/*
 *  ******************************************************************************
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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
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package org.nd4j.linalg.api.ops.impl.reduce.floating;

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.BaseReduceFloatOp;

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

public class  LogEntropy extends BaseReduceFloatOp {

    public LogEntropy(SameDiff sameDiff, SDVariable i_v, int[] dimensions) {
        super(sameDiff, i_v, dimensions);
    }

    public LogEntropy() {}

    public LogEntropy(INDArray x, INDArray z, int... dimensions) {
        super(x, null, z, dimensions);
    }

    public LogEntropy(INDArray x, int... dimensions) {
        super(x, dimensions);
    }

    @Override
    public int opNum() {
        return 9;
    }

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

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

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

    @Override
    public List<SDVariable> doDiff(List<SDVariable> f1) {
        //If y=log(x), and x=entropy(in) then dL/dx = dL/dy * dy/dx; d(log(x))/dx = 1/x
        List<SDVariable> entropyGrad = Entropy.grad(sameDiff, arg(), f1.get(0), dimensions);
        return Collections.singletonList(entropyGrad.get(0).div(sameDiff.math.exp(outputVariable())));
    }
}
