def install(h, pip, groups, save, save_dev, packages):
    """Install a pip or bower package."""
    if groups:
        groups = [text_type.strip(group) for group in groups.split(",")]
    else:
        groups = h.requirements.keys()

    if not packages:
        for group in groups:
            if group not in h.requirements:
                warning("{} not in requirements".format(group))
                continue
            install = (h.install_bower if group.startswith("bower")
                       else h.install_pip)
            for requirement in h.requirements[group]:
                install(str(requirement), save=False, save_dev=False)
    if pip:
        for package in packages:
            h.install_pip(package, save=save, save_dev=save_dev)
    else:
        for package in packages:
            h.install_bower(package, save=save, save_dev=save_dev)
def testWrapperWeights(self, wrapper):
    """Tests that wrapper weights contain wrapped cells weights."""
    base_cell = layers.SimpleRNNCell(1, name="basic_rnn_cell")
    rnn_cell = wrapper(base_cell)
    rnn_layer = layers.RNN(rnn_cell)
    inputs = ops.convert_to_tensor_v2_with_dispatch([[[1]]],
                                                    dtype=dtypes.float32)
    rnn_layer(inputs)

    wrapper_name = generic_utils.to_snake_case(wrapper.__name__)
    expected_weights = ["rnn/" + wrapper_name + "/" + var for var in
                        ("kernel:0", "recurrent_kernel:0", "bias:0")]
    self.assertLen(rnn_cell.weights, 3)
    self.assertCountEqual([v.name for v in rnn_cell.weights], expected_weights)
    self.assertCountEqual([v.name for v in rnn_cell.trainable_variables],
                          expected_weights)
    self.assertCountEqual([v.name for v in rnn_cell.non_trainable_variables],
                          [])
    self.assertCountEqual([v.name for v in rnn_cell.cell.weights],
                          expected_weights)