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 : in the below make generalised with propoperly 

1)AdversarialPatch 
"rotation_max",
"scale_min",
"scale_max",
"learning_rate",
"max_iter",
"batch_size",
"targeted",
"verbose",

2)AdversarialTexturePyTorch 
"patch_height",
"patch_width",
"xmin",
"ymin",
"step_size",
"max_iter",
"batch_size",
"verbose",
3)AutoAttack 
"norm",
"eps",
"eps_step",
"attacks",
"batch_size",
"estimator_orig",
"targeted",
"parallel",

4) AutoProjectedGradientDescent
"norm",
"eps",
"eps_step",
"max_iter",
"targeted",
"nb_random_init",
"batch_size",
"loss_type",
"verbose",

5) AutoConjugateGradient
"norm",
"eps",
"eps_step",
"max_iter",
"targeted",
"nb_random_init",
"batch_size",
"loss_type",
"verbose",
6) BrendelBethgeAttack
"norm",
"targeted",
"init_attack",
"overshoot",
"steps",
"lr",
"lr_decay",
"lr_num_decay",
"momentum",
"binary_search_steps",
"init_size",

7)  BoundaryAttack
"targeted",
"delta",
"epsilon",
"step_adapt",
"max_iter",
"num_trial",
"sample_size",
"init_size",
"batch_size",
"verbose",

8) CompositeAdversarialAttackPyTorch
"enabled_attack",
"hue_epsilon",
"sat_epsilon",
"rot_epsilon",
"bri_epsilon",
"con_epsilon",
"pgd_epsilon",
"early_stop",
"max_iter",
"max_inner_iter",
"schedule",
"batch_size",
"verbose",

9) CarliniL2Method , 10)  CarliniLInfMethod, 11) CarliniL0Method
"confidence",
"targeted",
"learning_rate",
"max_iter",
"binary_search_steps",
"initial_const",
"max_halving",
"max_doubling",
"batch_size",
"verbose",
12 )    DeepFool,
"max_iter",
"epsilon",
"nb_grads",
"batch_size",
"verbose",
13) DPatch
"patch_shape",
"learning_rate",
"max_iter",
"batch_size",
"verbose",

14) RobustDPatch
"patch_shape",
"learning_rate",
"max_iter",
"batch_size",
"patch_location",
"crop_range",
"brightness_range",
"rotation_weights",
"sample_size",
"targeted",
"summary_writer",
"verbose",
15) ElasticNet
"confidence",
"targeted",
"learning_rate",
"max_iter",
"beta",
"binary_search_steps",
"initial_const",
"batch_size",
"decision_rule",
"verbose",


remeaning all will do it 
import sys
import logging
from pathlib import Path
from typing import Any, Dict, List, Tuple, Type

import numpy as np  # Added for handling NumPy arrays
import torch
import torch.nn as nn
import torch.optim as optim
from art.estimators.classification import PyTorchClassifier

# Adjust the system path to include the parent directory
sys.path.append(str(Path(__file__).resolve().parents[0]))

from attacks import (
    AdversarialPatch,
    AdversarialPatchNumpy,
    AdversarialPatchTensorFlowV2,
    AdversarialPatchPyTorch,
    AdversarialTexturePyTorch,
    CarliniWagnerASR,
    AutoAttack,
    AutoProjectedGradientDescent,
    AutoConjugateGradient,
    BrendelBethgeAttack,
    BoundaryAttack,
    CompositeAdversarialAttackPyTorch,
    CarliniL2Method,
    CarliniLInfMethod,
    CarliniL0Method,
    DecisionTreeAttack,
    DeepFool,
    DPatch,
    RobustDPatch,
    ElasticNet,
    FastGradientMethod,
    FrameSaliencyAttack,
    FeatureAdversariesNumpy,
    FeatureAdversariesPyTorch,
    FeatureAdversariesTensorFlowV2,
    GeoDA,
    GRAPHITEBlackbox,
    GRAPHITEWhiteboxPyTorch,
    HighConfidenceLowUncertainty,
    HopSkipJump,
    ImperceptibleASR,
    ImperceptibleASRPyTorch,
    BasicIterativeMethod,
    LaserAttack,
    LowProFool,
    MomentumIterativeMethod,
    NewtonFool,
    OverloadPyTorch,
    MalwareGDTensorFlow,
    PixelAttack,
    ProjectedGradientDescent,
    ProjectedGradientDescentNumpy,
    ProjectedGradientDescentPyTorch,
    ProjectedGradientDescentTensorFlowV2,
    OverTheAirFlickeringPyTorch,
    SaliencyMapMethod,
    ShadowAttack,
    ShapeShifter,
    SimBA,
    SpatialTransformation,
    SquareAttack,
    ThresholdAttack,
    UniversalPerturbation,
    TargetedUniversalPerturbation,
    VirtualAdversarialMethod,
    Wasserstein,
    ZooAttack,
    SignOPTAttack,
)

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


class SimpleCNN(nn.Module):
    """A simple convolutional neural network."""

    def __init__(self, num_classes: int) -> None:
        """Initialize the CNN architecture.

