import dgl
from dgl.nn.pytorch import GraphConv
import torch
import torch.nn as nn
import torch.nn.functional as F

from .. import utils

class GCN(nn.Module):
    def __init__(self,
                 in_feats,
                 n_hidden,
                 n_classes,
                 n_layers,
                 activation,
                 dropout):
        super(GCN, self).__init__()
        self.layers = nn.ModuleList()
        # input layer
        self.layers.append(GraphConv(in_feats, n_hidden, activation=activation))
        # hidden layers
        for i in range(n_layers - 1):
            self.layers.append(GraphConv(n_hidden, n_hidden, activation=activation))
        # output layer
        self.layers.append(GraphConv(n_hidden, n_classes))
        self.dropout = nn.Dropout(p=dropout)

    def forward(self, g, features):
        h = features
        for i, layer in enumerate(self.layers):
            if i != 0:
                h = self.dropout(h)
            h = layer(g, h)
        return h

def evaluate(model, g, features, labels, mask):
    model.eval()
    with torch.no_grad():
        logits = model(g, 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) * 100

@utils.benchmark('acc')
@utils.parametrize('data', ['cora', 'pubmed'])
def track_acc(data):
    data = utils.process_data(data)
    device = utils.get_bench_device()

    g = data[0].to(device).int()

    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

    g = dgl.remove_self_loop(g)
    g = dgl.add_self_loop(g)

    # normalization
    degs = g.in_degrees().float()
    norm = torch.pow(degs, -0.5)
    norm[torch.isinf(norm)] = 0
    g.ndata['norm'] = norm.unsqueeze(1)

    # create GCN model
    model = GCN(in_feats, 16, n_classes, 1, F.relu, 0.5)
    loss_fcn = torch.nn.CrossEntropyLoss()

    model = model.to(device)
    model.train()

    # optimizer
    optimizer = torch.optim.Adam(model.parameters(),
                                 lr=1e-2,
                                 weight_decay=5e-4)
    for epoch in range(200):
        logits = model(g, features)
        loss = loss_fcn(logits[train_mask], labels[train_mask])
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

    acc = evaluate(model, g, features, labels, test_mask)
    return acc
