list_attack: List[str] = [
        'AdversarialPatch',
        'AutoProjectedGradientDescent',
        'AutoConjugateGradient', 
        'CompositeAdversarialAttackPyTorch',
        'CarliniL2Method',
        'CarliniLInfMethod',
        'DeepFool',
        'ElasticNet',
        'FastGradientMethod',
        'BasicIterativeMethod',
        'MomentumIterativeMethod',
        'NewtonFool',
        'PixelAttack',
        'ProjectedGradientDescent',
        'ProjectedGradientDescentPyTorch',
        'SpatialTransformation',
        'SquareAttack',
        'UniversalPerturbation',
        'Wasserstein',
        'ZooAttack',
    ]


I'll explain the key mathematical equations for each attack:

1. AdversarialPatch
```
patch_application = (1 - M) ⊙ x + M ⊙ p
where:
M = mask matrix
x = original image
p = adversarial patch
⊙ = element-wise multiplication
```

2. AutoProjectedGradientDescent (APGD)
```
x_{t+1} = Π(x_t - α_t∇L(x_t))
where:
Π = projection onto feasible set
α_t = step size at iteration t
L = loss function
```

3. AutoConjugateGradient
```
d_k = -∇f(x_k) + β_k d_{k-1}
x_{k+1} = x_k + α_k d_k
where:
β_k = Fletcher-Reeves formula
α_k = line search parameter
```

4. CarliniL2Method (C&W L2)
```
minimize ||δ||_2 + c⋅f(x+δ)
subject to x+δ ∈ [0,1]^n
where:
δ = perturbation
c = constant
f = objective function
```

5. CarliniLInfMethod
```
minimize ||δ||_∞ + c⋅f(x+δ)
subject to x+δ ∈ [0,1]^n
```

6. DeepFool
```
x_{i+1} = x_i + r_i
r_i = -(f(x_i)/||∇f(x_i)||_2^2)∇f(x_i)
where:
f = classifier function
r_i = perturbation at step i
```

7. ElasticNet
```
minimize ||δ||_2^2 + β||δ||_1 + c⋅f(x+δ)
subject to x+δ ∈ [0,1]^n
where:
β = L1 regularization parameter
```

8. FastGradientMethod (FGSM)
```
x_adv = x + ε⋅sign(∇_x J(θ,x,y))
where:
ε = perturbation magnitude
J = loss function
```

9. BasicIterativeMethod (BIM)
```
x_{t+1} = clip_ε(x_t + α⋅sign(∇_x J(θ,x_t,y)))
where:
α = step size
clip_ε = epsilon-neighborhood clipping
```

10. MomentumIterativeMethod
```
g_{t+1} = μ⋅g_t + ∇_x J(θ,x_t,y)/||∇_x J(θ,x_t,y)||_1
x_{t+1} = x_t + α⋅sign(g_{t+1})
where:
μ = momentum parameter
```

11. NewtonFool
```
x_{k+1} = x_k - (f(x_k)/||∇f(x_k)||_2^2)∇f(x_k)
```

12. PixelAttack
```
x_adv[i,j] = x[i,j] + δ[i,j]
where δ modifies limited pixels
```

13. ProjectedGradientDescent (PGD)
```
x_{t+1} = Π_ε(x_t + α⋅sign(∇_x J(θ,x_t,y)))
where:
Π_ε = projection onto ε-ball
```

14. SpatialTransformation
```
x_adv = T(x; θ)
where:
T = spatial transformation function
θ = transformation parameters
```

15. SquareAttack
```
x_adv = x + random_square_perturbation
subject to ||x_adv - x||_∞ ≤ ε
```

16. UniversalPerturbation
```
v = Σ δ_i
x_adv = x + v
subject to ||v||_p ≤ ε
where:
δ_i = individual perturbations
```

17. Wasserstein
```
minimize W(P_x, P_x_adv)
subject to ||x_adv - x||_p ≤ ε
where:
W = Wasserstein distance
P = probability distributions
```

18. ZooAttack
```
∇f(x) ≈ [f(x+hei) - f(x-hei)]/2h
x_{t+1} = x_t - η_t∇f(x_t)
where:
h = small constant
ei = standard basis vector
```


import logging
from typing import Any, Dict, List, Optional, Union

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import transforms
from torch.utils.data import DataLoader

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Define type aliases
Tensor = torch.Tensor
AttackConfig = Dict[str, Any]


class AdversarialAttack:
    """
    A class encapsulating various adversarial attack methods.
    """

    def __init__(
        self,
        model: nn.Module,
        device: Union[str, torch.device] = "cuda" if torch.cuda.is_available() else "cpu",
    ) -> None:
        """
        Initialize the AdversarialAttack class with a model and device.

