# -*- coding: utf-8 -*-
# @Date    : 2019-07-25
# @Author  : Xinyu Gong (xy_gong@tamu.edu)
# @Link    : None
# @Version : 0.0

from __future__ import absolute_import, division, print_function

import os
from copy import deepcopy

import numpy as np
import torch
import torch.nn as nn
from tensorboardX import SummaryWriter
from tqdm import tqdm

import cfg
import datasets
import models  # noqa
from functions import copy_params, LinearLrDecay, load_params, train, validate
from utils.fid_score import check_or_download_inception, create_inception_graph
from utils.inception_score import _init_inception
from utils.utils import create_logger, save_checkpoint, set_log_dir

torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True


def main():
    args = cfg.parse_args()
    torch.cuda.manual_seed(args.random_seed)

    # set tf env
    _init_inception()
    inception_path = check_or_download_inception(None)
    create_inception_graph(inception_path)

    # import network
    gen_net = eval("models." + args.gen_model + ".Generator")(args=args).cuda()
    dis_net = eval("models." + args.dis_model + ".Discriminator")(args=args).cuda()

    # weight init
    def weights_init(m):
        classname = m.__class__.__name__
        if classname.find("Conv2d") != -1:
            if args.init_type == "normal":
                nn.init.normal_(m.weight.data, 0.0, 0.02)
            elif args.init_type == "orth":
                nn.init.orthogonal_(m.weight.data)
            elif args.init_type == "xavier_uniform":
                nn.init.xavier_uniform(m.weight.data, 1.0)
            else:
                raise NotImplementedError(
                    "{} unknown inital type".format(args.init_type)
                )
        elif classname.find("BatchNorm2d") != -1:
            nn.init.normal_(m.weight.data, 1.0, 0.02)
            nn.init.constant_(m.bias.data, 0.0)

    gen_net.apply(weights_init)
    dis_net.apply(weights_init)

    # set optimizer
    gen_optimizer = torch.optim.Adam(
        filter(lambda p: p.requires_grad, gen_net.parameters()),
        args.g_lr,
        (args.beta1, args.beta2),
    )
    dis_optimizer = torch.optim.Adam(
        filter(lambda p: p.requires_grad, dis_net.parameters()),
        args.d_lr,
        (args.beta1, args.beta2),
    )
    gen_scheduler = LinearLrDecay(
        gen_optimizer, args.g_lr, 0.0, 0, args.max_iter * args.n_critic
    )
    dis_scheduler = LinearLrDecay(
        dis_optimizer, args.d_lr, 0.0, 0, args.max_iter * args.n_critic
    )

    # set up data_loader
    dataset = datasets.ImageDataset(args)
    train_loader = dataset.train

    # fid stat
    if args.dataset.lower() == "cifar10":
        fid_stat = "fid_stat/fid_stats_cifar10_train.npz"
    elif args.dataset.lower() == "stl10":
        fid_stat = "fid_stat/stl10_train_unlabeled_fid_stats_48.npz"
    else:
        raise NotImplementedError(f"no fid stat for {args.dataset.lower()}")
    assert os.path.exists(fid_stat)

    # epoch number for dis_net
    args.max_epoch = args.max_epoch * args.n_critic
    if args.max_iter:
        args.max_epoch = np.ceil(args.max_iter * args.n_critic / len(train_loader))

    # initial
    fixed_z = torch.cuda.FloatTensor(np.random.normal(0, 1, (25, args.latent_dim)))
    gen_avg_param = copy_params(gen_net)
    start_epoch = 0
    best_fid = 1e4

    # set writer
    if args.load_path:
        print(f"=> resuming from {args.load_path}")
        assert os.path.exists(args.load_path)
        checkpoint_file = os.path.join(args.load_path, "Model", "checkpoint.pth")
        assert os.path.exists(checkpoint_file)
        checkpoint = torch.load(checkpoint_file)
        start_epoch = checkpoint["epoch"]
        best_fid = checkpoint["best_fid"]
        gen_net.load_state_dict(checkpoint["gen_state_dict"])
        dis_net.load_state_dict(checkpoint["dis_state_dict"])
        gen_optimizer.load_state_dict(checkpoint["gen_optimizer"])
        dis_optimizer.load_state_dict(checkpoint["dis_optimizer"])
        avg_gen_net = deepcopy(gen_net)
        avg_gen_net.load_state_dict(checkpoint["avg_gen_state_dict"])
        gen_avg_param = copy_params(avg_gen_net)
        del avg_gen_net

        args.path_helper = checkpoint["path_helper"]
        logger = create_logger(args.path_helper["log_path"])
        logger.info(f"=> loaded checkpoint {checkpoint_file} (epoch {start_epoch})")
    else:
        # create new log dir
        assert args.exp_name
        args.path_helper = set_log_dir("logs", args.exp_name)
        logger = create_logger(args.path_helper["log_path"])

    logger.info(args)
    writer_dict = {
        "writer": SummaryWriter(args.path_helper["log_path"]),
        "train_global_steps": start_epoch * len(train_loader),
        "valid_global_steps": start_epoch // args.val_freq,
    }

    # train loop
    for epoch in tqdm(
        range(int(start_epoch), int(args.max_epoch)), desc="total progress"
    ):
        lr_schedulers = (gen_scheduler, dis_scheduler) if args.lr_decay else None
        train(
            args,
            gen_net,
            dis_net,
            gen_optimizer,
            dis_optimizer,
            gen_avg_param,
            train_loader,
            epoch,
            writer_dict,
            lr_schedulers,
        )

        if epoch and epoch % args.val_freq == 0 or epoch == int(args.max_epoch) - 1:
            backup_param = copy_params(gen_net)
            load_params(gen_net, gen_avg_param)
            inception_score, fid_score = validate(
                args, fixed_z, fid_stat, gen_net, writer_dict
            )
            logger.info(
                f"Inception score: {inception_score}, FID score: {fid_score} || @ epoch {epoch}."
            )
            load_params(gen_net, backup_param)
            if fid_score < best_fid:
                best_fid = fid_score
                is_best = True
            else:
                is_best = False
        else:
            is_best = False

        avg_gen_net = deepcopy(gen_net)
        load_params(avg_gen_net, gen_avg_param)
        save_checkpoint(
            {
                "epoch": epoch + 1,
                "gen_model": args.gen_model,
                "dis_model": args.dis_model,
                "gen_state_dict": gen_net.state_dict(),
                "dis_state_dict": dis_net.state_dict(),
                "avg_gen_state_dict": avg_gen_net.state_dict(),
                "gen_optimizer": gen_optimizer.state_dict(),
                "dis_optimizer": dis_optimizer.state_dict(),
                "best_fid": best_fid,
                "path_helper": args.path_helper,
            },
            is_best,
            args.path_helper["ckpt_path"],
        )
        del avg_gen_net


if __name__ == "__main__":
    main()
