
"""simple docstring"""

from __future__ import annotations
def 		UpperCAmelCase					(		a_			):





		'''simple docstring'''


		for r in range(1,						a_			):
				lowerCamelCase     :			Tuple		=    []
				for x in range(n - 1			):
						lowerCamelCase     :			List[str]		=    pow(a_,						a_,						a_			)
						if val in li:
								break
						li.append(a_			)
				else:
						return r
		return None


if __name__ == "__main__":
					_A            = int(input('Enter a prime number q: '))
					_A            = find_primitive(q)
					if a is None:
										print(F"""Cannot find the primitive for the value: {a!r}""")
					else:
										_A            = int(input('Enter private key of A: '))
										_A            = pow(a, a_private, q)
										_A            = int(input('Enter private key of B: '))
										_A            = pow(a, b_private, q)

										_A            = pow(b_public, a_private, q)
										_A            = pow(a_public, b_private, q)

										print('The key value generated by A is: ', a_secret)
										print('The key value generated by B is: ', b_secret)








"""simple docstring"""

import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class        _lowercase   (				__UpperCAmelCase		):
	lowercase_        =	(KDPMaAncestralDiscreteScheduler,)
	lowercase_        =	1_0




	def 			_UpperCamelCase    ( self		, **UpperCAmelCase_					)		->							Optional[int]:
			lowerCamelCase     :			Any		=    {
			    'num_train_timesteps': 1100,
			    'beta_start': 0.0001,
			    'beta_end': 0.02,
			    'beta_schedule': 'linear',
			}

			config.update(**UpperCAmelCase_					)
			return config




	def 			_UpperCamelCase    ( self					)		->							Any:
			for timesteps in [10, 50, 100, 1000]:
					self.check_over_configs(num_train_timesteps=UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							List[str]:
			for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]		, [0.0002, 0.002, 0.02]					):
					self.check_over_configs(beta_start=UpperCAmelCase_		, beta_end=UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							Optional[int]:
			for schedule in ["linear", "scaled_linear"]:
					self.check_over_configs(beta_schedule=UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							List[Any]:
			if torch_device == "mps":
					return
			lowerCamelCase     :			Any		=    self.scheduler_classes[0]
			lowerCamelCase     :			List[str]		=    self.get_scheduler_config()
			lowerCamelCase     :			Union[str, Any]		=    scheduler_class(**UpperCAmelCase_					)

			scheduler.set_timesteps(self.num_inference_steps					)

			lowerCamelCase     :			Optional[Any]		=    torch.manual_seed(0					)

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

			for i, t in enumerate(scheduler.timesteps					):
					lowerCamelCase     :			Tuple		=    scheduler.scale_model_input(UpperCAmelCase_		, UpperCAmelCase_					)

					lowerCamelCase     :			List[str]		=    model(UpperCAmelCase_		, UpperCAmelCase_					)

					lowerCamelCase     :			Tuple		=    scheduler.step(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, generator=UpperCAmelCase_					)
					lowerCamelCase     :			str		=    output.prev_sample

			lowerCamelCase     :			Optional[int]		=    torch.sum(torch.abs(UpperCAmelCase_					)					)
			lowerCamelCase     :			Tuple		=    torch.mean(torch.abs(UpperCAmelCase_					)					)

			assert abs(result_sum.item() - 13849.3877					) < 1E-2
			assert abs(result_mean.item() - 18.0331					) < 5E-3




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




	def 			_UpperCamelCase    ( self					)		->							List[str]:
			if torch_device == "mps":
					return
			lowerCamelCase     :			Dict		=    self.scheduler_classes[0]
			lowerCamelCase     :			Union[str, Any]		=    self.get_scheduler_config(prediction_type='v_prediction'					)
			lowerCamelCase     :			int		=    scheduler_class(**UpperCAmelCase_					)

			scheduler.set_timesteps(self.num_inference_steps					)

			lowerCamelCase     :			int		=    self.dummy_model()
			lowerCamelCase     :			Dict		=    self.dummy_sample_deter * scheduler.init_noise_sigma
			lowerCamelCase     :			Optional[int]		=    sample.to(UpperCAmelCase_					)

			lowerCamelCase     :			Dict		=    torch.manual_seed(0					)

			for i, t in enumerate(scheduler.timesteps					):
					lowerCamelCase     :			Dict		=    scheduler.scale_model_input(UpperCAmelCase_		, UpperCAmelCase_					)

					lowerCamelCase     :			Optional[Any]		=    model(UpperCAmelCase_		, UpperCAmelCase_					)

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

			lowerCamelCase     :			Union[str, Any]		=    torch.sum(torch.abs(UpperCAmelCase_					)					)
			lowerCamelCase     :			Optional[Any]		=    torch.mean(torch.abs(UpperCAmelCase_					)					)

			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
			lowerCamelCase     :			Union[str, Any]		=    self.scheduler_classes[0]
			lowerCamelCase     :			Dict		=    self.get_scheduler_config()
			lowerCamelCase     :			Dict		=    scheduler_class(**UpperCAmelCase_					)

			scheduler.set_timesteps(self.num_inference_steps		, device=UpperCAmelCase_					)
			lowerCamelCase     :			int		=    torch.manual_seed(0					)

			lowerCamelCase     :			List[Any]		=    self.dummy_model()
			lowerCamelCase     :			Union[str, Any]		=    self.dummy_sample_deter.to(UpperCAmelCase_					) * scheduler.init_noise_sigma

			for t in scheduler.timesteps:
					lowerCamelCase     :			Optional[int]		=    scheduler.scale_model_input(UpperCAmelCase_		, UpperCAmelCase_					)

					lowerCamelCase     :			str		=    model(UpperCAmelCase_		, UpperCAmelCase_					)

					lowerCamelCase     :			List[str]		=    scheduler.step(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, generator=UpperCAmelCase_					)
					lowerCamelCase     :			Dict		=    output.prev_sample

			lowerCamelCase     :			List[str]		=    torch.sum(torch.abs(UpperCAmelCase_					)					)
			lowerCamelCase     :			str		=    torch.mean(torch.abs(UpperCAmelCase_					)					)

			assert abs(result_sum.item() - 13849.3818					) < 1E-1
			assert abs(result_mean.item() - 18.0331					) < 1E-3






