/*
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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.
 *  *
 *  *  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.transforms.pairwise.arithmetic;

import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.imports.NoOpNameFoundException;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.impl.transforms.BaseDynamicTransformOp;
import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.bp.SquaredDifferenceBpOp;

import java.util.List;

public class SquaredDifferenceOp extends BaseDynamicTransformOp {
    public static final String OP_NAME = "squaredsubtract";

    public SquaredDifferenceOp() {}

    public SquaredDifferenceOp(SameDiff sameDiff, SDVariable x, SDVariable y, boolean inPlace) {
        super(sameDiff, new SDVariable[]{x,y}, inPlace);
    }

    public SquaredDifferenceOp(SameDiff sameDiff, SDVariable x, SDVariable y) {
        this(sameDiff, x, y, false);
    }

    public SquaredDifferenceOp(INDArray x, INDArray y, INDArray output) {
        super(new INDArray[]{x,y}, new INDArray[]{output});
    }

    public SquaredDifferenceOp(INDArray x, INDArray y) {
        addInputArgument(new INDArray[]{x,y});
    }

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


    @Override
    public String onnxName() {
        throw new NoOpNameFoundException("No onnx op opName found for " +  opName());
    }

    @Override
    public String tensorflowName() {
        return "SquaredDifference";
    }

    @Override
    public List<SDVariable> doDiff(List<SDVariable> i_v1) {
        return new SquaredDifferenceBpOp(sameDiff, new SDVariable[]{larg(), rarg(), i_v1.get(0)}).outputs();
    }

}
