



import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class    _SCREAMING_SNAKE_CASE						(UpperCamelCase			):
     lowerCAmelCase       =      (EulerAncestralDiscreteScheduler,)
     lowerCAmelCase       =      10





     def 						__snake_case     (							self			:				List[str]  ,	**UpperCamelCase			:				int					)->Optional[int]:
      __SCREAMING_SNAKE_CASE							:     Any							=						{
          "num_train_timesteps": 1_1_0_0,
          "beta_start": 0.0_0_0_1,
          "beta_end": 0.0_2,
          "beta_schedule": "linear",
      }

      config.update(**UpperCamelCase					)
      return config





     def 						__snake_case     (							self			:				Optional[Any]					)->Optional[int]:
      for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
       self.check_over_configs(num_train_timesteps=UpperCamelCase					)





     def 						__snake_case     (							self			:				List[str]					)->str:
      for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]  ,	[0.0_0_0_2, 0.0_0_2, 0.0_2]					):
       self.check_over_configs(beta_start=UpperCamelCase  ,	beta_end=UpperCamelCase					)





     def 						__snake_case     (							self			:				List[str]					)->Optional[int]:
      for schedule in ["linear", "scaled_linear"]:
       self.check_over_configs(beta_schedule=UpperCamelCase					)





     def 						__snake_case     (							self			:				Optional[int]					)->Union[str, Any]:
      for prediction_type in ["epsilon", "v_prediction"]:
       self.check_over_configs(prediction_type=UpperCamelCase					)





     def 						__snake_case     (							self			:				int					)->Any:
      __SCREAMING_SNAKE_CASE							:     str							=						self.scheduler_classes[0]
      __SCREAMING_SNAKE_CASE							:     int							=						self.get_scheduler_config()
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						scheduler_class(**UpperCamelCase					)

      scheduler.set_timesteps(self.num_inference_steps					)

      __SCREAMING_SNAKE_CASE							:     List[Any]							=						torch.manual_seed(0					)

      __SCREAMING_SNAKE_CASE							:     List[Any]							=						self.dummy_model()
      __SCREAMING_SNAKE_CASE							:     Tuple							=						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      __SCREAMING_SNAKE_CASE							:     Any							=						sample.to(UpperCamelCase					)

      for i, t in enumerate(scheduler.timesteps					):
       __SCREAMING_SNAKE_CASE							:     List[str]							=						scheduler.scale_model_input(UpperCamelCase  ,	UpperCamelCase					)

       __SCREAMING_SNAKE_CASE							:     Optional[int]							=						model(UpperCamelCase  ,	UpperCamelCase					)

       __SCREAMING_SNAKE_CASE							:     Any							=						scheduler.step(UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase  ,	generator=UpperCamelCase					)
       __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						output.prev_sample

      __SCREAMING_SNAKE_CASE							:     List[Any]							=						torch.sum(torch.abs(UpperCamelCase					)					)
      __SCREAMING_SNAKE_CASE							:     int							=						torch.mean(torch.abs(UpperCamelCase					)					)

      assert abs(result_sum.item() - 1_5_2.3_1_9_2					) < 1E-2
      assert abs(result_mean.item() - 0.1_9_8_3					) < 1E-3





     def 						__snake_case     (							self			:				Dict					)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE							:     int							=						self.scheduler_classes[0]
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.get_scheduler_config(prediction_type="v_prediction"					)
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						scheduler_class(**UpperCamelCase					)

      scheduler.set_timesteps(self.num_inference_steps					)

      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						torch.manual_seed(0					)

      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.dummy_model()
      __SCREAMING_SNAKE_CASE							:     List[str]							=						self.dummy_sample_deter * scheduler.init_noise_sigma
      __SCREAMING_SNAKE_CASE							:     List[str]							=						sample.to(UpperCamelCase					)

      for i, t in enumerate(scheduler.timesteps					):
       __SCREAMING_SNAKE_CASE							:     str							=						scheduler.scale_model_input(UpperCamelCase  ,	UpperCamelCase					)

       __SCREAMING_SNAKE_CASE							:     Any							=						model(UpperCamelCase  ,	UpperCamelCase					)

       __SCREAMING_SNAKE_CASE							:     Dict							=						scheduler.step(UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase  ,	generator=UpperCamelCase					)
       __SCREAMING_SNAKE_CASE							:     Tuple							=						output.prev_sample

      __SCREAMING_SNAKE_CASE							:     str							=						torch.sum(torch.abs(UpperCamelCase					)					)
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						torch.mean(torch.abs(UpperCamelCase					)					)

      assert abs(result_sum.item() - 1_0_8.4_4_3_9					) < 1E-2
      assert abs(result_mean.item() - 0.1_4_1_2					) < 1E-3






     def 						__snake_case     (							self			:				Optional[Any]					)->Optional[Any]:
      __SCREAMING_SNAKE_CASE							:     Any							=						self.scheduler_classes[0]
      __SCREAMING_SNAKE_CASE							:     List[str]							=						self.get_scheduler_config()
      __SCREAMING_SNAKE_CASE							:     int							=						scheduler_class(**UpperCamelCase					)

      scheduler.set_timesteps(self.num_inference_steps  ,	device=UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     List[str]							=						torch.manual_seed(0					)

      __SCREAMING_SNAKE_CASE							:     str							=						self.dummy_model()
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						sample.to(UpperCamelCase					)

      for t in scheduler.timesteps:
       __SCREAMING_SNAKE_CASE							:     List[str]							=						scheduler.scale_model_input(UpperCamelCase  ,	UpperCamelCase					)

       __SCREAMING_SNAKE_CASE							:     str							=						model(UpperCamelCase  ,	UpperCamelCase					)

       __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						scheduler.step(UpperCamelCase  ,	UpperCamelCase  ,	UpperCamelCase  ,	generator=UpperCamelCase					)
       __SCREAMING_SNAKE_CASE							:     Tuple							=						output.prev_sample

      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						torch.sum(torch.abs(UpperCamelCase					)					)
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						torch.mean(torch.abs(UpperCamelCase					)					)

      assert abs(result_sum.item() - 1_5_2.3_1_9_2					) < 1E-2
      assert abs(result_mean.item() - 0.1_9_8_3					) < 1E-3











def 						_lowerCAmelCase    (					__lowerCamelCase :  list  ):



 """simple docstring"""





 if len(__lowerCamelCase  ) <= 1:
  return data
 else:
  return [
      *quick_sort([e for e in data[1:] if e <= data[0]]  ),
      data[0],
      *quick_sort([e for e in data[1:] if e > data[0]]  ),
  ]


if __name__ == "__main__":
      import doctest

      doctest.testmod()






