#   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.v2 as 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(x=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(x=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()