import torch
import torchani
import os


buildins = torchani.neurochem.Buildins()
consts = buildins.consts
aev_computer = buildins.aev_computer
shift_energy = buildins.energy_shifter


def atomic():
    model = torch.nn.Sequential(
        torch.nn.Linear(384, 128),
        torch.nn.CELU(0.1),
        torch.nn.Linear(128, 128),
        torch.nn.CELU(0.1),
        torch.nn.Linear(128, 64),
        torch.nn.CELU(0.1),
        torch.nn.Linear(64, 1)
    )
    return model


def get_or_create_model(filename, device=torch.device('cpu')):
    model = torchani.ANIModel([atomic() for _ in range(4)])

    class Flatten(torch.nn.Module):

        def forward(self, x):
            return x[0], x[1].flatten()

    model = torch.nn.Sequential(aev_computer, model, Flatten())
    if os.path.isfile(filename):
        model.load_state_dict(torch.load(filename))
    else:
        torch.save(model.state_dict(), filename)
    return model.to(device)