        Args:
            model (nn.Module): The neural network model to attack.
            device (Union[str, torch.device], optional): The device to perform computations on.
                Defaults to 'cuda' if available, else 'cpu'.
        """
        self.device = device
        self.model = model.to(self.device)
        self.model.eval()
        logger.info(f"Model loaded on device: {self.device}")

    def _clamp(
        self, x: Tensor, min_val: float, max_val: float
    ) -> Tensor:
        """
        Clamp the tensor values between min_val and max_val.

        Args:
            x (Tensor): Input tensor.
            min_val (float): Minimum value.
            max_val (float): Maximum value.

        Returns:
            Tensor: Clamped tensor.
        """
        return torch.clamp(x, min=min_val, max=max_val)

    # ============================
    #       Basic Attacks
    # ============================

    def fgsm(
        self,
        x: Tensor,
        y: Tensor,
        epsilon: float = 0.007
    ) -> Tensor:
        """
        Fast Gradient Sign Method (FGSM) attack.

        Args:
            x (Tensor): Original input tensor.
            y (Tensor): True labels.
            epsilon (float, optional): Perturbation magnitude. Defaults to 0.007.

        Returns:
            Tensor: Adversarial example.
        """
        try:
            x_adv = x.clone().detach().to(self.device)
            x_adv.requires_grad = True

            outputs = self.model(x_adv).logits  # Extract logits
            loss = nn.CrossEntropyLoss()(outputs, y.to(self.device))
            self.model.zero_grad()
            loss.backward()
            grad_sign = x_adv.grad.sign()
            x_adv = x_adv + epsilon * grad_sign
            x_adv = self._clamp(x_adv, 0, 1)
            return x_adv.detach()
        except Exception as e:
            logger.error(f"FGSM attack failed: {e}")
            raise

    def fast_gradient_method(
        self,
        x: Tensor,
        y: Tensor,
        epsilon: float = 0.007
    ) -> Tensor:
        """
        Alias for FGSM to maintain consistency with naming conventions.

        Args:
            x (Tensor): Original input tensor.
            y (Tensor): True labels.
            epsilon (float, optional): Perturbation magnitude. Defaults to 0.007.

        Returns:
            Tensor: Adversarial example.
        """
        return self.fgsm(x, y, epsilon)

    def basic_iterative_method(
        self,
        x: Tensor,
        y: Tensor,
        epsilon: float = 0.03,
        alpha: float = 0.005,
        iters: int = 10
    ) -> Tensor:
        """
        Basic Iterative Method (BIM) attack.

        Args:
            x (Tensor): Original input tensor.
            y (Tensor): True labels.
            epsilon (float, optional): Maximum perturbation. Defaults to 0.03.
            alpha (float, optional): Step size. Defaults to 0.005.
            iters (int, optional): Number of iterations. Defaults to 10.

        Returns:
            Tensor: Adversarial example.
        """
        try:
            x_adv = x.clone().detach().to(self.device)
            y = y.to(self.device)

            for _ in range(iters):
                x_adv.requires_grad = True
                outputs = self.model(x_adv).logits
                loss = nn.CrossEntropyLoss()(outputs, y)
                self.model.zero_grad()
                loss.backward()
                grad = x_adv.grad.sign()
                x_adv = x_adv + alpha * grad
                eta = torch.clamp(x_adv - x, min=-epsilon, max=epsilon)
                x_adv = torch.clamp(x + eta, 0, 1).detach()

            return x_adv
        except Exception as e:
            logger.error(f"Basic Iterative Method attack failed: {e}")
            raise

    def momentum_iterative_method(
        self,
        x: Tensor,
        y: Tensor,
        alpha: float = 0.01,
        momentum: float = 1.0,
        epsilon: float = 0.03,
        iters: int = 40
    ) -> Tensor:
        """
        Momentum Iterative Method (MI-FGSM) attack.

