code: 
  
  import torch
  
  from diffusers import EulerAncestralDiscreteScheduler
  from diffusers.utils import torch_device
  
  from .test_schedulers import SchedulerCommonTest
  class 					A_   (      _a						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      a__     							=       (EulerAncestralDiscreteScheduler,)
      a__     							=       10
  
  
      def      lowerCAmelCase_						(self     ,      **lowercase__  )     ->      str:
          __UpperCAmelCase									=			{
              '''num_train_timesteps''': 1_100,
              '''beta_start''': 0.0001,
              '''beta_end''': 0.02,
              '''beta_schedule''': '''linear''',
          }
  
          config.update(**lowercase__  )
          return config
  
  
      def      lowerCAmelCase_						(self  )     ->      str:
          for timesteps in [10, 50, 100, 1_000]:
              self.check_over_configs(num_train_timesteps=lowercase__  )
  
  
      def      lowerCAmelCase_						(self  )     ->      List[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=lowercase__     ,      beta_end=lowercase__  )
  
  
      def      lowerCAmelCase_						(self  )     ->      Optional[Any]:
          for schedule in ["linear", "scaled_linear"]:
              self.check_over_configs(beta_schedule=lowercase__  )
  
  
      def      lowerCAmelCase_						(self  )     ->      Dict:
          for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=lowercase__  )
  
  
      def      lowerCAmelCase_						(self  )     ->      Tuple:
          __UpperCAmelCase									=			self.scheduler_classes[0]
          __UpperCAmelCase									=			self.get_scheduler_config()
          __UpperCAmelCase									=			scheduler_class(**lowercase__  )
  
          scheduler.set_timesteps(self.num_inference_steps  )
  
          __UpperCAmelCase									=			torch.manual_seed(0  )
  
          __UpperCAmelCase									=			self.dummy_model()
          __UpperCAmelCase									=			self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
          __UpperCAmelCase									=			sample.to(lowercase__  )
  
          for i, t in enumerate(scheduler.timesteps  ):
              __UpperCAmelCase									=			scheduler.scale_model_input(lowercase__     ,      lowercase__  )
  
              __UpperCAmelCase									=			model(lowercase__     ,      lowercase__  )
  
              __UpperCAmelCase									=			scheduler.step(lowercase__     ,      lowercase__     ,      lowercase__     ,      generator=lowercase__  )
              __UpperCAmelCase									=			output.prev_sample
  
          __UpperCAmelCase									=			torch.sum(torch.abs(lowercase__  )  )
          __UpperCAmelCase									=			torch.mean(torch.abs(lowercase__  )  )
  
          assert abs(result_sum.item() - 152.3192  ) < 1E-2
          assert abs(result_mean.item() - 0.1983  ) < 1E-3
  
  
      def      lowerCAmelCase_						(self  )     ->      List[str]:
          __UpperCAmelCase									=			self.scheduler_classes[0]
          __UpperCAmelCase									=			self.get_scheduler_config(prediction_type='''v_prediction'''  )
          __UpperCAmelCase									=			scheduler_class(**lowercase__  )
  
          scheduler.set_timesteps(self.num_inference_steps  )
  
          __UpperCAmelCase									=			torch.manual_seed(0  )
  
          __UpperCAmelCase									=			self.dummy_model()
          __UpperCAmelCase									=			self.dummy_sample_deter * scheduler.init_noise_sigma
          __UpperCAmelCase									=			sample.to(lowercase__  )
  
          for i, t in enumerate(scheduler.timesteps  ):
              __UpperCAmelCase									=			scheduler.scale_model_input(lowercase__     ,      lowercase__  )
  
              __UpperCAmelCase									=			model(lowercase__     ,      lowercase__  )
  
              __UpperCAmelCase									=			scheduler.step(lowercase__     ,      lowercase__     ,      lowercase__     ,      generator=lowercase__  )
              __UpperCAmelCase									=			output.prev_sample
  
          __UpperCAmelCase									=			torch.sum(torch.abs(lowercase__  )  )
          __UpperCAmelCase									=			torch.mean(torch.abs(lowercase__  )  )
  
          assert abs(result_sum.item() - 108.4439  ) < 1E-2
          assert abs(result_mean.item() - 0.1412  ) < 1E-3
  
  
  
  
  
  
      def      lowerCAmelCase_						(self  )     ->      Dict:
          __UpperCAmelCase									=			self.scheduler_classes[0]
          __UpperCAmelCase									=			self.get_scheduler_config()
          __UpperCAmelCase									=			scheduler_class(**lowercase__  )
  
          scheduler.set_timesteps(self.num_inference_steps     ,      device=lowercase__  )
          __UpperCAmelCase									=			torch.manual_seed(0  )
  
          __UpperCAmelCase									=			self.dummy_model()
          __UpperCAmelCase									=			self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
          __UpperCAmelCase									=			sample.to(lowercase__  )
  
          for t in scheduler.timesteps:
              __UpperCAmelCase									=			scheduler.scale_model_input(lowercase__     ,      lowercase__  )
  
              __UpperCAmelCase									=			model(lowercase__     ,      lowercase__  )
  
              __UpperCAmelCase									=			scheduler.step(lowercase__     ,      lowercase__     ,      lowercase__     ,      generator=lowercase__  )
              __UpperCAmelCase									=			output.prev_sample
  
          __UpperCAmelCase									=			torch.sum(torch.abs(lowercase__  )  )
          __UpperCAmelCase									=			torch.mean(torch.abs(lowercase__  )  )
  
          assert abs(result_sum.item() - 152.3192  ) < 1E-2
          assert abs(result_mean.item() - 0.1983  ) < 1E-3
  
  
  
  
  
style_context: 
  
  import numpy as np
  
  
  def       __a       (   SCREAMING_SNAKE_CASE      )      ->     Tuple:
  
  
  
  
  
  
      '''simple docstring'''
      __UpperCAmelCase					,     __UpperCAmelCase									=			a.shape
      __UpperCAmelCase									=			min(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )
      __UpperCAmelCase									=			np.eye(SCREAMING_SNAKE_CASE      )
      __UpperCAmelCase									=			a.copy()
  
      for k in range(t - 1      ):
          # select a column of modified matrix A':
          __UpperCAmelCase									=			r[k:, [k]]
          # construct first basis vector
          __UpperCAmelCase									=			np.zeros_like(SCREAMING_SNAKE_CASE      )
          __UpperCAmelCase									=			1.0
          # determine scaling factor
          __UpperCAmelCase									=			np.linalg.norm(SCREAMING_SNAKE_CASE      )
          # construct vector v for Householder reflection
          __UpperCAmelCase									=			x + np.sign(x[0]      ) * alpha * ea
          v /= np.linalg.norm(SCREAMING_SNAKE_CASE      )
  
          # construct the Householder matrix
          __UpperCAmelCase									=			np.eye(m - k      ) - 2.0 * v @ v.T
          # pad with ones and zeros as necessary
          __UpperCAmelCase									=			np.block([[np.eye(SCREAMING_SNAKE_CASE      ), np.zeros((k, m - k)      )], [np.zeros((m - k, k)      ), q_k]]      )
  
          __UpperCAmelCase									=			q @ q_k.T
          __UpperCAmelCase									=			q_k @ r
  
      return q, r
  
  
  if __name__ == "__main__":
         import doctest
  
         doctest.testmod()
  
  
  
  
  