# Copyright (c) 2019-2021, NVIDIA CORPORATION.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import gc

import numpy as np
import pytest

import cudf
import cugraph
from cugraph.tests import utils


# Temporarily suppress warnings till networkX fixes deprecation warnings
# (Using or importing the ABCs from 'collections' instead of from
# 'collections.abc' is deprecated, and in 3.8 it will stop working) for
# python 3.7.  Also, this import networkx needs to be relocated in the
# third-party group once this gets fixed.
import warnings

with warnings.catch_warnings():
    warnings.filterwarnings("ignore", category=DeprecationWarning)
    import networkx as nx


def compare_edges(cg, nxg):
    edgelist_df = cg.view_edge_list()
    assert cg.edgelist.weights is False
    assert len(edgelist_df) == nxg.size()
    for i in range(len(edgelist_df)):
        assert nxg.has_edge(
            edgelist_df["src"].iloc[i], edgelist_df["dst"].iloc[i]
        )
    return True


def cugraph_call(M, verts, directed=True):
    # directed is used to create either a Graph or DiGraph so the returned
    # cugraph can be compared to nx graph of same type.
    if directed:
        G = cugraph.DiGraph()
    else:
        G = cugraph.Graph()
    cu_M = cudf.DataFrame()
    cu_M["src"] = cudf.Series(M["0"])
    cu_M["dst"] = cudf.Series(M["1"])
    G.from_cudf_edgelist(cu_M, source="src", destination="dst")
    cu_verts = cudf.Series(verts)
    return cugraph.subgraph(G, cu_verts)


def nx_call(M, verts, directed=True):
    if directed:
        G = nx.from_pandas_edgelist(
            M, source="0", target="1", create_using=nx.DiGraph()
        )
    else:
        G = nx.from_pandas_edgelist(
            M, source="0", target="1", create_using=nx.Graph()
        )
    return nx.subgraph(G, verts)


@pytest.mark.parametrize("graph_file", utils.DATASETS)
def test_subgraph_extraction_DiGraph(graph_file):
    gc.collect()

    M = utils.read_csv_for_nx(graph_file)
    verts = np.zeros(3, dtype=np.int32)
    verts[0] = 0
    verts[1] = 1
    verts[2] = 17
    cu_sg = cugraph_call(M, verts)
    nx_sg = nx_call(M, verts)
    assert compare_edges(cu_sg, nx_sg)


@pytest.mark.parametrize("graph_file", utils.DATASETS)
def test_subgraph_extraction_Graph(graph_file):
    gc.collect()

    M = utils.read_csv_for_nx(graph_file)
    verts = np.zeros(3, dtype=np.int32)
    verts[0] = 0
    verts[1] = 1
    verts[2] = 17
    cu_sg = cugraph_call(M, verts, False)
    nx_sg = nx_call(M, verts, False)
    assert compare_edges(cu_sg, nx_sg)


@pytest.mark.parametrize("graph_file", utils.DATASETS)
def test_subgraph_extraction_Graph_nx(graph_file):
    gc.collect()
    directed = False
    verts = np.zeros(3, dtype=np.int32)
    verts[0] = 0
    verts[1] = 1
    verts[2] = 17

    M = utils.read_csv_for_nx(graph_file)

    if directed:
        G = nx.from_pandas_edgelist(
            M, source="0", target="1", create_using=nx.DiGraph()
        )
    else:
        G = nx.from_pandas_edgelist(
            M, source="0", target="1", create_using=nx.Graph()
        )

    nx_sub = nx.subgraph(G, verts)

    cu_verts = cudf.Series(verts)
    cu_sub = cugraph.subgraph(G, cu_verts)

    for (u, v) in cu_sub.edges():
        assert nx_sub.has_edge(u, v)


@pytest.mark.parametrize("graph_file", utils.DATASETS)
def test_subgraph_extraction_multi_column(graph_file):
    gc.collect()

    M = utils.read_csv_for_nx(graph_file)

    cu_M = cudf.DataFrame()
    cu_M["src_0"] = cudf.Series(M["0"])
    cu_M["dst_0"] = cudf.Series(M["1"])
    cu_M["src_1"] = cu_M["src_0"] + 1000
    cu_M["dst_1"] = cu_M["dst_0"] + 1000
    G1 = cugraph.Graph()
    G1.from_cudf_edgelist(cu_M, source=["src_0", "src_1"],
                          destination=["dst_0", "dst_1"])

    verts = cudf.Series([0, 1, 17])
    verts_G1 = cudf.DataFrame()
    verts_G1['v_0'] = verts
    verts_G1['v_1'] = verts + 1000

    sG1 = cugraph.subgraph(G1, verts_G1)

    G2 = cugraph.Graph()
    G2.from_cudf_edgelist(cu_M, source="src_0", destination="dst_0")

    sG2 = cugraph.subgraph(G2, verts)

    # FIXME: Replace with multi-column view_edge_list()
    edgelist_df = sG1.edgelist.edgelist_df
    edgelist_df_res = sG1.unrenumber(edgelist_df, "src")
    edgelist_df_res = sG1.unrenumber(edgelist_df_res, "dst")
    for i in range(len(edgelist_df_res)):
        assert sG2.has_edge(edgelist_df_res["0_src"].iloc[i],
                            edgelist_df_res["0_dst"].iloc[i])
