import io
import pickle
import unittest

import backend as F

import dgl
import dgl.function as fn
import networkx as nx
import pytest
import pytests_utils
import scipy.sparse as ssp
from dgl.graph_index import create_graph_index
from dgl.utils import toindex
from pytests_utils import get_cases, parametrize_idtype
from utils import assert_is_identical, assert_is_identical_hetero


def _assert_is_identical_nodeflow(nf1, nf2):
    assert nf1.number_of_nodes() == nf2.number_of_nodes()
    src, dst = nf1.all_edges()
    src2, dst2 = nf2.all_edges()
    assert F.array_equal(src, src2)
    assert F.array_equal(dst, dst2)

    assert nf1.num_layers == nf2.num_layers
    for i in range(nf1.num_layers):
        assert nf1.layer_size(i) == nf2.layer_size(i)
        assert nf1.layers[i].data.keys() == nf2.layers[i].data.keys()
        for k in nf1.layers[i].data:
            assert F.allclose(nf1.layers[i].data[k], nf2.layers[i].data[k])
    assert nf1.num_blocks == nf2.num_blocks
    for i in range(nf1.num_blocks):
        assert nf1.block_size(i) == nf2.block_size(i)
        assert nf1.blocks[i].data.keys() == nf2.blocks[i].data.keys()
        for k in nf1.blocks[i].data:
            assert F.allclose(nf1.blocks[i].data[k], nf2.blocks[i].data[k])


def _assert_is_identical_batchedgraph(bg1, bg2):
    assert_is_identical(bg1, bg2)
    assert bg1.batch_size == bg2.batch_size
    assert bg1.batch_num_nodes == bg2.batch_num_nodes
    assert bg1.batch_num_edges == bg2.batch_num_edges


def _assert_is_identical_batchedhetero(bg1, bg2):
    assert_is_identical_hetero(bg1, bg2)
    for ntype in bg1.ntypes:
        assert bg1.batch_num_nodes(ntype) == bg2.batch_num_nodes(ntype)
    for canonical_etype in bg1.canonical_etypes:
        assert bg1.batch_num_edges(canonical_etype) == bg2.batch_num_edges(
            canonical_etype
        )


def _assert_is_identical_index(i1, i2):
    assert i1.slice_data() == i2.slice_data()
    assert F.array_equal(i1.tousertensor(), i2.tousertensor())


def _reconstruct_pickle(obj):
    f = io.BytesIO()
    pickle.dump(obj, f)
    f.seek(0)
    obj = pickle.load(f)
    f.close()

    return obj


def test_pickling_index():
    # normal index
    i = toindex([1, 2, 3])
    i.tousertensor()
    i.todgltensor()  # construct a dgl tensor which is unpicklable
    i2 = _reconstruct_pickle(i)
    _assert_is_identical_index(i, i2)

    # slice index
    i = toindex(slice(5, 10))
    i2 = _reconstruct_pickle(i)
    _assert_is_identical_index(i, i2)


def test_pickling_graph_index():
    gi = create_graph_index(None, False)
    gi.add_nodes(3)
    src_idx = toindex([0, 0])
    dst_idx = toindex([1, 2])
    gi.add_edges(src_idx, dst_idx)

    gi2 = _reconstruct_pickle(gi)

    assert gi2.number_of_nodes() == gi.number_of_nodes()
    src_idx2, dst_idx2, _ = gi2.edges()
    assert F.array_equal(src_idx.tousertensor(), src_idx2.tousertensor())
    assert F.array_equal(dst_idx.tousertensor(), dst_idx2.tousertensor())


def _global_message_func(nodes):
    return {"x": nodes.data["x"]}


@unittest.skipIf(F._default_context_str == "gpu", reason="GPU not implemented")
@parametrize_idtype
@pytest.mark.parametrize(
    "g", get_cases(exclude=["dglgraph", "two_hetero_batch"])
)
def test_pickling_graph(g, idtype):
    g = g.astype(idtype)
    new_g = _reconstruct_pickle(g)
    pytests_utils.check_graph_equal(g, new_g, check_feature=True)


