






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   lowerCamelCase				(unittest.TestCase      ):







		'''simple docstring'''



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase=3_2       )     ->							int:
							set_seed(0       )
							UpperCAmelCase_   :				int						=						UNetaDModel(sample_size=_UpperCamelCase    ,		in_channels=3    ,		out_channels=3       )
							UpperCAmelCase_   :				Optional[Any]						=						torch.optim.SGD(model.parameters()    ,		lr=0.00_01       )
							return model, optimizer



		@slow
		def  __UpperCAmelCase      (							self       )     ->							List[str]:
							UpperCAmelCase_   :				str						=						'cpu'  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
							UpperCAmelCase_   :				Dict						=						DDPMScheduler(
							    num_train_timesteps=1_0_0_0    ,		beta_start=0.00_01    ,		beta_end=0.02    ,		beta_schedule='linear'    ,		clip_sample=_UpperCamelCase    ,		)
							UpperCAmelCase_   :				Optional[Any]						=						DDIMScheduler(
							    num_train_timesteps=1_0_0_0    ,		beta_start=0.00_01    ,		beta_end=0.02    ,		beta_schedule='linear'    ,		clip_sample=_UpperCamelCase    ,		)

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

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

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

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

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














def 					lowercase__				(					__snake_case   :							int  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Tuple						=						[1]

					UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :				Tuple						=						0, 0, 0
					UpperCAmelCase_   :				Union[str, Any]						=						ugly_nums[ia] * 2
					UpperCAmelCase_   :				Tuple						=						ugly_nums[ia] * 3
					UpperCAmelCase_   :				Union[str, Any]						=						ugly_nums[ia] * 5

					for _ in range(1					,	__snake_case  ):
										UpperCAmelCase_   :				Tuple						=						min(__snake_case					,	__snake_case					,	__snake_case  )
										ugly_nums.append(__snake_case  )
										if next_num == next_a:
															ia += 1
															UpperCAmelCase_   :				Union[str, Any]						=						ugly_nums[ia] * 2
										if next_num == next_a:
															ia += 1
															UpperCAmelCase_   :				Any						=						ugly_nums[ia] * 3
										if next_num == next_a:
															ia += 1
															UpperCAmelCase_   :				List[str]						=						ugly_nums[ia] * 5
					return ugly_nums[-1]


if __name__ == "__main__":
		from doctest import testmod

		testmod(verbose=True)
		print(F'{ugly_numbers(200) = }')






