from __future__ import absolute_import
from __future__ import print_function
import pytest

from keras.utils.test_utils import keras_test
from keras.models import Model, Sequential
from keras.layers import Dense, Input


@keras_test
def test_layer_trainability_switch():
    # with constructor argument, in Sequential
    model = Sequential()
    model.add(Dense(2, trainable=False, input_dim=1))
    assert model.trainable_weights == []

    # by setting the `trainable` argument, in Sequential
    model = Sequential()
    layer = Dense(2, input_dim=1)
    model.add(layer)
    assert model.trainable_weights == layer.trainable_weights
    layer.trainable = False
    assert model.trainable_weights == []

    # with constructor argument, in Model
    x = Input(shape=(1,))
    y = Dense(2, trainable=False)(x)
    model = Model(x, y)
    assert model.trainable_weights == []

    # by setting the `trainable` argument, in Model
    x = Input(shape=(1,))
    layer = Dense(2)
    y = layer(x)
    model = Model(x, y)
    assert model.trainable_weights == layer.trainable_weights
    layer.trainable = False
    assert model.trainable_weights == []


@keras_test
def test_model_trainability_switch():
    # a non-trainable model has no trainable weights
    x = Input(shape=(1,))
    y = Dense(2)(x)
    model = Model(x, y)
    model.trainable = False
    assert model.trainable_weights == []

    # same for Sequential
    model = Sequential()
    model.add(Dense(2, input_dim=1))
    model.trainable = False
    assert model.trainable_weights == []


@keras_test
def test_nested_model_trainability():
    # a Sequential inside a Model
    inner_model = Sequential()
    inner_model.add(Dense(2, input_dim=1))

    x = Input(shape=(1,))
    y = inner_model(x)
    outer_model = Model(x, y)
    assert outer_model.trainable_weights == inner_model.trainable_weights
    inner_model.trainable = False
    assert outer_model.trainable_weights == []
    inner_model.trainable = True
    inner_model.layers[-1].trainable = False
    assert outer_model.trainable_weights == []

    # a Sequential inside a Sequential
    inner_model = Sequential()
    inner_model.add(Dense(2, input_dim=1))
    outer_model = Sequential()
    outer_model.add(inner_model)
    assert outer_model.trainable_weights == inner_model.trainable_weights
    inner_model.trainable = False
    assert outer_model.trainable_weights == []
    inner_model.trainable = True
    inner_model.layers[-1].trainable = False
    assert outer_model.trainable_weights == []

    # a Model inside a Model
    x = Input(shape=(1,))
    y = Dense(2)(x)
    inner_model = Model(x, y)
    x = Input(shape=(1,))
    y = inner_model(x)
    outer_model = Model(x, y)
    assert outer_model.trainable_weights == inner_model.trainable_weights
    inner_model.trainable = False
    assert outer_model.trainable_weights == []
    inner_model.trainable = True
    inner_model.layers[-1].trainable = False
    assert outer_model.trainable_weights == []

    # a Model inside a Sequential
    x = Input(shape=(1,))
    y = Dense(2)(x)
    inner_model = Model(x, y)
    outer_model = Sequential()
    outer_model.add(inner_model)
    assert outer_model.trainable_weights == inner_model.trainable_weights
    inner_model.trainable = False
    assert outer_model.trainable_weights == []
    inner_model.trainable = True
    inner_model.layers[-1].trainable = False
    assert outer_model.trainable_weights == []


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