import torch
from torch_sparse import SparseTensor, sample_adj


def test_sample_adj():
    row = torch.tensor([0, 0, 0, 1, 1, 2, 2, 2, 2, 3, 4, 4, 5, 5])
    col = torch.tensor([1, 2, 3, 0, 2, 0, 1, 4, 5, 0, 2, 5, 2, 4])
    value = torch.arange(row.size(0))
    adj_t = SparseTensor(row=row, col=col, value=value, sparse_sizes=(6, 6))

    out, n_id = sample_adj(adj_t, torch.arange(2, 6), num_neighbors=-1)

    assert n_id.tolist() == [2, 3, 4, 5, 0, 1]

    row, col, val = out.coo()
    assert row.tolist() == [0, 0, 0, 0, 1, 2, 2, 3, 3]
    assert col.tolist() == [2, 3, 4, 5, 4, 0, 3, 0, 2]
    assert val.tolist() == [5, 6, 7, 8, 9, 10, 11, 12, 13]

    out, n_id = sample_adj(adj_t, torch.arange(2, 6), num_neighbors=2,
                           replace=True)
    assert out.nnz() == 8