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 : update below basic environment my logic

we have update environment : below logic 

 
model , dataset we have find (Batch_size,channel,height,weight) 
original_accuracy : 

after attack  using attack api , selection should not randomly , think impact of attack, 1) high impact high choice 2) low impact lower choice
attack_accuracy:
attack its impact : str , int

agent should come best defence for given attack , its should (for this attack this type of defence is good like that it should think)

after applying defence :
accuracy is should be high >=original_accuracy :
save model weight update to preserve robustness of model 

reward : what is recentage increases of robustness, make sure accuracy is >= original after robustness 


Note: every time initial weights are preserved , in the process weights weights for best defence if there any training (defence is there ) 

at the end weights should be robustness all type of attacks 


# adversarial_env.py

import gymnasium as gym
from gymnasium import spaces
from typing import List, Tuple, Dict, Any, Optional
import numpy as np
import torch
import torchvision
from torchvision import transforms
from art.estimators.classification import PyTorchClassifier
from art.attacks.evasion import FastGradientMethod, ProjectedGradientDescent
from art.defences.preprocessor.gaussian_augmentation import GaussianAugmentation
from art.defences.preprocessor.feature_squeezing import FeatureSqueezing

from stable_baselines3 import PPO
from stable_baselines3.common.env_checker import check_env
from typing import Any, List
import torch
import torchvision
from torchvision import transforms
from art.estimators.classification import PyTorchClassifier
from art.attacks.evasion import FastGradientMethod, ProjectedGradientDescent
from art.defences.preprocessor.gaussian_augmentation import GaussianAugmentation
from art.defences.preprocessor.feature_squeezing import FeatureSqueezing

class AdversarialEnv(gym.Env):
    """
    Custom Environment for Adversarial Image Classification
    """
    metadata = {'render_modes': ['human']}

    def __init__(
        self,
        model: torch.nn.Module,
        dataset: torch.utils.data.Dataset,
        attacks: List[Any],
        defenses: List[Any],
        device: str = 'cpu'
    ) -> None:
        super(AdversarialEnv, self).__init__()

        self.device = device
        self.model = model.to(self.device).eval()
        self.dataset = dataset
        self.attacks = attacks
        self.defenses = defenses

        # Define action and observation space
        # Actions: selecting which defenses to apply (multi-label)
        self.action_space = spaces.MultiBinary(len(self.defenses))

        # Observations: [original_accuracy, attack_type_one_hot, current_accuracy]
        self.num_attacks = len(self.attacks)
        self.observation_space = spaces.Box(
            low=0.0,
            high=1.0,
            shape=(2 + self.num_attacks,),
            dtype=np.float32
        )

        # Initialize ART classifier
        self.classifier = PyTorchClassifier(
            model=self.model,
            loss=torch.nn.CrossEntropyLoss(),
            optimizer=torch.optim.SGD(self.model.parameters(), lr=0.01),
            input_shape=(3, 32, 32),  # Example for CIFAR-10
            nb_classes=10,
            device_type=self.device
        )

        # Prepare data loader
        self.data_loader = torch.utils.data.DataLoader(
            self.dataset,
            batch_size=64,
            shuffle=False
        )

        self.current_attack = None
        self.current_defenses = []
        self.original_accuracy = 0.0
        self.robust_accuracy = 0.0

    def reset(
        self,
        *,
        seed: Optional[int] = None,
        options: Optional[Dict[str, Any]] = None
    ) -> Tuple[np.ndarray, Dict[str, Any]]:
        """
        Resets the environment to an initial state and returns an initial observation.

        Args:
            seed (Optional[int]): Seed for the environment's random number generator.
            options (Optional[Dict[str, Any]]): Additional options for resetting the environment.

        Returns:
            Tuple[np.ndarray, Dict[str, Any]]: The initial observation and additional info.
        """
        super().reset(seed=seed)  # Optionally set the seed

        # If a seed is provided, set it for reproducibility
        if seed is not None:
            np.random.seed(seed)
            torch.manual_seed(seed)

        # Select a random attack
        self.current_attack = np.random.choice(self.attacks)
        self.current_defenses = []

        # Evaluate original accuracy
        self.original_accuracy = self.evaluate_accuracy(self.classifier, self.dataset)

        # Apply attack
        adversarial_examples = self.apply_attack(self.current_attack)

        # Evaluate attacked accuracy
        attacked_accuracy = self.evaluate_accuracy(
            self.classifier, adversarial_examples, is_adversarial=True
        )

        # Initial observation
        attack_one_hot = self.attack_to_one_hot(self.current_attack)
        observation = np.array(
            [self.original_accuracy] + list(attack_one_hot) + [attacked_accuracy],
            dtype=np.float32
        )

        info = {
            'original_accuracy': self.original_accuracy,
            'attack': self.current_attack.__class__.__name__,
            'attacked_accuracy': attacked_accuracy
        }

        return observation, info

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

        Args:
            action (np.ndarray): The action taken by the agent.

