



'''simple docstring'''





import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class __A    (						A		):



 '''simple docstring'''







 __lowerCamelCase					: Dict    = (DDPMParallelScheduler,)



 def       a__						(self				,     **A )					->  str:


      """simple docstring"""



      _a											=						{
          '''num_train_timesteps''': 1_000,
          '''beta_start''': 0.0001,
          '''beta_end''': 0.02,
          '''beta_schedule''': '''linear''',
          '''variance_type''': '''fixed_small''',
          '''clip_sample''': True,
      }

      config.update(**A )
      return config



 def       a__						(self )					->  Any:


      """simple docstring"""



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



 def       a__						(self )					->  Dict:


      """simple docstring"""



      for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]				,     [0.002, 0.02, 0.2, 2] ):
           self.check_over_configs(beta_start=A				,     beta_end=A )



 def       a__						(self )					->  str:


      """simple docstring"""



      for schedule in ["linear", "squaredcos_cap_v2"]:
           self.check_over_configs(beta_schedule=A )



 def       a__						(self )					->  Dict:


      """simple docstring"""



      for variance in ["fixed_small", "fixed_large", "other"]:
           self.check_over_configs(variance_type=A )



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


      """simple docstring"""



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



 def       a__						(self )					->  str:


      """simple docstring"""



      self.check_over_configs(thresholding=A )
      for threshold in [0.5, 1.0, 2.0]:
           for prediction_type in ["epsilon", "sample", "v_prediction"]:
                self.check_over_configs(
                    thresholding=A				,     prediction_type=A				,     sample_max_value=A				,     )



 def       a__						(self )					->  Any:


      """simple docstring"""



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



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


      """simple docstring"""



      for t in [0, 500, 999]:
           self.check_over_forward(time_step=A )



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


      """simple docstring"""



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

      assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.00979 ) ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.02 ) ) < 1E-5



 def       a__						(self )					->  Any:


      """simple docstring"""



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

      _a											=						len(A )

      _a											=						self.dummy_model()
      _a											=						self.dummy_sample_deter
      _a											=						self.dummy_sample_deter + 0.1
      _a											=						self.dummy_sample_deter - 0.1

      _a											=						samplea.shape[0]
      _a											=						torch.stack([samplea, samplea, samplea]				,     dim=0 )
      _a											=						torch.arange(A )[0:3, None].repeat(1				,     A )

      _a											=						model(samples.flatten(0				,     1 )				,     timesteps.flatten(0				,     1 ) )
      _a											=						scheduler.batch_step_no_noise(A				,     timesteps.flatten(0				,     1 )				,     samples.flatten(0				,     1 ) )

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

      assert abs(result_sum.item() - 1153.1833 ) < 1E-2
      assert abs(result_mean.item() - 0.5005 ) < 1E-3



 def       a__						(self )					->  Tuple:


      """simple docstring"""



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

      _a											=						len(A )

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

      for t in reversed(range(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() - 258.9606 ) < 1E-2
      assert abs(result_mean.item() - 0.3372 ) < 1E-3



 def       a__						(self )					->  Any:


      """simple docstring"""



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

      _a											=						len(A )

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

      for t in reversed(range(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() - 202.0296 ) < 1E-2
      assert abs(result_mean.item() - 0.2631 ) < 1E-3



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


      """simple docstring"""



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

      _a											=						[100, 87, 50, 1, 0]

      scheduler.set_timesteps(timesteps=A )

      _a											=						scheduler.timesteps

      for i, timestep in enumerate(A ):
           if i == len(A ) - 1:
                _a											=						-1
           else:
                _a											=						timesteps[i + 1]

           _a											=						scheduler.previous_timestep(A )
           _a											=						prev_t.item()

           self.assertEqual(A				,     A )



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


      """simple docstring"""



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

      _a											=						[100, 87, 50, 51, 0]

      with self.assertRaises(A				,     msg='''`custom_timesteps` must be in descending order.''' ):
           scheduler.set_timesteps(timesteps=A )



 def       a__						(self )					->  Dict:


      """simple docstring"""



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

      _a											=						[100, 87, 50, 1, 0]
      _a											=						len(A )

      with self.assertRaises(A				,     msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.''' ):
           scheduler.set_timesteps(num_inference_steps=A				,     timesteps=A )



 def       a__						(self )					->  int:


      """simple docstring"""



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

      _a											=						[scheduler.config.num_train_timesteps]

      with self.assertRaises(
          A				,     msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''				,     ):
           scheduler.set_timesteps(timesteps=A )








'''simple docstring'''





def lowerCAmelCase						(__A):



     """simple docstring"""



     _a											=						set()
     # To detect a back edge, keep track of vertices currently in the recursion stack
     _a											=						set()
     return any(
         node not in visited and depth_first_search(__A						,			__A						,			__A						,			__A)
         for node in graph)




def lowerCAmelCase						(__A						,			__A						,			__A						,			__A):



     """simple docstring"""



     visited.add(__A)
     rec_stk.add(__A)

     for node in graph[vertex]:
          if node not in visited:
               if depth_first_search(__A						,			__A						,			__A						,			__A):
                    return True
          elif node in rec_stk:
               return True

    # The node needs to be removed from recursion stack before function ends
     rec_stk.remove(__A)
     return False


if __name__ == "__main__":
  from doctest import testmod

  testmod()



