code: 
  """simple docstring"""
  import torch
  
  from diffusers import LMSDiscreteScheduler
  from diffusers.utils import torch_device
  
  from .test_schedulers import SchedulerCommonTest
  
  
  class    _lowerCAmelCase						(						lowerCamelCase						):
  							lowercase_     :     List[Any]       =							(LMSDiscreteScheduler,)
  							lowercase_     :     Optional[Any]       =							10
  
  
  
  							def       _a   (      self ,						**a_   )							->							Any:
  								_UpperCAmelCase =						{
  								    "num_train_timesteps": 1100,
  								    "beta_start": 0.0001,
  								    "beta_end": 0.02,
  								    "beta_schedule": "linear",
  								}
  
  								config.update(**a_   )
  								return config
  
  
  
  							def       _a   (      self   )							->							List[Any]:
  								for timesteps in [10, 50, 100, 1000]:
  									self.check_over_configs(num_train_timesteps=a_   )
  
  
  
  							def       _a   (      self   )							->							str:
  								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   )							->							Dict:
  								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   )							->							Optional[Any]:
  								for t in [0, 500, 800]:
  									self.check_over_forward(time_step=a_   )
  
  
  
  							def       _a   (      self   )							->							Any:
  								_UpperCAmelCase =						self.scheduler_classes[0]
  								_UpperCAmelCase =						self.get_scheduler_config()
  								_UpperCAmelCase =						scheduler_class(**a_   )
  
  								scheduler.set_timesteps(self.num_inference_steps   )
  
  								_UpperCAmelCase =						self.dummy_model()
  								_UpperCAmelCase =						self.dummy_sample_deter * scheduler.init_noise_sigma
  
  								for i, t in enumerate(scheduler.timesteps   ):
  									_UpperCAmelCase =						scheduler.scale_model_input(a_ ,						a_   )
  
  									_UpperCAmelCase =						model(a_ ,						a_   )
  
  									_UpperCAmelCase =						scheduler.step(a_ ,						a_ ,						a_   )
  									_UpperCAmelCase =						output.prev_sample
  
  								_UpperCAmelCase =						torch.sum(torch.abs(a_   )   )
  								_UpperCAmelCase =						torch.mean(torch.abs(a_   )   )
  
  								assert abs(result_sum.item() - 1006.388   ) < 1e-2
  								assert abs(result_mean.item() - 1.31   ) < 1e-3
  
  
  
  							def       _a   (      self   )							->							str:
  								_UpperCAmelCase =						self.scheduler_classes[0]
  								_UpperCAmelCase =						self.get_scheduler_config(prediction_type="v_prediction"   )
  								_UpperCAmelCase =						scheduler_class(**a_   )
  
  								scheduler.set_timesteps(self.num_inference_steps   )
  
  								_UpperCAmelCase =						self.dummy_model()
  								_UpperCAmelCase =						self.dummy_sample_deter * scheduler.init_noise_sigma
  
  								for i, t in enumerate(scheduler.timesteps   ):
  									_UpperCAmelCase =						scheduler.scale_model_input(a_ ,						a_   )
  
  									_UpperCAmelCase =						model(a_ ,						a_   )
  
  									_UpperCAmelCase =						scheduler.step(a_ ,						a_ ,						a_   )
  									_UpperCAmelCase =						output.prev_sample
  
  								_UpperCAmelCase =						torch.sum(torch.abs(a_   )   )
  								_UpperCAmelCase =						torch.mean(torch.abs(a_   )   )
  
  								assert abs(result_sum.item() - 0.0017   ) < 1e-2
  								assert abs(result_mean.item() - 2.2_676e-06   ) < 1e-3
  
  
  
  							def       _a   (      self   )							->							Optional[int]:
  								_UpperCAmelCase =						self.scheduler_classes[0]
  								_UpperCAmelCase =						self.get_scheduler_config()
  								_UpperCAmelCase =						scheduler_class(**a_   )
  
  								scheduler.set_timesteps(self.num_inference_steps ,						device=a_   )
  
  								_UpperCAmelCase =						self.dummy_model()
  								_UpperCAmelCase =						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
  								_UpperCAmelCase =						sample.to(a_   )
  
  								for i, t in enumerate(scheduler.timesteps   ):
  									_UpperCAmelCase =						scheduler.scale_model_input(a_ ,						a_   )
  
  									_UpperCAmelCase =						model(a_ ,						a_   )
  
  									_UpperCAmelCase =						scheduler.step(a_ ,						a_ ,						a_   )
  									_UpperCAmelCase =						output.prev_sample
  
  								_UpperCAmelCase =						torch.sum(torch.abs(a_   )   )
  								_UpperCAmelCase =						torch.mean(torch.abs(a_   )   )
  
  								assert abs(result_sum.item() - 1006.388   ) < 1e-2
  								assert abs(result_mean.item() - 1.31   ) < 1e-3
  
  
  
  							def       _a   (      self   )							->							Tuple:
  								_UpperCAmelCase =						self.scheduler_classes[0]
  								_UpperCAmelCase =						self.get_scheduler_config()
  								_UpperCAmelCase =						scheduler_class(**a_ ,						use_karras_sigmas=a_   )
  
  								scheduler.set_timesteps(self.num_inference_steps ,						device=a_   )
  
  								_UpperCAmelCase =						self.dummy_model()
  								_UpperCAmelCase =						self.dummy_sample_deter.to(a_   ) * scheduler.init_noise_sigma
  								_UpperCAmelCase =						sample.to(a_   )
  
  								for t in scheduler.timesteps:
  									_UpperCAmelCase =						scheduler.scale_model_input(a_ ,						a_   )
  
  									_UpperCAmelCase =						model(a_ ,						a_   )
  
  									_UpperCAmelCase =						scheduler.step(a_ ,						a_ ,						a_   )
  									_UpperCAmelCase =						output.prev_sample
  
  								_UpperCAmelCase =						torch.sum(torch.abs(a_   )   )
  								_UpperCAmelCase =						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"""
  from collections import namedtuple
  
  __magic_name__		       =				namedtuple('''from_to''', '''from_ to''')
  
  __magic_name__		       =				{
      '''atm''': from_to(1, 1),
      '''pascal''': from_to(0.0000098, 10_13_25),
      '''bar''': from_to(0.986923, 1.01325),
      '''kilopascal''': from_to(0.00986923, 101.325),
      '''megapascal''': from_to(9.86923, 0.101325),
      '''psi''': from_to(0.068046, 14.6959),
      '''inHg''': from_to(0.0334211, 29.9213),
      '''torr''': from_to(0.00131579, 7_60),
  }
  
  
  
  
  def     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	if from_type not in PRESSURE_CONVERSION:
  		raise ValueError(
  		    f"Invalid 'from_type' value: {from_type!r}  Supported values are:\n"
  		    + ", ".join(UpperCamelCase__      )      )
  	if to_type not in PRESSURE_CONVERSION:
  		raise ValueError(
  		    f"Invalid 'to_type' value: {to_type!r}.  Supported values are:\n"
  		    + ", ".join(UpperCamelCase__      )      )
  	return (
  	    value * PRESSURE_CONVERSION[from_type].from_ * PRESSURE_CONVERSION[to_type].to
  	)
  
  
  if __name__ == "__main__":
  			import doctest
  
  			doctest.testmod()
  
  
  
  
  