import torch
import torch.nn as nn
import numpy as np


class ReconstructionLoss(nn.Module):
    def __init__(self, losstype='l2', eps=1e-6):
        super(ReconstructionLoss, self).__init__()
        self.losstype = losstype
        self.eps = eps

    def forward(self, x, target):
        if self.losstype == 'l2':
            return torch.mean(torch.sum((x - target) ** 2, (1, 2, 3)))
        elif self.losstype == 'l1':
            diff = x - target
            return torch.mean(torch.sum(torch.sqrt(diff * diff + self.eps), (1, 2, 3)))
        elif self.losstype == 'center':
            return torch.sum((x - target) ** 2, (1, 2, 3))

        else:
            print("reconstruction loss type error!")
            return 0


# Define GAN loss: [vanilla | lsgan | wgan-gp]
class GANLoss(nn.Module):
    def __init__(self, gan_type, real_label_val=1.0, fake_label_val=0.0):
        super(GANLoss, self).__init__()
        self.gan_type = gan_type.lower()
        self.real_label_val = real_label_val
        self.fake_label_val = fake_label_val

        if self.gan_type == 'gan' or self.gan_type == 'ragan':
            self.loss = nn.BCEWithLogitsLoss()
        elif self.gan_type == 'lsgan':
            self.loss = nn.MSELoss()
        elif self.gan_type == 'wgan-gp':

            def wgan_loss(input, target):
                # target is boolean
                return -1 * input.mean() if target else input.mean()

            self.loss = wgan_loss
        else:
            raise NotImplementedError('GAN type [{:s}] is not found'.format(self.gan_type))

    def get_target_label(self, input, target_is_real):
        if self.gan_type == 'wgan-gp':
            return target_is_real
        if target_is_real:
            return torch.empty_like(input).fill_(self.real_label_val)
        else:
            return torch.empty_like(input).fill_(self.fake_label_val)

    def forward(self, input, target_is_real):
        target_label = self.get_target_label(input, target_is_real)
        loss = self.loss(input, target_label)
        return loss


class GradientPenaltyLoss(nn.Module):
    def __init__(self, device=torch.device('cpu')):
        super(GradientPenaltyLoss, self).__init__()
        self.register_buffer('grad_outputs', torch.Tensor())
        self.grad_outputs = self.grad_outputs.to(device)

    def get_grad_outputs(self, input):
        if self.grad_outputs.size() != input.size():
            self.grad_outputs.resize_(input.size()).fill_(1.0)
        return self.grad_outputs

    def forward(self, interp, interp_crit):
        grad_outputs = self.get_grad_outputs(interp_crit)
        grad_interp = torch.autograd.grad(outputs=interp_crit, inputs=interp,
                                          grad_outputs=grad_outputs, create_graph=True,
                                          retain_graph=True, only_inputs=True)[0]
        grad_interp = grad_interp.view(grad_interp.size(0), -1)
        grad_interp_norm = grad_interp.norm(2, dim=1)

        loss = ((grad_interp_norm - 1) ** 2).mean()
        return loss
    

class ReconstructionMsgLoss(nn.Module):
    def __init__(self, losstype='mse'):
        super(ReconstructionMsgLoss, self).__init__()
        self.losstype = losstype
        self.mse_loss = nn.MSELoss()
        self.bce_loss = nn.BCELoss()
        self.bce_logits_loss = nn.BCEWithLogitsLoss()

    def forward(self, messages, decoded_messages): 
        if self.losstype == 'mse':
            return self.mse_loss(messages, decoded_messages)
        elif self.losstype == 'bce':
            return self.bce_loss(messages, decoded_messages)
        elif self.losstype == 'bce_logits':
            return self.bce_logits_loss(messages, decoded_messages)
        else:
            print("ReconstructionMsgLoss loss type error!")
            return 0