import torch
import pytorch_lightning as pl
import torch.nn.functional as F

import sys
sys.path.append('.')

from stable_diffusion.ldm.modules.diffusionmodules.model import Encoder, Decoder
from stable_diffusion.ldm.modules.distributions.distributions import DiagonalGaussianDistribution
from stable_diffusion.ldm.util import instantiate_from_config


class AutoencoderKL(pl.LightningModule):
    def __init__(self,
                 ddconfig,
                 lossconfig,  # torch.nn.Identity
                 embed_dim,  # embed_dim = 4
                 ckpt_path=None,
                 ignore_keys=[],
                 image_key="image",
                 colorize_nlabels=None,  # This is None
                 monitor=None,  # val/rec_loss
                 ):
        super().__init__()
        self.image_key = image_key  # 'image'

        # The encoder and decoder are reverse in the VQVAE
        # The encoder encodes the image to a latent space, and then transfer it to a Gaussian Distribution
        self.encoder = Encoder(**ddconfig)
        # Note, the output of the encoder is NOT directly fed into the decoder. The output channel size of the encoder is 2 * z_channel, as identified by the ddconfig['double_z']. This is becuase the output of the encoder is used to construct a Gaussian Distribution
        # The decoder decodes the latent space to an image
        self.decoder = Decoder(**ddconfig)

        # torch.nn.Identity
        self.loss = instantiate_from_config(lossconfig)  # Identity function

        # double_z = True.
        assert ddconfig["double_z"]

        # z_channels = 4
        self.quant_conv = torch.nn.Conv2d(2 * ddconfig["z_channels"], 2 * embed_dim, 1)
        self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)

        # embed_dim = 4
        self.embed_dim = embed_dim

        # colorize_nlabels is None
        if colorize_nlabels is not None:
            assert type(colorize_nlabels)==int
            self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))

        # monitor = val/rec_loss
        if monitor is not None:
            self.monitor = monitor

        # ckpt_path = None, the checkpoint loading of stable diffusion is conducted outside
        if ckpt_path is not None:
            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)

    def init_from_ckpt(self, path, ignore_keys=list()):
        sd = torch.load(path, map_location="cpu")["state_dict"]
        keys = list(sd.keys())
        for k in keys:
            for ik in ignore_keys:
                if k.startswith(ik):
                    print("Deleting key {} from state_dict.".format(k))
                    del sd[k]
        self.load_state_dict(sd, strict=False)
        print(f"Restored from {path}")

    def encode(self, x):
        # x: [bs, 3, 256, 256], h: [bs, 8, 32, 32]
        h = self.encoder(x)
        # serves as the mean and variance of the Gaussian distribution (halve the last dim)
        # moments: [bs, 8, 32, 32]
        moments = self.quant_conv(h)
        posterior = DiagonalGaussianDistribution(moments)
        return posterior

    def decode(self, z):
        # z: [bs, 4, 32, 32]
        z = self.post_quant_conv(z)
        # z: [bs, 4, 32, 32]
        dec = self.decoder(z)
        # dec: [bs, 3, 256, 256]
        return dec

    def forward(self, input, sample_posterior=True):
        posterior = self.encode(input)
        if sample_posterior:
            z = posterior.sample()  # a normal sampling
        else:
            z = posterior.mode()  # returns the mean
        dec = self.decode(z)
        return dec, posterior

    def get_input(self, batch, k):
        x = batch[k]
        if len(x.shape) == 3:
            x = x[..., None]
        x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
        return x

    def training_step(self, batch, batch_idx, optimizer_idx):  # in Stable Diffusion we use pretrained VAE and freeze it.
        inputs = self.get_input(batch, self.image_key)
        reconstructions, posterior = self(inputs)

        if optimizer_idx == 0:
            # train encoder+decoder+logvar
            aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
                                            last_layer=self.get_last_layer(), split="train")
            self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
            self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False)
            return aeloss

        if optimizer_idx == 1:
            # train the discriminator
            discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
                                                last_layer=self.get_last_layer(), split="train")

            self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
            self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False)
            return discloss

    def validation_step(self, batch, batch_idx):
        inputs = self.get_input(batch, self.image_key)
        reconstructions, posterior = self(inputs)
        aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step,
                                        last_layer=self.get_last_layer(), split="val")

        discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step,
                                            last_layer=self.get_last_layer(), split="val")

        self.log("val/rec_loss", log_dict_ae["val/rec_loss"])
        self.log_dict(log_dict_ae)
        self.log_dict(log_dict_disc)
        return self.log_dict

    def configure_optimizers(self):
        lr = self.learning_rate
        opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
                                  list(self.decoder.parameters())+
                                  list(self.quant_conv.parameters())+
                                  list(self.post_quant_conv.parameters()),
                                  lr=lr, betas=(0.5, 0.9))
        opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
                                    lr=lr, betas=(0.5, 0.9))
        return [opt_ae, opt_disc], []

    def get_last_layer(self):
        return self.decoder.conv_out.weight

    @torch.no_grad()
    def log_images(self, batch, only_inputs=False, **kwargs):
        log = dict()
        x = self.get_input(batch, self.image_key)
        x = x.to(self.device)
        if not only_inputs:
            xrec, posterior = self(x)
            if x.shape[1] > 3:
                # colorize with random projection
                assert xrec.shape[1] > 3
                x = self.to_rgb(x)
                xrec = self.to_rgb(xrec)
            log["samples"] = self.decode(torch.randn_like(posterior.sample()))
            log["reconstructions"] = xrec
        log["inputs"] = x
        return log

    def to_rgb(self, x):
        assert self.image_key == "segmentation"
        if not hasattr(self, "colorize"):
            self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
        x = F.conv2d(x, weight=self.colorize)
        x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
        return x


class IdentityFirstStage(torch.nn.Module):
    def __init__(self, *args, vq_interface=False, **kwargs):
        self.vq_interface = vq_interface  # TODO: Should be true by default but check to not break older stuff
        super().__init__()

    def encode(self, x, *args, **kwargs):
        return x

    def decode(self, x, *args, **kwargs):
        return x

    def quantize(self, x, *args, **kwargs):
        if self.vq_interface:
            return x, None, [None, None, None]
        return x

    def forward(self, x, *args, **kwargs):
        return x