"""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 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			str							=		(DPMSolverSDEScheduler,)
     _UpperCamelCase							:			Optional[int]							=		10






     def     __A  (		self				,     **a__  ):
       _lowerCAmelCase						:       Optional[int]  			=     {
           """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     __A  (		self  ):
       for timesteps in [10, 50, 100, 1000]:
         self.check_over_configs(num_train_timesteps=a__  )






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






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






     def     __A  (		self  ):
       _lowerCAmelCase						:       Tuple  			=     self.scheduler_classes[0]
       _lowerCAmelCase						:       Any  			=     self.get_scheduler_config()
       _lowerCAmelCase						:       Tuple  			=     scheduler_class(**a__  )

       scheduler.set_timesteps(self.num_inference_steps  )

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

       for i, t in enumerate(scheduler.timesteps  ):
         _lowerCAmelCase						:       int  			=     scheduler.scale_model_input(a__				,     a__  )

         _lowerCAmelCase						:       Optional[int]  			=     model(a__				,     a__  )

         _lowerCAmelCase						:       List[Any]  			=     scheduler.step(a__				,     a__				,     a__  )
         _lowerCAmelCase						:       Dict  			=     output.prev_sample

       _lowerCAmelCase						:       Dict  			=     torch.sum(torch.abs(a__  )  )
       _lowerCAmelCase						:       Optional[Any]  			=     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     __A  (		self  ):
       _lowerCAmelCase						:       Dict  			=     self.scheduler_classes[0]
       _lowerCAmelCase						:       str  			=     self.get_scheduler_config(prediction_type="""v_prediction"""  )
       _lowerCAmelCase						:       Union[str, Any]  			=     scheduler_class(**a__  )

       scheduler.set_timesteps(self.num_inference_steps  )

       _lowerCAmelCase						:       Any  			=     self.dummy_model()
       _lowerCAmelCase						:       List[str]  			=     self.dummy_sample_deter * scheduler.init_noise_sigma
       _lowerCAmelCase						:       List[Any]  			=     sample.to(a__  )

       for i, t in enumerate(scheduler.timesteps  ):
         _lowerCAmelCase						:       int  			=     scheduler.scale_model_input(a__				,     a__  )

         _lowerCAmelCase						:       int  			=     model(a__				,     a__  )

         _lowerCAmelCase						:       List[str]  			=     scheduler.step(a__				,     a__				,     a__  )
         _lowerCAmelCase						:       Optional[int]  			=     output.prev_sample

       _lowerCAmelCase						:       Any  			=     torch.sum(torch.abs(a__  )  )
       _lowerCAmelCase						:       str  			=     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     __A  (		self  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     self.scheduler_classes[0]
       _lowerCAmelCase						:       Optional[Any]  			=     self.get_scheduler_config()
       _lowerCAmelCase						:       str  			=     scheduler_class(**a__  )

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

       _lowerCAmelCase						:       Tuple  			=     self.dummy_model()
       _lowerCAmelCase						:       Optional[Any]  			=     self.dummy_sample_deter.to(a__  ) * scheduler.init_noise_sigma

       for t in scheduler.timesteps:
         _lowerCAmelCase						:       Union[str, Any]  			=     scheduler.scale_model_input(a__				,     a__  )

         _lowerCAmelCase						:       Tuple  			=     model(a__				,     a__  )

         _lowerCAmelCase						:       Optional[int]  			=     scheduler.step(a__				,     a__				,     a__  )
         _lowerCAmelCase						:       List[str]  			=     output.prev_sample

       _lowerCAmelCase						:       int  			=     torch.sum(torch.abs(a__  )  )
       _lowerCAmelCase						:       List[str]  			=     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     __A  (		self  ):
       _lowerCAmelCase						:       Any  			=     self.scheduler_classes[0]
       _lowerCAmelCase						:       Dict  			=     self.get_scheduler_config()
       _lowerCAmelCase						:       Optional[int]  			=     scheduler_class(**a__				,     use_karras_sigmas=a__  )