        Args:
            num_classes (int): Number of output classes.
        """
        super(SimpleCNN, self).__init__()
        self.conv1: nn.Conv2d = nn.Conv2d(
            in_channels=3, out_channels=32, kernel_size=3, stride=1, padding=1
        )
        self.conv2: nn.Conv2d = nn.Conv2d(
            in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1
        )
        self.adaptive_pool: nn.AdaptiveAvgPool2d = nn.AdaptiveAvgPool2d((32, 32))
        self.fc1: nn.Linear = nn.Linear(64 * 32 * 32, 128)
        self.fc2: nn.Linear = nn.Linear(128, num_classes)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """Define the forward pass.

        Args:
            x (torch.Tensor): Input tensor.

        Returns:
            torch.Tensor: Output logits.
        """
        x = torch.relu(self.conv1(x))
        x = torch.relu(self.conv2(x))
        x = self.adaptive_pool(x)
        x = torch.flatten(x, 1)
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x


def initialize_model(num_classes: int) -> nn.Module:
    """Initialize the CNN model.

    Args:
        num_classes (int): Number of output classes.

    Returns:
        nn.Module: Initialized model.
    """
    try:
        model = SimpleCNN(num_classes=num_classes)
        logger.info("Model initialized successfully.")
        return model
    except Exception as e:
        logger.error(f"Error initializing model: {e}")
        sys.exit(1)


def initialize_classifier(
    model: nn.Module,
    input_shape: Tuple[int, int, int],
    nb_classes: int,
    clip_values: Tuple[float, float],
    learning_rate: float = 0.001,
) -> PyTorchClassifier:
    """Initialize the ART PyTorchClassifier.

    Args:
        model (nn.Module): PyTorch model.
        input_shape (Tuple[int, int, int]): Shape of input data.
        nb_classes (int): Number of classes.
        clip_values (Tuple[float, float]): Min and max values for input data.
        learning_rate (float, optional): Learning rate for optimizer. Defaults to 0.001.

    Returns:
        PyTorchClassifier: Wrapped classifier.
    """
    try:
        loss_fn = nn.CrossEntropyLoss()
        optimizer = optim.Adam(model.parameters(), lr=learning_rate)
        classifier = PyTorchClassifier(
            model=model,
            loss=loss_fn,
            optimizer=optimizer,
            input_shape=input_shape,
            nb_classes=nb_classes,
            clip_values=clip_values,
        )
        logger.info("Classifier initialized successfully.")
        return classifier
    except Exception as e:
        logger.error(f"Error initializing classifier: {e}")
        sys.exit(1)


def generate_dummy_data(batch_size: int = 1) -> Tuple[np.ndarray, np.ndarray]:
    """Generate dummy input data and labels.

    Args:
        batch_size (int, optional): Number of samples. Defaults to 1.

    Returns:
        Tuple[np.ndarray, np.ndarray]: Inputs and labels.
    """
    try:
        # Generate random inputs as NumPy arrays
        inputs = np.random.randn(batch_size, 3, 32, 32).astype(np.float32)
        # Generate random labels between 0 and 999
        labels = np.random.randint(0, 1000, size=(batch_size,))
        logger.info("Dummy data generated successfully.")
        return inputs, labels
    except Exception as e:
        logger.error(f"Error generating dummy data: {e}")
        sys.exit(1)


AttackType = Type[Any]  # Define a generic type for attacks


def get_attack_parameters(attack: AttackType) -> Dict[str, Any]:
    """Retrieve default parameters for each attack.

    Args:
        attack (AttackType): The attack class.

