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.

NOTE: you developing policy code separate .py file 
TASK :  
Actor neural network all possible understanding of observation space , action space ,history , reward great detail 

Critic:neural network all possible understanding of observation space , action space ,history , reward great detail 


Policy come hybrid of PPO and TRPO


## Proximal Policy Optimization (PPO)
Proximal Policy Optimization (PPO) is a policy gradient method designed to improve training stability and efficiency. PPO addresses the limitations of earlier methods like TRPO by simplifying the optimization process while maintaining reliable performance.

Objective Function
PPO introduces a clipped surrogate objective to restrict policy updates within a trust region, preventing large, destabilizing parameter changes:


$$
L^{CLIP}(\theta) = \mathbb{E}_t \left[ \min \left( r_t(\theta) \hat{A}_t, \text{clip}(r_t(\theta), 1 - \epsilon, 1 + \epsilon) \hat{A}_t \right) \right]
$$
Where:

- $ r_t(\theta) = \frac{\pi_\theta(a_t|s_t)}{\pi_{\theta_{\text{old}}}(a_t|s_t)} $ is the probability ratio,
- $ \hat{A}_t $ is the estimated advantage at time $ t $,
- $ \epsilon $ is a hyperparameter dictating the clipping range.
Advantages of PPO
Simplicity: Easier to implement compared to TRPO, requiring fewer computational resources.
Performance: Demonstrates competitive performance across various benchmark tasks.
Stability: The clipping mechanism effectively prevents destructive policy updates, enhancing training stability.
## PPO Variants
- PPO-Penalty: Incorporates a penalty term based on the KL divergence between the new and old policies.
- PPO-Clip: Uses the clipped surrogate objective as described above, which is more commonly used due to its simplicity and effectiveness.
- PPO has become a popular choice in Deep RL due to its balance between performance and computational efficiency.

## Trust Region Policy Optimization (TRPO)
Trust Region Policy Optimization (TRPO) is a policy gradient method aimed at ensuring monotonic improvement in policy updates by constraining the step size in policy space.

Objective Function
TRPO maximizes the expected advantage while enforcing a trust region constraint on the KL divergence between the new and old policies:


$$
\max_{\theta} \mathbb{E}_t \left[ \frac{\pi_\theta(a_t|s_t)}{\pi_{\theta_{\text{old}}}(a_t|s_t)} \hat{A}_t \right] \quad \text{s.t.} \quad \mathbb{E}_t \left[ \text{KL} \left( \pi_{\theta_{\text{old}}}(\cdot|s_t) \| \pi_\theta(\cdot|s_t) \right) \right] \leq \delta
$$
Where:

- $ \delta $ is a predefined threshold for the KL divergence.
Conjugate Gradient and Fisher Information Matrix
To solve the constrained optimization problem, TRPO employs the conjugate gradient method combined with the Fisher Information Matrix to compute a natural gradient direction that respects the trust region constraint.

Advantages and Limitations
Advantages:

### Monotonic Improvement: Guarantees that each policy update does not degrade performance.
Theoretical Foundations: Strong theoretical underpinnings ensuring convergence properties.
Limitations:

### Computational Complexity: Involves expensive second-order computations, making it less scalable.
Implementation Complexity: More intricate to implement compared to simpler policy gradient methods.
TRPO paved the way for more efficient policy optimization algorithms like PPO by highlighting the importance of constrained policy updates for stable learning.



# environment.py
import sys
import gymnasium as gym
import numpy as np
import torch
from torch import nn
from typing import Tuple, Dict, Any, Optional, Callable, List
from art.estimators.classification import PyTorchClassifier
import logging
from collections import deque
import torch.optim as optim
from torch.utils.data import TensorDataset, DataLoader
import tkinter as tk
from tkinter import ttk, scrolledtext



from attacks import apply_attack, list_attack
from defences import DefenseManager

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


class RobustModelEnvironment(gym.Env):
    """
    Environment for training a robust model against various adversarial attacks.
    """

    metadata = {'render.modes': ['human']}

    def __init__(
        self,
        model: nn.Module,
        dataset: torch.utils.data.Dataset,
        batch_size: int = 32,
        device: Optional[str] = None,
        memory_efficient: bool = True,
        max_steps: int = 1000,
        num_classes: Optional[int] = None  # Optional parameter to specify number of classes
    ) -> None:
        """
        Initialize the RobustModelEnvironment.