        Args:
            x (Tensor): Original input tensor.
            y (Tensor): True labels.
            alpha (float, optional): Step size. Defaults to 0.01.
            momentum (float, optional): Momentum factor. Defaults to 1.0.
            epsilon (float, optional): Maximum perturbation. Defaults to 0.03.
            iters (int, optional): Number of iterations. Defaults to 40.

        Returns:
            Tensor: Adversarial example.
        """
        try:
            x_adv = x.clone().detach().to(self.device)
            y = y.to(self.device)
            momentum_acc = torch.zeros_like(x_adv).to(self.device)

            for _ in range(iters):
                x_adv.requires_grad = True
                outputs = self.model(x_adv).logits
                loss = nn.CrossEntropyLoss()(outputs, y)
                self.model.zero_grad()
                loss.backward()
                grad = x_adv.grad.sign()
                momentum_acc = momentum * momentum_acc + grad
                x_adv = x_adv + alpha * momentum_acc
                eta = torch.clamp(x_adv - x, min=-epsilon, max=epsilon)
                x_adv = torch.clamp(x + eta, 0, 1).detach()

            return x_adv
        except Exception as e:
            logger.error(f"Momentum Iterative Method attack failed: {e}")
            raise

    def elastic_net(
        self,
        x: Tensor,
        y: Tensor,
        alpha: float = 1.0,
        beta: float = 0.01,
        iters: int = 1000
    ) -> Tensor:
        """
        Elastic Net attack.

        Args:
            x (Tensor): Original input tensor.
            y (Tensor): True labels.
            alpha (float, optional): Weight for L1 regularization. Defaults to 1.0.
            beta (float, optional): Weight for L2 regularization. Defaults to 0.01.
            iters (int, optional): Number of iterations. Defaults to 1000.

        Returns:
            Tensor: Adversarial example.
        """
        try:
            x_adv = x.clone().detach().to(self.device)
            x_adv.requires_grad = True
            optimizer = optim.Adam([x_adv], lr=0.01)

            for _ in range(iters):
                outputs = self.model(x_adv).logits
                loss = nn.CrossEntropyLoss()(outputs, y.to(self.device)) + alpha * torch.norm(x_adv - x, p=1) + beta * torch.norm(x_adv - x, p=2)
                optimizer.zero_grad()
                loss.backward()
                optimizer.step()
                x_adv.data = torch.clamp(x_adv.data, 0, 1)

            return x_adv.detach()
        except Exception as e:
            logger.error(f"Elastic Net attack failed: {e}")
            raise

    # ============================
    #      Optimization-Based
    # ============================

    def cw_l2(
        self,
        x: Tensor,
        y: Tensor,
        c: float = 1e-4,
        target: Optional[int] = None,
        max_iter: int = 1000
    ) -> Tensor:
        """
        Carlini & Wagner L2 attack.

        Args:
            x (Tensor): Original input tensor.
            y (Tensor): True labels.
            c (float, optional): Configuration parameter. Defaults to 1e-4.
            target (Optional[int], optional): Target label for targeted attacks. Defaults to None.
            max_iter (int, optional): Maximum number of iterations. Defaults to 1000.

        Returns:
            Tensor: Adversarial example.
        """
        try:
            x_adv = x.clone().detach().to(self.device)
            x_adv.requires_grad = True
            optimizer = optim.Adam([x_adv], lr=0.01)

            if target is not None:
                target_tensor = torch.tensor([target]).to(self.device)
            else:
                target_tensor = y.to(self.device)

            for _ in range(max_iter):
                outputs = self.model(x_adv).logits
                loss_class = nn.CrossEntropyLoss()(outputs, target_tensor)
                loss_l2 = torch.norm(x_adv - x, p=2)
                loss = loss_class + c * loss_l2
                optimizer.zero_grad()
                loss.backward()
                optimizer.step()
                x_adv.data = torch.clamp(x_adv.data, 0, 1)
                x_adv.requires_grad = True

            return x_adv.detach()
        except Exception as e:
            logger.error(f"Carlini & Wagner L2 attack failed: {e}")
            raise

    def cw_linf(
        self,
        x: Tensor,
        y: Tensor,
        c: float = 1e-4,
        target: Optional[int] = None,
        max_iter: int = 1000
    ) -> Tensor:
        """
        Carlini & Wagner Linf attack.

