






import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class   SCREAMING_SNAKE_CASE (       snake_case    ):





							"""simple docstring"""

							A_       =    (EulerAncestralDiscreteScheduler,)
							A_       =    10

							def 					__A       (     self:      Any						,   **__A:      Union[str, Any]      )			-> List[Any]:
												_A									=       {
												    '''num_train_timesteps''': 11_00,
												    '''beta_start''': 0.0_001,
												    '''beta_end''': 0.02,
												    '''beta_schedule''': '''linear''',
												}

												config.update(**__A      )
												return config

							def 					__A       (     self:      Optional[Any]      )			-> int:
												for timesteps in [10, 50, 1_00, 10_00]:
																	self.check_over_configs(num_train_timesteps=__A      )

							def 					__A       (     self:      str      )			-> List[str]:
												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=__A						,   beta_end=__A      )

							def 					__A       (     self:      Union[str, Any]      )			-> str:
												for schedule in ["linear", "scaled_linear"]:
																	self.check_over_configs(beta_schedule=__A      )

							def 					__A       (     self:      int      )			-> Dict:
												for prediction_type in ["epsilon", "v_prediction"]:
																	self.check_over_configs(prediction_type=__A      )

							def 					__A       (     self:      Any      )			-> List[str]:
												_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.cpu()
												_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() - 152.3_192      ) < 1e-2
												assert abs(result_mean.item() - 0.1_983      ) < 1e-3

							def 					__A       (     self:      List[str]      )			-> int:
												_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									=       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() - 108.4_439      ) < 1e-2
												assert abs(result_mean.item() - 0.1_412      ) < 1e-3



							def 					__A       (     self:      str      )			-> List[Any]:
												_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 * scheduler.init_noise_sigma.cpu()
												_A									=       sample.to(__A      )

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











from math import factorial




def    __A    (   _lowercase    ,	_lowercase       ):

					'''simple docstring'''







					return factorial(_lowercase       ) / (factorial(_lowercase       ) * factorial(n - r       ))




def    __A    (   ):

					'''simple docstring'''







					_A									=       0

					for i in range(1    ,	1_01       ):
										for j in range(1    ,	i + 1       ):
															if combinations(_lowercase    ,	_lowercase       ) > 1e6:
																				total += 1
					return total


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



