



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_							:      Tuple      =  False






class      UpperCamelCase    (						unittest.TestCase     ):



	def 			__A							(       self			,	UpperCAmelCase__=32		):
							set_seed(0		)
							A__							=       UNetaDModel(sample_size=UpperCAmelCase__			,	in_channels=3			,	out_channels=3		)
							A__							=       torch.optim.SGD(model.parameters()			,	lr=0.0_001		)
							return model, optimizer



	@slow
	def 			__A							(       self		):
							A__							=       "cpu"  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
							A__							=       DDPMScheduler(
							    num_train_timesteps=1_000			,	beta_start=0.0_001			,	beta_end=0.02			,	beta_schedule="linear"			,	clip_sample=UpperCAmelCase__			,	)
							A__							=       DDIMScheduler(
							    num_train_timesteps=1_000			,	beta_start=0.0_001			,	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		)
							A__							=       [torch.randn((4, 3, 32, 32)		).clip(-1			,	1		).to(UpperCAmelCase__		) for _ in range(4		)]
							A__							=       [torch.randn((4, 3, 32, 32)		).to(UpperCAmelCase__		) for _ in range(4		)]
							A__							=       [torch.randint(0			,	1_000			,	(4,)		).long().to(UpperCAmelCase__		) for _ in range(4		)]

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










import os
import time

import pytest

from datasets.utils.filelock import FileLock, Timeout
def  UpperCamelCase						(       _A			:				Optional[int]    )->       List[Any]:





						"""simple docstring"""





						A__							=       FileLock(str(tmpdir / "foo.lock"    )    )
						A__							=       FileLock(str(tmpdir / "foo.lock"    )    )
						A__							=       0.01
						with locka.acquire():
												with pytest.raises(_A    ):
																		A__							=       time.time()
																		locka.acquire(_A    )
																		assert time.time() - _start > timeout
def  UpperCamelCase						(       _A			:				str    )->       List[Any]:





						"""simple docstring"""





						A__							=       "a" * 1000 + ".lock"
						A__							=       FileLock(str(tmpdir / filename    )    )
						assert locka._lock_file.endswith(".lock"    )
						assert not locka._lock_file.endswith(_A    )
						assert len(os.path.basename(locka._lock_file    )    ) <= 255
						A__							=       FileLock(tmpdir / filename    )
						with locka.acquire():
												with pytest.raises(_A    ):
																		locka.acquire(0    )