        Args:
            model (nn.Module): The PyTorch model to evaluate and train.
            dataset (torch.utils.data.Dataset): The dataset for training and evaluation.
            batch_size (int, optional): Batch size for data loading. Defaults to 32.
            device (Optional[str], optional): Device to run computations on. Defaults to 'cuda' if available.
            memory_efficient (bool, optional): Flag for memory-efficient operations. Defaults to True.
            max_steps (int, optional): Maximum number of steps per episode. Defaults to 1000.
            num_classes (Optional[int], optional): Number of distinct classes. If not provided, deduced from dataset.
        """
        super(RobustModelEnvironment, self).__init__()
        self.model = model.to(device or ('cuda' if torch.cuda.is_available() else 'cpu'))
        self.dataset = dataset
        self.batch_size = batch_size
        self.device = device or ('cuda' if torch.cuda.is_available() else 'cpu')
        self.memory_efficient = memory_efficient
        self.max_steps = max_steps
        self.steps = 0

        # Initialize logger first
        self.logger = self._setup_logger()

        self.initial_checkpoint = self._get_model_checkpoint()

        if num_classes is not None:
            self.num_classes = num_classes
            self.logger.info(f"Number of classes provided: {self.num_classes}")
        else:
            self.num_classes = self._get_num_classes()

        self.classifier = self._setup_classifier()
        self.original_accuracy = self._evaluate_accuracy()

        self.attacks = self._setup_attacks()
        self.defenses_manager = DefenseManager()
        self.defenses = self._setup_defenses()

        self.action_space = gym.spaces.Discrete(len(self.attacks) * len(self.defenses))
        self.observation_space = self._setup_observation_space()

        self.performance_history = deque(maxlen=100)
        self.performance_history.append(self.original_accuracy)

    def _setup_logger(self) -> logging.Logger:
        """
        Set up the logger for the environment.

        Returns:
            logging.Logger: Configured logger.
        """
        logger_env = logging.getLogger('RobustModelEnvironment')
        logger_env.setLevel(logging.INFO)
        if not logger_env.handlers:
            handler = logging.StreamHandler(sys.stdout)
            formatter = logging.Formatter('[%(levelname)s] %(message)s')
            handler.setFormatter(formatter)
            logger_env.addHandler(handler)
        return logger_env

    def _get_num_classes(self) -> int:
        """
        Get the number of classes from the dataset.

        Returns:
            int: Number of distinct classes.
        """
        try:
            if isinstance(self.dataset, TensorDataset):
                _, labels = self.dataset.tensors
                labels = labels.tolist()
            else:
                labels = [label for _, label in self.dataset]

            labels = [label if isinstance(label, int) else label.item() for label in labels]

            num_classes = len(set(labels))
            self.logger.info(f"Detected number of classes: {num_classes}")
            return num_classes
        except Exception as e:
            self.logger.error(f"Failed to get number of classes: {e}")
            raise

    def _setup_classifier(self) -> PyTorchClassifier:
        """
        Set up the PyTorch classifier using ART.

        Returns:
            PyTorchClassifier: Configured classifier.
        """
        try:
            return PyTorchClassifier(
                model=self.model,
                loss=nn.CrossEntropyLoss(),
                optimizer=optim.Adam(self.model.parameters()),
                input_shape=self._get_input_shape(),
                nb_classes=self.num_classes,
                device_type='cuda' if self.device.startswith('cuda') else 'cpu'
            )
        except Exception as e:
            self.logger.error(f"Failed to set up classifier: {e}")
            raise

    def _get_input_shape(self) -> Tuple[int, ...]:
        """
        Get the input shape from the dataset.

