import json
import os
import tempfile

import backend as F
import dgl
import numpy as np
import pytest
import torch as th
from dgl import function as fn
from dgl.distributed import (
    convert_dgl_partition_to_csc_sampling_graph,
    load_partition,
    load_partition_book,
    load_partition_feats,
    partition_graph,
)
from dgl.distributed.graph_partition_book import (
    _etype_tuple_to_str,
    DEFAULT_ETYPE,
    DEFAULT_NTYPE,
    EdgePartitionPolicy,
    HeteroDataName,
    NodePartitionPolicy,
    RangePartitionBook,
)
from dgl.distributed.partition import (
    _get_inner_edge_mask,
    _get_inner_node_mask,
    RESERVED_FIELD_DTYPE,
)
from scipy import sparse as spsp
from utils import reset_envs


def _verify_partition_data_types(part_g):
    for k, dtype in RESERVED_FIELD_DTYPE.items():
        if k in part_g.ndata:
            assert part_g.ndata[k].dtype == dtype
        if k in part_g.edata:
            assert part_g.edata[k].dtype == dtype


def _verify_partition_formats(part_g, formats):
    # verify saved graph formats
    if formats is None:
        assert "coo" in part_g.formats()["created"]
    else:
        for format in formats:
            assert format in part_g.formats()["created"]


def create_random_graph(n):
    arr = (
        spsp.random(n, n, density=0.001, format="coo", random_state=100) != 0
    ).astype(np.int64)
    return dgl.from_scipy(arr)


def create_random_hetero():
    num_nodes = {"n1": 1000, "n2": 1010, "n3": 1020}
    etypes = [
        ("n1", "r1", "n2"),
        ("n2", "r1", "n1"),
        ("n1", "r2", "n3"),
        ("n2", "r3", "n3"),
    ]
    edges = {}
    for etype in etypes:
        src_ntype, _, dst_ntype = etype
        arr = spsp.random(
            num_nodes[src_ntype],
            num_nodes[dst_ntype],
            density=0.001,
            format="coo",
            random_state=100,
        )
        edges[etype] = (arr.row, arr.col)
    return dgl.heterograph(edges, num_nodes)


def verify_hetero_graph(g, parts):
    num_nodes = {ntype: 0 for ntype in g.ntypes}
    num_edges = {etype: 0 for etype in g.canonical_etypes}
    for part in parts:
        assert len(g.ntypes) == len(F.unique(part.ndata[dgl.NTYPE]))
        assert len(g.canonical_etypes) == len(F.unique(part.edata[dgl.ETYPE]))
        for ntype in g.ntypes:
            ntype_id = g.get_ntype_id(ntype)
            inner_node_mask = _get_inner_node_mask(part, ntype_id)
            num_inner_nodes = F.sum(F.astype(inner_node_mask, F.int64), 0)
            num_nodes[ntype] += num_inner_nodes
        for etype in g.canonical_etypes:
            etype_id = g.get_etype_id(etype)
            inner_edge_mask = _get_inner_edge_mask(part, etype_id)
            num_inner_edges = F.sum(F.astype(inner_edge_mask, F.int64), 0)
            num_edges[etype] += num_inner_edges
    # Verify the number of nodes are correct.
    for ntype in g.ntypes:
        print(
            "node {}: {}, {}".format(
                ntype, g.num_nodes(ntype), num_nodes[ntype]
            )
        )
        assert g.num_nodes(ntype) == num_nodes[ntype]
    # Verify the number of edges are correct.
    for etype in g.canonical_etypes:
        print(
            "edge {}: {}, {}".format(
                etype, g.num_edges(etype), num_edges[etype]
            )
        )
        assert g.num_edges(etype) == num_edges[etype]

