code: 
  
  
  import torch
  
  from diffusers import EulerAncestralDiscreteScheduler
  from diffusers.utils import torch_device
  
  from .test_schedulers import SchedulerCommonTest
  
  
  
  
  class 					_UpperCamelCase (      lowerCamelCase__	):
  
  
  
  
  
  
  
  							'''simple docstring'''
  							_A  				=  (EulerAncestralDiscreteScheduler,)
  							_A  				=  1_0
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Dict		,				**SCREAMING_SNAKE_CASE_  :				Optional[int]		):
  													_a     =			{
  													    'num_train_timesteps': 1_1_0_0,
  													    'beta_start': 0.0001,
  													    'beta_end': 0.02,
  													    'beta_schedule': 'linear',
  													}
  
  													config.update(**SCREAMING_SNAKE_CASE_		)
  													return config
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				List[str]		):
  													for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
  																			self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_		)
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Tuple		):
  													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=SCREAMING_SNAKE_CASE_		,				beta_end=SCREAMING_SNAKE_CASE_		)
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				List[str]		):
  													for schedule in ["linear", "scaled_linear"]:
  																			self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_		)
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				List[str]		):
  													for prediction_type in ["epsilon", "v_prediction"]:
  																			self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_		)
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Tuple		):
  													_a     =			self.scheduler_classes[0]
  													_a     =			self.get_scheduler_config()
  													_a     =			scheduler_class(**SCREAMING_SNAKE_CASE_		)
  
  													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.cpu()
  													_a     =			sample.to(SCREAMING_SNAKE_CASE_		)
  
  													for i, t in enumerate(scheduler.timesteps		):
  																			_a     =			scheduler.scale_model_input(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		)
  
  																			_a     =			model(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		)
  
  																			_a     =			scheduler.step(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				generator=SCREAMING_SNAKE_CASE_		)
  																			_a     =			output.prev_sample
  
  													_a     =			torch.sum(torch.abs(SCREAMING_SNAKE_CASE_		)		)
  													_a     =			torch.mean(torch.abs(SCREAMING_SNAKE_CASE_		)		)
  
  													assert abs(result_sum.item() - 152.3192		) < 1e-2
  													assert abs(result_mean.item() - 0.1983		) < 1e-3
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Union[str, Any]		):
  													_a     =			self.scheduler_classes[0]
  													_a     =			self.get_scheduler_config(prediction_type='v_prediction'		)
  													_a     =			scheduler_class(**SCREAMING_SNAKE_CASE_		)
  
  													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(SCREAMING_SNAKE_CASE_		)
  
  													for i, t in enumerate(scheduler.timesteps		):
  																			_a     =			scheduler.scale_model_input(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		)
  
  																			_a     =			model(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		)
  
  																			_a     =			scheduler.step(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				generator=SCREAMING_SNAKE_CASE_		)
  																			_a     =			output.prev_sample
  
  													_a     =			torch.sum(torch.abs(SCREAMING_SNAKE_CASE_		)		)
  													_a     =			torch.mean(torch.abs(SCREAMING_SNAKE_CASE_		)		)
  
  													assert abs(result_sum.item() - 108.4439		) < 1e-2
  													assert abs(result_mean.item() - 0.1412		) < 1e-3
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Union[str, Any]		):
  													_a     =			self.scheduler_classes[0]
  													_a     =			self.get_scheduler_config()
  													_a     =			scheduler_class(**SCREAMING_SNAKE_CASE_		)
  
  													scheduler.set_timesteps(self.num_inference_steps		,				device=SCREAMING_SNAKE_CASE_		)
  													_a     =			torch.manual_seed(0		)
  
  													_a     =			self.dummy_model()
  													_a     =			self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
  													_a     =			sample.to(SCREAMING_SNAKE_CASE_		)
  
  													for t in scheduler.timesteps:
  																			_a     =			scheduler.scale_model_input(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		)
  
  																			_a     =			model(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		)
  
  																			_a     =			scheduler.step(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				generator=SCREAMING_SNAKE_CASE_		)
  																			_a     =			output.prev_sample
  
  													_a     =			torch.sum(torch.abs(SCREAMING_SNAKE_CASE_		)		)
  													_a     =			torch.mean(torch.abs(SCREAMING_SNAKE_CASE_		)		)
  
  													assert abs(result_sum.item() - 152.3192		) < 1e-2
  													assert abs(result_mean.item() - 0.1983		) < 1e-3
  
  
  
style_context: 
  
  
  from math import pi
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  float:
  						return degree / (180 / pi)
  
  
  if __name__ == "__main__":
  		from doctest import testmod
  
  		testmod()
  
  
  