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

       _lowerCAmelCase						:       Any  			=     self.dummy_model()
       _lowerCAmelCase						:       int  			=     self.dummy_sample_deter.to(a__  ) * scheduler.init_noise_sigma
       _lowerCAmelCase						:       int  			=     sample.to(a__  )

       for t in scheduler.timesteps:
         _lowerCAmelCase						:       Optional[Any]  			=     scheduler.scale_model_input(a__				,     a__  )

         _lowerCAmelCase						:       Optional[int]  			=     model(a__				,     a__  )

         _lowerCAmelCase						:       Union[str, Any]  			=     scheduler.step(a__				,     a__				,     a__  )
         _lowerCAmelCase						:       Tuple  			=     output.prev_sample

       _lowerCAmelCase						:       List[Any]  			=     torch.sum(torch.abs(a__  )  )
       _lowerCAmelCase						:       Dict  			=     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









'''simple docstring'''





import random




def lowerCAmelCase  (__A):



     """simple docstring"""



     _a      =  num - 1
     _a      =  0

     while s % 2 == 0:
          _a      =  s // 2
          t += 1

     for _ in range(5):
          _a      =  random.randrange(2  ,	num - 1)
          _a      =  pow(__A  ,	__A  ,	__A)
          if v != 1:
               _a      =  0
               while v != (num - 1):
                    if i == t - 1:
                         return False
                    else:
                         _a      =  i + 1
                         _a      =  (v**2) % num
     return True




def lowerCAmelCase  (__A):



     """simple docstring"""



     if num < 2:
          return False

     _a      =  [
         2,
         3,
         5,
         7,
         11,
         13,
         17,
         19,
         23,
         29,
         31,
         37,
         41,
         43,
         47,
         53,
         59,
         61,
         67,
         71,
         73,
         79,
         83,
         89,
         97,
         101,
         103,
         107,
         109,
         113,
         127,
         131,
         137,
         139,
         149,
         151,
         157,
         163,
         167,
         173,
         179,
         181,
         191,
         193,
         197,
         199,
         211,
         223,
         227,
         229,
         233,
         239,
         241,
         251,
         257,
         263,
         269,
         271,
         277,
         281,
         283,
         293,
         307,
         311,
         313,
         317,
         331,
         337,
         347,
         349,
         353,
         359,
         367,
         373,
         379,
         383,
         389,
         397,
         401,
         409,
         419,
         421,
         431,
         433,
         439,
         443,
         449,
         457,
         461,
         463,
         467,
         479,
         487,
         491,
         499,
         503,
         509,
         521,
         523,
         541,
         547,
         557,
         563,
         569,
         571,
         577,
         587,
         593,
         599,
         601,
         607,
         613,
         617,
         619,
         631,
         641,
         643,
         647,
         653,
         659,
         661,
         673,
         677,
         683,
         691,
         701,
         709,
         719,
         727,
         733,
         739,
         743,
         751,
         757,
         761,
         769,
         773,
         787,
         797,
         809,
         811,
         821,
         823,
         827,
         829,
         839,
         853,
         857,
         859,
         863,
         877,
         881,
         883,
         887,
         907,
         911,
         919,
         929,
         937,
         941,
         947,
         953,
         967,
         971,
         977,
         983,
         991,
         997,
     ]

     if num in low_primes:
          return True

     for prime in low_primes:
          if (num % prime) == 0:
               return False

     return rabin_miller(__A)




def lowerCAmelCase  (__A = 1_024):



     """simple docstring"""



     while True:
          _a      =  random.randrange(2 ** (keysize - 1)  ,	2 ** (keysize))
          if is_prime_low_num(__A):
               return num


if __name__ == "__main__":
  lowercase_		       =  generate_large_prime()
  print(("Prime number:", num))
  print(("is_prime_low_num:", is_prime_low_num(num)))



