


import unittest

import torch

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


snake_case							:	int					      =				False







class 	snake_case_       (unittest.TestCase				):
					def 			lowerCamelCase__(				self       :Optional[int]      ,__snake_case       :Any=32				) ->   int:
												set_seed(0				)
												a__					=					UNetaDModel(sample_size=__snake_case      ,in_channels=3      ,out_channels=3				)
												a__					=					torch.optim.SGD(model.parameters()      ,lr=0.00_01				)
												return model, optimizer



					@slow
					def 			lowerCamelCase__(				self       :str				) ->   Union[str, Any]:
												a__					=					'cpu'  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
												a__					=					DDPMScheduler(
												    num_train_timesteps=10_00      ,beta_start=0.00_01      ,beta_end=0.02      ,beta_schedule='linear'      ,clip_sample=__snake_case      ,)
												a__					=					DDIMScheduler(
												    num_train_timesteps=10_00      ,beta_start=0.00_01      ,beta_end=0.02      ,beta_schedule='linear'      ,clip_sample=__snake_case      ,)

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

												# shared batches for DDPM and DDIM
												set_seed(0				)
												a__					=					[torch.randn((4, 3, 32, 32)				).clip(-1      ,1				).to(__snake_case				) for _ in range(4				)]
												a__					=					[torch.randn((4, 3, 32, 32)				).to(__snake_case				) for _ in range(4				)]
												a__					=					[torch.randint(0      ,10_00      ,(4,)				).long().to(__snake_case				) for _ in range(4				)]

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

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

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










from __future__ import annotations






def 							__lowercase       (			__lowerCAmelCase							:    float ,   __lowerCAmelCase							:    float ,   __lowerCAmelCase							:    float       ):
							if (voltage, current, resistance).count(0       ) != 1:
														raise ValueError('One and only one argument must be 0'       )
							if resistance < 0:
														raise ValueError('Resistance cannot be negative'       )
							if voltage == 0:
														return {"voltage": float(current * resistance       )}
							elif current == 0:
														return {"current": voltage / resistance}
							elif resistance == 0:
														return {"resistance": voltage / current}
							else:
														raise ValueError('Exactly one argument must be 0'       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()




