import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import torch.optim as optim


def squared_l2_norm(x):
    flattened = x.view(x.unsqueeze(0).shape[0], -1)
    return (flattened ** 2).sum(1)


def l2_norm(x):
    return squared_l2_norm(x).sqrt()


def trades_loss(model,
                x_natural,
                y,
                optimizer,
                step_size=0.003,
                epsilon=0.031,
                perturb_steps=10,
                beta=1.0,
                distance='l_inf'):
    # define KL-loss
    criterion_kl = nn.KLDivLoss(size_average=False)
    model.eval()
    batch_size = len(x_natural)
    # generate adversarial example
    x_adv = x_natural.detach() + 0.001 * torch.randn(x_natural.shape).cuda().detach()
    if distance == 'l_inf':
        for _ in range(perturb_steps):
            x_adv.requires_grad_()
            with torch.enable_grad():
                loss_kl = criterion_kl(F.log_softmax(model(x_adv), dim=1),
                                       F.softmax(model(x_natural), dim=1))
            grad = torch.autograd.grad(loss_kl, [x_adv])[0]
            x_adv = x_adv.detach() + step_size * torch.sign(grad.detach())
            x_adv = torch.min(torch.max(x_adv, x_natural - epsilon), x_natural + epsilon)
            x_adv = torch.clamp(x_adv, 0.0, 1.0)
    elif distance == 'l_2':
        delta = 0.001 * torch.randn(x_natural.shape).cuda().detach()
        delta = Variable(delta.data, requires_grad=True)

        # Setup optimizers
        optimizer_delta = optim.SGD([delta], lr=epsilon / perturb_steps * 2)

        for _ in range(perturb_steps):
            adv = x_natural + delta

            # optimize
            optimizer_delta.zero_grad()
            with torch.enable_grad():
                loss = (-1) * criterion_kl(F.log_softmax(model(adv), dim=1),
                                           F.softmax(model(x_natural), dim=1))
            loss.backward()
            # renorming gradient
            grad_norms = delta.grad.view(batch_size, -1).norm(p=2, dim=1)
            delta.grad.div_(grad_norms.view(-1, 1, 1, 1))
            # avoid nan or inf if gradient is 0
            if (grad_norms == 0).any():
                delta.grad[grad_norms == 0] = torch.randn_like(delta.grad[grad_norms == 0])
            optimizer_delta.step()

            # projection
            delta.data.add_(x_natural)
            delta.data.clamp_(0, 1).sub_(x_natural)
            delta.data.renorm_(p=2, dim=0, maxnorm=epsilon)
        x_adv = Variable(x_natural + delta, requires_grad=False)
    else:
        x_adv = torch.clamp(x_adv, 0.0, 1.0)
    model.train()

    x_adv = Variable(torch.clamp(x_adv, 0.0, 1.0), requires_grad=False)
    # zero gradient
    optimizer.zero_grad()
    # calculate robust loss
    #logits = model(x_natural)
    
    output = model(x_natural)
    output_adv = model(x_adv)
    device = torch.device('cuda')
    with torch.no_grad():
        aa = torch.zeros(output.size()[1], output.size()[1]).to(device)
        for xx in output:
            aa += torch.kron(xx, xx.reshape((-1,1)))
        #aa = torch.abs(torch.inverse(aa))
        aa = -1*torch.abs(aa)
        aa_min, aa_indexes = torch.min(aa, 1)
        aa = aa-aa_min.reshape(-1,1)
        for i in range(len(aa)):
            aa[i,i]=0.
        aa = 0.8*aa/aa.sum(1).reshape(-1,1)
        for i in range(len(aa)):
            aa[i,i]=0.2
        yy = aa[y]

        aa_adv = torch.zeros(output_adv.size()[1], output_adv.size()[1]).to(device)
        for xx in output_adv:
            aa_adv += torch.kron(xx, xx.reshape((-1,1)))
        #aa_adv = torch.abs(torch.inverse(aa_adv))
        aa_adv = -1*torch.abs(aa_adv)
        aa_adv_min, aa_adv_indexes = torch.min(aa_adv, 1)
        aa_adv = aa_adv-aa_adv_min.reshape(-1,1)
        for i in range(len(aa_adv)):
            aa_adv[i,i]=0.
        aa_adv = 0.8*aa_adv/aa_adv.sum(1).reshape(-1,1)
        for i in range(len(aa_adv)):
            aa_adv[i,i]=0.2
        yy_adv = aa_adv[y]

    loss_yy = (1.0 / yy.size()[0]) * criterion_kl(F.log_softmax(model(x_natural), dim=1),yy)
    loss_yy_adv = (1.0 / yy_adv.size()[0]) * criterion_kl(F.log_softmax(model(x_adv), dim=1),yy_adv)
    
    loss_natural = F.cross_entropy(output, y)
    loss_robust = (1.0 / batch_size) * criterion_kl(F.log_softmax(model(x_adv), dim=1),
                                                    F.softmax(model(x_natural), dim=1))
    loss = loss_natural*0.7 + 0.3*0.5*(loss_yy+loss_yy_adv) + beta * loss_robust
    return loss

def trades_adv(model,
                x_natural,
                y,
                optimizer,
                step_size=0.003,
                epsilon=0.031,
                perturb_steps=10,
                beta=1.0,
                distance='l_inf'):
    # define KL-loss
    criterion_kl = nn.KLDivLoss(size_average=False)
    model.eval()
    batch_size = len(x_natural)
    # generate adversarial example
    x_adv = x_natural.detach() + 0.001 * torch.randn(x_natural.shape).cuda().detach()
    if distance == 'l_inf':
        for _ in range(perturb_steps):
            x_adv.requires_grad_()
            with torch.enable_grad():
                loss_kl = criterion_kl(F.log_softmax(model(x_adv), dim=1),
                                       F.softmax(model(x_natural), dim=1))
            grad = torch.autograd.grad(loss_kl, [x_adv])[0]
            x_adv = x_adv.detach() + step_size * torch.sign(grad.detach())
            x_adv = torch.min(torch.max(x_adv, x_natural - epsilon), x_natural + epsilon)
            x_adv = torch.clamp(x_adv, 0.0, 1.0)
    elif distance == 'l_2':
        delta = 0.001 * torch.randn(x_natural.shape).cuda().detach()
        delta = Variable(delta.data, requires_grad=True)

        # Setup optimizers
        optimizer_delta = optim.SGD([delta], lr=epsilon / perturb_steps * 2)

        for _ in range(perturb_steps):
            adv = x_natural + delta

            # optimize
            optimizer_delta.zero_grad()
            with torch.enable_grad():
                loss = (-1) * criterion_kl(F.log_softmax(model(adv), dim=1),
                                           F.softmax(model(x_natural), dim=1))
            loss.backward()
            # renorming gradient
            grad_norms = delta.grad.view(batch_size, -1).norm(p=2, dim=1)
            delta.grad.div_(grad_norms.view(-1, 1, 1, 1))
            # avoid nan or inf if gradient is 0
            if (grad_norms == 0).any():
                delta.grad[grad_norms == 0] = torch.randn_like(delta.grad[grad_norms == 0])
            optimizer_delta.step()

            # projection
            delta.data.add_(x_natural)
            delta.data.clamp_(0, 1).sub_(x_natural)
            delta.data.renorm_(p=2, dim=0, maxnorm=epsilon)
        x_adv = Variable(x_natural + delta, requires_grad=False)
    else:
        x_adv = torch.clamp(x_adv, 0.0, 1.0)
    model.train()

    x_adv = Variable(torch.clamp(x_adv, 0.0, 1.0), requires_grad=False)
    return x_adv