import argparse
import time

import dgl
import dgl.nn.pytorch as dglnn

import numpy as np
import torch as th
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import tqdm
from ogb.nodeproppred import DglNodePropPredDataset


class SAGE(nn.Module):
    def __init__(
        self, in_feats, n_hidden, n_classes, n_layers, activation, dropout
    ):
        super().__init__()
        self.n_layers = n_layers
        self.n_hidden = n_hidden
        self.n_classes = n_classes
        self.layers = nn.ModuleList()
        self.layers.append(dglnn.SAGEConv(in_feats, n_hidden, "mean"))
        for i in range(1, n_layers - 1):
            self.layers.append(dglnn.SAGEConv(n_hidden, n_hidden, "mean"))
        self.layers.append(dglnn.SAGEConv(n_hidden, n_classes, "mean"))
        self.dropout = nn.Dropout(dropout)
        self.activation = activation

    def forward(self, blocks, x):
        h = x
        for l, (layer, block) in enumerate(zip(self.layers, blocks)):
            # We need to first copy the representation of nodes on the RHS from the
            # appropriate nodes on the LHS.
            # Note that the shape of h is (num_nodes_LHS, D) and the shape of h_dst
            # would be (num_nodes_RHS, D)
            h_dst = h[: block.num_dst_nodes()]
            # Then we compute the updated representation on the RHS.
            # The shape of h now becomes (num_nodes_RHS, D)
            h = layer(block, (h, h_dst))
            if l != len(self.layers) - 1:
                h = self.activation(h)
                h = self.dropout(h)
        return h

    def inference(self, g, x, device):
        """
        Inference with the GraphSAGE model on full neighbors (i.e. without neighbor sampling).
        g : the entire graph.
        x : the input of entire node set.
        The inference code is written in a fashion that it could handle any number of nodes and
        layers.
        """
        # During inference with sampling, multi-layer blocks are very inefficient because
        # lots of computations in the first few layers are repeated.
        # Therefore, we compute the representation of all nodes layer by layer.  The nodes
        # on each layer are of course splitted in batches.
        # TODO: can we standardize this?
        for l, layer in enumerate(self.layers):
            y = th.zeros(
                g.num_nodes(),
                self.n_hidden if l != len(self.layers) - 1 else self.n_classes,
            ).to(device)

            sampler = dgl.dataloading.MultiLayerFullNeighborSampler(1)
            dataloader = dgl.dataloading.DataLoader(
                g,
                th.arange(g.num_nodes()),
                sampler,
                batch_size=args.batch_size,
                shuffle=True,
                drop_last=False,
                num_workers=args.num_workers,
            )

            for input_nodes, output_nodes, blocks in tqdm.tqdm(dataloader):
                block = blocks[0].int().to(device)

                h = x[input_nodes]
                h_dst = h[: block.num_dst_nodes()]
                h = layer(block, (h, h_dst))
                if l != len(self.layers) - 1:
                    h = self.activation(h)
                    h = self.dropout(h)

                y[output_nodes] = h

            x = y
        return y


def compute_acc(pred, labels):
    """
    Compute the accuracy of prediction given the labels.
    """
    return (th.argmax(pred, dim=1) == labels).float().sum() / len(pred)


def evaluate(model, g, nfeat, labels, val_nid, test_nid, device):
    """
    Evaluate the model on the validation set specified by ``val_mask``.
    g : The entire graph.
    inputs : The features of all the nodes.
    labels : The labels of all the nodes.
    val_mask : A 0-1 mask indicating which nodes do we actually compute the accuracy for.
    device : The GPU device to evaluate on.
    """
    model.eval()
    with th.no_grad():
        pred = model.inference(g, nfeat, device)
    model.train()
    return (
        compute_acc(pred[val_nid], labels[val_nid]),
        compute_acc(pred[test_nid], labels[test_nid]),
        pred,
    )


def load_subtensor(nfeat, labels, seeds, input_nodes):
    """
    Extracts features and labels for a set of nodes.
    """
    batch_inputs = nfeat[input_nodes]
    batch_labels = labels[seeds]
    return batch_inputs, batch_labels


#### Entry point
def run(args, device, data):
    # Unpack data
    train_nid, val_nid, test_nid, in_feats, labels, n_classes, nfeat, g = data

    # Create PyTorch DataLoader for constructing blocks
    sampler = dgl.dataloading.MultiLayerNeighborSampler(
        [int(fanout) for fanout in args.fan_out.split(",")]
    )
    dataloader = dgl.dataloading.DataLoader(
        g,
        train_nid,
        sampler,
        batch_size=args.batch_size,
        shuffle=True,
        drop_last=False,
        num_workers=args.num_workers,
    )

    # Define model and optimizer
    model = SAGE(
        in_feats,
        args.num_hidden,
        n_classes,
        args.num_layers,
        F.relu,
        args.dropout,
    )
    model = model.to(device)
    loss_fcn = nn.CrossEntropyLoss()
    optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.wd)