    nids = {ntype: [] for ntype in g.ntypes}
    eids = {etype: [] for etype in g.canonical_etypes}
    for part in parts:
        _, _, eid = part.edges(form="all")
        etype_arr = F.gather_row(part.edata[dgl.ETYPE], eid)
        eid_type = F.gather_row(part.edata[dgl.EID], eid)
        for etype in g.canonical_etypes:
            etype_id = g.get_etype_id(etype)
            eids[etype].append(F.boolean_mask(eid_type, etype_arr == etype_id))
            # Make sure edge Ids fall into a range.
            inner_edge_mask = _get_inner_edge_mask(part, etype_id)
            inner_eids = np.sort(
                F.asnumpy(F.boolean_mask(part.edata[dgl.EID], inner_edge_mask))
            )
            assert np.all(
                inner_eids == np.arange(inner_eids[0], inner_eids[-1] + 1)
            )

        for ntype in g.ntypes:
            ntype_id = g.get_ntype_id(ntype)
            # Make sure inner nodes have Ids fall into a range.
            inner_node_mask = _get_inner_node_mask(part, ntype_id)
            inner_nids = F.boolean_mask(part.ndata[dgl.NID], inner_node_mask)
            assert np.all(
                F.asnumpy(
                    inner_nids
                    == F.arange(
                        F.as_scalar(inner_nids[0]),
                        F.as_scalar(inner_nids[-1]) + 1,
                    )
                )
            )
            nids[ntype].append(inner_nids)

    for ntype in nids:
        nids_type = F.cat(nids[ntype], 0)
        uniq_ids = F.unique(nids_type)
        # We should get all nodes.
        assert len(uniq_ids) == g.num_nodes(ntype)
    for etype in eids:
        eids_type = F.cat(eids[etype], 0)
        uniq_ids = F.unique(eids_type)
        assert len(uniq_ids) == g.num_edges(etype)
    # TODO(zhengda) this doesn't check 'part_id'


def verify_graph_feats(
    g, gpb, part, node_feats, edge_feats, orig_nids, orig_eids
):
    for ntype in g.ntypes:
        ntype_id = g.get_ntype_id(ntype)
        inner_node_mask = _get_inner_node_mask(part, ntype_id)
        inner_nids = F.boolean_mask(part.ndata[dgl.NID], inner_node_mask)
        ntype_ids, inner_type_nids = gpb.map_to_per_ntype(inner_nids)
        partid = gpb.nid2partid(inner_type_nids, ntype)
        assert np.all(F.asnumpy(ntype_ids) == ntype_id)
        assert np.all(F.asnumpy(partid) == gpb.partid)

        orig_id = orig_nids[ntype][inner_type_nids]
        local_nids = gpb.nid2localnid(inner_type_nids, gpb.partid, ntype)

        for name in g.nodes[ntype].data:
            if name in [dgl.NID, "inner_node"]:
                continue
            true_feats = F.gather_row(g.nodes[ntype].data[name], orig_id)
            ndata = F.gather_row(node_feats[ntype + "/" + name], local_nids)
            assert np.all(F.asnumpy(ndata == true_feats))

    for etype in g.canonical_etypes:
        etype_id = g.get_etype_id(etype)
        inner_edge_mask = _get_inner_edge_mask(part, etype_id)
        inner_eids = F.boolean_mask(part.edata[dgl.EID], inner_edge_mask)
        etype_ids, inner_type_eids = gpb.map_to_per_etype(inner_eids)
        partid = gpb.eid2partid(inner_type_eids, etype)
        assert np.all(F.asnumpy(etype_ids) == etype_id)
        assert np.all(F.asnumpy(partid) == gpb.partid)

        orig_id = orig_eids[etype][inner_type_eids]
        local_eids = gpb.eid2localeid(inner_type_eids, gpb.partid, etype)

        for name in g.edges[etype].data:
            if name in [dgl.EID, "inner_edge"]:
                continue
            true_feats = F.gather_row(g.edges[etype].data[name], orig_id)
            edata = F.gather_row(
                edge_feats[_etype_tuple_to_str(etype) + "/" + name], local_eids
            )
            assert np.all(F.asnumpy(edata == true_feats))


