/*******************************************************************************
 * 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.nd4j.linalg.api.ops.impl.broadcast;

import lombok.NonNull;
import org.nd4j.autodiff.functions.DifferentialFunction;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.base.Preconditions;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.BaseBroadcastOp;
import org.nd4j.linalg.api.ops.DynamicCustomOp;

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

public class BiasAddGrad extends DynamicCustomOp {

    public BiasAddGrad(SameDiff sameDiff, SDVariable input, SDVariable bias, SDVariable gradient) {
        super(null, sameDiff, new SDVariable[]{input, bias, gradient});
    }

    public BiasAddGrad(@NonNull INDArray input, @NonNull INDArray bias, @NonNull INDArray gradient, INDArray output){
        super(new INDArray[]{input, bias, gradient}, wrapOrNull(output));
    }

    public BiasAddGrad() {}

    @Override
    public int opNum() {
        return 0;
    }

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



    @Override
    public List<SDVariable> doDiff(List<SDVariable> f1) {
        throw new UnsupportedOperationException("Differentiation not supported for op " + getClass().getSimpleName());
    }

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

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

    @Override
    public List<DataType> calculateOutputDataTypes(List<DataType> inputDataTypes){
        Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == 3, "Expected 3 input data types for %s, got %s", getClass(), inputDataTypes);
        return Arrays.asList(inputDataTypes.get(0), inputDataTypes.get(1));
    }
}
