





"""simple docstring"""





import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest




class 							snake_case							(			__UpperCAmelCase ):

      '''simple docstring'''



      _A     :       Optional[Any]         =       (DDPMScheduler,)

      def       A_					(							self       :      str						,				**__lowercase       :      Optional[Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[Any]       =						{
            '''num_train_timesteps''': 1_000,
            '''beta_start''': 0.0_0_0_1,
            '''beta_end''': 0.0_2,
            '''beta_schedule''': '''linear''',
            '''variance_type''': '''fixed_small''',
            '''clip_sample''': True,
        }

        config.update(**__lowerCAmelCase )
        return config

      def       A_					(							self       :      str ):


        '''simple docstring'''




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

      def       A_					(							self       :      int ):


        '''simple docstring'''




        for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]						,				[0.0_0_2, 0.0_2, 0.2, 2] ):
          self.check_over_configs(beta_start=__lowerCAmelCase						,				beta_end=__lowerCAmelCase )

      def       A_					(							self       :      Any ):


        '''simple docstring'''




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

      def       A_					(							self       :      Any ):


        '''simple docstring'''




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

      def       A_					(							self       :      Union[str, Any] ):


        '''simple docstring'''




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

      def       A_					(							self       :      str ):


        '''simple docstring'''




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

      def       A_					(							self       :      Optional[int] ):


        '''simple docstring'''




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

      def       A_					(							self       :      Optional[Any] ):


        '''simple docstring'''




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

      def       A_					(							self       :      Union[str, Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[int]       =						self.scheduler_classes[0]
        __UpperCAmelCase    :						Dict       =						self.get_scheduler_config()
        __UpperCAmelCase    :						Optional[int]       =						scheduler_class(**__lowerCAmelCase )

        assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_0_9_7_9 ) ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.0_2 ) ) < 1e-5

      def       A_					(							self       :      int ):


        '''simple docstring'''




        __UpperCAmelCase    :						int       =						self.scheduler_classes[0]
        __UpperCAmelCase    :						Any       =						self.get_scheduler_config()
        __UpperCAmelCase    :						Optional[int]       =						scheduler_class(**__lowerCAmelCase )

        __UpperCAmelCase    :						Tuple       =						len(__lowerCAmelCase )

        __UpperCAmelCase    :						str       =						self.dummy_model()
        __UpperCAmelCase    :						Optional[Any]       =						self.dummy_sample_deter
        __UpperCAmelCase    :						List[Any]       =						torch.manual_seed(0 )

        for t in reversed(range(__lowerCAmelCase ) ):
          # 1. predict noise residual
          __UpperCAmelCase    :						Tuple       =						model(__lowerCAmelCase						,				__lowerCAmelCase )

          # 2. predict previous mean of sample x_t-1
          __UpperCAmelCase    :						Optional[Any]       =						scheduler.step(__lowerCAmelCase						,				__lowerCAmelCase						,				__lowerCAmelCase						,				generator=__lowerCAmelCase ).prev_sample

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

        __UpperCAmelCase    :						Tuple       =						torch.sum(torch.abs(__lowerCAmelCase ) )
        __UpperCAmelCase    :						int       =						torch.mean(torch.abs(__lowerCAmelCase ) )

        assert abs(result_sum.item() - 2_5_8.9_6_0_6 ) < 1e-2
        assert abs(result_mean.item() - 0.3_3_7_2 ) < 1e-3

      def       A_					(							self       :      Optional[Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						List[Any]       =						self.scheduler_classes[0]
        __UpperCAmelCase    :						Dict       =						self.get_scheduler_config(prediction_type='''v_prediction''' )
        __UpperCAmelCase    :						Optional[int]       =						scheduler_class(**__lowerCAmelCase )

        __UpperCAmelCase    :						int       =						len(__lowerCAmelCase )

        __UpperCAmelCase    :						Optional[int]       =						self.dummy_model()
        __UpperCAmelCase    :						List[Any]       =						self.dummy_sample_deter
        __UpperCAmelCase    :						Tuple       =						torch.manual_seed(0 )

        for t in reversed(range(__lowerCAmelCase ) ):
          # 1. predict noise residual
          __UpperCAmelCase    :						Tuple       =						model(__lowerCAmelCase						,				__lowerCAmelCase )

          # 2. predict previous mean of sample x_t-1
          __UpperCAmelCase    :						Tuple       =						scheduler.step(__lowerCAmelCase						,				__lowerCAmelCase						,				__lowerCAmelCase						,				generator=__lowerCAmelCase ).prev_sample