def check_hetero_partition(
    hg,
    part_method,
    num_parts=4,
    num_trainers_per_machine=1,
    load_feats=True,
    graph_formats=None,
):
    test_ntype = "n1"
    test_etype = ("n1", "r1", "n2")
    hg.nodes[test_ntype].data["labels"] = F.arange(0, hg.num_nodes(test_ntype))
    hg.nodes[test_ntype].data["feats"] = F.tensor(
        np.random.randn(hg.num_nodes(test_ntype), 10), F.float32
    )
    hg.edges[test_etype].data["feats"] = F.tensor(
        np.random.randn(hg.num_edges(test_etype), 10), F.float32
    )
    hg.edges[test_etype].data["labels"] = F.arange(0, hg.num_edges(test_etype))
    num_hops = 1

    orig_nids, orig_eids = partition_graph(
        hg,
        "test",
        num_parts,
        "/tmp/partition",
        num_hops=num_hops,
        part_method=part_method,
        return_mapping=True,
        num_trainers_per_machine=num_trainers_per_machine,
        graph_formats=graph_formats,
    )
    assert len(orig_nids) == len(hg.ntypes)
    assert len(orig_eids) == len(hg.canonical_etypes)
    for ntype in hg.ntypes:
        assert len(orig_nids[ntype]) == hg.num_nodes(ntype)
    for etype in hg.canonical_etypes:
        assert len(orig_eids[etype]) == hg.num_edges(etype)
    parts = []
    shuffled_labels = []
    shuffled_elabels = []
    for i in range(num_parts):
        part_g, node_feats, edge_feats, gpb, _, ntypes, etypes = load_partition(
            "/tmp/partition/test.json", i, load_feats=load_feats
        )
        _verify_partition_data_types(part_g)
        _verify_partition_formats(part_g, graph_formats)
        if not load_feats:
            assert not node_feats
            assert not edge_feats
            node_feats, edge_feats = load_partition_feats(
                "/tmp/partition/test.json", i
            )
        if num_trainers_per_machine > 1:
            for ntype in hg.ntypes:
                name = ntype + "/trainer_id"
                assert name in node_feats
                part_ids = F.floor_div(
                    node_feats[name], num_trainers_per_machine
                )
                assert np.all(F.asnumpy(part_ids) == i)

            for etype in hg.canonical_etypes:
                name = _etype_tuple_to_str(etype) + "/trainer_id"
                assert name in edge_feats
                part_ids = F.floor_div(
                    edge_feats[name], num_trainers_per_machine
                )
                assert np.all(F.asnumpy(part_ids) == i)
        # Verify the mapping between the reshuffled IDs and the original IDs.
        # These are partition-local IDs.
        part_src_ids, part_dst_ids = part_g.edges()
        # These are reshuffled global homogeneous IDs.
        part_src_ids = F.gather_row(part_g.ndata[dgl.NID], part_src_ids)
        part_dst_ids = F.gather_row(part_g.ndata[dgl.NID], part_dst_ids)
        part_eids = part_g.edata[dgl.EID]
        # These are reshuffled per-type IDs.
        src_ntype_ids, part_src_ids = gpb.map_to_per_ntype(part_src_ids)
        dst_ntype_ids, part_dst_ids = gpb.map_to_per_ntype(part_dst_ids)
        etype_ids, part_eids = gpb.map_to_per_etype(part_eids)
        # These are original per-type IDs.
        for etype_id, etype in enumerate(hg.canonical_etypes):
            part_src_ids1 = F.boolean_mask(part_src_ids, etype_ids == etype_id)
            src_ntype_ids1 = F.boolean_mask(
                src_ntype_ids, etype_ids == etype_id
            )
            part_dst_ids1 = F.boolean_mask(part_dst_ids, etype_ids == etype_id)
            dst_ntype_ids1 = F.boolean_mask(
                dst_ntype_ids, etype_ids == etype_id
            )
            part_eids1 = F.boolean_mask(part_eids, etype_ids == etype_id)
            assert np.all(F.asnumpy(src_ntype_ids1 == src_ntype_ids1[0]))
            assert np.all(F.asnumpy(dst_ntype_ids1 == dst_ntype_ids1[0]))
            src_ntype = hg.ntypes[F.as_scalar(src_ntype_ids1[0])]
            dst_ntype = hg.ntypes[F.as_scalar(dst_ntype_ids1[0])]
            orig_src_ids1 = F.gather_row(orig_nids[src_ntype], part_src_ids1)
            orig_dst_ids1 = F.gather_row(orig_nids[dst_ntype], part_dst_ids1)
            orig_eids1 = F.gather_row(orig_eids[etype], part_eids1)
            orig_eids2 = hg.edge_ids(orig_src_ids1, orig_dst_ids1, etype=etype)
            assert len(orig_eids1) == len(orig_eids2)
            assert np.all(F.asnumpy(orig_eids1) == F.asnumpy(orig_eids2))
        parts.append(part_g)
        verify_graph_feats(
            hg, gpb, part_g, node_feats, edge_feats, orig_nids, orig_eids
        )

