"""GCN using builtin functions that enables SPMV optimization.

References:
- Semi-Supervised Classification with Graph Convolutional Networks
- Paper: https://arxiv.org/abs/1609.02907
- Code: https://github.com/tkipf/gcn
"""
import math
import torch
import torch.nn as nn
import dgl.function as fn
import argparse, time
import numpy as np
import networkx as nx
import torch.nn.functional as F
from dgl import DGLGraph 
from dgl.data import register_data_args, load_data

class GCNLayer(nn.Module):
    def __init__(self,
                 g,
                 in_feats,
                 out_feats,
                 activation,
                 dropout,
                 bias=True):
        super(GCNLayer, self).__init__()
        self.g = g
        self.weight = nn.Parameter(torch.Tensor(in_feats, out_feats))
        if bias:
            self.bias = nn.Parameter(torch.Tensor(out_feats))
        else:
            self.bias = None
        self.activation = activation
        if dropout:
            self.dropout = nn.Dropout(p=dropout)
        else:
            self.dropout = 0.
        self.reset_parameters()

    def reset_parameters(self):
        stdv = 1. / math.sqrt(self.weight.size(1))
        self.weight.data.uniform_(-stdv, stdv)
        if self.bias is not None:
            self.bias.data.uniform_(-stdv, stdv)

    def forward(self, h):
        if self.dropout:
            h = self.dropout(h)
        h = torch.mm(h, self.weight)
        # normalization by square root of src degree
        h = h * self.g.ndata['norm']
        self.g.ndata['h'] = h
        self.g.update_all(fn.copy_src(src='h', out='m'),
                          fn.sum(msg='m', out='h'))
        h = self.g.ndata.pop('h')
        # normalization by square root of dst degree
        h = h * self.g.ndata['norm']
        # bias
        if self.bias is not None:
            h = h + self.bias
        if self.activation:
            h = self.activation(h)
        return h

class GCN(nn.Module):
    def __init__(self,
                 g,
                 in_feats,
                 n_hidden,
                 n_classes,
                 n_layers,
                 activation,
                 dropout):
        super(GCN, self).__init__()
        self.layers = nn.ModuleList()
        # input layer
        self.layers.append(GCNLayer(g, in_feats, n_hidden, activation, 0.))
        # hidden layers
        for i in range(n_layers - 1):
            self.layers.append(GCNLayer(g, n_hidden, n_hidden, activation, dropout))
        # output layer
        self.layers.append(GCNLayer(g, n_hidden, n_classes, None, dropout))

    def forward(self, features):
        h = features
        for layer in self.layers:
            h = layer(h)
        return h


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)
    features = torch.FloatTensor(data.features)
    labels = torch.LongTensor(data.labels)
    if hasattr(torch, 'BoolTensor'):
        train_mask = torch.BoolTensor(data.train_mask)
        val_mask = torch.BoolTensor(data.val_mask)
        test_mask = torch.BoolTensor(data.test_mask)
    else:
        train_mask = torch.ByteTensor(data.train_mask)
        val_mask = torch.ByteTensor(data.val_mask)
        test_mask = torch.ByteTensor(data.test_mask)
    in_feats = features.shape[1]
    n_classes = data.num_labels
    n_edges = data.graph.number_of_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()))

    if args.gpu < 0:
        cuda = False
    else:
        cuda = True
        torch.cuda.set_device(args.gpu)
        features = features.cuda()
        labels = labels.cuda()
        train_mask = train_mask.cuda()
        val_mask = val_mask.cuda()
        test_mask = test_mask.cuda()

    # graph preprocess and calculate normalization factor
    g = data.graph
    # add self loop
    if args.self_loop:
        g.remove_edges_from(nx.selfloop_edges(g))
        g.add_edges_from(zip(g.nodes(), g.nodes()))
    g = DGLGraph(g)
    n_edges = g.number_of_edges()
    # normalization
    degs = g.in_degrees().float()
    norm = torch.pow(degs, -0.5)
    norm[torch.isinf(norm)] = 0
    if cuda:
        norm = norm.cuda()
    g.ndata['norm'] = norm.unsqueeze(1)

    # create GCN model
    model = GCN(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= []
    with torch.autograd.profiler.profile(use_cuda=True) as prof:
        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} | ". format(epoch, np.mean(dur), loss.item(), acc))

    print()
    acc = evaluate(model, features, labels, test_mask)
    print("Test accuracy {:.2%}".format(acc))

    print(prof.key_averages().table(sort_by="cuda_time_total"))



if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='GCN')
    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 gcn units")
    parser.add_argument("--n-layers", type=int, default=1,
            help="number of hidden gcn 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)
