


import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class        UpperCamelCase__   (	__magic_name__   ):
					__SCREAMING_SNAKE_CASE       :  Dict						=   (EulerDiscreteScheduler,)
					__SCREAMING_SNAKE_CASE       :  str						=   10

					def   UpperCAmelCase__       (     self       :   List[str]							,					**UpperCamelCase__       :   Any       ):






												'''simple docstring'''






												lowercase_  							=			{
												    """num_train_timesteps""": 1_100,
												    """beta_start""": 0.0_001,
												    """beta_end""": 0.02,
												    """beta_schedule""": """linear""",
												}

												config.update(**UpperCamelCase__       )
												return config

					def   UpperCAmelCase__       (     self       :   Optional[int]       ):






												'''simple docstring'''






												for timesteps in [10, 50, 100, 1_000]:
																			self.check_over_configs(num_train_timesteps=UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   Optional[Any]       ):






												'''simple docstring'''






												for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]							,					[0.0_002, 0.002, 0.02]       ):
																			self.check_over_configs(beta_start=UpperCamelCase__							,					beta_end=UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   List[str]       ):






												'''simple docstring'''






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

					def   UpperCAmelCase__       (     self       :   int       ):






												'''simple docstring'''






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

					def   UpperCAmelCase__       (     self       :   Dict       ):






												'''simple docstring'''






												lowercase_  							=			self.scheduler_classes[0]
												lowercase_  							=			self.get_scheduler_config()
												lowercase_  							=			scheduler_class(**UpperCamelCase__       )

												scheduler.set_timesteps(self.num_inference_steps       )

												lowercase_  							=			torch.manual_seed(0       )

												lowercase_  							=			self.dummy_model()
												lowercase_  							=			self.dummy_sample_deter * scheduler.init_noise_sigma
												lowercase_  							=			sample.to(UpperCamelCase__       )

												for i, t in enumerate(scheduler.timesteps       ):
																			lowercase_  							=			scheduler.scale_model_input(UpperCamelCase__							,					UpperCamelCase__       )

																			lowercase_  							=			model(UpperCamelCase__							,					UpperCamelCase__       )

																			lowercase_  							=			scheduler.step(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					generator=UpperCamelCase__       )
																			lowercase_  							=			output.prev_sample

												lowercase_  							=			torch.sum(torch.abs(UpperCamelCase__       )       )
												lowercase_  							=			torch.mean(torch.abs(UpperCamelCase__       )       )

												assert abs(result_sum.item() - 10.0_807       ) < 1e-2
												assert abs(result_mean.item() - 0.0_131       ) < 1e-3

					def   UpperCAmelCase__       (     self       :   Optional[int]       ):






												'''simple docstring'''






												lowercase_  							=			self.scheduler_classes[0]
												lowercase_  							=			self.get_scheduler_config(prediction_type="""v_prediction"""       )
												lowercase_  							=			scheduler_class(**UpperCamelCase__       )

												scheduler.set_timesteps(self.num_inference_steps       )

												lowercase_  							=			torch.manual_seed(0       )

												lowercase_  							=			self.dummy_model()
												lowercase_  							=			self.dummy_sample_deter * scheduler.init_noise_sigma
												lowercase_  							=			sample.to(UpperCamelCase__       )

												for i, t in enumerate(scheduler.timesteps       ):
																			lowercase_  							=			scheduler.scale_model_input(UpperCamelCase__							,					UpperCamelCase__       )

																			lowercase_  							=			model(UpperCamelCase__							,					UpperCamelCase__       )

																			lowercase_  							=			scheduler.step(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					generator=UpperCamelCase__       )
																			lowercase_  							=			output.prev_sample

												lowercase_  							=			torch.sum(torch.abs(UpperCamelCase__       )       )
												lowercase_  							=			torch.mean(torch.abs(UpperCamelCase__       )       )

												assert abs(result_sum.item() - 0.0_002       ) < 1e-2
												assert abs(result_mean.item() - 2.2676e-06       ) < 1e-3

					def   UpperCAmelCase__       (     self       :   int       ):






												'''simple docstring'''






												lowercase_  							=			self.scheduler_classes[0]
												lowercase_  							=			self.get_scheduler_config()
												lowercase_  							=			scheduler_class(**UpperCamelCase__       )

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

												lowercase_  							=			torch.manual_seed(0       )

												lowercase_  							=			self.dummy_model()
												lowercase_  							=			self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
												lowercase_  							=			sample.to(UpperCamelCase__       )

												for t in scheduler.timesteps:
																			lowercase_  							=			scheduler.scale_model_input(UpperCamelCase__							,					UpperCamelCase__       )

																			lowercase_  							=			model(UpperCamelCase__							,					UpperCamelCase__       )

																			lowercase_  							=			scheduler.step(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					generator=UpperCamelCase__       )
																			lowercase_  							=			output.prev_sample

												lowercase_  							=			torch.sum(torch.abs(UpperCamelCase__       )       )
												lowercase_  							=			torch.mean(torch.abs(UpperCamelCase__       )       )

												assert abs(result_sum.item() - 10.0_807       ) < 1e-2
												assert abs(result_mean.item() - 0.0_131       ) < 1e-3







					def   UpperCAmelCase__       (     self       :   Optional[Any]       ):






												'''simple docstring'''






												lowercase_  							=			self.scheduler_classes[0]
												lowercase_  							=			self.get_scheduler_config()
												lowercase_  							=			scheduler_class(**UpperCamelCase__							,					use_karras_sigmas=UpperCamelCase__       )

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

												lowercase_  							=			torch.manual_seed(0       )

												lowercase_  							=			self.dummy_model()
												lowercase_  							=			self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
												lowercase_  							=			sample.to(UpperCamelCase__       )

												for t in scheduler.timesteps:
																			lowercase_  							=			scheduler.scale_model_input(UpperCamelCase__							,					UpperCamelCase__       )

																			lowercase_  							=			model(UpperCamelCase__							,					UpperCamelCase__       )

																			lowercase_  							=			scheduler.step(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					generator=UpperCamelCase__       )
																			lowercase_  							=			output.prev_sample

												lowercase_  							=			torch.sum(torch.abs(UpperCamelCase__       )       )
												lowercase_  							=			torch.mean(torch.abs(UpperCamelCase__       )       )

												assert abs(result_sum.item() - 124.52_299_499_511_719       ) < 1e-2
												assert abs(result_mean.item() - 0.16_213_932_633_399_963       ) < 1e-3






def 			UpperCAmelCase_				(      UpperCAmelCase__=2_8_1_2_3  ):
					lowercase_  							=			[1] * (limit + 1)

					for i in range(2						,							int(limit**0.5  ) + 1  ):
										sum_divs[i * i] += i
										for k in range(i + 1						,							limit // i + 1  ):
															sum_divs[k * i] += k + i

					lowercase_  							=			set()
					lowercase_  							=			0

					for n in range(1						,							limit + 1  ):
										if sum_divs[n] > n:
															abundants.add(UpperCAmelCase__  )

										if not any((n - a in abundants) for a in abundants  ):
															res += n

					return res


if __name__ == "__main__":
							print(solution())


