import logging

import pytorch_lightning as pl
import torch

from imaginairy.modules.diffusion.model import Decoder, Encoder
from imaginairy.modules.distributions import DiagonalGaussianDistribution
from imaginairy.utils import instantiate_from_config

logger = logging.getLogger(__name__)


class AutoencoderKL(pl.LightningModule):
    def __init__(
        self,
        ddconfig,
        lossconfig,
        embed_dim,
        ckpt_path=None,
        ignore_keys=None,
        image_key="image",
        colorize_nlabels=None,
        monitor=None,
    ):
        super().__init__()
        ignore_keys = [] if ignore_keys is None else ignore_keys
        self.image_key = image_key
        self.encoder = Encoder(**ddconfig)
        self.decoder = Decoder(**ddconfig)
        self.loss = instantiate_from_config(lossconfig)
        assert ddconfig["double_z"]
        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)
        self.embed_dim = embed_dim
        if colorize_nlabels is not None:
            assert isinstance(colorize_nlabels, int)
            self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
        if monitor is not None:
            self.monitor = monitor
        if ckpt_path is not None:
            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)

    def init_from_ckpt(self, path, ignore_keys=None):
        ignore_keys = [] if ignore_keys is None else ignore_keys
        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):
                    logger.info(f"Deleting key {k} from state_dict.")
                    del sd[k]
        self.load_state_dict(sd, strict=False)
        logger.info(f"Restored from {path}")

    def encode(self, x):
        h = self.encoder(x)
        moments = self.quant_conv(h)
        posterior = DiagonalGaussianDistribution(moments)
        return posterior

    def decode(self, z):
        z = self.post_quant_conv(z)
        dec = self.decoder(z)
        return dec

    def forward(self, input, sample_posterior=True):  # noqa
        posterior = self.encode(input)
        if sample_posterior:
            z = posterior.sample()
        else:
            z = posterior.mode()
        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