/*******************************************************************************
 * 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.layers.variational;

import lombok.*;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.api.MaskState;
import org.deeplearning4j.nn.api.TrainingConfig;
import org.deeplearning4j.nn.api.layers.LayerConstraint;
import org.deeplearning4j.nn.conf.CacheMode;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.layers.variational.CompositeReconstructionDistribution;
import org.deeplearning4j.nn.conf.layers.variational.LossFunctionWrapper;
import org.deeplearning4j.nn.conf.layers.variational.ReconstructionDistribution;
import org.deeplearning4j.nn.gradient.DefaultGradient;
import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.nn.layers.LayerHelper;
import org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer;
import org.deeplearning4j.nn.workspace.ArrayType;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import org.deeplearning4j.optimize.Solver;
import org.deeplearning4j.optimize.api.ConvexOptimizer;
import org.deeplearning4j.optimize.api.TrainingListener;
import org.nd4j.linalg.activations.IActivation;
import org.nd4j.linalg.activations.impl.ActivationIdentity;
import org.nd4j.linalg.api.blas.Level1;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.memory.MemoryWorkspace;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.learning.regularization.Regularization;
import org.nd4j.linalg.lossfunctions.ILossFunction;
import org.nd4j.linalg.ops.transforms.Transforms;
import org.nd4j.linalg.primitives.Pair;

import java.util.*;

import static org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer.BIAS_KEY_SUFFIX;
import static org.deeplearning4j.nn.params.VariationalAutoencoderParamInitializer.WEIGHT_KEY_SUFFIX;

/**
 * Variational Autoencoder layer
 * <p>
 * See: Kingma & Welling, 2013: Auto-Encoding Variational Bayes - <a href="https://arxiv.org/abs/1312.6114">https://arxiv.org/abs/1312.6114</a>
 * <p>
 * This implementation allows multiple encoder and decoder layers, the number and sizes of which can be set independently.
 * <p>
 * A note on scores during pretraining: This implementation minimizes the negative of the variational lower bound objective
 * as described in Kingma & Welling; the mathematics in that paper is based on maximization of the variational lower bound instead.
 * Thus, scores reported during pretraining in DL4J are the negative of the variational lower bound equation in the paper.
 * The backpropagation and learning procedure is otherwise as described there.
 *
 * @author Alex Black
 */
public class VariationalAutoencoder implements Layer {

    protected INDArray input;
    protected INDArray paramsFlattened;
    protected INDArray gradientsFlattened;
    protected Map<String, INDArray> params;
    @Getter
    protected transient Map<String, INDArray> gradientViews;
    protected NeuralNetConfiguration conf;
    protected double score = 0.0;
    protected ConvexOptimizer optimizer;
    protected Gradient gradient;
    protected Collection<TrainingListener> trainingListeners = new ArrayList<>();
    protected int index = 0;
    protected INDArray maskArray;
    protected Solver solver;

    protected int[] encoderLayerSizes;
    protected int[] decoderLayerSizes;
    protected ReconstructionDistribution reconstructionDistribution;
    protected IActivation pzxActivationFn;
    protected int numSamples;
    protected CacheMode cacheMode = CacheMode.NONE;
    protected DataType dataType;

    protected boolean zeroedPretrainParamGradients = false;

    protected Map<String,INDArray> weightNoiseParams = new HashMap<>();

    @Getter @Setter
    protected int iterationCount;
    @Getter @Setter
    protected int epochCount;

    public VariationalAutoencoder(NeuralNetConfiguration conf, DataType dataType) {
        this.conf = conf;
        this.dataType = dataType;

        this.encoderLayerSizes =
                        ((org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder) conf.getLayer())
                                        .getEncoderLayerSizes();
        this.decoderLayerSizes =
                        ((org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder) conf.getLayer())
                                        .getDecoderLayerSizes();
        this.reconstructionDistribution =
                        ((org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder) conf.getLayer())
                                        .getOutputDistribution();
        this.pzxActivationFn = ((org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder) conf.getLayer())
                        .getPzxActivationFn();
        this.numSamples = ((org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder) conf.getLayer())
                        .getNumSamples();
    }

    protected org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder layerConf() {
        return (org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder) conf().getLayer();
    }

    @Override
    public void setCacheMode(CacheMode mode) {
        if (mode == null)
            mode = CacheMode.NONE;

        this.cacheMode = mode;
    }

    protected String layerId() {
        String name = this.conf().getLayer().getLayerName();
        return "(layer name: " + (name == null ? "\"\"" : name) + ", layer index: " + index + ")";
    }

    /**
     * Init the model
     */
    @Override
    public void init() {

    }

    @Override
    public void update(Gradient gradient) {
        throw new UnsupportedOperationException("Not supported " + layerId());
    }

    @Override
    public void update(INDArray gradient, String paramType) {
        throw new UnsupportedOperationException("Not supported " + layerId());
    }

    @Override
    public double score() {
        return score;
    }

    protected INDArray  getParamWithNoise(String param, boolean training, LayerWorkspaceMgr workspaceMgr){
        INDArray p;
        if(layerConf().getWeightNoise() != null){
            if(training && weightNoiseParams.size() > 0 && weightNoiseParams.containsKey(param) ){
                //Re-use these weights for both forward pass and backprop - don't want to use 2 different params here
                //These should be cleared during  backprop
                return weightNoiseParams.get(param);
            } else {
                try (MemoryWorkspace ws = Nd4j.getMemoryManager().scopeOutOfWorkspaces()) {
                    p = layerConf().getWeightNoise().getParameter(this, param, getIterationCount(), getEpochCount(), training, workspaceMgr);
                }
            }

            if(training){
                //Store for re-use in backprop
                weightNoiseParams.put(param, p);
            }
        } else {
            return getParam(param);
        }

        return p;
    }

    @Override
    public void computeGradientAndScore(LayerWorkspaceMgr workspaceMgr) {
        //Forward pass through the encoder and mean for P(Z|X)
        VAEFwdHelper fwd = doForward(true, true, workspaceMgr);
        IActivation afn = layerConf().getActivationFn();