        Returns:
            Tuple[int, ...]: Shape of a single input sample.
        """
        try:
            sample_input, _ = self.dataset[0]
            return sample_input.shape
        except Exception as e:
            self.logger.error(f"Failed to get input shape: {e}")
            raise

    def _setup_attacks(self) -> List[str]:
        """
        Retrieve the list of available attacks.

        Returns:
            List[str]: List of attack names.
        """
        try:
            attacks = list_attack
            self.logger.info(f"Available attacks: {attacks}")
            return attacks
        except Exception as e:
            self.logger.error(f"Failed to set up attacks: {e}")
            raise

    def _setup_defenses(self) -> List[str]:
        """
        Retrieve the list of available defenses from DefenseManager.

        Returns:
            List[str]: List of defense names.
        """
        try:
            defenses = self.defenses_manager.list_defenses()
            return defenses
        except Exception as e:
            self.logger.error(f"Failed to set up defenses: {e}")
            raise

    def _setup_observation_space(self) -> gym.spaces.Dict:
        """
        Set up the observation space.

        Returns:
            gym.spaces.Dict: Observation space definition.
        """
        return gym.spaces.Dict({
            'model_state': gym.spaces.Box(low=-np.inf, high=np.inf, shape=(1,), dtype=np.float32),
            'attack_impact': gym.spaces.Box(low=0.0, high=1.0, shape=(1,), dtype=np.float32),
            'current_accuracy': gym.spaces.Box(low=0.0, high=1.0, shape=(1,), dtype=np.float32),
            'step': gym.spaces.Box(low=0, high=self.max_steps, shape=(1,), dtype=np.int32)
        })

    def step(self, action: int) -> Tuple[Dict[str, np.ndarray], float, bool, bool, Dict[str, Any]]:
        """
        Execute one step in the environment.

        Args:
            action (int): Combined index for attack and defense.

        Returns:
            Tuple[Dict[str, np.ndarray], float, bool, bool, Dict[str, Any]]:
                - observation
                - reward
                - done
                - truncated
                - info
        """
        self.steps += 1
        attack_idx = action // len(self.defenses)
        defense_idx = action % len(self.defenses)

        try:
            attack_name = self.attacks[attack_idx]
            defense_name = self.defenses[defense_idx]
            self.logger.info(f"Step {self.steps}: Applying Attack '{attack_name}' and Defense '{defense_name}'")

            data_loader = DataLoader(self.dataset, batch_size=self.batch_size, shuffle=True)
            x_test, y_test = next(iter(data_loader))
            x_test, y_test = x_test.to(self.device), y_test.to(self.device)

            x_test_np = x_test.cpu().numpy()
            y_test_np = y_test.cpu().numpy()

            # Apply attack
            adversarial_x = apply_attack(
                attack_name=attack_name,
                classifier=self.classifier,
                x=x_test_np,
                y=y_test_np,
                # params=None  # Add parameters if needed
            )

            # Apply defense
            defended_x = self.defenses_manager.apply_defense(
                defense_name=defense_name,
                # classifier=self.classifier,
                x=adversarial_x,
                y=y_test_np,
                # params=None  # Add parameters if needed
            )

            # Evaluate model accuracy on adversarial examples
            y_pred_adv = self.classifier.predict(adversarial_x)
            if y_pred_adv.ndim > 1 and y_pred_adv.shape[1] > 1:
                y_pred_adv = np.argmax(y_pred_adv, axis=1)
            attack_accuracy = np.mean(y_pred_adv == y_test_np)
            attack_impact = self.original_accuracy - attack_accuracy

            # Evaluate model accuracy on defended examples
            y_pred_defended = self.classifier.predict(defended_x)
            if y_pred_defended.ndim > 1 and y_pred_defended.shape[1] > 1:
                y_pred_defended = np.argmax(y_pred_defended, axis=1)
            new_accuracy = np.mean(y_pred_defended == y_test_np)

            self.logger.info(f"Attack Accuracy: {attack_accuracy:.4f}, New Accuracy after Defense: {new_accuracy:.4f}")

            reward = self._calculate_reward(new_accuracy, attack_impact)
            done = self.steps >= self.max_steps
            observation = self._get_observation(new_accuracy, attack_impact)

            self.performance_history.append(new_accuracy)

