import pytorch_lightning as pl
import argparse
import pprint
from loguru import logger as loguru_logger

from src.config.default import get_cfg_defaults
from src.utils.profiler import build_profiler

from src.lightning.data import MultiSceneDataModule
from src.lightning.lightning_aspanformer import PL_ASpanFormer
import torch


def parse_args():
    # init a costum parser which will be added into pl.Trainer parser
    # check documentation: https://pytorch-lightning.readthedocs.io/en/latest/common/trainer.html#trainer-flags
    parser = argparse.ArgumentParser(
        formatter_class=argparse.ArgumentDefaultsHelpFormatter
    )
    parser.add_argument("data_cfg_path", type=str, help="data config path")
    parser.add_argument("main_cfg_path", type=str, help="main config path")
    parser.add_argument(
        "--ckpt_path",
        type=str,
        default="weights/indoor_ds.ckpt",
        help="path to the checkpoint",
    )
    parser.add_argument(
        "--dump_dir",
        type=str,
        default=None,
        help="if set, the matching results will be dump to dump_dir",
    )
    parser.add_argument(
        "--profiler_name",
        type=str,
        default=None,
        help="options: [inference, pytorch], or leave it unset",
    )
    parser.add_argument("--batch_size", type=int, default=1, help="batch_size per gpu")
    parser.add_argument("--num_workers", type=int, default=2)
    parser.add_argument(
        "--thr",
        type=float,
        default=None,
        help="modify the coarse-level matching threshold.",
    )
    parser.add_argument(
        "--mode",
        type=str,
        default="vanilla",
        help="modify the coarse-level matching threshold.",
    )
    parser = pl.Trainer.add_argparse_args(parser)
    return parser.parse_args()


if __name__ == "__main__":
    # parse arguments
    args = parse_args()
    pprint.pprint(vars(args))

    # init default-cfg and merge it with the main- and data-cfg
    config = get_cfg_defaults()
    config.merge_from_file(args.main_cfg_path)
    config.merge_from_file(args.data_cfg_path)
    pl.seed_everything(config.TRAINER.SEED)  # reproducibility

    # tune when testing
    if args.thr is not None:
        config.ASPAN.MATCH_COARSE.THR = args.thr

    loguru_logger.info(f"Args and config initialized!")

    # lightning module
    profiler = build_profiler(args.profiler_name)
    model = PL_ASpanFormer(
        config,
        pretrained_ckpt=args.ckpt_path,
        profiler=profiler,
        dump_dir=args.dump_dir,
    )
    loguru_logger.info(f"ASpanFormer-lightning initialized!")

    # lightning data
    data_module = MultiSceneDataModule(args, config)
    loguru_logger.info(f"DataModule initialized!")

    # lightning trainer
    trainer = pl.Trainer.from_argparse_args(
        args, replace_sampler_ddp=False, logger=False
    )

    loguru_logger.info(f"Start testing!")
    trainer.test(model, datamodule=data_module, verbose=False)