




"""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


a_				:    List[Any]										=     False

class __lowercase(      unittest.TestCase				):




					'''simple docstring'''


					def     snake_case_				(    self						,	__a=32     ):
								set_seed(0     )
								__lowerCamelCase					:	Union[str, Any]	       =				UNetaDModel(sample_size=__a						,	in_channels=3						,	out_channels=3     )
								__lowerCamelCase					:	List[Any]	       =				torch.optim.SGD(model.parameters()						,	lr=0.0_001     )
								return model, optimizer





					@slow
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	List[str]	       =				'cpu'  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
								__lowerCamelCase					:	int	       =				DDPMScheduler(
								    num_train_timesteps=1000						,	beta_start=0.0_001						,	beta_end=0.02						,	beta_schedule='linear'						,	clip_sample=__a						,	)
								__lowerCamelCase					:	Tuple	       =				DDIMScheduler(
								    num_train_timesteps=1000						,	beta_start=0.0_001						,	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     )
								__lowerCamelCase					:	Union[str, Any]	       =				[torch.randn((4, 3, 32, 32)     ).clip(-1						,	1     ).to(__a     ) for _ in range(4     )]
								__lowerCamelCase					:	Dict	       =				[torch.randn((4, 3, 32, 32)     ).to(__a     ) for _ in range(4     )]
								__lowerCamelCase					:	Optional[Any]	       =				[torch.randint(0						,	1000						,	(4,)     ).long().to(__a     ) for _ in range(4     )]

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

								# recreate the model and optimizer, and retry with DDIM
								__lowerCamelCase       ,      __lowerCamelCase					:	Optional[Any]	       =				self.get_model_optimizer(resolution=32     )
								model.train().to(__a     )
								for i in range(4     ):
											optimizer.zero_grad()
											__lowerCamelCase					:	Dict	       =				ddim_scheduler.add_noise(clean_images[i]						,	noise[i]						,	timesteps[i]     )
											__lowerCamelCase					:	Union[str, Any]	       =				model(__a						,	timesteps[i]     ).sample
											__lowerCamelCase					:	Optional[int]	       =				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"""
import pytest

import datasets


# Import fixture modules as plugins
a_				:    Tuple										=     ['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']





def 						UpperCAmelCase				(			A__:    List[Any]							,				A__:    Optional[Any]     )  ->     List[str]:
			# Mark tests as "unit" by default if not marked as "integration" (or already marked as "unit")
			for item in items:
						if any(marker in item.keywords for marker in ['integration', 'unit']     ):
									continue
						item.add_marker(pytest.mark.unit     )





def 						UpperCAmelCase				(			A__:    int     )  ->     Optional[int]:
			config.addinivalue_line('markers'							,				'torchaudio_latest: mark test to run with torchaudio>=0.12'     )





@pytest.fixture(autouse=A__     )
def 						UpperCAmelCase				(			A__:    Optional[Any]							,				A__:    List[Any]     )  ->     Any:
			# test_hf_cache_home = tmp_path_factory.mktemp("cache")  # TODO: why a cache dir per test function does not work?
			__lowerCamelCase					:	str	       =				tmp_path_factory.getbasetemp() / 'cache'
			__lowerCamelCase					:	Dict	       =				test_hf_cache_home / 'datasets'
			__lowerCamelCase					:	int	       =				test_hf_cache_home / 'metrics'
			__lowerCamelCase					:	List[str]	       =				test_hf_cache_home / 'modules'
			monkeypatch.setattr('datasets.config.HF_DATASETS_CACHE'							,				str(A__     )     )
			monkeypatch.setattr('datasets.config.HF_METRICS_CACHE'							,				str(A__     )     )
			monkeypatch.setattr('datasets.config.HF_MODULES_CACHE'							,				str(A__     )     )
			__lowerCamelCase					:	List[Any]	       =				test_hf_datasets_cache / 'downloads'
			monkeypatch.setattr('datasets.config.DOWNLOADED_DATASETS_PATH'							,				str(A__     )     )
			__lowerCamelCase					:	Optional[int]	       =				test_hf_datasets_cache / 'downloads' / 'extracted'
			monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH'							,				str(A__     )     )





@pytest.fixture(autouse=A__							,				scope='session'     )
def 						UpperCAmelCase				(			)  ->     Any:
			datasets.disable_progress_bar()





@pytest.fixture(autouse=A__     )
def 						UpperCAmelCase				(			A__:    Optional[Any]     )  ->     Dict:
			# don't take tests into account when counting downloads
			monkeypatch.setattr('datasets.config.HF_UPDATE_DOWNLOAD_COUNTS'							,				A__     )






@pytest.fixture
def 						UpperCAmelCase				(			A__:    Union[str, Any]     )  ->     Optional[Any]:
			# Required to suppress RemovedIn20Warning when feature(s) are not compatible with SQLAlchemy 2.0
			# To be removed once SQLAlchemy 2.0 supported
			monkeypatch.setattr('sqlalchemy.util.deprecations.SILENCE_UBER_WARNING'							,				A__     )