            # Simulate model weight degradation
            self._degrade_model_weights(attack_impact)

            return observation, reward, done, False, {
                'attack_name': attack_name,
                'defense_name': defense_name,
                'attack_accuracy': attack_accuracy,
                'new_accuracy': new_accuracy
            }

        except StopIteration:
            self.logger.warning("End of DataLoader reached. Resetting environment.")
            return self.reset()
        except IndexError:
            self.logger.error("Invalid action index.")
            return self._get_observation(self.original_accuracy, 0.0), 0.0, True, False, {}
        except Exception as e:
            self.logger.error(f"Error during step execution: {e}")
            return self._get_observation(self.original_accuracy, 0.0), 0.0, True, False, {}

    def reset(
        self,
        seed: Optional[int] = None,
        options: Optional[Dict[str, Any]] = None
    ) -> Tuple[Dict[str, np.ndarray], Dict[str, Any]]:
        """
        Reset the environment to its initial state.

        Args:
            seed (Optional[int], optional): Random seed. Defaults to None.
            options (Optional[Dict[str, Any]], optional): Additional options. Defaults to None.

        Returns:
            Tuple[Dict[str, np.ndarray], Dict[str, Any]]: Initial observation and info.
        """
        super().reset(seed=seed)
        self.steps = 0
        self._load_model_checkpoint(self.initial_checkpoint)
        self.performance_history = deque(maxlen=100)
        self.performance_history.append(self.original_accuracy)
        observation = self._get_observation(self.original_accuracy, 0.0)
        self.logger.info("Environment has been reset.")
        return observation, {}

    def _calculate_reward(self, new_accuracy: float, attack_impact: float) -> float:
        """
        Calculate the reward based on accuracy improvement and attack impact.

        Args:
            new_accuracy (float): Current model accuracy post-defense.
            attack_impact (float): Impact of the attack on the model.

        Returns:
            float: Calculated reward.
        """
        robustness_improvement = new_accuracy - self.original_accuracy
        reward = robustness_improvement * 100 + (1 - attack_impact) * 50
        self.logger.debug(f"Calculated Reward: {reward}")
        return reward

    def _get_model_checkpoint(self) -> Dict[str, torch.Tensor]:
        """
        Get a checkpoint of the model's current state.

        Returns:
            Dict[str, torch.Tensor]: Dictionary of model parameters.
        """
        return {name: param.data.clone() for name, param in self.model.named_parameters()}

    def _update_model_checkpoint(self) -> None:
        """
        Update the initial checkpoint with the current model state.
        """
        self.initial_checkpoint = self._get_model_checkpoint()

    def _load_model_checkpoint(self, checkpoint: Dict[str, torch.Tensor]) -> None:
        """
        Load a model checkpoint.

        Args:
            checkpoint (Dict[str, torch.Tensor]): Model parameters to load.
        """
        with torch.no_grad():
            for name, param in self.model.named_parameters():
                if name in checkpoint:
                    param.copy_(checkpoint[name])
        self.logger.info("Model checkpoint loaded.")

    def _evaluate_accuracy(self) -> float:
        """
        Evaluate the current accuracy of the model.

        Returns:
            float: Model accuracy.
        """
        self.model.eval()
        correct = 0
        total = 0
        try:
            data_loader = DataLoader(self.dataset, batch_size=self.batch_size, shuffle=False)
            with torch.no_grad():
                for inputs, labels in data_loader:
                    inputs, labels = inputs.to(self.device), labels.to(self.device)
                    outputs = self.model(inputs)
                    _, predicted = torch.max(outputs.data, 1)
                    total += labels.size(0)
                    correct += (predicted == labels).sum().item()
            accuracy = correct / total
            self.logger.info(f"Initial Model Accuracy: {accuracy:.4f}")
            return accuracy
        except Exception as e:
            self.logger.error(f"Failed to evaluate accuracy: {e}")
            return 0.0

    def _compute_accuracy_tensor(self, outputs: torch.Tensor, labels: torch.Tensor) -> float:
        """
        Compute accuracy given model outputs and labels.

