
'''simple docstring'''

import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest






class 				A       (		A__      ):
	snake_case__ :str				       =       (DDIMScheduler,)
	snake_case__ :Optional[Any]				       =       (('eta', 0.0), ('num_inference_steps', 50))





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Union[str, Any] ,							**__magic_name__     :    Optional[Any]							):


				"""simple docstring"""

				lowerCAmelCase__									=							{
				    "num_train_timesteps": 1000,
				    "beta_start": 0.0001,
				    "beta_end": 0.02,
				    "beta_schedule": "linear",
				    "clip_sample": True,
				}

				config.update(**__magic_name__							)
				return config





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


				"""simple docstring"""

				lowerCAmelCase__									=							self.scheduler_classes[0]
				lowerCAmelCase__									=							self.get_scheduler_config(**__magic_name__							)
				lowerCAmelCase__									=							scheduler_class(**__magic_name__							)

				lowerCAmelCase__		,lowerCAmelCase__									=							10, 0.0

				lowerCAmelCase__									=							self.dummy_model()
				lowerCAmelCase__									=							self.dummy_sample_deter

				scheduler.set_timesteps(__magic_name__							)

				for t in scheduler.timesteps:
							lowerCAmelCase__									=							model(__magic_name__ ,							__magic_name__							)
							lowerCAmelCase__									=							scheduler.step(__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__							).prev_sample

				return sample





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Dict							):


				"""simple docstring"""

				for timesteps in [100, 500, 1000]:
							self.check_over_configs(num_train_timesteps=__magic_name__							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[Any]							):


				"""simple docstring"""

				for steps_offset in [0, 1]:
							self.check_over_configs(steps_offset=__magic_name__							)

				lowerCAmelCase__									=							self.scheduler_classes[0]
				lowerCAmelCase__									=							self.get_scheduler_config(steps_offset=1							)
				lowerCAmelCase__									=							scheduler_class(**__magic_name__							)
				scheduler.set_timesteps(5							)
				assert torch.equal(scheduler.timesteps ,							torch.LongTensor([801, 601, 401, 201, 1]							)							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Union[str, Any]							):


				"""simple docstring"""

				for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] ,							[0.002, 0.02, 0.2, 2]							):
							self.check_over_configs(beta_start=__magic_name__ ,							beta_end=__magic_name__							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[Any]							):


				"""simple docstring"""

				for schedule in ["linear", "squaredcos_cap_v2"]:
							self.check_over_configs(beta_schedule=__magic_name__							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Tuple							):


				"""simple docstring"""

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[Any]							):


				"""simple docstring"""

				for clip_sample in [True, False]:
							self.check_over_configs(clip_sample=__magic_name__							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    str							):


				"""simple docstring"""

				for timestep_spacing in ["trailing", "leading"]:
							self.check_over_configs(timestep_spacing=__magic_name__							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[int]							):


				"""simple docstring"""

				for rescale_betas_zero_snr in [True, False]:
							self.check_over_configs(rescale_betas_zero_snr=__magic_name__							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Any							):


				"""simple docstring"""

				self.check_over_configs(thresholding=__magic_name__							)
				for threshold in [0.5, 1.0, 2.0]:
							for prediction_type in ["epsilon", "v_prediction"]:
										self.check_over_configs(
										    thresholding=__magic_name__ ,							prediction_type=__magic_name__ ,							sample_max_value=__magic_name__ ,							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    str							):


				"""simple docstring"""

				for t in [1, 10, 49]:
							self.check_over_forward(time_step=__magic_name__							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[str]							):


				"""simple docstring"""

				for t, num_inference_steps in zip([1, 10, 50] ,							[10, 50, 500]							):
							self.check_over_forward(time_step=__magic_name__ ,							num_inference_steps=__magic_name__							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Tuple							):


				"""simple docstring"""

				for t, eta in zip([1, 10, 49] ,							[0.0, 0.5, 1.0]							):
							self.check_over_forward(time_step=__magic_name__ ,							eta=__magic_name__							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    int							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.scheduler_classes[0]
				lowerCAmelCase__									=							self.get_scheduler_config()
				lowerCAmelCase__									=							scheduler_class(**__magic_name__							)

				assert torch.sum(torch.abs(scheduler._get_variance(0 ,							0							) - 0.0							)							) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(420 ,							400							) - 0.1_4771							)							) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(980 ,							960							) - 0.3_2460							)							) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(0 ,							0							) - 0.0							)							) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(487 ,							486							) - 0.0_0979							)							) < 1E-5
				assert torch.sum(torch.abs(scheduler._get_variance(999 ,							998							) - 0.02							)							) < 1E-5





	def 							__SCREAMING_SNAKE_CASE					(   self     :    str							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.full_loop()

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

				assert abs(result_sum.item() - 172.0067							) < 1E-2
				assert abs(result_mean.item() - 0.22_3967							) < 1E-3





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[str]							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.full_loop(prediction_type="v_prediction"							)

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

				assert abs(result_sum.item() - 52.5302							) < 1E-2
				assert abs(result_mean.item() - 0.0684							) < 1E-3





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[Any]							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.full_loop(set_alpha_to_one=__magic_name__ ,							beta_start=0.01							)
				lowerCAmelCase__									=							torch.sum(torch.abs(__magic_name__							)							)
				lowerCAmelCase__									=							torch.mean(torch.abs(__magic_name__							)							)

				assert abs(result_sum.item() - 149.8295							) < 1E-2
				assert abs(result_mean.item() - 0.1951							) < 1E-3







	def 							__SCREAMING_SNAKE_CASE					(   self     :    Tuple							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.full_loop(set_alpha_to_one=__magic_name__ ,							beta_start=0.01							)
				lowerCAmelCase__									=							torch.sum(torch.abs(__magic_name__							)							)
				lowerCAmelCase__									=							torch.mean(torch.abs(__magic_name__							)							)

				assert abs(result_sum.item() - 149.0784							) < 1E-2
				assert abs(result_mean.item() - 0.1941							) < 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()