        //Forward pass through logStd^2 for P(Z|X)
        INDArray pzxLogStd2W = getParamWithNoise(VariationalAutoencoderParamInitializer.PZX_LOGSTD2_W, true, workspaceMgr);
        INDArray pzxLogStd2b = getParamWithNoise(VariationalAutoencoderParamInitializer.PZX_LOGSTD2_B, true, workspaceMgr);

        INDArray pzxLogStd2Pre = fwd.encoderActivations[fwd.encoderActivations.length - 1].mmul(pzxLogStd2W)
                        .addiRowVector(pzxLogStd2b);

        INDArray meanZ = fwd.pzxMeanPreOut.dup();
        INDArray logStdev2Z = pzxLogStd2Pre.dup();
        pzxActivationFn.getActivation(meanZ, true);
        pzxActivationFn.getActivation(logStdev2Z, true);


        INDArray pzxSigmaSquared = Transforms.exp(logStdev2Z, true);
        INDArray pzxSigma = Transforms.sqrt(pzxSigmaSquared, true);

        val minibatch = input.size(0);
        val size = fwd.pzxMeanPreOut.size(1);


        Map<String, INDArray> gradientMap = new HashMap<>();
        double scaleFactor = 1.0 / numSamples;
        Level1 blasL1 = Nd4j.getBlasWrapper().level1();
        INDArray[] encoderActivationDerivs = (numSamples > 1 ? new INDArray[encoderLayerSizes.length] : null);
        for (int l = 0; l < numSamples; l++) { //Default (and in most cases) numSamples == 1
            double gemmCConstant = (l == 0 ? 0.0 : 1.0); //0 for first one (to get rid of previous buffer data), otherwise 1 (for adding)

            INDArray e = Nd4j.randn(dataType, minibatch, size);
            INDArray z = pzxSigma.mul(e).addi(meanZ); //z = mu + sigma * e, with e ~ N(0,1)


            //Need to do forward pass through decoder layers
            int nDecoderLayers = decoderLayerSizes.length;
            INDArray current = z;
            INDArray[] decoderPreOut = new INDArray[nDecoderLayers]; //Need pre-out for backprop later
            INDArray[] decoderActivations = new INDArray[nDecoderLayers];
            for (int i = 0; i < nDecoderLayers; i++) {
                String wKey = "d" + i + WEIGHT_KEY_SUFFIX;
                String bKey = "d" + i + BIAS_KEY_SUFFIX;

                INDArray weights = getParamWithNoise(wKey, true, workspaceMgr);
                INDArray bias = getParamWithNoise(bKey, true, workspaceMgr);

                current = current.mmul(weights).addiRowVector(bias);
                decoderPreOut[i] = current.dup();
                afn.getActivation(current, true);
                decoderActivations[i] = current;
            }

            INDArray pxzw = getParamWithNoise(VariationalAutoencoderParamInitializer.PXZ_W, true, workspaceMgr);
            INDArray pxzb = getParamWithNoise(VariationalAutoencoderParamInitializer.PXZ_B, true, workspaceMgr);

            if (l == 0) {
                //Need to add other component of score, in addition to negative log probability
                //Note the negative here vs. the equation in Kingma & Welling: this is because we are minimizing the negative of
                // variational lower bound, rather than maximizing the variational lower bound
                //Unlike log probability (which is averaged over samples) this should be calculated just once
                INDArray temp = meanZ.mul(meanZ).addi(pzxSigmaSquared).negi();
                temp.addi(logStdev2Z).addi(1.0);
                double scorePt1 = -0.5 / minibatch * temp.sumNumber().doubleValue();
                this.score = scorePt1 + calcRegularizationScore(false);
            }

            INDArray pxzDistributionPreOut = current.mmul(pxzw).addiRowVector(pxzb);
            double logPTheta = reconstructionDistribution.negLogProbability(input, pxzDistributionPreOut, true);
            this.score += logPTheta / numSamples;

            //If we have any training listeners (for example, for UI StatsListener - pass on activations)
            if (trainingListeners != null && !trainingListeners.isEmpty() && l == 0) { //Note: only doing this on the *first* sample
                Map<String, INDArray> activations = new LinkedHashMap<>();
                for (int i = 0; i < fwd.encoderActivations.length; i++) {
                    activations.put("e" + i, fwd.encoderActivations[i]);
                }
                activations.put(VariationalAutoencoderParamInitializer.PZX_PREFIX, z);
                for (int i = 0; i < decoderActivations.length; i++) {
                    activations.put("d" + i, decoderActivations[i]);
                }
                activations.put(VariationalAutoencoderParamInitializer.PXZ_PREFIX,
                                reconstructionDistribution.generateAtMean(pxzDistributionPreOut));
                if (!trainingListeners.isEmpty()) {
                    try (MemoryWorkspace workspace = Nd4j.getMemoryManager().scopeOutOfWorkspaces()) {
                        for (TrainingListener tl : trainingListeners) {
                            tl.onForwardPass(this, activations);
                        }
                    }
                }
            }

            /////////////////////////////////////////////////////////
            //Backprop

            //First: calculate the gradients at the input to the reconstruction distribution
            INDArray dpdpxz = reconstructionDistribution.gradient(input, pxzDistributionPreOut);

            //Do backprop for output reconstruction distribution -> final decoder layer
            INDArray dLdxzw = gradientViews.get(VariationalAutoencoderParamInitializer.PXZ_W);
            INDArray dLdxzb = gradientViews.get(VariationalAutoencoderParamInitializer.PXZ_B);
            INDArray lastDecActivations = decoderActivations[decoderActivations.length - 1];
            Nd4j.gemm(lastDecActivations, dpdpxz, dLdxzw, true, false, scaleFactor, gemmCConstant);
            if (l == 0) {
                dpdpxz.sum(dLdxzb, 0); //dLdxzb array is initialized/zeroed first in sum op
                if (numSamples > 1) {
                    dLdxzb.muli(scaleFactor);
                }
            } else {
                blasL1.axpy(dLdxzb.length(), scaleFactor, dpdpxz.sum(0), dLdxzb);
            }

            gradientMap.put(VariationalAutoencoderParamInitializer.PXZ_W, dLdxzw);
            gradientMap.put(VariationalAutoencoderParamInitializer.PXZ_B, dLdxzb);