        Args:
            x (Tensor): Original input tensor.
            y (Tensor): True labels.
            c (float, optional): Configuration parameter. Defaults to 1e-4.
            target (Optional[int], optional): Target label for targeted attacks. Defaults to None.
            max_iter (int, optional): Maximum number of iterations. Defaults to 1000.

        Returns:
            Tensor: Adversarial example.
        """
        try:
            x_adv = x.clone().detach().to(self.device)
            x_adv.requires_grad = True
            optimizer = optim.Adam([x_adv], lr=0.01)

            if target is not None:
                target_tensor = torch.tensor([target]).to(self.device)
            else:
                target_tensor = y.to(self.device)

            for _ in range(max_iter):
                outputs = self.model(x_adv).logits
                loss_class = nn.CrossEntropyLoss()(outputs, target_tensor)
                loss_linf = torch.norm(x_adv - x, p=float('inf'))
                loss = loss_class + c * loss_linf
                optimizer.zero_grad()
                loss.backward()
                optimizer.step()
                x_adv.data = torch.clamp(x_adv.data, 0, 1)
                x_adv.requires_grad = True

            return x_adv.detach()
        except Exception as e:
            logger.error(f"Carlini & Wagner Linf attack failed: {e}")
            raise

    # ============================
    #       Gradient-Based
    # ============================

    def projected_gradient_descent(
        self,
        x: Tensor,
        y: Tensor,
        epsilon: float = 0.03,
        alpha: float = 0.007,
        iters: int = 40
    ) -> Tensor:
        """
        Projected Gradient Descent (PGD) attack.

        Args:
            x (Tensor): Original input tensor.
            y (Tensor): True labels.
            epsilon (float, optional): Maximum perturbation. Defaults to 0.03.
            alpha (float, optional): Step size. Defaults to 0.007.
            iters (int, optional): Number of iterations. Defaults to 40.

        Returns:
            Tensor: Adversarial example.
        """
        try:
            x_adv = x.clone().detach().to(self.device)
            y = y.to(self.device)

            for _ in range(iters):
                x_adv.requires_grad = True
                outputs = self.model(x_adv).logits
                loss = nn.CrossEntropyLoss()(outputs, y)
                self.model.zero_grad()
                loss.backward()
                grad = x_adv.grad.sign()
                x_adv = x_adv + alpha * grad
                eta = torch.clamp(x_adv - x, min=-epsilon, max=epsilon)
                x_adv = torch.clamp(x + eta, 0, 1).detach()

            return x_adv
        except Exception as e:
            logger.error(f"Projected Gradient Descent attack failed: {e}")
            raise

    def auto_projected_gradient_descent(
        self,
        x: Tensor,
        y: Tensor,
        epsilon: float = 0.03,
        alpha: float = 0.007,
        iters: int = 40
    ) -> Tensor:
        """
        Auto Projected Gradient Descent attack.
        * Placeholder for a more advanced PGD variant.
        
        Args:
            x (Tensor): Original input tensor.
            y (Tensor): True labels.
            epsilon (float, optional): Maximum perturbation.
            alpha (float, optional): Step size.
            iters (int, optional): Number of iterations.

        Returns:
            Tensor: Adversarial example.
        """
        try:
            # This can be an implementation where parameters are dynamically adjusted
            # based on some criteria, such as loss or gradient magnitude.
            # For simplicity, we adopt a similar approach to PGD here.
            x_adv = x.clone().detach().to(self.device)
            y = y.to(self.device)

            for i in range(iters):
                x_adv.requires_grad = True
                outputs = self.model(x_adv).logits
                loss = nn.CrossEntropyLoss()(outputs, y)
                self.model.zero_grad()
                loss.backward()
                grad = x_adv.grad.sign()
                x_adv = x_adv + alpha * grad

                # Dynamic adjustment of alpha can be implemented here
                if i % 10 == 0 and i != 0:
                    alpha = alpha * 0.9  # Decaying step size

                eta = torch.clamp(x_adv - x, min=-epsilon, max=epsilon)
                x_adv = torch.clamp(x + eta, 0, 1).detach()

            return x_adv
        except Exception as e:
            logger.error(f"Auto Projected Gradient Descent attack failed: {e}")
            raise

    def auto_conjugate_gradient(
        self,
        x: Tensor,
        y: Tensor,
        epsilon: float = 0.03,
        alpha: float = 0.007,
        iters: int = 40
    ) -> Tensor:
        """
        Auto Conjugate Gradient attack.
        * Placeholder for an advanced attack using conjugate gradients.
        
