


'''simple docstring'''







import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 				__lowerCamelCase				( a_ ):
    """simple docstring"""
    a									=					(EulerAncestralDiscreteScheduler,)
    a									=					10


    def      A					(						self    :							int   ,							**SCREAMING_SNAKE_CASE    :							Union[str, Any]):
           _A  :			Any											=      {
               'num_train_timesteps': 1100,
               'beta_start': 0.0001,
               'beta_end': 0.02,
               'beta_schedule': 'linear',
           }

           config.update(**SCREAMING_SNAKE_CASE)
           return config


    def      A					(						self    :							Dict):
           for timesteps in [10, 50, 100, 1000]:
                  self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE)


    def      A					(						self    :							Optional[Any]):
           for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]   ,							[0.0002, 0.002, 0.02]):
                  self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE   ,							beta_end=SCREAMING_SNAKE_CASE)


    def      A					(						self    :							int):
           for schedule in ["linear", "scaled_linear"]:
                  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]):
           _A  :			str											=      self.scheduler_classes[0]
           _A  :			Optional[int]											=      self.get_scheduler_config()
           _A  :			Any											=      scheduler_class(**SCREAMING_SNAKE_CASE)

           scheduler.set_timesteps(self.num_inference_steps)

           _A  :			Tuple											=      torch.manual_seed(0)

           _A  :			Union[str, Any]											=      self.dummy_model()
           _A  :			Tuple											=      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
           _A  :			int											=      sample.to(SCREAMING_SNAKE_CASE)

           for i, t in enumerate(scheduler.timesteps):
                  _A  :			Tuple											=      scheduler.scale_model_input(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE)

                  _A  :			Optional[int]											=      model(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE)

                  _A  :			int											=      scheduler.step(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							generator=SCREAMING_SNAKE_CASE)
                  _A  :			int											=      output.prev_sample

           _A  :			Tuple											=      torch.sum(torch.abs(SCREAMING_SNAKE_CASE))
           _A  :			Tuple											=      torch.mean(torch.abs(SCREAMING_SNAKE_CASE))

           assert abs(result_sum.item() - 152.3192) < 1e-2
           assert abs(result_mean.item() - 0.1983) < 1e-3


    def      A					(						self    :							Optional[int]):
           _A  :			Optional[int]											=      self.scheduler_classes[0]
           _A  :			Optional[Any]											=      self.get_scheduler_config(prediction_type='v_prediction')
           _A  :			Any											=      scheduler_class(**SCREAMING_SNAKE_CASE)

           scheduler.set_timesteps(self.num_inference_steps)

           _A  :			Any											=      torch.manual_seed(0)

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

           for i, t in enumerate(scheduler.timesteps):
                  _A  :			Optional[int]											=      scheduler.scale_model_input(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE)

                  _A  :			Optional[Any]											=      model(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE)

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

           _A  :			int											=      torch.sum(torch.abs(SCREAMING_SNAKE_CASE))
           _A  :			Union[str, Any]											=      torch.mean(torch.abs(SCREAMING_SNAKE_CASE))

           assert abs(result_sum.item() - 108.4439) < 1e-2
           assert abs(result_mean.item() - 0.1412) < 1e-3


    def      A					(						self    :							Tuple):
           _A  :			Tuple											=      self.scheduler_classes[0]
           _A  :			str											=      self.get_scheduler_config()
           _A  :			List[str]											=      scheduler_class(**SCREAMING_SNAKE_CASE)

           scheduler.set_timesteps(self.num_inference_steps   ,							device=SCREAMING_SNAKE_CASE)
           _A  :			List[str]											=      torch.manual_seed(0)

           _A  :			List[Any]											=      self.dummy_model()
           _A  :			int											=      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
           _A  :			Optional[int]											=      sample.to(SCREAMING_SNAKE_CASE)

           for t in scheduler.timesteps:
                  _A  :			List[Any]											=      scheduler.scale_model_input(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE)

                  _A  :			Optional[Any]											=      model(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE)

                  _A  :			List[str]											=      scheduler.step(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							generator=SCREAMING_SNAKE_CASE)
                  _A  :			List[Any]											=      output.prev_sample

           _A  :			Dict											=      torch.sum(torch.abs(SCREAMING_SNAKE_CASE))
           _A  :			int											=      torch.mean(torch.abs(SCREAMING_SNAKE_CASE))

           assert abs(result_sum.item() - 152.3192) < 1e-2
           assert abs(result_mean.item() - 0.1983) < 1e-3










'''simple docstring'''







import itertools






def    lowerCAmelCase__ (       lowerCamelCase  :    int = 1000000     ):
       _A  :			str											=      [1]

       for i in itertools.count(len(lowerCamelCase     )     ):
              _A  :			Optional[Any]											=      0
              for j in itertools.count(1     ):
                     _A  :			int											=      -1 if j % 2 == 0 else +1
                     _A  :			Optional[Any]											=      (j * j * 3 - j) // 2
                     if index > i:
                            break
                     item += partitions[i - index] * sign
                     item %= number
                     index += j
                     if index > i:
                            break
                     item += partitions[i - index] * sign
                     item %= number

              if item == 0:
                     return i
              partitions.append(lowerCamelCase     )

       return 0


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   print(f"""{solution() = }""")






