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/*******************************************************************************
 * Copyright (c) 2015-2018 Skymind, Inc.
 *
 * 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.
 *
 * 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
 * under the License.
 *
 * SPDX-License-Identifier: Apache-2.0
 ******************************************************************************/

package org.deeplearning4j.nn.layers.convolution;

import lombok.val;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.gradient.DefaultGradient;
import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.nn.layers.AbstractLayer;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.indexing.INDArrayIndex;
import org.nd4j.linalg.indexing.NDArrayIndex;
import org.nd4j.linalg.primitives.Pair;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import org.deeplearning4j.nn.workspace.ArrayType;

/**
 * Zero padding layer for convolutional neural networks.
 * Allows padding to be done separately for top/bottom/left/right
 *
 * @author Alex Black
 */
public class ZeroPaddingLayer extends AbstractLayer {

    private int[] padding; //[padTop, padBottom, padLeft, padRight]

    public ZeroPaddingLayer(NeuralNetConfiguration conf, DataType dataType) {
        super(conf, dataType);
        this.padding = ((org.deeplearning4j.nn.conf.layers.ZeroPaddingLayer) conf.getLayer()).getPadding();
    }

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

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

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

    @Override
    public Pair backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr) {
        assertInputSet(true);
        val inShape = input.shape();

        INDArray epsNext = epsilon.get(NDArrayIndex.all(), NDArrayIndex.all(),
                        NDArrayIndex.interval(padding[0], padding[0] + inShape[2]),
                        NDArrayIndex.interval(padding[2], padding[2] + inShape[3]));

        epsNext = workspaceMgr.leverageTo(ArrayType.ACTIVATION_GRAD, epsNext);
        return new Pair<>((Gradient) new DefaultGradient(), epsNext);
    }


    @Override
    public INDArray activate(boolean training, LayerWorkspaceMgr workspaceMgr) {
        assertInputSet(false);
        val inShape = input.shape();
        val outH = inShape[2] + padding[0] + padding[1];
        val outW = inShape[3] + padding[2] + padding[3];
        val outShape = new long[] {inShape[0], inShape[1], outH, outW};

        INDArray out = workspaceMgr.create(ArrayType.ACTIVATIONS, input.dataType(), outShape, 'c');

        out.put(new INDArrayIndex[] {NDArrayIndex.all(), NDArrayIndex.all(),
                        NDArrayIndex.interval(padding[0], padding[0] + inShape[2]),
                        NDArrayIndex.interval(padding[2], padding[2] + inShape[3])}, input);

        return out;
    }

    @Override
    public Layer clone() {
        return new ZeroPaddingLayer(conf.clone(), dataType);
    }

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