import tensorflow as tf


@pytest.fixture
def mu(session_tf):
    return tf.convert_to_tensor(Datum.mu_data)

@pytest.fixture
def sqrt_diag(session_tf):
    return tf.convert_to_tensor(Datum.sqrt_diag_data)

@pytest.fixture
def K(session_tf):
    return tf.convert_to_tensor(Datum.K_data)

@pytest.fixture
def K_batch(session_tf):
    return tf.convert_to_tensor(Datum.K_batch_data)

@pytest.fixture
def sqrt(session_tf):
    return tf.convert_to_tensor(Datum.sqrt_data)

@pytest.fixture()
def I(session_tf):
    return tf.convert_to_tensor(Datum.I)

@pytest.mark.parametrize('white', [True, False])
def test_diags(session_tf, white, mu, sqrt_diag, K):
    """
    The covariance of q(x) can be Cholesky matrices or diagonal matrices.
    Here we make sure the behaviours overlap.
