import unittest

import backend as F
import dgl.graphbolt as gb
import pytest
import torch

from .. import gb_test_utils


@unittest.skipIf(
    F._default_context_str == "cpu",
    reason="Tests for pinned memory are only meaningful on GPU.",
)
@pytest.mark.parametrize(
    "indptr_dtype",
    [torch.int32, torch.int64],
)
@pytest.mark.parametrize(
    "indices_dtype",
    [torch.int8, torch.int16, torch.int32, torch.int64],
)
@pytest.mark.parametrize("idtype", [torch.int32, torch.int64])
@pytest.mark.parametrize("is_pinned", [False, True])
@pytest.mark.parametrize("output_size", [None, True])
def test_index_select_csc(
    indptr_dtype, indices_dtype, idtype, is_pinned, output_size
):
    """Original graph in COO:
    1   0   1   0   1   0
    1   0   0   1   0   1
    0   1   0   1   0   0
    0   1   0   0   1   0
    1   0   0   0   0   1
    0   0   1   0   1   0
    """
    indptr = torch.tensor([0, 3, 5, 7, 9, 12, 14], dtype=indptr_dtype)
    indices = torch.tensor(
        [0, 1, 4, 2, 3, 0, 5, 1, 2, 0, 3, 5, 1, 4], dtype=indices_dtype
    )
    index = torch.tensor([0, 5, 3], dtype=idtype)

    cpu_indptr, cpu_indices = torch.ops.graphbolt.index_select_csc(
        indptr, indices, index, None
    )
    if is_pinned:
        indptr = indptr.pin_memory()
        indices = indices.pin_memory()
    else:
        indptr = indptr.cuda()
        indices = indices.cuda()
    index = index.cuda()

    if output_size:
        output_size = len(cpu_indices)

    gpu_indptr, gpu_indices = torch.ops.graphbolt.index_select_csc(
        indptr, indices, index, output_size
    )
    assert not cpu_indptr.is_cuda
    assert not cpu_indices.is_cuda

    assert gpu_indptr.is_cuda
    assert gpu_indices.is_cuda

    assert torch.equal(cpu_indptr, gpu_indptr.cpu())
    assert torch.equal(cpu_indices, gpu_indices.cpu())


def test_InSubgraphSampler_homo():
    """Original graph in COO:
    1   0   1   0   1   0
    1   0   0   1   0   1
    0   1   0   1   0   0
    0   1   0   0   1   0
    1   0   0   0   0   1
    0   0   1   0   1   0
    """
    indptr = torch.LongTensor([0, 3, 5, 7, 9, 12, 14])
    indices = torch.LongTensor([0, 1, 4, 2, 3, 0, 5, 1, 2, 0, 3, 5, 1, 4])
    graph = gb.fused_csc_sampling_graph(indptr, indices).to(F.ctx())

    seed_nodes = torch.LongTensor([0, 5, 3])
    item_set = gb.ItemSet(seed_nodes, names="seed_nodes")
    batch_size = 1
    item_sampler = gb.ItemSampler(item_set, batch_size=batch_size).copy_to(
        F.ctx()
    )

    in_subgraph_sampler = gb.InSubgraphSampler(item_sampler, graph)

    it = iter(in_subgraph_sampler)

    def original_indices(minibatch):
        sampled_subgraph = minibatch.sampled_subgraphs[0]
        _indices = sampled_subgraph.original_row_node_ids[
            sampled_subgraph.sampled_csc.indices
        ]
        return _indices

    mn = next(it)
    assert torch.equal(mn.seeds, torch.LongTensor([0]).to(F.ctx()))
    assert torch.equal(
        mn.sampled_subgraphs[0].sampled_csc.indptr,
        torch.tensor([0, 3]).to(F.ctx()),
    )

    mn = next(it)
    assert torch.equal(mn.seeds, torch.LongTensor([5]).to(F.ctx()))
    assert torch.equal(
        mn.sampled_subgraphs[0].sampled_csc.indptr,
        torch.tensor([0, 2]).to(F.ctx()),
    )
    assert torch.equal(original_indices(mn), torch.tensor([1, 4]).to(F.ctx()))

    mn = next(it)
    assert torch.equal(mn.seeds, torch.LongTensor([3]).to(F.ctx()))
    assert torch.equal(
        mn.sampled_subgraphs[0].sampled_csc.indptr,
        torch.tensor([0, 2]).to(F.ctx()),
    )
    assert torch.equal(original_indices(mn), torch.tensor([1, 2]).to(F.ctx()))


