


"""simple docstring"""


import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class snake_case   (	_UpperCamelCase):
		__UpperCamelCase							     =      (KDPMaAncestralDiscreteScheduler,)
		__UpperCamelCase							     =      10







		def 			a_			(       self   :							int     , **a__   :							Union[str, Any]		)							->    Tuple:





						'''simple docstring'''







						_A       	=      {
						    "num_train_timesteps": 11_00,
						    "beta_start": 0.0_0_0_1,
						    "beta_end": 0.0_2,
						    "beta_schedule": "linear",
						}

						config.update(**a__		)
						return config







		def 			a_			(       self   :							str		)							->    Optional[int]:





						'''simple docstring'''







						for timesteps in [10, 50, 1_00, 10_00]:
										self.check_over_configs(num_train_timesteps=a__		)







		def 			a_			(       self   :							Tuple		)							->    Optional[int]:





						'''simple docstring'''







						for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]     , [0.0_0_0_2, 0.0_0_2, 0.0_2]		):
										self.check_over_configs(beta_start=a__     , beta_end=a__		)







		def 			a_			(       self   :							Any		)							->    Tuple:





						'''simple docstring'''







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







		def 			a_			(       self   :							Optional[int]		)							->    Any:





						'''simple docstring'''







						if torch_device == "mps":
										return
						_A       	=      self.scheduler_classes[0]
						_A       	=      self.get_scheduler_config()
						_A       	=      scheduler_class(**a__		)

						scheduler.set_timesteps(self.num_inference_steps		)

						_A       	=      torch.manual_seed(0		)

						_A       	=      self.dummy_model()
						_A       	=      self.dummy_sample_deter * scheduler.init_noise_sigma
						_A       	=      sample.to(a__		)

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

										_A       	=      model(a__     , a__		)

										_A       	=      scheduler.step(a__     , a__     , a__     , generator=a__		)
										_A       	=      output.prev_sample

						_A       	=      torch.sum(torch.abs(a__		)		)
						_A       	=      torch.mean(torch.abs(a__		)		)

						assert abs(result_sum.item() - 1_3_8_4_9.3_8_7_7		) < 1E-2
						assert abs(result_mean.item() - 1_8.0_3_3_1		) < 5E-3







		def 			a_			(       self   :							Optional[Any]		)							->    Optional[int]:





						'''simple docstring'''







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







		def 			a_			(       self   :							Optional[int]		)							->    int:





						'''simple docstring'''







						if torch_device == "mps":
										return
						_A       	=      self.scheduler_classes[0]
						_A       	=      self.get_scheduler_config(prediction_type="v_prediction"		)
						_A       	=      scheduler_class(**a__		)

						scheduler.set_timesteps(self.num_inference_steps		)

						_A       	=      self.dummy_model()
						_A       	=      self.dummy_sample_deter * scheduler.init_noise_sigma
						_A       	=      sample.to(a__		)

						_A       	=      torch.manual_seed(0		)

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

										_A       	=      model(a__     , a__		)

										_A       	=      scheduler.step(a__     , a__     , a__     , generator=a__		)
										_A       	=      output.prev_sample

						_A       	=      torch.sum(torch.abs(a__		)		)
						_A       	=      torch.mean(torch.abs(a__		)		)

						assert abs(result_sum.item() - 3_2_8.9_9_7_0		) < 1E-2
						assert abs(result_mean.item() - 0.4_2_8_4		) < 1E-3







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





						'''simple docstring'''







						if torch_device == "mps":
										return
						_A       	=      self.scheduler_classes[0]
						_A       	=      self.get_scheduler_config()
						_A       	=      scheduler_class(**a__		)

						scheduler.set_timesteps(self.num_inference_steps     , device=a__		)
						_A       	=      torch.manual_seed(0		)

						_A       	=      self.dummy_model()
						_A       	=      self.dummy_sample_deter.to(a__		) * scheduler.init_noise_sigma

						for t in scheduler.timesteps:
										_A       	=      scheduler.scale_model_input(a__     , a__		)

										_A       	=      model(a__     , a__		)

										_A       	=      scheduler.step(a__     , a__     , a__     , generator=a__		)
										_A       	=      output.prev_sample

						_A       	=      torch.sum(torch.abs(a__		)		)
						_A       	=      torch.mean(torch.abs(a__		)		)

						assert abs(result_sum.item() - 1_3_8_4_9.3_8_1_8		) < 1E-1
						assert abs(result_mean.item() - 1_8.0_3_3_1		) < 1E-3



"""simple docstring"""


from math import factorial






def   a__			(					__lowercase  ,      __lowercase )						->						List[Any]:
				return factorial(__lowercase ) / (factorial(__lowercase ) * factorial(n - r ))






def   a__			(					)						->						List[str]:
				_A       	=      0

				for i in range(1  ,      101 ):
								for j in range(1  ,      i + 1 ):
												if combinations(__lowercase  ,      __lowercase ) > 1E6:
																total += 1
				return total


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