import time

import dgl
import dgl.function as fn
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

from .. import utils


class NegativeSampler(object):
    def __init__(self, g, k, neg_share=False):
        self.weights = g.in_degrees().float() ** 0.75
        self.k = k
        self.neg_share = neg_share

    def __call__(self, g, eids):
        src, _ = g.find_edges(eids)
        n = len(src)
        if self.neg_share and n % self.k == 0:
            dst = self.weights.multinomial(n, replacement=True)
            dst = dst.view(-1, 1, self.k).expand(-1, self.k, -1).flatten()
        else:
            dst = self.weights.multinomial(n * self.k, replacement=True)
        src = src.repeat_interleave(self.k)
        return src, dst


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


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


class CrossEntropyLoss(nn.Module):
    def forward(self, block_outputs, pos_graph, neg_graph):
        with pos_graph.local_scope():
            pos_graph.ndata["h"] = block_outputs
            pos_graph.apply_edges(fn.u_dot_v("h", "h", "score"))
            pos_score = pos_graph.edata["score"]
        with neg_graph.local_scope():
            neg_graph.ndata["h"] = block_outputs
            neg_graph.apply_edges(fn.u_dot_v("h", "h", "score"))
            neg_score = neg_graph.edata["score"]

        score = th.cat([pos_score, neg_score])
        label = th.cat(
            [th.ones_like(pos_score), th.zeros_like(neg_score)]
        ).long()
        loss = F.binary_cross_entropy_with_logits(score, label.float())
        return loss


@utils.benchmark("time", 600)
@utils.parametrize("data", ["reddit"])
@utils.parametrize("num_negs", [2, 8, 32])
@utils.parametrize("batch_size", [1024, 2048, 8192])
def track_time(data, num_negs, batch_size):
    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_classes

    # 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 = 2
    num_hidden = 16
    num_layers = 2
    fan_out = "10,25"
    lr = 0.003
    dropout = 0.5
    num_workers = 4
    num_negs = 2
    iter_start = 3
    iter_count = 10

    n_edges = g.num_edges()
    train_seeds = np.arange(n_edges)

    # Create PyTorch DataLoader for constructing blocks
    sampler = dgl.dataloading.MultiLayerNeighborSampler(
        [int(fanout) for fanout in fan_out.split(",")]
    )
    sampler = dgl.dataloading.as_edge_prediction_sampler(
        sampler,
        exclude="reverse_id",
        # For each edge with ID e in Reddit dataset, the reverse edge is e ± |E|/2.
        reverse_eids=th.cat(
            [th.arange(n_edges // 2, n_edges), th.arange(0, n_edges // 2)]
        ),
        negative_sampler=NegativeSampler(g, num_negs),
    )
    dataloader = dgl.dataloading.DataLoader(
        g,
        train_seeds,
        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 = CrossEntropyLoss()
    loss_fcn = loss_fcn.to(device)
    optimizer = optim.Adam(model.parameters(), lr=lr)

    # Training loop
    avg = 0
    iter_tput = []
    for step, (input_nodes, pos_graph, neg_graph, blocks) in enumerate(
        dataloader
    ):
        # Load the input features as well as output labels
        batch_inputs = load_subtensor(g, input_nodes, device)

        pos_graph = pos_graph.to(device)
        neg_graph = neg_graph.to(device)
        blocks = [block.int().to(device) for block in blocks]
        # Compute loss and prediction
        batch_pred = model(blocks, batch_inputs)
        loss = loss_fcn(batch_pred, pos_graph, neg_graph)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        # start timer at before iter_start
        if step == iter_start - 1:
            t0 = time.time()
        elif step == iter_count + iter_start - 1:  # time iter_count iterations
            break

    t1 = time.time()

    return (t1 - t0) / iter_count
