




import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = (DDIMParallelScheduler,)
    lowerCAmelCase         = (('''eta''', 0.0), ('''num_inference_steps''', 50))





    def 				a__ ( self  ,**_SCREAMING_SNAKE_CASE    )					->						Any:
         UpperCAmelCase_     :				Dict  =     {
             '''num_train_timesteps''': 1_000,
             '''beta_start''': 0.00_01,
             '''beta_end''': 0.02,
             '''beta_schedule''': '''linear''',
             '''clip_sample''': True,
         }

         config.update(**_SCREAMING_SNAKE_CASE    )
         return config





    def 				a__ ( self  ,**_SCREAMING_SNAKE_CASE    )					->						Union[str, Any]:
         UpperCAmelCase_     :				List[str]  =     self.scheduler_classes[0]
         UpperCAmelCase_     :				List[Any]  =     self.get_scheduler_config(**_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Optional[int]  =     scheduler_class(**_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_,       UpperCAmelCase_     :				Tuple  =     10, 0.0

         UpperCAmelCase_     :				List[str]  =     self.dummy_model()
         UpperCAmelCase_     :				List[str]  =     self.dummy_sample_deter

         scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )

         for t in scheduler.timesteps:
              UpperCAmelCase_     :				Tuple  =     model(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
              UpperCAmelCase_     :				int  =     scheduler.step(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ).prev_sample

         return sample





    def 				a__ ( self    )					->						Optional[int]:
         for timesteps in [100, 500, 1_000]:
              self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						str:
         for steps_offset in [0, 1]:
              self.check_over_configs(steps_offset=_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				List[str]  =     self.scheduler_classes[0]
         UpperCAmelCase_     :				Dict  =     self.get_scheduler_config(steps_offset=1    )
         UpperCAmelCase_     :				str  =     scheduler_class(**_SCREAMING_SNAKE_CASE    )
         scheduler.set_timesteps(5    )
         assert torch.equal(scheduler.timesteps  ,torch.LongTensor([801, 601, 401, 201, 1]    )    )





    def 				a__ ( self    )					->						Optional[int]:
         for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]  ,[0.0_02, 0.02, 0.2, 2]    ):
              self.check_over_configs(beta_start=_SCREAMING_SNAKE_CASE  ,beta_end=_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						List[Any]:
         for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						Optional[Any]:
         for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						Union[str, Any]:
         for clip_sample in [True, False]:
              self.check_over_configs(clip_sample=_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						Any:
         for timestep_spacing in ["trailing", "leading"]:
              self.check_over_configs(timestep_spacing=_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						int:
         for rescale_betas_zero_snr in [True, False]:
              self.check_over_configs(rescale_betas_zero_snr=_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						Dict:
         self.check_over_configs(thresholding=_SCREAMING_SNAKE_CASE    )
         for threshold in [0.5, 1.0, 2.0]:
              for prediction_type in ["epsilon", "v_prediction"]:
                   self.check_over_configs(
                       thresholding=_SCREAMING_SNAKE_CASE  ,prediction_type=_SCREAMING_SNAKE_CASE  ,sample_max_value=_SCREAMING_SNAKE_CASE  ,)





    def 				a__ ( self    )					->						str:
         for t in [1, 10, 49]:
              self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						int:
         for t, num_inference_steps in zip([1, 10, 50]  ,[10, 50, 500]    ):
              self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE  ,num_inference_steps=_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						Optional[Any]:
         for t, eta in zip([1, 10, 49]  ,[0.0, 0.5, 1.0]    ):
              self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE  ,eta=_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						Tuple:
         UpperCAmelCase_     :				Union[str, Any]  =     self.scheduler_classes[0]
         UpperCAmelCase_     :				str  =     self.get_scheduler_config()
         UpperCAmelCase_     :				int  =     scheduler_class(**_SCREAMING_SNAKE_CASE    )

         assert torch.sum(torch.abs(scheduler._get_variance(0  ,0    ) - 0.0    )    ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(420  ,400    ) - 0.1_47_71    )    ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(980  ,960    ) - 0.3_24_60    )    ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(0  ,0    ) - 0.0    )    ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(487  ,486    ) - 0.0_09_79    )    ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(999  ,998    ) - 0.02    )    ) < 1e-5





    def 				a__ ( self    )					->						Any:
         UpperCAmelCase_     :				Optional[Any]  =     self.scheduler_classes[0]
         UpperCAmelCase_     :				Optional[Any]  =     self.get_scheduler_config()
         UpperCAmelCase_     :				Union[str, Any]  =     scheduler_class(**_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_,       UpperCAmelCase_     :				Any  =     10, 0.0
         scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				Optional[Any]  =     self.dummy_model()
         UpperCAmelCase_     :				Optional[Any]  =     self.dummy_sample_deter
         UpperCAmelCase_     :				Dict  =     self.dummy_sample_deter + 0.1
         UpperCAmelCase_     :				Union[str, Any]  =     self.dummy_sample_deter - 0.1

