import math
import os
import sys
import time
from collections import defaultdict

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import tqdm
from numpy import random
from torch.nn.parameter import Parameter
from utils import *

import dgl
import dgl.function as fn


def get_graph(network_data, vocab):
    """Build graph, treat all nodes as the same type

    Parameters
    ----------
    network_data: a dict
        keys describing the edge types, values representing edges
    vocab: a dict
        mapping node IDs to node indices
    Output
    ------
    DGLHeteroGraph
        a heterogenous graph, with one node type and different edge types
    """
    graphs = []

    node_type = "_N"  # '_N' can be replaced by an arbitrary name
    data_dict = dict()
    num_nodes_dict = {node_type: len(vocab)}

    for edge_type in network_data:
        tmp_data = network_data[edge_type]
        src = []
        dst = []
        for edge in tmp_data:
            src.extend([vocab[edge[0]], vocab[edge[1]]])
            dst.extend([vocab[edge[1]], vocab[edge[0]]])
        data_dict[(node_type, edge_type, node_type)] = (src, dst)
    graph = dgl.heterograph(data_dict, num_nodes_dict)

    return graph


class NeighborSampler(object):
    def __init__(self, g, num_fanouts):
        self.g = g
        self.num_fanouts = num_fanouts

    def sample(self, pairs):
        pairs = np.stack(pairs)
        heads, tails, types = pairs[:, 0], pairs[:, 1], pairs[:, 2]
        seeds, head_invmap = torch.unique(
            torch.LongTensor(heads), return_inverse=True
        )
        blocks = []
        for fanout in reversed(self.num_fanouts):
            sampled_graph = dgl.sampling.sample_neighbors(self.g, seeds, fanout)
            sampled_block = dgl.to_block(sampled_graph, seeds)
            seeds = sampled_block.srcdata[dgl.NID]
            blocks.insert(0, sampled_block)
        return (
            blocks,
            torch.LongTensor(head_invmap),
            torch.LongTensor(tails),
            torch.LongTensor(types),
        )


class DGLGATNE(nn.Module):
    def __init__(
        self,
        num_nodes,
        embedding_size,
        embedding_u_size,
        edge_types,
        edge_type_count,
        dim_a,
    ):
        super(DGLGATNE, self).__init__()
        self.num_nodes = num_nodes
        self.embedding_size = embedding_size
        self.embedding_u_size = embedding_u_size
        self.edge_types = edge_types
        self.edge_type_count = edge_type_count
        self.dim_a = dim_a

        self.node_embeddings = nn.Embedding(
            num_nodes, embedding_size, sparse=True
        )
        self.node_type_embeddings = nn.Embedding(
            num_nodes * edge_type_count, embedding_u_size, sparse=True
        )
        self.trans_weights = Parameter(
            torch.FloatTensor(edge_type_count, embedding_u_size, embedding_size)
        )
        self.trans_weights_s1 = Parameter(
            torch.FloatTensor(edge_type_count, embedding_u_size, dim_a)
        )
        self.trans_weights_s2 = Parameter(
            torch.FloatTensor(edge_type_count, dim_a, 1)
        )

        self.reset_parameters()

    def reset_parameters(self):
        self.node_embeddings.weight.data.uniform_(-1.0, 1.0)
        self.node_type_embeddings.weight.data.uniform_(-1.0, 1.0)
        self.trans_weights.data.normal_(
            std=1.0 / math.sqrt(self.embedding_size)
        )
        self.trans_weights_s1.data.normal_(
            std=1.0 / math.sqrt(self.embedding_size)
        )
        self.trans_weights_s2.data.normal_(
            std=1.0 / math.sqrt(self.embedding_size)
        )

    # embs: [batch_size, embedding_size]
    def forward(self, block):
        input_nodes = block.srcdata[dgl.NID]
        output_nodes = block.dstdata[dgl.NID]
        batch_size = block.number_of_dst_nodes()
        node_type_embed = []

        with block.local_scope():
            for i in range(self.edge_type_count):
                edge_type = self.edge_types[i]
                block.srcdata[edge_type] = self.node_type_embeddings(
                    input_nodes * self.edge_type_count + i
                )
                block.dstdata[edge_type] = self.node_type_embeddings(
                    output_nodes * self.edge_type_count + i
                )
                block.update_all(
                    fn.copy_u(edge_type, "m"),
                    fn.sum("m", edge_type),
                    etype=edge_type,
                )
                node_type_embed.append(block.dstdata[edge_type])

            node_type_embed = torch.stack(node_type_embed, 1)
            tmp_node_type_embed = node_type_embed.unsqueeze(2).view(
                -1, 1, self.embedding_u_size
            )
            trans_w = (
                self.trans_weights.unsqueeze(0)
                .repeat(batch_size, 1, 1, 1)
                .view(-1, self.embedding_u_size, self.embedding_size)
            )
            trans_w_s1 = (
                self.trans_weights_s1.unsqueeze(0)
                .repeat(batch_size, 1, 1, 1)
                .view(-1, self.embedding_u_size, self.dim_a)
            )
            trans_w_s2 = (
                self.trans_weights_s2.unsqueeze(0)
                .repeat(batch_size, 1, 1, 1)
                .view(-1, self.dim_a, 1)
            )

