


import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest



@require_torchsde
class 	__lowerCamelCase			(_a						):
       _lowercase											=     (DPMSolverSDEScheduler,)
       _lowercase											=     10







       def 		snake_case_	(			self: Tuple,**A_: List[str]					):






            '''simple docstring'''




            __UpperCamelCase =			{
                'num_train_timesteps': 1100,
                'beta_start': 0.0_0_0_1,
                'beta_end': 0.0_2,
                'beta_schedule': 'linear',
                'noise_sampler_seed': 0,
            }

            config.update(**A_					)
            return config







       def 		snake_case_	(			self: List[Any]					):






            '''simple docstring'''




            for timesteps in [10, 50, 100, 1000]:
                 self.check_over_configs(num_train_timesteps=A_					)







       def 		snake_case_	(			self: str					):






            '''simple docstring'''




            for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1],[0.0_0_0_2, 0.0_0_2, 0.0_2]					):
                 self.check_over_configs(beta_start=A_,beta_end=A_					)







       def 		snake_case_	(			self: Dict					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: List[str]					):






            '''simple docstring'''




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







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






            '''simple docstring'''




            __UpperCamelCase =			self.scheduler_classes[0]
            __UpperCamelCase =			self.get_scheduler_config()
            __UpperCamelCase =			scheduler_class(**A_					)

            scheduler.set_timesteps(self.num_inference_steps					)

            __UpperCamelCase =			self.dummy_model()
            __UpperCamelCase =			self.dummy_sample_deter * scheduler.init_noise_sigma
            __UpperCamelCase =			sample.to(A_					)

            for i, t in enumerate(scheduler.timesteps					):
                 __UpperCamelCase =			scheduler.scale_model_input(A_,A_					)

                 __UpperCamelCase =			model(A_,A_					)

                 __UpperCamelCase =			scheduler.step(A_,A_,A_					)
                 __UpperCamelCase =			output.prev_sample

            __UpperCamelCase =			torch.sum(torch.abs(A_					)					)
            __UpperCamelCase =			torch.mean(torch.abs(A_					)					)

            if torch_device in ["mps"]:
                 assert abs(result_sum.item() - 1_6_7.4_7_8_2_1_0_4_4_9_2_1_8_7_5					) < 1E-2
                 assert abs(result_mean.item() - 0.2_1_7_8_7_0_5_9_6_4_5_6_5_2_7_7					) < 1E-3
            elif torch_device in ["cuda"]:
                 assert abs(result_sum.item() - 1_7_1.5_9_3_5_2_1_1_1_8_1_6_4_0_6					) < 1E-2
                 assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_6_8_9_2_2_9_9_6_5_2					) < 1E-3
            else:
                 assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2					) < 1E-2
                 assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6					) < 1E-3







       def 		snake_case_	(			self: str					):






            '''simple docstring'''




            __UpperCamelCase =			self.scheduler_classes[0]
            __UpperCamelCase =			self.get_scheduler_config(prediction_type='v_prediction'					)
            __UpperCamelCase =			scheduler_class(**A_					)

            scheduler.set_timesteps(self.num_inference_steps					)

            __UpperCamelCase =			self.dummy_model()
            __UpperCamelCase =			self.dummy_sample_deter * scheduler.init_noise_sigma
            __UpperCamelCase =			sample.to(A_					)

            for i, t in enumerate(scheduler.timesteps					):
                 __UpperCamelCase =			scheduler.scale_model_input(A_,A_					)

                 __UpperCamelCase =			model(A_,A_					)

                 __UpperCamelCase =			scheduler.step(A_,A_,A_					)
                 __UpperCamelCase =			output.prev_sample

            __UpperCamelCase =			torch.sum(torch.abs(A_					)					)
            __UpperCamelCase =			torch.mean(torch.abs(A_					)					)

