

'''simple docstring'''




import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest






class     _SCREAMING_SNAKE_CASE       (       lowercase_      ):
 __SCREAMING_SNAKE_CASE	:int     			=	(DDPMScheduler,)



 def 					snake_case__    (       self						:       Optional[int]					, **a__						:       Union[str, Any]     ):
      __magic_name__		  =							{
          '''num_train_timesteps''': 1000,
          '''beta_start''': 0.0_001,
          '''beta_end''': 0.02,
          '''beta_schedule''': '''linear''',
          '''variance_type''': '''fixed_small''',
          '''clip_sample''': True,
      }

      config.update(**_lowercase     )
      return config



 def 					snake_case__    (       self						:       Dict     ):
      for timesteps in [1, 5, 100, 1000]:
           self.check_over_configs(num_train_timesteps=_lowercase     )



 def 					snake_case__    (       self						:       Dict     ):
      for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]					, [0.002, 0.02, 0.2, 2]     ):
           self.check_over_configs(beta_start=_lowercase					, beta_end=_lowercase     )



 def 					snake_case__    (       self						:       Any     ):
      for schedule in ["linear", "squaredcos_cap_v2"]:
           self.check_over_configs(beta_schedule=_lowercase     )



 def 					snake_case__    (       self						:       Optional[Any]     ):
      for variance in ["fixed_small", "fixed_large", "other"]:
           self.check_over_configs(variance_type=_lowercase     )



 def 					snake_case__    (       self						:       str     ):
      for clip_sample in [True, False]:
           self.check_over_configs(clip_sample=_lowercase     )



 def 					snake_case__    (       self						:       Any     ):
      self.check_over_configs(thresholding=_lowercase     )
      for threshold in [0.5, 1.0, 2.0]:
           for prediction_type in ["epsilon", "sample", "v_prediction"]:
                self.check_over_configs(
                    thresholding=_lowercase					, prediction_type=_lowercase					, sample_max_value=_lowercase					, )



 def 					snake_case__    (       self						:       str     ):
      for prediction_type in ["epsilon", "sample", "v_prediction"]:
           self.check_over_configs(prediction_type=_lowercase     )



 def 					snake_case__    (       self						:       List[str]     ):
      for t in [0, 500, 999]:
           self.check_over_forward(time_step=_lowercase     )



 def 					snake_case__    (       self						:       Dict     ):
      __magic_name__		  =							self.scheduler_classes[0]
      __magic_name__		  =							self.get_scheduler_config()
      __magic_name__		  =							scheduler_class(**_lowercase     )

      assert torch.sum(torch.abs(scheduler._get_variance(0     ) - 0.0     )     ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(487     ) - 0.00_979     )     ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(999     ) - 0.02     )     ) < 1E-5



 def 					snake_case__    (       self						:       int     ):
      __magic_name__		  =							self.scheduler_classes[0]
      __magic_name__		  =							self.get_scheduler_config()
      __magic_name__		  =							scheduler_class(**_lowercase     )

      __magic_name__		  =							len(_lowercase     )

      __magic_name__		  =							self.dummy_model()
      __magic_name__		  =							self.dummy_sample_deter
      __magic_name__		  =							torch.manual_seed(0     )

      for t in reversed(range(_lowercase     )     ):
           # 1. predict noise residual
           __magic_name__		  =							model(_lowercase					, _lowercase     )

           # 2. predict previous mean of sample x_t-1
           __magic_name__		  =							scheduler.step(_lowercase					, _lowercase					, _lowercase					, generator=_lowercase     ).prev_sample

           # if t > 0:
           #     noise = self.dummy_sample_deter
           #     variance = scheduler.get_variance(t) ** (0.5) * noise
           #
           # sample = pred_prev_sample + variance
           __magic_name__		  =							pred_prev_sample

