# -*- coding: utf-8 -*-
"""
Created on Wed Nov  6 15:00:36 2019

@author: Nancy
"""
import torch
import torch.nn as nn
from torch.nn import init
import functools
from torch.optim import lr_scheduler
import itertools
from modelutil.image_pool import ImagePool
import numpy as np
from modelutil.usage import *
from modelutil.base_model import BaseModel
from Loss import *

def init_net(net, init_type='normal', init_gain=0.02, gpu_ids=[]):
    """Initialize a network: 1. register CPU/GPU device (with multi-GPU support); 2. initialize the network weights
    Parameters:
        net (network)      -- the network to be initialized
        init_type (str)    -- the name of an initialization method: normal | xavier | kaiming | orthogonal
        gain (float)       -- scaling factor for normal, xavier and orthogonal.
        gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2
    Return an initialized network.
    """
    if len(gpu_ids) > 0:
        assert(torch.cuda.is_available())
        net.to(gpu_ids[0])
        net = torch.nn.DataParallel(net, gpu_ids)  # multi-GPUs
    init_weights(net, init_type, init_gain=init_gain)
    return net

def define_G(input_nc, output_nc, ngf, netG, norm='batch', use_dropout=False, init_type='normal', init_gain=0.02, gpu_ids=[]):
    """Create a generator
    Parameters:
        input_nc (int) -- the number of channels in input images
        output_nc (int) -- the number of channels in output images
        ngf (int) -- the number of filters in the last conv layer
        netG (str) -- the architecture's name: resnet_9blocks | resnet_6blocks | unet_256 | unet_128
        norm (str) -- the name of normalization layers used in the network: batch | instance | none
        use_dropout (bool) -- if use dropout layers.
        init_type (str)    -- the name of our initialization method.
        init_gain (float)  -- scaling factor for normal, xavier and orthogonal.
        gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2
    Returns a generator
    Our current implementation provides two types of generators:
        U-Net: [unet_128] (for 128x128 input images) and [unet_256] (for 256x256 input images)
        The original U-Net paper: https://arxiv.org/abs/1505.04597
        Resnet-based generator: [resnet_6blocks] (with 6 Resnet blocks) and [resnet_9blocks] (with 9 Resnet blocks)
        Resnet-based generator consists of several Resnet blocks between a few downsampling/upsampling operations.
        We adapt Torch code from Justin Johnson's neural style transfer project (https://github.com/jcjohnson/fast-neural-style).
    The generator has been initialized by <init_net>. It uses RELU for non-linearity.
    """
    net = None
    norm_layer = get_norm_layer(norm_type=norm)

    if netG == 'resnet_9blocks':
        net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=9)
    elif netG == 'resnet_6blocks':
        net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=6)
    elif netG == 'unet_128':
        net = UnetGenerator(input_nc, output_nc, 7, ngf, norm_layer=norm_layer, use_dropout=use_dropout)
    elif netG == 'unet_256':
        net = UnetGenerator(input_nc, output_nc, 8, ngf, norm_layer=norm_layer, use_dropout=use_dropout)
    else:
        raise NotImplementedError('Generator model name [%s] is not recognized' % netG)
    return init_net(net, init_type, init_gain, gpu_ids)


def define_D(input_nc, ndf, netD, n_layers_D=3, norm='batch', init_type='normal', init_gain=0.02, gpu_ids=[]):
    """Create a discriminator
    Parameters:
        input_nc (int)     -- the number of channels in input images
        ndf (int)          -- the number of filters in the first conv layer
        netD (str)         -- the architecture's name: basic | n_layers | pixel
        n_layers_D (int)   -- the number of conv layers in the discriminator; effective when netD=='n_layers'
        norm (str)         -- the type of normalization layers used in the network.
        init_type (str)    -- the name of the initialization method.
        init_gain (float)  -- scaling factor for normal, xavier and orthogonal.
        gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2
    Returns a discriminator
    Our current implementation provides three types of discriminators:
        [basic]: 'PatchGAN' classifier described in the original pix2pix paper.
        It can classify whether 70×70 overlapping patches are real or fake.
        Such a patch-level discriminator architecture has fewer parameters
        than a full-image discriminator and can work on arbitrarily-sized images
        in a fully convolutional fashion.
        [n_layers]: With this mode, you cna specify the number of conv layers in the discriminator
        with the parameter <n_layers_D> (default=3 as used in [basic] (PatchGAN).)
        [pixel]: 1x1 PixelGAN discriminator can classify whether a pixel is real or not.
        It encourages greater color diversity but has no effect on spatial statistics.
    The discriminator has been initialized by <init_net>. It uses Leakly RELU for non-linearity.
    """
    net = None
    norm_layer = get_norm_layer(norm_type=norm)

    if netD == 'basic':  # default PatchGAN classifier
        net = NLayerDiscriminator(input_nc, ndf, n_layers=3, norm_layer=norm_layer)
    elif netD == 'n_layers':  # more options
        net = NLayerDiscriminator(input_nc, ndf, n_layers_D, norm_layer=norm_layer)
    else:
        raise NotImplementedError('Discriminator model name [%s] is not recognized' % netD)
    return init_net(net, init_type, init_gain, gpu_ids)


