



"""simple docstring"""






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 	__snake_case	(     _lowercase):
  snake_case__    :       List[str]        =						(DPMSolverSDEScheduler,)
  snake_case__    :       List[Any]        =						1_0




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[str]						, **__lowerCAmelCase					:						Any		):






         """simple docstring"""






         _lowerCamelCase						:    Optional[int]         =			{
             '''num_train_timesteps''': 1_1_0_0,
             '''beta_start''': 0.00_01,
             '''beta_end''': 0.02,
             '''beta_schedule''': '''linear''',
             '''noise_sampler_seed''': 0,
         }

         config.update(**__lowerCAmelCase		)
         return config




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






         """simple docstring"""






         for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
                self.check_over_configs(num_train_timesteps=__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Dict		):






         """simple docstring"""






         for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]						, [0.00_02, 0.0_02, 0.02]		):
                self.check_over_configs(beta_start=__lowerCAmelCase						, beta_end=__lowerCAmelCase		)




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






         """simple docstring"""






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




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[int]		):






         """simple docstring"""






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




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[Any]		):






         """simple docstring"""






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

         scheduler.set_timesteps(self.num_inference_steps		)

         _lowerCamelCase						:    Tuple         =			self.dummy_model()
         _lowerCamelCase						:    Optional[int]         =			self.dummy_sample_deter * scheduler.init_noise_sigma
         _lowerCamelCase						:    int         =			sample.to(__lowerCAmelCase		)

         for i, t in enumerate(scheduler.timesteps		):
                _lowerCamelCase						:    Optional[Any]         =			scheduler.scale_model_input(__lowerCAmelCase						, __lowerCAmelCase		)

                _lowerCamelCase						:    Tuple         =			model(__lowerCAmelCase						, __lowerCAmelCase		)

                _lowerCamelCase						:    Tuple         =			scheduler.step(__lowerCAmelCase						, __lowerCAmelCase						, __lowerCAmelCase		)
                _lowerCamelCase						:    Optional[Any]         =			output.prev_sample

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

         if torch_device in ["mps"]:
                assert abs(result_sum.item() - 1_67.47_82_10_44_92_18_75		) < 1E-2
                assert abs(result_mean.item() - 0.21_78_70_59_64_56_52_77		) < 1E-3
         elif torch_device in ["cuda"]:
                assert abs(result_sum.item() - 1_71.59_35_21_11_81_64_06		) < 1E-2
                assert abs(result_mean.item() - 0.2_23_42_90_68_92_29_96_52		) < 1E-3
         else:
                assert abs(result_sum.item() - 1_62.52_38_34_22_85_15_62		) < 1E-2
                assert abs(result_mean.item() - 0.2_11_61_95_70_85_13_26		) < 1E-3




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[int]		):






         """simple docstring"""






         _lowerCamelCase						:    int         =			self.scheduler_classes[0]
         _lowerCamelCase						:    Tuple         =			self.get_scheduler_config(prediction_type='''v_prediction'''		)
         _lowerCamelCase						:    Tuple         =			scheduler_class(**__lowerCAmelCase		)

         scheduler.set_timesteps(self.num_inference_steps		)

         _lowerCamelCase						:    str         =			self.dummy_model()
         _lowerCamelCase						:    Optional[int]         =			self.dummy_sample_deter * scheduler.init_noise_sigma
         _lowerCamelCase						:    Union[str, Any]         =			sample.to(__lowerCAmelCase		)

         for i, t in enumerate(scheduler.timesteps		):
                _lowerCamelCase						:    Dict         =			scheduler.scale_model_input(__lowerCAmelCase						, __lowerCAmelCase		)

                _lowerCamelCase						:    Tuple         =			model(__lowerCAmelCase						, __lowerCAmelCase		)

                _lowerCamelCase						:    Tuple         =			scheduler.step(__lowerCAmelCase						, __lowerCAmelCase						, __lowerCAmelCase		)
                _lowerCamelCase						:    str         =			output.prev_sample

         _lowerCamelCase						:    Optional[Any]         =			torch.sum(torch.abs(__lowerCAmelCase		)		)
         _lowerCamelCase						:    Tuple         =			torch.mean(torch.abs(__lowerCAmelCase		)		)

         if torch_device in ["mps"]:
                assert abs(result_sum.item() - 1_24.77_14_92_00_43_94_53		) < 1E-2
                assert abs(result_mean.item() - 0.1_62_26_28_90_14_81_62_84		) < 1E-3
         elif torch_device in ["cuda"]:
                assert abs(result_sum.item() - 1_28.1_66_33_60_59_57_03		) < 1E-2
                assert abs(result_mean.item() - 0.1_66_88_32_60_01_16_72_97		) < 1E-3
         else:
                assert abs(result_sum.item() - 1_19.8_48_75_48_82_81_25		) < 1E-2
                assert abs(result_mean.item() - 0.15_60_53_06_62_53_66_21		) < 1E-3




  def 						SCREAMING_SNAKE_CASE    (			self					:						str		):






