"""Modeling Relational Data with Graph Convolutional Networks
Paper: https://arxiv.org/abs/1703.06103
Reference Code: https://github.com/tkipf/relational-gcn
"""
import argparse
import time

import numpy as np
import torch as th
import torch.nn as nn
import torch.nn.functional as F

from dgl.data.rdf import AIFBDataset, AMDataset, BGSDataset, MUTAGDataset
from model import EntityClassify


def main(args):
    # load graph data
    if args.dataset == "aifb":
        dataset = AIFBDataset()
    elif args.dataset == "mutag":
        dataset = MUTAGDataset()
    elif args.dataset == "bgs":
        dataset = BGSDataset()
    elif args.dataset == "am":
        dataset = AMDataset()
    else:
        raise ValueError()

    g = dataset[0]
    category = dataset.predict_category
    num_classes = dataset.num_classes
    train_mask = g.nodes[category].data.pop("train_mask")
    test_mask = g.nodes[category].data.pop("test_mask")
    train_idx = th.nonzero(train_mask, as_tuple=False).squeeze()
    test_idx = th.nonzero(test_mask, as_tuple=False).squeeze()
    labels = g.nodes[category].data.pop("labels")
    category_id = len(g.ntypes)
    for i, ntype in enumerate(g.ntypes):
        if ntype == category:
            category_id = i

    # split dataset into train, validate, test
    if args.validation:
        val_idx = train_idx[: len(train_idx) // 5]
        train_idx = train_idx[len(train_idx) // 5 :]
    else:
        val_idx = train_idx

    # check cuda
    use_cuda = args.gpu >= 0 and th.cuda.is_available()
    if use_cuda:
        th.cuda.set_device(args.gpu)
        g = g.to("cuda:%d" % args.gpu)
        labels = labels.cuda()
        train_idx = train_idx.cuda()
        test_idx = test_idx.cuda()

    # create model
    model = EntityClassify(
        g,
        args.n_hidden,
        num_classes,
        num_bases=args.n_bases,
        num_hidden_layers=args.n_layers - 2,
        dropout=args.dropout,
        use_self_loop=args.use_self_loop,
    )

    if use_cuda:
        model.cuda()

    # optimizer
    optimizer = th.optim.Adam(
        model.parameters(), lr=args.lr, weight_decay=args.l2norm
    )

    # training loop
    print("start training...")
    dur = []
    model.train()
    for epoch in range(args.n_epochs):
        optimizer.zero_grad()
        if epoch > 5:
            t0 = time.time()
        logits = model()[category]
        loss = F.cross_entropy(logits[train_idx], labels[train_idx])
        loss.backward()
        optimizer.step()
        t1 = time.time()

        if epoch > 5:
            dur.append(t1 - t0)
        train_acc = th.sum(
            logits[train_idx].argmax(dim=1) == labels[train_idx]
        ).item() / len(train_idx)
        val_loss = F.cross_entropy(logits[val_idx], labels[val_idx])
        val_acc = th.sum(
            logits[val_idx].argmax(dim=1) == labels[val_idx]
        ).item() / len(val_idx)
        print(
            "Epoch {:05d} | Train Acc: {:.4f} | Train Loss: {:.4f} | Valid Acc: {:.4f} | Valid loss: {:.4f} | Time: {:.4f}".format(
                epoch,
                train_acc,
                loss.item(),
                val_acc,
                val_loss.item(),
                np.average(dur),
            )
        )
    print()
    if args.model_path is not None:
        th.save(model.state_dict(), args.model_path)

    model.eval()
    logits = model.forward()[category]
    test_loss = F.cross_entropy(logits[test_idx], labels[test_idx])
    test_acc = th.sum(
        logits[test_idx].argmax(dim=1) == labels[test_idx]
    ).item() / len(test_idx)
    print(
        "Test Acc: {:.4f} | Test loss: {:.4f}".format(
            test_acc, test_loss.item()
        )
    )
    print()


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="RGCN")
    parser.add_argument(
        "--dropout", type=float, default=0, help="dropout probability"
    )
    parser.add_argument(
        "--n-hidden", type=int, default=16, help="number of hidden units"
    )
    parser.add_argument("--gpu", type=int, default=-1, help="gpu")
    parser.add_argument("--lr", type=float, default=1e-2, help="learning rate")
    parser.add_argument(
        "--n-bases",
        type=int,
        default=-1,
        help="number of filter weight matrices, default: -1 [use all]",
    )
    parser.add_argument(
        "--n-layers", type=int, default=2, help="number of propagation rounds"
    )
    parser.add_argument(
        "-e",
        "--n-epochs",
        type=int,
        default=50,
        help="number of training epochs",
    )
    parser.add_argument(
        "-d", "--dataset", type=str, required=True, help="dataset to use"
    )
    parser.add_argument(
        "--model_path", type=str, default=None, help="path for save the model"
    )
    parser.add_argument("--l2norm", type=float, default=0, help="l2 norm coef")
    parser.add_argument(
        "--use-self-loop",
        default=False,
        action="store_true",
        help="include self feature as a special relation",
    )
    fp = parser.add_mutually_exclusive_group(required=False)
    fp.add_argument("--validation", dest="validation", action="store_true")
    fp.add_argument("--testing", dest="validation", action="store_false")
    parser.set_defaults(validation=True)

    args = parser.parse_args()
    print(args)
    main(args)
