from __future__ import print_function, division
import os
import random
import argparse
import torch
import pandas as pd
from skimage import io, transform
import numpy as np
import matplotlib.pyplot as plt
from torch.utils.data import Dataset, DataLoader
import torchvision.utils as vutils
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision.datasets as dSet
from IPython.display import HTML
import matplotlib.animation as animation
from torchvision import transforms, utils
import datetime
seed = 123321
#random.seed(seed)
#torch.manual_seed(int(datetime.datetime.now().strftime("%H%M%S")))


##Hyperparamètres
ABSOLUTE = 'D:/Documents/Prepa/TIPE'
pathImage = ABSOLUTE + '/Images/Creasteph/'
pathModels = ABSOLUTE + "/Models/"

batchSize = 4 #10 pour Mosa et Mosa2 et 4 pour Mosa3
imSize = 128 #Ok 128 pour Mosa et Mosa2 et Mosa3
channelsNumber = 3 #Couleurs !
inputSize = 5000 #Entrée du générateur 100 pour Mosa, 5000 pour Mosa2 et Mosa3 et Mosa4
featuresGenerator = 64 #64 pour Mosa, Mosa2 et Mosa3, 128 pour Mosa4
featuresDiscriminator = 64 #De même
learningRate = 0.0002 #0.0002 pour Mosa, Mosa2 Mosa3
beta1 = 0.5


setImages = dSet.ImageFolder(root = pathImage, transform = transforms.Compose([transforms.RandomCrop((imSize, imSize)), transforms.RandomHorizontalFlip(), transforms.RandomVerticalFlip(), transforms.ColorJitter(hue = 0.5), transforms.ToTensor()]))
imagesLoader = torch.utils.data.DataLoader(setImages, batch_size = batchSize, shuffle = True, num_workers=0)

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")


def weightsInit(m):
    classname = m.__class__.__name__
    if classname.find('conv') != -1:
        nn.init.normal_(m.weight.data, 0.0, 0.02)
    elif classname.find('BatchNorm') != -1:
        nn.init.normal_(m.weight.data, 1.0, 0.02)
        nn.init.constant_(m.bias.data, 0)

## générateur

class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()
        self.main = nn.Sequential(
            nn.ConvTranspose2d(inputSize, featuresGenerator * 16, 4, 1, 0, bias = False),
            nn.BatchNorm2d(featuresGenerator * 16),
            nn.ReLU(True),

            nn.ConvTranspose2d(featuresGenerator * 16, featuresGenerator * 8, 4, 2, 1, bias = False),
            nn.BatchNorm2d(featuresGenerator * 8),
            nn.ReLU(True),

            nn.ConvTranspose2d(featuresGenerator * 8, featuresGenerator * 4, 4, 2, 1, bias = False),
            nn.BatchNorm2d(featuresGenerator * 4),
            nn.ReLU(True),

            nn.ConvTranspose2d(featuresGenerator * 4, featuresGenerator * 2, 4, 2, 1, bias = False),
            nn.BatchNorm2d(featuresGenerator * 2),
            nn.ReLU(True),

            nn.ConvTranspose2d(featuresGenerator * 2, featuresGenerator, 4, 2, 1, bias = False),
            nn.BatchNorm2d(featuresGenerator),
            nn.ReLU(True),

            nn.ConvTranspose2d(featuresGenerator, channelsNumber, 4, 2, 1, bias = False),
            nn.Tanh()
        )

    def forward(self, input):
        return self.main(input)

class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()
        self.main = nn.Sequential(
            nn.Conv2d(channelsNumber, featuresDiscriminator, 4, 2, 1, bias=False),
            nn.LeakyReLU(0.2, inplace=True),

            nn.Conv2d(featuresDiscriminator, featuresDiscriminator * 2, 4, 2, 1, bias=False),
            nn.BatchNorm2d(featuresDiscriminator * 2),
            nn.LeakyReLU(0.2, inplace=True),

            nn.Conv2d(featuresDiscriminator * 2, featuresDiscriminator * 4, 4, 2, 1, bias=False),
            nn.BatchNorm2d(featuresDiscriminator * 4),
            nn.LeakyReLU(0.2, inplace=True),

