  Here is the Python code that performs the tasks described in the instruction using the avalanche library:
```
import avalanche as av

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

# Define transformations for training and testing data
transformations = av.Transformation(
    av.ToTensor(),
    av.Normalize(mean=[0.5], std=[0.5]),
    av.RandomHorizontalFlip(p=0.5),
    av.RandomCrop(size=32, padding=4)
)

# Create a benchmark using the MNIST dataset with the defined transformations
benchmark = av.Benchmark(
    av.MNIST(transformations=transformations),
    av.StreamDiskUsage(disk_usage_metrics),
    av.StreamCPUUsage(cpu_usage_metrics)
)

# Create a simple MLP model with the number of classes equal to the number of classes in the benchmark
model = av.create_model(
    av.MLP(num_classes=benchmark.num_classes),
    av.SGD(lr=0.01),
    av.CrossEntropyLoss()
)

# Define various loggers including a text logger, an interactive logger, a CSV logger, and a Tensorboard logger
loggers = [
    av.TextLogger(),
    av.InteractiveLogger(),
    av.CSVLogger(),
    av.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.EvaluationPlugin(
    model,
    av.CrossEntropyLoss(),
    loggers,
    metrics=[
        av.accuracy_metrics,
        av.class_accuracy_metrics,
        av.AMCA(),
        av.forgetting_metrics,
        av.backward_transfer_metrics,
        av.forward_transfer_metrics,
        av.CPUUsage(),
        av.timing_metrics,
        av.ram_usage_metrics,
        av.gpu_usage_metrics,
        av.disk_usage_metrics,
        av.MAC(),
        av.labels_repartition_metrics
    ]
)

# Create a Naive continual learning strategy using the defined model, an SGD optimizer, a CrossEntropyLoss loss function, and the defined evaluation plugin
strategy = av.NaiveContinualLearningStrategy(
    model,
    av.SGD(lr=0.01),
    av.CrossEntropyLoss(),
    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}")
```
Note that this code assumes that the `args` variable is a dictionary containing the command line arguments, and that the `av` module is imported as `avalanche`.