         """simple docstring"""






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

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

         _lowerCamelCase						:    Union[str, Any]         =			self.dummy_model()
         _lowerCamelCase						:    List[str]         =			self.dummy_sample_deter.to(__lowerCAmelCase		) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
                _lowerCamelCase						:    List[str]         =			scheduler.scale_model_input(__lowerCAmelCase						, __lowerCAmelCase		)

                _lowerCamelCase						:    List[Any]         =			model(__lowerCAmelCase						, __lowerCAmelCase		)

                _lowerCamelCase						:    List[str]         =			scheduler.step(__lowerCAmelCase						, __lowerCAmelCase						, __lowerCAmelCase		)
                _lowerCamelCase						:    Optional[Any]         =			output.prev_sample

         _lowerCamelCase						:    Optional[Any]         =			torch.sum(torch.abs(__lowerCAmelCase		)		)
         _lowerCamelCase						:    Optional[Any]         =			torch.mean(torch.abs(__lowerCAmelCase		)		)

         if torch_device in ["mps"]:
                assert abs(result_sum.item() - 1_67.46_95_73_97_46_09_38		) < 1E-2
                assert abs(result_mean.item() - 0.2_18_05_93_46_07_98_26_35		) < 1E-3
         elif torch_device in ["cuda"]:
                assert abs(result_sum.item() - 1_71.59_35_36_37_69_53_12		) < 1E-2
                assert abs(result_mean.item() - 0.2_23_42_90_83_82_41_57_71		) < 1E-3
         else:
                assert abs(result_sum.item() - 1_62.52_38_34_22_85_15_62		) < 1E-2
                assert abs(result_mean.item() - 0.2_11_61_95_70_85_13_26		) < 1E-3




  def 						SCREAMING_SNAKE_CASE    (			self					:						int		):






         """simple docstring"""






         _lowerCamelCase						:    Any         =			self.scheduler_classes[0]
         _lowerCamelCase						:    List[Any]         =			self.get_scheduler_config()
         _lowerCamelCase						:    Dict         =			scheduler_class(**__lowerCAmelCase						, use_karras_sigmas=__lowerCAmelCase		)

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

         _lowerCamelCase						:    Dict         =			self.dummy_model()
         _lowerCamelCase						:    Optional[Any]         =			self.dummy_sample_deter.to(__lowerCAmelCase		) * scheduler.init_noise_sigma
         _lowerCamelCase						:    Optional[int]         =			sample.to(__lowerCAmelCase		)

         for t in scheduler.timesteps:
                _lowerCamelCase						:    Dict         =			scheduler.scale_model_input(__lowerCAmelCase						, __lowerCAmelCase		)

                _lowerCamelCase						:    List[Any]         =			model(__lowerCAmelCase						, __lowerCAmelCase		)

                _lowerCamelCase						:    Optional[Any]         =			scheduler.step(__lowerCAmelCase						, __lowerCAmelCase						, __lowerCAmelCase		)
                _lowerCamelCase						:    str         =			output.prev_sample

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

         if torch_device in ["mps"]:
                assert abs(result_sum.item() - 1_76.66_97_41_35_74_21_88		) < 1E-2
                assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11		) < 1E-2
         elif torch_device in ["cuda"]:
                assert abs(result_sum.item() - 1_77.63_65_35_64_45_31_25		) < 1E-2
                assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11		) < 1E-2
         else:
                assert abs(result_sum.item() - 1_70.3_13_52_23_38_86_72		) < 1E-2
                assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11		) < 1E-2







"""simple docstring"""






import math






def 	snake_case_  (				A_ :		int						):


       '''simple docstring'''





       return math.sqrt(A_						) * math.sqrt(A_						) == num






def 	snake_case_  (				A_ :		int						):


       '''simple docstring'''





       _lowerCamelCase						:    Dict         =			0
       _lowerCamelCase						:    Tuple         =			n
       while left <= right:
              _lowerCamelCase						:    List[str]         =			(left + right) // 2
              if mid**2 == n:
                     return True
              elif mid**2 > n:
                     _lowerCamelCase						:    int         =			mid - 1
              else:
                     _lowerCamelCase						:    Optional[Any]         =			mid + 1
       return False


if __name__ == "__main__":
   import doctest

   doctest.testmod()


