code: 
  import torch
  
  from diffusers import EulerAncestralDiscreteScheduler
  from diffusers.utils import torch_device
  
  from .test_schedulers import SchedulerCommonTest
  
  
  
  
  class      __lowercase	(	__snake_case					):
  	_A   			=      (EulerAncestralDiscreteScheduler,)
  	_A   			=      10
  
  
  
  
  
  
  	def 			_a(self							:   Any			,  **snake_case							:   Any						)   -> Optional[Any]:
  							_lowercase					:  Any    			=      {
  							    "num_train_timesteps": 1100,
  							    "beta_start": 0.00_01,
  							    "beta_end": 0.02,
  							    "beta_schedule": "linear",
  							}
  
  							config.update(**snake_case						)
  							return config
  
  
  
  
  
  
  	def 			_a(self							:   Dict						)   -> Union[str, Any]:
  							for timesteps in [10, 50, 100, 1000]:
  													self.check_over_configs(num_train_timesteps=snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   int						)   -> int:
  							for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]			,  [0.00_02, 0.0_02, 0.02]						):
  													self.check_over_configs(beta_start=snake_case			,  beta_end=snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   Optional[int]						)   -> Optional[Any]:
  							for schedule in ["linear", "scaled_linear"]:
  													self.check_over_configs(beta_schedule=snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   Dict						)   -> Optional[Any]:
  							for prediction_type in ["epsilon", "v_prediction"]:
  													self.check_over_configs(prediction_type=snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   List[str]						)   -> Union[str, Any]:
  							_lowercase					:  List[str]    			=      self.scheduler_classes[0]
  							_lowercase					:  List[str]    			=      self.get_scheduler_config()
  							_lowercase					:  Optional[Any]    			=      scheduler_class(**snake_case						)
  
  							scheduler.set_timesteps(self.num_inference_steps						)
  
  							_lowercase					:  Optional[int]    			=      torch.manual_seed(0						)
  
  							_lowercase					:  Any    			=      self.dummy_model()
  							_lowercase					:  List[Any]    			=      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
  							_lowercase					:  Any    			=      sample.to(snake_case						)
  
  							for i, t in enumerate(scheduler.timesteps						):
  													_lowercase					:  int    			=      scheduler.scale_model_input(snake_case			,  snake_case						)
  
  													_lowercase					:  List[str]    			=      model(snake_case			,  snake_case						)
  
  													_lowercase					:  List[str]    			=      scheduler.step(snake_case			,  snake_case			,  snake_case			,  generator=snake_case						)
  													_lowercase					:  Any    			=      output.prev_sample
  
  							_lowercase					:  Union[str, Any]    			=      torch.sum(torch.abs(snake_case						)						)
  							_lowercase					:  Union[str, Any]    			=      torch.mean(torch.abs(snake_case						)						)
  
  							assert abs(result_sum.item() - 1_52.31_92						) < 1e-2
  							assert abs(result_mean.item() - 0.19_83						) < 1e-3
  
  
  
  
  
  
  	def 			_a(self							:   List[str]						)   -> Tuple:
  							_lowercase					:  Union[str, Any]    			=      self.scheduler_classes[0]
  							_lowercase					:  Dict    			=      self.get_scheduler_config(prediction_type="v_prediction"						)
  							_lowercase					:  Any    			=      scheduler_class(**snake_case						)
  
  							scheduler.set_timesteps(self.num_inference_steps						)
  
  							_lowercase					:  int    			=      torch.manual_seed(0						)
  
  							_lowercase					:  List[Any]    			=      self.dummy_model()
  							_lowercase					:  Tuple    			=      self.dummy_sample_deter * scheduler.init_noise_sigma
  							_lowercase					:  int    			=      sample.to(snake_case						)
  
  							for i, t in enumerate(scheduler.timesteps						):
  													_lowercase					:  List[str]    			=      scheduler.scale_model_input(snake_case			,  snake_case						)
  
  													_lowercase					:  str    			=      model(snake_case			,  snake_case						)
  
  													_lowercase					:  str    			=      scheduler.step(snake_case			,  snake_case			,  snake_case			,  generator=snake_case						)
  													_lowercase					:  Union[str, Any]    			=      output.prev_sample
  
  							_lowercase					:  Any    			=      torch.sum(torch.abs(snake_case						)						)
  							_lowercase					:  str    			=      torch.mean(torch.abs(snake_case						)						)
  
  							assert abs(result_sum.item() - 1_08.44_39						) < 1e-2
  							assert abs(result_mean.item() - 0.14_12						) < 1e-3
  
  
  
  
  
  
  	def 			_a(self							:   List[str]						)   -> Union[str, Any]:
  							_lowercase					:  Optional[int]    			=      self.scheduler_classes[0]
  							_lowercase					:  int    			=      self.get_scheduler_config()
  							_lowercase					:  Dict    			=      scheduler_class(**snake_case						)
  
  							scheduler.set_timesteps(self.num_inference_steps			,  device=snake_case						)
  							_lowercase					:  Any    			=      torch.manual_seed(0						)
  
  							_lowercase					:  Union[str, Any]    			=      self.dummy_model()
  							_lowercase					:  int    			=      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
  							_lowercase					:  Dict    			=      sample.to(snake_case						)
  
  							for t in scheduler.timesteps:
  													_lowercase					:  Optional[Any]    			=      scheduler.scale_model_input(snake_case			,  snake_case						)
  
  													_lowercase					:  List[Any]    			=      model(snake_case			,  snake_case						)
  
  													_lowercase					:  Optional[Any]    			=      scheduler.step(snake_case			,  snake_case			,  snake_case			,  generator=snake_case						)
  													_lowercase					:  Optional[Any]    			=      output.prev_sample
  
  							_lowercase					:  Any    			=      torch.sum(torch.abs(snake_case						)						)
  							_lowercase					:  Optional[int]    			=      torch.mean(torch.abs(snake_case						)						)
  
  							assert abs(result_sum.item() - 1_52.31_92						) < 1e-2
  							assert abs(result_mean.item() - 0.19_83						) < 1e-3
  
  
  
  
  
style_context: 
  import os
  
  
  
  def 		UpperCamelCase    (					)      -> Optional[int]:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						_lowercase					:  Tuple    			=      0
  						_lowercase					:  List[Any]    			=      0
  						with open(os.path.dirname(_UpperCAmelCase     ) + "/p022_names.txt"     ) as file:
  												_lowercase					:  Any    			=      str(file.readlines()[0]     )
  												_lowercase					:  int    			=      name.replace("\""   , ""     ).split(","     )
  
  						name.sort()
  						for i in range(len(_UpperCAmelCase     )     ):
  												for j in name[i]:
  																		temp_sum += ord(_UpperCAmelCase     ) - ord("A"     ) + 1
  												total_sum += (i + 1) * temp_sum
  												_lowercase					:  Tuple    			=      0
  						return total_sum
  
  
  if __name__ == "__main__":
  							print(solution())
  
  
  
  
  