






'''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 UpperCAmelCase_					(    __A				):




      """simple docstring"""




      UpperCamelCase_								=			(DPMSolverSDEScheduler,)
      UpperCamelCase_								=			10






      def 				A__      ( self			:		int     ,      **UpperCAmelCase			:		Optional[Any]		)  ->  Optional[Any]:


       '''simple docstring'''

       lowercase					:   List[str]				  ={
           '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(**UpperCamelCase__		)
       return config






      def 				A__      ( self			:		str		)  ->  Tuple:


       '''simple docstring'''

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






      def 				A__      ( self			:		Any		)  ->  List[Any]:


       '''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=UpperCamelCase__     ,      beta_end=UpperCamelCase__		)






      def 				A__      ( self			:		List[Any]		)  ->  Tuple:


       '''simple docstring'''

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






      def 				A__      ( self			:		Any		)  ->  Any:


       '''simple docstring'''

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






      def 				A__      ( self			:		Optional[int]		)  ->  Optional[int]:


       '''simple docstring'''

       lowercase					:   List[str]				  =self.scheduler_classes[0]
       lowercase					:   List[Any]				  =self.get_scheduler_config()
       lowercase					:   List[str]				  =scheduler_class(**UpperCamelCase__		)

       scheduler.set_timesteps(self.num_inference_steps		)

       lowercase					:   Union[str, Any]				  =self.dummy_model()
       lowercase					:   Any				  =self.dummy_sample_deter * scheduler.init_noise_sigma
       lowercase					:   Any				  =sample.to(UpperCamelCase__		)

       for i, t in enumerate(scheduler.timesteps		):
        lowercase					:   Any				  =scheduler.scale_model_input(UpperCamelCase__     ,      UpperCamelCase__		)

        lowercase					:   List[str]				  =model(UpperCamelCase__     ,      UpperCamelCase__		)

        lowercase					:   Optional[Any]				  =scheduler.step(UpperCamelCase__     ,      UpperCamelCase__     ,      UpperCamelCase__		)
        lowercase					:   Optional[Any]				  =output.prev_sample

       lowercase					:   Tuple				  =torch.sum(torch.abs(UpperCamelCase__		)		)
       lowercase					:   Optional[Any]				  =torch.mean(torch.abs(UpperCamelCase__		)		)

       if torch_device in ["mps"]:
        assert abs(result_sum.item() - 167.47_8210_4492_1875		) < 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() - 171.59_3521_1181_6406		) < 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() - 162.52_3834_2285_1562		) < 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 				A__      ( self			:		int		)  ->  Optional[Any]:


       '''simple docstring'''

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

       scheduler.set_timesteps(self.num_inference_steps		)

       lowercase					:   Dict				  =self.dummy_model()
       lowercase					:   Optional[Any]				  =self.dummy_sample_deter * scheduler.init_noise_sigma
       lowercase					:   Tuple				  =sample.to(UpperCamelCase__		)

       for i, t in enumerate(scheduler.timesteps		):
        lowercase					:   Tuple				  =scheduler.scale_model_input(UpperCamelCase__     ,      UpperCamelCase__		)

        lowercase					:   str				  =model(UpperCamelCase__     ,      UpperCamelCase__		)

        lowercase					:   List[Any]				  =scheduler.step(UpperCamelCase__     ,      UpperCamelCase__     ,      UpperCamelCase__		)
        lowercase					:   Optional[int]				  =output.prev_sample

       lowercase					:   List[Any]				  =torch.sum(torch.abs(UpperCamelCase__		)		)
       lowercase					:   Union[str, Any]				  =torch.mean(torch.abs(UpperCamelCase__		)		)

       if torch_device in ["mps"]:
        assert abs(result_sum.item() - 124.77_1492_0043_9453		) < 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() - 128.1_6633_6059_5703		) < 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() - 119.8_4875_4882_8125		) < 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 				A__      ( self			:		int		)  ->  Any:


       '''simple docstring'''

       lowercase					:   Any				  =self.scheduler_classes[0]
       lowercase					:   List[Any]				  =self.get_scheduler_config()
       lowercase					:   int				  =scheduler_class(**UpperCamelCase__		)

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

       lowercase					:   List[Any]				  =self.dummy_model()
       lowercase					:   Dict				  =self.dummy_sample_deter.to(UpperCamelCase__		) * scheduler.init_noise_sigma

       for t in scheduler.timesteps:
        lowercase					:   Tuple				  =scheduler.scale_model_input(UpperCamelCase__     ,      UpperCamelCase__		)

        lowercase					:   Any				  =model(UpperCamelCase__     ,      UpperCamelCase__		)

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

       lowercase					:   List[Any]				  =torch.sum(torch.abs(UpperCamelCase__		)		)
       lowercase					:   Dict				  =torch.mean(torch.abs(UpperCamelCase__		)		)

       if torch_device in ["mps"]:
        assert abs(result_sum.item() - 167.46_9573_9746_0938		) < 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() - 171.59_3536_3769_5312		) < 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() - 162.52_3834_2285_1562		) < 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 				A__      ( self			:		Optional[int]		)  ->  Optional[Any]:


       '''simple docstring'''

       lowercase					:   List[str]				  =self.scheduler_classes[0]
       lowercase					:   List[Any]				  =self.get_scheduler_config()
       lowercase					:   Optional[Any]				  =scheduler_class(**UpperCamelCase__     ,      use_karras_sigmas=UpperCamelCase__		)

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

       lowercase					:   Dict				  =self.dummy_model()
       lowercase					:   str				  =self.dummy_sample_deter.to(UpperCamelCase__		) * scheduler.init_noise_sigma
       lowercase					:   int				  =sample.to(UpperCamelCase__		)

       for t in scheduler.timesteps:
        lowercase					:   Optional[int]				  =scheduler.scale_model_input(UpperCamelCase__     ,      UpperCamelCase__		)

        lowercase					:   List[str]				  =model(UpperCamelCase__     ,      UpperCamelCase__		)

        lowercase					:   Dict				  =scheduler.step(UpperCamelCase__     ,      UpperCamelCase__     ,      UpperCamelCase__		)
        lowercase					:   Optional[Any]				  =output.prev_sample

       lowercase					:   int				  =torch.sum(torch.abs(UpperCamelCase__		)		)
       lowercase					:   int				  =torch.mean(torch.abs(UpperCamelCase__		)		)

       if torch_device in ["mps"]:
        assert abs(result_sum.item() - 176.66_9741_3574_2188		) < 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() - 177.63_6535_6445_3125		) < 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() - 170.3_1352_2338_8672		) < 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











'''simple docstring'''



def 							lowercase_				(    __A :       int = 6_0_0_8_5_1_4_7_5_1_4_3			)       ->      int:



 """simple docstring"""




 try:
  lowercase					:   Any				  =int(__A			)
 except (TypeError, ValueError):
  raise TypeError('''Parameter n must be int or castable to int.'''			)
 if n <= 0:
  raise ValueError('''Parameter n must be greater than or equal to one.'''			)
 lowercase					:   Optional[Any]				  =1
 lowercase					:   Dict				  =2
 while i * i <= n:
  while n % i == 0:
   lowercase					:   Optional[int]				  =i
   n //= i
  i += 1
 if n > 1:
  lowercase					:   Dict				  =n
 return int(__A			)


if __name__ == "__main__":
      print(f"""{solution() = }""")