class CycleGANModel(BaseModel):
    """
    This class implements the CycleGAN model, for learning image-to-image translation without paired data.
    The model training requires '--dataset_mode unaligned' dataset.
    By default, it uses a '--netG resnet_9blocks' ResNet generator,
    a '--netD basic' discriminator (PatchGAN introduced by pix2pix),
    and a least-square GANs objective ('--gan_mode lsgan').
    CycleGAN paper: https://arxiv.org/pdf/1703.10593.pdf
    """
    @staticmethod
    def modify_commandline_options(parser, is_train=True):
        """Add new dataset-specific options, and rewrite default values for existing options.
        Parameters:
            parser          -- original option parser
            is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options.
        Returns:
            the modified parser.
        For CycleGAN, in addition to GAN losses, we introduce lambda_A, lambda_B, and lambda_identity for the following losses.
        A (source domain), B (target domain).
        Generators: G_A: A -> B; G_B: B -> A.
        Discriminators: D_A: G_A(A) vs. B; D_B: G_B(B) vs. A.
        Forward cycle loss:  lambda_A * ||G_B(G_A(A)) - A|| (Eqn. (2) in the paper)
        Backward cycle loss: lambda_B * ||G_A(G_B(B)) - B|| (Eqn. (2) in the paper)
        Identity loss (optional): lambda_identity * (||G_A(B) - B|| * lambda_B + ||G_B(A) - A|| * lambda_A) (Sec 5.2 "Photo generation from paintings" in the paper)
        Dropout is not used in the original CycleGAN paper.
        """
        parser.set_defaults(no_dropout=True)  # default CycleGAN did not use dropout
        if is_train:
            parser.add_argument('--lambda_A', type=float, default=10.0, help='weight for cycle loss (A -> B -> A)')
            parser.add_argument('--lambda_B', type=float, default=10.0, help='weight for cycle loss (B -> A -> B)')
            parser.add_argument('--lambda_identity', type=float, default=0.5, help='use identity mapping. Setting lambda_identity other than 0 has an effect of scaling the weight of the identity mapping loss. For example, if the weight of the identity loss should be 10 times smaller than the weight of the reconstruction loss, please set lambda_identity = 0.1')

        return parser

    def __init__(self, opt):
        """Initialize the CycleGAN class.
        Parameters:
            opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
        """
        BaseModel.__init__(self, opt)
        # specify the training losses you want to print out. The training/test scripts will call <BaseModel.get_current_losses>
        self.loss_names = ['D_A', 'G_A', 'cycle_A', 'idt_A', 'D_B', 'G_B', 'cycle_B', 'idt_B']
        # specify the images you want to save/display. The training/test scripts will call <BaseModel.get_current_visuals>
        visual_names_A = ['real_A', 'fake_B', 'rec_A']
        visual_names_B = ['real_B', 'fake_A', 'rec_B']
        if self.isTrain and self.opt.lambda_identity > 0.0:  # if identity loss is used, we also visualize idt_B=G_A(B) ad idt_A=G_A(B)
            visual_names_A.append('idt_B')
            visual_names_B.append('idt_A')

        self.visual_names = visual_names_A + visual_names_B  # combine visualizations for A and B
        # specify the models you want to save to the disk. The training/test scripts will call <BaseModel.save_networks> and <BaseModel.load_networks>.
        if self.isTrain:
            self.model_names = ['G_A', 'G_B', 'D_A', 'D_B']
        else:  # during test time, only load Gs
            self.model_names = ['G_A', 'G_B']

        # define networks (both Generators and discriminators)
        # The naming is different from those used in the paper.
        # Code (vs. paper): G_A (G), G_B (F), D_A (D_Y), D_B (D_X)
        self.netG_A = define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.netG, opt.norm,
                                        not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)
        self.netG_B = define_G(opt.output_nc, opt.input_nc, opt.ngf, opt.netG, opt.norm,
                                        not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)

        if self.isTrain:  # define discriminators
            self.netD_A = define_D(opt.output_nc, opt.ndf, opt.netD,
                                            opt.n_layers_D, opt.norm, opt.init_type, opt.init_gain, self.gpu_ids)
            self.netD_B = define_D(opt.input_nc, opt.ndf, opt.netD,
                                            opt.n_layers_D, opt.norm, opt.init_type, opt.init_gain, self.gpu_ids)

        if self.isTrain:
            if opt.lambda_identity > 0.0:  # only works when input and output images have the same number of channels
                assert(opt.input_nc == opt.output_nc)
            self.fake_A_pool = ImagePool(opt.pool_size)  # create image buffer to store previously generated images
            self.fake_B_pool = ImagePool(opt.pool_size)  # create image buffer to store previously generated images
            # define loss functions
            self.criterionGAN = GANLoss(opt.gan_mode).to(self.device)  # define GAN loss.
            self.criterionCycle = torch.nn.L1Loss()
            self.criterionIdt = torch.nn.L1Loss()
            # initialize optimizers; schedulers will be automatically created by function <BaseModel.setup>.
            self.optimizer_G = torch.optim.Adam(itertools.chain(self.netG_A.parameters(), self.netG_B.parameters()), lr=opt.lr, betas=(opt.beta1, 0.999))
            self.optimizer_D = torch.optim.Adam(itertools.chain(self.netD_A.parameters(), self.netD_B.parameters()), lr=opt.lr, betas=(opt.beta1, 0.999))
            self.optimizers.append(self.optimizer_G)
            self.optimizers.append(self.optimizer_D)

