# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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 time
import pytest
import numpy as np
from mindspore import context
from mindspore import Tensor
from model_zoo.official.gnn.gcn.src.gcn import GCN
from model_zoo.official.gnn.gcn.src.metrics import LossAccuracyWrapper, TrainNetWrapper
from model_zoo.official.gnn.gcn.src.config import ConfigGCN
from model_zoo.official.gnn.gcn.src.dataset import get_adj_features_labels, get_mask


DATA_DIR = '/home/workspace/mindspore_dataset/cora/cora_mr/cora_mr'
TRAIN_NODE_NUM = 140
EVAL_NODE_NUM = 500
TEST_NODE_NUM = 1000
SEED = 20


@pytest.mark.level0
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_onecard
def test_gcn():
    print("test_gcn begin")
    np.random.seed(SEED)
    context.set_context(mode=context.GRAPH_MODE,
                        device_target="Ascend", save_graphs=False)
    config = ConfigGCN()
    config.dropout = 0.0
    adj, feature, label_onehot, _ = get_adj_features_labels(DATA_DIR)

    nodes_num = label_onehot.shape[0]
    train_mask = get_mask(nodes_num, 0, TRAIN_NODE_NUM)
    eval_mask = get_mask(nodes_num, TRAIN_NODE_NUM, TRAIN_NODE_NUM + EVAL_NODE_NUM)
    test_mask = get_mask(nodes_num, nodes_num - TEST_NODE_NUM, nodes_num)

    class_num = label_onehot.shape[1]
    input_dim = feature.shape[1]
    gcn_net = GCN(config, input_dim, class_num)
    gcn_net.add_flags_recursive(fp16=True)

    adj = Tensor(adj)
    feature = Tensor(feature)

    eval_net = LossAccuracyWrapper(gcn_net, label_onehot, eval_mask, config.weight_decay)
    test_net = LossAccuracyWrapper(gcn_net, label_onehot, test_mask, config.weight_decay)
    train_net = TrainNetWrapper(gcn_net, label_onehot, train_mask, config)

    loss_list = []
    for epoch in range(config.epochs):
        t = time.time()

        train_net.set_train()
        train_result = train_net(adj, feature)
        train_loss = train_result[0].asnumpy()
        train_accuracy = train_result[1].asnumpy()

        eval_net.set_train(False)
        eval_result = eval_net(adj, feature)
        eval_loss = eval_result[0].asnumpy()
        eval_accuracy = eval_result[1].asnumpy()

        loss_list.append(eval_loss)
        print("Epoch:", '%04d' % (epoch + 1), "train_loss=", "{:.5f}".format(train_loss),
              "train_acc=", "{:.5f}".format(train_accuracy), "val_loss=", "{:.5f}".format(eval_loss),
              "val_acc=", "{:.5f}".format(eval_accuracy), "time=", "{:.5f}".format(time.time() - t))

        if epoch > config.early_stopping and loss_list[-1] > np.mean(loss_list[-(config.early_stopping+1):-1]):
            print("Early stopping...")
            break

    test_net.set_train(False)
    test_result = test_net(adj, feature)
    test_loss = test_result[0].asnumpy()
    test_accuracy = test_result[1].asnumpy()
    print("Test set results:", "loss=", "{:.5f}".format(test_loss),
          "accuracy=", "{:.5f}".format(test_accuracy))
    assert test_accuracy > 0.812