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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.
 *  *
 *  *  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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 *  * SPDX-License-Identifier: Apache-2.0
 *  *****************************************************************************
 */

package org.nd4j.linalg.api.ops.impl.reduce.custom;

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.reduce.bp.SumBp;

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

public class LogSumExp extends DynamicCustomOp {

    protected boolean keepDims;

    public LogSumExp(SameDiff sameDiff, SDVariable i_v, boolean keepDims, int[] dimensions) {
        super(sameDiff, i_v);
        if(dimensions != null) {
            addIArgument(dimensions);
            this.dimensions = dimensions;
        }
        addTArgument(keepDims ? 1.0 : 0.0);
        this.keepDims = keepDims;
    }

    public LogSumExp(SameDiff sameDiff, SDVariable i_v, int[] dimensions) {
        this(sameDiff, i_v, false, dimensions);
    }

    public LogSumExp() {}

    public LogSumExp(INDArray x, int... dimensions) {
        this(x, false, dimensions);
    }

    public LogSumExp(INDArray x, boolean keepDim, int... dimensions) {
        this(x, null, keepDim, dimensions);
    }

    public LogSumExp(INDArray x, INDArray z, boolean keepDim, int... dimensions) {
        super(null, x,z, Collections.singletonList(keepDim ? 1.0 : 0.0), dimensions);
    }

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


    @Override
    public List calculateOutputDataTypes(List dataTypes){
        Preconditions.checkState(dataTypes != null && (dataTypes.size() == 1 || dataTypes.size() == 2),
                "Expected 1 or 2 input datatypes for %s, got %s", getClass(), dataTypes);
        return Collections.singletonList(dataTypes.get(0));
    }


    @Override
    public List doDiff(List f1) {
        //z = log(sum_i exp(x_i)) = log(s)
        //dL/dx = dL/dz * dz/ds * ds/dx
        //dz/ds = 1/s
        SDVariable exp = sameDiff.math.exp(arg());
        SDVariable sumExp =  null;
        if(dimensions == null) {
            if(args().length < 2) {
                dimensions = new int[]{Integer.MAX_VALUE};
                sumExp = exp.sum(dimensions);
            } else {
                sumExp = sameDiff.math().sum(exp,arg(1));
            }
        }

        SDVariable gradProd = f1.get(0).div(sumExp);
        if(dimensions == null && args().length > 1) {
            SDVariable dSumExpdx = new SumBp(sameDiff, arg(), gradProd, keepDims, arg(1)).outputVariable().mul(exp);
            return Collections.singletonList(dSumExpdx);


        } else {
            SDVariable dSumExpdx = new SumBp(sameDiff, arg(), gradProd, keepDims, dimensions).outputVariable().mul(exp);
            return Collections.singletonList(dSumExpdx);
        }


    }

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