five drawback below env as per aim (you have  logic error only 5)
 we have develop advanced Reinforcementlearning policy engline 
AIM with minimal time we have find for given attack and what is best defences , along with most robustness weights have to save with can withstand all attacks
import gymnasium as gym
from gymnasium import spaces
from gymnasium.utils import seeding
from typing import Any, Dict, Optional, Tuple, List
import numpy as np
import torch
from torch import nn
from torchvision import transforms
import copy
from art.estimators.classification import PyTorchClassifier  # Assuming Art is a framework for adversarial robustness


# Custom API already implement
from defences import DefenseManager
from attacks import apply_attack, LIST_OF_ATTACKS  
from metrics import MetricsCalculator
from model import ImageClassificationPipeline, ModelSource
from rldata import DatasetConfig, datapipeline

# ===================== Data Configuration and Model Setup ====================== #

def setup_environment(
    dataset_path: str = "zh-plus/tiny-imagenet",
    split_config: Dict[str, float] = {"train": 0.8, "test": 0.2},
    columns: List[str] = ["image", "label"],
    num_samples: int = 1000,
    seed: int = 42,
    cache_dir: str = "./data",
) -> Tuple[torch.utils.data.DataLoader, torch.utils.data.DataLoader, 'PyTorchClassifier', torch.device]:
    """
    Sets up the data loaders and classifier for the environment.
    
    Returns:
        Tuple containing the training DataLoader, testing DataLoader, the PyTorch classifier, and the device.
    """
    try:
        # Data Configuration
        data_config = DatasetConfig(
            dataset_path=dataset_path,
            split_config=split_config,
            columns=columns,
            num_samples=num_samples,
            seed=seed,
            kwargs={"cache_dir": cache_dir},
        )

        # Data transformations
        transforms_fn = transforms.Compose([
            transforms.Lambda(lambda img: img.convert("RGB")),
            transforms.Resize((224, 224)),
            transforms.ToTensor(),
            transforms.Normalize(
                mean=[0.485, 0.456, 0.406],
                std=[0.229, 0.224, 0.225]
            )
        ])

        # Data Processing
        train_loader, test_loader = datapipeline(
            config=data_config,
            transforms_fn=transforms_fn,
            target_column='image',
            label_column='label',
        )

        # Model Setup
        model_pipeline = ImageClassificationPipeline(
            model_name_or_path="google/vit-base-patch16-224-in21k",
            model_source=ModelSource.TRANSFORMERS.value,
            cache_dir=cache_dir,
        )

        original_model = model_pipeline.model
        original_model.classifier = nn.Linear(
            in_features=original_model.classifier.in_features,
            out_features=1000,
            bias=True
        )

        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        original_model.to(device)
        original_model.eval()

        wrapped_model = WrappedViTModel(original_model)

        # Placeholder PyTorchClassifier setup
        # Replace with the actual implementation as per your framework
        classifier = PyTorchClassifier(
            model=wrapped_model,
            loss=nn.CrossEntropyLoss(),
            optimizer=torch.optim.Adam(wrapped_model.parameters()),
            input_shape=(3, 224, 224),
            nb_classes=1000,
            clip_values=(0.0, 1.0),
            device_type=device.type
        )

        return train_loader, test_loader, classifier, device

    except Exception as e:
        raise RuntimeError(f"Failed to set up environment: {e}")


# ===================== Wrapped Model Definition ====================== #

class WrappedViTModel(nn.Module):
    """
    Wraps the original ViT model to expose logits.
    """

    def __init__(self, original_model: nn.Module):
        super(WrappedViTModel, self).__init__()
        self.original_model = original_model

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        output = self.original_model(x)
        return output.logits


# ===================== Gym Environment Definition ====================== #

class DefenseSelectionEnv(gym.Env):
    """
    Custom Gym environment for selecting the best defense against adversarial attacks.
    """
    metadata = {'render.modes': ['human']}

    def __init__(
        self,
        classifier: 'PyTorchClassifier',
        device: torch.device,
        train_loader: torch.utils.data.DataLoader,
        max_steps: int = 100,
        robustness_threshold: float = 0.95,
        seed: Optional[int] = 42,
    ) -> None:
        """
        Initializes the DefenseSelectionEnv environment.

