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 : we have to think all possible action , agent select defences , environment make all possible adversarial possible (in future attacks and defence may increases) 

make sure model weights degrade or spoils , make sure end of episode model will make robustness of model  all type of attacks its see in that environment 
make advanced GUI interface to see what going on in the env

in the below code sample or simple reference code :
import gymnasium as gym
import numpy as np
import torch
from torch import nn
from typing import Tuple, Dict, Any, List, Optional
from art.attacks.evasion import FastGradientMethod, ProjectedGradientDescent, DeepFool
from art.defences.preprocessor import FeatureSqueezing, SpatialSmoothing
from art.estimators.classification import PyTorchClassifier
import logging
from collections import deque
from transformers import BertConfig,BertModel
import torch
import torch.nn as nn
import torch.optim as optim
from torch.distributions import Categorical
from typing import Dict, List, Tuple, Any,Tuple
import numpy as np
from collections import deque
import logging
class RobustModelEnvironment(gym.Env):
    """
    Environment for training a robust model against various attacks.
    """

    def __init__(
        self,
        model: nn.Module,
        dataset: torch.utils.data.Dataset,
        batch_size: int = 32,
        device: str = 'cuda',
        memory_efficient: bool = True,
        max_steps: int = 1000
    ) -> None:
        super().__init__()
        self.model = model
        self.dataset = dataset
        self.batch_size = batch_size
        self.device = device
        self.memory_efficient = memory_efficient
        self.max_steps = max_steps
        self.steps = 0

        self.initial_checkpoint = self._get_model_checkpoint()

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

        self.attacks = self._setup_attacks()
        self.defences = self._setup_defences()

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

        self.logger = logging.getLogger(__name__)
        self.logger.setLevel(logging.INFO)

        self.performance_history = deque(maxlen=100)

    def _setup_classifier(self) -> PyTorchClassifier:
        """Set up the PyTorch classifier."""
        try:
            return PyTorchClassifier(
                model=self.model,
                loss=nn.CrossEntropyLoss(),
                input_shape=self.dataset[0][0].shape,
                nb_classes=len(set(self.dataset.targets)),
                device_type=self.device
            )
        except Exception as e:
            self.logger.error(f"Failed to setup classifier: {e}")
            raise

    def _setup_attacks(self) -> Dict[str, Any]:
        """Set up the attack methods."""
        return {
            'fgsm': lambda: FastGradientMethod(estimator=self.classifier, eps=0.1),
            'pgd': lambda: ProjectedGradientDescent(estimator=self.classifier, eps=0.1, max_iter=10),
            'deepfool': lambda: DeepFool(classifier=self.classifier, max_iter=50)
        }

    def _setup_defences(self) -> Dict[str, Any]:
        """Set up the defence methods."""
        return {
            'feature_squeezing': lambda: FeatureSqueezing(bit_depth=5),
            'spatial_smoothing': lambda: SpatialSmoothing(window_size=3)
        }

    def _setup_observation_space(self) -> gym.spaces.Dict:
        """Set up the observation space."""
        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, high=1, shape=(1,), dtype=np.float32),
            'current_accuracy': gym.spaces.Box(low=0, high=1, 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."""
        self.steps += 1
        attack_idx = action // len(self.defences)
        defence_idx = action % len(self.defences)

        attack = list(self.attacks.values())[attack_idx]()
        defence = list(self.defences.values())[defence_idx]()

        try:
            x_test, y_test = next(iter(torch.utils.data.DataLoader(self.dataset, batch_size=self.batch_size)))
            x_test_adv = attack.generate(x=x_test.numpy())
            attack_accuracy = self.classifier.compute_accuracy(x_test_adv, y_test.numpy())
            attack_impact = self.original_accuracy - attack_accuracy

            x_test_defended = defence(x_test_adv)[0]
            new_accuracy = self.classifier.compute_accuracy(x_test_defended, y_test.numpy())

            reward = self._calculate_reward(new_accuracy, attack_impact)

            if new_accuracy > max(self.performance_history, default=self.original_accuracy) * 1.05:
                self._update_model_checkpoint()

            done = (new_accuracy >= self.original_accuracy) or (self.steps >= self.max_steps)

            observation = self._get_observation(new_accuracy, attack_impact)

            info = {
                'attack_name': list(self.attacks.keys())[attack_idx],
                'defence_name': list(self.defences.keys())[defence_idx],
                'attack_accuracy': attack_accuracy,
                'new_accuracy': new_accuracy
            }

            self.performance_history.append(new_accuracy)

            return observation, reward, done, False, info
        except Exception as e:
            self.logger.error(f"Error during step execution: {e}")
            return self._get_observation(self.original_accuracy, 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."""
        super().reset(seed=seed)
        self.steps = 0
        self._load_model_checkpoint(self.initial_checkpoint)
        observation = self._get_observation(self.original_accuracy, 0)
        return observation, {}

    def _calculate_reward(self, new_accuracy: float, attack_impact: float) -> float:
        """Calculate the reward based on accuracy improvement and attack impact."""
        robustness_improvement = new_accuracy - self.original_accuracy
        return robustness_improvement * 100 + (1 - attack_impact) * 50

    def _get_model_checkpoint(self) -> Dict[str, torch.Tensor]:
        """Get a checkpoint of the model's current state."""
        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."""
        with torch.no_grad():
            for name, param in self.model.named_parameters():
                param.copy_(checkpoint[name])

    def _evaluate_accuracy(self) -> float:
        """Evaluate the current accuracy of the model."""
        self.model.eval()
        correct = 0
        total = 0
        with torch.no_grad():
            for inputs, labels in torch.utils.data.DataLoader(self.dataset, batch_size=self.batch_size):
                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()
        return correct / total

    def _get_observation(self, accuracy: float, attack_impact: float) -> Dict[str, np.ndarray]:
        """Get the current observation of the environment."""
        return {
            'model_state': np.array([sum(p.sum().item() for p in self.model.parameters())]),
            'attack_impact': np.array([attack_impact]),
            'current_accuracy': np.array([accuracy]),
            'step': np.array([self.steps])
        }

    def render(self, mode: str = 'human') -> None:
        """Render the current state of the environment."""
        self.logger.info(f"Step: {self.steps}, Current Accuracy: {self.performance_history[-1]:.4f}")

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