





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_		:				str				=						False


class 			lowercase				(							unittest.TestCase     ):



						"""simple docstring"""

						def 		__UpperCAmelCase       (    self			:		Optional[int] ,				lowerCamelCase_			:		Dict=32					):





										'''simple docstring'''







										set_seed(0					)
										_snake_case :			List[Any]  						=   UNetaDModel(sample_size=lowerCamelCase_ ,				in_channels=3 ,				out_channels=3					)
										_snake_case :			Dict  						=   torch.optim.SGD(model.parameters() ,				lr=0.0001					)
										return model, optimizer







						@slow
						def 		__UpperCAmelCase       (    self			:		Optional[Any]					):





										'''simple docstring'''







										_snake_case :			Any  						=   'cpu'  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
										_snake_case :			Optional[int]  						=   DDPMScheduler(
										    num_train_timesteps=10_00 ,				beta_start=0.0001 ,				beta_end=0.02 ,				beta_schedule='linear' ,				clip_sample=lowerCamelCase_ ,				)
										_snake_case :			Dict  						=   DDIMScheduler(
										    num_train_timesteps=10_00 ,				beta_start=0.0001 ,				beta_end=0.02 ,				beta_schedule='linear' ,				clip_sample=lowerCamelCase_ ,				)

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

										# shared batches for DDPM and DDIM
										set_seed(0					)
										_snake_case :			Tuple  						=   [torch.randn((4, 3, 32, 32)					).clip(-1 ,				1					).to(lowerCamelCase_					) for _ in range(4					)]
										_snake_case :			Any  						=   [torch.randn((4, 3, 32, 32)					).to(lowerCamelCase_					) for _ in range(4					)]
										_snake_case :			int  						=   [torch.randint(0 ,				10_00 ,				(4,)					).long().to(lowerCamelCase_					) for _ in range(4					)]

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

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

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













import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


lowercase_		:				Optional[Any]				=						pytest.mark.integration

@pytest.mark.parametrize('path'	,			['paws', 'csv'] )
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				inspect_dataset(__lowerCAmelCase	,			__lowerCAmelCase )
				_snake_case :			str  						=   path + '.py'
				assert script_name in os.listdir(__lowerCAmelCase )
				assert "__pycache__" not in os.listdir(__lowerCAmelCase )

@pytest.mark.filterwarnings('ignore:inspect_metric is deprecated:FutureWarning' )
@pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning' )
@pytest.mark.parametrize('path'	,			['accuracy'] )
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				inspect_metric(__lowerCAmelCase	,			__lowerCAmelCase )
				_snake_case :			Tuple  						=   path + '.py'
				assert script_name in os.listdir(__lowerCAmelCase )
				assert "__pycache__" not in os.listdir(__lowerCAmelCase )

@pytest.mark.parametrize(
    'path, config_name, expected_splits'	,			[
        ('squad', 'plain_text', ['train', 'validation']),
        ('dalle-mini/wit', 'dalle-mini--wit', ['train']),
        ('paws', 'labeled_final', ['train', 'test', 'validation']),
    ]	,			)
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			Union[str, Any]  						=   get_dataset_config_info(__lowerCAmelCase	,			config_name=__lowerCAmelCase )
				assert info.config_name == config_name
				assert list(info.splits.keys() ) == expected_splits

@pytest.mark.parametrize(
    'path, config_name, expected_exception'	,			[
        ('paws', None, ValueError),
    ]	,			)
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				with pytest.raises(__lowerCAmelCase ):
								get_dataset_config_info(__lowerCAmelCase	,			config_name=__lowerCAmelCase )

@pytest.mark.parametrize(
    'path, expected'	,			[
        ('squad', 'plain_text'),
        ('acronym_identification', 'default'),
        ('lhoestq/squad', 'plain_text'),
        ('lhoestq/test', 'default'),
        ('lhoestq/demo1', 'lhoestq--demo1'),
        ('dalle-mini/wit', 'dalle-mini--wit'),
    ]	,			)
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			Optional[Any]  						=   get_dataset_config_names(__lowerCAmelCase )
				assert expected in config_names

@pytest.mark.parametrize(
    'path, expected_configs, expected_splits_in_first_config'	,			[
        ('squad', ['plain_text'], ['train', 'validation']),
        ('dalle-mini/wit', ['dalle-mini--wit'], ['train']),
        ('paws', ['labeled_final', 'labeled_swap', 'unlabeled_final'], ['train', 'test', 'validation']),
    ]	,			)
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			List[Any]  						=   get_dataset_infos(__lowerCAmelCase )
				assert list(infos.keys() ) == expected_configs
				_snake_case :			Any  						=   expected_configs[0]
				assert expected_config in infos
				_snake_case :			str  						=   infos[expected_config]
				assert info.config_name == expected_config
				assert list(info.splits.keys() ) == expected_splits_in_first_config

@pytest.mark.parametrize(
    'path, expected_config, expected_splits'	,			[
        ('squad', 'plain_text', ['train', 'validation']),
        ('dalle-mini/wit', 'dalle-mini--wit', ['train']),
        ('paws', 'labeled_final', ['train', 'test', 'validation']),
    ]	,			)
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			Any  						=   get_dataset_infos(__lowerCAmelCase )
				assert expected_config in infos
				_snake_case :			Any  						=   infos[expected_config]
				assert info.config_name == expected_config
				assert list(info.splits.keys() ) == expected_splits

@pytest.mark.parametrize(
    'path, config_name, expected_exception'	,			[
        ('paws', None, ValueError),
    ]	,			)
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				with pytest.raises(__lowerCAmelCase ):
								get_dataset_split_names(__lowerCAmelCase	,			config_name=__lowerCAmelCase )






