from dataclasses import dataclass
import gc
import os
from pathlib import Path
import subprocess

from omegaconf import OmegaConf
from rich import print
import torch

from refined_v4.train import Config as TrainConfig, run
from refined_v4.study import model_inventory
from .data import ROOT, TASKS


@dataclass
class Config:
    root: str = str(ROOT / 'smoke')
    output: str = str(ROOT / 'smoke-flow')
    task: str = ''
    device: str = 'auto'


def main(config: Config):
    base = next(row for row in model_inventory() if row['name'] == '8m')
    for task in TASKS:
        if config.task and config.task != task:
            continue
        settings = TrainConfig(task=task, model='8m', root=config.root,
            checkpoint=base['checkpoint'], output=str(Path(config.output) / task),
            sft_tokens=4096, rl_rollouts=128, samples=32, groups=4,
            eval_queries=16, eval_samples=2, interval=128, recovery_interval=128,
            workers=2, threads=2, wandb_mode='disabled', smoke=True, device=config.device)
        if not run(settings):
            if os.environ.get('SLURM_JOB_ID'):
                subprocess.run(['scontrol', 'requeue', os.environ['SLURM_JOB_ID']], check=True)
                return
            raise RuntimeError('Smoke training interrupted')
        print(f'[green]{task}: real SFT + RL smoke complete[/green]')
        gc.collect()
        if torch.cuda.is_available():
            torch.cuda.empty_cache()


if __name__ == '__main__':
    main(OmegaConf.to_object(OmegaConf.merge(OmegaConf.structured(Config), OmegaConf.from_cli())))