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 : develop only environment and policy and critic and actor nn (keep my api developed )



1. Environment Overview
Purpose: The environment is designed to simulate conditions for testing the model’s accuracy under various attack scenarios and their corresponding defense mechanisms. This setup enables an assessment of how resilient the model remains when exposed to adversarial conditions, determining the most effective defense strategies for each type of attack.
Environment Variables:
Accuracy: Initially, the model’s baseline accuracy, or “original accuracy,” is calculated to provide a benchmark before any attacks are applied.
Attack Parameters: These define the nature of the attack and include:
Type (str): The specific type of attack (e.g., FGSM, PGD).
Batch Size: The number of data samples per attack iteration.
Labels: The target labels that might be modified under attack scenarios.
Impact of Attack: After applying an attack, the environment calculates the “attack accuracy,” reflecting the degradation in performance. This value helps in comparing how much each attack influences the model’s accuracy, assisting in evaluating defenses.
2. Observation Space
Purpose: The observation space provides the agent with structured information about factors affecting the model’s performance under various attack conditions. This is key for dynamically adapting defense strategies.
Components:
Current Accuracy: The current real-time accuracy of the model under either normal or attack conditions, providing a baseline for evaluating defense impact.
Attack Type (str): Specifies the type of attack affecting the model. This descriptor allows agents to adjust defenses according to the type of attack encountered.
Impact of Attack: Measures the drop in accuracy or rise in error rate due to the attack, giving insight into the severity and type of counteraction needed.
3. Agent
Role: Its primary role is to evaluate the attack’s nature and impact, then select the most effective defense.
Action Logic:
Defense Strategy Selection: Using the observation space information, the agent evaluates the effectiveness of various defenses based on specific attack types. It then chooses the optimal strategy to mitigate the effects.
Adaptive Decision-Making: The agent is designed to adjust defenses as attack conditions change, enhancing the model’s robustness over time. It continually adapts to maximize both resilience and accuracy under varying adversarial conditions.(EXTRA Memory neural network)
4. Accuracy Metrics
Original Accuracy: The model’s baseline accuracy in a non-adversarial setting, used as a benchmark for comparison.
Attack Accuracy: Reflects the model’s performance under a specific attack, showing how much the accuracy has degraded due to the attack.
Defense Accuracy: Measures the model’s accuracy post-defense, demonstrating the extent to which the applied defense restores or maintains performance relative to both the original and attack conditions.
5. Reward Calculator
Purpose: The reward calculator quantifies the robustness of the model by measuring how effectively it maintains accuracy when defenses are applied against attacks.
Calculation Logic:
The reward is calculated based on the proximity of the defense accuracy to the original accuracy. If the defense brings the accuracy close to or exceeds the original, a higher reward is assigned.
Rewards are also boosted if the defense demonstrates improved robustness over multiple attack types, indicating an enhanced, generalized resilience.

Model-Based Reinforcement Learning (RL) Structure
Environment Representation (Actor Network): The environment is represented within the observation space using an actor neural network, which interacts with the agent by continuously supplying updated state information.
Agent Representation (Critic Network): The agent, equipped with a critic neural network, evaluates the state-action pairs and helps to determine the best defense strategies by assessing potential future rewards.
Policy Structure: The agent’s policy is a hybrid, combining elements of Proximal Policy Optimization (PPO) and Trust Region Policy Optimization (TRPO). 
This hybrid approach allows for:
Exploration Benefits: PPO enables controlled exploration of defense strategies.
Exploitation Benefits: TRPO maintains stability in the learning process, improving the agent’s ability to select optimal defenses.
Visualization: Real-time environment interaction and the agent’s decision-making process are visualized using Pygame, making the dynamics of the attacks and defenses more interpretable.

















Note:using below api we develop from starch already implement
from rlpedata import datapipeline, DatasetConfig
from Imageclassificationmetrics import MetricsCalculator
from attacks import AdversarialAttack,AttackConfig
from defence import *
from modelloader import ImageClassificationPipeline, ModelSource
import logging
from torchvision import transforms
from typing import Any, Dict, List, Optional, Union
import torch
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)



def print_metrics(metrics: Dict[str, Any]) -> None:
    """
    Print the classification metrics.