            attention = (
                F.softmax(
                    torch.matmul(
                        torch.tanh(
                            torch.matmul(tmp_node_type_embed, trans_w_s1)
                        ),
                        trans_w_s2,
                    )
                    .squeeze(2)
                    .view(-1, self.edge_type_count),
                    dim=1,
                )
                .unsqueeze(1)
                .repeat(1, self.edge_type_count, 1)
            )

            node_type_embed = torch.matmul(attention, node_type_embed).view(
                -1, 1, self.embedding_u_size
            )
            node_embed = self.node_embeddings(output_nodes).unsqueeze(1).repeat(
                1, self.edge_type_count, 1
            ) + torch.matmul(node_type_embed, trans_w).view(
                -1, self.edge_type_count, self.embedding_size
            )
            last_node_embed = F.normalize(node_embed, dim=2)

            return (
                last_node_embed  # [batch_size, edge_type_count, embedding_size]
            )


class NSLoss(nn.Module):
    def __init__(self, num_nodes, num_sampled, embedding_size):
        super(NSLoss, self).__init__()
        self.num_nodes = num_nodes
        self.num_sampled = num_sampled
        self.embedding_size = embedding_size

        # [ (log(i+2) - log(i+1)) / log(num_nodes + 1)]
        self.sample_weights = F.normalize(
            torch.Tensor(
                [
                    (math.log(k + 2) - math.log(k + 1))
                    / math.log(num_nodes + 1)
                    for k in range(num_nodes)
                ]
            ),
            dim=0,
        )
        self.weights = nn.Embedding(num_nodes, embedding_size, sparse=True)
        self.reset_parameters()

    def reset_parameters(self):
        self.weights.weight.data.normal_(
            std=1.0 / math.sqrt(self.embedding_size)
        )

    def forward(self, input, embs, label):
        n = input.shape[0]
        log_target = torch.log(
            torch.sigmoid(torch.sum(torch.mul(embs, self.weights(label)), 1))
        )
        negs = (
            torch.multinomial(
                self.sample_weights, self.num_sampled * n, replacement=True
            )
            .view(n, self.num_sampled)
            .to(input.device)
        )
        noise = torch.neg(self.weights(negs))
        sum_log_sampled = torch.sum(
            torch.log(torch.sigmoid(torch.bmm(noise, embs.unsqueeze(2)))), 1
        ).squeeze()

        loss = log_target + sum_log_sampled
        return -loss.sum() / n


def train_model(network_data):
    index2word, vocab, type_nodes = generate_vocab(network_data)

    edge_types = list(network_data.keys())
    num_nodes = len(index2word)
    edge_type_count = len(edge_types)
    epochs = args.epoch
    batch_size = args.batch_size
    embedding_size = args.dimensions
    embedding_u_size = args.edge_dim
    u_num = edge_type_count
    num_sampled = args.negative_samples
    dim_a = args.att_dim
    att_head = 1
    neighbor_samples = args.neighbor_samples
    num_workers = args.workers

    device = torch.device(
        "cuda" if args.gpu is not None and torch.cuda.is_available() else "cpu"
    )

    g = get_graph(network_data, vocab)
    all_walks = []
    for i in range(edge_type_count):
        nodes = torch.LongTensor(type_nodes[i] * args.num_walks)
        traces, types = dgl.sampling.random_walk(
            g, nodes, metapath=[edge_types[i]] * (neighbor_samples - 1)
        )
        all_walks.append(traces)

    train_pairs = generate_pairs(all_walks, args.window_size, num_workers)
    neighbor_sampler = NeighborSampler(g, [neighbor_samples])
    train_dataloader = torch.utils.data.DataLoader(
        train_pairs,
        batch_size=batch_size,
        collate_fn=neighbor_sampler.sample,
        shuffle=True,
        num_workers=num_workers,
        pin_memory=True,
    )

    model = DGLGATNE(
        num_nodes,
        embedding_size,
        embedding_u_size,
        edge_types,
        edge_type_count,
        dim_a,
    )

    nsloss = NSLoss(num_nodes, num_sampled, embedding_size)

    model.to(device)
    nsloss.to(device)

    embeddings_params = list(
        map(id, model.node_embeddings.parameters())
    ) + list(map(id, model.node_type_embeddings.parameters()))
    weights_params = list(map(id, nsloss.weights.parameters()))

    optimizer = torch.optim.Adam(
        [
            {
                "params": filter(
                    lambda p: id(p) not in embeddings_params,
                    model.parameters(),
                )
            },
            {
                "params": filter(
                    lambda p: id(p) not in weights_params,
                    nsloss.parameters(),
                )
            },
        ],
        lr=1e-3,
    )

