


"""simple docstring"""




import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 			__snake_case   (lowerCamelCase  ):
							__a					 =       (EulerDiscreteScheduler,)
							__a					 =       10




							def   __a   (     self:    Any     ,							**A_:    Optional[Any]   ):
										__lowerCamelCase          =      {
										    """num_train_timesteps""": 11_00,
										    """beta_start""": 0.0_001,
										    """beta_end""": 0.02,
										    """beta_schedule""": """linear""",
										}

										config.update(**A_   )
										return config




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




							def   __a   (     self:    List[Any]   ):
										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=A_     ,							beta_end=A_   )




							def   __a   (     self:    Optional[Any]   ):
										for schedule in ["linear", "scaled_linear"]:
													self.check_over_configs(beta_schedule=A_   )




							def   __a   (     self:    Union[str, Any]   ):
										for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=A_   )




							def   __a   (     self:    List[Any]   ):
										__lowerCamelCase          =      self.scheduler_classes[0]
										__lowerCamelCase          =      self.get_scheduler_config()
										__lowerCamelCase          =      scheduler_class(**A_   )

										scheduler.set_timesteps(self.num_inference_steps   )

										__lowerCamelCase          =      torch.manual_seed(0   )

										__lowerCamelCase          =      self.dummy_model()
										__lowerCamelCase          =      self.dummy_sample_deter * scheduler.init_noise_sigma
										__lowerCamelCase          =      sample.to(A_   )

										for i, t in enumerate(scheduler.timesteps   ):
													__lowerCamelCase          =      scheduler.scale_model_input(A_     ,							A_   )

													__lowerCamelCase          =      model(A_     ,							A_   )

													__lowerCamelCase          =      scheduler.step(A_     ,							A_     ,							A_     ,							generator=A_   )
													__lowerCamelCase          =      output.prev_sample

										__lowerCamelCase          =      torch.sum(torch.abs(A_   )   )
										__lowerCamelCase          =      torch.mean(torch.abs(A_   )   )

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




							def   __a   (     self:    Dict   ):
										__lowerCamelCase          =      self.scheduler_classes[0]
										__lowerCamelCase          =      self.get_scheduler_config(prediction_type="""v_prediction"""   )
										__lowerCamelCase          =      scheduler_class(**A_   )

										scheduler.set_timesteps(self.num_inference_steps   )

										__lowerCamelCase          =      torch.manual_seed(0   )

										__lowerCamelCase          =      self.dummy_model()
										__lowerCamelCase          =      self.dummy_sample_deter * scheduler.init_noise_sigma
										__lowerCamelCase          =      sample.to(A_   )

										for i, t in enumerate(scheduler.timesteps   ):
													__lowerCamelCase          =      scheduler.scale_model_input(A_     ,							A_   )

													__lowerCamelCase          =      model(A_     ,							A_   )

													__lowerCamelCase          =      scheduler.step(A_     ,							A_     ,							A_     ,							generator=A_   )
													__lowerCamelCase          =      output.prev_sample

										__lowerCamelCase          =      torch.sum(torch.abs(A_   )   )
										__lowerCamelCase          =      torch.mean(torch.abs(A_   )   )

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




							def   __a   (     self:    List[Any]   ):
										__lowerCamelCase          =      self.scheduler_classes[0]
										__lowerCamelCase          =      self.get_scheduler_config()
										__lowerCamelCase          =      scheduler_class(**A_   )

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

										__lowerCamelCase          =      torch.manual_seed(0   )

										__lowerCamelCase          =      self.dummy_model()
										__lowerCamelCase          =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
										__lowerCamelCase          =      sample.to(A_   )

										for t in scheduler.timesteps:
													__lowerCamelCase          =      scheduler.scale_model_input(A_     ,							A_   )

													__lowerCamelCase          =      model(A_     ,							A_   )

													__lowerCamelCase          =      scheduler.step(A_     ,							A_     ,							A_     ,							generator=A_   )
													__lowerCamelCase          =      output.prev_sample

										__lowerCamelCase          =      torch.sum(torch.abs(A_   )   )
										__lowerCamelCase          =      torch.mean(torch.abs(A_   )   )

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




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

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

										__lowerCamelCase          =      torch.manual_seed(0   )

										__lowerCamelCase          =      self.dummy_model()
										__lowerCamelCase          =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
										__lowerCamelCase          =      sample.to(A_   )

										for t in scheduler.timesteps:
													__lowerCamelCase          =      scheduler.scale_model_input(A_     ,							A_   )

													__lowerCamelCase          =      model(A_     ,							A_   )

													__lowerCamelCase          =      scheduler.step(A_     ,							A_     ,							A_     ,							generator=A_   )
													__lowerCamelCase          =      output.prev_sample

										__lowerCamelCase          =      torch.sum(torch.abs(A_   )   )
										__lowerCamelCase          =      torch.mean(torch.abs(A_   )   )

										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








"""simple docstring"""




def       a_ (	lowercase__ :str     ):
			__lowerCamelCase          =      [int(lowercase__     ) for i in ip_va_address.split("""."""     ) if i.isdigit()]
			return len(lowercase__     ) == 4 and all(0 <= int(lowercase__     ) <= 254 for octet in octets     )


if __name__ == "__main__":
					__magic_name__						: Tuple    =				input().strip()
					__magic_name__						: Optional[int]    =				'valid' if is_ip_va_address_valid(ip) else 'invalid'
					print(f"""{ip} is a {valid_or_invalid} IP v4 address.""")




