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/*
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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
 *  * under the License.
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package org.nd4j.linalg.api.ops.impl.reduce3;

import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.autodiff.util.SameDiffUtils;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;

import java.util.Arrays;
import java.util.List;

public class EuclideanDistance extends BaseReduce3Op {
    public static final String OP_NAME = "euclidean";

    public EuclideanDistance(SameDiff sameDiff, SDVariable i_v, int[] dimensions) {
        super(sameDiff, i_v, dimensions);
    }

    public EuclideanDistance(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, int[] dimensions) {
        super(sameDiff, i_v, i_v2, dimensions);
    }

    public EuclideanDistance() {}



    public EuclideanDistance(INDArray x, INDArray y, int... dimensions) {
        this(x, y, null, dimensions);
    }

    public EuclideanDistance(INDArray x, INDArray y, INDArray z) {
        this(x, y, z, null);
    }

    public EuclideanDistance(INDArray x, INDArray y, INDArray z, int... dimensions) {
        super(x, y, z,dimensions);
        extraArgs = new Object[]{0.0f, 0.0f};
    }

    public EuclideanDistance(INDArray x, INDArray y, boolean allDistances, int... dimensions) {
        this(x, y, null, allDistances, dimensions);
    }

    public EuclideanDistance(INDArray x, INDArray y, INDArray z, boolean allDistances, int... dimensions) {
        this(x, y, z, false, allDistances, dimensions);
    }

    public EuclideanDistance(INDArray x, INDArray y, INDArray z, boolean keepDims, boolean allDistances, int... dimensions){
        super(x, y, z, keepDims, allDistances, dimensions);
        extraArgs = new Object[]{0.0f, 0.0f};
    }

    @Override
    public int opNum() {
        return 1;
    }

    @Override
    public String opName() {
        return OP_NAME;
    }


    @Override
    public List doDiff(List i_v1) {
        //ddist(x,y)/dxi = (xi-yi)/dist(x,y)
        SDVariable euc = outputVariables()[0];
        SDVariable difference = larg().sub(rarg());
        SDVariable divBroadcastable = i_v1.get(0).div(euc);
        if(!keepDims && !(dimensions == null || dimensions.length == 0 || (dimensions.length == 1 && dimensions[0] == Integer.MAX_VALUE))){
            //Not keep dims, and not full array reduction -> need to make broadcastable
            divBroadcastable = SameDiffUtils.reductionBroadcastableWithOrigShape(arg(), sameDiff.constant(Nd4j.createFromArray(dimensions)), divBroadcastable);
        }

        SDVariable gradX = difference.mul(divBroadcastable);
        SDVariable gradY = sameDiff.math.neg(gradX);
        return Arrays.asList(gradX, gradY);
    }
}