
'''simple docstring'''

import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 				A       (		SCREAMING_SNAKE_CASE__      ):
	snake_case__ :Tuple				       =       (KDPMaAncestralDiscreteScheduler,)
	snake_case__ :Any				       =       10





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[Any] ,							**__magic_name__     :    Optional[int]							):


				"""simple docstring"""

				lowerCAmelCase__									=							{
				    "num_train_timesteps": 1100,
				    "beta_start": 0.0001,
				    "beta_end": 0.02,
				    "beta_schedule": "linear",
				}

				config.update(**__magic_name__							)
				return config





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Tuple							):


				"""simple docstring"""

				for timesteps in [10, 50, 100, 1000]:
							self.check_over_configs(num_train_timesteps=__magic_name__							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[Any]							):


				"""simple docstring"""

				for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] ,							[0.0002, 0.002, 0.02]							):
							self.check_over_configs(beta_start=__magic_name__ ,							beta_end=__magic_name__							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    str							):


				"""simple docstring"""

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[int]							):


				"""simple docstring"""

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

				scheduler.set_timesteps(self.num_inference_steps							)

				lowerCAmelCase__									=							torch.manual_seed(0							)

				lowerCAmelCase__									=							self.dummy_model()
				lowerCAmelCase__									=							self.dummy_sample_deter * scheduler.init_noise_sigma
				lowerCAmelCase__									=							sample.to(__magic_name__							)

				for i, t in enumerate(scheduler.timesteps							):
							lowerCAmelCase__									=							scheduler.scale_model_input(__magic_name__ ,							__magic_name__							)

							lowerCAmelCase__									=							model(__magic_name__ ,							__magic_name__							)

							lowerCAmelCase__									=							scheduler.step(__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							generator=__magic_name__							)
							lowerCAmelCase__									=							output.prev_sample

				lowerCAmelCase__									=							torch.sum(torch.abs(__magic_name__							)							)
				lowerCAmelCase__									=							torch.mean(torch.abs(__magic_name__							)							)

				assert abs(result_sum.item() - 1_3849.3877							) < 1E-2
				assert abs(result_mean.item() - 18.0331							) < 5E-3





	def 							__SCREAMING_SNAKE_CASE					(   self     :    str							):


				"""simple docstring"""

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[str]							):


				"""simple docstring"""

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

				scheduler.set_timesteps(self.num_inference_steps							)

				lowerCAmelCase__									=							self.dummy_model()
				lowerCAmelCase__									=							self.dummy_sample_deter * scheduler.init_noise_sigma
				lowerCAmelCase__									=							sample.to(__magic_name__							)

				lowerCAmelCase__									=							torch.manual_seed(0							)

				for i, t in enumerate(scheduler.timesteps							):
							lowerCAmelCase__									=							scheduler.scale_model_input(__magic_name__ ,							__magic_name__							)

							lowerCAmelCase__									=							model(__magic_name__ ,							__magic_name__							)

							lowerCAmelCase__									=							scheduler.step(__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							generator=__magic_name__							)
							lowerCAmelCase__									=							output.prev_sample

				lowerCAmelCase__									=							torch.sum(torch.abs(__magic_name__							)							)
				lowerCAmelCase__									=							torch.mean(torch.abs(__magic_name__							)							)

				assert abs(result_sum.item() - 328.9970							) < 1E-2
				assert abs(result_mean.item() - 0.4284							) < 1E-3







	def 							__SCREAMING_SNAKE_CASE					(   self     :    int							):


				"""simple docstring"""

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

				scheduler.set_timesteps(self.num_inference_steps ,							device=__magic_name__							)
				lowerCAmelCase__									=							torch.manual_seed(0							)

				lowerCAmelCase__									=							self.dummy_model()
				lowerCAmelCase__									=							self.dummy_sample_deter.to(__magic_name__							) * scheduler.init_noise_sigma

				for t in scheduler.timesteps:
							lowerCAmelCase__									=							scheduler.scale_model_input(__magic_name__ ,							__magic_name__							)

							lowerCAmelCase__									=							model(__magic_name__ ,							__magic_name__							)

							lowerCAmelCase__									=							scheduler.step(__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							generator=__magic_name__							)
							lowerCAmelCase__									=							output.prev_sample

				lowerCAmelCase__									=							torch.sum(torch.abs(__magic_name__							)							)
				lowerCAmelCase__									=							torch.mean(torch.abs(__magic_name__							)							)

				assert abs(result_sum.item() - 1_3849.3818							) < 1E-1
				assert abs(result_mean.item() - 18.0331							) < 1E-3







'''simple docstring'''

import math







def 					A       ( UpperCamelCase_   :       int )   ->    list:







			'''simple docstring'''



			if n <= 0:
						raise ValueError("Only positive integers have prime factors" )
			lowerCAmelCase__									=							[]
			while n % 2 == 0:
						pf.append(2 )
						lowerCAmelCase__									=							int(n / 2 )
			for i in range(3  ,		int(math.sqrt(UpperCamelCase_ ) ) + 1  ,		2 ):
						while n % i == 0:
									pf.append(UpperCamelCase_ )
									lowerCAmelCase__									=							int(n / i )
			if n > 2:
						pf.append(UpperCamelCase_ )
			return pf







def 					A       ( UpperCamelCase_   :       int )   ->    int:







			'''simple docstring'''



			if n <= 0:
						raise ValueError("Only positive numbers are accepted" )
			lowerCAmelCase__									=							1
			lowerCAmelCase__									=							1
			while n % 2 == 0:
						temp += 1
						lowerCAmelCase__									=							int(n / 2 )
			div *= temp
			for i in range(3  ,		int(math.sqrt(UpperCamelCase_ ) ) + 1  ,		2 ):
						lowerCAmelCase__									=							1
						while n % i == 0:
									temp += 1
									lowerCAmelCase__									=							int(n / i )
						div *= temp
			if n > 1:
						div *= 2
			return div







def 					A       ( UpperCamelCase_   :       int )   ->    int:







			'''simple docstring'''



			if n <= 0:
						raise ValueError("Only positive numbers are accepted" )
			lowerCAmelCase__									=							1
			lowerCAmelCase__									=							1
			while n % 2 == 0:
						temp += 1
						lowerCAmelCase__									=							int(n / 2 )
			if temp > 1:
						s *= (2**temp - 1) / (2 - 1)
			for i in range(3  ,		int(math.sqrt(UpperCamelCase_ ) ) + 1  ,		2 ):
						lowerCAmelCase__									=							1
						while n % i == 0:
									temp += 1
									lowerCAmelCase__									=							int(n / i )
						if temp > 1:
									s *= (i**temp - 1) / (i - 1)
			return int(UpperCamelCase_ )







def 					A       ( UpperCamelCase_   :       int )   ->    int:







			'''simple docstring'''



			lowerCAmelCase__									=							n
			for x in set(prime_factors(UpperCamelCase_ ) ):
						s *= (x - 1) / x
			return int(UpperCamelCase_ )


if __name__ == "__main__":
		import doctest

		doctest.testmod()





