import torch
import torch.nn as nn
import torch.nn.functional as F
import torchmetrics.functional as MF
import dgl
import dgl.nn as dglnn
import time
import numpy as np
from ogb.nodeproppred import DglNodePropPredDataset
import tqdm
import argparse

class SAGE(nn.Module):
    def __init__(self, in_feats, n_hidden, n_classes):
        super().__init__()
        self.layers = nn.ModuleList()
        self.layers.append(dglnn.SAGEConv(in_feats, n_hidden, 'mean'))
        self.layers.append(dglnn.SAGEConv(n_hidden, n_hidden, 'mean'))
        self.layers.append(dglnn.SAGEConv(n_hidden, n_classes, 'mean'))
        self.dropout = nn.Dropout(0.5)
        self.n_hidden = n_hidden
        self.n_classes = n_classes

    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 = F.relu(h)
                h = self.dropout(h)
        return h

    def inference(self, g, device, batch_size, num_workers, buffer_device=None):
        # The difference between this inference function and the one in the official
        # example is that the intermediate results can also benefit from prefetching.
        feat = g.ndata['feat']
        sampler = dgl.dataloading.MultiLayerFullNeighborSampler(1, prefetch_node_feats=['feat'])
        dataloader = dgl.dataloading.NodeDataLoader(
                g, torch.arange(g.num_nodes()).to(g.device), sampler, device=device,
                batch_size=batch_size, shuffle=False, drop_last=False,
                num_workers=num_workers)

        if buffer_device is None:
            buffer_device = device

        for l, layer in enumerate(self.layers):
            y = torch.empty(
                g.num_nodes(), self.n_hidden if l != len(self.layers) - 1 else self.n_classes,
                device=buffer_device, pin_memory=True)
            feat = feat.to(device)
            for input_nodes, output_nodes, blocks in tqdm.tqdm(dataloader):
                # use an explicitly contuous slice
                x = feat[input_nodes]
                h = layer(blocks[0], x)
                if l != len(self.layers) - 1:
                    h = F.relu(h)
                    h = self.dropout(h)
                # be design, our output nodes are contiguous so we can take
                # advantage of that here
                y[output_nodes[0]:output_nodes[-1]+1] = h.to(buffer_device)
            feat = y
        return y



dataset = DglNodePropPredDataset('ogbn-products')
graph, labels = dataset[0]
graph.ndata['label'] = labels.squeeze()
split_idx = dataset.get_idx_split()
train_idx, valid_idx, test_idx = split_idx['train'], split_idx['valid'], split_idx['test']

device = 'cuda'
train_idx = train_idx.to(device)
valid_idx = valid_idx.to(device)
test_idx = test_idx.to(device)

graph = graph.to(device)

model = SAGE(graph.ndata['feat'].shape[1], 256, dataset.num_classes).to(device)
opt = torch.optim.Adam(model.parameters(), lr=0.001, weight_decay=5e-4)

sampler = dgl.dataloading.NeighborSampler(
        [15, 10, 5], prefetch_node_feats=['feat'], prefetch_labels=['label'])
train_dataloader = dgl.dataloading.DataLoader(
        graph, train_idx, sampler, device=device, batch_size=1024, shuffle=True,
        drop_last=False, num_workers=0, use_uva=False)
valid_dataloader = dgl.dataloading.NodeDataLoader(
        graph, valid_idx, sampler, device=device, batch_size=1024, shuffle=True,
        drop_last=False, num_workers=0, use_uva=False)

durations = []
for _ in range(10):
    model.train()
    t0 = time.time()
    for it, (input_nodes, output_nodes, blocks) in enumerate(train_dataloader):
        x = blocks[0].srcdata['feat']
        y = blocks[-1].dstdata['label']
        y_hat = model(blocks, x)
        loss = F.cross_entropy(y_hat, y)
        opt.zero_grad()
        loss.backward()
        opt.step()
        if it % 20 == 0:
            acc = MF.accuracy(torch.argmax(y_hat, dim=1), y)
            mem = torch.cuda.max_memory_allocated() / 1000000
            print('Loss', loss.item(), 'Acc', acc.item(), 'GPU Mem', mem, 'MB')
    tt = time.time()
    print(tt - t0)
    durations.append(tt - t0)

    model.eval()
    ys = []
    y_hats = []
    for it, (input_nodes, output_nodes, blocks) in enumerate(valid_dataloader):
        with torch.no_grad():
            x = blocks[0].srcdata['feat']
            ys.append(blocks[-1].dstdata['label'])
            y_hats.append(torch.argmax(model(blocks, x), dim=1))
    acc = MF.accuracy(torch.cat(y_hats), torch.cat(ys))
    print('Validation acc:', acc.item())

print(np.mean(durations[4:]), np.std(durations[4:]))

# Test accuracy and offline inference of all nodes
model.eval()
with torch.no_grad():
    pred = model.inference(graph, device, 4096, 0, 'cpu')
    pred = pred[test_idx].to(device)
    label = graph.ndata['label'][test_idx]
    acc = MF.accuracy(torch.argmax(pred, dim=1), label)
    print('Test acc:', acc.item())