        Args:
            outputs (torch.Tensor): Model outputs.
            labels (torch.Tensor): Ground truth labels.

        Returns:
            float: Computed accuracy.
        """
        try:
            _, predicted = torch.max(outputs.data, 1)
            correct = (predicted == labels).sum().item()
            accuracy = correct / labels.size(0)
            return accuracy
        except Exception as e:
            self.logger.error(f"Failed to compute accuracy: {e}")
            return 0.0

    def _get_observation(self, accuracy: float, attack_impact: float) -> Dict[str, np.ndarray]:
        """
        Get the current observation of the environment.

        Args:
            accuracy (float): Current model accuracy.
            attack_impact (float): Impact of the latest attack.

        Returns:
            Dict[str, np.ndarray]: Observation dictionary.
        """
        model_state_sum = sum(p.sum().item() for p in self.model.parameters())
        return {
            'model_state': np.array([model_state_sum], dtype=np.float32),
            'attack_impact': np.array([attack_impact], dtype=np.float32),
            'current_accuracy': np.array([accuracy], dtype=np.float32),
            'step': np.array([self.steps], dtype=np.int32)
        }

    def _degrade_model_weights(self, attack_impact: float) -> None:
        """
        Simulate model weight degradation based on attack impact.

        Args:
            attack_impact (float): Impact of the attack on the model.
        """
        try:
            degradation_factor = attack_impact * 0.01  # Scale factor for degradation
            with torch.no_grad():
                for param in self.model.parameters():
                    noise = torch.randn(param.size()).to(self.device) * degradation_factor
                    param.add_(noise)
            self.logger.debug(f"Model weights degraded by factor: {degradation_factor:.4f}")
        except Exception as e:
            self.logger.error(f"Failed to degrade model weights: {e}")

    def render(self, mode: str = 'human') -> None:
        """
        Render the current state of the environment.

        Args:
            mode (str, optional): Mode to render with. Defaults to 'human'.
        """
        if mode == 'human':
            current_accuracy = self.performance_history[-1]
            self.logger.info(f"Step: {self.steps}, Current Accuracy: {current_accuracy:.4f}")

    def close(self) -> None:
        """
        Clean up resources used by the environment.
        """
        self.logger.info("Closing environment.")
        pass


class EnvironmentGUI:
    """
    Advanced GUI for visualizing the RobustModelEnvironment.
    """

    def __init__(self, env: RobustModelEnvironment) -> None:
        """
        Initialize the GUI with the given environment.

        Args:
            env (RobustModelEnvironment): The environment to visualize.
        """
        self.env = env
        self.root = tk.Tk()
        self.root.title("Robust Model Environment Visualization")
        self._setup_gui_elements()
        self._update_gui()

    def _setup_gui_elements(self) -> None:
        """
        Set up GUI components.
        """
        # Frame for Metrics
        metrics_frame = ttk.LabelFrame(self.root, text="Metrics")
        metrics_frame.grid(column=0, row=0, padx=10, pady=10, sticky="nsew")

        # Metrics Labels
        self.step_var = tk.StringVar(value="Step: 0")
        self.accuracy_var = tk.StringVar(value="Accuracy: 0.0")
        self.attack_impact_var = tk.StringVar(value="Attack Impact: 0.0")

        ttk.Label(metrics_frame, textvariable=self.step_var).grid(column=0, row=0, sticky="w", padx=5, pady=5)
        ttk.Label(metrics_frame, textvariable=self.accuracy_var).grid(column=0, row=1, sticky="w", padx=5, pady=5)
        ttk.Label(metrics_frame, textvariable=self.attack_impact_var).grid(column=0, row=2, sticky="w", padx=5, pady=5)

