int      =							torch.mean(torch.abs(lowerCAmelCase__ ) )

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






							def        __a  (  self )      ->					str:
								a :						List[str]      =							self.scheduler_classes[0]
								a :						Dict      =							self.get_scheduler_config()
								a :						List[Any]      =							scheduler_class(**lowerCAmelCase__ )

								a :						int      =							[106, 0]
								scheduler.set_timesteps(timesteps=lowerCAmelCase__ )
								a :						Union[str, Any]      =							scheduler.timesteps

								a :						Dict      =							torch.manual_seed(0 )

								a :						Tuple      =							self.dummy_model()
								a :						List[Any]      =							self.dummy_sample_deter * scheduler.init_noise_sigma

								for t in timesteps:
									# 1. scale model input
									a :						List[Any]      =							scheduler.scale_model_input(lowerCAmelCase__ ,   lowerCAmelCase__ )

									# 2. predict noise residual
									a :						List[Any]      =							model(lowerCAmelCase__ ,   lowerCAmelCase__ )

									# 3. predict previous sample x_t-1
									a :						List[str]      =							scheduler.step(lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   generator=lowerCAmelCase__ ).prev_sample

									a :						int      =							pred_prev_sample

								a :						Dict      =							torch.sum(torch.abs(lowerCAmelCase__ ) )
								a :						str      =							torch.mean(torch.abs(lowerCAmelCase__ ) )

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






							def        __a  (  self )      ->					Tuple:
								a :						Dict      =							self.scheduler_classes[0]
								a :						Dict      =							self.get_scheduler_config()
								a :						Union[str, Any]      =							scheduler_class(**lowerCAmelCase__ )

								a :						Optional[Any]      =							[39, 30, 12, 15, 0]

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






							def        __a  (  self )      ->					Optional[Any]:
								a :						List[str]      =							self.scheduler_classes[0]
								a :						int      =							self.get_scheduler_config()
								a :						List[Any]      =							scheduler_class(**lowerCAmelCase__ )

								a :						Dict      =							[39, 30, 12, 1, 0]
								a :						Dict      =							len(lowerCAmelCase__ )

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






							def        __a  (  self )      ->					List[str]:
								a :						Dict      =							self.scheduler_classes[0]
								a :						Union[str, Any]      =							self.get_scheduler_config()
								a :						str      =							scheduler_class(**lowerCAmelCase__ )

								a :						List[Any]      =							[scheduler.config.num_train_timesteps]

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









"""simple docstring"""




def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       list[int]   )					->float:






	'''simple docstring'''






	if not nums:  # Makes sure that the list is not empty
		raise ValueError("List is empty"   )

	a :						Dict      =							sum(_lowercase   ) / len(_lowercase   )  # Calculate the average
	return sum(abs(x - average   ) for x in nums   ) / len(_lowercase   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()


