/*******************************************************************************
 * 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.deeplearning4j.nn.params;

import lombok.val;
import org.deeplearning4j.nn.api.ParamInitializer;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.layers.BatchNormalization;
import org.deeplearning4j.nn.conf.layers.Layer;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.indexing.NDArrayIndex;

import java.util.*;

/**
 * Batch normalization variable init
 */

public class BatchNormalizationParamInitializer implements ParamInitializer {

    private static final BatchNormalizationParamInitializer INSTANCE = new BatchNormalizationParamInitializer();

    public static BatchNormalizationParamInitializer getInstance() {
        return INSTANCE;
    }

    public static final String GAMMA = "gamma";
    public static final String BETA = "beta";
    public static final String GLOBAL_MEAN = "mean";
    public static final String GLOBAL_VAR = "var";
    public static final String GLOBAL_LOG_STD = "log10stdev";

    @Override
    public long numParams(NeuralNetConfiguration conf) {
        return numParams(conf.getLayer());
    }

    @Override
    public long numParams(Layer l) {
        BatchNormalization layer = (BatchNormalization) l;
        //Parameters in batch norm:
        //gamma, beta, global mean estimate, global variance estimate
        // latter 2 are treated as parameters, which greatly simplifies spark training and model serialization

        if (layer.isLockGammaBeta()) {
            //Special case: gamma and beta are fixed values for all outputs -> no parameters for gamma and  beta in this case
            return 2 * layer.getNOut();
        } else {
            //Standard case: gamma and beta are learned per output; additional 2*nOut for global mean/variance estimate
            return 4 * layer.getNOut();
        }
    }

    @Override
    public List<String> paramKeys(Layer layer) {
        if(((BatchNormalization)layer).isUseLogStd()){
            return Arrays.asList(GAMMA, BETA, GLOBAL_MEAN, GLOBAL_LOG_STD);
        } else {
            return Arrays.asList(GAMMA, BETA, GLOBAL_MEAN, GLOBAL_VAR);
        }
    }

    @Override
    public List<String> weightKeys(Layer layer) {
        return Collections.emptyList();
    }

    @Override
    public List<String> biasKeys(Layer layer) {
        return Collections.emptyList();
    }

    @Override
    public boolean isWeightParam(Layer layer, String key) {
        return false;
    }

    @Override
    public boolean isBiasParam(Layer layer, String key) {
        return false;
    }

    @Override
    public Map<String, INDArray> init(NeuralNetConfiguration conf, INDArray paramView, boolean initializeParams) {
        Map<String, INDArray> params = Collections.synchronizedMap(new LinkedHashMap<String, INDArray>());
        // TODO setup for RNN
        BatchNormalization layer = (BatchNormalization) conf.getLayer();
        val nOut = layer.getNOut();

        long meanOffset = 0;
        if (!layer.isLockGammaBeta()) { //No gamma/beta parameters when gamma/beta are locked
            INDArray gammaView = paramView.get(NDArrayIndex.interval(0,0,true), NDArrayIndex.interval(0, nOut));
            INDArray betaView = paramView.get(NDArrayIndex.interval(0,0,true), NDArrayIndex.interval(nOut, 2 * nOut));

            params.put(GAMMA, createGamma(conf, gammaView, initializeParams));
            conf.addVariable(GAMMA);
            params.put(BETA, createBeta(conf, betaView, initializeParams));
            conf.addVariable(BETA);

            meanOffset = 2 * nOut;
        }

        INDArray globalMeanView =
                        paramView.get(NDArrayIndex.interval(0,0,true), NDArrayIndex.interval(meanOffset, meanOffset + nOut));
        INDArray globalVarView = paramView.get(NDArrayIndex.interval(0,0,true),
                        NDArrayIndex.interval(meanOffset + nOut, meanOffset + 2 * nOut));

        if (initializeParams) {
            globalMeanView.assign(0);
            if(layer.isUseLogStd()){
                //Global log stdev: assign 0.0 as initial value (s=sqrt(v), and log10(s) = log10(sqrt(v)) -> log10(1) = 0
                globalVarView.assign(0);
            } else {
                //Global variance view: assign 1.0 as initial value
                globalVarView.assign(1);
            }
        }

        params.put(GLOBAL_MEAN, globalMeanView);
        conf.addVariable(GLOBAL_MEAN);
        if(layer.isUseLogStd()){
            params.put(GLOBAL_LOG_STD, globalVarView);
            conf.addVariable(GLOBAL_LOG_STD);
        } else {
            params.put(GLOBAL_VAR, globalVarView);
            conf.addVariable(GLOBAL_VAR);
        }

        return params;
    }

    @Override
    public Map<String, INDArray> getGradientsFromFlattened(NeuralNetConfiguration conf, INDArray gradientView) {
        BatchNormalization layer = (BatchNormalization) conf.getLayer();
        val nOut = layer.getNOut();

        Map<String, INDArray> out = new LinkedHashMap<>();
        long meanOffset = 0;
        if (!layer.isLockGammaBeta()) {
            INDArray gammaView = gradientView.get(NDArrayIndex.interval(0,0,true), NDArrayIndex.interval(0, nOut));
            INDArray betaView = gradientView.get(NDArrayIndex.interval(0,0,true), NDArrayIndex.interval(nOut, 2 * nOut));
            out.put(GAMMA, gammaView);
            out.put(BETA, betaView);
            meanOffset = 2 * nOut;
        }

        out.put(GLOBAL_MEAN,
                        gradientView.get(NDArrayIndex.interval(0,0,true), NDArrayIndex.interval(meanOffset, meanOffset + nOut)));
        if(layer.isUseLogStd()){
            out.put(GLOBAL_LOG_STD, gradientView.get(NDArrayIndex.interval(0,0,true),
                    NDArrayIndex.interval(meanOffset + nOut, meanOffset + 2 * nOut)));
        } else {
            out.put(GLOBAL_VAR, gradientView.get(NDArrayIndex.interval(0,0,true),
                    NDArrayIndex.interval(meanOffset + nOut, meanOffset + 2 * nOut)));
        }

        return out;
    }

    private INDArray createBeta(NeuralNetConfiguration conf, INDArray betaView, boolean initializeParams) {
        BatchNormalization layer = (BatchNormalization) conf.getLayer();
        if (initializeParams)
            betaView.assign(layer.getBeta());
        return betaView;
    }

    private INDArray createGamma(NeuralNetConfiguration conf, INDArray gammaView, boolean initializeParams) {
        BatchNormalization layer = (BatchNormalization) conf.getLayer();
        if (initializeParams)
            gammaView.assign(layer.getGamma());
        return gammaView;
    }
}