      __magic_name__		  =							torch.sum(torch.abs(_lowercase     )     )
      __magic_name__		  =							torch.mean(torch.abs(_lowercase     )     )

      assert abs(result_sum.item() - 258.9_606     ) < 1E-2
      assert abs(result_mean.item() - 0.3_372     ) < 1E-3



 def 					snake_case__    (       self						:       Any     ):
      __magic_name__		  =							self.scheduler_classes[0]
      __magic_name__		  =							self.get_scheduler_config(prediction_type='''v_prediction'''     )
      __magic_name__		  =							scheduler_class(**_lowercase     )

      __magic_name__		  =							len(_lowercase     )

      __magic_name__		  =							self.dummy_model()
      __magic_name__		  =							self.dummy_sample_deter
      __magic_name__		  =							torch.manual_seed(0     )

      for t in reversed(range(_lowercase     )     ):
           # 1. predict noise residual
           __magic_name__		  =							model(_lowercase					, _lowercase     )

           # 2. predict previous mean of sample x_t-1
           __magic_name__		  =							scheduler.step(_lowercase					, _lowercase					, _lowercase					, generator=_lowercase     ).prev_sample

           # if t > 0:
           #     noise = self.dummy_sample_deter
           #     variance = scheduler.get_variance(t) ** (0.5) * noise
           #
           # sample = pred_prev_sample + variance
           __magic_name__		  =							pred_prev_sample

      __magic_name__		  =							torch.sum(torch.abs(_lowercase     )     )
      __magic_name__		  =							torch.mean(torch.abs(_lowercase     )     )

      assert abs(result_sum.item() - 202.0_296     ) < 1E-2
      assert abs(result_mean.item() - 0.2_631     ) < 1E-3



 def 					snake_case__    (       self						:       int     ):
      __magic_name__		  =							self.scheduler_classes[0]
      __magic_name__		  =							self.get_scheduler_config()
      __magic_name__		  =							scheduler_class(**_lowercase     )

      __magic_name__		  =							[100, 87, 50, 1, 0]

      scheduler.set_timesteps(timesteps=_lowercase     )

      __magic_name__		  =							scheduler.timesteps

      for i, timestep in enumerate(_lowercase     ):
           if i == len(_lowercase     ) - 1:
                __magic_name__		  =							-1
           else:
                __magic_name__		  =							timesteps[i + 1]

           __magic_name__		  =							scheduler.previous_timestep(_lowercase     )
           __magic_name__		  =							prev_t.item()

           self.assertEqual(_lowercase					, _lowercase     )



 def 					snake_case__    (       self						:       Union[str, Any]     ):
      __magic_name__		  =							self.scheduler_classes[0]
      __magic_name__		  =							self.get_scheduler_config()
      __magic_name__		  =							scheduler_class(**_lowercase     )

      __magic_name__		  =							[100, 87, 50, 51, 0]

      with self.assertRaises(_lowercase					, msg='''`custom_timesteps` must be in descending order.'''     ):
           scheduler.set_timesteps(timesteps=_lowercase     )



 def 					snake_case__    (       self						:       Optional[int]     ):
      __magic_name__		  =							self.scheduler_classes[0]
      __magic_name__		  =							self.get_scheduler_config()
      __magic_name__		  =							scheduler_class(**_lowercase     )

      __magic_name__		  =							[100, 87, 50, 1, 0]
      __magic_name__		  =							len(_lowercase     )

      with self.assertRaises(_lowercase					, msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''     ):
           scheduler.set_timesteps(num_inference_steps=_lowercase					, timesteps=_lowercase     )



 def 					snake_case__    (       self						:       Optional[Any]     ):
      __magic_name__		  =							self.scheduler_classes[0]
      __magic_name__		  =							self.get_scheduler_config()
      __magic_name__		  =							scheduler_class(**_lowercase     )

      __magic_name__		  =							[scheduler.config.num_train_timesteps]

      with self.assertRaises(
          _lowercase					, msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''					, ):
           scheduler.set_timesteps(timesteps=_lowercase     )