    Args:
        metrics (Dict[str, Any]): Dictionary of metric names and values.
    """
    for metric, value in metrics.items():
        if isinstance(value, torch.Tensor):
            if value.numel() == 1:
                print(f"{metric}: {value.item()}")
            else:
                print(f"{metric}: {value.cpu().numpy()}")
        elif isinstance(value, float):
            print(f"{metric}: {value}")
        else:
            # Handle other possible types (e.g., None should have been removed)
            print(f"{metric}: {value}")



def applyingattacks() -> None:
    """
    Main function to execute adversarial attacks on the dataset.
    """
 

    # Dataset configuration
    config = DatasetConfig(
        dataset_path="zh-plus/tiny-imagenet",
        split_config={"train": 0.8, "test": 0.2},
        columns=["image", "label"],
        num_samples=1000,
        seed=42,
        kwargs={"cache_dir": "./data_test"},
    )

    # Data transformations
    trans = transforms.Compose([
        transforms.Lambda(lambda img: img.convert("RGB")),
        transforms.Resize((224, 224)),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406],
                             std=[0.229, 0.224, 0.225])
    ])

    # Data loaders
    try:
        train_loader, test_loader = datapipeline(
            config=config,
            transforms_fn=trans,
            target_column="image",
            label_column="label"
        )
        logger.info("Data loaders created successfully.")
    except Exception as e:
        logger.error(f"Failed to create data loaders: {e}")
        return

    # Load model
    try:
        model_pipeline=ImageClassificationPipeline(
        model_name_or_path="google/vit-base-patch16-224",
        model_source=ModelSource.TRANSFORMERS.value,
        )


        model=model_pipeline.model 

    except Exception as e:
        logger.error(f"Failed to load model: {e}")
        return

    # Initialize AdversarialAttack
    adversary = AdversarialAttack(model=model)

    # Define list of attacks
    list_attack: List[str] = [
        'FGSM',
        'FastGradientMethod',
        'PGD',
        'AutoProjectedGradientDescent',
        'DeepFool',
        'CW_L2',
        'CW_Linf',
        'ElasticNet',
        'BasicIterativeMethod',
        'MomentumIterativeMethod',
        'AutoConjugateGradient',
        'PixelAttack',
        'SquareAttack',
        'SquareAttackBatch',
        'SnowAttack',  # Assuming 'SpatialTransformation' was intended as 'SnowAttack'
        'AdversarialPatch',
        'UniversalPerturbation',
        'ZooAttack',
        'CompositeAdversarialAttackPyTorch',
        'SquareAttackBatch',  # Duplicate removed or kept based on use-case
    ]

    # Iterate over the training data and apply attacks
    try:
        for batch in train_loader:
            images, labels = batch["images"], batch["labels"]
            images = images.to(adversary.device)
            labels = labels.to(adversary.device)

