





import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class 					lowerCAmelCase							(   _lowerCAmelCase					):
  __lowerCamelCase		=			(EulerAncestralDiscreteScheduler,)
  __lowerCamelCase		=			10







  def   UpperCAmelCase     (			self  :Optional[int]    ,							**_lowercase  :int					):






     '''simple docstring'''





     lowercase__    =  {
         "num_train_timesteps": 11_00,
         "beta_start": 0.0001,
         "beta_end": 0.02,
         "beta_schedule": "linear",
     }

     config.update(**A_					)
     return config







  def   UpperCAmelCase     (			self  :Optional[int]					):






     '''simple docstring'''





     for timesteps in [10, 50, 1_00, 10_00]:
        self.check_over_configs(num_train_timesteps=A_					)







  def   UpperCAmelCase     (			self  :List[Any]					):






     '''simple docstring'''





     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   UpperCAmelCase     (			self  :str					):






     '''simple docstring'''





     for schedule in ["linear", "scaled_linear"]:
        self.check_over_configs(beta_schedule=A_					)







  def   UpperCAmelCase     (			self  :Dict					):






     '''simple docstring'''





     for prediction_type in ["epsilon", "v_prediction"]:
        self.check_over_configs(prediction_type=A_					)







  def   UpperCAmelCase     (			self  :List[str]					):






     '''simple docstring'''





     lowercase__    =  self.scheduler_classes[0]
     lowercase__    =  self.get_scheduler_config()
     lowercase__    =  scheduler_class(**A_					)

     scheduler.set_timesteps(self.num_inference_steps					)

     lowercase__    =  torch.manual_seed(0					)

     lowercase__    =  self.dummy_model()
     lowercase__    =  self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
     lowercase__    =  sample.to(A_					)

     for i, t in enumerate(scheduler.timesteps					):
        lowercase__    =  scheduler.scale_model_input(A_    ,							A_					)

        lowercase__    =  model(A_    ,							A_					)

        lowercase__    =  scheduler.step(A_    ,							A_    ,							A_    ,							generator=A_					)
        lowercase__    =  output.prev_sample

     lowercase__    =  torch.sum(torch.abs(A_					)					)
     lowercase__    =  torch.mean(torch.abs(A_					)					)

     assert abs(result_sum.item() - 1_52.31_92					) < 1e-2
     assert abs(result_mean.item() - 0.1983					) < 1e-3







  def   UpperCAmelCase     (			self  :Tuple					):






     '''simple docstring'''





     lowercase__    =  self.scheduler_classes[0]
     lowercase__    =  self.get_scheduler_config(prediction_type="v_prediction"					)
     lowercase__    =  scheduler_class(**A_					)

     scheduler.set_timesteps(self.num_inference_steps					)

     lowercase__    =  torch.manual_seed(0					)

     lowercase__    =  self.dummy_model()
     lowercase__    =  self.dummy_sample_deter * scheduler.init_noise_sigma
     lowercase__    =  sample.to(A_					)

     for i, t in enumerate(scheduler.timesteps					):
        lowercase__    =  scheduler.scale_model_input(A_    ,							A_					)

        lowercase__    =  model(A_    ,							A_					)

        lowercase__    =  scheduler.step(A_    ,							A_    ,							A_    ,							generator=A_					)
        lowercase__    =  output.prev_sample

     lowercase__    =  torch.sum(torch.abs(A_					)					)
     lowercase__    =  torch.mean(torch.abs(A_					)					)

     assert abs(result_sum.item() - 1_08.44_39					) < 1e-2
     assert abs(result_mean.item() - 0.1412					) < 1e-3







  def   UpperCAmelCase     (			self  :List[str]					):






     '''simple docstring'''





     lowercase__    =  self.scheduler_classes[0]
     lowercase__    =  self.get_scheduler_config()
     lowercase__    =  scheduler_class(**A_					)

     scheduler.set_timesteps(self.num_inference_steps    ,							device=A_					)
     lowercase__    =  torch.manual_seed(0					)

     lowercase__    =  self.dummy_model()
     lowercase__    =  self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
     lowercase__    =  sample.to(A_					)

     for t in scheduler.timesteps:
        lowercase__    =  scheduler.scale_model_input(A_    ,							A_					)

        lowercase__    =  model(A_    ,							A_					)

        lowercase__    =  scheduler.step(A_    ,							A_    ,							A_    ,							generator=A_					)
        lowercase__    =  output.prev_sample

     lowercase__    =  torch.sum(torch.abs(A_					)					)
     lowercase__    =  torch.mean(torch.abs(A_					)					)

     assert abs(result_sum.item() - 1_52.31_92					) < 1e-2
     assert abs(result_mean.item() - 0.1983					) < 1e-3











'''simple docstring'''

def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						int = 4_000_000							)			->						int:







	"""simple docstring"""
	lowerCAmelCase    =	1
	lowerCAmelCase    =	2
	lowerCAmelCase    =	0
	while j <= n:
		if j % 2 == 0:
			total += j
		lowerCAmelCase,   lowerCAmelCase    =	j, i + j

	return total


if __name__ == "__main__":
				print(F'''{solution() = }''')