import dgl
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 torch.multiprocessing as mp
from torch.utils.data import DataLoader
import dgl.function as fn
import dgl.nn.pytorch as dglnn
import time
import argparse
from _thread import start_new_thread
from functools import wraps
from dgl.data import RedditDataset
import tqdm
import traceback
from ogb.nodeproppred import DglNodePropPredDataset
from functools import partial

from sampler import ClusterIter, subgraph_collate_fn

#### Neighbor sampler

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, g, x):
        h = x
        for l, conv in enumerate(self.layers):
            h = conv(g, h)
            if l != len(self.layers) - 1:
                h = self.activation(h)
                h = self.dropout(h)
        return h

    def inference(self, g, x, batch_size, 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?
        h = x
        for l, conv in enumerate(self.layers):
            h = conv(g, h)
            if l != len(self.layers) - 1:
                h = self.activation(h)

        return h

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, labels, val_nid, test_nid, batch_size, 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.
    batch_size : Number of nodes to compute at the same time.
    device : The GPU device to evaluate on.
    """
    model.eval()
    with th.no_grad():
        inputs = g.ndata['feat']
        model = model.cpu()
        pred = model.inference(g, inputs, batch_size, device)
    model.train()
    return compute_acc(pred[val_nid], labels[val_nid]), compute_acc(pred[test_nid], labels[test_nid]), pred

def load_subtensor(g, labels, seeds, input_nodes, device):
    """
    Copys features and labels of a set of nodes onto GPU.
    """
    batch_inputs = g.ndata['feat'][input_nodes].to(device)
    batch_labels = labels[seeds].to(device)
    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, g, cluster_iterator = data


    # 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()
    loss_fcn = loss_fcn.to(device)
    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):
        iter_load = 0
        iter_far = 0
        iter_back = 0
        iter_tl = 0
        tic = time.time()

        # Loop over the dataloader to sample the computation dependency graph as a list of
        # blocks.
        tic_start = time.time()
        for step, cluster in enumerate(cluster_iterator):
            cluster = cluster.int().to(device)
            mask = cluster.ndata['train_mask'].to(device)
            if mask.sum() == 0:
                continue
            feat = cluster.ndata['feat'].to(device)
            batch_labels = cluster.ndata['labels'].to(device)
            tic_step = time.time()

            batch_pred = model(cluster, feat)
            batch_pred = batch_pred[mask]
            batch_labels = batch_labels[mask]
            loss = loss_fcn(batch_pred, batch_labels)
            optimizer.zero_grad()
            tic_far = time.time()
            loss.backward()
            optimizer.step()
            tic_back = time.time()
            iter_load += (tic_step - tic_start)
            iter_far += (tic_far - tic_step)
            iter_back += (tic_back - tic_far)

            tic_start = time.time()
            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} | GPU {:.1f} MB'.format(
                    epoch, step, loss.item(), acc.item(), gpu_mem_alloc))

        toc = time.time()
        print('Epoch Time(s): {:.4f} Load {:.4f} Forward {:.4f} Backward {:.4f}'.format(toc - tic, iter_load, iter_far, iter_back))
        if epoch >= 5:
            avg += toc - tic

        if epoch % args.eval_every == 0 and epoch != 0:
            eval_acc, test_acc, pred = evaluate(model, g, labels, val_nid, test_nid, args.val_batch_size, device)
            model = model.to(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=30)
    argparser.add_argument('--num-hidden', type=int, default=256)
    argparser.add_argument('--num-layers', type=int, default=3)
    argparser.add_argument('--batch-size', type=int, default=32)
    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=0.001)
    argparser.add_argument('--dropout', type=float, default=0.5)
    argparser.add_argument('--save-pred', type=str, default='')
    argparser.add_argument('--wd', type=float, default=0)
    argparser.add_argument('--num_partitions', type=int, default=15000)
    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]
    labels = labels[:, 0]
    num_nodes = train_idx.shape[0] + val_idx.shape[0] + test_idx.shape[0]
    assert num_nodes == graph.number_of_nodes()
    graph.ndata['labels'] = labels
    mask = th.zeros(num_nodes, dtype=th.bool)
    mask[train_idx] = True
    graph.ndata['train_mask'] = mask
    mask = th.zeros(num_nodes, dtype=th.bool)
    mask[val_idx] = True
    graph.ndata['valid_mask'] = mask
    mask = th.zeros(num_nodes, dtype=th.bool)
    mask[test_idx] = True
    graph.ndata['test_mask'] = mask

    graph.in_degree(0)
    graph.out_degree(0)
    graph.find_edges(0)

    cluster_iter_data = ClusterIter(
                    'ogbn-products', graph, args.num_partitions, args.batch_size, th.cat([train_idx, val_idx, test_idx]))
    idx = th.arange(args.num_partitions // args.batch_size)
    cluster_iterator = DataLoader(cluster_iter_data, batch_size=32, shuffle=True, pin_memory=True, num_workers=4, collate_fn=partial(subgraph_collate_fn, graph))

    in_feats = graph.ndata['feat'].shape[1]
    print(in_feats)
    n_classes = (labels.max() + 1).item()
    # Pack data
    data = train_idx, val_idx, test_idx, in_feats, labels, n_classes, graph, cluster_iterator

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