
import logging
from typing import Any

from haskell.haskell_bridge import HaskellDivination

logger = logging.getLogger(__name__)

class DharmaConstraints:
    """Dharma Constraints Bridge — The Symbolic Guardrails.
    Translates Haskell-defined Dharmic invariants into solver constraints.
    """

    def __init__(self) -> None:
        self.div: HaskellDivination | None = None
        try:
            self.div = HaskellDivination()
            logger.info("☸️ Dharma Bridge: Connected to Haskell backend.")
        except Exception as e:
            logger.warning(f"☸️ Dharma Bridge: Haskell backend unavailable: {e}")

    def get_invariants(self, node_count: int) -> list[dict[str, Any]]:
        """Query the grimoire for invariants relevant to a system of the given size.
        """
        invariants = []

        # 1. Try Haskell backend first
        if self.div:
            try:
                # Real Haskell queries for Dharmic balance
                if node_count >= 6:
                    # Check if a 6-node hexagram (e.g. all 1s/0s) is balanced
                    # This is symbolic: we use Haskell to define what 'balance' means
                    res = self.div.create_and_query([1, 0, 1, 0, 1, 0])
                    if res.get("is_balanced"):
                        invariants.append({
                            "type": "balanced_hexagram",
                            "indices": list(range(6)),
                            "description": f"Haskell confirmed King Wen #{res['king_wen_number']} equilibrium required.",
                        })
                return invariants
            except Exception as e:
                logger.debug(f"Haskell query failed: {e}")

        # 2. Python Fallback (Resilient Dharma)
        logger.debug("☸️ Dharma Bridge: Using Python native fallback logic.")
        if node_count >= 6:
            invariants.append({
                "type": "balanced_hexagram",
                "indices": list(range(6)),
                "description": "The first 6 nodes must form a balanced hexagram (Python-fallback).",
            })

        return invariants

    def apply_to_solver(self, constraints: list[Any], x: Any, node_to_idx: dict[str, int]) -> Any:
        """Apply Dharmic invariants as hard constraints to a cvxpy problem.
        """
        # This is a placeholder for the LMO (Linear Minimization Oracle)
        # to incorporate these during the Frank-Wolfe step or as hard CVXPY limits.
        pass

_dharma: DharmaConstraints | None = None

def get_dharma_bridge() -> DharmaConstraints:
    global _dharma
    if _dharma is None:
        _dharma = DharmaConstraints()
    return _dharma
