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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
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package org.nd4j.linalg.api.ops.impl.transforms.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.transforms.gradient.LogSoftMaxDerivative;
import org.nd4j.linalg.factory.Nd4j;

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

public class LogSoftMax extends DynamicCustomOp {

    private Integer dimension = null;

    public LogSoftMax(SameDiff sameDiff, SDVariable i_v) {
        super(sameDiff, i_v);
    }

    public LogSoftMax() {
    }

    public LogSoftMax(INDArray x, INDArray z) {
        super(null, x, z, null, null);
    }

    public LogSoftMax(INDArray x) {
        this(x, x);
    }

    public LogSoftMax(INDArray x, int dimension) {
        this(x, null);
        this.dimension = dimension;
    }

    public LogSoftMax(SameDiff sameDiff, SDVariable i_v, int dimension) {
        this(sameDiff, i_v);
        this.dimension = dimension;
        addIArgument(dimension);
    }


    @Override
    public String opName() {
        return "log_softmax";
    }
    @Override
    public String tensorflowName() {
        return "LogSoftmax";
    }

    @Override
    public List doDiff(List i_v) {
        if(dimension == null) {
            return new LogSoftMaxDerivative(sameDiff, arg(), i_v.get(0)).outputs();
        } else {
            return new LogSoftMaxDerivative(sameDiff, arg(), i_v.get(0), dimension).outputs();
        }
    }

    @Override
    public List calculateOutputDataTypes(List inTypes){
        Preconditions.checkState(inTypes != null && inTypes.size() == 1, "Expected 1 input datatype for %s, got %s",
                getClass(), inTypes);
        if(inTypes.get(0).isFPType())
            return Collections.singletonList(inTypes.get(0));
        return Collections.singletonList(Nd4j.defaultFloatingPointType());
    }
}