import torch
import torchani
import os


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)
    )
    model.output_length = 1
    return model


def get_or_create_model(filename, benchmark=False,
                        device=torchani.default_device):
    aev_computer = torchani.SortedAEV(benchmark=benchmark, device=device)
    prepare = torchani.PrepareInput(aev_computer.species, aev_computer.device)
    model = torchani.models.CustomModel(
        reducer=torch.sum,
        benchmark=benchmark,
        per_species={
            'C': atomic(),
            'H': atomic(),
            'N': atomic(),
            'O': atomic(),
        })

    class Flatten(torch.nn.Module):

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

    model = torch.nn.Sequential(prepare, 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)