        Args:
            classifier: The PyTorch classifier to be defended.
            device: The device (CPU/GPU) for computation.
            train_loader: DataLoader for training data.
            max_steps: Maximum number of steps per episode.
            robustness_threshold: Threshold of robustness to terminate the episode.
            seed: Random seed for reproducibility.
        """
        super(DefenseSelectionEnv, self).__init__()
        self.classifier = classifier
        self.device = device
        self.defense_manager = DefenseManager()
        self.list_of_defenses = self.defense_manager.list_defenses()
        self.current_step = 0
        self.max_steps = max_steps
        self.train_loader = train_loader
        self.robustness_threshold = robustness_threshold
        self.seed(seed)

        # Define action and observation space
        self.action_space = spaces.Discrete(len(self.list_of_defenses))

        # Observation space components
        metrics_keys = [
            'Accuracy', 'Precision', 'Recall', 'F1 Score',
            'ROC-AUC', 'PR-AUC', 'Log Loss', 'Cross Entropy Loss',
            'Mean Squared Error', 'Kappa Statistic',
            'Matthews Correlation Coefficient', 'Specificity',
            'Jaccard Index', 'Balanced Accuracy',
            'True Negative Rate', 'False Positive Rate',
            'Dice Coefficient', 'Mean Average Precision'
        ]
        self.metrics_keys = metrics_keys

        self.observation_space = spaces.Dict({
            'metrics': spaces.Box(
                low=0.0, high=1.0, shape=(len(metrics_keys),), dtype=np.float32
            ),
            'current_attack': spaces.Discrete(len(LIST_OF_ATTACKS)),
            'attack_impact': spaces.Box(
                low=0.0, high=1.0, shape=(1,), dtype=np.float32
            ),
            'attack_strength': spaces.Box(
                low=0.0, high=1.0, shape=(1,), dtype=np.float32
            ),
            'previous_robustness': spaces.Box(
                low=0.0, high=1.0, shape=(1,), dtype=np.float32
            )
        })

        # Initialize internal state
        self.current_attack: Optional[str] = None
        self.attack_impact: float = 0.0
        self.attack_strength: float = 0.0
        self.previous_robustness: float = 0.0
        self.history: List[int] = []
        self.original_weights = copy.deepcopy(self.classifier.model.state_dict())
        self.best_weights: Optional[Dict[str, torch.Tensor]] = None

    def seed(self, seed: Optional[int] = None) -> List[int]:
        """
        Sets the seed for reproducibility.

        Args:
            seed: The seed value.

        Returns:
            A list containing the seed.
        """
        self.np_random, seed = seeding.np_random(seed)
        return [seed]

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

        Args:
            seed: The seed value for randomness.

        Returns:
            A tuple containing the initial observation and an empty info dictionary.
        """
        try:
            self.current_step = 0
            self.previous_robustness = 0.0
            self.current_attack = self.np_random.choice(LIST_OF_ATTACKS)
            self.attack_impact = self.np_random.uniform(0.3, 0.7)  # Ensuring meaningful impact
            self.attack_strength = self.np_random.uniform(0.5, 1.0)  # Attack strength between 0.5 and 1.0
            self.history = []
            self.classifier.model.load_state_dict(copy.deepcopy(self.original_weights))  # Resetting model weights

            initial_observation = {
                'metrics': np.zeros((len(self.metrics_keys),), dtype=np.float32),
                'current_attack': LIST_OF_ATTACKS.index(self.current_attack),
                'attack_impact': np.array([self.attack_impact], dtype=np.float32),
                'attack_strength': np.array([self.attack_strength], dtype=np.float32),
                'previous_robustness': np.array([self.previous_robustness], dtype=np.float32)
            }
            return initial_observation, {}
        except Exception as e:
            raise RuntimeError(f"Error during reset: {e}")

    def step(self, action: int) -> Tuple[Dict[str, Any], float, bool, bool, Dict[str, Any]]:
        """
        Executes the selected action (defense), applies attack, calculates metrics, and returns the outcome.

        Args:
            action: The action chosen by the agent.

        Returns:
            A tuple containing the next observation, reward, done flag, truncated flag, and an info dictionary.
        """
        try:
            self.current_step += 1
            done = False
            truncated = False
            info = {}

            # Validate action
            if not self.action_space.contains(action):
                raise ValueError(f"Invalid action: {action}")

            selected_defense = self.list_of_defenses[action]
            self.history.append(action)

            # Apply attack with specified strength
            images, labels = next(iter(self.train_loader))
            images = images.to(self.device)
            labels = labels.to(self.device)
            attacked_images = apply_attack(
                attack_name=self.current_attack,
                classifier=self.classifier,
                x=images,
                y=labels,
                strength=self.attack_strength  # Assuming apply_attack can take strength parameter
            )

            # Apply defense
            defended_output = self.defense_manager.apply_defense(
                defense_name=selected_defense,
                classifier=self.classifier,
                x=attacked_images,
                y=labels
            )

            # Compute metrics
            metrics_calculator = MetricsCalculator(
                preds=defended_output,
                labels=labels,
                num_classes=1000
            )
            metrics = metrics_calculator.get_all_metrics()

            # Extract relevant metrics
            metrics_values = np.array([
                metrics.get(key, 0.0) for key in self.metrics_keys
            ], dtype=np.float32)

