code: 
  
  
  
  import torch
  
  from diffusers import LMSDiscreteScheduler
  from diffusers.utils import torch_device
  
  from .test_schedulers import SchedulerCommonTest
  
  
  
  class 		_snake_case      (			lowerCamelCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   lowerCamelCase_          =			(LMSDiscreteScheduler,)
   lowerCamelCase_          =			1_0
   def lowercase_		(						self	,    **a					)	->							Optional[Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    {
         '''num_train_timesteps''': 1_1_0_0,
         '''beta_start''': 0.0_001,
         '''beta_end''': 0.02,
         '''beta_schedule''': '''linear''',
     }
  
     config.update(**a					)
     return config
   def lowercase_		(						self					)	->							Optional[int]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
       self.check_over_configs(num_train_timesteps=a					)
   def lowercase_		(						self					)	->							Any:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]	,    [0.0_002, 0.002, 0.02]					):
       self.check_over_configs(beta_start=a	,    beta_end=a					)
   def lowercase_		(						self					)	->							Dict:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     for schedule in ["linear", "scaled_linear"]:
       self.check_over_configs(beta_schedule=a					)
   def lowercase_		(						self					)	->							Optional[int]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     for prediction_type in ["epsilon", "v_prediction"]:
       self.check_over_configs(prediction_type=a					)
   def lowercase_		(						self					)	->							List[str]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     for t in [0, 5_0_0, 8_0_0]:
       self.check_over_forward(time_step=a					)
   def lowercase_		(						self					)	->							Union[str, Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    self.scheduler_classes[0]
     _A           =    self.get_scheduler_config()
     _A           =    scheduler_class(**a					)
  
     scheduler.set_timesteps(self.num_inference_steps					)
  
     _A           =    self.dummy_model()
     _A           =    self.dummy_sample_deter * scheduler.init_noise_sigma
  
     for i, t in enumerate(scheduler.timesteps					):
       _A           =    scheduler.scale_model_input(a	,    a					)
  
       _A           =    model(a	,    a					)
  
       _A           =    scheduler.step(a	,    a	,    a					)
       _A           =    output.prev_sample
  
     _A           =    torch.sum(torch.abs(a					)					)
     _A           =    torch.mean(torch.abs(a					)					)
  
     assert abs(result_sum.item() - 1_006.388					) < 1e-2
     assert abs(result_mean.item() - 1.31					) < 1e-3
   def lowercase_		(						self					)	->							int:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    self.scheduler_classes[0]
     _A           =    self.get_scheduler_config(prediction_type='''v_prediction'''					)
     _A           =    scheduler_class(**a					)
  
     scheduler.set_timesteps(self.num_inference_steps					)
  
     _A           =    self.dummy_model()
     _A           =    self.dummy_sample_deter * scheduler.init_noise_sigma
  
     for i, t in enumerate(scheduler.timesteps					):
       _A           =    scheduler.scale_model_input(a	,    a					)
  
       _A           =    model(a	,    a					)
  
       _A           =    scheduler.step(a	,    a	,    a					)
       _A           =    output.prev_sample
  
     _A           =    torch.sum(torch.abs(a					)					)
     _A           =    torch.mean(torch.abs(a					)					)
  
     assert abs(result_sum.item() - 0.0_017					) < 1e-2
     assert abs(result_mean.item() - 2.2_676e-06					) < 1e-3
   def lowercase_		(						self					)	->							int:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    self.scheduler_classes[0]
     _A           =    self.get_scheduler_config()
     _A           =    scheduler_class(**a					)
  
     scheduler.set_timesteps(self.num_inference_steps	,    device=a					)
  
     _A           =    self.dummy_model()
     _A           =    self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
     _A           =    sample.to(a					)
  
     for i, t in enumerate(scheduler.timesteps					):
       _A           =    scheduler.scale_model_input(a	,    a					)
  
       _A           =    model(a	,    a					)
  
       _A           =    scheduler.step(a	,    a	,    a					)
       _A           =    output.prev_sample
  
     _A           =    torch.sum(torch.abs(a					)					)
     _A           =    torch.mean(torch.abs(a					)					)
  
     assert abs(result_sum.item() - 1_006.388					) < 1e-2
     assert abs(result_mean.item() - 1.31					) < 1e-3
  
  
  
   def lowercase_		(						self					)	->							Tuple:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    self.scheduler_classes[0]
     _A           =    self.get_scheduler_config()
     _A           =    scheduler_class(**a	,    use_karras_sigmas=a					)
  
     scheduler.set_timesteps(self.num_inference_steps	,    device=a					)
  
     _A           =    self.dummy_model()
     _A           =    self.dummy_sample_deter.to(a					) * scheduler.init_noise_sigma
     _A           =    sample.to(a					)
  
     for t in scheduler.timesteps:
       _A           =    scheduler.scale_model_input(a	,    a					)
  
       _A           =    model(a	,    a					)
  
       _A           =    scheduler.step(a	,    a	,    a					)
       _A           =    output.prev_sample
  
     _A           =    torch.sum(torch.abs(a					)					)
     _A           =    torch.mean(torch.abs(a					)					)
  
     assert abs(result_sum.item() - 3_812.9_927					) < 2e-2
     assert abs(result_mean.item() - 4.9_648					) < 1e-3
style_context: 
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case						,				__snake_case						,				__snake_case						,				__snake_case							)   -> None:
    if target == 0:
      answer.append(path.copy()							)
    else:
      for index in range(__snake_case						,				len(__snake_case							)							):
        if target >= candidates[index]:
          path.append(candidates[index]							)
          backtrack(__snake_case						,				__snake_case						,				__snake_case						,				target - candidates[index]						,				__snake_case							)
          path.pop(len(__snake_case							) - 1							)
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case							)   -> list:
    _A           =    []  # type: list[int]
    _A           =    []  # type: list[int]
    backtrack(__snake_case						,				__snake_case						,				__snake_case						,				__snake_case						,				0							)
    return answer
  
  
  
  
  def     UpperCAmelCase__ (							)   -> None:
    print(combination_sum([-8, 2.3, 0]						,				1							)							)
  
  
  if __name__ == "__main__":
      import doctest
  
      doctest.testmod()
      main()