# Copyright (c) 2019, 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
from itertools import product
import time

import pytest
import numpy as np

import cugraph
from cugraph.tests import utils
import rmm

# 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


print('Networkx version : {} '.format(nx.__version__))

SOURCES = [1]


@pytest.mark.parametrize('managed, pool',
                         list(product([False, True], [False, True])))
@pytest.mark.parametrize('graph_file', ['../datasets/netscience.csv'])
@pytest.mark.parametrize('source', SOURCES)
def test_filter_unreachable(managed, pool, graph_file, source):
    gc.collect()

    rmm.reinitialize(
        managed_memory=managed,
        pool_allocator=pool,
        initial_pool_size=2 << 27
    )

    assert(rmm.is_initialized())

    cu_M = utils.read_csv_file(graph_file)

    print('sources size = ' + str(len(cu_M)))
    print('destinations size = ' + str(len(cu_M)))

    # cugraph Pagerank Call
    G = cugraph.DiGraph()
    G.from_cudf_edgelist(cu_M, source='0', destination='1', edge_attr='2')

    print('cugraph Solving... ')
    t1 = time.time()

    df = cugraph.sssp(G, source)

    t2 = time.time() - t1
    print('Time : '+str(t2))

    reachable_df = cugraph.filter_unreachable(df)

    if(np.issubdtype(df['distance'].dtype, np.integer)):
        inf = np.iinfo(reachable_df['distance'].dtype).max  # noqa: F841
        assert len(reachable_df.query("distance == @inf")) == 0
    elif(np.issubdtype(df['distance'].dtype, np.inexact)):
        inf = np.finfo(reachable_df['distance'].dtype).max  # noqa: F841
        assert len(reachable_df.query("distance == @inf")) == 0

    assert len(reachable_df) != 0
