from typing import List, Any, Dict
import numpy as np

try:
    from art.metrics import empirical_robustness
    ART_AVAILABLE = True
except ImportError:
    ART_AVAILABLE = False

class ARTAdapter:
    """
    Adapter to interface ERH data with Adversarial Robustness Toolbox (ART).
    """
    
    def __init__(self):
        if not ART_AVAILABLE:
            print("Warning: adversarial-robustness-toolbox (art) not found.")

    def check_robustness(self, actions: List[Any], judge_model: Any = None) -> Dict[str, float]:
        """
        Mock robustness check using ART concepts.
        In a real scenario, we would wrap the 'judge' as a classifier 
        and test its robustness against perturbations in 'complexity'.
        """
        if not ART_AVAILABLE:
            return {"error": "art not installed"}

        # This would require a fully differentiable mock of the judge 
        # compatible with ART's classifiers.
        # For this PoC, we return a placeholder metric based on 
        # how close the 'mistake' boundary is.
        
        return {
            "mock_robustness_score": 0.85,
            "description": "Placeholder for ART empirical robustness"
        }
