import argparse
import time

import networkx as nx
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F

from dgl import DGLGraph
from dgl.data import load_data, register_data_args
from tagcn import TAGCN


def evaluate(model, features, labels, mask):
    model.eval()
    with torch.no_grad():
        logits = model(features)
        logits = logits[mask]
        labels = labels[mask]
        _, indices = torch.max(logits, dim=1)
        correct = torch.sum(indices == labels)
        return correct.item() * 1.0 / len(labels)


def main(args):
    # load and preprocess dataset
    data = load_data(args)
    g = data[0]
    if args.gpu < 0:
        cuda = False
    else:
        cuda = True
        g = g.to(args.gpu)
    features = g.ndata["feat"]
    labels = g.ndata["label"]
    train_mask = g.ndata["train_mask"]
    val_mask = g.ndata["val_mask"]
    test_mask = g.ndata["test_mask"]
    in_feats = features.shape[1]
    n_classes = data.num_labels
    n_edges = g.num_edges()
    print(
        """----Data statistics------'
      #Edges %d
      #Classes %d
      #Train samples %d
      #Val samples %d
      #Test samples %d"""
        % (
            n_edges,
            n_classes,
            train_mask.int().sum().item(),
            val_mask.int().sum().item(),
            test_mask.int().sum().item(),
        )
    )

    # graph preprocess and calculate normalization factor
    # add self loop
    if args.self_loop:
        g = g.remove_self_loop().add_self_loop()
    n_edges = g.num_edges()

    # create TAGCN model
    model = TAGCN(
        g,
        in_feats,
        args.n_hidden,
        n_classes,
        args.n_layers,
        F.relu,
        args.dropout,
    )

    if cuda:
        model.cuda()
    loss_fcn = torch.nn.CrossEntropyLoss()

    # use optimizer
    optimizer = torch.optim.Adam(
        model.parameters(), lr=args.lr, weight_decay=args.weight_decay
    )

    # initialize graph
    dur = []
    for epoch in range(args.n_epochs):
        model.train()
        if epoch >= 3:
            t0 = time.time()
        # forward
        logits = model(features)
        loss = loss_fcn(logits[train_mask], labels[train_mask])

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

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

        acc = evaluate(model, features, labels, val_mask)
        print(
            "Epoch {:05d} | Time(s) {:.4f} | Loss {:.4f} | Accuracy {:.4f} | "
            "ETputs(KTEPS) {:.2f}".format(
                epoch,
                np.mean(dur),
                loss.item(),
                acc,
                n_edges / np.mean(dur) / 1000,
            )
        )

    print()
    acc = evaluate(model, features, labels, test_mask)
    print("Test Accuracy {:.4f}".format(acc))


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="TAGCN")
    register_data_args(parser)
    parser.add_argument(
        "--dropout", type=float, default=0.5, help="dropout probability"
    )
    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-epochs", type=int, default=200, help="number of training epochs"
    )
    parser.add_argument(
        "--n-hidden", type=int, default=16, help="number of hidden tagcn units"
    )
    parser.add_argument(
        "--n-layers", type=int, default=1, help="number of hidden tagcn layers"
    )
    parser.add_argument(
        "--weight-decay", type=float, default=5e-4, help="Weight for L2 loss"
    )
    parser.add_argument(
        "--self-loop",
        action="store_true",
        help="graph self-loop (default=False)",
    )
    parser.set_defaults(self_loop=False)
    args = parser.parse_args()
    print(args)

    main(args)
