
import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      UpperCamelCase_    : Tuple									=(KDPMaAncestralDiscreteScheduler,)
      UpperCamelCase_    : List[Any]									=10
      def    UpperCAmelCase						(    self   ,    **SCREAMING_SNAKE_CASE_			)    ->				Optional[Any]:
          UpperCamelCase	:List[Any]     							=       {
              '''num_train_timesteps''': 1100,
              '''beta_start''': 0.0001,
              '''beta_end''': 0.02,
              '''beta_schedule''': '''linear''',
          }

          config.update(**SCREAMING_SNAKE_CASE_			)
          return config
      def    UpperCAmelCase						(    self			)    ->				Optional[int]:
          for timesteps in [10, 50, 100, 1000]:
              self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_			)
      def    UpperCAmelCase						(    self			)    ->				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    UpperCAmelCase						(    self			)    ->				int:
          for schedule in ["linear", "scaled_linear"]:
              self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_			)
      def    UpperCAmelCase						(    self			)    ->				Union[str, Any]:
          if torch_device == "mps":
              return
          UpperCamelCase	:List[str]     							=       self.scheduler_classes[0]
          UpperCamelCase	:Optional[Any]     							=       self.get_scheduler_config()
          UpperCamelCase	:Dict     							=       scheduler_class(**SCREAMING_SNAKE_CASE_			)

          scheduler.set_timesteps(self.num_inference_steps			)

          UpperCamelCase	:Any     							=       torch.manual_seed(0			)

          UpperCamelCase	:Optional[Any]     							=       self.dummy_model()
          UpperCamelCase	:str     							=       self.dummy_sample_deter * scheduler.init_noise_sigma
          UpperCamelCase	:Any     							=       sample.to(SCREAMING_SNAKE_CASE_			)

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

              UpperCamelCase	:List[str]     							=       model(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)

              UpperCamelCase	:Union[str, Any]     							=       scheduler.step(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    generator=SCREAMING_SNAKE_CASE_			)
              UpperCamelCase	:Optional[int]     							=       output.prev_sample

          UpperCamelCase	:Optional[Any]     							=       torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
          UpperCamelCase	:Any     							=       torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

          assert abs(result_sum.item() - 1_3849.3877			) < 1e-2
          assert abs(result_mean.item() - 18.0331			) < 5e-3
      def    UpperCAmelCase						(    self			)    ->				Union[str, Any]:
          for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_			)
      def    UpperCAmelCase						(    self			)    ->				Optional[Any]:
          if torch_device == "mps":
              return
          UpperCamelCase	:Union[str, Any]     							=       self.scheduler_classes[0]
          UpperCamelCase	:Union[str, Any]     							=       self.get_scheduler_config(prediction_type='''v_prediction'''			)
          UpperCamelCase	:Optional[Any]     							=       scheduler_class(**SCREAMING_SNAKE_CASE_			)

          scheduler.set_timesteps(self.num_inference_steps			)

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

          UpperCamelCase	:int     							=       torch.manual_seed(0			)

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

              UpperCamelCase	:Any     							=       model(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)

              UpperCamelCase	:int     							=       scheduler.step(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    generator=SCREAMING_SNAKE_CASE_			)
              UpperCamelCase	:List[str]     							=       output.prev_sample

          UpperCamelCase	:Any     							=       torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
          UpperCamelCase	:Union[str, Any]     							=       torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

          assert abs(result_sum.item() - 328.9970			) < 1e-2
          assert abs(result_mean.item() - 0.4284			) < 1e-3




      def    UpperCAmelCase						(    self			)    ->				Any:
          if torch_device == "mps":
              return
          UpperCamelCase	:Any     							=       self.scheduler_classes[0]
          UpperCamelCase	:List[Any]     							=       self.get_scheduler_config()
          UpperCamelCase	:int     							=       scheduler_class(**SCREAMING_SNAKE_CASE_			)

          scheduler.set_timesteps(self.num_inference_steps   ,    device=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:Optional[Any]     							=       torch.manual_seed(0			)

          UpperCamelCase	:int     							=       self.dummy_model()
          UpperCamelCase	:List[str]     							=       self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE_			) * scheduler.init_noise_sigma

          for t in scheduler.timesteps:
              UpperCamelCase	:Dict     							=       scheduler.scale_model_input(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)

              UpperCamelCase	:List[str]     							=       model(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)

              UpperCamelCase	:Any     							=       scheduler.step(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    generator=SCREAMING_SNAKE_CASE_			)
              UpperCamelCase	:int     							=       output.prev_sample

          UpperCamelCase	:int     							=       torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
          UpperCamelCase	:Optional[int]     							=       torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

          assert abs(result_sum.item() - 1_3849.3818			) < 1e-1
          assert abs(result_mean.item() - 18.0331			) < 1e-3













def   _A       (    SCREAMING_SNAKE_CASE__  :     int	):
    UpperCamelCase	:Optional[Any]     							=       9

    while num > 0:
        if num % 10 != digit:
            return False
        num //= 100
        digit -= 1

    return True


def   _A       (    ):
    UpperCamelCase	:List[str]     							=       138902663

    while not is_square_form(num * num	):
        if num % 10 == 3:
            num -= 6  # (3 - 6) % 10 = 7
        else:
            num -= 4  # (7 - 4) % 10 = 3

    return num * 10


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





