# Mnist LTN with Reconstruction module
import torch
from utils.args import *
from utils.conf import get_device
from utils.losses import *
from models.cext import CExt
from models.utils.utils_problog import build_worlds_queries_matrix
from utils.mnist_ltn_loss import ADDMNIST_SAT_AGG


def get_parser() -> ArgumentParser:
    """Returns argument parser from this architecture

    Returns:
        argpars: argumentparser
    """
    parser = ArgumentParser(description="Learning via" "Concept Extractor .")
    add_management_args(parser)
    add_experiment_args(parser)
    return parser


class MnistLTNRec(CExt):
    """LTN with reconstruction term"""

    NAME = "mnistltnrec"
    """
    MNIST OPERATIONS AMONG TWO DIGITS. IT WORKS ONLY IN THIS CONFIGURATION.
    """

    def __init__(self, encoder, decoder, n_images=2, c_split=(), args=None):
        super(MnistLTNRec, self).__init__(
            encoder=encoder, n_images=n_images, c_split=c_split
        )
        """Initialize method

        Args:
            self: instance
            encoder (nn.Module): encoder network
            decoder (nn.Module): decoder network
            n_images (int, default=2): number of images
            c_split: concept split
            args (default=None): command line arguments

        Returns:
            None: This function does not return a value.
        """
        self.p = 2
        self.decoder = decoder

        # opt and device
        self.opt = None
        self.device = get_device()

    def forward(self, x):
        """Forward method

        Args:
            self: instance
            x (torch.tensor): input vector

        Returns:
            out_dict: output dict
        """
        # Image encoding
        cs, mus, logvars = [], [], []
        latents = []
        recs = []
        xs = torch.split(x, x.size(-1) // self.n_images, -1)
        for i in range(self.n_images):
            c, mu, logvar = self.encoder(xs[i])
            cs.append(c)
            mus.append(mu)
            logvars.append(logvar)

            # extract decodings

            # 1) add variational vars + discrete
            eps = torch.randn_like(logvar)
            L = len(eps)
            latents.append((mu + eps * logvar.exp()).view(L, -1))

            for i in range(len(self.c_split)):
                latents.append(F.gumbel_softmax(c[:, i, :], tau=1, hard=True, dim=-1))

        latents = torch.cat(latents, dim=1)

        # 2) pass to decoder
        recs = self.decoder(latents)

        # return everything
        clen = len(cs[0].shape)
        cs = torch.stack(cs, dim=1) if clen == 2 else torch.cat(cs, dim=1)
        mus = torch.stack(mus, dim=-1) if clen == 2 else torch.cat(mus, dim=1)
        logvars = (
            torch.stack(logvars, dim=-1) if clen == 2 else torch.cat(logvars, dim=1)
        )

        # normalize concept preditions
        pCs = self.normalize_concepts(cs)

        # normalize concept preditions

        pred = torch.argmax(pCs[:, 0, :], dim=-1) + torch.argmax(pCs[:, 1, :], dim=-1)

        return {
            "YS": F.one_hot(pred, 19),
            "CS": cs,
            "pCS": pCs,
            "MUS": mus,
            "LOGVARS": logvars,
            "RECS": recs,
        }

    def normalize_concepts(self, z, split=2):
        """Computes the probability for each ProbLog fact given the latent vector z

        Args:
            self: instance
            z: latents
            split (int, default=2): number of splits

        Returns:
            vec: normalized concepts
        """
        # Extract probs for each digit

        prob_digit1, prob_digit2 = z[:, 0, :], z[:, 1, :]

        # Sotfmax on digits_probs (the 10 digits values are mutually exclusive)
        prob_digit1 = torch.nn.Softmax(dim=1)(prob_digit1)
        prob_digit2 = torch.nn.Softmax(dim=1)(prob_digit2)

        # Clamp digits_probs to avoid underflow
        eps = 1e-5
        prob_digit1 = prob_digit1 + eps
        with torch.no_grad():
            Z1 = torch.sum(prob_digit1, dim=-1, keepdim=True)
        prob_digit1 = prob_digit1 / Z1  # Normalization
        prob_digit2 = prob_digit2 + eps
        with torch.no_grad():
            Z2 = torch.sum(prob_digit2, dim=-1, keepdim=True)
        prob_digit2 = prob_digit2 / Z2  # Normalization

        return torch.stack([prob_digit1, prob_digit2], dim=1).view(-1, 2, 10)

    def get_loss(self, args):
        """Returns the loss function for this architecture

        Args:
            self: instance
            args: command line arguments

        Returns:
            loss: loss function

        Raises:
            err: NotImplementedError if loss function is not specified
        """
        if args.dataset in ["addmnist", "shortmnist"]:
            return ADDMNIST_SAT_AGG(ADDMNIST_Cumulative)
        else:
            return NotImplementedError("Wrong dataset choice")

    def start_optim(self, args):
        """Initialize the optimizer for this architecture

        Args:
            self: instance
            args: command line arguments

        Returns:
            None: This function does not return a value.
        """
        self.opt = torch.optim.Adam(
            self.parameters(), args.lr, weight_decay=args.weight_decay
        )
