import functools
from math import exp

import torch
import torch.nn as nn
from torch.nn import init
from torch.autograd import Variable
import torch.nn.functional as F
import torch.nn.utils.spectral_norm as SpectralNorm
from torchvision import models

def save(net,path,gpu_id):
    if isinstance(net, nn.DataParallel):
        torch.save(net.module.cpu().state_dict(),path)
    else:
        torch.save(net.cpu().state_dict(),path) 
    if gpu_id != '-1':
        net.cuda()

################################## initialization ##################################
def get_norm_layer(norm_type='instance',mod = '2d'):
    if norm_type == 'batch':
        if mod == '2d':
            norm_layer = functools.partial(nn.BatchNorm2d, affine=True)
        elif mod == '3d':
            norm_layer = functools.partial(nn.BatchNorm3d, affine=True)
    elif norm_type == 'instance':
        if mod == '2d':
            norm_layer = functools.partial(nn.InstanceNorm2d, affine=False, track_running_stats=True)
        elif mod =='3d':
            norm_layer = functools.partial(nn.InstanceNorm3d, affine=False, track_running_stats=True)
    elif norm_type == 'none':
        norm_layer = None
    else:
        raise NotImplementedError('normalization layer [%s] is not found' % norm_type)

    return norm_layer

def init_weights(net, init_type='normal', gain=0.02):
    def init_func(m):
        classname = m.__class__.__name__
        if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1):
            if init_type == 'normal':
                init.normal_(m.weight.data, 0.0, gain)
            elif init_type == 'xavier':
                init.xavier_normal_(m.weight.data, gain=gain)
            elif init_type == 'kaiming':
                init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
            elif init_type == 'orthogonal':
                init.orthogonal_(m.weight.data, gain=gain)
            else:
                raise NotImplementedError('initialization method [%s] is not implemented' % init_type)
            if hasattr(m, 'bias') and m.bias is not None:
                init.constant_(m.bias.data, 0.0)
        elif classname.find('BatchNorm2d') != -1:
            init.normal_(m.weight.data, 1.0, gain)
            init.constant_(m.bias.data, 0.0)

    # print('initialize network with %s' % init_type)
    net.apply(init_func)

################################## Network structure ##################################
class ResnetBlockSpectralNorm(nn.Module):
    def __init__(self, dim, padding_type, activation=nn.LeakyReLU(0.2), use_dropout=False):
        super(ResnetBlockSpectralNorm, self).__init__()
        self.conv_block = self.build_conv_block(dim, padding_type, activation, use_dropout)

    def build_conv_block(self, dim, padding_type, activation, use_dropout):
        conv_block = []
        p = 0
        if padding_type == 'reflect':
            conv_block += [nn.ReflectionPad2d(1)]
        elif padding_type == 'replicate':
            conv_block += [nn.ReplicationPad2d(1)]
        elif padding_type == 'zero':
            p = 1
        else:
            raise NotImplementedError('padding [%s] is not implemented' % padding_type)

        conv_block += [SpectralNorm(nn.Conv2d(dim, dim, kernel_size=3, padding=p)),
                       activation]
        if use_dropout:
            conv_block += [nn.Dropout(0.5)]

        p = 0
        if padding_type == 'reflect':
            conv_block += [nn.ReflectionPad2d(1)]
        elif padding_type == 'replicate':
            conv_block += [nn.ReplicationPad2d(1)]
        elif padding_type == 'zero':
            p = 1
        else:
            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
        conv_block += [SpectralNorm(nn.Conv2d(dim, dim, kernel_size=3, padding=p))]

        return nn.Sequential(*conv_block)

    def forward(self, x):
        out = x + self.conv_block(x)
        return out

################################## Loss function ##################################
class HingeLossD(nn.Module):
    def __init__(self):
        super(HingeLossD, self).__init__()

    def forward(self, dis_fake, dis_real):
        loss_real = torch.mean(F.relu(1. - dis_real))
        loss_fake = torch.mean(F.relu(1. + dis_fake))
        return loss_real + loss_fake