        # Frame for Controls
        controls_frame = ttk.LabelFrame(self.root, text="Controls")
        controls_frame.grid(column=0, row=1, padx=10, pady=10, sticky="nsew")

        # Control Buttons
        self.start_button = ttk.Button(controls_frame, text="Start", command=self.start_environment)
        self.start_button.grid(column=0, row=0, padx=5, pady=5)

        self.pause_button = ttk.Button(controls_frame, text="Pause", command=self.pause_environment)
        self.pause_button.grid(column=1, row=0, padx=5, pady=5)

        self.reset_button = ttk.Button(controls_frame, text="Reset", command=self.reset_environment)
        self.reset_button.grid(column=2, row=0, padx=5, pady=5)

        # Frame for Logs
        logs_frame = ttk.LabelFrame(self.root, text="Logs")
        logs_frame.grid(column=0, row=2, padx=10, pady=10, sticky="nsew")

        # Scrolled Text for Logs
        self.log_text = scrolledtext.ScrolledText(logs_frame, width=80, height=20, state='disabled')
        self.log_text.grid(column=0, row=0, padx=5, pady=5)

        # Configure Grid Weights
        self.root.columnconfigure(0, weight=1)
        self.root.rowconfigure(2, weight=1)
        metrics_frame.columnconfigure(0, weight=1)
        controls_frame.columnconfigure((0, 1, 2), weight=1)
        logs_frame.columnconfigure(0, weight=1)

        # Redirect logger to GUI
        self._redirect_logging()

    def _redirect_logging(self) -> None:
        """
        Redirect logging output to the GUI log text widget.
        """
        class TextHandler(logging.Handler):
            def __init__(self, text_widget: scrolledtext.ScrolledText) -> None:
                super().__init__()
                self.text_widget = text_widget

            def emit(self, record: logging.LogRecord) -> None:
                msg = self.format(record) + '\n'
                self.text_widget.configure(state='normal')
                self.text_widget.insert(tk.END, msg)
                self.text_widget.configure(state='disabled')
                self.text_widget.yview(tk.END)

        handler = TextHandler(self.log_text)
        handler.setFormatter(logging.Formatter('[%(levelname)s] %(message)s'))
        self.env.logger.addHandler(handler)

    def start_environment(self) -> None:
        """
        Start the environment's training loop.
        """
        if not getattr(self, 'running', False):
            self.env.logger.info("Starting environment...")
            self.running = True
            self._run_steps()

    def pause_environment(self) -> None:
        """
        Pause the environment's training loop.
        """
        if getattr(self, 'running', False):
            self.env.logger.info("Pausing environment...")
            self.running = False

    def reset_environment(self) -> None:
        """
        Reset the environment.
        """
        self.env.reset()
        self.env.logger.info("Environment has been reset.")
        self._update_gui()

    def _run_steps(self) -> None:
        """
        Run environment steps in a loop.
        """
        if self.running and self.env.steps < self.env.max_steps:
            action = self._select_action()
            observation, reward, done, _, info = self.env.step(action)
            self._update_gui(observation)
            if done:
                self.running = False
                self.env.logger.info("Episode finished.")
                return
            # Schedule next step
            self.root.after(100, self._run_steps)

    def _select_action(self) -> int:
        """
        Select an action for the agent. Placeholder for agent logic.

        Returns:
            int: Selected action index.
        """
        # Placeholder: Select random action
        attack_length = len(self.env.attacks)
        defense_length = len(self.env.defenses)
        total_actions = attack_length * defense_length
        return np.random.randint(0, total_actions)

    def _update_gui(self, observation: Optional[Dict[str, np.ndarray]] = None) -> None:
        """
        Update GUI elements with the latest observation.

