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 *  *  information regarding copyright ownership.
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 *  * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
 *  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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package org.deeplearning4j.nn.layers.feedforward.elementwise;


import org.deeplearning4j.nn.params.ElementWiseParamInitializer;
import org.deeplearning4j.exception.DL4JInvalidInputException;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.gradient.DefaultGradient;
import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.nn.layers.BaseLayer;
import org.deeplearning4j.nn.params.DefaultParamInitializer;
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;

import java.util.Arrays;

public class ElementWiseMultiplicationLayer extends BaseLayer<org.deeplearning4j.nn.conf.layers.misc.ElementWiseMultiplicationLayer> {

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

    @Override
    public Pair<Gradient, INDArray> backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr) {
        //If this layer is layer L, then epsilon for this layer is ((w^(L+1)*(delta^(L+1))^T))^T (or equivalent)
        INDArray z = preOutput(true, workspaceMgr); //Note: using preOutput(INDArray) can't be used as this does a setInput(input) and resets the 'appliedDropout' flag
        INDArray delta = layerConf().getActivationFn().backprop(z, epsilon).getFirst(); //TODO handle activation function params

        if (maskArray != null) {
            applyMask(delta);
        }

        INDArray input = this.input.castTo(dataType);

        Gradient ret = new DefaultGradient();

        INDArray weightGrad =  gradientViews.get(ElementWiseParamInitializer.WEIGHT_KEY);
        weightGrad.subi(weightGrad);

        weightGrad.addi(input.mul(delta).sum(0));

        INDArray biasGrad = gradientViews.get(ElementWiseParamInitializer.BIAS_KEY);
        delta.sum(biasGrad, 0); //biasGrad is initialized/zeroed first

        ret.gradientForVariable().put(ElementWiseParamInitializer.WEIGHT_KEY, weightGrad);
        ret.gradientForVariable().put(ElementWiseParamInitializer.BIAS_KEY, biasGrad);

//      epsilonNext is a 2d matrix
        INDArray epsilonNext = delta.mulRowVector(params.get(ElementWiseParamInitializer.WEIGHT_KEY));
        epsilonNext = workspaceMgr.leverageTo(ArrayType.ACTIVATION_GRAD, epsilonNext);

        epsilonNext = backpropDropOutIfPresent(epsilonNext);
        return new Pair<>(ret, epsilonNext);
    }


    /**
     * Returns true if the layer can be trained in an unsupervised/pretrain manner (VAE, RBMs etc)
     *
     * @return true if the layer can be pretrained (using fit(INDArray), false otherwise
     */
    @Override
    public boolean isPretrainLayer() {
        return false;
    }

    @Override
    public INDArray preOutput(boolean training, LayerWorkspaceMgr workspaceMgr) {
        INDArray b = getParam(DefaultParamInitializer.BIAS_KEY);
        INDArray W = getParam(DefaultParamInitializer.WEIGHT_KEY);

        if ( input.columns() != W.columns()) {
            throw new DL4JInvalidInputException(
                    "Input size (" + input.columns() + " columns; shape = " + Arrays.toString(input.shape())
                            + ") is invalid: does not match layer input size (layer # inputs = "
                            + W.shapeInfoToString() + ") " + layerId());
        }

        INDArray input = this.input.castTo(dataType);

        applyDropOutIfNecessary(training, workspaceMgr);

        INDArray ret = workspaceMgr.createUninitialized(ArrayType.ACTIVATIONS, input.dataType(), input.shape(), 'c');

        ret.assign(input.mulRowVector(W).addiRowVector(b));

        if (maskArray != null) {
            applyMask(ret);
        }

        return ret;
    }

}