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

from .. import utils

class GAT(nn.Module):
    def __init__(self,
                 num_layers,
                 in_dim,
                 num_hidden,
                 num_classes,
                 heads,
                 activation,
                 feat_drop,
                 attn_drop,
                 negative_slope,
                 residual):
        super(GAT, self).__init__()
        self.num_layers = num_layers
        self.gat_layers = nn.ModuleList()
        self.activation = activation
        # input projection (no residual)
        self.gat_layers.append(GATConv(
            in_dim, num_hidden, heads[0],
            feat_drop, attn_drop, negative_slope, False, self.activation))
        # hidden layers
        for l in range(1, num_layers):
            # due to multi-head, the in_dim = num_hidden * num_heads
            self.gat_layers.append(GATConv(
                num_hidden * heads[l-1], num_hidden, heads[l],
                feat_drop, attn_drop, negative_slope, residual, self.activation))
        # output projection
        self.gat_layers.append(GATConv(
            num_hidden * heads[-2], num_classes, heads[-1],
            feat_drop, attn_drop, negative_slope, residual, None))

    def forward(self, g, inputs):
        h = inputs
        for l in range(self.num_layers):
            h = self.gat_layers[l](g, h).flatten(1)
        # output projection
        logits = self.gat_layers[-1](g, h).mean(1)
        return logits

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)

    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)

    # create model
    model = GAT(1, in_feats, 8, n_classes, [8, 1], F.elu,
                0.6, 0.6, 0.2, False)
    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