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

        __UpperCAmelCase    :						List[Any]       =						torch.sum(torch.abs(__lowerCAmelCase ) )
        __UpperCAmelCase    :						Any       =						torch.mean(torch.abs(__lowerCAmelCase ) )

        assert abs(result_sum.item() - 2_0_2.0_2_9_6 ) < 1e-2
        assert abs(result_mean.item() - 0.2_6_3_1 ) < 1e-3

      def       A_					(							self       :      Union[str, Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[Any]       =						self.scheduler_classes[0]
        __UpperCAmelCase    :						Union[str, Any]       =						self.get_scheduler_config()
        __UpperCAmelCase    :						int       =						scheduler_class(**__lowerCAmelCase )

        __UpperCAmelCase    :						Optional[int]       =						[100, 87, 50, 1, 0]

        scheduler.set_timesteps(timesteps=__lowerCAmelCase )

        __UpperCAmelCase    :						Optional[int]       =						scheduler.timesteps

        for i, timestep in enumerate(__lowerCAmelCase ):
          if i == len(__lowerCAmelCase ) - 1:
            __UpperCAmelCase    :						int       =						-1
          else:
            __UpperCAmelCase    :						Dict       =						timesteps[i + 1]

          __UpperCAmelCase    :						Union[str, Any]       =						scheduler.previous_timestep(__lowerCAmelCase )
          __UpperCAmelCase    :						Optional[int]       =						prev_t.item()

          self.assertEqual(__lowerCAmelCase						,				__lowerCAmelCase )

      def       A_					(							self       :      List[Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Tuple       =						self.scheduler_classes[0]
        __UpperCAmelCase    :						Any       =						self.get_scheduler_config()
        __UpperCAmelCase    :						Union[str, Any]       =						scheduler_class(**__lowerCAmelCase )

        __UpperCAmelCase    :						int       =						[100, 87, 50, 51, 0]

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

      def       A_					(							self       :      Union[str, Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						List[str]       =						self.scheduler_classes[0]
        __UpperCAmelCase    :						Tuple       =						self.get_scheduler_config()
        __UpperCAmelCase    :						Dict       =						scheduler_class(**__lowerCAmelCase )

        __UpperCAmelCase    :						Optional[int]       =						[100, 87, 50, 1, 0]
        __UpperCAmelCase    :						str       =						len(__lowerCAmelCase )

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







      def       A_					(							self       :      str ):


        '''simple docstring'''




        __UpperCAmelCase    :						Union[str, Any]       =						self.scheduler_classes[0]
        __UpperCAmelCase    :						Optional[Any]       =						self.get_scheduler_config()
        __UpperCAmelCase    :						int       =						scheduler_class(**__lowerCAmelCase )

        __UpperCAmelCase    :						Union[str, Any]       =						[scheduler.config.num_train_timesteps]

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






"""simple docstring"""





def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->Tuple:





  """simple docstring"""


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


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_=0  )						->Dict:





  """simple docstring"""


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


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_=float('''inf'''  )  )						->str:





  """simple docstring"""


  for i in range(points_counts - 1  ):
    for j in range(i + 1  ,			UpperCAmelCase_  ):
      __UpperCAmelCase    :						List[Any]       =						euclidean_distance_sqr(points[i]  ,			points[j]  )
      if current_dis < min_dis:
        __UpperCAmelCase    :						Tuple       =						current_dis
  return min_dis


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_=float('''inf'''  )  )						->str:





  """simple docstring"""


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


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->Any:





  """simple docstring"""


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

  # recursion
  __UpperCAmelCase    :						Any       =						points_counts // 2
  __UpperCAmelCase    :						Any       =						closest_pair_of_points_sqr(
      UpperCAmelCase_  ,			points_sorted_on_y[:mid]  ,			UpperCAmelCase_  )
  __UpperCAmelCase    :						Tuple       =						closest_pair_of_points_sqr(
      UpperCAmelCase_  ,			points_sorted_on_y[mid:]  ,			points_counts - mid  )
  __UpperCAmelCase    :						List[Any]       =						min(UpperCAmelCase_  ,			UpperCAmelCase_  )
  __UpperCAmelCase    :						int       =						[]
  for point in points_sorted_on_x:
    if abs(point[0] - points_sorted_on_x[mid][0]  ) < closest_pair_dis:
      cross_strip.append(UpperCAmelCase_  )

  __UpperCAmelCase    :						Union[str, Any]       =						dis_between_closest_in_strip(
      UpperCAmelCase_  ,			len(UpperCAmelCase_  )  ,			UpperCAmelCase_  )
  return min(UpperCAmelCase_  ,			UpperCAmelCase_  )


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->List[Any]:





  """simple docstring"""


  __UpperCAmelCase    :						str       =						column_based_sort(UpperCAmelCase_  ,			column=0  )
  __UpperCAmelCase    :						Any       =						column_based_sort(UpperCAmelCase_  ,			column=1  )
  return (
      closest_pair_of_points_sqr(
          UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )
  ) ** 0.5


if __name__ == "__main__":
  lowercase__				:Optional[Any]				   =       [(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)))