




import unittest

import torch

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


UpperCamelCase__    			=			False
class 			a__    (					unittest.TestCase      ):






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A=3_2			):




											"""simple docstring"""



											set_seed(0			)
											__lowerCAmelCase				   =       UNetaDModel(sample_size=__lowerCAmelCase		,			in_channels=3		,			out_channels=3			)
											__lowerCAmelCase				   =       torch.optim.SGD(model.parameters()		,			lr=0.00_01			)
											return model, optimizer






						@slow
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       '''cpu'''  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
											__lowerCAmelCase				   =       DDPMScheduler(
											    num_train_timesteps=1_0_0_0		,			beta_start=0.00_01		,			beta_end=0.02		,			beta_schedule="linear"		,			clip_sample=__lowerCAmelCase		,			)
											__lowerCAmelCase				   =       DDIMScheduler(
											    num_train_timesteps=1_0_0_0		,			beta_start=0.00_01		,			beta_end=0.02		,			beta_schedule="linear"		,			clip_sample=__lowerCAmelCase		,			)

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

											# shared batches for DDPM and DDIM
											set_seed(0			)
											__lowerCAmelCase				   =       [torch.randn((4, 3, 3_2, 3_2)			).clip(-1		,			1			).to(__lowerCAmelCase			) for _ in range(4			)]
											__lowerCAmelCase				   =       [torch.randn((4, 3, 3_2, 3_2)			).to(__lowerCAmelCase			) for _ in range(4			)]
											__lowerCAmelCase				   =       [torch.randint(0		,			1_0_0_0		,			(4,)			).long().to(__lowerCAmelCase			) for _ in range(4			)]

											# train with a DDPM scheduler
											__lowerCAmelCase				   =       self.get_model_optimizer(resolution=3_2			)
											model.train().to(__lowerCAmelCase			)
											for i in range(4			):
																optimizer.zero_grad()
																__lowerCAmelCase				   =       ddpm_scheduler.add_noise(clean_images[i]		,			noise[i]		,			timesteps[i]			)
																__lowerCAmelCase				   =       model(__lowerCAmelCase		,			timesteps[i]			).sample
																__lowerCAmelCase				   =       torch.nn.functional.mse_loss(__lowerCAmelCase		,			noise[i]			)
																loss.backward()
																optimizer.step()
											del model, optimizer

											# recreate the model and optimizer, and retry with DDIM
											__lowerCAmelCase				   =       self.get_model_optimizer(resolution=3_2			)
											model.train().to(__lowerCAmelCase			)
											for i in range(4			):
																optimizer.zero_grad()
																__lowerCAmelCase				   =       ddim_scheduler.add_noise(clean_images[i]		,			noise[i]		,			timesteps[i]			)
																__lowerCAmelCase				   =       model(__lowerCAmelCase		,			timesteps[i]			).sample
																__lowerCAmelCase				   =       torch.nn.functional.mse_loss(__lowerCAmelCase		,			noise[i]			)
																loss.backward()
																optimizer.step()
											del model, optimizer

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





'''simple docstring'''

def 			__UpperCamelCase       (   UpperCAmelCase     ):
 lowercase__				:    List[str]  =					0
 while num > 0:
  digit_sum += num % 10
  num //= 10
 return digit_sum
def 			__UpperCamelCase       (   UpperCAmelCase = 100     ):
 lowercase__				:    Dict  =					1
 lowercase__				:    Optional[int]  =					2

 for i in range(2		,     max_n + 1     ):
  lowercase__				:    List[str]  =					pre_numerator
  lowercase__				:    Optional[Any]  =					2 * i // 3 if i % 3 == 0 else 1
  lowercase__				:    Union[str, Any]  =					cur_numerator
  lowercase__				:    int  =					e_cont * pre_numerator + temp

 return sum_digits(UpperCAmelCase     )


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