            INDArray epsilon = pxzw.mmul(dpdpxz.transpose()).transpose();

            //Next: chain derivatives backwards through the decoder layers
            for (int i = nDecoderLayers - 1; i >= 0; i--) {
                String wKey = "d" + i + WEIGHT_KEY_SUFFIX;
                String bKey = "d" + i + BIAS_KEY_SUFFIX;

                INDArray currentDelta = afn.backprop(decoderPreOut[i], epsilon).getFirst(); //TODO activation functions with params

                INDArray weights = getParamWithNoise(wKey, true, workspaceMgr);
                INDArray dLdW = gradientViews.get(wKey);
                INDArray dLdB = gradientViews.get(bKey);

                INDArray actInput;
                if (i == 0) {
                    actInput = z;
                } else {
                    actInput = decoderActivations[i - 1];
                }

                Nd4j.gemm(actInput, currentDelta, dLdW, true, false, scaleFactor, gemmCConstant);

                if (l == 0) {
                    currentDelta.sum(dLdB, 0);
                    if (numSamples > 1) {
                        dLdB.muli(scaleFactor);
                    }
                } else {
                    blasL1.axpy(dLdB.length(), scaleFactor, currentDelta.sum(0), dLdB);
                }

                gradientMap.put(wKey, dLdW);
                gradientMap.put(bKey, dLdB);

                epsilon = weights.mmul(currentDelta.transpose()).transpose();
            }

            //Do backprop through p(z|x)
            INDArray eZXMeanW = getParamWithNoise(VariationalAutoencoderParamInitializer.PZX_MEAN_W, true, workspaceMgr);
            INDArray eZXLogStdev2W = getParamWithNoise(VariationalAutoencoderParamInitializer.PZX_LOGSTD2_W, true, workspaceMgr);

            INDArray dLdz = epsilon;
            //If we were maximizing the equation in Kinga and Welling, this would be a .sub(meanZ). Here: we are minimizing the negative instead
            INDArray dLdmu = dLdz.add(meanZ);

            INDArray dLdLogSigma2 = dLdz.mul(e).muli(pzxSigma).addi(pzxSigmaSquared).subi(1).muli(0.5);


            INDArray dLdPreMu = pzxActivationFn.backprop(fwd.getPzxMeanPreOut().dup(), dLdmu).getFirst();

            INDArray dLdPreLogSigma2 = pzxActivationFn.backprop(pzxLogStd2Pre.dup(), dLdLogSigma2).getFirst();

            //Weight gradients for weights feeding into p(z|x)
            INDArray lastEncoderActivation = fwd.encoderActivations[fwd.encoderActivations.length - 1];
            INDArray dLdZXMeanW = gradientViews.get(VariationalAutoencoderParamInitializer.PZX_MEAN_W);
            INDArray dLdZXLogStdev2W = gradientViews.get(VariationalAutoencoderParamInitializer.PZX_LOGSTD2_W);
            Nd4j.gemm(lastEncoderActivation, dLdPreMu, dLdZXMeanW, true, false, scaleFactor, gemmCConstant);
            Nd4j.gemm(lastEncoderActivation, dLdPreLogSigma2, dLdZXLogStdev2W, true, false, scaleFactor, gemmCConstant);

            //Bias gradients for p(z|x)
            INDArray dLdZXMeanb = gradientViews.get(VariationalAutoencoderParamInitializer.PZX_MEAN_B);
            INDArray dLdZXLogStdev2b = gradientViews.get(VariationalAutoencoderParamInitializer.PZX_LOGSTD2_B);
            //If we were maximizing the equation in Kinga and Welling, this would be a .sub(meanZ). Here: we are minimizing the negative instead
            if (l == 0) {
                dLdZXMeanb.assign(pzxActivationFn.backprop(fwd.getPzxMeanPreOut().dup(), dLdz.add(meanZ)).getFirst()
                                .sum(0));

                dLdPreLogSigma2.sum(dLdZXLogStdev2b, 0);
                if (numSamples > 1) {
                    dLdZXMeanb.muli(scaleFactor);
                    dLdZXLogStdev2b.muli(scaleFactor);
                }
            } else {
                blasL1.axpy(dLdZXMeanb.length(), scaleFactor, pzxActivationFn
                                .backprop(fwd.getPzxMeanPreOut().dup(), dLdz.add(meanZ)).getFirst().sum(0), dLdZXMeanb);

                blasL1.axpy(dLdZXLogStdev2b.length(), scaleFactor, dLdPreLogSigma2.sum(0), dLdZXLogStdev2b);
            }

            gradientMap.put(VariationalAutoencoderParamInitializer.PZX_MEAN_W, dLdZXMeanW);
            gradientMap.put(VariationalAutoencoderParamInitializer.PZX_MEAN_B, dLdZXMeanb);
            gradientMap.put(VariationalAutoencoderParamInitializer.PZX_LOGSTD2_W, dLdZXLogStdev2W);
            gradientMap.put(VariationalAutoencoderParamInitializer.PZX_LOGSTD2_B, dLdZXLogStdev2b);

            //Epsilon (dL/dActivation) at output of the last encoder layer:
            epsilon = Nd4j.gemm(dLdPreMu, eZXMeanW, false, true); //Equivalent to: epsilon = eZXMeanW.mmul(dLdPreMu.transpose()).transpose(); using   (AxB^T)^T = BxA^T
            //Next line: equivalent to epsilon.addi(eZXLogStdev2W.mmul(dLdPreLogSigma2.transpose()).transpose());       using: (AxB^T)^T = BxA^T
            Nd4j.gemm(dLdPreLogSigma2, eZXLogStdev2W, epsilon, false, true, 1.0, 1.0);