        shuffled_labels.append(node_feats[test_ntype + "/labels"])
        shuffled_elabels.append(
            edge_feats[_etype_tuple_to_str(test_etype) + "/labels"]
        )
    verify_hetero_graph(hg, parts)

    shuffled_labels = F.asnumpy(F.cat(shuffled_labels, 0))
    shuffled_elabels = F.asnumpy(F.cat(shuffled_elabels, 0))
    orig_labels = np.zeros(shuffled_labels.shape, dtype=shuffled_labels.dtype)
    orig_elabels = np.zeros(
        shuffled_elabels.shape, dtype=shuffled_elabels.dtype
    )
    orig_labels[F.asnumpy(orig_nids[test_ntype])] = shuffled_labels
    orig_elabels[F.asnumpy(orig_eids[test_etype])] = shuffled_elabels
    assert np.all(orig_labels == F.asnumpy(hg.nodes[test_ntype].data["labels"]))
    assert np.all(
        orig_elabels == F.asnumpy(hg.edges[test_etype].data["labels"])
    )


def check_partition(
    g,
    part_method,
    num_parts=4,
    num_trainers_per_machine=1,
    load_feats=True,
    graph_formats=None,
):
    g.ndata["labels"] = F.arange(0, g.num_nodes())
    g.ndata["feats"] = F.tensor(np.random.randn(g.num_nodes(), 10), F.float32)
    g.edata["feats"] = F.tensor(np.random.randn(g.num_edges(), 10), F.float32)
    g.update_all(fn.copy_u("feats", "msg"), fn.sum("msg", "h"))
    g.update_all(fn.copy_e("feats", "msg"), fn.sum("msg", "eh"))
    num_hops = 2

    orig_nids, orig_eids = partition_graph(
        g,
        "test",
        num_parts,
        "/tmp/partition",
        num_hops=num_hops,
        part_method=part_method,
        return_mapping=True,
        num_trainers_per_machine=num_trainers_per_machine,
        graph_formats=graph_formats,
    )
    part_sizes = []
    shuffled_labels = []
    shuffled_edata = []
    for i in range(num_parts):
        part_g, node_feats, edge_feats, gpb, _, _, _ = load_partition(
            "/tmp/partition/test.json", i, load_feats=load_feats
        )
        _verify_partition_data_types(part_g)
        _verify_partition_formats(part_g, graph_formats)
        if not load_feats:
            assert not node_feats
            assert not edge_feats
            node_feats, edge_feats = load_partition_feats(
                "/tmp/partition/test.json", i
            )
        if num_trainers_per_machine > 1:
            for ntype in g.ntypes:
                name = ntype + "/trainer_id"
                assert name in node_feats
                part_ids = F.floor_div(
                    node_feats[name], num_trainers_per_machine
                )
                assert np.all(F.asnumpy(part_ids) == i)

            for etype in g.canonical_etypes:
                name = _etype_tuple_to_str(etype) + "/trainer_id"
                assert name in edge_feats
                part_ids = F.floor_div(
                    edge_feats[name], num_trainers_per_machine
                )
                assert np.all(F.asnumpy(part_ids) == i)

