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:only develop environment using import gymnasium as gym  module 

good 
from gymnasium.spaces.box import Box
from gymnasium.spaces.dict import Dict
from gymnasium.spaces.discrete import Discrete
from gymnasium.spaces.graph import Graph, GraphInstance
from gymnasium.spaces.multi_binary import MultiBinary
from gymnasium.spaces.multi_discrete import MultiDiscrete
from gymnasium.spaces.oneof import OneOf
from gymnasium.spaces.sequence import Sequence
from gymnasium.spaces.space import Space
from gymnasium.spaces.text import Text
from gymnasium.spaces.tuple import Tuple
from gymnasium.spaces.utils import flatdim, flatten, flatten_space, unflatten


TASK : 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



environment :

observation space:
1) top three  metrics details we have 20 metrics we develop with return # gives  Dict[str, Any] they are (
already implemented
'Accuracy': self.accuracy(),
            'Precision': self.precision(),
            'Recall': self.recall(),
            'F1 Score': self.f1_score(),
            'Confusion Matrix': self.confusion_matrix(),
            'ROC-AUC': self.roc_auc(),
            'PR-AUC': self.pr_auc(),
            'Top-5 Accuracy': self.top_k_accuracy(k=5) if self.preds.ndim > 1 else None,
            'Log Loss': self.log_loss(),
            'Cross Entropy Loss': self.cross_entropy_loss(),
            'Mean Squared Error': self.mean_squared_error(),
            'Kappa Statistic': self.kappa_score(),
            'Matthews Correlation Coefficient': self.matthews_correlation_coefficient(),
            'Specificity': self.specificity(),
            'Jaccard Index': self.jaccard_index(),
            'Balanced Accuracy': self.balanced_accuracy(),
            'True Negative Rate': self.true_negative_rate(),
            'False Positive Rate': self.false_positive_rate(),
            'Dice Coefficient': self.dice_coefficient(),
            'Mean Average Precision': self.mean_average_precision(),

)
   
2) what is attack also include 
3) what is impact of attack 
4) what robustness previously/originally


action space : given observation , and previous history 

what is best defenvce I can select



rewards: 1) agent find best defence and 2) how robustness is increased 



below all api already implemented 

     




very sample environment and refeernec env

DRawback
1) how observation including what is impact of attack and how strong attack is ?? this logic is missing
2) model weights depreciation / model loss its functionslaity keep on attack acting on it , some logic deal ethically (to evualte impact of attack)
3) agent how to select best defence logic is missing 
4) observations , what attack going on , if let assume a1, previous what is impact ,
5) rewarding , minimum time for find best defence for given attack along with robustness for all attcks
6) truncation logic is missing
terminations  logic is missing 
reset logic is missing
closing and rendering logic is missing 

 



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

# Assuming these APIs are already implemented as per user instructions
from art.estimators.classification import PyTorchClassifier
from defences import DefenseManager
from attacks import apply_attack, LIST_OF_ATTACTS
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[gym.Env, PyTorchClassifier]:
    """
    Sets up the data loaders and classifier for the environment.
    
    Returns:
        Tuple containing the Gym environment and the PyTorch classifier.
    """
    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)

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

    def __init__(self, 
                 classifier: PyTorchClassifier,
                 device: torch.device,
                 max_steps: int = 100):
        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.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.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_ATTACTS)),
            'attack_impact': 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 = None
        self.attack_impact = 0.0
        self.previous_robustness = 0.0
        self.train_loader = None  # To be set externally or passed as a parameter

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

    def reset(self, seed: Optional[int] = None) -> Dict[str, Any]:
        """
        Resets the environment to an initial state and returns the initial observation.
        """
        try:
            self.current_step = 0
            self.previous_robustness = 0.0
            self.current_attack = self.np_random.choice(LIST_OF_ATTACTS)
            self.attack_impact = self.np_random.uniform(0.0, 1.0)

            initial_observation = {
                'metrics': np.zeros(self.observation_space['metrics'].shape, dtype=np.float32),
                'current_attack': LIST_OF_ATTACTS.index(self.current_attack),
                'attack_impact': np.array([self.attack_impact], 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.
        """
        try:
            self.current_step += 1
            done = False
            info = {}

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

            selected_defense = self.list_of_defenses[action]

            # Apply attack
            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
            )

            # 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('Accuracy', 0.0),
                metrics.get('Precision', 0.0),
                metrics.get('Recall', 0.0),
                metrics.get('F1 Score', 0.0),
                metrics.get('ROC-AUC', 0.0),
                metrics.get('PR-AUC', 0.0),
                metrics.get('Log Loss', 0.0),
                metrics.get('Cross Entropy Loss', 0.0),
                metrics.get('Mean Squared Error', 0.0),
                metrics.get('Kappa Statistic', 0.0),
                metrics.get('Matthews Correlation Coefficient', 0.0),
                metrics.get('Specificity', 0.0),
                metrics.get('Jaccard Index', 0.0),
                metrics.get('Balanced Accuracy', 0.0),
                metrics.get('True Negative Rate', 0.0),
                metrics.get('False Positive Rate', 0.0),
                metrics.get('Dice Coefficient', 0.0),
                metrics.get('Mean Average Precision', 0.0)
            ], 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

            # Define rewards
            reward = robustness_improvement * 10  # Scaling factor for reward

            # Check if episode is done
            if self.current_step >= self.max_steps:
                done = True

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

            return next_observation, reward, done, False, info

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

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

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

# =====================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.

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

if __name__ == "__main__":
    try:
        # Setup environment components
        train_loader, test_loader, classifier, device = setup_environment()
        
        # Initialize Gym environment
        env = DefenseSelectionEnv(classifier=classifier, device=device, max_steps=50)
        env.train_loader = train_loader  # Assign the data loader

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

        done = False
        total_reward = 0.0

        while not done:
            # Example: random action for demonstration
            action = env.action_space.sample()
            observation, reward, done, truncated, info = env.step(action)
            total_reward += reward
            print(f"Step Reward: {reward}, Total Reward: {total_reward}")

        print("Episode finished.")

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