def test_InSubgraphSampler_hetero():
    """Original graph in COO:
    1   0   1   0   1   0
    1   0   0   1   0   1
    0   1   0   1   0   0
    0   1   0   0   1   0
    1   0   0   0   0   1
    0   0   1   0   1   0
    node_type_0: [0, 1, 2]
    node_type_1: [3, 4, 5]
    edge_type_0: node_type_0 -> node_type_0
    edge_type_1: node_type_0 -> node_type_1
    edge_type_2: node_type_1 -> node_type_0
    edge_type_3: node_type_1 -> node_type_1
    """
    ntypes = {
        "N0": 0,
        "N1": 1,
    }
    etypes = {
        "N0:R0:N0": 0,
        "N0:R1:N1": 1,
        "N1:R2:N0": 2,
        "N1:R3:N1": 3,
    }
    indptr = torch.LongTensor([0, 3, 5, 7, 9, 12, 14])
    indices = torch.LongTensor([0, 1, 4, 2, 3, 0, 5, 1, 2, 0, 3, 5, 1, 4])
    node_type_offset = torch.LongTensor([0, 3, 6])
    type_per_edge = torch.LongTensor([0, 0, 2, 0, 2, 0, 2, 1, 1, 1, 3, 3, 1, 3])
    graph = gb.fused_csc_sampling_graph(
        csc_indptr=indptr,
        indices=indices,
        node_type_offset=node_type_offset,
        type_per_edge=type_per_edge,
        node_type_to_id=ntypes,
        edge_type_to_id=etypes,
    ).to(F.ctx())

    item_set = gb.ItemSetDict(
        {
            "N0": gb.ItemSet(torch.LongTensor([1, 0, 2]), names="seed_nodes"),
            "N1": gb.ItemSet(torch.LongTensor([0, 2, 1]), names="seed_nodes"),
        }
    )
    batch_size = 2
    item_sampler = gb.ItemSampler(item_set, batch_size=batch_size).copy_to(
        F.ctx()
    )

    in_subgraph_sampler = gb.InSubgraphSampler(item_sampler, graph)

    it = iter(in_subgraph_sampler)

    mn = next(it)
    assert torch.equal(mn.seeds["N0"], torch.LongTensor([1, 0]).to(F.ctx()))
    expected_sampled_csc = {
        "N0:R0:N0": gb.CSCFormatBase(
            indptr=torch.LongTensor([0, 1, 3]),
            indices=torch.LongTensor([2, 1, 0]),
        ),
        "N0:R1:N1": gb.CSCFormatBase(
            indptr=torch.LongTensor([0]), indices=torch.LongTensor([])
        ),
        "N1:R2:N0": gb.CSCFormatBase(
            indptr=torch.LongTensor([0, 1, 2]), indices=torch.LongTensor([0, 1])
        ),
        "N1:R3:N1": gb.CSCFormatBase(
            indptr=torch.LongTensor([0]), indices=torch.LongTensor([])
        ),
    }
    for etype, pairs in mn.sampled_subgraphs[0].sampled_csc.items():
        assert torch.equal(
            pairs.indices, expected_sampled_csc[etype].indices.to(F.ctx())
        )
        assert torch.equal(
            pairs.indptr, expected_sampled_csc[etype].indptr.to(F.ctx())
        )

    mn = next(it)
    assert mn.seeds == {
        "N0": torch.LongTensor([2]).to(F.ctx()),
        "N1": torch.LongTensor([0]).to(F.ctx()),
    }
    expected_sampled_csc = {
        "N0:R0:N0": gb.CSCFormatBase(
            indptr=torch.LongTensor([0, 1]), indices=torch.LongTensor([1])
        ),
        "N0:R1:N1": gb.CSCFormatBase(
            indptr=torch.LongTensor([0, 2]), indices=torch.LongTensor([2, 0])
        ),
        "N1:R2:N0": gb.CSCFormatBase(
            indptr=torch.LongTensor([0, 1]), indices=torch.LongTensor([1])
        ),
        "N1:R3:N1": gb.CSCFormatBase(
            indptr=torch.LongTensor([0, 0]), indices=torch.LongTensor([])
        ),
    }
    for etype, pairs in mn.sampled_subgraphs[0].sampled_csc.items():
        assert torch.equal(
            pairs.indices, expected_sampled_csc[etype].indices.to(F.ctx())
        )
        assert torch.equal(
            pairs.indptr, expected_sampled_csc[etype].indptr.to(F.ctx())
        )

    mn = next(it)
    assert torch.equal(mn.seeds["N1"], torch.LongTensor([2, 1]).to(F.ctx()))
    expected_sampled_csc = {
        "N0:R0:N0": gb.CSCFormatBase(
            indptr=torch.LongTensor([0]), indices=torch.LongTensor([])
        ),
        "N0:R1:N1": gb.CSCFormatBase(
            indptr=torch.LongTensor([0, 1, 2]), indices=torch.LongTensor([0, 1])
        ),
        "N1:R2:N0": gb.CSCFormatBase(
            indptr=torch.LongTensor([0]), indices=torch.LongTensor([])
        ),
        "N1:R3:N1": gb.CSCFormatBase(
            indptr=torch.LongTensor([0, 1, 3]),
            indices=torch.LongTensor([1, 2, 0]),
        ),
    }
    if graph.csc_indptr.is_cuda:
        expected_sampled_csc["N0:R1:N1"] = gb.CSCFormatBase(
            indptr=torch.LongTensor([0, 1, 2]), indices=torch.LongTensor([1, 0])
        )
    for etype, pairs in mn.sampled_subgraphs[0].sampled_csc.items():
        assert torch.equal(
            pairs.indices, expected_sampled_csc[etype].indices.to(F.ctx())
        )
        assert torch.equal(
            pairs.indptr, expected_sampled_csc[etype].indptr.to(F.ctx())
        )
