text: # 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 pytest
  
  import cudf
  import cugraph
  from cugraph.tests import utils
  import rmm
  from rmm import rmm_config
  
  # 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 cugraph_call(M, edgevals=False):
      M = M.tocoo()
      rows = cudf.Series(M.row)
      cols = cudf.Series(M.col)
      if edgevals is False:
          values = None
      else:
          values = cudf.Series(M.data)
      G = cugraph.Graph()
      G.add_edge_list(rows, cols, values)
      return cugraph.triangles(G)
  
  
  def networkx_call(M):
      Gnx = nx.Graph(M)
      dic = nx.triangles(Gnx)
      count = 0
      for i in range(len(dic)):
          count += dic[i]
      return count
  
  
  DATASETS = ['../datasets/dolphins.csv',
              '../datasets/karate.csv',
              '../datasets/netscience.csv']
  
  
  # Test all combinations of default/managed and pooled/non-pooled allocation
  @pytest.mark.parametrize('managed, pool',
                           list(product([False, True], [False, True])))
  @pytest.mark.parametrize('graph_file', DATASETS)
  def test_triangles(managed, pool, graph_file):
      gc.collect()
  
      rmm.finalize()
      rmm_config.use_managed_memory = managed
      rmm_config.use_pool_allocator = pool
      rmm_config.initial_pool_size = 2 << 27
      rmm.initialize()
  
      assert(rmm.is_initialized())
  
      M = utils.read_csv_for_nx(graph_file)
      cu_count = cugraph_call(M)
      nx_count = networkx_call(M)
      assert cu_count == nx_count
  
  
  # Test all combinations of default/managed and pooled/non-pooled allocation
  @pytest.mark.parametrize('managed, pool',
                           list(product([False, True], [False, True])))
  @pytest.mark.parametrize('graph_file', DATASETS)
  def test_triangles_edge_vals(managed, pool, graph_file):
      gc.collect()
  
      rmm.finalize()
      rmm_config.use_managed_memory = managed
      rmm_config.use_pool_allocator = pool
      rmm_config.initial_pool_size = 2 << 27
      rmm.initialize()
  
      assert(rmm.is_initialized())
  
      M = utils.read_csv_for_nx(graph_file)
      cu_count = cugraph_call(M, edgevals=True)
      nx_count = networkx_call(M)
      assert cu_count == nx_count
  