



"""simple docstring"""







import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest







class 				UpperCamelCase__       (     lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					(CMStochasticIterativeScheduler,)
      _SCREAMING_SNAKE_CASE      =					10



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple   ,      **SCREAMING_SNAKE_CASE_    :				Any	):
        lowerCAmelCase_       :    List[str]								=			{
            'num_train_timesteps': 2_0_1,
            'sigma_min': 0.0_02,
            'sigma_max': 80.0,
        }

        config.update(**SCREAMING_SNAKE_CASE_	)
        return config



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]	):
        lowerCAmelCase_       :    str								=			1_0

        lowerCAmelCase_       :    Dict								=			self.get_scheduler_config()
        lowerCAmelCase_       :    str								=			self.scheduler_classes[0](**SCREAMING_SNAKE_CASE_	)

        scheduler.set_timesteps(SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_       :    Optional[Any]								=			scheduler.timesteps[0]
        lowerCAmelCase_       :    Tuple								=			scheduler.timesteps[1]

        lowerCAmelCase_       :    Optional[Any]								=			self.dummy_sample
        lowerCAmelCase_       :    Dict								=			0.1 * sample

        lowerCAmelCase_       :    str								=			scheduler.step(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	).prev_sample
        lowerCAmelCase_       :    str								=			scheduler.step(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	).prev_sample

        self.assertEqual(output_a.shape   ,      sample.shape	)
        self.assertEqual(output_a.shape   ,      output_a.shape	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
        for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
          self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
        for clip_denoised in [True, False]:
          self.check_over_configs(clip_denoised=SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple	):
        lowerCAmelCase_       :    List[str]								=			self.scheduler_classes[0]
        lowerCAmelCase_       :    Tuple								=			self.get_scheduler_config()
        lowerCAmelCase_       :    Tuple								=			scheduler_class(**SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_       :    Any								=			1
        scheduler.set_timesteps(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Tuple								=			scheduler.timesteps

        lowerCAmelCase_       :    List[Any]								=			torch.manual_seed(0	)

        lowerCAmelCase_       :    Union[str, Any]								=			self.dummy_model()
        lowerCAmelCase_       :    Optional[int]								=			self.dummy_sample_deter * scheduler.init_noise_sigma

        for i, t in enumerate(SCREAMING_SNAKE_CASE_	):
          # 1. scale model input
          lowerCAmelCase_       :    str								=			scheduler.scale_model_input(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)

          # 2. predict noise residual
          lowerCAmelCase_       :    Any								=			model(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)

          # 3. predict previous sample x_t-1
          lowerCAmelCase_       :    Tuple								=			scheduler.step(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_   ,      generator=SCREAMING_SNAKE_CASE_	).prev_sample

          lowerCAmelCase_       :    Optional[int]								=			pred_prev_sample

        lowerCAmelCase_       :    List[str]								=			torch.sum(torch.abs(SCREAMING_SNAKE_CASE_	)	)
        lowerCAmelCase_       :    Optional[Any]								=			torch.mean(torch.abs(SCREAMING_SNAKE_CASE_	)	)

        assert abs(result_sum.item() - 1_92.76_14	) < 1E-2
        assert abs(result_mean.item() - 0.25_10	) < 1E-3



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]	):
        lowerCAmelCase_       :    Optional[Any]								=			self.scheduler_classes[0]
        lowerCAmelCase_       :    Tuple								=			self.get_scheduler_config()
        lowerCAmelCase_       :    Tuple								=			scheduler_class(**SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_       :    Dict								=			[1_0_6, 0]
        scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Optional[int]								=			scheduler.timesteps

        lowerCAmelCase_       :    Tuple								=			torch.manual_seed(0	)

        lowerCAmelCase_       :    Tuple								=			self.dummy_model()
        lowerCAmelCase_       :    str								=			self.dummy_sample_deter * scheduler.init_noise_sigma

        for t in timesteps:
          # 1. scale model input
          lowerCAmelCase_       :    Optional[int]								=			scheduler.scale_model_input(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)

          # 2. predict noise residual
          lowerCAmelCase_       :    Union[str, Any]								=			model(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)

          # 3. predict previous sample x_t-1
          lowerCAmelCase_       :    Tuple								=			scheduler.step(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_   ,      generator=SCREAMING_SNAKE_CASE_	).prev_sample

          lowerCAmelCase_       :    Optional[Any]								=			pred_prev_sample

        lowerCAmelCase_       :    Tuple								=			torch.sum(torch.abs(SCREAMING_SNAKE_CASE_	)	)
        lowerCAmelCase_       :    Tuple								=			torch.mean(torch.abs(SCREAMING_SNAKE_CASE_	)	)

        assert abs(result_sum.item() - 3_47.63_57	) < 1E-2
        assert abs(result_mean.item() - 0.45_27	) < 1E-3



