"""
Bartholomew Quantum Tunneling State Traversal Engine (QTST v1.0)
================================================================
Applies wave-mechanical state traversal principles to solve two fundamental
problems in autonomous multi-agent networks:

1. Multi-Party Metastable Gridlock (Deadlock Collapse):
   Evaluates cyclic dependency loops across N agents as a single unified
   action integral (\\oint p dq = 0), allowing atomic transition from S_initial
   to S_final without serializing blocked intermediary states.

2. Orthogonal WKB Barrier Penetration (Trapped Agent Escape):
   Calculates WKB transmission amplitudes (T = exp(-2 * kappa * dx)) to allow
   agents trapped in local failure minima to tunnel into viable orthogonal execution
   branches with zero token waste.

3. Infinite Potential Zero-Tunneling Invariant (V_0 -> inf):
   Enforces absolute zero transmission (T = 0) for destructive/hostile payloads.
"""

import time
import math
import hashlib
from typing import Dict, Any, List, Tuple, Optional

try:
    from src.trust_protocol import BartholomewTrustAuthority
    from src.rfc8785 import rfc8785_canonicalize
    from src.polyglot_ast_validator import PolyglotASTValidator
except ImportError:
    from btp_guard.src.trust_protocol import BartholomewTrustAuthority
    from btp_guard.src.rfc8785 import rfc8785_canonicalize
    from btp_guard.src.polyglot_ast_validator import PolyglotASTValidator


class QuantumTunnelingStateEngine:
    """
    Wave-mechanical state traversal and zero-latency gridlock resolution engine.
    """

    def __init__(self, hbar_eff: float = 1.0, authority: Optional[BartholomewTrustAuthority] = None):
        self.hbar_eff = hbar_eff  # Effective informational Planck constant
        self.authority = authority or BartholomewTrustAuthority()

    def resolve_multi_party_gridlock(
        self,
        agents_state: Dict[str, Dict[str, Any]],
        cyclic_dependency_chain: List[str]
    ) -> Dict[str, Any]:
        """
        Solves cyclic multi-agent deadlocks (A -> B -> C -> A) via non-local phase collapse.
        Computes closed-loop invariant conservation and executes atomic state teleportation.
        """
        t0 = time.perf_counter()

        if len(cyclic_dependency_chain) < 2:
            raise ValueError("Cyclic dependency chain must contain at least 2 nodes.")

        # 1. Verify closed loop continuity
        is_closed = cyclic_dependency_chain[0] == cyclic_dependency_chain[-1] or (
            len(cyclic_dependency_chain) >= 3 and cyclic_dependency_chain[-1] != cyclic_dependency_chain[0]
        )

        # 2. Compute Net Invariant Action Integral \oint p dq
        net_invariant_delta = 0.0
        for agent_id in set(cyclic_dependency_chain):
            agent_data = agents_state.get(agent_id, {})
            # Invariant balance: resources claimed minus resources released
            claimed = agent_data.get("claim_weight", 1.0)
            released = agent_data.get("release_weight", 1.0)
            net_invariant_delta += (claimed - released)

        is_conserved = abs(net_invariant_delta) < 1e-6

        if not is_conserved:
            return {
                "tunneled": False,
                "reason": f"Action integral not conserved (Delta = {net_invariant_delta})",
                "latency_us": (time.perf_counter() - t0) * 1_000_000
            }

        # 3. Wave Packet Phase Collapse (Atomic State Transition)
        collapsed_state = {}
        for agent_id in set(cyclic_dependency_chain):
            collapsed_state[agent_id] = {
                "status": "RESOLVED_CONCURRENT",
                "acquired_resource": agents_state[agent_id].get("target_resource"),
                "released_resource": agents_state[agent_id].get("held_resource")
            }

        latency_us = (time.perf_counter() - t0) * 1_000_000

        # 4. Mint Ed25519 Atomic Attestation Receipt
        receipt = self.authority.evaluate_intent(
            agent_id="qtst-gridlock-resolver",
            action_type="ATOMIC_PHASE_COLLAPSE",
            payload={
                "cycle": cyclic_dependency_chain,
                "collapsed_state": collapsed_state,
                "net_delta": net_invariant_delta,
                "latency_us": latency_us
            }
        )

        return {
            "tunneled": True,
            "resolution_mode": "NON_LOCAL_ATOMIC_COLLAPSE",
            "cycle_length": len(set(cyclic_dependency_chain)),
            "new_state": collapsed_state,
            "receipt": receipt,
            "latency_us": round(latency_us, 2)
        }

    def calculate_wkb_tunneling_escape(
        self,
        current_state: Dict[str, Any],
        failure_barrier_height: float,
        barrier_thickness: float,
        candidate_bypass_payload: str
    ) -> Dict[str, Any]:
        """
        Calculates WKB transmission probability and synthesizes orthogonal tunneling vector
        to allow trapped agents to penetrate local failure barriers without infinite retry loops.
        """
        t0 = time.perf_counter()

        # 1. Polyglot AST Hard Barrier Check (Infinite Potential V_0 -> inf)
        is_safe, msg, meta = PolyglotASTValidator.validate_code(candidate_bypass_payload)
        if not is_safe:
            # Infinite Potential Barrier -> Transmission strictly zero
            return {
                "tunneled": False,
                "transmission_coefficient": 0.0,
                "reason": f"Infinite Potential Barrier (V_0 -> inf): Destructive Invariant Violation: {msg}",
                "latency_us": (time.perf_counter() - t0) * 1_000_000
            }

        # 2. Compute WKB Transmission Coefficient: T = exp(-2 * kappa * dx)
        # kappa = sqrt(2 * m * (V - E)) / hbar
        effective_mass = 1.0
        energy_level = current_state.get("kinetic_energy", 0.5)

        delta_v = max(0.01, failure_barrier_height - energy_level)
        kappa = math.sqrt(2 * effective_mass * delta_v) / self.hbar_eff
        dx = max(0.01, barrier_thickness)

        exponent = -2.0 * kappa * dx
        # Bound exponent to prevent underflow
        transmission_probability = math.exp(max(-50.0, exponent))

        latency_us = (time.perf_counter() - t0) * 1_000_000

        if transmission_probability > 0.05:  # Viable tunneling threshold
            return {
                "tunneled": True,
                "transmission_coefficient": round(transmission_probability, 6),
                "orthogonal_vector": candidate_bypass_payload,
                "escape_mode": "WKB_ORTHOGONAL_PHASE_TUNNEL",
                "barrier_thickness": dx,
                "latency_us": round(latency_us, 2)
            }
        else:
            return {
                "tunneled": False,
                "transmission_coefficient": round(transmission_probability, 6),
                "reason": "Barrier too thick for coherent tunneling (requires architectural elevation)",
                "latency_us": round(latency_us, 2)
            }