


"""simple docstring"""




import unittest

import torch

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


lowercase_			     =  False
class 					__lowerCAmelCase					(   unittest.TestCase       ):


		'''simple docstring'''



		def 			__UpperCAmelCase       (  self							, _a=32      ):
				set_seed(0      )
				__a           =			UNetaDModel(sample_size=_a							, in_channels=3							, out_channels=3      )
				__a           =			torch.optim.SGD(model.parameters()							, lr=0.0001      )
				return model, optimizer





		@slow
		def 			__UpperCAmelCase       (  self      ):
				__a           =			'''cpu'''  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
				__a           =			DDPMScheduler(
				    num_train_timesteps=1_000							, beta_start=0.0001							, beta_end=0.02							, beta_schedule='''linear'''							, clip_sample=_a							, )
				__a           =			DDIMScheduler(
				    num_train_timesteps=1_000							, beta_start=0.0001							, beta_end=0.02							, 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      )
				__a           =			[torch.randn((4, 3, 32, 32)      ).clip(-1							, 1      ).to(_a      ) for _ in range(4      )]
				__a           =			[torch.randn((4, 3, 32, 32)      ).to(_a      ) for _ in range(4      )]
				__a           =			[torch.randint(0							, 1_000							, (4,)      ).long().to(_a      ) for _ in range(4      )]

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










"""simple docstring"""




def        lowercase							(		lowerCAmelCase__ :     list			) ->      bool:
		if not isinstance(lowerCAmelCase__    ,					lowerCAmelCase__			):
				raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''			)
		if len(lowerCAmelCase__			) == 0:
				raise ValueError('''Input list must be a non empty list'''			)
		if len(lowerCAmelCase__			) == 1:
				return True
		__a           =			series[1] - series[0]
		for index in range(len(lowerCAmelCase__			) - 1			):
				if series[index + 1] - series[index] != common_diff:
						return False
		return True

def        lowercase							(		lowerCAmelCase__ :     list			) ->      float:
		if not isinstance(lowerCAmelCase__    ,					lowerCAmelCase__			):
				raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''			)
		if len(lowerCAmelCase__			) == 0:
				raise ValueError('''Input list must be a non empty list'''			)
		__a           =			0
		for val in series:
				answer += val
		return answer / len(lowerCAmelCase__			)


if __name__ == "__main__":
				import doctest

				doctest.testmod()






