import jittor as jt
from jittor import init, rand, randint
import os
import numpy as np
from jittor import nn
from mydataset import myDataset

if jt.has_cuda:
    jt.flags.use_cuda = 1

class option:
    def __init__(self,n=100,
                      batch_size=4,
                      lr=0.0005,
                      b1=0.5,
                      b2=0.9,
                      n_cpu=8,
                      latent_dim=100,
                      n_classes=17,
                      img_size=180,
                      channels=3,
                      sample_interval=100) :
        self.n_epochs=n
        self.batch_size=batch_size
        self.lr=lr
        self.b1=b1
        self.b2=b2
        self.cpu=n_cpu
        self.latent_dim=latent_dim
        self.n_classes=n_classes
        self.img_size=img_size
        self.channels=channels
        self.sample_interval=sample_interval

opt=option()
# here is to set command line options, use -h operation we can check these arguments
# parser = argparse.ArgumentParser()
# parser.add_argument('--n_epochs', type=int, default=100, help='number of epochs of training')
# parser.add_argument('--batch_size', type=int, default=8, help='size of the batches')
# parser.add_argument('--lr', type=float, default=0.0005, 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('--n_cpu', type=int, default=8, help='number of cpu threads to use during batch generation')
# parser.add_argument('--latent_dim', type=int, default=100, help='dimensionality of the latent space')
# parser.add_argument('--n_classes', type=int, default=4, help='number of classes for dataset')
# parser.add_argument('--img_size', type=int, default=80, help='size of each image dimension')
# parser.add_argument('--channels', type=int, default=3, help='number of image channels')
# parser.add_argument('--sample_interval', type=int, default=100, help='interval between image sampling')
# opt = parser.parse_args()  # assign all args to opt
print(opt) 

img_shape = (opt.channels, opt.img_size, opt.img_size) # 3*32*32 dimention of the pic, used when reshaping


# using cnn to generate:

class Generator(nn.Module):

    def __init__(self):
        super(Generator, self).__init__()
        self.label_emb = nn.Embedding(opt.n_classes, 10)
        input_dim = ((opt.latent_dim + 10) )
        self.init_size = (opt.img_size // 4)
        self.l1 = nn.Sequential(nn.Linear(input_dim, (128 * (self.init_size ** 2))))
        self.conv_blocks = nn.Sequential(nn.BatchNorm(128),
                                         nn.Upsample(scale_factor=2), 
                                         nn.Conv(128, 128, 3, stride=1, padding=1), 
                                         nn.BatchNorm(128, eps=0.8), 
                                         nn.LeakyReLU(scale=0.2), 
                                         nn.Upsample(scale_factor=2), 
                                         nn.Conv(128, 64, 3, stride=1, padding=1), 
                                         nn.BatchNorm(64, eps=0.8), 
                                         nn.LeakyReLU(scale=0.2), 
                                         nn.Conv(64, opt.channels, 3, stride=1, padding=1), 
                                         nn.Tanh())
        def weights_init_normal(m):
            classname = m.__class__.__name__
            if (classname.find('Conv') != (- 1)):
                init.gauss_(m.weight, mean=0.0, std=0.02)
            elif (classname.find('BatchNorm') != (- 1)):
                init.gauss_(m.weight, mean=1.0, std=0.02)
                init.constant_(m.bias, value=0.0)
        for m in self.modules():
            weights_init_normal(m)

    def execute(self, noise, labels):
        gen_input = jt.contrib.concat((self.label_emb(labels), noise), dim=1)        
        out = self.l1(gen_input)
        out = out.view((out.shape[0], 128, self.init_size, self.init_size))
        img = self.conv_blocks(out)
        return img



# using all fc layer:

# class Generator(nn.Module):
#     def __init__(self):
#         super(Generator, self).__init__() # use nn.Module's __init__() to initialize generator. super means generator is a superclass of nn.Module
#         self.label_emb = nn.Embedding(opt.n_classes, opt.n_classes) #generate data from 1~10, and each for 10, so it's 10*10

#         # use block to define layers, used in nn.Sequential to generate sequential layers
#         def block(in_feat, out_feat, normalize=True):
#             """
#             each block:
#                 1. linner layer (in_feat->out_feat)
#                 2. if normalize==true, add batchnorm1d layer
#                 3. leakyrelu layer
#             """
#             layers = [nn.Linear(in_feat, out_feat)]
#             if normalize:  # BatchNorm就是在深度神经网络训练过程中使得每一层神经网络的输入保持相同分布的。
#                 layers.append(nn.BatchNorm1d(out_feat, 0.8))
#             layers.append(nn.LeakyReLU(0.2))
#             return layers
#         # Sequential would add activated funtion automatically
#         self.model = nn.Sequential(*block((opt.latent_dim + opt.n_classes), 128, normalize=False),
#                                    *block(128, 256), 
#                                    *block( 256,512), 
#                                    *block(512, 1024), 
#                                    nn.Linear(1024, int(np.prod(img_shape))), # generate a picture
#                                    nn.Tanh())