        Args:
            observation (Optional[Dict[str, np.ndarray]], optional): Latest observation. Defaults to None.
        """
        if observation:
            self.step_var.set(f"Step: {observation['step'][0]}")
            self.accuracy_var.set(f"Accuracy: {observation['current_accuracy'][0]:.4f}")
            self.attack_impact_var.set(f"Attack Impact: {observation['attack_impact'][0]:.4f}")
        else:
            self.step_var.set(f"Step: {self.env.steps}")
            if self.env.performance_history:
                self.accuracy_var.set(f"Accuracy: {self.env.performance_history[-1]:.4f}")
                attack_impact = self.env.original_accuracy - self.env.performance_history[-1]
                self.attack_impact_var.set(f"Attack Impact: {attack_impact:.4f}")

    def run(self) -> None:
        """
        Start the GUI event loop.
        """
        self.running = False
        self.root.mainloop()


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 neural network model.

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

    Returns:
        nn.Module: Initialized model.
    """
    input_size = 3 * 32 * 32  # Example for CIFAR-10
    model = SimpleCNN(num_classes)
    logger.info("Model initialized.")
    return model


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

    Args:
        model (nn.Module): The PyTorch model.
        input_shape (Tuple[int, ...]): Shape of input data.
        nb_classes (int): Number of output classes.
        clip_values (Tuple[float, float]): Min and max values for input data.

    Returns:
        PyTorchClassifier: Initialized classifier.
    """
    classifier = PyTorchClassifier(
        model=model,
        loss=nn.CrossEntropyLoss(),
        optimizer=optim.Adam(model.parameters()),
        input_shape=input_shape,
        nb_classes=nb_classes,
        clip_values=clip_values,
        device_type='cuda' if torch.cuda.is_available() else 'cpu'
    )
    logger.info("Classifier initialized.")
    return classifier


def generate_dummy_data(batch_size: int) -> Tuple[Any, Any]:
    """
    Generate dummy data for testing purposes.

    Args:
        batch_size (int): Number of samples to generate.

    Returns:
        Tuple[Any, Any]: Inputs and labels.
    """
    inputs = torch.randn(batch_size, 3, 32, 32)
    labels = torch.randint(0, 1000, (batch_size,))
    logger.info("Dummy data generated.")
    return inputs, labels


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)
    logger.info("Model initialized successfully.")

    # 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,
    )
    logger.info("Classifier initialized successfully.")

    # Generate dummy data
    inputs, labels = generate_dummy_data(batch_size=32)
    logger.info("Dummy data generated successfully.")

    # Dynamically list available attacks
    available_attacks = list_attack
    logger.info(f"Available Attacks: {available_attacks}")

    if not available_attacks:
        logger.error("No attacks are registered. Exiting.")
        sys.exit(1)

    # Select an attack (for demonstration, select the first available attack)
    selected_attack = available_attacks[0]
    logger.info(f"Selected Attack: {selected_attack}")

    adversarial_x = apply_attack(
        attack_name=selected_attack,
        classifier=classifier,
        x=inputs.cpu().numpy(),
        y=labels.cpu().numpy(),
        # params=None
    )

    # Initialize DefenseManager
    manager = DefenseManager()

    # List available defenses
    available_defenses = manager.list_defenses()
    logger.info(f"Available Defenses: {available_defenses}")

    if not available_defenses:
        logger.error("No defenses are available. Exiting.")
        sys.exit(1)

    # Select a defense (for demonstration, select the first available defense)
    selected_defense = available_defenses[0]
    logger.info(f"Selected Defense: {selected_defense}")

    defended_x = manager.apply_defense(
        defense_name=selected_defense,
        x=inputs.cpu().numpy(),  # Example image data
        y=labels.cpu().numpy(),  # Example labels
    )

    logger.info("Defense applied successfully.")

    # Initialize environment
    dataset = TensorDataset(inputs, labels)
    env = RobustModelEnvironment(
        model=model,
        dataset=dataset,
        batch_size=32,
        device='cuda' if torch.cuda.is_available() else 'cpu',
        max_steps=100,
        num_classes=num_classes
    )
    logger.info("Environment initialized successfully.")

    # Initialize and run GUI
    gui = EnvironmentGUI(env)
    gui.run()


if __name__ == "__main__":
    main()