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package org.deeplearning4j.nn.layers.samediff;

import lombok.val;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.api.MaskState;
import org.deeplearning4j.nn.api.TrainingConfig;
import org.deeplearning4j.nn.conf.layers.samediff.SDVertexParams;
import org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex;
import org.deeplearning4j.nn.gradient.DefaultGradient;
import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.graph.vertex.BaseGraphVertex;
import org.deeplearning4j.nn.params.SameDiffParamInitializer;
import org.deeplearning4j.nn.workspace.ArrayType;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.autodiff.samediff.array.SingleThreadArrayHolder;
import org.nd4j.autodiff.samediff.internal.InferenceSession;
import org.nd4j.autodiff.samediff.internal.SessionMemMgr;
import org.nd4j.autodiff.util.SameDiffUtils;
import org.nd4j.common.base.Preconditions;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.memory.MemoryWorkspace;
import org.nd4j.linalg.api.memory.conf.WorkspaceConfiguration;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.impl.layers.ExternalErrorsFunction;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.common.primitives.Pair;

import java.util.*;

public class SameDiffGraphVertex extends BaseGraphVertex {

    protected SameDiffVertex config;
    protected SameDiff sameDiff;
    protected SDVariable outputVar;
    protected ExternalErrorsFunction fn;
    protected String outputKey;
    protected Map inputVars;
    protected INDArray[] maskArrays;

    protected INDArray params;
    protected INDArray gradients;
    protected Map paramTable;
    protected Map gradTable;
    private MaskState currentMaskState;
    private int minibatchSize;

    public SameDiffGraphVertex(SameDiffVertex config, ComputationGraph graph, String name, int vertexIndex,
                                  INDArray paramsView, boolean initParams, DataType dataType) {
        super(graph, name, vertexIndex, null, null, dataType);
        this.config = config;
        SDVertexParams vp = config.getVertexParams();
        paramTable = SameDiffParamInitializer.getInstance().subsetAndReshape(vp.getParameterKeys(),
                vp.getParamShapes(), paramsView, null, config);
        if(initParams){
            config.initializeParameters(paramTable);
        }
        this.params = paramsView;
    }

    @Override
    public String toString() {
        return null;
    }

    @Override
    public boolean hasLayer() {
        return false;
    }

    @Override
    public Layer getLayer() {
        return null;
    }

    @Override
    public INDArray doForward(boolean training, LayerWorkspaceMgr workspaceMgr) {
        try(MemoryWorkspace ws = Nd4j.getWorkspaceManager().scopeOutOfWorkspaces()) {
            if (sameDiff == null) {
                doInit();
            }
        }

        Map phMap = new HashMap<>();
        config.validateInput(inputs);
        for(int i=0; i doBackward(boolean tbptt, LayerWorkspaceMgr workspaceMgr) {
        Gradient g = new DefaultGradient();

        try(MemoryWorkspace ws = Nd4j.getWorkspaceManager().scopeOutOfWorkspaces()) {
            if (sameDiff == null) {
                doInit();
            }
        }

        List inputNames = config.getVertexParams().getInputs();
        if(!sameDiff.hasGradientFunction()) {
            //Create when scoped out, to ensure any arrays are not in WS
            String[] inArr = inputNames.toArray(new String[inputNames.size()]);
            sameDiff.createGradFunction(inArr);
        }
        config.validateInput(inputs);

        //Configure memory management for SameDiff instance - use DL4J workspaces
        Map sessionMap = sameDiff.getFunction("grad").getSessions();
        if(!sessionMap.containsKey(Thread.currentThread().getId())){
            sessionMap.put(Thread.currentThread().getId(), SameDiff.getInferenceFactory().create(sameDiff.getFunction("grad")));
        }
        String wsNameWorking = workspaceMgr.getWorkspaceName(ArrayType.BP_WORKING_MEM);
        String wsNameActGrad = workspaceMgr.getWorkspaceName(ArrayType.ACTIVATION_GRAD);
        WorkspaceConfiguration confWorking = workspaceMgr.getConfiguration(ArrayType.BP_WORKING_MEM);
        WorkspaceConfiguration confOutput = workspaceMgr.getConfiguration(ArrayType.ACTIVATION_GRAD);

        boolean actGradScopedOut = workspaceMgr.isScopedOut(ArrayType.ACTIVATION_GRAD);
        Preconditions.checkState(actGradScopedOut || wsNameActGrad != null, "Activation gradients must have a workspace or be scoped out");
        SessionMemMgr mmgr = new DL4JSameDiffMemoryMgr(wsNameWorking, wsNameActGrad, confWorking, confOutput);
        sessionMap.get(Thread.currentThread().getId()).setMmgr(mmgr);



