





'''simple docstring'''







import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            =(EulerDiscreteScheduler,)
				__a            =10




				def   UpperCamelCase__   (  self     :					List[Any]					,				**__a     :					Union[str, Any]					):
								_a								= {
								    "num_train_timesteps": 11_00,
								    "beta_start": 0.0001,
								    "beta_end": 0.02,
								    "beta_schedule": "linear",
								}

								config.update(**__a					)
								return config




				def   UpperCamelCase__   (  self     :					int					):
								for timesteps in [10, 50, 1_00, 10_00]:
												self.check_over_configs(num_train_timesteps=__a					)




				def   UpperCamelCase__   (  self     :					str					):
								for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]					,				[0.0002, 0.002, 0.02]					):
												self.check_over_configs(beta_start=__a					,				beta_end=__a					)




				def   UpperCamelCase__   (  self     :					int					):
								for schedule in ["linear", "scaled_linear"]:
												self.check_over_configs(beta_schedule=__a					)




				def   UpperCamelCase__   (  self     :					List[Any]					):
								for prediction_type in ["epsilon", "v_prediction"]:
												self.check_over_configs(prediction_type=__a					)




				def   UpperCamelCase__   (  self     :					Any					):
								_a								= self.scheduler_classes[0]
								_a								= self.get_scheduler_config()
								_a								= scheduler_class(**__a					)

								scheduler.set_timesteps(self.num_inference_steps					)

								_a								= torch.manual_seed(0					)

								_a								= self.dummy_model()
								_a								= self.dummy_sample_deter * scheduler.init_noise_sigma
								_a								= sample.to(__a					)

								for i, t in enumerate(scheduler.timesteps					):
												_a								= scheduler.scale_model_input(__a					,				__a					)

												_a								= model(__a					,				__a					)

												_a								= scheduler.step(__a					,				__a					,				__a					,				generator=__a					)
												_a								= output.prev_sample

								_a								= torch.sum(torch.abs(__a					)					)
								_a								= torch.mean(torch.abs(__a					)					)

								assert abs(result_sum.item() - 10.0807					) < 1e-2
								assert abs(result_mean.item() - 0.0131					) < 1e-3




				def   UpperCamelCase__   (  self     :					Any					):
								_a								= self.scheduler_classes[0]
								_a								= self.get_scheduler_config(prediction_type="v_prediction"					)
								_a								= scheduler_class(**__a					)

								scheduler.set_timesteps(self.num_inference_steps					)

								_a								= torch.manual_seed(0					)

								_a								= self.dummy_model()
								_a								= self.dummy_sample_deter * scheduler.init_noise_sigma
								_a								= sample.to(__a					)

								for i, t in enumerate(scheduler.timesteps					):
												_a								= scheduler.scale_model_input(__a					,				__a					)

												_a								= model(__a					,				__a					)

												_a								= scheduler.step(__a					,				__a					,				__a					,				generator=__a					)
												_a								= output.prev_sample

								_a								= torch.sum(torch.abs(__a					)					)
								_a								= torch.mean(torch.abs(__a					)					)

								assert abs(result_sum.item() - 0.0002					) < 1e-2
								assert abs(result_mean.item() - 2.2_6_7_6e-0_6					) < 1e-3




				def   UpperCamelCase__   (  self     :					List[str]					):
								_a								= self.scheduler_classes[0]
								_a								= self.get_scheduler_config()
								_a								= scheduler_class(**__a					)

								scheduler.set_timesteps(self.num_inference_steps					,				device=__a					)

								_a								= torch.manual_seed(0					)

								_a								= self.dummy_model()
								_a								= self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
								_a								= sample.to(__a					)

								for t in scheduler.timesteps:
												_a								= scheduler.scale_model_input(__a					,				__a					)

												_a								= model(__a					,				__a					)

												_a								= scheduler.step(__a					,				__a					,				__a					,				generator=__a					)
												_a								= output.prev_sample

								_a								= torch.sum(torch.abs(__a					)					)
								_a								= torch.mean(torch.abs(__a					)					)

								assert abs(result_sum.item() - 10.0807					) < 1e-2
								assert abs(result_mean.item() - 0.0131					) < 1e-3




				def   UpperCamelCase__   (  self     :					Optional[Any]					):
								_a								= self.scheduler_classes[0]
								_a								= self.get_scheduler_config()
								_a								= scheduler_class(**__a					,				use_karras_sigmas=__a					)

								scheduler.set_timesteps(self.num_inference_steps					,				device=__a					)

								_a								= torch.manual_seed(0					)

								_a								= self.dummy_model()
								_a								= self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
								_a								= sample.to(__a					)

								for t in scheduler.timesteps:
												_a								= scheduler.scale_model_input(__a					,				__a					)

												_a								= model(__a					,				__a					)

												_a								= scheduler.step(__a					,				__a					,				__a					,				generator=__a					)
												_a								= output.prev_sample

								_a								= torch.sum(torch.abs(__a					)					)
								_a								= torch.mean(torch.abs(__a					)					)

								assert abs(result_sum.item() - 124.52299499511719					) < 1e-2
								assert abs(result_mean.item() - 0.16213932633399963					) < 1e-3








def 						_a    (      a :int )							->			bool:
 a		  =      n ** (1 / 3)
 return (val * val * val) == n


if __name__ == "__main__":
     print(perfect_cube(27))
     print(perfect_cube(4))