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple	):
        lowerCAmelCase_       :    str								=			self.scheduler_classes[0]
        lowerCAmelCase_       :    Dict								=			self.get_scheduler_config()
        lowerCAmelCase_       :    Tuple								=			scheduler_class(**SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_       :    Any								=			[3_9, 3_0, 1_2, 1_5, 0]

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



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Any	):
        lowerCAmelCase_       :    int								=			self.scheduler_classes[0]
        lowerCAmelCase_       :    Optional[int]								=			self.get_scheduler_config()
        lowerCAmelCase_       :    Tuple								=			scheduler_class(**SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_       :    Dict								=			[3_9, 3_0, 1_2, 1, 0]
        lowerCAmelCase_       :    Dict								=			len(SCREAMING_SNAKE_CASE_	)

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





      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]	):
        lowerCAmelCase_       :    Optional[int]								=			self.scheduler_classes[0]
        lowerCAmelCase_       :    List[Any]								=			self.get_scheduler_config()
        lowerCAmelCase_       :    Union[str, Any]								=			scheduler_class(**SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_       :    Optional[int]								=			[scheduler.config.num_train_timesteps]

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










"""simple docstring"""







def     UpperCamelCase_   (   lowerCAmelCase__				:    Optional[Any]   ,       lowerCAmelCase__				:    str						)						->       List[Any]:

  """simple docstring"""




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

def     UpperCamelCase_   (   lowerCAmelCase__				:    List[Any]   ,       lowerCAmelCase__				:    str=0						)						->       Union[str, Any]:

  """simple docstring"""




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

def     UpperCamelCase_   (   lowerCAmelCase__				:    Dict   ,       lowerCAmelCase__				:    Optional[int]   ,       lowerCAmelCase__				:    Optional[Any]=float('inf'						)						)						->       Optional[int]:

  """simple docstring"""




  for i in range(points_counts - 1						):
    for j in range(i + 1   ,       lowerCAmelCase__						):
      lowerCAmelCase_       :    Union[str, Any]								=			euclidean_distance_sqr(points[i]   ,       points[j]						)
      if current_dis < min_dis:
        lowerCAmelCase_       :    Optional[int]								=			current_dis
  return min_dis

def     UpperCamelCase_   (   lowerCAmelCase__				:    List[Any]   ,       lowerCAmelCase__				:    Dict   ,       lowerCAmelCase__				:    Union[str, Any]=float('inf'						)						)						->       Dict:

  """simple docstring"""




  for i in range(min(6   ,       points_counts - 1						)   ,       lowerCAmelCase__						):
    for j in range(max(0   ,       i - 6						)   ,       lowerCAmelCase__						):
      lowerCAmelCase_       :    Any								=			euclidean_distance_sqr(points[i]   ,       points[j]						)
      if current_dis < min_dis:
        lowerCAmelCase_       :    Union[str, Any]								=			current_dis
  return min_dis

def     UpperCamelCase_   (   lowerCAmelCase__				:    Dict   ,       lowerCAmelCase__				:    int   ,       lowerCAmelCase__				:    Optional[int]						)						->       Dict:

  """simple docstring"""




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

  # recursion
  lowerCAmelCase_       :    int								=			points_counts // 2
  lowerCAmelCase_       :    Optional[Any]								=			closest_pair_of_points_sqr(
      lowerCAmelCase__   ,       points_sorted_on_y[:mid]   ,       lowerCAmelCase__						)
  lowerCAmelCase_       :    Optional[Any]								=			closest_pair_of_points_sqr(
      lowerCAmelCase__   ,       points_sorted_on_y[mid:]   ,       points_counts - mid						)
  lowerCAmelCase_       :    Any								=			min(lowerCAmelCase__   ,       lowerCAmelCase__						)
  lowerCAmelCase_       :    str								=			[]
  for point in points_sorted_on_x:
    if abs(point[0] - points_sorted_on_x[mid][0]						) < closest_pair_dis:
      cross_strip.append(lowerCAmelCase__						)

  lowerCAmelCase_       :    List[Any]								=			dis_between_closest_in_strip(
      lowerCAmelCase__   ,       len(lowerCAmelCase__						)   ,       lowerCAmelCase__						)
  return min(lowerCAmelCase__   ,       lowerCAmelCase__						)

def     UpperCamelCase_   (   lowerCAmelCase__				:    str   ,       lowerCAmelCase__				:    Tuple						)						->       List[Any]:

  """simple docstring"""




  lowerCAmelCase_       :    List[str]								=			column_based_sort(lowerCAmelCase__   ,       column=0						)
  lowerCAmelCase_       :    Dict								=			column_based_sort(lowerCAmelCase__   ,       column=1						)
  return (
      closest_pair_of_points_sqr(
          lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__						)
  ) ** 0.5


if __name__ == "__main__":
     lowercase__		:    List[str]				   =     [(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)))