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

    def forward(self, dis_fake):
        loss_fake = F.relu(-torch.mean(dis_fake))
        return loss_fake

class VGGLoss(nn.Module):
    def __init__(self, gpu_id):
        super(VGGLoss, self).__init__()  

        self.vgg = Vgg19()
        if gpu_id != '-1' and len(gpu_id) == 1:
            self.vgg.cuda()
        elif gpu_id != '-1' and len(gpu_id) > 1:
            self.vgg = nn.DataParallel(self.vgg)
            self.vgg.cuda()

        self.criterion = nn.MSELoss()
        self.weights = [1.0/32, 1.0/16, 1.0/8, 1.0/4, 1.0]        

    def forward(self, x, y):              
        x_vgg, y_vgg = self.vgg(x), self.vgg(y)
        loss = 0
        for i in range(len(x_vgg)):
            loss += self.weights[i] * self.criterion(x_vgg[i], y_vgg[i].detach())        
        return loss

class Vgg19(torch.nn.Module):
    def __init__(self, requires_grad=False):
        super(Vgg19, self).__init__()
        vgg_pretrained_features = models.vgg19(pretrained=True).features
        self.slice1 = torch.nn.Sequential()
        self.slice2 = torch.nn.Sequential()
        self.slice3 = torch.nn.Sequential()
        self.slice4 = torch.nn.Sequential()
        self.slice5 = torch.nn.Sequential()
        for x in range(2):
            self.slice1.add_module(str(x), vgg_pretrained_features[x])
        for x in range(2, 7):
            self.slice2.add_module(str(x), vgg_pretrained_features[x])
        for x in range(7, 12):
            self.slice3.add_module(str(x), vgg_pretrained_features[x])
        for x in range(12, 21):
            self.slice4.add_module(str(x), vgg_pretrained_features[x])
        for x in range(21, 30):
            self.slice5.add_module(str(x), vgg_pretrained_features[x])
        if not requires_grad:
            for param in self.parameters():
                param.requires_grad = False

    def forward(self, X):
        h_relu1 = self.slice1(X)
        h_relu2 = self.slice2(h_relu1)        
        h_relu3 = self.slice3(h_relu2)        
        h_relu4 = self.slice4(h_relu3)        
        h_relu5 = self.slice5(h_relu4)                
        out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5]
        return out

################################## Evaluation ##################################
'''https://github.com/Po-Hsun-Su/pytorch-ssim

img1 = Variable(torch.rand(1, 1, 256, 256))
img2 = Variable(torch.rand(1, 1, 256, 256))

if torch.cuda.is_available():
    img1 = img1.cuda()
    img2 = img2.cuda()

print(pytorch_ssim.ssim(img1, img2))

ssim_loss = pytorch_ssim.SSIM(window_size = 11)

print(ssim_loss(img1, img2))
'''

def gaussian(window_size, sigma):
    gauss = torch.Tensor([exp(-(x - window_size//2)**2/float(2*sigma**2)) for x in range(window_size)])
    return gauss/gauss.sum()

def create_window(window_size, channel):
    _1D_window = gaussian(window_size, 1.5).unsqueeze(1)
    _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
    window = Variable(_2D_window.expand(channel, 1, window_size, window_size).contiguous())
    return window

def _ssim(img1, img2, window, window_size, channel, size_average = True):
    mu1 = F.conv2d(img1, window, padding = window_size//2, groups = channel)
    mu2 = F.conv2d(img2, window, padding = window_size//2, groups = channel)

    mu1_sq = mu1.pow(2)
    mu2_sq = mu2.pow(2)
    mu1_mu2 = mu1*mu2

    sigma1_sq = F.conv2d(img1*img1, window, padding = window_size//2, groups = channel) - mu1_sq
    sigma2_sq = F.conv2d(img2*img2, window, padding = window_size//2, groups = channel) - mu2_sq
    sigma12 = F.conv2d(img1*img2, window, padding = window_size//2, groups = channel) - mu1_mu2

    C1 = 0.01**2
    C2 = 0.03**2

    ssim_map = ((2*mu1_mu2 + C1)*(2*sigma12 + C2))/((mu1_sq + mu2_sq + C1)*(sigma1_sq + sigma2_sq + C2))

    if size_average:
        return ssim_map.mean()
    else:
        return ssim_map.mean(1).mean(1).mean(1)

class SSIM(torch.nn.Module):
    def __init__(self, window_size = 11, size_average = True):
        super(SSIM, self).__init__()
        self.window_size = window_size
        self.size_average = size_average
        self.channel = 1
        self.window = create_window(window_size, self.channel)

    def forward(self, img1, img2):
        (_, channel, _, _) = img1.size()

        if channel == self.channel and self.window.data.type() == img1.data.type():
            window = self.window
        else:
            window = create_window(self.window_size, channel)
            
            if img1.is_cuda:
                window = window.cuda(img1.get_device())
            window = window.type_as(img1)
            
            self.window = window
            self.channel = channel


        return _ssim(img1, img2, window, self.window_size, channel, self.size_average)

def ssim(img1, img2, window_size = 11, size_average = True):
    (_, channel, _, _) = img1.size()
    window = create_window(window_size, channel)
    
    if img1.is_cuda:
        window = window.cuda(img1.get_device())
    window = window.type_as(img1)
    
    return _ssim(img1, img2, window, window_size, channel, size_average)
