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

import lombok.NoArgsConstructor;
import lombok.NonNull;
import lombok.val;
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.DynamicCustomOp;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.util.ArrayUtil;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;

import java.util.*;

/**
 * Bias addition gradient operation.
 */
@NoArgsConstructor
public class BiasAdd extends DynamicCustomOp {


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

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

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

    @Override
    public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
        super.initFromTensorFlow(nodeDef, initWith, attributesForNode, graph);

    }

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

    @Override
    public String[] tensorflowNames() {
        return new String[]{"BiasAdd","BiasAddV1"};
    }

    @Override
    public List doDiff(List gradient){
        return Arrays.asList(f().biasAddBp(arg(0), arg(1), gradient.get(0)));
    }

    @Override
    public List calculateOutputDataTypes(List inputDataTypes){
        Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == 2, "Expected 2 input data types for %s, got %s", getClass(), inputDataTypes);
        return Collections.singletonList(inputDataTypes.get(0));
    }
}