




import unittest

import torch

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


a													=			False




class       _A				(		unittest.TestCase     ):






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






		@slow
		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			"""cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
								_UpperCAmelCase      =			DDPMScheduler(
								    num_train_timesteps=1000						,		beta_start=0.0001						,		beta_end=0.02						,		beta_schedule="""linear"""						,		clip_sample=_SCREAMING_SNAKE_CASE						,		)
								_UpperCAmelCase      =			DDIMScheduler(
								    num_train_timesteps=1000						,		beta_start=0.0001						,		beta_end=0.02						,		beta_schedule="""linear"""						,		clip_sample=_SCREAMING_SNAKE_CASE						,		)

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

								# shared batches for DDPM and DDIM
								set_seed(0			)
								_UpperCAmelCase      =			[torch.randn((4, 3, 32, 32)			).clip(-1						,		1			).to(_SCREAMING_SNAKE_CASE			) for _ in range(4			)]
								_UpperCAmelCase      =			[torch.randn((4, 3, 32, 32)			).to(_SCREAMING_SNAKE_CASE			) for _ in range(4			)]
								_UpperCAmelCase      =			[torch.randint(0						,		1000						,		(4,)			).long().to(_SCREAMING_SNAKE_CASE			) for _ in range(4			)]

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

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

								self.assertTrue(torch.allclose(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		atol=1e-5			)			)
								self.assertTrue(torch.allclose(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		atol=1e-5			)			)





from typing import Union

import fire
import torch
from tqdm import tqdm




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case = "cpu"			,     snake_case = None       )						->				None:
						_UpperCAmelCase      =			torch.load(snake_case			,     map_location=snake_case       )
						for k, v in tqdm(state_dict.items()       ):
												if not isinstance(snake_case			,     torch.Tensor       ):
																		raise TypeError("""FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin"""       )
												_UpperCAmelCase      =			v.half()
						if save_path is None:  # overwrite src_path
												_UpperCAmelCase      =			src_path
						torch.save(snake_case			,     snake_case       )


if __name__ == "__main__":
				fire.Fire(convert)