"""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 itertools
import numpy as np
import time
import torch as th
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from functools import partial

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

def extract_embed(node_embed, input_nodes):
    emb = {}
    for ntype, nid in input_nodes.items():
        nid = input_nodes[ntype]
        emb[ntype] = node_embed[ntype][nid]
    return emb

def evaluate(model, loader, node_embed, labels, category, device):
    model.eval()
    total_loss = 0
    total_acc = 0
    count = 0
    for input_nodes, seeds, blocks in loader:
        blocks = [blk.to(device) for blk in blocks]
        seeds = seeds[category]
        emb = extract_embed(node_embed, input_nodes)
        emb = {k : e.to(device) for k, e in emb.items()}
        lbl = labels[seeds].to(device)
        logits = model(emb, blocks)[category]
        loss = F.cross_entropy(logits, lbl)
        acc = th.sum(logits.argmax(dim=1) == lbl).item()
        total_loss += loss.item() * len(seeds)
        total_acc += acc
        count += len(seeds)
    return total_loss / count, total_acc / count

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).squeeze()
    test_idx = th.nonzero(test_mask).squeeze()
    labels = g.nodes[category].data.pop('labels')

    # 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
    device = 'cpu'
    use_cuda = args.gpu >= 0 and th.cuda.is_available()
    if use_cuda:
        th.cuda.set_device(args.gpu)
        device = 'cuda:%d' % args.gpu

    train_label = labels[train_idx]
    val_label = labels[val_idx]
    test_label = labels[test_idx]

    # create embeddings
    embed_layer = RelGraphEmbed(g, args.n_hidden)
    node_embed = embed_layer()
    # 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()

    # train sampler
    sampler = dgl.sampling.MultiLayerNeighborSampler([args.fanout] * args.n_layers)
    loader = dgl.sampling.NodeDataLoader(
        g, {category: train_idx}, sampler,
        batch_size=args.batch_size, shuffle=True, num_workers=0)

    # validation sampler
    # we do not use full neighbor to save computation resources
    val_sampler = dgl.sampling.MultiLayerNeighborSampler([args.fanout] * args.n_layers)
    val_loader = dgl.sampling.NodeDataLoader(
        g, {category: val_idx}, val_sampler,
        batch_size=args.batch_size, shuffle=True, num_workers=0)

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

    # training loop
    print("start training...")
    dur = []
    for epoch in range(args.n_epochs):
        model.train()
        optimizer.zero_grad()
        if epoch > 3:
            t0 = time.time()

        for i, (input_nodes, seeds, blocks) in enumerate(loader):
            blocks = [blk.to(device) for blk in blocks]
            seeds = seeds[category]     # we only predict the nodes with type "category"
            batch_tic = time.time()
            emb = extract_embed(node_embed, input_nodes)
            lbl = labels[seeds]
            if use_cuda:
                emb = {k : e.cuda() for k, e in emb.items()}
                lbl = lbl.cuda()
            logits = model(emb, blocks)[category]
            loss = F.cross_entropy(logits, lbl)
            loss.backward()
            optimizer.step()

            train_acc = th.sum(logits.argmax(dim=1) == lbl).item() / len(seeds)
            print("Epoch {:05d} | Batch {:03d} | Train Acc: {:.4f} | Train Loss: {:.4f} | Time: {:.4f}".
                  format(epoch, i, train_acc, loss.item(), time.time() - batch_tic))

        if epoch > 3:
            dur.append(time.time() - t0)

        val_loss, val_acc = evaluate(model, val_loader, node_embed, labels, category, device)
        print("Epoch {:05d} | Valid Acc: {:.4f} | Valid loss: {:.4f} | Time: {:.4f}".
              format(epoch, val_acc, val_loss, np.average(dur)))
    print()
    if args.model_path is not None:
        th.save(model.state_dict(), args.model_path)

    output = model.inference(
        g, args.batch_size, 'cuda' if use_cuda else 'cpu', 0, node_embed)
    test_pred = output[category][test_idx]
    test_labels = labels[test_idx]
    test_acc = (test_pred.argmax(1) == test_labels).float().mean()
    print("Test Acc: {:.4f}".format(test_acc))
    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=20,
            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")
    parser.add_argument("--batch-size", type=int, default=100,
            help="Mini-batch size. If -1, use full graph training.")
    parser.add_argument("--fanout", type=int, default=4,
            help="Fan-out of neighbor sampling.")
    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)