#     def execute(self, noise, labels):
#         gen_input = jt.contrib.concat((self.label_emb(labels), noise), dim=1)
#         img = self.model(gen_input)
#         img = img.view((img.shape[0], *img_shape))
#         return img

class Discriminator(nn.Module):

    def __init__(self):
        super(Discriminator, self).__init__()
        self.label_embedding = nn.Embedding(opt.n_classes, 10)
        
        self.model = nn.Sequential(nn.Linear((10 + int(np.prod(img_shape))), 256),
                                   nn.LeakyReLU(0.2), 
                                   nn.Linear(256, 256), 
                                   nn.Dropout(0.4), 
                                   nn.LeakyReLU(0.2), 
                                   nn.Linear(256, 128), 
                                   nn.Dropout(0.4), 
                                   nn.LeakyReLU(0.2), 
                                   nn.Linear(128, 1))
                      

    def execute(self, img, labels):
        d_in = jt.contrib.concat((img.view((img.shape[0], (- 1))), self.label_embedding(labels)), dim=1)
        validity = self.model(d_in)
        return validity

# Loss functions
adversarial_loss = nn.MSELoss()

generator = Generator()
discriminator = Discriminator()

# Configure data loader
# from jittor.dataset.mnist import MNIST
# import jittor.transform as transform
# transform = transform.Compose([
#     transform.Resize(opt.img_size),
#     transform.Gray(),
#     transform.ImageNormalize(mean=[0.5], std=[0.5]),
# ])
# dataloader = MNIST(train=True, transform=transform).set_attrs(batch_size=opt.batch_size, shuffle=True)

dogData=myDataset()
dataloader = dogData.set_attrs(batch_size=opt.batch_size, shuffle=True)

optimizer_G = nn.Adam(generator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
optimizer_D = nn.Adam(discriminator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))

os.makedirs("images", exist_ok=True)
os.makedirs("saved_models", exist_ok=True)

from PIL import Image
def save_image(img, path, nrow=10, padding=5):
    N,C,W,H = img.shape
    if (N%nrow!=0):
        print("N%nrow!=0")
        return
    ncol=int(N/nrow)
    img_all = []
    for i in range(ncol):
        img_ = []
        for j in range(nrow):
            img_.append(img[i*nrow+j])
            img_.append(np.zeros((C,W,padding)))
        img_all.append(np.concatenate(img_, 2))
        img_all.append(np.zeros((C,padding,img_all[0].shape[2])))
    img = np.concatenate(img_all, 1)
    img = np.concatenate([np.zeros((C,padding,img.shape[2])), img], 1)
    img = np.concatenate([np.zeros((C,img.shape[1],padding)), img], 2)
    min_=img.min()
    max_=img.max()
    img=(img-min_)/(max_-min_)*255
    img=img.transpose((1,2,0))
    if C==3:
        img = img[:,:,::-1]
    elif C==1:
        img = img[:,:,0]
    Image.fromarray(np.uint8(img)).save(path)

def sample_image(n_row, batches_done):
    # Sample noise
    z = jt.array(np.random.normal(0, 1, (n_row ** 2, opt.latent_dim))).float32().stop_grad()
    labels = jt.array(np.array([num for _ in range(n_row) for num in range(n_row)])).float32().stop_grad()
    gen_imgs = generator(z, labels)
    save_image(gen_imgs.numpy(), "images/%d.png" % batches_done, nrow=n_row)

def sample_generate(batches_done):
    label=randint(0,opt.n_classes)
    z = jt.array(np.random.normal(0, 1, (1, opt.latent_dim))).float32().stop_grad()
    gen_imgs = generator(z, label)
    save_image(gen_imgs.numpy(), "images/%dclass%d.png" % (batches_done,label), nrow=1)



# ----------
#  Training
# ----------

for epoch in range(opt.n_epochs):
    for i, (imgs, labels) in enumerate(dataloader):
        batch_size = imgs.shape[0]

        # Adversarial ground truths
        valid = jt.ones([batch_size, 1]).float32().stop_grad()
        fake = jt.zeros([batch_size, 1]).float32().stop_grad()

        # Configure input
        real_imgs = jt.array(imgs)
        labels = jt.array(labels)

        # -----------------
        #  Train Generator
        # -----------------

        # Sample noise and labels as generator input
        z = jt.array(np.random.normal(0, 1, (batch_size, opt.latent_dim))).float32()
        gen_labels = jt.array(np.random.randint(0, opt.n_classes, batch_size)).float32()

        # Generate a batch of images
        gen_imgs = generator(z, gen_labels)
        # Loss measures generator's ability to fool the discriminator
        validity = discriminator(gen_imgs, gen_labels)
        g_loss = adversarial_loss(validity, valid)
        g_loss.sync()
        optimizer_G.step(g_loss)

        # ---------------------
        #  Train Discriminator
        # ---------------------

        # Loss for real images
        validity_real = discriminator(real_imgs, labels)
        d_real_loss = adversarial_loss(validity_real, valid)

        # Loss for fake images
        validity_fake = discriminator(gen_imgs.stop_grad(), gen_labels)
        d_fake_loss = adversarial_loss(validity_fake, fake)

        # Total discriminator loss
        d_loss = (d_real_loss + d_fake_loss) / 2
        d_loss.sync()
        optimizer_D.step(d_loss)
        if i  % 20 == 0:
            print(
                "[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f]"
                % (epoch, opt.n_epochs, i, len(dataloader), d_loss.data, g_loss.data)
            )
        # if g_loss<0.2:
        #     sample_generate( batches_done=batches_done)

        batches_done = epoch * len(dataloader) + i
        if batches_done % opt.sample_interval == 0:
            sample_generate( batches_done=batches_done)

    sample_generate( batches_done=batches_done)
    if epoch % 5 == 0:
        generator.save("saved_models/generator_last.pkl")
        discriminator.save("saved_models/discriminator_last.pkl")
    if epoch %10 ==0:
        opt.lr*=0.9