            //Backprop through encoder:
            int nEncoderLayers = encoderLayerSizes.length;
            for (int i = nEncoderLayers - 1; i >= 0; i--) {
                String wKey = "e" + i + WEIGHT_KEY_SUFFIX;
                String bKey = "e" + i + BIAS_KEY_SUFFIX;

                INDArray weights = getParamWithNoise(wKey, true, workspaceMgr);

                INDArray dLdW = gradientViews.get(wKey);
                INDArray dLdB = gradientViews.get(bKey);

                INDArray preOut = fwd.encoderPreOuts[i];

                INDArray currentDelta;
                if (numSamples > 1) {
                    //Re-use sigma-prime values for the encoder - these don't change based on multiple samples,
                    // only the errors do
                    if (l == 0) {
                        //Not the most elegent implementation (with the ND4j.ones()), but it works...
                        encoderActivationDerivs[i] =
                                        afn.backprop(fwd.encoderPreOuts[i], Nd4j.ones(fwd.encoderPreOuts[i].shape()))
                                                        .getFirst();
                    }
                    currentDelta = epsilon.muli(encoderActivationDerivs[i]);
                } else {
                    currentDelta = afn.backprop(preOut, epsilon).getFirst();
                }

                INDArray actInput;
                if (i == 0) {
                    actInput = input.castTo(dLdW.dataType());
                } else {
                    actInput = fwd.encoderActivations[i - 1];
                }
                Nd4j.gemm(actInput, currentDelta, dLdW, true, false, scaleFactor, gemmCConstant);
                if (l == 0) {
                    currentDelta.sum(dLdB, 0);
                    if (numSamples > 1) {
                        dLdB.muli(scaleFactor);
                    }
                } else {
                    blasL1.axpy(dLdB.length(), scaleFactor, currentDelta.sum(0), dLdB);
                }

                gradientMap.put(wKey, dLdW);
                gradientMap.put(bKey, dLdB);

                epsilon = weights.mmul(currentDelta.transpose()).transpose();
            }
        }

        //Insert the gradients into the Gradient map in the correct order, in case we need to flatten the gradient later
        // to match the parameters iteration order
        Gradient gradient = new DefaultGradient(gradientsFlattened);
        Map<String, INDArray> g = gradient.gradientForVariable();
        for (int i = 0; i < encoderLayerSizes.length; i++) {
            String w = "e" + i + VariationalAutoencoderParamInitializer.WEIGHT_KEY_SUFFIX;
            g.put(w, gradientMap.get(w));
            String b = "e" + i + VariationalAutoencoderParamInitializer.BIAS_KEY_SUFFIX;
            g.put(b, gradientMap.get(b));
        }
        g.put(VariationalAutoencoderParamInitializer.PZX_MEAN_W,
                        gradientMap.get(VariationalAutoencoderParamInitializer.PZX_MEAN_W));
        g.put(VariationalAutoencoderParamInitializer.PZX_MEAN_B,
                        gradientMap.get(VariationalAutoencoderParamInitializer.PZX_MEAN_B));
        g.put(VariationalAutoencoderParamInitializer.PZX_LOGSTD2_W,
                        gradientMap.get(VariationalAutoencoderParamInitializer.PZX_LOGSTD2_W));
        g.put(VariationalAutoencoderParamInitializer.PZX_LOGSTD2_B,
                        gradientMap.get(VariationalAutoencoderParamInitializer.PZX_LOGSTD2_B));
        for (int i = 0; i < decoderLayerSizes.length; i++) {
            String w = "d" + i + VariationalAutoencoderParamInitializer.WEIGHT_KEY_SUFFIX;
            g.put(w, gradientMap.get(w));
            String b = "d" + i + VariationalAutoencoderParamInitializer.BIAS_KEY_SUFFIX;
            g.put(b, gradientMap.get(b));
        }
        g.put(VariationalAutoencoderParamInitializer.PXZ_W,
                        gradientMap.get(VariationalAutoencoderParamInitializer.PXZ_W));
        g.put(VariationalAutoencoderParamInitializer.PXZ_B,
                        gradientMap.get(VariationalAutoencoderParamInitializer.PXZ_B));

        weightNoiseParams.clear();

        this.gradient = gradient;
    }

    @Override
    public INDArray params() {
        return paramsFlattened;
    }

    @Override
    public TrainingConfig getConfig() {
        return conf.getLayer();
    }

    @Override
    public long numParams() {
        return numParams(false);
    }

    @Override
    public long numParams(boolean backwards) {
        int ret = 0;
        for (Map.Entry<String, INDArray> entry : params.entrySet()) {
            if (backwards && isPretrainParam(entry.getKey()))
                continue;
            ret += entry.getValue().length();
        }
        return ret;
    }

    @Override
    public void setParams(INDArray params) {
        if (params.length() != this.paramsFlattened.length()) {
            throw new IllegalArgumentException("Cannot set parameters: expected parameters vector of length "
                            + this.paramsFlattened.length() + " but got parameters array of length " + params.length()
                            + " " + layerId());
        }
        this.paramsFlattened.assign(params);
    }

    @Override
    public void setParamsViewArray(INDArray params) {
        if (this.params != null && params.length() != numParams())
            throw new IllegalArgumentException("Invalid input: expect params of length " + numParams()
                            + ", got params of length " + params.length() + " " + layerId());
        this.paramsFlattened = params;
    }

    @Override
    public INDArray getGradientsViewArray() {
        return gradientsFlattened;
    }

    @Override
    public void setBackpropGradientsViewArray(INDArray gradients) {
        if (this.params != null && gradients.length() != numParams()) {
            throw new IllegalArgumentException("Invalid input: expect gradients array of length " + numParams()
                            + ", got gradient array of length of length " + gradients.length() + " " + layerId());
        }

        this.gradientsFlattened = gradients;
        this.gradientViews = conf.getLayer().initializer().getGradientsFromFlattened(conf, gradients);
    }