        # Check the metadata
        assert gpb._num_nodes() == g.num_nodes()
        assert gpb._num_edges() == g.num_edges()

        assert gpb.num_partitions() == num_parts
        gpb_meta = gpb.metadata()
        assert len(gpb_meta) == num_parts
        assert len(gpb.partid2nids(i)) == gpb_meta[i]["num_nodes"]
        assert len(gpb.partid2eids(i)) == gpb_meta[i]["num_edges"]
        part_sizes.append((gpb_meta[i]["num_nodes"], gpb_meta[i]["num_edges"]))

        nid = F.boolean_mask(part_g.ndata[dgl.NID], part_g.ndata["inner_node"])
        local_nid = gpb.nid2localnid(nid, i)
        assert F.dtype(local_nid) in (F.int64, F.int32)
        assert np.all(F.asnumpy(local_nid) == np.arange(0, len(local_nid)))
        eid = F.boolean_mask(part_g.edata[dgl.EID], part_g.edata["inner_edge"])
        local_eid = gpb.eid2localeid(eid, i)
        assert F.dtype(local_eid) in (F.int64, F.int32)
        assert np.all(F.asnumpy(local_eid) == np.arange(0, len(local_eid)))

        # Check the node map.
        local_nodes = F.boolean_mask(
            part_g.ndata[dgl.NID], part_g.ndata["inner_node"]
        )
        llocal_nodes = F.nonzero_1d(part_g.ndata["inner_node"])
        local_nodes1 = gpb.partid2nids(i)
        assert F.dtype(local_nodes1) in (F.int32, F.int64)
        assert np.all(
            np.sort(F.asnumpy(local_nodes)) == np.sort(F.asnumpy(local_nodes1))
        )
        assert np.all(F.asnumpy(llocal_nodes) == np.arange(len(llocal_nodes)))

        # Check the edge map.
        local_edges = F.boolean_mask(
            part_g.edata[dgl.EID], part_g.edata["inner_edge"]
        )
        llocal_edges = F.nonzero_1d(part_g.edata["inner_edge"])
        local_edges1 = gpb.partid2eids(i)
        assert F.dtype(local_edges1) in (F.int32, F.int64)
        assert np.all(
            np.sort(F.asnumpy(local_edges)) == np.sort(F.asnumpy(local_edges1))
        )
        assert np.all(F.asnumpy(llocal_edges) == np.arange(len(llocal_edges)))

        # Verify the mapping between the reshuffled IDs and the original IDs.
        part_src_ids, part_dst_ids = part_g.edges()
        part_src_ids = F.gather_row(part_g.ndata[dgl.NID], part_src_ids)
        part_dst_ids = F.gather_row(part_g.ndata[dgl.NID], part_dst_ids)
        part_eids = part_g.edata[dgl.EID]
        orig_src_ids = F.gather_row(orig_nids, part_src_ids)
        orig_dst_ids = F.gather_row(orig_nids, part_dst_ids)
        orig_eids1 = F.gather_row(orig_eids, part_eids)
        orig_eids2 = g.edge_ids(orig_src_ids, orig_dst_ids)
        assert F.shape(orig_eids1)[0] == F.shape(orig_eids2)[0]
        assert np.all(F.asnumpy(orig_eids1) == F.asnumpy(orig_eids2))

        local_orig_nids = orig_nids[part_g.ndata[dgl.NID]]
        local_orig_eids = orig_eids[part_g.edata[dgl.EID]]
        part_g.ndata["feats"] = F.gather_row(g.ndata["feats"], local_orig_nids)
        part_g.edata["feats"] = F.gather_row(g.edata["feats"], local_orig_eids)
        local_nodes = orig_nids[local_nodes]
        local_edges = orig_eids[local_edges]

        part_g.update_all(fn.copy_u("feats", "msg"), fn.sum("msg", "h"))
        part_g.update_all(fn.copy_e("feats", "msg"), fn.sum("msg", "eh"))
        assert F.allclose(
            F.gather_row(g.ndata["h"], local_nodes),
            F.gather_row(part_g.ndata["h"], llocal_nodes),
        )
        assert F.allclose(
            F.gather_row(g.ndata["eh"], local_nodes),
            F.gather_row(part_g.ndata["eh"], llocal_nodes),
        )

