code: 
  
  
  '''simple docstring'''
  
  
  
  
  import torch
  
  from diffusers import LMSDiscreteScheduler
  from diffusers.utils import torch_device
  
  from .test_schedulers import SchedulerCommonTest
  
  
  
  
  
  class _snake_case   ( a__   ):
  							"""simple docstring"""
  
  
  
  
  
  							__a												=     (LMSDiscreteScheduler,)
  							__a												=     1_0
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				**__A	)     ->Any:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Union[str, Any]											=					{
  											    "num_train_timesteps": 1100,
  											    "beta_start": 0.0001,
  											    "beta_end": 0.02,
  											    "beta_schedule": "linear",
  											}
  
  											config.update(**__A	)
  											return config
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Tuple:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											for timesteps in [10, 50, 100, 1000]:
  															self.check_over_configs(num_train_timesteps=__A	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Tuple:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]			,				[0.0002, 0.002, 0.02]	):
  															self.check_over_configs(beta_start=__A			,				beta_end=__A	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->str:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											for schedule in ["linear", "scaled_linear"]:
  															self.check_over_configs(beta_schedule=__A	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->List[Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											for prediction_type in ["epsilon", "v_prediction"]:
  															self.check_over_configs(prediction_type=__A	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->List[Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											for t in [0, 500, 800]:
  															self.check_over_forward(time_step=__A	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Any:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Dict											=					self.scheduler_classes[0]
  											__lowerCamelCase		:      List[Any]											=					self.get_scheduler_config()
  											__lowerCamelCase		:      Any											=					scheduler_class(**__A	)
  
  											scheduler.set_timesteps(self.num_inference_steps	)
  
  											__lowerCamelCase		:      Union[str, Any]											=					self.dummy_model()
  											__lowerCamelCase		:      Dict											=					self.dummy_sample_deter * scheduler.init_noise_sigma
  
  											for i, t in enumerate(scheduler.timesteps	):
  															__lowerCamelCase		:      Tuple											=					scheduler.scale_model_input(__A			,				__A	)
  
  															__lowerCamelCase		:      Optional[int]											=					model(__A			,				__A	)
  
  															__lowerCamelCase		:      Dict											=					scheduler.step(__A			,				__A			,				__A	)
  															__lowerCamelCase		:      List[Any]											=					output.prev_sample
  
  											__lowerCamelCase		:      List[Any]											=					torch.sum(torch.abs(__A	)	)
  											__lowerCamelCase		:      str											=					torch.mean(torch.abs(__A	)	)
  
  											assert abs(result_sum.item() - 1006.388	) < 1E-2
  											assert abs(result_mean.item() - 1.31	) < 1E-3
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Any:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      str											=					self.scheduler_classes[0]
  											__lowerCamelCase		:      Optional[int]											=					self.get_scheduler_config(prediction_type="v_prediction"	)
  											__lowerCamelCase		:      Union[str, Any]											=					scheduler_class(**__A	)
  
  											scheduler.set_timesteps(self.num_inference_steps	)
  
  											__lowerCamelCase		:      int											=					self.dummy_model()
  											__lowerCamelCase		:      str											=					self.dummy_sample_deter * scheduler.init_noise_sigma
  
  											for i, t in enumerate(scheduler.timesteps	):
  															__lowerCamelCase		:      Any											=					scheduler.scale_model_input(__A			,				__A	)
  
  															__lowerCamelCase		:      Optional[int]											=					model(__A			,				__A	)
  
  															__lowerCamelCase		:      List[Any]											=					scheduler.step(__A			,				__A			,				__A	)
  															__lowerCamelCase		:      int											=					output.prev_sample
  
  											__lowerCamelCase		:      List[str]											=					torch.sum(torch.abs(__A	)	)
  											__lowerCamelCase		:      List[str]											=					torch.mean(torch.abs(__A	)	)
  
  											assert abs(result_sum.item() - 0.0017	) < 1E-2
  											assert abs(result_mean.item() - 2.2676E-06	) < 1E-3
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->int:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Dict											=					self.scheduler_classes[0]
  											__lowerCamelCase		:      Tuple											=					self.get_scheduler_config()
  											__lowerCamelCase		:      Optional[Any]											=					scheduler_class(**__A	)
  
  											scheduler.set_timesteps(self.num_inference_steps			,				device=__A	)
  
  											__lowerCamelCase		:      List[str]											=					self.dummy_model()
  											__lowerCamelCase		:      Optional[Any]											=					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
  											__lowerCamelCase		:      Union[str, Any]											=					sample.to(__A	)
  
  											for i, t in enumerate(scheduler.timesteps	):
  															__lowerCamelCase		:      str											=					scheduler.scale_model_input(__A			,				__A	)
  
  															__lowerCamelCase		:      Any											=					model(__A			,				__A	)
  
  															__lowerCamelCase		:      int											=					scheduler.step(__A			,				__A			,				__A	)
  															__lowerCamelCase		:      List[str]											=					output.prev_sample
  
  											__lowerCamelCase		:      str											=					torch.sum(torch.abs(__A	)	)
  											__lowerCamelCase		:      Union[str, Any]											=					torch.mean(torch.abs(__A	)	)
  
  											assert abs(result_sum.item() - 1006.388	) < 1E-2
  											assert abs(result_mean.item() - 1.31	) < 1E-3
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Tuple:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Union[str, Any]											=					self.scheduler_classes[0]
  											__lowerCamelCase		:      Dict											=					self.get_scheduler_config()
  											__lowerCamelCase		:      Dict											=					scheduler_class(**__A			,				use_karras_sigmas=__A	)
  
  											scheduler.set_timesteps(self.num_inference_steps			,				device=__A	)
  
  											__lowerCamelCase		:      str											=					self.dummy_model()
  											__lowerCamelCase		:      Any											=					self.dummy_sample_deter.to(__A	) * scheduler.init_noise_sigma
  											__lowerCamelCase		:      Union[str, Any]											=					sample.to(__A	)
  
  											for t in scheduler.timesteps:
  															__lowerCamelCase		:      str											=					scheduler.scale_model_input(__A			,				__A	)
  
  															__lowerCamelCase		:      Optional[int]											=					model(__A			,				__A	)
  
  															__lowerCamelCase		:      List[str]											=					scheduler.step(__A			,				__A			,				__A	)
  															__lowerCamelCase		:      Dict											=					output.prev_sample
  
  											__lowerCamelCase		:      List[str]											=					torch.sum(torch.abs(__A	)	)
  											__lowerCamelCase		:      str											=					torch.mean(torch.abs(__A	)	)
  
  											assert abs(result_sum.item() - 3812.9927	) < 2E-2
  											assert abs(result_mean.item() - 4.9648	) < 1E-3
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  
  def 							a_       (						lowercase    )			->str:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				return " ".join(sorted(set(sentence.split()    )    )    )
  
  
  if __name__ == "__main__":
  				import doctest
  
  				doctest.testmod()
  
  
  
  
  
  