



'''simple docstring'''





import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest






class __A     (    A		):



 '''simple docstring'''







 __lowerCamelCase       :			List[Any]		=			(DDPMScheduler,)



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


      """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			)       ->	Union[str, Any]:


      """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 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			)       ->	int:


      """simple docstring"""



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



 def    a__    (self			)       ->	str:


      """simple docstring"""



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



 def    a__    (self			)       ->	Any:


      """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			)       ->	str:


      """simple docstring"""



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



 def    a__    (self			)       ->	int:


      """simple docstring"""



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



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


      """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			)       ->	Optional[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           =    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

           # if t > 0:
           #     noise = self.dummy_sample_deter
           #     variance = scheduler.get_variance(t) ** (0.5) * noise
           #
           # sample = pred_prev_sample + variance
           _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			)       ->	Dict:


      """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

           # if t > 0:
           #     noise = self.dummy_sample_deter
           #     variance = scheduler.get_variance(t) ** (0.5) * noise
           #
           # sample = pred_prev_sample + variance
           _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[Any]:


      """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			)       ->	Tuple:


      """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			)       ->	str:


      """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			)







def 		_A		(		_lowercase					,  _lowercase )					-> int:


     """simple docstring"""


     return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2






def 		_A		(		_lowercase					,  _lowercase=0 )					-> Dict:


     """simple docstring"""


     return sorted(_lowercase					,  key=lambda _lowercase : x[column] )






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase=float('inf' ) )					-> List[Any]:


     """simple docstring"""


     for i in range(points_counts - 1 ):
          for j in range(i + 1					,  _lowercase ):
               __UpperCamelCase =			euclidean_distance_sqr(points[i]					,  points[j] )
               if current_dis < min_dis:
                    __UpperCamelCase =			current_dis
     return min_dis






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase=float('inf' ) )					-> Tuple:


     """simple docstring"""


     for i in range(min(6					,  points_counts - 1 )					,  _lowercase ):
          for j in range(max(0					,  i - 6 )					,  _lowercase ):
               __UpperCamelCase =			euclidean_distance_sqr(points[i]					,  points[j] )
               if current_dis < min_dis:
                    __UpperCamelCase =			current_dis
     return min_dis






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase )					-> Optional[Any]:


     """simple docstring"""


     if points_counts <= 3:
          return dis_between_closest_pair(_lowercase					,  _lowercase )

     # recursion
     __UpperCamelCase =			points_counts // 2
     __UpperCamelCase =			closest_pair_of_points_sqr(
         _lowercase					,  points_sorted_on_y[:mid]					,  _lowercase )
     __UpperCamelCase =			closest_pair_of_points_sqr(
         _lowercase					,  points_sorted_on_y[mid:]					,  points_counts - mid )
     __UpperCamelCase =			min(_lowercase					,  _lowercase )
     __UpperCamelCase =			[]
     for point in points_sorted_on_x:
          if abs(point[0] - points_sorted_on_x[mid][0] ) < closest_pair_dis:
               cross_strip.append(_lowercase )

     __UpperCamelCase =			dis_between_closest_in_strip(
         _lowercase					,  len(_lowercase )					,  _lowercase )
     return min(_lowercase					,  _lowercase )






def 		_A		(		_lowercase					,  _lowercase )					-> Optional[int]:


     """simple docstring"""


     __UpperCamelCase =			column_based_sort(_lowercase					,  column=0 )
     __UpperCamelCase =			column_based_sort(_lowercase					,  column=1 )
     return (
         closest_pair_of_points_sqr(
             _lowercase					,  _lowercase					,  _lowercase )
     ) ** 0.5


if __name__ == "__main__":
    __snake_case   							=						[(2, 3), (1_2, 3_0), (4_0, 5_0), (5, 1), (1_2, 1_0), (3, 4)]
    print('''Distance:''', closest_pair_of_points(points, len(points)))