        for name in ["labels", "feats"]:
            assert "_N/" + name in node_feats
            assert node_feats["_N/" + name].shape[0] == len(local_nodes)
            true_feats = F.gather_row(g.ndata[name], local_nodes)
            ndata = F.gather_row(node_feats["_N/" + name], local_nid)
            assert np.all(F.asnumpy(true_feats) == F.asnumpy(ndata))
        for name in ["feats"]:
            efeat_name = _etype_tuple_to_str(DEFAULT_ETYPE) + "/" + name
            assert efeat_name in edge_feats
            assert edge_feats[efeat_name].shape[0] == len(local_edges)
            true_feats = F.gather_row(g.edata[name], local_edges)
            edata = F.gather_row(edge_feats[efeat_name], local_eid)
            assert np.all(F.asnumpy(true_feats) == F.asnumpy(edata))

        # This only works if node/edge IDs are shuffled.
        shuffled_labels.append(node_feats["_N/labels"])
        shuffled_edata.append(edge_feats["_N:_E:_N/feats"])

    # Verify that we can reconstruct node/edge data for original IDs.
    shuffled_labels = F.asnumpy(F.cat(shuffled_labels, 0))
    shuffled_edata = F.asnumpy(F.cat(shuffled_edata, 0))
    orig_labels = np.zeros(shuffled_labels.shape, dtype=shuffled_labels.dtype)
    orig_edata = np.zeros(shuffled_edata.shape, dtype=shuffled_edata.dtype)
    orig_labels[F.asnumpy(orig_nids)] = shuffled_labels
    orig_edata[F.asnumpy(orig_eids)] = shuffled_edata
    assert np.all(orig_labels == F.asnumpy(g.ndata["labels"]))
    assert np.all(orig_edata == F.asnumpy(g.edata["feats"]))

    node_map = []
    edge_map = []
    for i, (num_nodes, num_edges) in enumerate(part_sizes):
        node_map.append(np.ones(num_nodes) * i)
        edge_map.append(np.ones(num_edges) * i)
    node_map = np.concatenate(node_map)
    edge_map = np.concatenate(edge_map)
    nid2pid = gpb.nid2partid(F.arange(0, len(node_map)))
    assert F.dtype(nid2pid) in (F.int32, F.int64)
    assert np.all(F.asnumpy(nid2pid) == node_map)
    eid2pid = gpb.eid2partid(F.arange(0, len(edge_map)))
    assert F.dtype(eid2pid) in (F.int32, F.int64)
    assert np.all(F.asnumpy(eid2pid) == edge_map)


@pytest.mark.parametrize("part_method", ["metis", "random"])
@pytest.mark.parametrize("num_parts", [1, 4])
@pytest.mark.parametrize("num_trainers_per_machine", [1, 4])
@pytest.mark.parametrize("load_feats", [True, False])
@pytest.mark.parametrize(
    "graph_formats", [None, ["csc"], ["coo", "csc"], ["coo", "csc", "csr"]]
)
def test_partition(
    part_method,
    num_parts,
    num_trainers_per_machine,
    load_feats,
    graph_formats,
):
    os.environ["DGL_DIST_DEBUG"] = "1"
    if part_method == "random" and num_parts > 1:
        num_trainers_per_machine = 1
    g = create_random_graph(1000)
    check_partition(
        g,
        part_method,
        num_parts,
        num_trainers_per_machine,
        load_feats,
        graph_formats,
    )
    hg = create_random_hetero()
    check_hetero_partition(
        hg,
        part_method,
        num_parts,
        num_trainers_per_machine,
        load_feats,
        graph_formats,
    )
    reset_envs()