            if torch_device in ["mps"]:
                 assert abs(result_sum.item() - 1_2_4.7_7_1_4_9_2_0_0_4_3_9_4_5_3					) < 1E-2
                 assert abs(result_mean.item() - 0.1_6_2_2_6_2_8_9_0_1_4_8_1_6_2_8_4					) < 1E-3
            elif torch_device in ["cuda"]:
                 assert abs(result_sum.item() - 1_2_8.1_6_6_3_3_6_0_5_9_5_7_0_3					) < 1E-2
                 assert abs(result_mean.item() - 0.1_6_6_8_8_3_2_6_0_0_1_1_6_7_2_9_7					) < 1E-3
            else:
                 assert abs(result_sum.item() - 1_1_9.8_4_8_7_5_4_8_8_2_8_1_2_5					) < 1E-2
                 assert abs(result_mean.item() - 0.1_5_6_0_5_3_0_6_6_2_5_3_6_6_2_1					) < 1E-3







       def 		snake_case_	(			self: List[Any]					):






            '''simple docstring'''




            __UpperCamelCase =			self.scheduler_classes[0]
            __UpperCamelCase =			self.get_scheduler_config()
            __UpperCamelCase =			scheduler_class(**A_					)

            scheduler.set_timesteps(self.num_inference_steps,device=A_					)

            __UpperCamelCase =			self.dummy_model()
            __UpperCamelCase =			self.dummy_sample_deter.to(A_					) * scheduler.init_noise_sigma

            for t in scheduler.timesteps:
                 __UpperCamelCase =			scheduler.scale_model_input(A_,A_					)

                 __UpperCamelCase =			model(A_,A_					)

                 __UpperCamelCase =			scheduler.step(A_,A_,A_					)
                 __UpperCamelCase =			output.prev_sample

            __UpperCamelCase =			torch.sum(torch.abs(A_					)					)
            __UpperCamelCase =			torch.mean(torch.abs(A_					)					)

            if torch_device in ["mps"]:
                 assert abs(result_sum.item() - 1_6_7.4_6_9_5_7_3_9_7_4_6_0_9_3_8					) < 1E-2
                 assert abs(result_mean.item() - 0.2_1_8_0_5_9_3_4_6_0_7_9_8_2_6_3_5					) < 1E-3
            elif torch_device in ["cuda"]:
                 assert abs(result_sum.item() - 1_7_1.5_9_3_5_3_6_3_7_6_9_5_3_1_2					) < 1E-2
                 assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_8_3_8_2_4_1_5_7_7_1					) < 1E-3
            else:
                 assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2					) < 1E-2
                 assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6					) < 1E-3







       def 		snake_case_	(			self: Dict					):






            '''simple docstring'''




            __UpperCamelCase =			self.scheduler_classes[0]
            __UpperCamelCase =			self.get_scheduler_config()
            __UpperCamelCase =			scheduler_class(**A_,use_karras_sigmas=A_					)

            scheduler.set_timesteps(self.num_inference_steps,device=A_					)

            __UpperCamelCase =			self.dummy_model()
            __UpperCamelCase =			self.dummy_sample_deter.to(A_					) * scheduler.init_noise_sigma
            __UpperCamelCase =			sample.to(A_					)

            for t in scheduler.timesteps:
                 __UpperCamelCase =			scheduler.scale_model_input(A_,A_					)

                 __UpperCamelCase =			model(A_,A_					)

                 __UpperCamelCase =			scheduler.step(A_,A_,A_					)
                 __UpperCamelCase =			output.prev_sample

            __UpperCamelCase =			torch.sum(torch.abs(A_					)					)
            __UpperCamelCase =			torch.mean(torch.abs(A_					)					)

            if torch_device in ["mps"]:
                 assert abs(result_sum.item() - 1_7_6.6_6_9_7_4_1_3_5_7_4_2_1_8_8					) < 1E-2
                 assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1					) < 1E-2
            elif torch_device in ["cuda"]:
                 assert abs(result_sum.item() - 1_7_7.6_3_6_5_3_5_6_4_4_5_3_1_2_5					) < 1E-2
                 assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1					) < 1E-2
            else:
                 assert abs(result_sum.item() - 1_7_0.3_1_3_5_2_2_3_3_8_8_6_7_2					) < 1E-2
                 assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1					) < 1E-2






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)))


