






"""simple docstring"""





import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class 						_a  (		lowercase_       ):





   '''simple docstring'''






   UpperCamelCase__     	=							(EulerAncestralDiscreteScheduler,)
   UpperCamelCase__     	=							10





   def __lowercase							(       self					, **UpperCAmelCase_)			->   Optional[Any]:




          '''simple docstring'''




          lowercase__:							Optional[int]         =     {
              "num_train_timesteps": 1_100,
              "beta_start": 0.00_01,
              "beta_end": 0.02,
              "beta_schedule": "linear",
          }

          config.update(**UpperCAmelCase_)
          return config





   def __lowercase							(       self)			->   List[str]:




          '''simple docstring'''




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





   def __lowercase							(       self)			->   Optional[Any]:




          '''simple docstring'''




          for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]					, [0.00_02, 0.0_02, 0.02]):
                 self.check_over_configs(beta_start=UpperCAmelCase_					, beta_end=UpperCAmelCase_)





   def __lowercase							(       self)			->   Dict:




          '''simple docstring'''




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





   def __lowercase							(       self)			->   Optional[Any]:




          '''simple docstring'''




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





   def __lowercase							(       self)			->   List[Any]:




          '''simple docstring'''




          lowercase__:							Optional[Any]         =     self.scheduler_classes[0]
          lowercase__:							Dict         =     self.get_scheduler_config()
          lowercase__:							int         =     scheduler_class(**UpperCAmelCase_)

          scheduler.set_timesteps(self.num_inference_steps)

          lowercase__:							Union[str, Any]         =     torch.manual_seed(0)

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

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

                 lowercase__:							str         =     model(UpperCAmelCase_					, UpperCAmelCase_)

                 lowercase__:							int         =     scheduler.step(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, generator=UpperCAmelCase_)
                 lowercase__:							Optional[Any]         =     output.prev_sample

          lowercase__:							Optional[int]         =     torch.sum(torch.abs(UpperCAmelCase_))
          lowercase__:							Any         =     torch.mean(torch.abs(UpperCAmelCase_))

          assert abs(result_sum.item() - 1_52.31_92) < 1E-2
          assert abs(result_mean.item() - 0.19_83) < 1E-3





   def __lowercase							(       self)			->   int:




          '''simple docstring'''




          lowercase__:							Any         =     self.scheduler_classes[0]
          lowercase__:							Union[str, Any]         =     self.get_scheduler_config(prediction_type="v_prediction")
          lowercase__:							str         =     scheduler_class(**UpperCAmelCase_)

          scheduler.set_timesteps(self.num_inference_steps)

          lowercase__:							List[str]         =     torch.manual_seed(0)

          lowercase__:							Any         =     self.dummy_model()
          lowercase__:							Tuple         =     self.dummy_sample_deter * scheduler.init_noise_sigma
          lowercase__:							List[Any]         =     sample.to(UpperCAmelCase_)

          for i, t in enumerate(scheduler.timesteps):
                 lowercase__:							int         =     scheduler.scale_model_input(UpperCAmelCase_					, UpperCAmelCase_)

                 lowercase__:							Optional[int]         =     model(UpperCAmelCase_					, UpperCAmelCase_)

                 lowercase__:							Any         =     scheduler.step(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, generator=UpperCAmelCase_)
                 lowercase__:							Optional[Any]         =     output.prev_sample

          lowercase__:							int         =     torch.sum(torch.abs(UpperCAmelCase_))
          lowercase__:							Dict         =     torch.mean(torch.abs(UpperCAmelCase_))

          assert abs(result_sum.item() - 1_08.44_39) < 1E-2
          assert abs(result_mean.item() - 0.14_12) < 1E-3





   def __lowercase							(       self)			->   Union[str, Any]:




          '''simple docstring'''




          lowercase__:							Optional[Any]         =     self.scheduler_classes[0]
          lowercase__:							int         =     self.get_scheduler_config()
          lowercase__:							str         =     scheduler_class(**UpperCAmelCase_)

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

          lowercase__:							Dict         =     self.dummy_model()
          lowercase__:							int         =     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
          lowercase__:							Union[str, Any]         =     sample.to(UpperCAmelCase_)

          for t in scheduler.timesteps:
                 lowercase__:							int         =     scheduler.scale_model_input(UpperCAmelCase_					, UpperCAmelCase_)

                 lowercase__:							int         =     model(UpperCAmelCase_					, UpperCAmelCase_)

                 lowercase__:							Optional[Any]         =     scheduler.step(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, generator=UpperCAmelCase_)
                 lowercase__:							Dict         =     output.prev_sample

          lowercase__:							str         =     torch.sum(torch.abs(UpperCAmelCase_))
          lowercase__:							Optional[int]         =     torch.mean(torch.abs(UpperCAmelCase_))

          assert abs(result_sum.item() - 1_52.31_92) < 1E-2
          assert abs(result_mean.item() - 0.19_83) < 1E-3









"""simple docstring"""




import sys

sys.set_int_max_str_digits(0)  # type: ignore
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					A     =   [0] * 11
					A     =   [0] * 11

					# mark last 9 numbers
					for _ in range(9 ):
										A     =   1
										A     =   number // 10
					# flag
					A     =   True

					# check last 9 numbers for pandigitality

					for x in range(9 ):
										if not check_last[x + 1]:
															A     =   False
					if not f:
										return f

					# mark first 9 numbers
					A     =   int(str(lowercase__ )[:9] )

					for _ in range(9 ):
										A     =   1
										A     =   number // 10

					# check first 9 numbers for pandigitality

					for x in range(9 ):
										if not check_front[x + 1]:
															A     =   False
					return f
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					A     =   [0] * 11

					# mark last 9 numbers
					for _ in range(9 ):
										A     =   1
										A     =   number // 10
					# flag
					A     =   True

					# check last 9 numbers for pandigitality

					for x in range(9 ):
										if not check_last[x + 1]:
															A     =   False
					return f
def   __SCREAMING_SNAKE_CASE		(		):
					"""simple docstring"""
					A     =   1
					A     =   1
					A     =   2
					# temporary Fibonacci numbers

					A     =   1
					A     =   1
					A     =   2
					# temporary Fibonacci numbers mod 1e9

					# mod m=1e9, done for fast optimisation
					A     =   [0] * 1_000_000
					A     =   1_000_000_000

					for x in range(1_000_000 ):
										A     =   (aa + ba) % m
										A     =   ba % m
										A     =   ca % m
										if checka(lowercase__ ):
															A     =   1

					for x in range(1_000_000 ):
										A     =   a + b
										A     =   b
										A     =   c
										# perform check only if in tocheck
										if tocheck[x + 3] and check(lowercase__ ):
															return x + 3  # first 2 already done
					return -1


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