    sparse_optimizer = torch.optim.SparseAdam(
        [
            {"params": model.node_embeddings.parameters()},
            {"params": model.node_type_embeddings.parameters()},
            {"params": nsloss.weights.parameters()},
        ],
        lr=1e-3,
    )

    best_score = 0
    patience = 0
    for epoch in range(epochs):
        model.train()

        random.shuffle(train_pairs)

        data_iter = tqdm.tqdm(
            train_dataloader,
            desc="epoch %d" % (epoch),
            total=(len(train_pairs) + (batch_size - 1)) // batch_size,
        )
        avg_loss = 0.0

        for i, (block, head_invmap, tails, block_types) in enumerate(data_iter):
            optimizer.zero_grad()
            sparse_optimizer.zero_grad()
            # embs: [batch_size, edge_type_count, embedding_size]
            block_types = block_types.to(device)
            embs = model(block[0].to(device))[head_invmap]
            embs = embs.gather(
                1,
                block_types.view(-1, 1, 1).expand(
                    embs.shape[0], 1, embs.shape[2]
                ),
            )[:, 0]
            loss = nsloss(
                block[0].dstdata[dgl.NID][head_invmap].to(device),
                embs,
                tails.to(device),
            )
            loss.backward()
            optimizer.step()
            sparse_optimizer.step()
            avg_loss += loss.item()

            post_fix = {
                "epoch": epoch,
                "iter": i,
                "avg_loss": avg_loss / (i + 1),
                "loss": loss.item(),
            }
            data_iter.set_postfix(post_fix)

        model.eval()
        # {'1': {}, '2': {}}
        final_model = dict(
            zip(edge_types, [dict() for _ in range(edge_type_count)])
        )
        for i in range(num_nodes):
            train_inputs = (
                torch.tensor([i for _ in range(edge_type_count)])
                .unsqueeze(1)
                .to(device)
            )  # [i, i]
            train_types = (
                torch.tensor(list(range(edge_type_count)))
                .unsqueeze(1)
                .to(device)
            )  # [0, 1]
            pairs = torch.cat(
                (train_inputs, train_inputs, train_types), dim=1
            )  # (2, 3)
            (
                train_blocks,
                train_invmap,
                fake_tails,
                train_types,
            ) = neighbor_sampler.sample(pairs.cpu())

            node_emb = model(train_blocks[0].to(device))[train_invmap]
            node_emb = node_emb.gather(
                1,
                train_types.to(device)
                .view(-1, 1, 1)
                .expand(node_emb.shape[0], 1, node_emb.shape[2]),
            )[:, 0]

            for j in range(edge_type_count):
                final_model[edge_types[j]][index2word[i]] = (
                    node_emb[j].cpu().detach().numpy()
                )

        valid_aucs, valid_f1s, valid_prs = [], [], []
        test_aucs, test_f1s, test_prs = [], [], []
        for i in range(edge_type_count):
            if args.eval_type == "all" or edge_types[i] in args.eval_type.split(
                ","
            ):
                tmp_auc, tmp_f1, tmp_pr = evaluate(
                    final_model[edge_types[i]],
                    valid_true_data_by_edge[edge_types[i]],
                    valid_false_data_by_edge[edge_types[i]],
                    num_workers,
                )
                valid_aucs.append(tmp_auc)
                valid_f1s.append(tmp_f1)
                valid_prs.append(tmp_pr)

                tmp_auc, tmp_f1, tmp_pr = evaluate(
                    final_model[edge_types[i]],
                    testing_true_data_by_edge[edge_types[i]],
                    testing_false_data_by_edge[edge_types[i]],
                    num_workers,
                )
                test_aucs.append(tmp_auc)
                test_f1s.append(tmp_f1)
                test_prs.append(tmp_pr)
        print("valid auc:", np.mean(valid_aucs))
        print("valid pr:", np.mean(valid_prs))
        print("valid f1:", np.mean(valid_f1s))

        average_auc = np.mean(test_aucs)
        average_f1 = np.mean(test_f1s)
        average_pr = np.mean(test_prs)

        cur_score = np.mean(valid_aucs)
        if cur_score > best_score:
            best_score = cur_score
            patience = 0
        else:
            patience += 1
            if patience > args.patience:
                print("Early Stopping")
                break
    return average_auc, average_f1, average_pr


if __name__ == "__main__":
    args = parse_args()
    file_name = args.input
    print(args)

    training_data_by_type = load_training_data(file_name + "/train.txt")
    valid_true_data_by_edge, valid_false_data_by_edge = load_testing_data(
        file_name + "/valid.txt"
    )
    testing_true_data_by_edge, testing_false_data_by_edge = load_testing_data(
        file_name + "/test.txt"
    )

    start = time.time()
    average_auc, average_f1, average_pr = train_model(training_data_by_type)
    end = time.time()

    print("Overall ROC-AUC:", average_auc)
    print("Overall PR-AUC", average_pr)
    print("Overall F1:", average_f1)
    print("Training Time", end - start)
