code: 
  
  
  import torch
  
  from diffusers import EulerAncestralDiscreteScheduler
  from diffusers.utils import torch_device
  
  from .test_schedulers import SchedulerCommonTest
  
  
  
  
  
  
  
  class 					__lowercase					(       a_     ):
  
  
  
    """simple docstring"""
  
    UpperCamelCase    :				int 					=		(EulerAncestralDiscreteScheduler,)
    UpperCamelCase    :				List[str] 					=		1_0
  
  
  
  
  
  
  
    def 				__A      (   self		,			**A  )     ->   List[Any]:
  
  
  
  
  
  
  
       '''simple docstring'''
  
  
  
  
  
  
  
       lowerCamelCase								=     {
           """num_train_timesteps""": 11_00,
           """beta_start""": 0.0001,
           """beta_end""": 0.02,
           """beta_schedule""": """linear""",
       }
  
       config.update(**A  )
       return config
  
  
  
  
  
  
  
    def 				__A      (   self  )     ->   Dict:
  
  
  
  
  
  
  
       '''simple docstring'''
  
  
  
  
  
  
  
       for timesteps in [10, 50, 1_00, 10_00]:
          self.check_over_configs(num_train_timesteps=A  )
  
  
  
  
  
  
  
    def 				__A      (   self  )     ->   Optional[Any]:
  
  
  
  
  
  
  
       '''simple docstring'''
  
  
  
  
  
  
  
       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 				__A      (   self  )     ->   List[str]:
  
  
  
  
  
  
  
       '''simple docstring'''
  
  
  
  
  
  
  
       for schedule in ["linear", "scaled_linear"]:
          self.check_over_configs(beta_schedule=A  )
  
  
  
  
  
  
  
    def 				__A      (   self  )     ->   Tuple:
  
  
  
  
  
  
  
       '''simple docstring'''
  
  
  
  
  
  
  
       for prediction_type in ["epsilon", "v_prediction"]:
          self.check_over_configs(prediction_type=A  )
  
  
  
  
  
  
  
    def 				__A      (   self  )     ->   Any:
  
  
  
  
  
  
  
       '''simple docstring'''
  
  
  
  
  
  
  
       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.cpu()
       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() - 152.3192  ) < 1e-2
       assert abs(result_mean.item() - 0.1983  ) < 1e-3
  
  
  
  
  
  
  
    def 				__A      (   self  )     ->   Dict:
  
  
  
  
  
  
  
       '''simple docstring'''
  
  
  
  
  
  
  
       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() - 108.4439  ) < 1e-2
       assert abs(result_mean.item() - 0.1412  ) < 1e-3
  
  
  
  
  
  
  
    def 				__A      (   self  )     ->   List[Any]:
  
  
  
  
  
  
  
       '''simple docstring'''
  
  
  
  
  
  
  
       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() - 152.3192  ) < 1e-2
       assert abs(result_mean.item() - 0.1983  ) < 1e-3
  
  
style_context: 
  
  
  def   __lowerCamelCase	(       lowerCamelCase__		:	int = 100     ,       lowerCamelCase__		:	int = 100			):
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  			return max(
  			    sum(int(lowerCamelCase__			) for x in str(base**power			)			)
  			    for base in range(lowerCamelCase__			)
  			    for power in range(lowerCamelCase__			)			)
  
  
  # Tests
  if __name__ == "__main__":
  		import doctest
  
  		doctest.testmod()
  
  