from .modules import *
from torch.nn.functional import mse_loss
from torch import Tensor


#######################
# Basic AE-Implementation
class AutoEncoder(Module, ABC):

    @property
    def name(self):
        return self.__class__.__name__

    def __init__(self, dataParams, **kwargs):
        super(AutoEncoder, self).__init__()
        self.dataParams = dataParams
        self.latent_dim = kwargs.get('latent_dim', 2)
        self.encoder = Encoder(self.latent_dim)
        self.decoder = Decoder(self.latent_dim, self.dataParams['features'])

    def forward(self, batch: Tensor):
        # Encoder
        #  outputs, hidden (Batch, Timesteps aka. Size, Features / Latent Dim Size)
        z = self.encoder(batch)
        # Decoder
        # First repeat the data accordingly to the batch size
        z_repeatet = Repeater((batch.shape[0], self.dataParams['size'], -1))(z)
        x_hat = self.decoder(z_repeatet)
        return z, x_hat


class AutoEncoderLightningOverrides:

    def forward(self, x):
        return self.network.forward(x)

    def training_step(self, x, batch_nb):
        # z, x_hat
        _, x_hat = self.forward(x)
        loss = mse_loss(x, x_hat)
        return {'loss': loss}


if __name__ == '__main__':
    raise PermissionError('Get out of here - never run this module')