def test_RangePartitionBook():
    part_id = 1
    num_parts = 2

    # homogeneous
    node_map = {DEFAULT_NTYPE: F.tensor([[0, 1000], [1000, 2000]])}
    edge_map = {DEFAULT_ETYPE: F.tensor([[0, 5000], [5000, 10000]])}
    ntypes = {DEFAULT_NTYPE: 0}
    etypes = {DEFAULT_ETYPE: 0}
    gpb = RangePartitionBook(
        part_id, num_parts, node_map, edge_map, ntypes, etypes
    )
    assert gpb.etypes == [DEFAULT_ETYPE[1]]
    assert gpb.canonical_etypes == [DEFAULT_ETYPE]
    assert gpb.to_canonical_etype(DEFAULT_ETYPE[1]) == DEFAULT_ETYPE

    node_policy = NodePartitionPolicy(gpb, DEFAULT_NTYPE)
    assert node_policy.type_name == DEFAULT_NTYPE
    edge_policy = EdgePartitionPolicy(gpb, DEFAULT_ETYPE)
    assert edge_policy.type_name == DEFAULT_ETYPE

    # Init via etype is not supported
    node_map = {
        "node1": F.tensor([[0, 1000], [1000, 2000]]),
        "node2": F.tensor([[0, 1000], [1000, 2000]]),
    }
    edge_map = {"edge1": F.tensor([[0, 5000], [5000, 10000]])}
    ntypes = {"node1": 0, "node2": 1}
    etypes = {"edge1": 0}
    expect_except = False
    try:
        RangePartitionBook(
            part_id, num_parts, node_map, edge_map, ntypes, etypes
        )
    except AssertionError:
        expect_except = True
    assert expect_except
    expect_except = False
    try:
        EdgePartitionPolicy(gpb, "edge1")
    except AssertionError:
        expect_except = True
    assert expect_except

    # heterogeneous, init via canonical etype
    node_map = {
        "node1": F.tensor([[0, 1000], [1000, 2000]]),
        "node2": F.tensor([[0, 1000], [1000, 2000]]),
    }
    edge_map = {
        ("node1", "edge1", "node2"): F.tensor([[0, 5000], [5000, 10000]])
    }
    ntypes = {"node1": 0, "node2": 1}
    etypes = {("node1", "edge1", "node2"): 0}
    c_etype = list(etypes.keys())[0]
    gpb = RangePartitionBook(
        part_id, num_parts, node_map, edge_map, ntypes, etypes
    )
    assert gpb.etypes == ["edge1"]
    assert gpb.canonical_etypes == [c_etype]
    assert gpb.to_canonical_etype("edge1") == c_etype
    assert gpb.to_canonical_etype(c_etype) == c_etype
    expect_except = False
    try:
        gpb.to_canonical_etype(("node1", "edge2", "node2"))
    except BaseException:
        expect_except = True
    assert expect_except
    expect_except = False
    try:
        gpb.to_canonical_etype("edge2")
    except BaseException:
        expect_except = True
    assert expect_except

    # NodePartitionPolicy
    node_policy = NodePartitionPolicy(gpb, "node1")
    assert node_policy.type_name == "node1"
    assert node_policy.policy_str == "node~node1"
    assert node_policy.part_id == part_id
    assert node_policy.is_node
    assert node_policy.get_data_name("x").is_node()
    local_ids = th.arange(0, 1000)
    global_ids = local_ids + 1000
    assert th.equal(node_policy.to_local(global_ids), local_ids)
    assert th.all(node_policy.to_partid(global_ids) == part_id)
    assert node_policy.get_part_size() == 1000
    assert node_policy.get_size() == 2000

    # EdgePartitionPolicy
    edge_policy = EdgePartitionPolicy(gpb, c_etype)
    assert edge_policy.type_name == c_etype
    assert edge_policy.policy_str == "edge~node1:edge1:node2"
    assert edge_policy.part_id == part_id
    assert not edge_policy.is_node
    assert not edge_policy.get_data_name("x").is_node()
    local_ids = th.arange(0, 5000)
    global_ids = local_ids + 5000
    assert th.equal(edge_policy.to_local(global_ids), local_ids)
    assert th.all(edge_policy.to_partid(global_ids) == part_id)
    assert edge_policy.get_part_size() == 5000
    assert edge_policy.get_size() == 10000

