





"""simple docstring"""
import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class    _UpperCAmelCase  (      lowercase_					):
					UpperCamelCase						=				(KDPMaAncestralDiscreteScheduler,)
					UpperCamelCase						=				1_0


					def       lowerCamelCase			(   self     :int	,			**__UpperCamelCase     :str						):
												A						=    {
												    "num_train_timesteps": 11_00,
												    "beta_start": 0.0_001,
												    "beta_end": 0.02,
												    "beta_schedule": "linear",
												}

												config.update(**__UpperCamelCase						)
												return config


					def       lowerCamelCase			(   self     :Any						):
												for timesteps in [10, 50, 1_00, 10_00]:
																			self.check_over_configs(num_train_timesteps=__UpperCamelCase						)


					def       lowerCamelCase			(   self     :Any						):
												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=__UpperCamelCase	,			beta_end=__UpperCamelCase						)


					def       lowerCamelCase			(   self     :Dict						):
												for schedule in ["linear", "scaled_linear"]:
																			self.check_over_configs(beta_schedule=__UpperCamelCase						)


					def       lowerCamelCase			(   self     :int						):
												if torch_device == "mps":
																			return
												A						=    self.scheduler_classes[0]
												A						=    self.get_scheduler_config()
												A						=    scheduler_class(**__UpperCamelCase						)

												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(__UpperCamelCase						)

												for i, t in enumerate(scheduler.timesteps						):
																			A						=    scheduler.scale_model_input(__UpperCamelCase	,			__UpperCamelCase						)

																			A						=    model(__UpperCamelCase	,			__UpperCamelCase						)

																			A						=    scheduler.step(__UpperCamelCase	,			__UpperCamelCase	,			__UpperCamelCase	,			generator=__UpperCamelCase						)
																			A						=    output.prev_sample

												A						=    torch.sum(torch.abs(__UpperCamelCase						)						)
												A						=    torch.mean(torch.abs(__UpperCamelCase						)						)

												assert abs(result_sum.item() - 13_849.3_877						) < 1e-2
												assert abs(result_mean.item() - 18.0_331						) < 5e-3


					def       lowerCamelCase			(   self     :int						):
												for prediction_type in ["epsilon", "v_prediction"]:
																			self.check_over_configs(prediction_type=__UpperCamelCase						)


					def       lowerCamelCase			(   self     :List[str]						):
												if torch_device == "mps":
																			return
												A						=    self.scheduler_classes[0]
												A						=    self.get_scheduler_config(prediction_type="v_prediction"						)
												A						=    scheduler_class(**__UpperCamelCase						)

												scheduler.set_timesteps(self.num_inference_steps						)

												A						=    self.dummy_model()
												A						=    self.dummy_sample_deter * scheduler.init_noise_sigma
												A						=    sample.to(__UpperCamelCase						)

												A						=    torch.manual_seed(0						)

												for i, t in enumerate(scheduler.timesteps						):
																			A						=    scheduler.scale_model_input(__UpperCamelCase	,			__UpperCamelCase						)

																			A						=    model(__UpperCamelCase	,			__UpperCamelCase						)

																			A						=    scheduler.step(__UpperCamelCase	,			__UpperCamelCase	,			__UpperCamelCase	,			generator=__UpperCamelCase						)
																			A						=    output.prev_sample

												A						=    torch.sum(torch.abs(__UpperCamelCase						)						)
												A						=    torch.mean(torch.abs(__UpperCamelCase						)						)

												assert abs(result_sum.item() - 328.9_970						) < 1e-2
												assert abs(result_mean.item() - 0.4_284						) < 1e-3


					def       lowerCamelCase			(   self     :int						):
												if torch_device == "mps":
																			return
												A						=    self.scheduler_classes[0]
												A						=    self.get_scheduler_config()
												A						=    scheduler_class(**__UpperCamelCase						)

												scheduler.set_timesteps(self.num_inference_steps	,			device=__UpperCamelCase						)
												A						=    torch.manual_seed(0						)

												A						=    self.dummy_model()
												A						=    self.dummy_sample_deter.to(__UpperCamelCase						) * scheduler.init_noise_sigma

												for t in scheduler.timesteps:
																			A						=    scheduler.scale_model_input(__UpperCamelCase	,			__UpperCamelCase						)

																			A						=    model(__UpperCamelCase	,			__UpperCamelCase						)

																			A						=    scheduler.step(__UpperCamelCase	,			__UpperCamelCase	,			__UpperCamelCase	,			generator=__UpperCamelCase						)
																			A						=    output.prev_sample

												A						=    torch.sum(torch.abs(__UpperCamelCase						)						)
												A						=    torch.mean(torch.abs(__UpperCamelCase						)						)

												assert abs(result_sum.item() - 13_849.3_818						) < 1e-1
												assert abs(result_mean.item() - 18.0_331						) < 1e-3









"""simple docstring"""
from __future__ import annotations






def    A__							(     UpperCamelCase			):
							A						=    42
							A						=    list(UpperCamelCase			)
							A,      A						=    _vector[:-1], int(_vector[-1]			)
							A						=    [int(UpperCamelCase			) for digit in __vector]

							vector.reverse()
							for i, digit in enumerate(UpperCamelCase			):
														if i & 1 == 0:
																					A						=    digit * 2
																					if doubled > 9:
																												doubled -= 9
																					check_digit += doubled
														else:
																					check_digit += digit

							return check_digit % 10 == 0


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		assert is_luhn('79927398713')
		assert not is_luhn('79927398714')


