import torch
import torch.nn as nn
import torch.nn.functional as F
import sys
from ldm.util import exists
sys.path.insert(0, '.')  # nopep8
from ldm.modules.discriminator.model import (NLayerDiscriminator, NLayerDiscriminator1dFeats,
                                                   NLayerDiscriminator1dSpecs,
                                                   weights_init)
# from ldm.modules.losses_audio.lpaps import LPAPS
from ldm.modules.losses.vqperceptual import l1, l2, measure_perplexity, hinge_d_loss, vanilla_d_loss, adopt_weight



class DummyLoss(nn.Module):
    def __init__(self):
        super().__init__()

class VQLPAPSWithDiscriminator(nn.Module):
    def __init__(self, disc_start, codebook_weight=1.0, pixelloss_weight=1.0,
                 disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0,
                 perceptual_weight=1.0, use_actnorm=False, disc_conditional=False,
                 disc_ndf=64, disc_loss="hinge", n_classes=None, pixel_loss="l1"):
        super().__init__()
        assert disc_loss in ["hinge", "vanilla"]
        self.codebook_weight = codebook_weight
        self.pixel_weight = pixelloss_weight
        self.perceptual_loss = None # LPAPS().eval()
        self.perceptual_weight = perceptual_weight

        if pixel_loss == "l1":
            self.pixel_loss = l1
        else:
            self.pixel_loss = l2

        self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels,
                                                 n_layers=disc_num_layers,
                                                 use_actnorm=use_actnorm,
                                                 ndf=disc_ndf
                                                 ).apply(weights_init)
        self.discriminator_iter_start = disc_start
        if disc_loss == "hinge":
            self.disc_loss = hinge_d_loss
        elif disc_loss == "vanilla":
            self.disc_loss = vanilla_d_loss
        else:
            raise ValueError(f"Unknown GAN loss '{disc_loss}'.")
        print(f"VQLPAPSWithDiscriminator running with {disc_loss} loss.")
        self.disc_factor = disc_factor
        self.discriminator_weight = disc_weight
        self.disc_conditional = disc_conditional
        self.n_classes = n_classes

    def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None):
        if last_layer is not None:
            nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0]
            g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]
        else:
            nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0]
            g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0]

        d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
        d_weight = torch.clamp(d_weight, 0.0, 1e4).detach()
        d_weight = d_weight * self.discriminator_weight
        return d_weight

    def forward(self, codebook_loss, inputs, reconstructions, optimizer_idx,
                global_step, last_layer=None, cond=None, split="train", predicted_indices=None):
        if not exists(codebook_loss):
            codebook_loss = torch.tensor([0.]).to(inputs.device)
        rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous())
        if self.perceptual_weight > 0:
            p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous())
            rec_loss = rec_loss + self.perceptual_weight * p_loss
        else:
            p_loss = torch.tensor([0.0])

        nll_loss = rec_loss
        # nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
        nll_loss = torch.mean(nll_loss)

        # now the GAN part
        if optimizer_idx == 0:
            # generator update
            if cond is None:
                assert not self.disc_conditional
                logits_fake = self.discriminator(reconstructions.contiguous())
            else:
                assert self.disc_conditional
                logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1))
            g_loss = -torch.mean(logits_fake)

            try:
                d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer)
            except RuntimeError:
                assert not self.training
                d_weight = torch.tensor(0.0)

            disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
            loss = nll_loss + d_weight * disc_factor * g_loss + self.codebook_weight * codebook_loss.mean()

            log = {"{}/total_loss".format(split): loss.clone().detach().mean(),
                   "{}/quant_loss".format(split): codebook_loss.detach().mean(),
                   "{}/nll_loss".format(split): nll_loss.detach().mean(),
                   "{}/rec_loss".format(split): rec_loss.detach().mean(),
                   "{}/p_loss".format(split): p_loss.detach().mean(),
                   "{}/d_weight".format(split): d_weight.detach(),
                   "{}/disc_factor".format(split): torch.tensor(disc_factor),
                   "{}/g_loss".format(split): g_loss.detach().mean(),
                   }
            # if predicted_indices is not None:
            #     assert self.n_classes is not None
            #     with torch.no_grad():
            #         perplexity, cluster_usage = measure_perplexity(predicted_indices, self.n_classes)
            #     log[f"{split}/perplexity"] = perplexity
            #     log[f"{split}/cluster_usage"] = cluster_usage
            return loss, log

        if optimizer_idx == 1:
            # second pass for discriminator update
            if cond is None:
                logits_real = self.discriminator(inputs.contiguous().detach())
                logits_fake = self.discriminator(reconstructions.contiguous().detach())
            else:
                logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1))
                logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1))

            disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
            d_loss = disc_factor * self.disc_loss(logits_real, logits_fake)

            log = {"{}/disc_loss".format(split): d_loss.clone().detach().mean(),
                   "{}/logits_real".format(split): logits_real.detach().mean(),
                   "{}/logits_fake".format(split): logits_fake.detach().mean()
                   }
            return d_loss, log