    def set_input(self, input):
        """Unpack input data from the dataloader and perform necessary pre-processing steps.
        Parameters:
            input (dict): include the data itself and its metadata information.
        The option 'direction' can be used to swap domain A and domain B.
        """
        AtoB = self.opt.direction == 'AtoB'
        self.real_A = input['A' if AtoB else 'B'].to(self.device)
        self.real_B = input['B' if AtoB else 'A'].to(self.device)
        self.image_paths = input['A_paths' if AtoB else 'B_paths']

    def forward(self):
        """Run forward pass; called by both functions <optimize_parameters> and <test>."""
        self.fake_B = self.netG_A(self.real_A)  # G_A(A)
        self.rec_A = self.netG_B(self.fake_B)   # G_B(G_A(A))
        self.fake_A = self.netG_B(self.real_B)  # G_B(B)
        self.rec_B = self.netG_A(self.fake_A)   # G_A(G_B(B))

    def backward_D_basic(self, netD, real, fake):
        """Calculate GAN loss for the discriminator
        Parameters:
            netD (network)      -- the discriminator D
            real (tensor array) -- real images
            fake (tensor array) -- images generated by a generator
        Return the discriminator loss.
        We also call loss_D.backward() to calculate the gradients.
        """
        # Real
        pred_real = netD(real)
        loss_D_real = self.criterionGAN(pred_real, True)
        # Fake
        pred_fake = netD(fake.detach())
        loss_D_fake = self.criterionGAN(pred_fake, False)
        # Combined loss and calculate gradients
        loss_D = (loss_D_real + loss_D_fake) * 0.5
        loss_D.backward()
        return loss_D

    def backward_D_A(self):
        """Calculate GAN loss for discriminator D_A"""
        fake_B = self.fake_B_pool.query(self.fake_B)
        self.loss_D_A = self.backward_D_basic(self.netD_A, self.real_B, fake_B)

    def backward_D_B(self):
        """Calculate GAN loss for discriminator D_B"""
        fake_A = self.fake_A_pool.query(self.fake_A)
        self.loss_D_B = self.backward_D_basic(self.netD_B, self.real_A, fake_A)

    def backward_G(self):
        """Calculate the loss for generators G_A and G_B"""
        lambda_idt = self.opt.lambda_identity
        lambda_A = self.opt.lambda_A
        lambda_B = self.opt.lambda_B
        # Identity loss
        if lambda_idt > 0:
            # G_A should be identity if real_B is fed: ||G_A(B) - B||
            self.idt_A = self.netG_A(self.real_B)
            self.loss_idt_A = self.criterionIdt(self.idt_A, self.real_B) * lambda_B * lambda_idt
            # G_B should be identity if real_A is fed: ||G_B(A) - A||
            self.idt_B = self.netG_B(self.real_A)
            self.loss_idt_B = self.criterionIdt(self.idt_B, self.real_A) * lambda_A * lambda_idt
        else:
            self.loss_idt_A = 0
            self.loss_idt_B = 0

        # GAN loss D_A(G_A(A))
        self.loss_G_A = self.criterionGAN(self.netD_A(self.fake_B), True)
        # GAN loss D_B(G_B(B))
        self.loss_G_B = self.criterionGAN(self.netD_B(self.fake_A), True)
        # Forward cycle loss || G_B(G_A(A)) - A||
        self.loss_cycle_A = self.criterionCycle(self.rec_A, self.real_A) * lambda_A
        # Backward cycle loss || G_A(G_B(B)) - B||
        self.loss_cycle_B = self.criterionCycle(self.rec_B, self.real_B) * lambda_B
        # combined loss and calculate gradients
        self.loss_G = self.loss_G_A + self.loss_G_B + self.loss_cycle_A + self.loss_cycle_B + self.loss_idt_A + self.loss_idt_B
        self.loss_G.backward()

    def optimize_parameters(self):
        """Calculate losses, gradients, and update network weights; called in every training iteration"""
        # forward
        self.forward()      # compute fake images and reconstruction images.
        # G_A and G_B
        self.set_requires_grad([self.netD_A, self.netD_B], False)  # Ds require no gradients when optimizing Gs
        self.optimizer_G.zero_grad()  # set G_A and G_B's gradients to zero
        self.backward_G()             # calculate gradients for G_A and G_B
        self.optimizer_G.step()       # update G_A and G_B's weights
        # D_A and D_B
        self.set_requires_grad([self.netD_A, self.netD_B], True)
        self.optimizer_D.zero_grad()   # set D_A and D_B's gradients to zero
        self.backward_D_A()      # calculate gradients for D_A
        self.backward_D_B()      # calculate graidents for D_B
        self.optimizer_D.step()  # update D_A and D_B's weights


    