"""
LBFGS tutorial on mnist using advbox tool.
LBFGS method only supports targeted attack.
"""
import sys
sys.path.append("..")

import matplotlib.pyplot as plt
import paddle.fluid as fluid
import paddle.v2 as paddle

from advbox.adversary import Adversary
from advbox.attacks.lbfgs import LBFGS
from advbox.models.paddle import PaddleModel
from tutorials.mnist_model import mnist_cnn_model


def main():
    """
    Advbox demo which demonstrate how to use advbox.
    """
    TOTAL_NUM = 500
    IMG_NAME = 'img'
    LABEL_NAME = 'label'

    img = fluid.layers.data(name=IMG_NAME, shape=[1, 28, 28], dtype='float32')
    # gradient should flow
    img.stop_gradient = False
    label = fluid.layers.data(name=LABEL_NAME, shape=[1], dtype='int64')
    logits = mnist_cnn_model(img)
    cost = fluid.layers.cross_entropy(input=logits, label=label)
    avg_cost = fluid.layers.mean(x=cost)

    # use CPU
    place = fluid.CPUPlace()
    # use GPU
    # place = fluid.CUDAPlace(0)
    exe = fluid.Executor(place)

    BATCH_SIZE = 1
    train_reader = paddle.batch(
        paddle.reader.shuffle(
            paddle.dataset.mnist.train(), buf_size=128 * 10),
        batch_size=BATCH_SIZE)

    test_reader = paddle.batch(
        paddle.reader.shuffle(
            paddle.dataset.mnist.test(), buf_size=128 * 10),
        batch_size=BATCH_SIZE)

    fluid.io.load_params(
        exe, "./mnist/", main_program=fluid.default_main_program())

    # advbox demo
    m = PaddleModel(
        fluid.default_main_program(),
        IMG_NAME,
        LABEL_NAME,
        logits.name,
        avg_cost.name, (-1, 1),
        channel_axis=1)
    attack = LBFGS(m)
    attack_config = {"epsilon": 0.001, }

    # use train data to generate adversarial examples
    total_count = 0
    fooling_count = 0
    for data in train_reader():
        total_count += 1
        adversary = Adversary(data[0][0], data[0][1])

        # LBFGS targeted attack
        tlabel = 0
        adversary.set_target(is_targeted_attack=True, target_label=tlabel)
        adversary = attack(adversary, **attack_config)

        if adversary.is_successful():
            fooling_count += 1
            print(
                'attack success, original_label=%d, adversarial_label=%d, count=%d'
                % (data[0][1], adversary.adversarial_label, total_count))
            # plt.imshow(adversary.target, cmap='Greys_r')
            # plt.show()
            # np.save('adv_img', adversary.target)
        else:
            print('attack failed, original_label=%d, count=%d' %
                  (data[0][1], total_count))

        if total_count >= TOTAL_NUM:
            print(
                "[TRAIN_DATASET]: fooling_count=%d, total_count=%d, fooling_rate=%f"
                % (fooling_count, total_count,
                   float(fooling_count) / total_count))
            break

    # use test data to generate adversarial examples
    total_count = 0
    fooling_count = 0
    for data in test_reader():
        total_count += 1
        adversary = Adversary(data[0][0], data[0][1])

        # LBFGS targeted attack
        tlabel = 0
        adversary.set_target(is_targeted_attack=True, target_label=tlabel)
        adversary = attack(adversary, **attack_config)

        if adversary.is_successful():
            fooling_count += 1
            print(
                'attack success, original_label=%d, adversarial_label=%d, count=%d'
                % (data[0][1], adversary.adversarial_label, total_count))
            # plt.imshow(adversary.target, cmap='Greys_r')
            # plt.show()
            # np.save('adv_img', adversary.target)
        else:
            print('attack failed, original_label=%d, count=%d' %
                  (data[0][1], total_count))

        if total_count >= TOTAL_NUM:
            print(
                "[TEST_DATASET]: fooling_count=%d, total_count=%d, fooling_rate=%f"
                % (fooling_count, total_count,
                   float(fooling_count) / total_count))
            break
    print("lbfgs attack done")


if __name__ == '__main__':
    main()
