import dgl
from dgl.data import CoraGraphDataset
import torch
import pickle
from copy import deepcopy
import scipy.sparse as sp
import numpy as np
import os


def mask_test_edges(adj_orig, val_frac, test_frac):

    # Remove diagonal elements
    adj = deepcopy(adj_orig)
    # set diag as all zero
    adj.setdiag(0)
    adj.eliminate_zeros()
    # Check that diag is zero:
    # assert np.diag(adj.todense()).sum() == 0

    adj_triu = sp.triu(adj, 1)
    edges = sparse_to_tuple(adj_triu)[0]
    num_test = int(np.floor(edges.shape[0] * test_frac))
    num_val = int(np.floor(edges.shape[0] * val_frac))

    all_edge_idx = list(range(edges.shape[0]))
    np.random.shuffle(all_edge_idx)
    val_edge_idx = all_edge_idx[:num_val]
    test_edge_idx = all_edge_idx[num_val : (num_val + num_test)]
    test_edges = edges[test_edge_idx]
    val_edges = edges[val_edge_idx]
    train_edges = edges[all_edge_idx[num_val + num_test :]]

    noedge_mask = np.ones(adj.shape) - adj
    noedges = np.asarray(sp.triu(noedge_mask, 1).nonzero()).T
    all_edge_idx = list(range(noedges.shape[0]))
    np.random.shuffle(all_edge_idx)
    val_edge_idx = all_edge_idx[:num_val]
    test_edge_idx = all_edge_idx[num_val : (num_val + num_test)]
    test_edges_false = noedges[test_edge_idx]
    val_edges_false = noedges[val_edge_idx]

    data = np.ones(train_edges.shape[0])
    adj_train = sp.csr_matrix(
        (data, (train_edges[:, 0], train_edges[:, 1])), shape=adj.shape
    )
    adj_train = adj_train + adj_train.T

    train_mask = np.ones(adj_train.shape)
    for edges_tmp in [val_edges, val_edges_false, test_edges, test_edges_false]:
        for e in edges_tmp:
            assert e[0] < e[1]
        train_mask[edges_tmp.T[0], edges_tmp.T[1]] = 0
        train_mask[edges_tmp.T[1], edges_tmp.T[0]] = 0

    train_edges = np.asarray(sp.triu(adj_train, 1).nonzero()).T
    train_edges_false = np.asarray(
        (sp.triu(train_mask, 1) - sp.triu(adj_train, 1)).nonzero()
    ).T

    # NOTE: all these edge lists only contain single direction of edge!
    return (
        train_edges,
        train_edges_false,
        val_edges,
        val_edges_false,
        test_edges,
        test_edges_false,
    )


def sparse_to_tuple(sparse_mx):
    if not sp.isspmatrix_coo(sparse_mx):
        sparse_mx = sparse_mx.tocoo()
    coords = np.vstack((sparse_mx.row, sparse_mx.col)).transpose()
    values = sparse_mx.data
    shape = sparse_mx.shape
    return coords, values, shape


if __name__ == "__main__":
    os.mkdir("links")
    os.mkdir("pretrain_labels")
    g = CoraGraphDataset()[0]
    total_pos_edges = torch.randperm(g.num_edges())
    adj_train = g.adjacency_matrix(scipy_fmt="csr")
    (
        train_edges,
        train_edges_false,
        val_edges,
        val_edges_false,
        test_edges,
        test_edges_false,
    ) = mask_test_edges(adj_train, 0.1, 0.2)
    tvt_edges_file = "links/cora_tvtEdges.pkl"
    pickle.dump(
        (
            train_edges,
            train_edges_false,
            val_edges,
            val_edges_false,
            test_edges,
            test_edges_false,
        ),
        open(tvt_edges_file, "wb"),
    )
    node_assignment = dgl.metis_partition_assignment(g, 10)
    torch.save(node_assignment, "pretrain_labels/metis_label_cora.pt")