  import argparse
import torch
from avalanche.benchmarks.classic import CelebA
from avalanche.evaluation.metrics import accuracy_metrics
from avalanche.models import SimpleMLP
from avalanche.training.strategies import Replay
from avalanche.training.storage_policies import ReservoirSamplingBuffer

def main(args):
    # Set up the necessary configurations and transformations
    device = torch.device(f'cuda:{args.cuda_device}')
    scenario = CelebA(train_stream=True, test_stream=True, device=device)
    model = SimpleMLP(scenario.train_stream.target_shape, 10)
    optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
    metrics = accuracy_metrics(scenario.train_stream.class_labels)
    logger = TqdmUpTo(10)

    # Create an online continual learning scenario using the MNIST dataset for training and testing
    scenario = OnlineCLScenario(scenario, model, optimizer, metrics, logger)

    # Create a Replay strategy instance with a ReservoirSamplingBuffer storage policy
    strategy = Replay(scenario, ReservoirSamplingBuffer(1000))

    # Implement a training loop where it trains on the online train stream of the scenario and evaluates on the test stream
    results = []
    for experience in scenario.train_stream:
        strategy.train(experience)
        results.append(strategy.eval(scenario.test_stream))

    # Store the results of the evaluation in a list
    print(results)

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--cuda_device', type=int, default=0)
    args = parser.parse_args()
    main(args)