import torch.distributed as dist
import torch.multiprocessing as mp

import os
import os.path as osp
import sys
import numpy as np
import copy

from lib.cfg_holder import cfg_unique_holder as cfguh
from lib.cfg_helper import \
    get_command_line_args, \
    cfg_initiates

from lib.model_zoo.sd import version
from lib.utils import get_obj_from_str

if __name__ == "__main__":
    cfg = get_command_line_args()
    cfg = cfg_initiates(cfg)

    if 'train' in cfg: 
        trainer = get_obj_from_str(cfg.train.main)(cfg)
        tstage = get_obj_from_str(cfg.train.stage)()
        if 'eval' in cfg:
            tstage.nested_eval_stage = get_obj_from_str(cfg.eval.stage)()
        trainer.register_stage(tstage)
        if cfg.env.gpu_count == 1:
            trainer(0)
        else:
            mp.spawn(trainer,
                     args=(),
                     nprocs=cfg.env.gpu_count,
                     join=True)
        trainer.destroy()
    else:
        evaler = get_obj_from_str(cfg.eval.main)(cfg)
        estage = get_obj_from_str(cfg.eval.stage)()
        evaler.register_stage(estage)
        if cfg.env.gpu_count == 1:
            evaler(0)
        else:
            mp.spawn(evaler,
                     args=(),
                     nprocs=cfg.env.gpu_count,
                     join=True)
        evaler.destroy()