"""Toy Loihi-like backend for NeuPlan MVP.

This is a lightweight simulator that pretends to run a neuromorphic graph on
Loihi-like hardware. It converts a NeuPlan NIR (toy dict) into a latency/energy
estimate and returns a simple executable plan skeleton.
"""
from __future__ import annotations

from typing import Dict, Any, List


class LoihiBackend:
    def __init__(self, quantization_bits: int = 8, time_scale: float = 1.0) -> None:
        self.quantization_bits = quantization_bits
        self.time_scale = time_scale

    def run(self, nir: Dict[str, Any], time_budget_s: float) -> Dict[str, Any]:
        nodes: List[Dict[str, Any]] = nir.get("nodes", [])
        # naive latency model: 5ms per node scaled by time_scale
        latency = max(0.001, len(nodes) * 0.005 / max(1e-6, self.time_scale))
        status = "ok" if latency <= time_budget_s else "timeout"
        energy = max(0.01, len(nodes) * 0.02)
        plan = {"steps": [{"node": n} for n in nodes]}
        return {
            "status": status,
            "latency_s": latency,
            "energy_j": energy,
            "plan": plan,
        }