



'''simple docstring'''





import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       str          =       (UnCLIPScheduler,)



 def 					a__  (self ,   **A       )    ->     List[Any]:


      """simple docstring"""



      _a      =  {
          '''num_train_timesteps''': 1_000,
          '''variance_type''': '''fixed_small_log''',
          '''clip_sample''': True,
          '''clip_sample_range''': 1.0,
          '''prediction_type''': '''epsilon''',
      }

      config.update(**A       )
      return config



 def 					a__  (self       )    ->     Optional[int]:


      """simple docstring"""



      for timesteps in [1, 5, 100, 1_000]:
           self.check_over_configs(num_train_timesteps=A       )



 def 					a__  (self       )    ->     Union[str, Any]:


      """simple docstring"""



      for variance in ["fixed_small_log", "learned_range"]:
           self.check_over_configs(variance_type=A       )



 def 					a__  (self       )    ->     int:


      """simple docstring"""



      for clip_sample in [True, False]:
           self.check_over_configs(clip_sample=A       )



 def 					a__  (self       )    ->     List[str]:


      """simple docstring"""



      for clip_sample_range in [1, 5, 10, 20]:
           self.check_over_configs(clip_sample_range=A       )



 def 					a__  (self       )    ->     Dict:


      """simple docstring"""



      for prediction_type in ["epsilon", "sample"]:
           self.check_over_configs(prediction_type=A       )



 def 					a__  (self       )    ->     str:


      """simple docstring"""



      for time_step in [0, 500, 999]:
           for prev_timestep in [None, 5, 100, 250, 500, 750]:
                if prev_timestep is not None and prev_timestep >= time_step:
                     continue

                self.check_over_forward(time_step=A ,   prev_timestep=A       )



 def 					a__  (self       )    ->     Any:


      """simple docstring"""



      _a      =  self.scheduler_classes[0]
      _a      =  self.get_scheduler_config(variance_type='''fixed_small_log'''       )
      _a      =  scheduler_class(**A       )

      assert torch.sum(torch.abs(scheduler._get_variance(0       ) - 1.0000E-10       )       ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(487       ) - 0.0549625       )       ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(999       ) - 0.9994987       )       ) < 1E-5



 def 					a__  (self       )    ->     Dict:


      """simple docstring"""



      _a      =  self.scheduler_classes[0]
      _a      =  self.get_scheduler_config(variance_type='''learned_range'''       )
      _a      =  scheduler_class(**A       )

      _a      =  0.5

      assert scheduler._get_variance(1 ,   predicted_variance=A       ) - -10.1712790 < 1E-5
      assert scheduler._get_variance(487 ,   predicted_variance=A       ) - -5.7998052 < 1E-5
      assert scheduler._get_variance(999 ,   predicted_variance=A       ) - -0.0010011 < 1E-5



 def 					a__  (self       )    ->     str:


      """simple docstring"""



      _a      =  self.scheduler_classes[0]
      _a      =  self.get_scheduler_config()
      _a      =  scheduler_class(**A       )

      _a      =  scheduler.timesteps

      _a      =  self.dummy_model()
      _a      =  self.dummy_sample_deter
      _a      =  torch.manual_seed(0       )

      for i, t in enumerate(A       ):
           # 1. predict noise residual
           _a      =  model(A ,   A       )

           # 2. predict previous mean of sample x_t-1
           _a      =  scheduler.step(A ,   A ,   A ,   generator=A       ).prev_sample

           _a      =  pred_prev_sample

      _a      =  torch.sum(torch.abs(A       )       )
      _a      =  torch.mean(torch.abs(A       )       )

      assert abs(result_sum.item() - 252.2682495       ) < 1E-2
      assert abs(result_mean.item() - 0.3284743       ) < 1E-3



 def 					a__  (self       )    ->     Optional[int]:


      """simple docstring"""



      _a      =  self.scheduler_classes[0]
      _a      =  self.get_scheduler_config()
      _a      =  scheduler_class(**A       )

      scheduler.set_timesteps(25       )

      _a      =  scheduler.timesteps

      _a      =  self.dummy_model()
      _a      =  self.dummy_sample_deter
      _a      =  torch.manual_seed(0       )

      for i, t in enumerate(A       ):
           # 1. predict noise residual
           _a      =  model(A ,   A       )

           if i + 1 == timesteps.shape[0]:
                _a      =  None
           else:
                _a      =  timesteps[i + 1]

           # 2. predict previous mean of sample x_t-1
           _a      =  scheduler.step(
               A ,   A ,   A ,   prev_timestep=A ,   generator=A       ).prev_sample

           _a      =  pred_prev_sample

      _a      =  torch.sum(torch.abs(A       )       )
      _a      =  torch.mean(torch.abs(A       )       )

      assert abs(result_sum.item() - 258.2044983       ) < 1E-2
      assert abs(result_mean.item() - 0.3362038       ) < 1E-3



 def 					a__  (self       )    ->     int:


      """simple docstring"""



      pass



 def 					a__  (self       )    ->     str:


      """simple docstring"""



      pass








'''simple docstring'''





def lowerCAmelCase  (__A):



     """simple docstring"""



     if not isinstance(__A  ,	__A):
          raise ValueError('''multiplicative_persistence() only accepts integral values''')
     if num < 0:
          raise ValueError('''multiplicative_persistence() does not accept negative values''')

     _a      =  0
     _a      =  str(__A)

     while len(__A) != 1:
          _a      =  [int(__A) for i in num_string]

          _a      =  1
          for i in range(0  ,	len(__A)):
               total *= numbers[i]

          _a      =  str(__A)

          steps += 1
     return steps




def lowerCAmelCase  (__A):



     """simple docstring"""



     if not isinstance(__A  ,	__A):
          raise ValueError('''additive_persistence() only accepts integral values''')
     if num < 0:
          raise ValueError('''additive_persistence() does not accept negative values''')

     _a      =  0
     _a      =  str(__A)

     while len(__A) != 1:
          _a      =  [int(__A) for i in num_string]

          _a      =  0
          for i in range(0  ,	len(__A)):
               total += numbers[i]

          _a      =  str(__A)

          steps += 1
     return steps


if __name__ == "__main__":
  import doctest

  doctest.testmod()