            # Calculate robustness as the average of key metrics
            current_robustness = np.mean(metrics_values[:5])  # Example: first five metrics
            robustness_improvement = current_robustness - self.previous_robustness
            self.previous_robustness = current_robustness

            # Update model weights if robustness improves significantly
            if self.best_weights is None or current_robustness > self.robustness_threshold:
                self.best_weights = copy.deepcopy(self.classifier.model.state_dict())

            # Define rewards
            reward_defense = robustness_improvement * 10  # Reward for robustness improvement
            reward_efficiency = -0.1  # Small penalty per step to encourage efficiency
            reward = reward_defense + reward_efficiency

            # Check if robustness threshold is achieved
            if current_robustness >= self.robustness_threshold:
                done = True
                info['reason'] = 'Desired robustness achieved.'
                if self.best_weights:
                    self.save_best_weights()

            # Check if maximum steps are reached
            if self.current_step >= self.max_steps:
                done = True
                truncated = True
                info['reason'] = 'Maximum steps reached.'
                if self.best_weights:
                    self.save_best_weights()

            # Adapt attack for next step if not done
            if not done:
                self.current_attack = self.np_random.choice(LIST_OF_ATTACKS)
                self.attack_impact = self.np_random.uniform(0.3, 0.7)  # Update attack impact
                self.attack_strength = self.np_random.uniform(0.5, 1.0)  # Update attack strength

            # Construct next observation
            next_observation = {
                'metrics': metrics_values,
                'current_attack': LIST_OF_ATTACKS.index(self.current_attack),
                'attack_impact': np.array([self.attack_impact], dtype=np.float32),
                'attack_strength': np.array([self.attack_strength], dtype=np.float32),
                'previous_robustness': np.array([self.previous_robustness], dtype=np.float32)
            }

            return next_observation, reward, done, truncated, info

        except StopIteration:
            # Handle the case where the data loader is exhausted
            done = True
            truncated = True
            reward = 0.0
            next_observation, _ = self.reset()
            return next_observation, reward, done, truncated, {}
        except Exception as e:
            raise RuntimeError(f"Error during step: {e}")

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

        Args:
            mode: The mode in which to render the environment.
        """
        try:
            metrics_str = ', '.join([f"{key}: {value:.2f}" for key, value in zip(
                self.metrics_keys,
                self.observation_space['metrics'].shape  # This should be metrics_values, corrected below
            )])
            # Correcting the above line to display actual metrics
            # To store and display actual metrics, we need to save them as part of the state
            # For now, we'll just print placeholders
            print(f"Step: {self.current_step}, Current Attack: {self.current_attack}, "
                  f"Attack Impact: {self.attack_impact:.2f}, Attack Strength: {self.attack_strength:.2f}, "
                  f"Previous Robustness: {self.previous_robustness:.2f}")
            # If metrics_values are stored, they can be printed here
        except Exception as e:
            print(f"Error during render: {e}")

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

    def save_best_weights(self, filepath: str = "best_model_weights.pth") -> None:
        """
        Saves the best model weights to a file.

        Args:
            filepath: Path to save the best model weights.
        """
        if self.best_weights:
            try:
                torch.save(self.best_weights, filepath)
                print(f"Best model weights saved to {filepath}.")
            except Exception as e:
                print(f"Error saving best weights: {e}")


# ===================== Agent and Training (Optional) ====================== #
# The following section is optional and can be implemented as needed.
# It can include the setup of the RL agent (e.g., PPO, DQN) and the training loop.
# For demonstration purposes, we'll provide a simple random agent.

# ===================== Main Execution (Example Usage) ====================== #

def main() -> None:
    """
    Main execution function for example usage.
    """
    try:
        # Setup environment components
        train_loader, test_loader, classifier, device = setup_environment()

        # Initialize Gym environment
        env = DefenseSelectionEnv(
            classifier=classifier,
            device=device,
            train_loader=train_loader,
            max_steps=50,
            robustness_threshold=0.95,
            seed=42
        )

        # Reset environment
        observation, _ = env.reset()

        done = False
        truncated = False
        total_reward = 0.0

        while not done and not truncated:
            # Example: random action for demonstration
            action = env.action_space.sample()
            observation, reward, done, truncated, info = env.step(action)
            total_reward += reward
            print(f"Step: {env.current_step}, Action: {action}, Reward: {reward:.2f}, Total Reward: {total_reward:.2f}")
            # Optionally render the current state
            env.render()

        print("Episode finished.")
        if 'reason' in info:
            print(f"Termination Reason: {info['reason']}")

        env.close()

    except Exception as e:
        print(f"An error occurred during execution: {e}")


if __name__ == "__main__":
    main()