Following this instruction :
1. Strictly adhere to PEP-8 standards, ensuring code is clean, well-structured, and follows Python's style guide for readability.
2. Use appropriate modules like `typing` for type hints, ensuring code clarity, maintainability, and leveraging Python's standard libraries effectively.
3. Focus on time, space, robustness, scalability; write optimized, efficient code that can handle large datasets and scale seamlessly.
4. Implement comprehensive error handling, catching exceptions gracefully, providing meaningful error messages, and ensuring code robustness under unexpected conditions.
5. Ensure the developer agent writes advanced, maintainable Python code, balancing performance and readability while adhering to best practices.



TASK : 

make user it very fast using gpu and , mutiprocessing and very fast 
import sys
import torch
import logging
import torchvision.transforms as transforms
import torchvision.models as models
from torch.utils.data import DataLoader, TensorDataset
from attacks import get_all_attacks, initialize_classifier, apply_attack
from defences import DefenceManager,get_defense_parameters

# Set up logging configuration
logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s [%(levelname)s] %(message)s",
    handlers=[logging.StreamHandler(sys.stdout)],
)
logger = logging.getLogger(__name__)

# Check if GPU is available
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
logger.info(f"Using device: {device}")

# Define transformations for the dataset with 224x224 size
transform = transforms.Compose([
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])  # Normalize with ImageNet statistics
])

# Create dummy data (100 random images, each 224x224 with 3 channels)
num_samples = 100
dummy_images = torch.rand((num_samples, 3, 224, 224)).to(device)  # Random tensor with shape (100, 3, 224, 224)
dummy_labels = torch.randint(0, 10, (num_samples,)).to(device)  # Random labels (for 10 classes)

# Create a dataset and DataLoader for dummy data
dummy_dataset = TensorDataset(dummy_images, dummy_labels)
data_loader = DataLoader(dummy_dataset, batch_size=32, shuffle=True, num_workers=0, pin_memory=True)  # Set num_workers=0 for debugging

def run_adversarial_attacks() -> torch.Tensor:
    """Executes adversarial attacks on the Vision Transformer (ViT) model and returns adversarial samples."""
    logger.info("Starting adversarial attack tests.")
    
    # Load the pre-trained Vision Transformer (ViT) model and move it to GPU
    vit_model = models.vit_b_16(weights=models.ViT_B_16_Weights.DEFAULT).to(device)
    vit_model.eval()  # Set model to evaluation mode
    
    # Load a batch of data
    inputs, labels = next(iter(data_loader))  # Fetch data from DataLoader
    inputs, labels = inputs.to('cuda'), labels.to('cuda')  # Ensure inputs are on CPU

    # Move inputs and labels to GPU
    inputs, labels = inputs.to(device), labels.to(device)

    # Initialize classifier
    num_classes = 10
    input_shape = (3, 224, 224)
    clip_values = (0.0, 1.0)
    classifier = initialize_classifier(
        model=vit_model,
        input_shape=input_shape,
        nb_classes=num_classes,
        clip_values=clip_values,
    )

    # Retrieve all attacks
    attacks = get_all_attacks()

    adversarial_examples = None  # Placeholder for adversarial examples
    # Test each attack on the model
    for attack_class in attacks:
        adversarial_examples, adversarial_labels = apply_attack(attack_class, classifier, inputs, labels)
        logger.info(f"Attack {attack_class} applied. Generated {len(adversarial_examples)} adversarial examples.")
        break  # Assume we only use one attack for demonstration purposes
    
    logger.info("Adversarial attack tests completed.")
    return adversarial_examples, labels

def apply_defense(adversarial_samples: torch.Tensor, labels: torch.Tensor) -> None:
    """Applies defense on adversarial samples using DefenceManager."""
    logger.info("Starting defense application.")

    manager = DefenceManager()
    available_defenses = manager.list_defenses()
    logger.info(f"Available defenses: {available_defenses}")

    # Select a defense to apply
    selected_defense = "SpatialSmoothing"  # Example defense selection
    logger.info(f"Selected defense: {selected_defense}")

    try:
        defended_inputs = manager.apply_defense(defense_name=selected_defense, x=adversarial_samples, y=labels)
        logger.info(f"Defense '{selected_defense}' applied successfully.")
    except Exception as e:
        logger.error(f"Failed to apply defense '{selected_defense}': {e}")

if __name__ == "__main__":
    # Run adversarial attack and obtain adversarial samples
    adversarial_samples, labels = run_adversarial_attacks()

    # Apply defense on the generated adversarial samples
    apply_defense(adversarial_samples, labels)  # Apply defense using adversarial samples and labels