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#  MIT License
#
#  Copyright (c) 2021 Nathan Juraj Michlo
#
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#  of this software and associated documentation files (the "Software"), to deal
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from dataclasses import dataclass

import numpy as np

from disent.frameworks.vae._unsupervised__betavae import BetaVae

# ========================================================================= #
# Swapped Target BetaVAE                                                    #
# ========================================================================= #


class SwappedInputBetaVae(BetaVae):
    """
    This is a naive example of a framework that randomly swaps the inputs to
    the VAE, effectively creating a type of denoising auto-encoder.
    """

    REQUIRED_OBS = 2

    @dataclass
    class cfg(BetaVae.cfg):
        swap_chance: float = 0.1

    def __init__(self, model: "AutoEncoder", cfg: cfg = None, batch_augment=None):
        super().__init__(model=model, cfg=cfg, batch_augment=batch_augment)
        assert cfg.swap_chance >= 0

    def do_training_step(self, batch, batch_idx):
        (x0, x1), (x0_targ, x1_targ) = self._get_xs_and_targs(batch, batch_idx)

        # random change for the target not to be equal to the input
        if np.random.random() < self.cfg.swap_chance:
            x0, x1 = x1, x0

        return super(SwappedInputBetaVae, self).do_training_step(
            {
                "x": (x0,),
                "x_targ": (x0_targ,),
            },
            batch_idx,
        )


# ========================================================================= #
# END                                                                       #
# ========================================================================= #
