from decagon_pytorch.layer import InputLayer
from decagon_pytorch.data import Data
import torch
import pytest


def _some_data():
    d = Data()
    d.add_node_type('Gene', 1000)
    d.add_node_type('Drug', 100)
    d.add_relation_type('Target', 1, 0, None)
    d.add_relation_type('Interaction', 0, 0, None)
    d.add_relation_type('Side Effect: Nausea', 1, 1, None)
    d.add_relation_type('Side Effect: Infertility', 1, 1, None)
    d.add_relation_type('Side Effect: Death', 1, 1, None)
    return d


def test_input_layer_01():
    d = _some_data()
    for dimensionality in [32, 64, 128]:
        layer = InputLayer(d, dimensionality)
        assert layer.dimensionality == dimensionality
        assert len(layer.node_reps) == 2
        assert layer.node_reps[0].shape == (1000, dimensionality)
        assert layer.node_reps[1].shape == (100, dimensionality)
        assert layer.data == d


def test_input_layer_02():
    d = _some_data()
    layer = InputLayer(d, 32)
    res = layer()
    assert isinstance(res[0], torch.Tensor)
    assert isinstance(res[1], torch.Tensor)
    assert res[0].shape == (1000, 32)
    assert res[1].shape == (100, 32)
    assert torch.all(res[0] == layer.node_reps[0])
    assert torch.all(res[1] == layer.node_reps[1])


def test_input_layer_03():
    if torch.cuda.device_count() == 0:
        pytest.skip('No CUDA devices on this host')
    d = _some_data()
    layer = InputLayer(d, 32)
    device = torch.device('cuda:0')
    layer = layer.to(device)
    print(list(layer.parameters()))
    # assert layer.device.type == 'cuda:0'
    assert layer.node_reps[0].device == device
    assert layer.node_reps[1].device == device