





import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


_SCREAMING_SNAKE_CASE 		=						False
class SCREAMING_SNAKE_CASE_  (   unittest.TestCase   ):






						def UpperCAmelCase_			( self : Dict						,				_A : Any=32		)							->      int:







							"""simple docstring"""


							set_seed(0		)
							snake_case_						:     Optional[int]     =		UNetaDModel(sample_size=_A						,				in_channels=3						,				out_channels=3		)
							snake_case_						:     Union[str, Any]     =		torch.optim.SGD(model.parameters()						,				lr=0.0_0_0_1		)
							return model, optimizer






						@slow
						def UpperCAmelCase_			( self : Any		)							->      Optional[int]:







							"""simple docstring"""


							snake_case_						:     List[Any]     =		'cpu'  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
							snake_case_						:     int     =		DDPMScheduler(
							    num_train_timesteps=1000						,				beta_start=0.0_0_0_1						,				beta_end=0.0_2						,				beta_schedule='linear'						,				clip_sample=_A						,				)
							snake_case_						:     Union[str, Any]     =		DDIMScheduler(
							    num_train_timesteps=1000						,				beta_start=0.0_0_0_1						,				beta_end=0.0_2						,				beta_schedule='linear'						,				clip_sample=_A						,				)

							assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

							# shared batches for DDPM and DDIM
							set_seed(0		)
							snake_case_						:     Any     =		[torch.randn((4, 3, 32, 32)		).clip(-1						,				1		).to(_A		) for _ in range(4		)]
							snake_case_						:     Optional[int]     =		[torch.randn((4, 3, 32, 32)		).to(_A		) for _ in range(4		)]
							snake_case_						:     List[str]     =		[torch.randint(0						,				1000						,				(4,)		).long().to(_A		) for _ in range(4		)]

							# train with a DDPM scheduler
							snake_case_  ,snake_case_						:     Dict     =		self.get_model_optimizer(resolution=32		)
							model.train().to(_A		)
							for i in range(4		):
								optimizer.zero_grad()
								snake_case_						:     List[Any]     =		ddpm_scheduler.add_noise(clean_images[i]						,				noise[i]						,				timesteps[i]		)
								snake_case_						:     str     =		model(_A						,				timesteps[i]		).sample
								snake_case_						:     Dict     =		torch.nn.functional.mse_loss(_A						,				noise[i]		)
								loss.backward()
								optimizer.step()
							del model, optimizer

							# recreate the model and optimizer, and retry with DDIM
							snake_case_  ,snake_case_						:     List[str]     =		self.get_model_optimizer(resolution=32		)
							model.train().to(_A		)
							for i in range(4		):
								optimizer.zero_grad()
								snake_case_						:     Any     =		ddim_scheduler.add_noise(clean_images[i]						,				noise[i]						,				timesteps[i]		)
								snake_case_						:     int     =		model(_A						,				timesteps[i]		).sample
								snake_case_						:     Tuple     =		torch.nn.functional.mse_loss(_A						,				noise[i]		)
								loss.backward()
								optimizer.step()
							del model, optimizer

							self.assertTrue(torch.allclose(_A						,				_A						,				atol=1E-5		)		)
							self.assertTrue(torch.allclose(_A						,				_A						,				atol=1E-5		)		)








def 							SCREAMING_SNAKE_CASE__  (      __a = 60_08_51_47_51_43				):

	try:
		snake_case_						:     Optional[Any]     =		int(__a				)
	except (TypeError, ValueError):
		raise TypeError('Parameter n must be int or castable to int.'				)
	if n <= 0:
		raise ValueError('Parameter n must be greater than or equal to one.'				)
	snake_case_						:     Any     =		2
	snake_case_						:     Any     =		0
	if n == 2:
		return 2
	while n > 2:
		while n % i != 0:
			i += 1
		snake_case_						:     Tuple     =		i
		while n % i == 0:
			snake_case_						:     List[str]     =		n // i
		i += 1
	return int(__a				)


if __name__ == "__main__":
						print(F'''{solution() = }''')