        Args:
            x (Tensor): Original input tensor.
            y (Tensor): True labels.
            epsilon (float, optional): Maximum perturbation.
            alpha (float, optional): Step size.
            iters (int, optional): Number of iterations.

        Returns:
            Tensor: Adversarial example.
        """
        try:
            # Implement conjugate gradient-based optimization for perturbations.
            # This requires a more complex setup and is beyond the scope of this example.
            # Placeholder implementation:
            raise NotImplementedError("AutoConjugateGradient attack is not implemented yet.")
        except Exception as e:
            logger.error(f"Auto Conjugate Gradient attack failed: {e}")
            raise

    # ============================
    #      Choice-Based
    # ============================

    def deepfool(
        self,
        x: Tensor,
        y: Tensor,
        max_iter: int = 50,
        overshoot: float = 0.02
    ) -> Tensor:
        """
        DeepFool attack.

        Args:
            x (Tensor): Original input tensor.
            y (Tensor): True labels.
            max_iter (int, optional): Maximum number of iterations. Defaults to 50.
            overshoot (float, optional): Overshoot value. Defaults to 0.02.

        Returns:
            Tensor: Adversarial example.
        """
        try:
            x_adv = x.clone().detach().to(self.device)
            y = y.to(self.device)
            x_adv.requires_grad = True

            for _ in range(max_iter):
                outputs = self.model(x_adv).logits
                _, pred = torch.max(outputs, 1)
                if pred.item() != y.item():
                    break

                loss = nn.CrossEntropyLoss()(outputs, y)
                self.model.zero_grad()
                loss.backward()
                grad = x_adv.grad.data

                # Simple step in the direction of the gradient
                x_adv = x_adv + overshoot * grad.sign()
                x_adv = self._clamp(x_adv, 0, 1).detach()
                x_adv.requires_grad = True

            return x_adv.detach()
        except Exception as e:
            logger.error(f"DeepFool attack failed: {e}")
            raise

    def newton_fool(
        self,
        x: Tensor,
        y: Tensor,
        max_iter: int = 50,
        overshoot: float = 0.02
    ) -> Tensor:
        """
        NewtonFool attack.
        * Placeholder for NewtonFool implementation.
        
        Args:
            x (Tensor): Original input tensor.
            y (Tensor): True labels.
            max_iter (int, optional): Maximum number of iterations.
            overshoot (float, optional): Overshoot value.

        Returns:
            Tensor: Adversarial example.
        """
        try:
            # Implement NewtonFool attack which uses second-order information.
            # This requires Hessian computations and is more involved.
            # Placeholder implementation:
            raise NotImplementedError("NewtonFool attack is not implemented yet.")
        except Exception as e:
            logger.error(f"NewtonFool attack failed: {e}")
            raise

    def universal_perturbation(
        self,
        x: Tensor,
        y: Tensor,
        xi: float = 0.03,
        delta: float = 0.2,
        max_iter_uni: int = 10,
        max_iter_df: int = 50
    ) -> Tensor:
        """
        Universal Perturbation attack.

        Args:
            x (Tensor): Original input tensor.
            y (Tensor): True labels.
            xi (float, optional): L-infty norm of perturbation.
            delta (float, optional): Target misclassification rate.
            max_iter_uni (int, optional): Number of iterations for universal perturbation.
            max_iter_df (int, optional): Number of iterations for DeepFool.