    Returns:
        Dict[str, Any]: Dictionary of parameters.
    """
    # Define default parameters for each attack
    default_params: Dict[Type[Any], Dict[str, Any]] = {
    AdversarialPatch: {"patch_shape": (3, 10, 10)},
    AdversarialPatchNumpy: {"patch_shape": (3, 10, 10)},
    DeepFool: {"epsilon": 0.1, "max_iter": 50, "nb_grads": 10},
    FastGradientMethod: {"norm": 2, "eps": 0.3},
    AdversarialTexturePyTorch: { "patch_height":10,"patch_width": 10},
    CarliniL2Method: {"max_iterations": 10},
    CarliniLInfMethod: {"max_iterations": 10},
    CarliniL0Method: {"max_iterations": 10},
    MomentumIterativeMethod: {"norm": 2, "eps": 0.3, "steps": 40, "decay_factor": 1.0},
    BasicIterativeMethod: {"norm": 2, "eps": 0.3, "max_iter": 40},
    ProjectedGradientDescent: {"norm": 2, "eps": 0.3, "max_iter": 10},
    # Add more attacks with their default parameters as needed
}
    return default_params.get(attack, {})


def test_attack(
    attack_class: AttackType,
    classifier: PyTorchClassifier,
    inputs: np.ndarray,
    labels: np.ndarray,
) -> None:
    """Test a single adversarial attack.

    Args:
        attack_class (AttackType): The attack class to test.
        classifier (PyTorchClassifier): The classifier to attack.
        inputs (np.ndarray): Input data.
        labels (np.ndarray): True labels.
    """
    attack_name = attack_class.__name__
    logger.info(f"Testing attack: {attack_name}")

    try:
        params = get_attack_parameters(attack_class)
        attack = attack_class(classifier, **params)
        adversarial_examples = attack.generate(x=inputs, y=labels)
        logger.info(f"{attack_name} performed successfully.")
        # Optionally, you can add more checks or validations here
    except NotImplementedError:
        logger.warning(f"{attack_name} is not implemented for PyTorchClassifier.")
    except Exception as e:
        logger.error(f"Error performing {attack_name}: {e}")


def get_all_attacks() -> List[AttackType]:
    """Retrieve all attack classes to be tested.

    Returns:
        List[AttackType]: List of attack classes.
    """
    return [
        AdversarialPatch,
        AdversarialTexturePyTorch,
        AutoAttack,
        AutoProjectedGradientDescent,
        AutoConjugateGradient,
        BrendelBethgeAttack,
        BoundaryAttack,
        CompositeAdversarialAttackPyTorch,
        CarliniL2Method,
        CarliniLInfMethod,
        CarliniL0Method,
        DeepFool,
        DPatch,
        RobustDPatch,
        ElasticNet,
        FastGradientMethod,
        FrameSaliencyAttack,
        FeatureAdversariesNumpy,
        FeatureAdversariesPyTorch,
        FeatureAdversariesTensorFlowV2,
        GeoDA,
        GRAPHITEBlackbox,
        GRAPHITEWhiteboxPyTorch,
        HighConfidenceLowUncertainty,
        HopSkipJump,
        ImperceptibleASR,
        ImperceptibleASRPyTorch,
        BasicIterativeMethod,
        LaserAttack,
        LowProFool,
        MomentumIterativeMethod,
        NewtonFool,
        OverloadPyTorch,
        MalwareGDTensorFlow,
        PixelAttack,
        ProjectedGradientDescent,
        ProjectedGradientDescentNumpy,
        ProjectedGradientDescentPyTorch,
        ProjectedGradientDescentTensorFlowV2,
        OverTheAirFlickeringPyTorch,
        SaliencyMapMethod,
        ShadowAttack,
        ShapeShifter,
        SimBA,
        SpatialTransformation,
        SquareAttack,
        ThresholdAttack,
        UniversalPerturbation,
        TargetedUniversalPerturbation,
        VirtualAdversarialMethod,
        Wasserstein,
        ZooAttack,
        SignOPTAttack,
    ]


def main() -> None:
    """Main function to execute the end-to-end attack tests."""
    logger.info("Starting end-to-end adversarial attack tests.")

    # Initialize model
    num_classes = 1000
    model = initialize_model(num_classes=num_classes)

    # Initialize classifier
    input_shape = (3, 32, 32)
    clip_values = (0.0, 1.0)
    classifier = initialize_classifier(
        model=model,
        input_shape=input_shape,
        nb_classes=num_classes,
        clip_values=clip_values,
    )

    # Generate dummy data
    inputs, labels = generate_dummy_data(batch_size=1)

    # Retrieve all attacks
    attacks = get_all_attacks()

    # Test each attack
    for attack_class in attacks:
        test_attack(attack_class, classifier, inputs, labels)

    logger.info("All adversarial attack tests completed.")


if __name__ == "__main__":
    main()