


'''simple docstring'''

import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class 					snake_case   (   lowercase      ):





  """simple docstring"""




  _lowerCamelCase		   =  (EulerAncestralDiscreteScheduler,)
  _lowerCamelCase		   =  10






  def   snake_case		(   self      ,  **UpperCamelCase					):







   """simple docstring"""
   lowerCamelCase_								=  {
       "num_train_timesteps": 1100,
       "beta_start": 0.0_001,
       "beta_end": 0.02,
       "beta_schedule": "linear",
   }

   config.update(**UpperCamelCase					)
   return config






  def   snake_case		(   self					):







   """simple docstring"""
   for timesteps in [10, 50, 100, 1000]:
    self.check_over_configs(num_train_timesteps=UpperCamelCase					)






  def   snake_case		(   self					):







   """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=UpperCamelCase      ,  beta_end=UpperCamelCase					)






  def   snake_case		(   self					):







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






  def   snake_case		(   self					):







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






  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  self.scheduler_classes[0]
   lowerCamelCase_								=  self.get_scheduler_config()
   lowerCamelCase_								=  scheduler_class(**UpperCamelCase					)

   scheduler.set_timesteps(self.num_inference_steps					)

   lowerCamelCase_								=  torch.manual_seed(0					)

   lowerCamelCase_								=  self.dummy_model()
   lowerCamelCase_								=  self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
   lowerCamelCase_								=  sample.to(UpperCamelCase					)

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

    lowerCamelCase_								=  model(UpperCamelCase      ,  UpperCamelCase					)

    lowerCamelCase_								=  scheduler.step(UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase      ,  generator=UpperCamelCase					)
    lowerCamelCase_								=  output.prev_sample

   lowerCamelCase_								=  torch.sum(torch.abs(UpperCamelCase					)					)
   lowerCamelCase_								=  torch.mean(torch.abs(UpperCamelCase					)					)

   assert abs(result_sum.item() - 152.3_192					) < 1e-2
   assert abs(result_mean.item() - 0.1_983					) < 1e-3






  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  self.scheduler_classes[0]
   lowerCamelCase_								=  self.get_scheduler_config(prediction_type="v_prediction"					)
   lowerCamelCase_								=  scheduler_class(**UpperCamelCase					)

   scheduler.set_timesteps(self.num_inference_steps					)

   lowerCamelCase_								=  torch.manual_seed(0					)

   lowerCamelCase_								=  self.dummy_model()
   lowerCamelCase_								=  self.dummy_sample_deter * scheduler.init_noise_sigma
   lowerCamelCase_								=  sample.to(UpperCamelCase					)

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

    lowerCamelCase_								=  model(UpperCamelCase      ,  UpperCamelCase					)

    lowerCamelCase_								=  scheduler.step(UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase      ,  generator=UpperCamelCase					)
    lowerCamelCase_								=  output.prev_sample

   lowerCamelCase_								=  torch.sum(torch.abs(UpperCamelCase					)					)
   lowerCamelCase_								=  torch.mean(torch.abs(UpperCamelCase					)					)

   assert abs(result_sum.item() - 108.4_439					) < 1e-2
   assert abs(result_mean.item() - 0.1_412					) < 1e-3







  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  self.scheduler_classes[0]
   lowerCamelCase_								=  self.get_scheduler_config()
   lowerCamelCase_								=  scheduler_class(**UpperCamelCase					)

   scheduler.set_timesteps(self.num_inference_steps      ,  device=UpperCamelCase					)
   lowerCamelCase_								=  torch.manual_seed(0					)

   lowerCamelCase_								=  self.dummy_model()
   lowerCamelCase_								=  self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
   lowerCamelCase_								=  sample.to(UpperCamelCase					)

   for t in scheduler.timesteps:
    lowerCamelCase_								=  scheduler.scale_model_input(UpperCamelCase      ,  UpperCamelCase					)

    lowerCamelCase_								=  model(UpperCamelCase      ,  UpperCamelCase					)

    lowerCamelCase_								=  scheduler.step(UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase      ,  generator=UpperCamelCase					)
    lowerCamelCase_								=  output.prev_sample

   lowerCamelCase_								=  torch.sum(torch.abs(UpperCamelCase					)					)
   lowerCamelCase_								=  torch.mean(torch.abs(UpperCamelCase					)					)

   assert abs(result_sum.item() - 152.3_192					) < 1e-2
   assert abs(result_mean.item() - 0.1_983					) < 1e-3







'''simple docstring'''

def __snake_case (				UpperCAmelCase_						:						float		):
 return -num if num < 0 else num





def __snake_case (				UpperCAmelCase_						:						list[int]		):
 if len(UpperCAmelCase_		) == 0:
  raise ValueError("abs_min() arg is an empty sequence"		)
 lowerCamelCase_								=  x[0]
 for i in x:
  if abs_val(UpperCAmelCase_		) < abs_val(UpperCAmelCase_		):
   lowerCamelCase_								=  i
 return j





def __snake_case (				UpperCAmelCase_						:						list[int]		):
 if len(UpperCAmelCase_		) == 0:
  raise ValueError("abs_max() arg is an empty sequence"		)
 lowerCamelCase_								=  x[0]
 for i in x:
  if abs(UpperCAmelCase_		) > abs(UpperCAmelCase_		):
   lowerCamelCase_								=  i
 return j





def __snake_case (				UpperCAmelCase_						:						list[int]		):
 if len(UpperCAmelCase_		) == 0:
  raise ValueError("abs_max_sort() arg is an empty sequence"		)
 return sorted(UpperCAmelCase_ ,    key=UpperCAmelCase_		)[-1]





def __snake_case (				):
 assert abs_val(0		) == 0
 assert abs_val(34		) == 34
 assert abs_val(-100000000000		) == 100000000000

 lowerCamelCase_								=  [-3, -1, 2, -11]
 assert abs_max(UpperCAmelCase_		) == -11
 assert abs_max_sort(UpperCAmelCase_		) == -11
 assert abs_min(UpperCAmelCase_		) == -1


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     test_abs_val()
     print(abs_val(-34))  # --> 34



