"""
To run this template just do:
python generative_adversarial_net.py

After a few epochs, launch TensorBoard to see the images being generated at every batch:

tensorboard --logdir default
"""
import os
from argparse import ArgumentParser
from collections import OrderedDict

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from torchvision.datasets import MNIST

from pytorch_lightning.core import LightningModule
from pytorch_lightning.trainer import Trainer


class Generator(nn.Module):
    def __init__(self, latent_dim, img_shape):
        super().__init__()
        self.img_shape = img_shape

        def block(in_feat, out_feat, normalize=True):
            layers = [nn.Linear(in_feat, out_feat)]
            if normalize:
                layers.append(nn.BatchNorm1d(out_feat, 0.8))
            layers.append(nn.LeakyReLU(0.2, inplace=True))
            return layers

        self.model = nn.Sequential(
            *block(latent_dim, 128, normalize=False),
            *block(128, 256),
            *block(256, 512),
            *block(512, 1024),
            nn.Linear(1024, int(np.prod(img_shape))),
            nn.Tanh()
        )

    def forward(self, z):
        img = self.model(z)
        img = img.view(img.size(0), *self.img_shape)
        return img


class Discriminator(nn.Module):
    def __init__(self, img_shape):
        super().__init__()

        self.model = nn.Sequential(
            nn.Linear(int(np.prod(img_shape)), 512),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Linear(512, 256),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Linear(256, 1),
            nn.Sigmoid(),
        )

    def forward(self, img):
        img_flat = img.view(img.size(0), -1)
        validity = self.model(img_flat)

        return validity


class GAN(LightningModule):

    def __init__(self, hparams):
        super().__init__()
        self.hparams = hparams

        # networks
        mnist_shape = (1, 28, 28)
        self.generator = Generator(latent_dim=hparams.latent_dim, img_shape=mnist_shape)
        self.discriminator = Discriminator(img_shape=mnist_shape)

        # cache for generated images
        self.generated_imgs = None
        self.last_imgs = None

    def forward(self, z):
        return self.generator(z)

    def adversarial_loss(self, y_hat, y):
        return F.binary_cross_entropy(y_hat, y)

    def training_step(self, batch, batch_idx, optimizer_idx):
        imgs, _ = batch
        self.last_imgs = imgs

        # train generator
        if optimizer_idx == 0:
            # sample noise
            z = torch.randn(imgs.shape[0], self.hparams.latent_dim)
            z = z.type_as(imgs)

            # generate images
            self.generated_imgs = self(z)

            # log sampled images
            # sample_imgs = self.generated_imgs[:6]
            # grid = torchvision.utils.make_grid(sample_imgs)
            # self.logger.experiment.add_image('generated_images', grid, 0)

            # ground truth result (ie: all fake)
            # put on GPU because we created this tensor inside training_loop
            valid = torch.ones(imgs.size(0), 1)
            valid = valid.type_as(imgs)

            # adversarial loss is binary cross-entropy
            g_loss = self.adversarial_loss(self.discriminator(self.generated_imgs), valid)
            tqdm_dict = {'g_loss': g_loss}
            output = OrderedDict({
                'loss': g_loss,
                'progress_bar': tqdm_dict,
                'log': tqdm_dict
            })
            return output

        # train discriminator
        if optimizer_idx == 1:
            # Measure discriminator's ability to classify real from generated samples

            # how well can it label as real?
            valid = torch.ones(imgs.size(0), 1)
            valid = valid.type_as(imgs)

            real_loss = self.adversarial_loss(self.discriminator(imgs), valid)

            # how well can it label as fake?
            fake = torch.zeros(imgs.size(0), 1)
            fake = fake.type_as(fake)

            fake_loss = self.adversarial_loss(
                self.discriminator(self.generated_imgs.detach()), fake)

            # discriminator loss is the average of these
            d_loss = (real_loss + fake_loss) / 2
            tqdm_dict = {'d_loss': d_loss}
            output = OrderedDict({
                'loss': d_loss,
                'progress_bar': tqdm_dict,
                'log': tqdm_dict
            })
            return output

    def configure_optimizers(self):
        lr = self.hparams.lr
        b1 = self.hparams.b1
        b2 = self.hparams.b2

        opt_g = torch.optim.Adam(self.generator.parameters(), lr=lr, betas=(b1, b2))
        opt_d = torch.optim.Adam(self.discriminator.parameters(), lr=lr, betas=(b1, b2))
        return [opt_g, opt_d], []

    def train_dataloader(self):
        transform = transforms.Compose([transforms.ToTensor(),
                                        transforms.Normalize([0.5], [0.5])])
        dataset = MNIST(os.getcwd(), train=True, download=True, transform=transform)
        return DataLoader(dataset, batch_size=self.hparams.batch_size)

    def on_epoch_end(self):
        z = torch.randn(8, self.hparams.latent_dim)
        z = z.type_as(self.last_imgs)

        # log sampled images
        sample_imgs = self(z)
        grid = torchvision.utils.make_grid(sample_imgs)
        self.logger.experiment.add_image(f'generated_images', grid, self.current_epoch)


def main(hparams):
    # ------------------------
    # 1 INIT LIGHTNING MODEL
    # ------------------------
    model = GAN(hparams)

    # ------------------------
    # 2 INIT TRAINER
    # ------------------------
    trainer = Trainer()

    # ------------------------
    # 3 START TRAINING
    # ------------------------
    trainer.fit(model)


if __name__ == '__main__':
    parser = ArgumentParser()
    parser.add_argument("--batch_size", type=int, default=64, help="size of the batches")
    parser.add_argument("--lr", type=float, default=0.0002, help="adam: learning rate")
    parser.add_argument("--b1", type=float, default=0.5,
                        help="adam: decay of first order momentum of gradient")
    parser.add_argument("--b2", type=float, default=0.999,
                        help="adam: decay of first order momentum of gradient")
    parser.add_argument("--latent_dim", type=int, default=100,
                        help="dimensionality of the latent space")

    hparams = parser.parse_args()

    main(hparams)