"""Siamese GLVQ example using all four dimensions of the Iris dataset."""

import prototorch as pt
import pytorch_lightning as pl
import torch


class Backbone(torch.nn.Module):
    """Two fully connected layers with ReLU activation."""
    def __init__(self, input_size=4, hidden_size=10, latent_size=2):
        super().__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.latent_size = latent_size
        self.dense1 = torch.nn.Linear(self.input_size, self.hidden_size)
        self.dense2 = torch.nn.Linear(self.hidden_size, self.latent_size)
        self.relu = torch.nn.ReLU()

    def forward(self, x):
        x = self.relu(self.dense1(x))
        out = self.relu(self.dense2(x))
        return out


if __name__ == "__main__":
    # Dataset
    train_ds = pt.datasets.Iris()

    # Reproducibility
    pl.utilities.seed.seed_everything(seed=2)

    # Dataloaders
    train_loader = torch.utils.data.DataLoader(train_ds,
                                               num_workers=0,
                                               batch_size=150)

    # Hyperparameters
    hparams = dict(
        distribution=[1, 2, 3],
        proto_lr=0.01,
        bb_lr=0.01,
    )

    # Initialize the model
    model = pt.models.SiameseGLVQ(
        hparams,
        prototype_initializer=pt.components.SMI(train_ds),
        backbone_module=Backbone,
    )

    # Model summary
    print(model)

    # Callbacks
    vis = pt.models.VisSiameseGLVQ2D(data=train_ds, border=0.1)

    # Setup trainer
    trainer = pl.Trainer(max_epochs=100, callbacks=[vis], gpus=0)

    # Training loop
    trainer.fit(model, train_loader)