import dgl
import argparse
import torch as th
import torch.optim as optim
from torch.nn.functional import softmax
from sklearn.metrics import roc_auc_score, recall_score

from utils import EarlyStopping
from model_sampling import CAREGNN, CARESampler, _l1_dist


def evaluate(model, loss_fn, dataloader, device='cpu'):
    loss = 0
    auc = 0
    recall = 0
    num_blocks = 0
    for input_nodes, output_nodes, blocks in dataloader:
        blocks = [b.to(device) for b in blocks]
        feature = blocks[0].srcdata['feature']
        label = blocks[-1].dstdata['label']
        logits_gnn, logits_sim = model(blocks, feature)

        # compute loss
        loss += loss_fn(logits_gnn, label).item() + args.sim_weight * loss_fn(logits_sim, label).item()
        recall += recall_score(label.cpu(), logits_gnn.argmax(dim=1).detach().cpu())
        auc += roc_auc_score(label.cpu(), softmax(logits_gnn, dim=1)[:, 1].detach().cpu())
        num_blocks += 1

    return recall / num_blocks, auc / num_blocks, loss / num_blocks


def main(args):
    # Step 1: Prepare graph data and retrieve train/validation/test index ============================= #
    # Load dataset
    dataset = dgl.data.FraudDataset(args.dataset, train_size=0.4)
    graph = dataset[0]
    num_classes = dataset.num_classes

    # check cuda
    if args.gpu >= 0 and th.cuda.is_available():
        device = 'cuda:{}'.format(args.gpu)
        args.num_workers = 0
    else:
        device = 'cpu'

    # retrieve labels of ground truth
    labels = graph.ndata['label'].to(device)

    # Extract node features
    feat = graph.ndata['feature'].to(device)
    layers_feat = feat.expand(args.num_layers, -1, -1)

    # retrieve masks for train/validation/test
    train_mask = graph.ndata['train_mask']
    val_mask = graph.ndata['val_mask']
    test_mask = graph.ndata['test_mask']

    train_idx = th.nonzero(train_mask, as_tuple=False).squeeze(1).to(device)
    val_idx = th.nonzero(val_mask, as_tuple=False).squeeze(1).to(device)
    test_idx = th.nonzero(test_mask, as_tuple=False).squeeze(1).to(device)

    # Reinforcement learning module only for positive training nodes
    rl_idx = th.nonzero(train_mask.to(device) & labels.bool(), as_tuple=False).squeeze(1)

    graph = graph.to(device)

    # Step 2: Create model =================================================================== #
    model = CAREGNN(in_dim=feat.shape[-1],
                    num_classes=num_classes,
                    hid_dim=args.hid_dim,
                    num_layers=args.num_layers,
                    activation=th.tanh,
                    step_size=args.step_size,
                    edges=graph.canonical_etypes)

    model = model.to(device)

    # Step 3: Create training components ===================================================== #
    _, cnt = th.unique(labels, return_counts=True)
    loss_fn = th.nn.CrossEntropyLoss(weight=1 / cnt)
    optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
    if args.early_stop:
        stopper = EarlyStopping(patience=100)

    # Step 4: training epochs =============================================================== #
    for epoch in range(args.max_epoch):
        # calculate the distance of each edges and sample based on the distance
        dists = []
        p = []
        for i in range(args.num_layers):
            dist = {}
            graph.ndata['nd'] = th.tanh(model.layers[i].MLP(layers_feat[i]))
            for etype in graph.canonical_etypes:
                graph.apply_edges(_l1_dist, etype=etype)
                dist[etype] = graph.edges[etype].data.pop('ed').detach().cpu()
            dists.append(dist)
            p.append(model.layers[i].p)
        graph.ndata.pop('nd')
        sampler = CARESampler(p, dists, args.num_layers)