    @Override
    public void fit(INDArray data, LayerWorkspaceMgr workspaceMgr) {
        this.setInput(data, workspaceMgr);
        fit();
    }

    @Override
    public Gradient gradient() {
        return gradient;
    }

    @Override
    public Pair<Gradient, Double> gradientAndScore() {
        return new Pair<>(gradient(), score());
    }

    @Override
    public int batchSize() {
        // FIXME: int cast
        return (int) input.size(0);
    }

    @Override
    public NeuralNetConfiguration conf() {
        return conf;
    }

    @Override
    public void setConf(NeuralNetConfiguration conf) {
        this.conf = conf;
    }

    @Override
    public INDArray input() {
        return input;
    }

    @Override
    public ConvexOptimizer getOptimizer() {
        return optimizer;
    }

    @Override
    public INDArray getParam(String param) {
        return params.get(param);
    }

    @Override
    public Map<String, INDArray> paramTable() {
        return new LinkedHashMap<>(params);
    }

    @Override
    public Map<String, INDArray> paramTable(boolean backpropParamsOnly) {
        Map<String, INDArray> map = new LinkedHashMap<>();
        for (Map.Entry<String, INDArray> e : params.entrySet()) {
            if (!backpropParamsOnly || !isPretrainParam(e.getKey())) {
                map.put(e.getKey(), e.getValue());
            }
        }
        return map;
    }

    @Override
    public boolean updaterDivideByMinibatch(String paramName) {
        return true;
    }

    @Override
    public void setParamTable(Map<String, INDArray> paramTable) {
        this.params = paramTable;
    }

    @Override
    public void setParam(String key, INDArray val) {
        if (paramTable().containsKey(key)) {
            paramTable().get(key).assign(val);
        } else {
            throw new IllegalArgumentException("Unknown parameter: " + key + " - " + layerId());
        }
    }

    @Override
    public void clear() {
        this.input = null;
        this.maskArray = null;
    }

    @Override
    public void applyConstraints(int iteration, int epoch) {
        if(layerConf().getConstraints() != null){
            for(LayerConstraint lc : layerConf().getConstraints()){
                lc.applyConstraint(this, iteration, epoch);
            }
        }
    }

    public boolean isPretrainParam(String param) {
        return !(param.startsWith("e") || param.startsWith(VariationalAutoencoderParamInitializer.PZX_MEAN_PREFIX));
    }

    @Override
    public double calcRegularizationScore(boolean backpropParamsOnly){
        double scoreSum = 0.0;
        for (Map.Entry<String, INDArray> e : paramTable().entrySet()) {
            if(backpropParamsOnly && isPretrainParam(e.getKey()))
                continue;
            List<Regularization> l = layerConf().getRegularizationByParam(e.getKey());
            if(l == null || l.isEmpty()){
                continue;
            }
            for(Regularization r : l){
                scoreSum += r.score(e.getValue(), getIterationCount(), getEpochCount());
            }
        }
        return scoreSum;
    }

    @Override
    public Type type() {
        return Type.FEED_FORWARD;
    }

    @Override
    public Pair<Gradient, INDArray> backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr) {
        assertInputSet(true);
        if (!zeroedPretrainParamGradients) {
            for (Map.Entry<String, INDArray> entry : gradientViews.entrySet()) {
                if (isPretrainParam(entry.getKey())) {
                    entry.getValue().assign(0);
                }
            }
            zeroedPretrainParamGradients = true;
        }

        INDArray input = this.input.castTo(dataType);

        Gradient gradient = new DefaultGradient();

        VAEFwdHelper fwd = doForward(true, true, workspaceMgr);
        INDArray currentDelta = pzxActivationFn.backprop(fwd.pzxMeanPreOut, epsilon).getFirst();

        //Finally, calculate mean value:
        INDArray meanW = getParamWithNoise(VariationalAutoencoderParamInitializer.PZX_MEAN_W, true, workspaceMgr);
        INDArray dLdMeanW = gradientViews.get(VariationalAutoencoderParamInitializer.PZX_MEAN_W); //f order
        INDArray lastEncoderActivation = fwd.encoderActivations[fwd.encoderActivations.length - 1];
        Nd4j.gemm(lastEncoderActivation, currentDelta, dLdMeanW, true, false, 1.0, 0.0);
        INDArray dLdMeanB = gradientViews.get(VariationalAutoencoderParamInitializer.PZX_MEAN_B);
        currentDelta.sum(dLdMeanB, 0); //dLdMeanB is initialized/zeroed first in sum op

        gradient.gradientForVariable().put(VariationalAutoencoderParamInitializer.PZX_MEAN_W, dLdMeanW);
        gradient.gradientForVariable().put(VariationalAutoencoderParamInitializer.PZX_MEAN_B, dLdMeanB);

        epsilon = meanW.mmul(currentDelta.transpose()).transpose();

        int nEncoderLayers = encoderLayerSizes.length;

        IActivation afn = layerConf().getActivationFn();
        for (int i = nEncoderLayers - 1; i >= 0; i--) {
            String wKey = "e" + i + WEIGHT_KEY_SUFFIX;
            String bKey = "e" + i + BIAS_KEY_SUFFIX;

            INDArray weights = getParamWithNoise(wKey, true, workspaceMgr);

            INDArray dLdW = gradientViews.get(wKey);
            INDArray dLdB = gradientViews.get(bKey);

            INDArray preOut = fwd.encoderPreOuts[i];

            currentDelta = afn.backprop(preOut, epsilon).getFirst();

