import numpy as np
from pathlib import Path
from ...logger import logger
from ...config import load_config
from ..energy.free_energy import FreeEnergyEngine

_cfg = load_config()

class EvolutionaryLoRA:
    def __init__(self, brain):
        self.brain = brain
        self.free_energy_engine = FreeEnergyEngine()
        self.out_path = Path(_cfg["storage_root"]) / "storage" / "lora_delta_evo.json"

    def run_generation(self) -> None:
        # Simulated local teacher-forcing evaluation
        fake_logprob = -np.random.rand()
        self.free_energy_engine.ingest_observation(fake_logprob)
        
        if self.free_energy_engine.free_energy < 0.5:
            self.out_path.parent.mkdir(parents=True, exist_ok=True)
            self.out_path.touch()
            logger.info(f"LoRA improvement kept (free-energy={self.free_energy_engine.free_energy:.3f})")
        else:
            logger.info(f"LoRA discarded (free-energy={self.free_energy_engine.free_energy:.3f})")