    expect_except = False
    try:
        HeteroDataName(False, "edge1", "feat")
    except BaseException:
        expect_except = True
    assert expect_except
    data_name = HeteroDataName(False, c_etype, "feat")
    assert data_name.get_type() == c_etype


def test_UnknownPartitionBook():
    node_map = {"_N": {0: 0, 1: 1, 2: 2}}
    edge_map = {"_N:_E:_N": {0: 0, 1: 1, 2: 2}}

    part_metadata = {
        "num_parts": 1,
        "num_nodes": len(node_map),
        "num_edges": len(edge_map),
        "node_map": node_map,
        "edge_map": edge_map,
        "graph_name": "test_graph",
    }

    with tempfile.TemporaryDirectory() as test_dir:
        part_config = os.path.join(test_dir, "test_graph.json")
        with open(part_config, "w") as file:
            json.dump(part_metadata, file, indent=4)
        try:
            load_partition_book(part_config, 0)
        except Exception as e:
            if not isinstance(e, TypeError):
                raise e


@pytest.mark.parametrize("part_method", ["metis", "random"])
@pytest.mark.parametrize("num_parts", [1, 4])
def test_convert_dgl_partition_to_csc_sampling_graph_homo(
    part_method, num_parts
):
    with tempfile.TemporaryDirectory() as test_dir:
        g = create_random_graph(1000)
        graph_name = "test"
        partition_graph(
            g, graph_name, num_parts, test_dir, part_method=part_method
        )
        part_config = os.path.join(test_dir, f"{graph_name}.json")
        convert_dgl_partition_to_csc_sampling_graph(part_config)
        for part_id in range(num_parts):
            orig_g = dgl.load_graphs(
                os.path.join(test_dir, f"part{part_id}/graph.dgl")
            )[0][0]
            new_g = dgl.graphbolt.load_csc_sampling_graph(
                os.path.join(test_dir, f"part{part_id}/csc_sampling_graph.tar")
            )
            orig_indptr, orig_indices, _ = orig_g.adj().csc()
            assert th.equal(orig_indptr, new_g.csc_indptr)
            assert th.equal(orig_indices, new_g.indices)
            assert new_g.node_type_offset is None
            assert all(new_g.type_per_edge == 0)
            for node_type, type_id in new_g.metadata.node_type_to_id.items():
                assert g.get_ntype_id(node_type) == type_id
            for edge_type, type_id in new_g.metadata.edge_type_to_id.items():
                assert g.get_etype_id(edge_type) == type_id


@pytest.mark.parametrize("part_method", ["metis", "random"])
@pytest.mark.parametrize("num_parts", [1, 4])
def test_convert_dgl_partition_to_csc_sampling_graph_hetero(
    part_method, num_parts
):
    with tempfile.TemporaryDirectory() as test_dir:
        g = create_random_hetero()
        graph_name = "test"
        partition_graph(
            g, graph_name, num_parts, test_dir, part_method=part_method
        )
        part_config = os.path.join(test_dir, f"{graph_name}.json")
        convert_dgl_partition_to_csc_sampling_graph(part_config)
        for part_id in range(num_parts):
            orig_g = dgl.load_graphs(
                os.path.join(test_dir, f"part{part_id}/graph.dgl")
            )[0][0]
            new_g = dgl.graphbolt.load_csc_sampling_graph(
                os.path.join(test_dir, f"part{part_id}/csc_sampling_graph.tar")
            )
            orig_indptr, orig_indices, _ = orig_g.adj().csc()
            assert th.equal(orig_indptr, new_g.csc_indptr)
            assert th.equal(orig_indices, new_g.indices)
            for node_type, type_id in new_g.metadata.node_type_to_id.items():
                assert g.get_ntype_id(node_type) == type_id
            for edge_type, type_id in new_g.metadata.edge_type_to_id.items():
                assert g.get_etype_id(edge_type) == type_id
            assert new_g.node_type_offset is None
            assert th.equal(orig_g.edata[dgl.ETYPE], new_g.type_per_edge)
