# Copyright 2019 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.
"""
Natural adversarial defense test.
"""
import logging

import numpy as np
import pytest
from mindspore import context
from mindspore import nn
from mindspore.nn.optim.momentum import Momentum

from mock_net import Net
from mindarmour.defenses.natural_adversarial_defense import \
    NaturalAdversarialDefense
from mindarmour.utils.logger import LogUtil

LOGGER = LogUtil.get_instance()
TAG = 'Nad_Test'


@pytest.mark.level0
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_card
@pytest.mark.component_mindarmour
def test_nad():
    """UT for natural adversarial defense."""
    num_classes = 10
    batch_size = 32

    sparse = False
    context.set_context(mode=context.GRAPH_MODE)
    context.set_context(device_target='Ascend')

    # create test data
    inputs = np.random.rand(batch_size, 1, 32, 32).astype(np.float32)
    labels = np.random.randint(num_classes, size=batch_size).astype(np.int32)
    if not sparse:
        labels = np.eye(num_classes)[labels].astype(np.float32)

    net = Net()
    loss_fn = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=sparse)
    optimizer = Momentum(net.trainable_params(), 0.001, 0.9)

    # defense
    nad = NaturalAdversarialDefense(net, loss_fn=loss_fn, optimizer=optimizer)
    LOGGER.set_level(logging.DEBUG)
    LOGGER.debug(TAG, '---start natural adversarial defense--')
    loss = nad.defense(inputs, labels)
    LOGGER.debug(TAG, '---end natural adversarial defense--')
    assert np.any(loss >= 0.0)