        Returns:
            Tensor: Adversarial example.
        """
        try:
            v = torch.zeros_like(x).to(self.device)
            num_images = x.size(0)
            fooling_count = 0

            for _ in range(max_iter_uni):
                for i in range(num_images):
                    x_img = x[i:i+1].clone().detach().to(self.device)
                    y_img = y[i:i+1].to(self.device)
                    x_adv = x_img + v
                    x_adv = self._clamp(x_adv, 0, 1)
                    x_adv.requires_grad = True
                    outputs = self.model(x_adv).logits
                    _, pred = torch.max(outputs, 1)
                    if pred.item() != y_img.item():
                        fooling_count += 1
                        continue
                    loss = nn.CrossEntropyLoss()(outputs, y_img)
                    self.model.zero_grad()
                    loss.backward()
                    grad = x_adv.grad.sign()
                    v = v + (xi * grad)
                    v = torch.clamp(v, -xi, xi)
            return self._clamp(x + v, 0, 1)
        except Exception as e:
            logger.error(f"Universal Perturbation attack failed: {e}")
            raise

    # ============================
    #      Spatial Transformation
    # ============================

    def spatial_transformation(
        self,
        x: Tensor,
        y: Tensor,
        alpha: float = 0.1,
        beta: float = 0.1
    ) -> Tensor:
        """
        Spatial Transformation attack.
        * Placeholder for spatial transformation-based attack.
        
        Args:
            x (Tensor): Original input tensor.
            y (Tensor): True labels.
            alpha (float, optional): Translation along the width.
            beta (float, optional): Translation along the height.

        Returns:
            Tensor: Adversarial example.
        """
        try:
            # Implement spatial transformations such as rotation, translation, scaling.
            # This can be done using affine transformations.
            grid = nn.functional.affine_grid(
                theta=torch.tensor([
                    [1, 0, alpha],
                    [0, 1, beta]
                ], dtype=torch.float).unsqueeze(0).to(self.device),
                size=x.size()
            )
            x_adv = nn.functional.grid_sample(x, grid)
            x_adv = self._clamp(x_adv, 0, 1)
            return x_adv
        except Exception as e:
            logger.error(f"Spatial Transformation attack failed: {e}")
            raise

    # ============================
    #      Patch-Based
    # ============================

    def adversarial_patch(
        self,
        x: Tensor,
        y: Tensor,
        patch_size: int = 50,
        epsilon: float = 0.03,
        iters: int = 100
    ) -> Tensor:
        """
        Adversarial Patch attack.

        Args:
            x (Tensor): Original input tensor.
            y (Tensor): True labels.
            patch_size (int, optional): Size of the adversarial patch.
            epsilon (float, optional): Perturbation magnitude.
            iters (int, optional): Number of iterations.

        Returns:
            Tensor: Adversarial example with patch.
        """
        try:
            # Initialize patch
            patch = torch.zeros((1, 3, patch_size, patch_size), requires_grad=True).to(self.device)

            optimizer = optim.Adam([patch], lr=0.1)

            for _ in range(iters):
                # Define the region to place the patch (e.g., top-left corner)
                x_adv = x.clone().detach().to(self.device)
                x_adv.requires_grad = True

                # Place the patch
                x_adv[:, :, :patch_size, :patch_size] = patch

                outputs = self.model(x_adv).logits
                loss = nn.CrossEntropyLoss()(outputs, y.to(self.device))
                optimizer.zero_grad()
                loss.backward()
                optimizer.step()

                # Clamp the patch to maintain perturbation bounds
                patch.data = torch.clamp(patch.data, -epsilon, epsilon)
                patch.data = torch.clamp(x_adv[:, :, :patch_size, :patch_size], 0, 1)

            # Apply the final patch
            x_adv = x.clone().detach().to(self.device)
            x_adv[:, :, :patch_size, :patch_size] = patch
            x_adv = self._clamp(x_adv, 0, 1)

            return x_adv
        except Exception as e:
            logger.error(f"Adversarial Patch attack failed: {e}")
            raise

    # ============================
    #      Square Attack
    # ============================

    def square_attack(
        self,
        x: Tensor,
        y: Tensor,
        n_queries: int = 1000,
        p_init: float = 0.05,
        p_max: float = 0.4,
        p_min: float = 0.001,
        s_init: float = 0.5,
        s_min: float = 0.05,
        s_max: float = 0.9
    ) -> Tensor:
        """
        Square Attack.
        * Placeholder for SquareAttack implementation.