@unittest.skipIf(F._default_context_str == "gpu", reason="GPU not implemented")
def test_pickling_batched_heterograph():
    # copied from test_heterograph.create_test_heterograph()
    g = dgl.heterograph(
        {
            ("user", "follows", "user"): ([0, 1], [1, 2]),
            ("user", "plays", "game"): ([0, 1, 2, 1], [0, 0, 1, 1]),
            ("user", "wishes", "game"): ([0, 2], [1, 0]),
            ("developer", "develops", "game"): ([0, 1], [0, 1]),
        }
    )
    g2 = dgl.heterograph(
        {
            ("user", "follows", "user"): ([0, 1], [1, 2]),
            ("user", "plays", "game"): ([0, 1, 2, 1], [0, 0, 1, 1]),
            ("user", "wishes", "game"): ([0, 2], [1, 0]),
            ("developer", "develops", "game"): ([0, 1], [0, 1]),
        }
    )

    g.nodes["user"].data["u_h"] = F.randn((3, 4))
    g.nodes["game"].data["g_h"] = F.randn((2, 5))
    g.edges["plays"].data["p_h"] = F.randn((4, 6))
    g2.nodes["user"].data["u_h"] = F.randn((3, 4))
    g2.nodes["game"].data["g_h"] = F.randn((2, 5))
    g2.edges["plays"].data["p_h"] = F.randn((4, 6))

    bg = dgl.batch([g, g2])
    new_bg = _reconstruct_pickle(bg)
    pytests_utils.check_graph_equal(bg, new_bg)


@unittest.skipIf(
    F._default_context_str == "gpu",
    reason="GPU edge_subgraph w/ relabeling not implemented",
)
def test_pickling_subgraph():
    f1 = io.BytesIO()
    f2 = io.BytesIO()
    g = dgl.rand_graph(10000, 100000)
    g.ndata["x"] = F.randn((10000, 4))
    g.edata["x"] = F.randn((100000, 5))
    pickle.dump(g, f1)
    sg = g.subgraph([0, 1])
    sgx = sg.ndata["x"]  # materialize
    pickle.dump(sg, f2)
    # TODO(BarclayII): How should I test that the size of the subgraph pickle file should not
    # be as large as the size of the original pickle file?
    assert f1.tell() > f2.tell() * 50

    f2.seek(0)
    f2.truncate()
    sgx = sg.edata["x"]  # materialize
    pickle.dump(sg, f2)
    assert f1.tell() > f2.tell() * 50

    f2.seek(0)
    f2.truncate()
    sg = g.edge_subgraph([0])
    sgx = sg.edata["x"]  # materialize
    pickle.dump(sg, f2)
    assert f1.tell() > f2.tell() * 50

    f2.seek(0)
    f2.truncate()
    sgx = sg.ndata["x"]  # materialize
    pickle.dump(sg, f2)
    assert f1.tell() > f2.tell() * 50

    f1.close()
    f2.close()


@unittest.skipIf(F._default_context_str != "gpu", reason="Need GPU for pin")
@unittest.skipIf(
    dgl.backend.backend_name == "tensorflow",
    reason="TensorFlow create graph on gpu when unpickle",
)
@parametrize_idtype
def test_pickling_is_pinned(idtype):
    from copy import deepcopy

    g = dgl.rand_graph(10, 20, idtype=idtype, device=F.cpu())
    hg = dgl.heterograph(
        {
            ("user", "follows", "user"): ([0, 1], [1, 2]),
            ("user", "plays", "game"): ([0, 1, 2, 1], [0, 0, 1, 1]),
            ("user", "wishes", "game"): ([0, 2], [1, 0]),
            ("developer", "develops", "game"): ([0, 1], [0, 1]),
        },
        idtype=idtype,
        device=F.cpu(),
    )
    for graph in [g, hg]:
        assert not graph.is_pinned()
        graph.pin_memory_()
        assert graph.is_pinned()
        pg = _reconstruct_pickle(graph)
        assert pg.is_pinned()
        pg.unpin_memory_()
        dg = deepcopy(graph)
        assert dg.is_pinned()
        dg.unpin_memory_()
        graph.unpin_memory_()


if __name__ == "__main__":
    test_pickling_index()
    test_pickling_graph_index()
    test_pickling_frame()
    test_pickling_graph()
    test_pickling_nodeflow()
    test_pickling_batched_graph()
    test_pickling_heterograph()
    test_pickling_batched_heterograph()
    test_pickling_is_pinned()