#!/usr/bin/env python
import argparse
import importlib
import json
import os
import pprint

import torch

from core.config import SystemConfig
from core.dbs import datasets
from core.nnet.py_factory import NetworkFactory
from core.test import test_func

torch.backends.cudnn.benchmark = False


def parse_args():
    parser = argparse.ArgumentParser(description="Evaluation Script")
    parser.add_argument("cfg_file", help="config file", type=str)
    parser.add_argument("--testiter", dest="testiter",
                        help="test at iteration i",
                        default=None, type=int)
    parser.add_argument("--split", dest="split",
                        help="which split to use",
                        default="validation", type=str)
    parser.add_argument("--suffix", dest="suffix", default=None, type=str)
    parser.add_argument("--debug", action="store_true")

    args = parser.parse_args()
    return args


def make_dirs(directories):
    for directory in directories:
        if not os.path.exists(directory):
            os.makedirs(directory)


def test(db, system_config, model, args):
    split = args.split
    testiter = args.testiter
    debug = args.debug
    suffix = args.suffix

    result_dir = system_config.result_dir
    result_dir = os.path.join(result_dir, str(testiter), split)

    if suffix is not None:
        result_dir = os.path.join(result_dir, suffix)

    make_dirs([result_dir])

    test_iter = system_config.max_iter if testiter is None else testiter
    print("loading parameters at iteration: {}".format(test_iter))

    print("building neural network...")
    nnet = NetworkFactory(system_config, model)
    print("loading parameters...")
    nnet.load_params(test_iter)

    nnet.cuda()
    nnet.eval_mode()
    test_func(system_config, db, nnet, result_dir, debug=debug)


def main(args):
    if args.suffix is None:
        cfg_file = os.path.join("./configs", args.cfg_file + ".json")
    else:
        cfg_file = os.path.join("./configs", args.cfg_file + "-{}.json".format(args.suffix))
    print("cfg_file: {}".format(cfg_file))

    with open(cfg_file, "r") as f:
        config = json.load(f)

    config["system"]["snapshot_name"] = args.cfg_file
    system_config = SystemConfig().update_config(config["system"])

    model_file = "core.models.{}".format(args.cfg_file)
    model_file = importlib.import_module(model_file)
    model = model_file.model()

    train_split = system_config.train_split
    val_split = system_config.val_split
    test_split = system_config.test_split

    split = {
        "training": train_split,
        "validation": val_split,
        "testing": test_split
    }[args.split]

    print("loading all datasets...")
    dataset = system_config.dataset
    print("split: {}".format(split))
    testing_db = datasets[dataset](config["db"], split=split, sys_config=system_config)

    print("system config...")
    pprint.pprint(system_config.full)

    print("db config...")
    pprint.pprint(testing_db.configs)

    test(testing_db, system_config, model, args)


if __name__ == "__main__":
    args = parse_args()
    main(args)