         UpperCAmelCase_     :				Optional[Any]  =     samplea.shape[0]
         UpperCAmelCase_     :				Any  =     torch.stack([samplea, samplea, samplea]  ,dim=0    )
         UpperCAmelCase_     :				Optional[Any]  =     torch.arange(_SCREAMING_SNAKE_CASE    )[0:3, None].repeat(1  ,_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				int  =     model(samples.flatten(0  ,1    )  ,timesteps.flatten(0  ,1    )    )
         UpperCAmelCase_     :				Optional[Any]  =     scheduler.batch_step_no_noise(_SCREAMING_SNAKE_CASE  ,timesteps.flatten(0  ,1    )  ,samples.flatten(0  ,1    )  ,_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				Dict  =     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE    )    )
         UpperCAmelCase_     :				List[str]  =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

         assert abs(result_sum.item() - 11_47.79_04    ) < 1e-2
         assert abs(result_mean.item() - 0.49_82    ) < 1e-3





    def 				a__ ( self    )					->						List[str]:
         UpperCAmelCase_     :				str  =     self.full_loop()

         UpperCAmelCase_     :				Optional[Any]  =     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE    )    )
         UpperCAmelCase_     :				Tuple  =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

         assert abs(result_sum.item() - 1_72.00_67    ) < 1e-2
         assert abs(result_mean.item() - 0.22_39_67    ) < 1e-3





    def 				a__ ( self    )					->						Tuple:
         UpperCAmelCase_     :				List[Any]  =     self.full_loop(prediction_type='''v_prediction'''    )

         UpperCAmelCase_     :				Union[str, Any]  =     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE    )    )
         UpperCAmelCase_     :				Any  =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

         assert abs(result_sum.item() - 52.53_02    ) < 1e-2
         assert abs(result_mean.item() - 0.06_84    ) < 1e-3





    def 				a__ ( self    )					->						Union[str, Any]:
         # We specify different beta, so that the first alpha is 0.99
         UpperCAmelCase_     :				List[Any]  =     self.full_loop(set_alpha_to_one=_SCREAMING_SNAKE_CASE  ,beta_start=0.01    )
         UpperCAmelCase_     :				Optional[Any]  =     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE    )    )
         UpperCAmelCase_     :				int  =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

         assert abs(result_sum.item() - 1_49.82_95    ) < 1e-2
         assert abs(result_mean.item() - 0.19_51    ) < 1e-3





    def 				a__ ( self    )					->						str:
         # We specify different beta, so that the first alpha is 0.99
         UpperCAmelCase_     :				List[Any]  =     self.full_loop(set_alpha_to_one=_SCREAMING_SNAKE_CASE  ,beta_start=0.01    )
         UpperCAmelCase_     :				Union[str, Any]  =     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE    )    )
         UpperCAmelCase_     :				Any  =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

         assert abs(result_sum.item() - 1_49.07_84    ) < 1e-2
         assert abs(result_mean.item() - 0.19_41    ) < 1e-3





from functools import reduce

__a							     =					(
    '73167176531330624919225119674426574742355349194934'
    '96983520312774506326239578318016984801869478851843'
    '85861560789112949495459501737958331952853208805511'
    '12540698747158523863050715693290963295227443043557'
    '66896648950445244523161731856403098711121722383113'
    '62229893423380308135336276614282806444486645238749'
    '30358907296290491560440772390713810515859307960866'
    '70172427121883998797908792274921901699720888093776'
    '65727333001053367881220235421809751254540594752243'
    '52584907711670556013604839586446706324415722155397'
    '53697817977846174064955149290862569321978468622482'
    '83972241375657056057490261407972968652414535100474'
    '82166370484403199890008895243450658541227588666881'
    '16427171479924442928230863465674813919123162824586'
    '17866458359124566529476545682848912883142607690042'
    '24219022671055626321111109370544217506941658960408'
    '07198403850962455444362981230987879927244284909188'
    '84580156166097919133875499200524063689912560717606'
    '05886116467109405077541002256983155200055935729725'
    '71636269561882670428252483600823257530420752963450'
)







def lowerCamelCase__  (     _lowercase = N ):

     '''simple docstring'''


     return max(
         # mypy cannot properly interpret reduce
         int(reduce(lambda _lowercase					,		_lowercase : str(int(_lowercase ) * int(_lowercase ) )					,		n[i : i + 13] ) )
         for i in range(len(_lowercase ) - 12 ) )


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