'''simple docstring'''

def 							_UpperCamelCase				(   lowerCAmelCase__:					int		,lowerCAmelCase__:					bool = False   )      -> bool:
							if n == 2:
														return True
							if not n % 2 or n < 2:
														return False
							if n > 5 and n % 10 not in (1, 3, 7, 9):  # can quickly check last digit
														return False
							if n > 3_3170_4406_4679_8873_8596_1981 and not allow_probable:
														raise ValueError(
														    'Warning: upper bound of deterministic test is exceeded. '
														    'Pass allow_probable=True to allow probabilistic test. '
														    'A return value of True indicates a probable prime.'   )
							# array bounds provided by analysis
							SCREAMING_SNAKE_CASE_				=				[
							    2047,
							    137_3653,
							    2532_6001,
							    32_1503_1751,
							    2_1523_0289_8747,
							    3_4747_4966_0383,
							    341_5500_7172_8321,
							    1,
							    382_5123_0565_4641_3051,
							    1,
							    1,
							    3186_6585_7834_0311_5116_7461,
							    3_3170_4406_4679_8873_8596_1981,
							]

							SCREAMING_SNAKE_CASE_				=				[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
							for idx, _p in enumerate(lowerCAmelCase__		,1   ):
														if n < _p:
																					# then we have our last prime to check
																					SCREAMING_SNAKE_CASE_				=				primes[:idx]
																					break
							SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				n - 1, 0
							# break up n -1 into a power of 2 (s) and
							# remaining odd component
							# essentially, solve for d * 2 ** s == n - 1
							while d % 2 == 0:
														d //= 2
														s += 1
							for prime in plist:
														SCREAMING_SNAKE_CASE_				=				False
														for r in range(lowerCAmelCase__   ):
																					SCREAMING_SNAKE_CASE_				=				pow(lowerCAmelCase__		,d * 2**r		,lowerCAmelCase__   )
																					# see article for analysis explanation for m
																					if (r == 0 and m == 1) or ((m + 1) % n == 0):
																												SCREAMING_SNAKE_CASE_				=				True
																												# this loop will not determine compositeness
																												break
														if pr:
																					continue
														# if pr is False, then the above loop never evaluated to true,
														# and the n MUST be composite
														return False
							return True




def 							_UpperCamelCase				(   )      -> None:
							assert not miller_rabin(561   )
							assert miller_rabin(563   )
							# 2047

							assert not miller_rabin(83_8201   )
							assert miller_rabin(83_8207   )
							# 1_373_653

							assert not miller_rabin(1731_6001   )
							assert miller_rabin(1731_6017   )
							# 25_326_001

							assert not miller_rabin(30_7838_6641   )
							assert miller_rabin(30_7838_6653   )
							# 3_215_031_751

							assert not miller_rabin(1_7130_4557_4801   )
							assert miller_rabin(1_7130_4557_4819   )
							# 2_152_302_898_747

							assert not miller_rabin(2_7797_9972_8307   )
							assert miller_rabin(2_7797_9972_8327   )
							# 3_474_749_660_383

							assert not miller_rabin(113_8500_2390_9441   )
							assert miller_rabin(113_8500_2390_9527   )
							# 341_550_071_728_321

							assert not miller_rabin(127_5041_0188_4880_4351   )
							assert miller_rabin(127_5041_0188_4880_4391   )
							# 3_825_123_056_546_413_051

							assert not miller_rabin(796_6646_4458_5077_8779_1867   )
							assert miller_rabin(796_6646_4458_5077_8779_1951   )
							# 318_665_857_834_031_151_167_461

							assert not miller_rabin(5528_4067_7446_6478_9766_0333   )
							assert miller_rabin(5528_4067_7446_6478_9766_0359   )
							# 3_317_044_064_679_887_385_961_981
							# upper limit for probabilistic test


if __name__ == "__main__":
							test_miller_rabin()