        Returns:
            Tuple[np.ndarray, float, bool, bool, Dict[str, Any]]:
                - observation (np.ndarray): The next observation.
                - reward (float): The reward obtained.
                - done (bool): Whether the episode has ended.
                - truncated (bool): Whether the episode was truncated.
                - info (Dict[str, Any]): Additional information.
        """
        done = False
        truncated = False
        info = {}

        # Decode action: which defenses to apply
        self.current_defenses = [
            defense for idx, defense in enumerate(self.defenses) if action[idx] == 1
        ]

        # Apply defenses
        defended_classifier = self.apply_defenses(self.classifier, self.current_defenses)

        # Evaluate defended accuracy
        defended_accuracy = self.evaluate_accuracy(defended_classifier, self.dataset)

        # Calculate reward (robustness improvement)
        reward = defended_accuracy - self.original_accuracy

        # Prepare next observation
        attack_one_hot = self.attack_to_one_hot(self.current_attack)
        observation = np.array(
            [self.original_accuracy] + list(attack_one_hot) + [defended_accuracy],
            dtype=np.float32
        )

        info = {
            'defended_accuracy': defended_accuracy,
            'defenses_applied': [defense.__class__.__name__ for defense in self.current_defenses],
            'reward': reward
        }

        # For simplicity, we'll end the episode after one step
        done = True

        return observation, reward, done, truncated, info

    def render(self, mode='human') -> None:
        """
        Renders the environment.

        Args:
            mode (str): The mode to render with.
        """
        print(f"Original Accuracy: {self.original_accuracy:.4f}")
        print(f"Applied Attack: {self.current_attack.__class__.__name__}")
        print(f"Defenses Applied: {[defense.__class__.__name__ for defense in self.current_defenses]}")

    def close(self) -> None:
        """
        Performs any necessary cleanup.
        """
        pass

    def evaluate_accuracy(
        self,
        classifier: PyTorchClassifier,
        dataset: torch.utils.data.Dataset,
        is_adversarial: bool = False
    ) -> float:
        """
        Evaluates the accuracy of the classifier on the given dataset.

        Args:
            classifier (PyTorchClassifier): The classification model.
            dataset (torch.utils.data.Dataset): The dataset to evaluate on.
            is_adversarial (bool): Whether the dataset is adversarially perturbed.

        Returns:
            float: The accuracy of the model.
        """
        correct = 0
        total = 0

        loader = torch.utils.data.DataLoader(
            dataset,
            batch_size=64,
            shuffle=False
        )

        for data, target in loader:
            data, target = data.to(self.device), target.to(self.device)
            preds = classifier.predict(data.cpu().numpy())
            preds = np.argmax(preds, axis=1)
            correct += np.sum(preds == target.cpu().numpy())
            total += target.size(0)

        accuracy = correct / total
        return accuracy

    def apply_attack(self, attack: Any) -> torch.utils.data.Dataset:
        """
        Applies the specified adversarial attack to the dataset.

        Args:
            attack (Any): The adversarial attack instance.

        Returns:
            torch.utils.data.Dataset: The adversarially perturbed dataset.
        """
        adversarial_examples = []
        adversarial_labels = []

        for data, target in self.data_loader:
            data_np = data.cpu().numpy()
            target_np = target.numpy()
            adversarial_data = attack.generate(x=data_np, y=target_np)
            adversarial_examples.append(torch.tensor(adversarial_data))
            adversarial_labels.append(torch.tensor(target_np))

        adversarial_data = torch.utils.data.TensorDataset(
            torch.cat(adversarial_examples),
            torch.cat(adversarial_labels)
        )

        return adversarial_data

    def apply_defenses(
        self,
        classifier: PyTorchClassifier,
        defenses: List[Any]
    ) -> PyTorchClassifier:
        """
        Applies the specified defenses to the classifier.

        Args:
            classifier (PyTorchClassifier): The classification model.
            defenses (List[Any]): A list of defense instances.

        Returns:
            PyTorchClassifier: The defended classifier.
        """
        defended_classifier = classifier
        for defense in defenses:
            defended_classifier = defense.defend(defended_classifier)
        return defended_classifier

    def attack_to_one_hot(self, attack: Any) -> List[int]:
        """
        Converts the current attack to a one-hot encoded list.

        Args:
            attack (Any): The current adversarial attack.

        Returns:
            List[int]: One-hot encoded representation of the attack.
        """
        one_hot = [0] * self.num_attacks
        attack_index = self.attacks.index(attack)
        one_hot[attack_index] = 1
        return one_hot