    # Training loop
    avg = 0
    iter_tput = []
    best_eval_acc = 0
    best_test_acc = 0
    for epoch in range(args.num_epochs):
        tic = time.time()

        # Loop over the dataloader to sample the computation dependency graph as a list of
        # blocks.
        for step, (input_nodes, seeds, blocks) in enumerate(dataloader):
            tic_step = time.time()

            # copy block to gpu
            blocks = [blk.int().to(device) for blk in blocks]

            # Load the input features as well as output labels
            batch_inputs, batch_labels = load_subtensor(
                nfeat, labels, seeds, input_nodes
            )

            # Compute loss and prediction
            batch_pred = model(blocks, batch_inputs)
            loss = loss_fcn(batch_pred, batch_labels)
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

            iter_tput.append(len(seeds) / (time.time() - tic_step))
            if step % args.log_every == 0:
                acc = compute_acc(batch_pred, batch_labels)
                gpu_mem_alloc = (
                    th.cuda.max_memory_allocated() / 1000000
                    if th.cuda.is_available()
                    else 0
                )
                print(
                    "Epoch {:05d} | Step {:05d} | Loss {:.4f} | Train Acc {:.4f} | Speed (samples/sec) {:.4f} | GPU {:.1f} MB".format(
                        epoch,
                        step,
                        loss.item(),
                        acc.item(),
                        np.mean(iter_tput[3:]),
                        gpu_mem_alloc,
                    )
                )

        toc = time.time()
        print("Epoch Time(s): {:.4f}".format(toc - tic))
        if epoch >= 5:
            avg += toc - tic
        if epoch % args.eval_every == 0 and epoch != 0:
            eval_acc, test_acc, pred = evaluate(
                model, g, nfeat, labels, val_nid, test_nid, device
            )
            if args.save_pred:
                np.savetxt(
                    args.save_pred + "%02d" % epoch,
                    pred.argmax(1).cpu().numpy(),
                    "%d",
                )
            print("Eval Acc {:.4f}".format(eval_acc))
            if eval_acc > best_eval_acc:
                best_eval_acc = eval_acc
                best_test_acc = test_acc
            print(
                "Best Eval Acc {:.4f} Test Acc {:.4f}".format(
                    best_eval_acc, best_test_acc
                )
            )

    print("Avg epoch time: {}".format(avg / (epoch - 4)))
    return best_test_acc


if __name__ == "__main__":
    argparser = argparse.ArgumentParser("multi-gpu training")
    argparser.add_argument(
        "--gpu",
        type=int,
        default=0,
        help="GPU device ID. Use -1 for CPU training",
    )
    argparser.add_argument("--num-epochs", type=int, default=20)
    argparser.add_argument("--num-hidden", type=int, default=256)
    argparser.add_argument("--num-layers", type=int, default=3)
    argparser.add_argument("--fan-out", type=str, default="5,10,15")
    argparser.add_argument("--batch-size", type=int, default=1000)
    argparser.add_argument("--val-batch-size", type=int, default=10000)
    argparser.add_argument("--log-every", type=int, default=20)
    argparser.add_argument("--eval-every", type=int, default=1)
    argparser.add_argument("--lr", type=float, default=0.003)
    argparser.add_argument("--dropout", type=float, default=0.5)
    argparser.add_argument(
        "--num-workers",
        type=int,
        default=4,
        help="Number of sampling processes. Use 0 for no extra process.",
    )
    argparser.add_argument("--save-pred", type=str, default="")
    argparser.add_argument("--wd", type=float, default=0)
    args = argparser.parse_args()

    if args.gpu >= 0:
        device = th.device("cuda:%d" % args.gpu)
    else:
        device = th.device("cpu")

    # load ogbn-products data
    data = DglNodePropPredDataset(name="ogbn-products")
    splitted_idx = data.get_idx_split()
    train_idx, val_idx, test_idx = (
        splitted_idx["train"],
        splitted_idx["valid"],
        splitted_idx["test"],
    )
    graph, labels = data[0]
    nfeat = graph.ndata.pop("feat").to(device)
    labels = labels[:, 0].to(device)

    in_feats = nfeat.shape[1]
    n_classes = (labels.max() + 1).item()
    # Create csr/coo/csc formats before launching sampling processes
    # This avoids creating certain formats in each data loader process, which saves momory and CPU.
    graph.create_formats_()
    # Pack data
    data = (
        train_idx,
        val_idx,
        test_idx,
        in_feats,
        labels,
        n_classes,
        nfeat,
        graph,
    )

    # Run 10 times
    test_accs = []
    for i in range(10):
        test_accs.append(run(args, device, data).cpu().numpy())
        print(
            "Average test accuracy:", np.mean(test_accs), "±", np.std(test_accs)
        )
