import dgl
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.nn.pytorch as dglnn
import time

from .. import utils

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)):
            h = layer(block, h)
            if l != len(self.layers) - 1:
                h = self.activation(h)
                h = self.dropout(h)
        return h

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

@utils.benchmark('time', 3600)
@utils.parametrize('data', ['reddit', 'ogbn-products'])
def track_time(data):
    data = utils.process_data(data)
    device = utils.get_bench_device()
    g = data[0]
    g.ndata['features'] = g.ndata['feat']
    g.ndata['labels'] = g.ndata['label']
    in_feats = g.ndata['features'].shape[1]
    n_classes = data.num_labels

    # Create csr/coo/csc formats before launching training processes with multi-gpu.
    # This avoids creating certain formats in each sub-process, which saves momory and CPU.
    g.create_formats_()

    num_epochs = 20
    num_hidden = 16
    num_layers = 2
    fan_out = '10,25'
    batch_size = 1024
    lr = 0.003
    dropout = 0.5
    num_workers = 4

    train_nid = th.nonzero(g.ndata['train_mask'], as_tuple=True)[0]

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

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

    # dry run one epoch
    for step, (input_nodes, seeds, blocks) in enumerate(dataloader):
        # Load the input features as well as output labels
        #batch_inputs, batch_labels = load_subtensor(g, seeds, input_nodes, device)
        blocks = [block.int().to(device) for block in blocks]
        batch_inputs = blocks[0].srcdata['features']
        batch_labels = blocks[-1].dstdata['labels']

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

    # Training loop
    avg = 0
    iter_tput = []
    t0 = time.time()
    for epoch in range(num_epochs):
        # Loop over the dataloader to sample the computation dependency graph as a list of
        # blocks.
        for step, (input_nodes, seeds, blocks) in enumerate(dataloader):
            # Load the input features as well as output labels
            #batch_inputs, batch_labels = load_subtensor(g, seeds, input_nodes, device)
            blocks = [block.int().to(device) for block in blocks]
            batch_inputs = blocks[0].srcdata['features']
            batch_labels = blocks[-1].dstdata['labels']

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

    t1 = time.time()

    return (t1 - t0) / num_epochs
