






'''simple docstring'''





import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class        __snake_case(					__UpperCamelCase      ):
 lowercase :       Union[str, Any]		      =		(EulerAncestralDiscreteScheduler,)
 lowercase :       Optional[Any]		      =		10



 def 						UpperCAmelCase    (	self   :   int ,		**UpperCAmelCase__   :   Optional[int]	):

    """simple docstring"""






    lowercase__						:	Tuple         = {
        """num_train_timesteps""": 1_100,
        """beta_start""": 0.0_001,
        """beta_end""": 0.02,
        """beta_schedule""": """linear""",
    }

    config.update(**UpperCAmelCase__	)
    return config



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

    """simple docstring"""






    for timesteps in [10, 50, 100, 1_000]:
       self.check_over_configs(num_train_timesteps=UpperCAmelCase__	)



 def 						UpperCAmelCase    (	self   :   Optional[int]	):

    """simple docstring"""






    for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001] ,		[0.0_002, 0.002, 0.02]	):
       self.check_over_configs(beta_start=UpperCAmelCase__ ,		beta_end=UpperCAmelCase__	)



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

    """simple docstring"""






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



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

    """simple docstring"""






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



 def 						UpperCAmelCase    (	self   :   List[Any]	):

    """simple docstring"""






    lowercase__						:	Any         = self.scheduler_classes[0]
    lowercase__						:	Tuple         = self.get_scheduler_config()
    lowercase__						:	int         = scheduler_class(**UpperCAmelCase__	)

    scheduler.set_timesteps(self.num_inference_steps	)

    lowercase__						:	Tuple         = torch.manual_seed(0	)

    lowercase__						:	str         = self.dummy_model()
    lowercase__						:	Tuple         = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
    lowercase__						:	Dict         = sample.to(UpperCAmelCase__	)

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

       lowercase__						:	List[Any]         = model(UpperCAmelCase__ ,		UpperCAmelCase__	)

       lowercase__						:	List[Any]         = scheduler.step(UpperCAmelCase__ ,		UpperCAmelCase__ ,		UpperCAmelCase__ ,		generator=UpperCAmelCase__	)
       lowercase__						:	List[Any]         = output.prev_sample

    lowercase__						:	int         = torch.sum(torch.abs(UpperCAmelCase__	)	)
    lowercase__						:	Tuple         = torch.mean(torch.abs(UpperCAmelCase__	)	)

    assert abs(result_sum.item() - 152.3_192	) < 1e-2
    assert abs(result_mean.item() - 0.1_983	) < 1e-3



 def 						UpperCAmelCase    (	self   :   Tuple	):

    """simple docstring"""






    lowercase__						:	Tuple         = self.scheduler_classes[0]
    lowercase__						:	Optional[Any]         = self.get_scheduler_config(prediction_type="""v_prediction"""	)
    lowercase__						:	Optional[int]         = scheduler_class(**UpperCAmelCase__	)

    scheduler.set_timesteps(self.num_inference_steps	)

    lowercase__						:	Tuple         = torch.manual_seed(0	)

    lowercase__						:	int         = self.dummy_model()
    lowercase__						:	Any         = self.dummy_sample_deter * scheduler.init_noise_sigma
    lowercase__						:	Optional[Any]         = sample.to(UpperCAmelCase__	)

    for i, t in enumerate(scheduler.timesteps	):
       lowercase__						:	Dict         = scheduler.scale_model_input(UpperCAmelCase__ ,		UpperCAmelCase__	)

       lowercase__						:	List[str]         = model(UpperCAmelCase__ ,		UpperCAmelCase__	)

       lowercase__						:	Union[str, Any]         = scheduler.step(UpperCAmelCase__ ,		UpperCAmelCase__ ,		UpperCAmelCase__ ,		generator=UpperCAmelCase__	)
       lowercase__						:	Tuple         = output.prev_sample

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

    assert abs(result_sum.item() - 108.4_439	) < 1e-2
    assert abs(result_mean.item() - 0.1_412	) < 1e-3







 def 						UpperCAmelCase    (	self   :   Any	):

    """simple docstring"""






    lowercase__						:	Union[str, Any]         = self.scheduler_classes[0]
    lowercase__						:	str         = self.get_scheduler_config()
    lowercase__						:	Any         = scheduler_class(**UpperCAmelCase__	)

    scheduler.set_timesteps(self.num_inference_steps ,		device=UpperCAmelCase__	)
    lowercase__						:	Optional[int]         = torch.manual_seed(0	)

    lowercase__						:	List[str]         = self.dummy_model()
    lowercase__						:	Tuple         = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
    lowercase__						:	Optional[int]         = sample.to(UpperCAmelCase__	)

    for t in scheduler.timesteps:
       lowercase__						:	str         = scheduler.scale_model_input(UpperCAmelCase__ ,		UpperCAmelCase__	)

       lowercase__						:	str         = model(UpperCAmelCase__ ,		UpperCAmelCase__	)

       lowercase__						:	Tuple         = scheduler.step(UpperCAmelCase__ ,		UpperCAmelCase__ ,		UpperCAmelCase__ ,		generator=UpperCAmelCase__	)
       lowercase__						:	Any         = output.prev_sample

    lowercase__						:	Optional[Any]         = torch.sum(torch.abs(UpperCAmelCase__	)	)
    lowercase__						:	List[str]         = torch.mean(torch.abs(UpperCAmelCase__	)	)

    assert abs(result_sum.item() - 152.3_192	) < 1e-2
    assert abs(result_mean.item() - 0.1_983	) < 1e-3










a    =       10**8
def       a__						(  A					):
   if n % 10 == 0:
      return False
   lowercase__						:	Optional[Any]         = str(A					)
   return s == s[::-1]
def       a__						(  ):
   lowercase__						:	Union[str, Any]         = set()
   lowercase__						:	Dict         = 1
   lowercase__						:	List[str]         = 5
   while sum_squares < LIMIT:
      lowercase__						:	Tuple         = first_square + 1
      while sum_squares < LIMIT:
         if is_palindrome(A					):
            answer.add(A					)
         last_square += 1
         sum_squares += last_square**2
      first_square += 1
      lowercase__						:	Any         = first_square**2 + (first_square + 1) ** 2

   return sum(A					)


if __name__ == "__main__":
 print(solution())


