import time
import math
import numpy as np
import scipy.sparse as sp
import networkx as nx
import torch as th
import dgl
import utils as U

def test_graph_creation():
    g = dgl.DGLGraph()
    # test add nodes with data
    g.add_nodes(5)
    g.add_nodes(5, {'h' : th.ones((5, 2))})
    ans = th.cat([th.zeros(5, 2), th.ones(5, 2)], 0)
    U.allclose(ans, g.ndata['h'])
    g.ndata['w'] = 2 * th.ones((10, 2))
    assert U.allclose(2 * th.ones((10, 2)), g.ndata['w'])
    # test add edges with data
    g.add_edges([2, 3], [3, 4])
    g.add_edges([0, 1], [1, 2], {'m' : th.ones((2, 2))})
    ans = th.cat([th.zeros(2, 2), th.ones(2, 2)], 0)
    assert U.allclose(ans, g.edata['m'])
    # test clear and add again
    g.clear()
    g.add_nodes(5)
    g.ndata['h'] = 3 * th.ones((5, 2))
    assert U.allclose(3 * th.ones((5, 2)), g.ndata['h'])

def test_create_from_elist():
    elist = [(2, 1), (1, 0), (2, 0), (3, 0), (0, 2)]
    g = dgl.DGLGraph(elist)
    for i, (u, v) in enumerate(elist):
        assert g.edge_id(u, v) == i
    # immutable graph
    g = dgl.DGLGraph(elist, readonly=True)
    for i, (u, v) in enumerate(elist):
        assert g.edge_id(u, v) == i

def test_adjmat_speed():
    n = 1000
    p = 10 * math.log(n) / n
    a = sp.random(n, n, p, data_rvs=lambda n: np.ones(n))
    g = dgl.DGLGraph(a)
    # the first call should contruct the adj
    t0 = time.time()
    g.adjacency_matrix()
    dur1 = time.time() - t0
    # the second call should be cached and should be very fast
    t0 = time.time()
    g.adjacency_matrix()
    dur2 = time.time() - t0
    print('first time {}, second time {}'.format(dur1, dur2))
    assert dur2 < dur1

def test_incmat():
    g = dgl.DGLGraph()
    g.add_nodes(4)
    g.add_edge(0, 1) # 0
    g.add_edge(0, 2) # 1
    g.add_edge(0, 3) # 2
    g.add_edge(2, 3) # 3
    g.add_edge(1, 1) # 4
    assert U.allclose(
            g.incidence_matrix('in').to_dense(),
            th.tensor([[0., 0., 0., 0., 0.],
                       [1., 0., 0., 0., 1.],
                       [0., 1., 0., 0., 0.],
                       [0., 0., 1., 1., 0.]]))
    assert U.allclose(
            g.incidence_matrix('out').to_dense(),
            th.tensor([[1., 1., 1., 0., 0.],
                       [0., 0., 0., 0., 1.],
                       [0., 0., 0., 1., 0.],
                       [0., 0., 0., 0., 0.]]))
    assert U.allclose(
            g.incidence_matrix('both').to_dense(),
            th.tensor([[-1., -1., -1., 0., 0.],
                       [1., 0., 0., 0., 0.],
                       [0., 1., 0., -1., 0.],
                       [0., 0., 1., 1., 0.]]))

def test_incmat_speed():
    n = 1000
    p = 2 * math.log(n) / n
    a = sp.random(n, n, p, data_rvs=lambda n: np.ones(n))
    g = dgl.DGLGraph(a)
    # the first call should contruct the adj
    t0 = time.time()
    g.incidence_matrix("in")
    dur1 = time.time() - t0
    # the second call should be cached and should be very fast
    t0 = time.time()
    g.incidence_matrix("in")
    dur2 = time.time() - t0
    print('first time {}, second time {}'.format(dur1, dur2))
    assert dur2 < dur1

if __name__ == '__main__':
    test_graph_creation()
    test_create_from_elist()
    test_adjmat_speed()
    test_incmat()
    test_incmat_speed()