        Args:
            x (Tensor): Original input tensor.
            y (Tensor): True labels.
            n_queries (int, optional): Number of queries.
            p_init (float, optional): Initial perturbation probability.
            p_max (float, optional): Maximum perturbation probability.
            p_min (float, optional): Minimum perturbation probability.
            s_init (float, optional): Initial square size.
            s_min (float, optional): Minimum square size.
            s_max (float, optional): Maximum square size.

        Returns:
            Tensor: Adversarial example.
        """
        try:
            # Implement Square Attack which applies square-shaped perturbations
            # This requires random selection and updating of square regions.
            # Placeholder implementation:
            raise NotImplementedError("SquareAttack is not implemented yet.")
        except Exception as e:
            logger.error(f"Square Attack failed: {e}")
            raise

    # ============================
    #      Composition-Based
    # ============================

    def composite_adversarial_attack_pytorch(
        self,
        x: Tensor,
        y: Tensor,
        attacks: List[str],
        configs: Optional[List[AttackConfig]] = None
    ) -> Tensor:
        """
        Composite Adversarial Attack using multiple attack methods.

        Args:
            x (Tensor): Original input tensor.
            y (Tensor): True labels.
            attacks (List[str]): List of attack names to compose.
            configs (Optional[List[AttackConfig]], optional): List of configurations for each attack.

        Returns:
            Tensor: Adversarial example.
        """
        try:
            if configs is None:
                configs = [{} for _ in attacks]

            x_adv = x.clone().detach().to(self.device)

            for attack_name, config in zip(attacks, configs):
                x_adv = self.attack(attack_name, x_adv, y, config)

            return x_adv
        except Exception as e:
            logger.error(f"Composite Adversarial Attack failed: {e}")
            raise

    # ============================
    #      Other Attacks
    # ============================

    def pixel_attack(
        self,
        x: Tensor,
        y: Tensor,
        num_pixels: int = 100,
        epsilon: float = 0.02,
        iters: int = 1000
    ) -> Tensor:
        """
        Pixel Attack.

        Args:
            x (Tensor): Original input tensor.
            y (Tensor): True labels.
            num_pixels (int, optional): Number of pixels to perturb.
            epsilon (float, optional): Perturbation magnitude per pixel.
            iters (int, optional): Number of iterations.

        Returns:
            Tensor: Adversarial example.
        """
        try:
            x_adv = x.clone().detach().to(self.device)

            for _ in range(iters):
                # Randomly select pixels to perturb
                indices = torch.randint(0, x_adv.numel(), (num_pixels,), device=self.device)
                perturb = torch.zeros_like(x_adv).view(-1)
                perturb[indices] = epsilon * torch.sign(torch.randn_like(perturb[indices]))
                perturb = perturb.view_as(x_adv)
                x_adv = self._clamp(x_adv + perturb, 0, 1)

                outputs = self.model(x_adv).logits
                _, pred = torch.max(outputs, 1)
                if pred.item() != y.item():
                    break

            return x_adv
        except Exception as e:
            logger.error(f"Pixel Attack failed: {e}")
            raise

    def square_attack_batch(
        self,
        x: Tensor,
        y: Tensor,
        n_queries: int = 1000,
        p_init: float = 0.05,
        p_max: float = 0.4,
        p_min: float = 0.001,
        s_init: float = 0.5,
        s_min: float = 0.05,
        s_max: float = 0.9
    ) -> Tensor:
        """
        Square Attack for batch inputs.
        * Placeholder for more advanced SquareAttack implementation.

        Args:
            x (Tensor): Original input tensor.
            y (Tensor): True labels.
            n_queries (int, optional): Number of queries.
            p_init (float, optional): Initial perturbation probability.
            p_max (float, optional): Maximum perturbation probability.
            p_min (float, optional): Minimum perturbation probability.
            s_init (float, optional): Initial square size.
            s_min (float, optional): Minimum square size.
            s_max (float, optional): Maximum square size.

        Returns:
            Tensor: Adversarial example.
        """
        try:
            # Implement Square Attack for batches.
            # This is an extension of the single image SquareAttack.
            # Placeholder implementation:
            raise NotImplementedError("SquareAttackBatch is not implemented yet.")
        except Exception as e:
            logger.error(f"Square Attack Batch failed: {e}")
            raise

    def zoo_attack(
        self,
        x: Tensor,
        y: Tensor,
        n_queries: int = 1000,
        epsilon: float = 0.007
    ) -> Tensor:
        """
        Zoo Attack.

