#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
#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.
"""
FGSM demos on mnist using advbox tool.
"""
import paddle.v2 as paddle
import paddle.v2.fluid as fluid
import matplotlib.pyplot as plt
import numpy as np

from advbox.models.paddle import PaddleModel
from advbox.attacks.gradientsign import GradientSignAttack


def cnn_model(img):
    """
    Mnist cnn model
    Args:
        img(Varaible): the input image to be recognized
    Returns:
        Variable: the label prediction
    """
    #conv1 = fluid.nets.conv2d()
    conv_pool_1 = fluid.nets.simple_img_conv_pool(
        input=img,
        num_filters=20,
        filter_size=5,
        pool_size=2,
        pool_stride=2,
        act='relu')

    conv_pool_2 = fluid.nets.simple_img_conv_pool(
        input=conv_pool_1,
        num_filters=50,
        filter_size=5,
        pool_size=2,
        pool_stride=2,
        act='relu')

    logits = fluid.layers.fc(input=conv_pool_2, size=10, act='softmax')
    return logits


def main():
    """
    Advbox demo which demonstrate how to use advbox.
    """
    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 = cnn_model(img)
    cost = fluid.layers.cross_entropy(input=logits, label=label)
    avg_cost = fluid.layers.mean(x=cost)

    place = fluid.CPUPlace()
    exe = fluid.Executor(place)

    BATCH_SIZE = 1
    train_reader = paddle.batch(
        paddle.reader.shuffle(
            paddle.dataset.mnist.train(), buf_size=500),
        batch_size=BATCH_SIZE)
    feeder = fluid.DataFeeder(
        feed_list=[IMG_NAME, LABEL_NAME],
        place=place,
        program=fluid.default_main_program())

    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))
    att = GradientSignAttack(m)
    for data in train_reader():
        # fgsm attack
        adv_img = att(data)
        plt.imshow(n[0][0], cmap='Greys_r')
        plt.show()
        #np.save('adv_img', adv_img)
        break


if __name__ == '__main__':
    main()
