/*
 *  ******************************************************************************
 *  *
 *  *
 *  * 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.lossfunctions.impl;

import lombok.EqualsAndHashCode;
import lombok.Getter;
import org.nd4j.common.base.Preconditions;
import org.nd4j.linalg.activations.IActivation;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.lossfunctions.ILossFunction;
import org.nd4j.linalg.lossfunctions.LossUtil;
import org.nd4j.common.primitives.Pair;
import org.nd4j.serde.jackson.shaded.NDArrayTextDeSerializer;
import org.nd4j.serde.jackson.shaded.NDArrayTextSerializer;
import com.fasterxml.jackson.annotation.JsonInclude;
import com.fasterxml.jackson.annotation.JsonProperty;
import com.fasterxml.jackson.databind.annotation.JsonDeserialize;
import com.fasterxml.jackson.databind.annotation.JsonSerialize;

@EqualsAndHashCode
@JsonInclude(JsonInclude.Include.NON_NULL)
@Getter
public class LossL2 implements ILossFunction {

    @JsonSerialize(using = NDArrayTextSerializer.class)
    @JsonDeserialize(using = NDArrayTextDeSerializer.class)
    protected final INDArray weights;

    public LossL2() {
        this(null);

    }

    /**
     * L2 loss function where each the output is (optionally) weighted/scaled by a flags scalar value.
     * Note that the weights array must be a row vector, of length equal to the labels/output dimension 1 size.
     * A weight vector of 1s should give identical results to no weight vector.
     *
     * @param weights Weights array (row vector). May be null.
     */
    public LossL2(@JsonProperty("weights") INDArray weights) {
        if (weights != null && !weights.isRowVector()) {
            throw new IllegalArgumentException("Weights array must be a row vector");
        }
        this.weights = weights;
    }

    protected INDArray scoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask) {
        if(!labels.equalShapes(preOutput)){
            Preconditions.throwEx("Labels and preOutput must have equal shapes: got shapes %s vs %s", labels.shape(), preOutput.shape());
        }
        labels = labels.castTo(preOutput.dataType());   //No-op if already correct dtype
        INDArray output = activationFn.getActivation(preOutput.dup(), true);
        INDArray scoreArr = output.rsubi(labels);
        scoreArr = scoreArr.muli(scoreArr);

        //Weighted loss function
        if (weights != null) {
            if (weights.length() != output.size(1)) {
                throw new IllegalStateException("Weights vector (length " + weights.length()
                        + ") does not match output.size(1)=" + output.size(1));
            }
            scoreArr.muliRowVector(weights.castTo(scoreArr.dataType()));
        }

        //Loss function with masking
        if (mask != null) {
            LossUtil.applyMask(scoreArr, mask);
        }
        return scoreArr;
    }

    @Override
    public double computeScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask,
                               boolean average) {
        INDArray scoreArr = scoreArray(labels, preOutput, activationFn, mask);

        double score = scoreArr.sumNumber().doubleValue();

        if (average)
            score /= scoreArr.size(0);

        return score;
    }

    @Override
    public INDArray computeScoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask) {
        INDArray scoreArr = scoreArray(labels, preOutput, activationFn, mask);
        return scoreArr.sum(true,1);
    }

    @Override
    public INDArray computeGradient(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask) {
        if(!labels.equalShapes(preOutput)){
            Preconditions.throwEx("Labels and preOutput must have equal shapes: got shapes %s vs %s", labels.shape(), preOutput.shape());
        }
        labels = labels.castTo(preOutput.dataType());   //No-op if already correct dtype
        INDArray output = activationFn.getActivation(preOutput.dup(), true);

        INDArray dLda = output.subi(labels).muli(2);

        if (weights != null) {
            dLda.muliRowVector(weights.castTo(dLda.dataType()));
        }

        if (mask != null && LossUtil.isPerOutputMasking(dLda, mask)) {
            //For *most* activation functions: we don't actually need to mask dL/da in addition to masking dL/dz later
            //but: some, like softmax, require both (due to dL/dz_i being a function of dL/da_j, for i != j)
            //We could add a special case for softmax (activationFn instanceof ActivationSoftmax) but that would be
            // error prone - but buy us a tiny bit of performance
            LossUtil.applyMask(dLda, mask);
        }

        INDArray gradients = activationFn.backprop(preOutput, dLda).getFirst(); //TODO handle activation function parameter gradients

        //Loss function with masking
        if (mask != null) {
            LossUtil.applyMask(gradients, mask);
        }

        return gradients;
    }

    @Override
    public Pair<Double, INDArray> computeGradientAndScore(INDArray labels,
                                                          INDArray preOutput, IActivation activationFn, INDArray mask, boolean average) {
        //TODO: probably a more efficient way to do this...

        return new Pair<>(computeScore(labels, preOutput, activationFn, mask, average),
                computeGradient(labels, preOutput, activationFn, mask));
    }


    /**
     * The opName of this function
     *
     * @return
     */
    @Override
    public String name() {
        return toString();
    }

    @Override
    public String toString() {
        if (weights == null)
            return "LossL2()";
        return "LossL2(weights=" + weights + ")";
    }
}
