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 *  *  information regarding copyright ownership.
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package org.nd4j.linalg.api.ops.impl.layers.recurrent;

import lombok.Getter;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.common.base.Preconditions;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.weights.GRUWeights;

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


public class GRUCell extends DynamicCustomOp {

    @Getter
    private GRUWeights weights;

    public GRUCell() {
    }

    public GRUCell(SameDiff sameDiff, SDVariable x, SDVariable hLast, GRUWeights weights) {
        super(null, sameDiff, weights.argsWithInputs(x, hLast));
        this.weights = weights;
    }

    public GRUCell(INDArray x, INDArray hLast, GRUWeights gruWeights) {
        super(null, null, gruWeights.argsWithInputs(x, hLast));
        this.weights = gruWeights;
    }


    @Override
    public String opName() {
        return "gruCell";
    }


    @Override
    public String onnxName() {
        return "GRU";
    }

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

    @Override
    public String[] onnxNames() {
        return super.onnxNames();
    }

    @Override
    public List calculateOutputDataTypes(List inputDataTypes){
        Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == 6, "Expected exactly 6 inputs to GRUCell, got %s", inputDataTypes);
        //4 outputs, all of same type as input
        DataType dt = inputDataTypes.get(0);
        Preconditions.checkState(dt.isFPType(), "Input type 0 must be a floating point type, got %s", dt);
        return Arrays.asList(dt, dt, dt, dt);
    }

    @Override
    public List doDiff(List grads){
        throw new UnsupportedOperationException("Not yet implemented");
    }
}




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