        Map phMap = new HashMap<>();
        List inputs = config.getVertexParams().getInputs();
        int i=0;
        for(String s : inputs){
            phMap.put(s, this.inputs[i++]);
        }
        for( int j=0; j required = new ArrayList<>(config.getVertexParams().getInputs());     //Ensure that the input placeholder gradients are calculated
        required.addAll(paramTable.keySet());

        Map gradsMap = sameDiff.calculateGradients(phMap, required);
        for(String s : paramTable.keySet() ){
            INDArray sdGrad = gradsMap.get(s);
            INDArray dl4jGrad = gradTable.get(s);
            dl4jGrad.assign(sdGrad);                                            //TODO OPTIMIZE THIS
            g.gradientForVariable().put(s, dl4jGrad);
        }

        INDArray[] dLdIns = new INDArray[inputs.size()];
        String fnName = fn.getGradPlaceholderName();
        for(int j=0; j(g, dLdIns);
    }

    @Override
    public void setBackpropGradientsViewArray(INDArray backpropGradientsViewArray) {
        SDVertexParams vp = config.getVertexParams();
        gradTable = SameDiffParamInitializer.getInstance().subsetAndReshape(vp.getParameterKeys(),
                vp.getParamShapes(), backpropGradientsViewArray, null, config);
    }

    @Override
    public Pair feedForwardMaskArrays(INDArray[] maskArrays, MaskState currentMaskState, int minibatchSize) {
        this.maskArrays = maskArrays;
        this.currentMaskState = currentMaskState;

        return config.feedForwardMaskArrays(maskArrays, currentMaskState, minibatchSize);
    }


    protected void doInit(){
        try(MemoryWorkspace ws = Nd4j.getWorkspaceManager().scopeOutOfWorkspaces()) {
            sameDiff = SameDiff.create();
            //Use SingleThreadArrayHolder so we can use views (also don't nede multithreading here, DL4J is not thread safe)
            sameDiff.setArrayHolders(new SingleThreadArrayHolder(), new SingleThreadArrayHolder(), false);

            inputVars = new LinkedHashMap<>();
            LinkedHashMap maskVars = new LinkedHashMap<>();
            int i=0;
            for(String s : config.getVertexParams().getInputs()){
                val inputShape = inputs[i++].shape().clone();
                INDArray maskTemp = createMask(dataType, inputShape);
                inputShape[0] = -1;
                SDVariable inputVar = sameDiff.placeHolder(s, dataType, inputShape);
                inputVars.put(s, inputVar);
                long[] maskShape = maskTemp.shape().clone();
                maskShape[0] = -1;
                SDVariable maskVar = sameDiff.placeHolder(s + "_mask", maskTemp.dataType(), maskShape);
                maskVars.put(s, maskVar);
            }

            Map paramShapes = config.getVertexParams().getParamShapes();
            Map params = new LinkedHashMap<>();
            for (String s : paramShapes.keySet()) {
                val ps = paramShapes.get(s);
                SDVariable v = sameDiff.var(s, dataType, ps);
                params.put(s, v);
            }
            SDVariable layerOutput = config.defineVertex(sameDiff, inputVars, params, maskVars);
            Preconditions.checkNotNull(layerOutput, "Invalid output: layer output is null");
            outputVar = layerOutput;

            for (Map.Entry e : paramTable.entrySet()) {
                sameDiff.associateArrayWithVariable(e.getValue(), sameDiff.getVariable(e.getKey()));
            }

            //Define the function for external errors:
            fn = SameDiffUtils.externalErrors(sameDiff, null, layerOutput);
            fn.outputVariable();

            this.outputKey = outputVar.name();
        }
    }

    @Override
    public void clearVertex() {
        clear();
    }

    @Override
    public Map paramTable(boolean backpropOnly) {
        return paramTable;
    }

    @Override
    public TrainingConfig getConfig() {
        return config;
    }

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

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

    //Package private
    static INDArray createMask(DataType dataType, long[] shape){
        switch (shape.length){
            case 2: // FF-Type input
                return Nd4j.ones(dataType,shape[0], 1);
            case 3: // RNN-Type input
                return Nd4j.ones(dataType, shape[0], shape[2]);
            case 4: //CNN input
                return Nd4j.ones(dataType, shape[0], 1, 1, 1);
            default:
                Preconditions.throwEx("Can not create all-ones-mask for given input shape %s.", Arrays.toString(shape));
                return null;
        }
    }
}