            nn.Conv2d(featuresDiscriminator * 4, featuresDiscriminator * 8, 4, 2, 1, bias=False),
            nn.BatchNorm2d(featuresDiscriminator * 8),
            nn.LeakyReLU(0.2, inplace=True),

            nn.Conv2d(featuresDiscriminator * 8, featuresDiscriminator * 16, 4, 2, 1, bias=False),
            nn.BatchNorm2d(featuresDiscriminator * 16),
            nn.LeakyReLU(0.2, inplace=True),

            nn.Conv2d(featuresDiscriminator * 16, 1, 4, 1, 0, bias=False),
            nn.Sigmoid()
        )

    def forward(self, input):
        return self.main(input)

netG = Generator().to(device)
netG.apply(weightsInit)

netD = Discriminator().to(device)
netD.apply(weightsInit)


criterion = nn.BCELoss()
fixedNoise = torch.randn(1, inputSize, 1, 1, device = device)

realLabel = 1
fakeLabel = 0

optimD = optim.Adam(netD.parameters(), lr = learningRate, betas = (beta1, 0.999))
optimG = optim.Adam(netG.parameters(), lr = learningRate, betas = (beta1, 0.999))


imgList = []
GLoss = []
DLoss = []


def train(number):
    iters = 0
    for epoch in range(number):
        for i, data in enumerate(imagesLoader, 0):
            netD.zero_grad()
            real_cpu = data[0].to(device)
            b_size = real_cpu.size(0)
            label = torch.full((b_size,), realLabel, device = device)
            output = netD(real_cpu).view(-1)
            errD_real = criterion(output, label)
            errD_real.backward()
            D_x = output.mean().item()

            noise = torch.randn(b_size, inputSize, 1, 1, device = device)
            fake = netG(noise)
            label.fill_(fakeLabel)
            output = netD(fake.detach()).view(-1)
            errD_fake = criterion(output, label)
            errD_fake.backward()
            D_G_z1 = output.mean().item()
            errD = errD_real + errD_fake
            optimD.step()

            netG.zero_grad()
            label.fill_(realLabel)
            output = netD(fake).view(-1)
            errG = criterion(output, label)
            errG.backward()
            D_G_z2 = output.mean().item()
            optimG.step()

            if i % 50 == 0:
                print('[%d/%d][%d/%d]\tLoss_D: %.4f\tLoss_G: %.4f\tD(x): %.4f\tD(G(z)): %.4f / %.4f'
                      % (epoch, number, i, len(imagesLoader),
                         errD.item(), errG.item(), D_x, D_G_z1, D_G_z2))
            GLoss.append(errG.item())
            DLoss.append(errD.item())
            if (iters % 500 == 0) or ((epoch == number) and (i == len(imagesLoader)-1)):
                with torch.no_grad():
                    fake = netG(fixedNoise).detach().cpu()
                imgList.append(vutils.make_grid(fake, padding=2, normalize=True))
            iters += 1

def show():
    fig = plt.figure(figsize=(10,10))
    plt.axis("off")
    ims = [[plt.imshow(np.transpose(i,(1,2,0)), animated=True)] for i in imgList]
    ani = animation.ArtistAnimation(fig, ims, interval=1000, repeat_delay=1000, blit=True)

    HTML(ani.to_jshtml())
    plt.show()

def clear():
    imgList = []

def test():
    w = 5
    h = 5
    fig = plt.figure(figsize = (10,10))
    lol = torch.randn(25, inputSize, 1, 1, device = device)
    image = netG(lol).detach().cpu()
    for i in range(image.size()[0]):
        fig.add_subplot(w, h, i + 1)
        lel = (image[i].numpy().transpose((1, 2, 0)) * 255).astype(np.uint8)
        lel = np.roll(lel, np.random.randint(0, 3), 2)
        plt.imshow(lel)
    plt.show()

def saveModel(nom):
    torch.save(netD.state_dict(), pathModels +  'D-' + nom + '.pt')
    torch.save(netG.state_dict(), pathModels +  'G-' + nom + '.pt')

def loadModel(nom):
    netD.load_state_dict(torch.load(pathModels + 'D-' + nom + '.pt'))
    netG.load_state_dict(torch.load(pathModels + 'G-' + nom + '.pt'))
