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 *  * This program and the accompanying materials are made available under the
 *  * terms of the Apache License, Version 2.0 which is available at
 *  * https://www.apache.org/licenses/LICENSE-2.0.
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 *  *  See the NOTICE file distributed with this work for additional
 *  *  information regarding copyright ownership.
 *  * Unless required by applicable law or agreed to in writing, software
 *  * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
 *  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
 *  * License for the specific language governing permissions and limitations
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package org.deeplearning4j.nn.layers;


import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.gradient.DefaultGradient;
import org.deeplearning4j.nn.gradient.Gradient;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.common.primitives.Pair;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import org.deeplearning4j.nn.workspace.ArrayType;


public class ActivationLayer extends AbstractLayer<org.deeplearning4j.nn.conf.layers.ActivationLayer> {

    public ActivationLayer(NeuralNetConfiguration conf, DataType dataType) {
        super(conf, dataType);
    }

    @Override
    public double calcRegularizationScore(boolean backpropParamsOnly){
        return 0;
    }

    @Override
    public Type type() {
        return Type.FEED_FORWARD;
    }

    @Override
    public Pair<Gradient, INDArray> backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr) {
        assertInputSet(true);
        INDArray temp = workspaceMgr.dup(ArrayType.ACTIVATION_GRAD, input, input.ordering());
        INDArray delta = layerConf().getActivationFn().backprop(temp, epsilon).getFirst(); //TODO handle activation function params
        if(delta == epsilon ){
            //Edge case: identity activation + external errors -> no-op
            delta = workspaceMgr.dup(ArrayType.ACTIVATION_GRAD, delta);
        }

        delta = workspaceMgr.leverageTo(ArrayType.ACTIVATION_GRAD, delta);  //Usually a no-op (except for perhaps identity)
        Gradient ret = new DefaultGradient();
        return new Pair<>(ret, delta);
    }

    @Override
    public INDArray activate(boolean training, LayerWorkspaceMgr mgr) {
        assertInputSet(false);

        INDArray in;
        if (training) {
            //dup required: need to keep original input for backprop
            in = mgr.dup(ArrayType.ACTIVATIONS, input, input.ordering());
        } else {
            in = mgr.leverageTo(ArrayType.ACTIVATIONS, input);
        }

        return layerConf().getActivationFn().getActivation(in, training);
    }

    @Override
    public boolean isPretrainLayer() {
        return false;
    }

    @Override
    public void clearNoiseWeightParams() {
        //No op
    }


    @Override
    public INDArray params() {
        return null;
    }

}