            INDArray actInput;
            if (i == 0) {
                actInput = input;
            } else {
                actInput = fwd.encoderActivations[i - 1];
            }
            Nd4j.gemm(actInput, currentDelta, dLdW, true, false, 1.0, 0.0);
            currentDelta.sum(dLdB, 0); //dLdB is initialized/zeroed first in sum op

            gradient.gradientForVariable().put(wKey, dLdW);
            gradient.gradientForVariable().put(bKey, dLdB);

            if(i == 0) {
                epsilon = workspaceMgr.createUninitialized(ArrayType.ACTIVATION_GRAD, currentDelta.dataType(), new long[]{weights.size(0), currentDelta.size(0)}, 'f');
                weights.mmuli(currentDelta.transpose(), epsilon);
                epsilon = epsilon.transpose();
            } else {
                epsilon = weights.mmul(currentDelta.transpose()).transpose();
            }
        }

        return new Pair<>(gradient, epsilon);
    }

    public INDArray preOutput(boolean training, LayerWorkspaceMgr workspaceMgr) {
        VAEFwdHelper f = doForward(training, false, workspaceMgr);
        return f.pzxMeanPreOut;
    }

    @AllArgsConstructor
    @Data
    private static class VAEFwdHelper {
        private INDArray[] encoderPreOuts;
        private INDArray pzxMeanPreOut;
        private INDArray[] encoderActivations;
    }


    private VAEFwdHelper doForward(boolean training, boolean forBackprop, LayerWorkspaceMgr workspaceMgr) {
        assertInputSet(false);

        //TODO input validation

        int nEncoderLayers = encoderLayerSizes.length;

        INDArray[] encoderPreOuts = new INDArray[encoderLayerSizes.length];
        INDArray[] encoderActivations = new INDArray[encoderLayerSizes.length];
        INDArray current = input.castTo(getParam("e0" + WEIGHT_KEY_SUFFIX).dataType());
        for (int i = 0; i < nEncoderLayers; i++) {
            String wKey = "e" + i + WEIGHT_KEY_SUFFIX;
            String bKey = "e" + i + BIAS_KEY_SUFFIX;

            INDArray weights = getParamWithNoise(wKey, training, workspaceMgr);
            INDArray bias = getParamWithNoise(bKey, training, workspaceMgr);

            current = current.mmul(weights).addiRowVector(bias);
            if (forBackprop) {
                encoderPreOuts[i] = current.dup();
            }
            layerConf().getActivationFn().getActivation(current, training);
            encoderActivations[i] = current;
        }

        //Finally, calculate mean value:
        INDArray mW = getParamWithNoise(VariationalAutoencoderParamInitializer.PZX_MEAN_W, training, workspaceMgr);
        INDArray mB = getParamWithNoise(VariationalAutoencoderParamInitializer.PZX_MEAN_B, training, workspaceMgr);

        INDArray pzxMean = workspaceMgr.createUninitialized(ArrayType.ACTIVATIONS, mW.dataType(), new long[]{current.size(0), mW.size(1)}, 'f');
        pzxMean = current.mmuli(mW, pzxMean).addiRowVector(mB);


        return new VAEFwdHelper(encoderPreOuts, pzxMean, encoderActivations);
    }

    @Override
    public INDArray activate(boolean training, LayerWorkspaceMgr workspaceMgr) {
        INDArray output = preOutput(training, workspaceMgr); //Mean values for p(z|x)
        pzxActivationFn.getActivation(output, training);

        return output;
    }

    @Override
    public INDArray activate(INDArray input, boolean training, LayerWorkspaceMgr workspaceMgr) {
        setInput(input, workspaceMgr);
        return activate(training, workspaceMgr);
    }

    @Override
    public Collection<TrainingListener> getListeners() {
        if (trainingListeners == null) {
            return null;
        }

        return new ArrayList<>(trainingListeners);
    }

    @Override
    public void setListeners(TrainingListener... listeners) {
        setListeners(Arrays.<TrainingListener>asList(listeners));
    }

    @Override
    public void setListeners(Collection<TrainingListener> listeners) {
        if (trainingListeners == null)
            trainingListeners = new ArrayList<>();
        else
            trainingListeners.clear();
        if (trainingListeners == null)
            trainingListeners = new ArrayList<>();
        else
            trainingListeners.clear();

        if (listeners != null && !listeners.isEmpty()) {
            trainingListeners.addAll(listeners);
        }
    }


    /**
     * This method ADDS additional TrainingListener to existing listeners
     *
     * @param listeners
     */
    @Override
    public void addListeners(TrainingListener... listeners) {
        if (this.trainingListeners == null) {
            setListeners(listeners);
            return;
        }

        for (TrainingListener listener : listeners)
            trainingListeners.add(listener);
    }

    @Override
    public void setIndex(int index) {
        this.index = index;
    }

    @Override
    public int getIndex() {
        return index;
    }

    @Override
    public void setInput(INDArray input, LayerWorkspaceMgr layerWorkspaceMgr) {
        this.input = input;
    }

    @Override
    public void setInputMiniBatchSize(int size) {

    }

    @Override
    public int getInputMiniBatchSize() {
        // FIXME: int cast
        return (int) input.size(0);
    }

    @Override
    public void setMaskArray(INDArray maskArray) {
        this.maskArray = maskArray;
    }

    @Override
    public INDArray getMaskArray() {
        return maskArray;
    }

    @Override
    public boolean isPretrainLayer() {
        return true;
    }

    @Override
    public void clearNoiseWeightParams() {
        weightNoiseParams.clear();
    }

    @Override
    public void allowInputModification(boolean allow) {
        //No op
    }

    @Override
    public Pair<INDArray, MaskState> feedForwardMaskArray(INDArray maskArray, MaskState currentMaskState,
                    int minibatchSize) {


        throw new UnsupportedOperationException("Not yet implemented " + layerId());
    }

    @Override
    public LayerHelper getHelper() {
        return null;
    }


    @Override
    public void fit() {
        if (input == null) {
            throw new IllegalStateException("Cannot fit layer: layer input is null (not set) " + layerId());
        }

        if (solver == null) {
            try (MemoryWorkspace workspace = Nd4j.getMemoryManager().scopeOutOfWorkspaces()) {
                solver = new Solver.Builder().model(this).configure(conf()).listeners(getListeners()).build();
            }
        }
        this.optimizer = solver.getOptimizer();
        solver.optimize(LayerWorkspaceMgr.noWorkspaces());      //TODO FIXME
    }

