



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




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()