





import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest






class  _SCREAMING_SNAKE_CASE(       _a  ):
       SCREAMING_SNAKE_CASE_       :				List[str]     						=  (UnCLIPScheduler,)




       def    _UpperCamelCase					(						self		,**SCREAMING_SNAKE_CASE__							)      ->				int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Tuple		    =  {
                 '''num_train_timesteps''': 10_00,
                 '''variance_type''': '''fixed_small_log''',
                 '''clip_sample''': True,
                 '''clip_sample_range''': 1.0,
                 '''prediction_type''': '''epsilon''',
             }

             config.update(**__lowerCAmelCase							)
             return config




       def    _UpperCamelCase					(						self							)      ->				Dict:





             """simple docstring"""






             for timesteps in [1, 5, 1_00, 10_00]:
                   self.check_over_configs(num_train_timesteps=__lowerCAmelCase							)




       def    _UpperCamelCase					(						self							)      ->				int:





             """simple docstring"""






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




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





             """simple docstring"""






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




       def    _UpperCamelCase					(						self							)      ->				Dict:





             """simple docstring"""






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




       def    _UpperCamelCase					(						self							)      ->				str:





             """simple docstring"""






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




       def    _UpperCamelCase					(						self							)      ->				Tuple:





             """simple docstring"""






             for time_step in [0, 5_00, 9_99]:
                   for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
                         if prev_timestep is not None and prev_timestep >= time_step:
                               continue

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




       def    _UpperCamelCase					(						self							)      ->				Tuple:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  self.scheduler_classes[0]
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  self.get_scheduler_config(variance_type='''fixed_small_log'''							)
             __SCREAMING_SNAKE_CASE       :Tuple		    =  scheduler_class(**__lowerCAmelCase							)

             assert torch.sum(torch.abs(scheduler._get_variance(0							) - 1.0_000E-10							)							) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(4_87							) - 0.0_5_4_9_6_2_5							)							) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(9_99							) - 0.9_9_9_4_9_8_7							)							) < 1E-5




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  self.scheduler_classes[0]
             __SCREAMING_SNAKE_CASE       :List[str]		    =  self.get_scheduler_config(variance_type='''learned_range'''							)
             __SCREAMING_SNAKE_CASE       :int		    =  scheduler_class(**__lowerCAmelCase							)

             __SCREAMING_SNAKE_CASE       :Any		    =  0.5

             assert scheduler._get_variance(1		,predicted_variance=__lowerCAmelCase							) - -1_0.1_7_1_2_7_9_0 < 1E-5
             assert scheduler._get_variance(4_87		,predicted_variance=__lowerCAmelCase							) - -5.7_9_9_8_0_5_2 < 1E-5
             assert scheduler._get_variance(9_99		,predicted_variance=__lowerCAmelCase							) - -0.0_0_1_0_0_1_1 < 1E-5




       def    _UpperCamelCase					(						self							)      ->				str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[Any]		    =  self.scheduler_classes[0]
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  self.get_scheduler_config()
             __SCREAMING_SNAKE_CASE       :Dict		    =  scheduler_class(**__lowerCAmelCase							)

             __SCREAMING_SNAKE_CASE       :Tuple		    =  scheduler.timesteps

             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  self.dummy_model()
             __SCREAMING_SNAKE_CASE       :Dict		    =  self.dummy_sample_deter
             __SCREAMING_SNAKE_CASE       :Dict		    =  torch.manual_seed(0							)

             for i, t in enumerate(__lowerCAmelCase							):
                   # 1. predict noise residual
                   __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  model(__lowerCAmelCase		,__lowerCAmelCase							)

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

                   __SCREAMING_SNAKE_CASE       :str		    =  pred_prev_sample

             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  torch.sum(torch.abs(__lowerCAmelCase							)							)
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  torch.mean(torch.abs(__lowerCAmelCase							)							)

             assert abs(result_sum.item() - 2_5_2.2_6_8_2_4_9_5							) < 1E-2
             assert abs(result_mean.item() - 0.3_2_8_4_7_4_3							) < 1E-3




       def    _UpperCamelCase					(						self							)      ->				Any:





             """simple docstring"""






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

             scheduler.set_timesteps(25							)

             __SCREAMING_SNAKE_CASE       :Dict		    =  scheduler.timesteps

             __SCREAMING_SNAKE_CASE       :Tuple		    =  self.dummy_model()
             __SCREAMING_SNAKE_CASE       :List[str]		    =  self.dummy_sample_deter
             __SCREAMING_SNAKE_CASE       :Dict		    =  torch.manual_seed(0							)

             for i, t in enumerate(__lowerCAmelCase							):
                   # 1. predict noise residual
                   __SCREAMING_SNAKE_CASE       :int		    =  model(__lowerCAmelCase		,__lowerCAmelCase							)

                   if i + 1 == timesteps.shape[0]:
                         __SCREAMING_SNAKE_CASE       :Optional[int]		    =  None
                   else:
                         __SCREAMING_SNAKE_CASE       :int		    =  timesteps[i + 1]

                   # 2. predict previous mean of sample x_t-1
                   __SCREAMING_SNAKE_CASE       :List[str]		    =  scheduler.step(
                       __lowerCAmelCase		,__lowerCAmelCase		,__lowerCAmelCase		,prev_timestep=__lowerCAmelCase		,generator=__lowerCAmelCase							).prev_sample

                   __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  pred_prev_sample

             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  torch.sum(torch.abs(__lowerCAmelCase							)							)
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  torch.mean(torch.abs(__lowerCAmelCase							)							)

             assert abs(result_sum.item() - 2_5_8.2_0_4_4_9_8_3							) < 1E-2
             assert abs(result_mean.item() - 0.3_3_6_2_0_3_8							) < 1E-3




       def    _UpperCamelCase					(						self							)      ->				int:





             """simple docstring"""






             pass




       def    _UpperCamelCase					(						self							)      ->				Dict:





             """simple docstring"""






             pass






"""simple docstring"""
def 					__lowerCamelCase       (							a_					:		Union[str, Any]			,       a_					:		Optional[Any]							)      ->				Union[str, Any]:
      return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2



def 					__lowerCamelCase       (							a_					:		Optional[int]			,       a_					:		Any=0							)      ->				Optional[Any]:
      return sorted(a_			,       key=lambda a_							: x[column]							)



def 					__lowerCamelCase       (							a_					:		Optional[Any]			,       a_					:		Optional[int]			,       a_					:		str=float('''inf'''							)							)      ->				str:

      for i in range(points_counts - 1							):
            for j in range(i + 1			,       a_							):
                  __SCREAMING_SNAKE_CASE       :Dict		    =  euclidean_distance_sqr(points[i]			,       points[j]							)
                  if current_dis < min_dis:
                        __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  current_dis
      return min_dis



def 					__lowerCamelCase       (							a_					:		List[Any]			,       a_					:		Any			,       a_					:		Optional[int]=float('''inf'''							)							)      ->				Optional[Any]:

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



def 					__lowerCamelCase       (							a_					:		str			,       a_					:		List[Any]			,       a_					:		int							)      ->				Optional[int]:

      # base case
      if points_counts <= 3:
            return dis_between_closest_pair(a_			,       a_							)

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

      __SCREAMING_SNAKE_CASE       :Dict		    =  dis_between_closest_in_strip(
          a_			,       len(a_							)			,       a_							)
      return min(a_			,       a_							)



def 					__lowerCamelCase       (							a_					:		int			,       a_					:		Tuple							)      ->				List[Any]:
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  column_based_sort(a_			,       column=0							)
      __SCREAMING_SNAKE_CASE       :int		    =  column_based_sort(a_			,       column=1							)
      return (
          closest_pair_of_points_sqr(
              a_			,       a_			,       a_							)
      ) ** 0.5


if __name__ == "__main__":
  lowerCamelCase_       =		[(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)))