            # Iterate over each attack
            for attack_name in list_attack:
                try:
                    config_attack: Optional[AttackConfig] = None
                    # Define specific configurations for each attack if needed
                    if attack_name == 'FGSM':
                        config_attack = {'epsilon': 0.007}
                    elif attack_name == 'FastGradientMethod':
                        config_attack = {'epsilon': 0.007}
                    elif attack_name == 'PGD':
                        config_attack = {'epsilon': 0.03, 'alpha': 0.007, 'iters': 40}
                    elif attack_name == 'AutoProjectedGradientDescent':
                        config_attack = {'epsilon': 0.03, 'alpha': 0.007, 'iters': 40}
                    elif attack_name == 'DeepFool':
                        config_attack = {'max_iter': 50, 'overshoot': 0.02}
                    elif attack_name == 'CW_L2':
                        config_attack = {'c': 1e-4, 'max_iter': 1000}
                    elif attack_name == 'CW_Linf':
                        config_attack = {'c': 1e-4, 'max_iter': 1000}
                    elif attack_name == 'ElasticNet':
                        config_attack = {'alpha': 1.0, 'beta': 0.01, 'iters': 1000}
                    elif attack_name == 'BasicIterativeMethod':
                        config_attack = {'epsilon': 0.03, 'alpha': 0.005, 'iters': 10}
                    elif attack_name == 'MomentumIterativeMethod':
                        config_attack = {'alpha': 0.01, 'momentum': 1.0, 'epsilon': 0.03, 'iters': 40}
                    elif attack_name == 'AutoConjugateGradient':
                        config_attack = {'epsilon': 0.03, 'alpha': 0.007, 'iters': 40}
                    elif attack_name == 'PixelAttack':
                        config_attack = {'num_pixels': 100, 'epsilon': 0.02, 'iters': 1000}
                    elif attack_name == 'SquareAttack':
                        config_attack = {'n_queries': 1000, 'p_init': 0.05, 'p_max': 0.4,
                                         'p_min': 0.001, 's_init': 0.5, 's_min': 0.05, 's_max': 0.9}
                    elif attack_name == 'SquareAttackBatch':
                        config_attack = {'n_queries': 1000, 'p_init': 0.05, 'p_max': 0.4,
                                         'p_min': 0.001, 's_init': 0.5, 's_min': 0.05, 's_max': 0.9}
                    elif attack_name == 'AdversarialPatch':
                        config_attack = {'patch_size': 50, 'epsilon': 0.03, 'iters': 100}
                    elif attack_name == 'UniversalPerturbation':
                        config_attack = {'xi': 0.03, 'delta': 0.2, 'max_iter_uni': 10, 'max_iter_df': 50}
                    elif attack_name == 'ZooAttack':
                        config_attack = {'n_queries': 1000, 'epsilon': 0.007}
                    elif attack_name == 'CompositeAdversarialAttackPyTorch':
                        # Example composite: FGSM followed by PGD
                        config_attack = {
                            'attacks': ['FGSM', 'PGD'],
                            'configs': [
                                {'epsilon': 0.007},
                                {'epsilon': 0.03, 'alpha': 0.007, 'iters': 40}
                            ]
                        }
                    elif attack_name == 'SpatialTransformation':
                        config_attack = {'alpha': 0.1, 'beta': 0.1}
                    else:
                        logger.warning(f"No configuration defined for attack '{attack_name}'. Using defaults.")

                    adv_images = adversary.attack(attack_name, images, labels, config_attack)

                    # Forward pass with adversarial examples
                    model_output = model(adv_images).logits  # Extract logits
                    metrics_calc = MetricsCalculator(preds=model_output, labels=labels, num_classes=1000)
                    metrics = metrics_calc.get_all_metrics()
                    print(f"\nAttack: {attack_name}")
                    print_metrics(metrics)

                except ValueError as ve:
                    logger.warning(f"Skipping unsupported attack '{attack_name}': {ve}")
                except NotImplementedError as nie:
                    logger.warning(f"Attack '{attack_name}' is not implemented: {nie}")
                except RuntimeError as re:
                    logger.error(f"Attack '{attack_name}' failed: {re}")
    except Exception as e:
            logger.error(f"Unexpected error during attack '{attack_name}': {e}")

if __name__ == "__main__":
    # Example tensor
    x = torch.randn(32, 3, 224, 224)
    y = torch.randint(0, 100, (32,))  # Corrected line

    # Initialize defenses
    gaussian_defense = GaussianNoiseDefense(sigma=0.02)
    label_smoothing_defense = LabelSmoothingDefense(alpha=0.1, num_classes=3)
    mixup_defense = MixupDefense(alpha=0.4)
    feature_squeezing_defense = FeatureSqueezingDefense(bit_depth=4)
    spatial_smoothing_defense = SpatialSmoothingDefense(kernel_size=5)
    cutout_defense = CutoutDefense(mask_size=50)
    
    # Ensure PIL is installed for JpegCompressionDefense
    try:
        from PIL import Image
        jpeg_defense = JpegCompressionDefense(quality=50)
    except ImportError:
        logger.error("PIL is required for JpegCompressionDefense. Please install it using `pip install Pillow`.")
        jpeg_defense = None

    # Apply defenses
    x_noisy = gaussian_defense.apply(x)
    y_smooth = label_smoothing_defense.apply(y)
    x_mixup, y_mixup = mixup_defense.apply(x, y)
    x_squeezed = feature_squeezing_defense.apply(x)
    x_smooth = spatial_smoothing_defense.apply(x)
    x_cutout = cutout_defense.apply(x)
    print(x_cutout.shape)
    
    if jpeg_defense:
        x_compressed = jpeg_defense.apply(x)
        print(x_compressed.shape)