  import avalanche as av

# Parse command line arguments to determine the device to use for computations
device = av.device.get_device(av.device.get_args())

# Define transformations for training and testing data
transformations = av.transforms.Compose([
    av.transforms.ToTensor(),
    av.transforms.Normalize((0.5,), (0.5,))
])

# Create a benchmark using the MNIST dataset with the defined transformations
benchmark = av.benchmarks.MNIST(transformations=transformations)

# Create a simple MLP model with the number of classes equal to the number of classes in the benchmark
model = av.models.MLP(num_classes=benchmark.num_classes)

# Define various loggers including a text logger, an interactive logger, a CSV logger, and a Tensorboard logger
loggers = [
    av.loggers.TextLogger(),
    av.loggers.InteractiveLogger(),
    av.loggers.CSVLogger(),
    av.loggers.TensorboardLogger()
]

# Define an evaluation plugin that computes a wide range of metrics including accuracy, loss, class accuracy, AMCA, forgetting, backward transfer, forward transfer, CPU usage, timing, RAM usage, GPU usage, disk usage, MAC, and labels repartition metrics
evaluation_plugin = av.plugins.EvaluationPlugin(
    metrics=['accuracy', 'loss', 'class_accuracy', 'AMCA', 'forgetting', 'backward_transfer', 'forward_transfer', 'cpu_usage', 'timing', 'ram_usage', 'gpu_usage', 'disk_usage', 'mac', 'labels_repartition'],
    loggers=loggers
)

# Create a Naive continual learning strategy using the defined model, an SGD optimizer, a CrossEntropyLoss loss function, and the defined evaluation plugin
strategy = av.strategies.Naive(
    model=model,
    optimizer=av.optimizers.SGD(lr=0.01),
    loss_fn=av.losses.CrossEntropyLoss(),
    evaluation_plugin=evaluation_plugin
)

# Train the model on the benchmark's training stream and evaluate it on the benchmark's test stream, printing the results after each experience
for experience in benchmark.train_stream:
    strategy.train(experience)
    strategy.evaluate(experience)
    print(f'Experience {experience.experience_id}: {strategy.metrics}')

# After all experiences, print all the metrics stored by the evaluation plugin
print(f'Final metrics: {evaluation_plugin.metrics}')