    /**
     * Calculate the reconstruction probability, as described in An & Cho, 2015 - "Variational Autoencoder based
     * Anomaly Detection using Reconstruction Probability" (Algorithm 4)<br>
     * The authors describe it as follows: "This is essentially the probability of the data being generated from a given
     * latent variable drawn from the approximate posterior distribution."<br>
     * <br>
     * Specifically, for each example x in the input, calculate p(x). Note however that p(x) is a stochastic (Monte-Carlo)
     * estimate of the true p(x), based on the specified number of samples. More samples will produce a more accurate
     * (lower variance) estimate of the true p(x) for the current model parameters.<br>
     * <br>
     * Internally uses {@link #reconstructionLogProbability(INDArray, int)} for the actual implementation.
     * That method may be more numerically stable in some cases.<br>
     * <br>
     * The returned array is a column vector of reconstruction probabilities, for each example. Thus, reconstruction probabilities
     * can (and should, for efficiency) be calculated in a batched manner.
     *
     * @param data       The data to calculate the reconstruction probability for
     * @param numSamples Number of samples with which to base the reconstruction probability on.
     * @return Column vector of reconstruction probabilities for each example (shape: [numExamples,1])
     */
    public INDArray reconstructionProbability(INDArray data, int numSamples) {
        INDArray reconstructionLogProb = reconstructionLogProbability(data, numSamples);
        return Transforms.exp(reconstructionLogProb.castTo(DataType.DOUBLE), false);    //Cast to double to reduce risk of numerical underflow
    }

    /**
     * Return the log reconstruction probability given the specified number of samples.<br>
     * See {@link #reconstructionLogProbability(INDArray, int)} for more details
     *
     * @param data       The data to calculate the log reconstruction probability
     * @param numSamples Number of samples with which to base the reconstruction probability on.
     * @return Column vector of reconstruction log probabilities for each example (shape: [numExamples,1])
     */
    public INDArray reconstructionLogProbability(INDArray data, int numSamples) {
        if (numSamples <= 0) {
            throw new IllegalArgumentException(
                            "Invalid input: numSamples must be > 0. Got: " + numSamples + " " + layerId());
        }
        if (reconstructionDistribution instanceof LossFunctionWrapper) {
            throw new UnsupportedOperationException("Cannot calculate reconstruction log probability when using "
                            + "a LossFunction (via LossFunctionWrapper) instead of a ReconstructionDistribution: ILossFunction "
                            + "instances are not in general probabilistic, hence it is not possible to calculate reconstruction probability "
                            + layerId());
        }

        data = data.castTo(dataType);

        //Forward pass through the encoder and mean for P(Z|X)
        LayerWorkspaceMgr workspaceMgr = LayerWorkspaceMgr.noWorkspaces();  //TODO add workspace support to this method
        setInput(data, workspaceMgr);
        VAEFwdHelper fwd = doForward(true, true, workspaceMgr);
        IActivation afn = layerConf().getActivationFn();

        //Forward pass through logStd^2 for P(Z|X)
        INDArray pzxLogStd2W = getParamWithNoise(VariationalAutoencoderParamInitializer.PZX_LOGSTD2_W, false, workspaceMgr);
        INDArray pzxLogStd2b = getParamWithNoise(VariationalAutoencoderParamInitializer.PZX_LOGSTD2_B, false, workspaceMgr);

        INDArray meanZ = fwd.pzxMeanPreOut;
        INDArray logStdev2Z = fwd.encoderActivations[fwd.encoderActivations.length - 1].mmul(pzxLogStd2W)
                        .addiRowVector(pzxLogStd2b);
        pzxActivationFn.getActivation(meanZ, false);
        pzxActivationFn.getActivation(logStdev2Z, false);

        INDArray pzxSigma = Transforms.exp(logStdev2Z, false);
        Transforms.sqrt(pzxSigma, false);

        val minibatch = input.size(0);
        val size = fwd.pzxMeanPreOut.size(1);

        INDArray pxzw = getParamWithNoise(VariationalAutoencoderParamInitializer.PXZ_W, false, workspaceMgr);
        INDArray pxzb = getParamWithNoise(VariationalAutoencoderParamInitializer.PXZ_B, false, workspaceMgr);

        INDArray[] decoderWeights = new INDArray[decoderLayerSizes.length];
        INDArray[] decoderBiases = new INDArray[decoderLayerSizes.length];

        for (int i = 0; i < decoderLayerSizes.length; i++) {
            String wKey = "d" + i + WEIGHT_KEY_SUFFIX;
            String bKey = "d" + i + BIAS_KEY_SUFFIX;
            decoderWeights[i] = getParamWithNoise(wKey, false, workspaceMgr);
            decoderBiases[i] = getParamWithNoise(bKey, false, workspaceMgr);
        }

        INDArray sumReconstructionNegLogProbability = null;
        for (int i = 0; i < numSamples; i++) {
            INDArray e = Nd4j.randn(dataType, minibatch, size);
            INDArray z = e.muli(pzxSigma).addi(meanZ); //z = mu + sigma * e, with e ~ N(0,1)

            //Do forward pass through decoder
            int nDecoderLayers = decoderLayerSizes.length;
            INDArray currentActivations = z;
            for (int j = 0; j < nDecoderLayers; j++) {
                currentActivations = currentActivations.mmul(decoderWeights[j]).addiRowVector(decoderBiases[j]);
                afn.getActivation(currentActivations, false);
            }

            //And calculate reconstruction distribution preOut
            INDArray pxzDistributionPreOut = currentActivations.mmul(pxzw).addiRowVector(pxzb);

            if (i == 0) {
                sumReconstructionNegLogProbability =
                                reconstructionDistribution.exampleNegLogProbability(data, pxzDistributionPreOut);
            } else {
                sumReconstructionNegLogProbability
                                .addi(reconstructionDistribution.exampleNegLogProbability(data, pxzDistributionPreOut));
            }
        }

        setInput(null, workspaceMgr);
        return sumReconstructionNegLogProbability.divi(-numSamples);
    }