        Args:
            x (Tensor): Original input tensor.
            y (Tensor): True labels.
            n_queries (int, optional): Number of queries.
            epsilon (float, optional): Perturbation magnitude.

        Returns:
            Tensor: Adversarial example.
        """
        try:
            # ZOO Attack is a zeroth-order optimization-based attack.
            # It requires access to gradients through finite differences or similar methods.
            # Implementing ZOO is complex and resource-intensive.
            # Placeholder implementation:
            raise NotImplementedError("ZooAttack is not implemented yet.")
        except Exception as e:
            logger.error(f"Zoo Attack failed: {e}")
            raise

    def composite_adversarial_attack_pytorch(
        self,
        x: Tensor,
        y: Tensor,
        attacks: List[str],
        configs: Optional[List[AttackConfig]] = None
    ) -> Tensor:
        """
        Composite Adversarial Attack using multiple attack methods.

        Args:
            x (Tensor): Original input tensor.
            y (Tensor): True labels.
            attacks (List[str]): List of attack names to compose.
            configs (Optional[List[AttackConfig]], optional): List of configurations for each attack.

        Returns:
            Tensor: Adversarial example.
        """
        try:
            if configs is None:
                configs = [{} for _ in attacks]

            x_adv = x.clone().detach().to(self.device)

            for attack_name, config in zip(attacks, configs):
                x_adv = self.attack(attack_name, x_adv, y, config)

            return x_adv
        except Exception as e:
            logger.error(f"Composite Adversarial Attack failed: {e}")
            raise

    # ============================
    #      Attack Interface
    # ============================

    def attack(
        self,
        attack_name: str,
        x: Tensor,
        y: Tensor,
        config: Optional[AttackConfig] = None
    ) -> Tensor:
        """
        General interface to perform specified adversarial attack.

        Args:
            attack_name (str): Name of the attack to perform.
            x (Tensor): Original input tensor.
            y (Tensor): True labels.
            config (Optional[AttackConfig], optional): Configuration parameters for the attack.
                Defaults to None.

        Returns:
            Tensor: Adversarial example.

        Raises:
            ValueError: If the attack name is not supported.
        """
        attack_methods = {
            'FGSM': self.fgsm,
            'FastGradientMethod': self.fast_gradient_method,
            'PGD': self.projected_gradient_descent,
            'AutoProjectedGradientDescent': self.auto_projected_gradient_descent,
            'DeepFool': self.deepfool,
            'CW_L2': self.cw_l2,
            'CW_Linf': self.cw_linf,
            'ElasticNet': self.elastic_net,
            'BasicIterativeMethod': self.basic_iterative_method,
            'MomentumIterativeMethod': self.momentum_iterative_method,
            'AutoConjugateGradient': self.auto_conjugate_gradient,
            'UniversalPerturbation': self.universal_perturbation,
            'SpatialTransformation': self.spatial_transformation,
            'AdversarialPatch': self.adversarial_patch,
            'PixelAttack': self.pixel_attack,
            'SquareAttack': self.square_attack,
            'SquareAttackBatch': self.square_attack_batch,
            'ZooAttack': self.zoo_attack,
            'CompositeAdversarialAttackPyTorch': self.composite_adversarial_attack_pytorch,
            # Add additional attacks here
        }

        attack = attack_methods.get(attack_name)
        if not attack:
            logger.error(f"Attack '{attack_name}' is not supported.")
            raise ValueError(f"Attack '{attack_name}' is not supported.")

        try:
            if config:
                adversarial_example = attack(x, y, **config)
            else:
                adversarial_example = attack(x, y)
            logger.info(f"Attack '{attack_name}' executed successfully.")
            return adversarial_example
        except NotImplementedError as nie:
            logger.error(f"Attack '{attack_name}' is not implemented: {nie}")
            raise
        except Exception as e:
            logger.error(f"Error during attack '{attack_name}': {e}")
            raise RuntimeError(f"Attack '{attack_name}' failed.") from e