#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# 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 errno
import math
import os

import matplotlib
import numpy

import paddle
import paddle.fluid as fluid

matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec

NOISE_SIZE = 100
NUM_PASS = 1000
NUM_REAL_IMGS_IN_BATCH = 121
NUM_TRAIN_TIMES_OF_DG = 3
LEARNING_RATE = 2e-5


def D(x):
    hidden = fluid.layers.fc(input=x,
                             size=200,
                             act='relu',
                             param_attr='D.w1',
                             bias_attr='D.b1')
    logits = fluid.layers.fc(input=hidden,
                             size=1,
                             act=None,
                             param_attr='D.w2',
                             bias_attr='D.b2')
    return logits


def G(x):
    hidden = fluid.layers.fc(input=x,
                             size=200,
                             act='relu',
                             param_attr='G.w1',
                             bias_attr='G.b1')
    img = fluid.layers.fc(input=hidden,
                          size=28 * 28,
                          act='tanh',
                          param_attr='G.w2',
                          bias_attr='G.b2')
    return img


def plot(gen_data):
    gen_data.resize(gen_data.shape[0], 28, 28)
    n = int(math.ceil(math.sqrt(gen_data.shape[0])))
    fig = plt.figure(figsize=(n, n))
    gs = gridspec.GridSpec(n, n)
    gs.update(wspace=0.05, hspace=0.05)

    for i, sample in enumerate(gen_data):
        ax = plt.subplot(gs[i])
        plt.axis('off')
        ax.set_xticklabels([])
        ax.set_yticklabels([])
        ax.set_aspect('equal')
        plt.imshow(sample.reshape(28, 28), cmap='Greys_r')

    return fig


def main():
    try:
        os.makedirs("./out")
    except OSError as e:
        if e.errno != errno.EEXIST:
            raise

    startup_program = fluid.Program()
    d_program = fluid.Program()
    dg_program = fluid.Program()

    with fluid.program_guard(d_program, startup_program):
        img = fluid.layers.data(name='img', shape=[784], dtype='float32')
        d_loss = fluid.layers.sigmoid_cross_entropy_with_logits(
            x=D(img),
            label=fluid.layers.data(
                name='label', shape=[1], dtype='float32'))
        d_loss = fluid.layers.mean(d_loss)

    with fluid.program_guard(dg_program, startup_program):
        noise = fluid.layers.data(
            name='noise', shape=[NOISE_SIZE], dtype='float32')
        g_img = G(x=noise)
        g_program = dg_program.clone()
        dg_loss = fluid.layers.sigmoid_cross_entropy_with_logits(
            x=D(g_img),
            label=fluid.layers.fill_constant_batch_size_like(
                input=noise, dtype='float32', shape=[-1, 1], value=1.0))
        dg_loss = fluid.layers.mean(dg_loss)

    opt = fluid.optimizer.Adam(learning_rate=LEARNING_RATE)

    opt.minimize(loss=d_loss, startup_program=startup_program)
    opt.minimize(
        loss=dg_loss,
        startup_program=startup_program,
        parameter_list=[
            p.name for p in g_program.global_block().all_parameters()
        ])
    exe = fluid.Executor(fluid.CPUPlace())
    exe.run(startup_program)

    num_true = NUM_REAL_IMGS_IN_BATCH
    train_reader = paddle.batch(
        paddle.reader.shuffle(
            paddle.dataset.mnist.train(), buf_size=60000),
        batch_size=num_true)

    for pass_id in range(NUM_PASS):
        for batch_id, data in enumerate(train_reader()):
            num_true = len(data)
            n = numpy.random.uniform(
                low=-1.0, high=1.0,
                size=[num_true * NOISE_SIZE]).astype('float32').reshape(
                    [num_true, NOISE_SIZE])
            generated_img = exe.run(g_program,
                                    feed={'noise': n},
                                    fetch_list={g_img})[0]
            real_data = numpy.array(map(lambda x: x[0], data)).astype('float32')
            real_data = real_data.reshape(num_true, 784)
            total_data = numpy.concatenate([real_data, generated_img])
            total_label = numpy.concatenate([
                numpy.ones(
                    shape=[real_data.shape[0], 1], dtype='float32'),
                numpy.zeros(
                    shape=[real_data.shape[0], 1], dtype='float32')
            ])
            d_loss_np = exe.run(d_program,
                                feed={'img': total_data,
                                      'label': total_label},
                                fetch_list={d_loss})[0]
            for _ in xrange(NUM_TRAIN_TIMES_OF_DG):
                n = numpy.random.uniform(
                    low=-1.0, high=1.0,
                    size=[2 * num_true * NOISE_SIZE]).astype('float32').reshape(
                        [2 * num_true, NOISE_SIZE, 1, 1])
                dg_loss_np = exe.run(dg_program,
                                     feed={'noise': n},
                                     fetch_list={dg_loss})[0]
            print("Pass ID={0}, Batch ID={1}, D-Loss={2}, DG-Loss={3}".format(
                pass_id, batch_id, d_loss_np, dg_loss_np))
        # generate image each batch
        fig = plot(generated_img)
        plt.savefig(
            'out/{0}.png'.format(str(pass_id).zfill(3)), bbox_inches='tight')
        plt.close(fig)


if __name__ == '__main__':
    main()