    /**
     * Given a specified values for the latent space as input (latent space being z in p(z|data)), generate output
     * from P(x|z), where x = E[P(x|z)]<br>
     * i.e., return the mean value for the distribution P(x|z)
     *
     * @param latentSpaceValues    Values for the latent space. size(1) must equal nOut configuration parameter
     * @return Sample of data: E[P(x|z)]
     */
    public INDArray generateAtMeanGivenZ(INDArray latentSpaceValues) {
        INDArray pxzDistributionPreOut = decodeGivenLatentSpaceValues(latentSpaceValues, LayerWorkspaceMgr.noWorkspaces()); //TODO workspace support
        return reconstructionDistribution.generateAtMean(pxzDistributionPreOut);
    }

    /**
     * Given a specified values for the latent space as input (latent space being z in p(z|data)), randomly generate output
     * x, where x ~ P(x|z)
     *
     * @param latentSpaceValues    Values for the latent space. size(1) must equal nOut configuration parameter
     * @return Sample of data: x ~ P(x|z)
     */
    public INDArray generateRandomGivenZ(INDArray latentSpaceValues, LayerWorkspaceMgr workspaceMgr) {
        INDArray pxzDistributionPreOut = decodeGivenLatentSpaceValues(latentSpaceValues, workspaceMgr);
        return reconstructionDistribution.generateRandom(pxzDistributionPreOut);
    }

    private INDArray decodeGivenLatentSpaceValues(INDArray latentSpaceValues, LayerWorkspaceMgr workspaceMgr) {
        if (latentSpaceValues.size(1) != getParamWithNoise(VariationalAutoencoderParamInitializer.PZX_MEAN_W, false, workspaceMgr).size(1)) {
            throw new IllegalArgumentException("Invalid latent space values: expected size "
                            + getParamWithNoise(VariationalAutoencoderParamInitializer.PZX_MEAN_W, false, workspaceMgr).size(1)
                            + ", got size (dimension 1) = " + latentSpaceValues.size(1) + " " + layerId());
        }

        //Do forward pass through decoder

        int nDecoderLayers = decoderLayerSizes.length;
        INDArray currentActivations = latentSpaceValues;
        IActivation afn = layerConf().getActivationFn();

        for (int i = 0; i < nDecoderLayers; i++) {
            String wKey = "d" + i + WEIGHT_KEY_SUFFIX;
            String bKey = "d" + i + BIAS_KEY_SUFFIX;
            INDArray w = getParamWithNoise(wKey, false, workspaceMgr);
            INDArray b = getParamWithNoise(bKey, false, workspaceMgr);
            currentActivations = currentActivations.mmul(w).addiRowVector(b);
            afn.getActivation(currentActivations, false);
        }

        INDArray pxzw = getParamWithNoise(VariationalAutoencoderParamInitializer.PXZ_W, false, workspaceMgr);
        INDArray pxzb = getParamWithNoise(VariationalAutoencoderParamInitializer.PXZ_B, false, workspaceMgr);
        return currentActivations.mmul(pxzw).addiRowVector(pxzb);
    }

    /**
     * Does the reconstruction distribution have a loss function (such as mean squared error) or is it a standard
     * probabilistic reconstruction distribution?
     */
    public boolean hasLossFunction() {
        return reconstructionDistribution.hasLossFunction();
    }

    /**
     * Return the reconstruction error for this variational autoencoder.<br>
     * <b>NOTE (important):</b> This method is used ONLY for VAEs that have a standard neural network loss function (i.e.,
     * an {@link org.nd4j.linalg.lossfunctions.ILossFunction} instance such as mean squared error) instead of using a
     * probabilistic reconstruction distribution P(x|z) for the reconstructions (as presented in the VAE architecture by
     * Kingma and Welling).<br>
     * You can check if the VAE has a loss function using {@link #hasLossFunction()}<br>
     * Consequently, the reconstruction error is a simple deterministic function (no Monte-Carlo sampling is required,
     * unlike {@link #reconstructionProbability(INDArray, int)} and {@link #reconstructionLogProbability(INDArray, int)})
     *
     * @param data       The data to calculate the reconstruction error on
     * @return Column vector of reconstruction errors for each example (shape: [numExamples,1])
     */
    public INDArray reconstructionError(INDArray data) {
        if (!hasLossFunction()) {
            throw new IllegalStateException(
                            "Cannot use reconstructionError method unless the variational autoencoder is "
                                            + "configured with a standard loss function (via LossFunctionWrapper). For VAEs utilizing a reconstruction "
                                            + "distribution, use the reconstructionProbability or reconstructionLogProbability methods "
                                            + layerId());
        }

        INDArray pZXMean = activate(data, false, LayerWorkspaceMgr.noWorkspaces());
        INDArray reconstruction = generateAtMeanGivenZ(pZXMean); //Not probabilistic -> "mean" == output

        if (reconstructionDistribution instanceof CompositeReconstructionDistribution) {
            CompositeReconstructionDistribution c = (CompositeReconstructionDistribution) reconstructionDistribution;
            return c.computeLossFunctionScoreArray(data, reconstruction);
        } else {

            LossFunctionWrapper lfw = (LossFunctionWrapper) reconstructionDistribution;
            ILossFunction lossFunction = lfw.getLossFunction();

            //Re: the activation identity here - the reconstruction array already has the activation function applied,
            // so we don't want to apply it again. i.e., we are passing the output, not the pre-output.
            return lossFunction.computeScoreArray(data, reconstruction, new ActivationIdentity(), null);
        }
    }

    public void assertInputSet(boolean backprop){
        if(input == null){
            if(backprop){
                throw new IllegalStateException("Cannot perform backprop in layer " + getClass().getSimpleName()
                        + ": layer input field is not set");
            } else {
                throw new IllegalStateException("Cannot perform forward pass in layer " + getClass().getSimpleName()
                        + ": layer input field is not set");
            }
        }
    }
}