        # train
        model.train()
        tr_loss = 0
        tr_recall = 0
        tr_auc = 0
        tr_blk = 0
        train_dataloader = dgl.dataloading.DataLoader(
            graph, train_idx, sampler, batch_size=args.batch_size,
            shuffle=True,  drop_last=False, num_workers=args.num_workers)

        for input_nodes, output_nodes, blocks in train_dataloader:
            blocks = [b.to(device) for b in blocks]
            train_feature = blocks[0].srcdata['feature']
            train_label = blocks[-1].dstdata['label']
            logits_gnn, logits_sim = model(blocks, train_feature)

            # compute loss
            blk_loss = loss_fn(logits_gnn, train_label) + args.sim_weight * loss_fn(logits_sim, train_label)
            tr_loss += blk_loss.item()
            tr_recall += recall_score(train_label.cpu(), logits_gnn.argmax(dim=1).detach().cpu())
            tr_auc += roc_auc_score(train_label.cpu(), softmax(logits_gnn, dim=1)[:, 1].detach().cpu())
            tr_blk += 1

            # backward
            optimizer.zero_grad()
            blk_loss.backward()
            optimizer.step()

        # Reinforcement learning module
        model.RLModule(graph, epoch, rl_idx, dists)

        # validation
        model.eval()
        val_dataloader = dgl.dataloading.DataLoader(
            graph, val_idx, sampler, batch_size=args.batch_size,
            shuffle=True, drop_last=False, num_workers=args.num_workers)

        val_recall, val_auc, val_loss = evaluate(model, loss_fn, val_dataloader, device)

        # Print out performance
        print("In epoch {}, Train Recall: {:.4f} | Train AUC: {:.4f} | Train Loss: {:.4f}; "
              "Valid Recall: {:.4f} | Valid AUC: {:.4f} | Valid loss: {:.4f}".
              format(epoch, tr_recall / tr_blk, tr_auc / tr_blk, tr_loss / tr_blk, val_recall, val_auc, val_loss))

        if args.early_stop:
            if stopper.step(val_auc, model):
                break

    # Test with mini batch after all epoch
    model.eval()
    if args.early_stop:
        model.load_state_dict(th.load('es_checkpoint.pt'))
    test_dataloader = dgl.dataloading.DataLoader(
        graph, test_idx, sampler, batch_size=args.batch_size,
        shuffle=True, drop_last=False, num_workers=args.num_workers)

    test_recall, test_auc, test_loss = evaluate(model, loss_fn, test_dataloader, device)

    print("Test Recall: {:.4f} | Test AUC: {:.4f} | Test loss: {:.4f}".format(test_recall, test_auc, test_loss))


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='GCN-based Anti-Spam Model')
    parser.add_argument("--dataset", type=str, default="amazon", help="DGL dataset for this model (yelp, or amazon)")
    parser.add_argument("--gpu", type=int, default=-1, help="GPU index. Default: -1, using CPU.")
    parser.add_argument("--hid_dim", type=int, default=64, help="Hidden layer dimension")
    parser.add_argument("--num_layers", type=int, default=1, help="Number of layers")
    parser.add_argument("--batch_size", type=int, default=256, help="Size of mini-batch")
    parser.add_argument("--max_epoch", type=int, default=30, help="The max number of epochs. Default: 30")
    parser.add_argument("--lr", type=float, default=0.01, help="Learning rate. Default: 0.01")
    parser.add_argument("--weight_decay", type=float, default=0.001, help="Weight decay. Default: 0.001")
    parser.add_argument("--step_size", type=float, default=0.02, help="RL action step size (lambda 2). Default: 0.02")
    parser.add_argument("--sim_weight", type=float, default=2, help="Similarity loss weight (lambda 1). Default: 0.001")
    parser.add_argument("--num_workers", type=int, default=4, help="Number of node dataloader")
    parser.add_argument('--early-stop', action='store_true', default=False, help="indicates whether to use early stop")

    args = parser.parse_args()
    th.manual_seed(717)
    print(args)
    main(args)
