import pytest
from keras.utils.test_utils import layer_test
from keras import layers
from keras import backend as K


@pytest.mark.parametrize('activation_layer',
                         [layers.LeakyReLU,
                          layers.ELU])
@pytest.mark.parametrize('alpha', [0., .5, -1.])
def test_linear_unit_activations(activation_layer,
                                 alpha):
    layer_test(activation_layer, kwargs={'alpha': alpha},
               input_shape=(2, 3, 4))


def test_prelu():
    layer_test(layers.PReLU, kwargs={},
               input_shape=(2, 3, 4))


def test_prelu_share():
    layer_test(layers.PReLU, kwargs={'shared_axes': 1},
               input_shape=(2, 3, 4))


def test_thresholded_relu():
    layer_test(layers.ThresholdedReLU, kwargs={'theta': 0.5},
               input_shape=(2, 3, 4))


@pytest.mark.parametrize('axis', [1, -1])
def test_softmax(axis):
    layer_test(layers.Softmax, kwargs={'axis': axis},
               input_shape=(2, 3, 4))


def test_relu():
    layer_test(layers.ReLU,
               kwargs={'max_value': 10,
                       'negative_slope': 0.2,
                       'threshold': 3.0},
               input_shape=(2, 3, 4))
    layer_test(layers.ReLU,
               kwargs={'max_value': 6},
               input_shape=(2, 3, 4))
    layer_test(layers.ReLU,
               kwargs={'negative_slope': 0.2},
               input_shape=(2, 3, 4))

    # max_value of ReLU layer cannot be negative value
    with pytest.raises(ValueError):
        layer_test(layers.ReLU, kwargs={'max_value': -2.0},
                   input_shape=(2, 3, 4))

    # negative_slope of ReLU layer cannot be negative value
    with pytest.raises(ValueError):
        layer_test(layers.ReLU, kwargs={'negative_slope': -2.0},
                   input_shape=(2, 3, 4))



if __name__ == '__main__':
    pytest.main([__file__])
