import os.path as osp

import torch
import torch.nn.functional as F
from sklearn.model_selection import StratifiedKFold
from torch_geometric.datasets import Planetoid
from torch_geometric.nn import DNAConv

dataset = 'Cora'
path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', dataset)
dataset = Planetoid(path, dataset)
data = dataset[0]
data.train_mask = data.val_mask = data.test_mask = None


def gen_uniform_20_20_60_split(data):
    skf = StratifiedKFold(5, shuffle=True, random_state=55)
    idx = [torch.from_numpy(i) for _, i in skf.split(data.y, data.y)]
    data.train_idx = idx[0].to(torch.long)
    data.val_idx = idx[1].to(torch.long)
    data.test_idx = torch.cat(idx[2:], dim=0).to(torch.long)
    return data


data = gen_uniform_20_20_60_split(data)


class Net(torch.nn.Module):
    def __init__(self,
                 in_channels,
                 hidden_channels,
                 out_channels,
                 num_layers,
                 heads=1,
                 groups=1):
        super(Net, self).__init__()
        self.hidden_channels = hidden_channels
        self.lin1 = torch.nn.Linear(in_channels, hidden_channels)
        self.convs = torch.nn.ModuleList()
        for i in range(num_layers):
            self.convs.append(
                DNAConv(
                    hidden_channels, heads, groups, dropout=0.8, cached=True))
        self.lin2 = torch.nn.Linear(hidden_channels, out_channels)

    def reset_parameters(self):
        self.lin1.reset_parameters()
        for conv in self.convs:
            conv.reset_parameters()
        self.lin2.reset_parameters()

    def forward(self, x, edge_index):
        x = F.relu(self.lin1(x))
        x = F.dropout(x, p=0.5, training=self.training)
        x_all = x.view(-1, 1, self.hidden_channels)
        for conv in self.convs:
            x = F.relu(conv(x_all, edge_index))
            x = x.view(-1, 1, self.hidden_channels)
            x_all = torch.cat([x_all, x], dim=1)
        x = x_all[:, -1]
        x = F.dropout(x, p=0.5, training=self.training)
        x = self.lin2(x)
        return torch.log_softmax(x, dim=1)


device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = Net(
    in_channels=dataset.num_features,
    hidden_channels=128,
    out_channels=dataset.num_classes,
    num_layers=5,
    heads=8,
    groups=16)
model, data = model.to(device), data.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.005, weight_decay=0.0005)


def train():
    model.train()
    optimizer.zero_grad()
    out = model(data.x, data.edge_index)
    loss = F.nll_loss(out[data.train_idx], data.y[data.train_idx])
    loss.backward()
    optimizer.step()


def test():
    model.eval()
    logits, accs = model(data.x, data.edge_index), []
    for _, idx in data('train_idx', 'val_idx', 'test_idx'):
        pred = logits[idx].max(1)[1]
        acc = pred.eq(data.y[idx]).sum().item() / idx.numel()
        accs.append(acc)
    return accs


best_val_acc = test_acc = 0
for epoch in range(1, 201):
    train()
    train_acc, val_acc, tmp_test_acc = test()
    if val_acc > best_val_acc:
        best_val_acc = val_acc
        test_acc = tmp_test_acc
    log = 'Epoch: {:03d}, Train: {:.4f}, Val: {:.4f}, Test: {:.4f}'
    print(log.format(epoch, train_acc, best_val_acc, test_acc))