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/*
 *
 *  * Copyright 2016 Skymind,Inc.
 *  *
 *  *    Licensed under the Apache License, Version 2.0 (the "License");
 *  *    you may not use this file except in compliance with the License.
 *  *    You may obtain a copy of the License at
 *  *
 *  *        http://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.
 *
 */

package org.deeplearning4j.nn.graph.vertex.impl;

import lombok.Data;
import org.deeplearning4j.berkeley.Pair;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.api.layers.IOutputLayer;
import org.deeplearning4j.nn.api.layers.RecurrentLayer;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.graph.vertex.BaseGraphVertex;
import org.deeplearning4j.nn.graph.vertex.VertexIndices;
import org.deeplearning4j.nn.layers.BaseOutputLayer;
import org.nd4j.linalg.api.ndarray.INDArray;

import java.util.Arrays;

/**
 * LayerVertex is a GraphVertex with a neural network Layer (and, optionally an {@link InputPreProcessor}) in it
 *
 * @author Alex Black
 */
@Data
public class LayerVertex extends BaseGraphVertex {

    private final Layer layer;
    private final InputPreProcessor layerPreProcessor;
    //Set outputVertex to true when Layer is an OutputLayer, OR For use in specialized situations like reinforcement learning
    // For RL situations, this Layer insn't an OutputLayer, but is the last layer in a graph, that gets its error/epsilon
    // passed in externally
    private final boolean outputVertex;

    /**
     * Create a network input vertex:
     */
    public LayerVertex(ComputationGraph graph, String name, int vertexIndex, Layer layer, InputPreProcessor layerPreProcessor, boolean outputVertex) {
        this(graph, name, vertexIndex, null, null, layer, layerPreProcessor, outputVertex);
    }

    public LayerVertex(ComputationGraph graph, String name, int vertexIndex, VertexIndices[] inputVertices, VertexIndices[] outputVertices,
                       Layer layer, InputPreProcessor layerPreProcessor, boolean outputVertex) {
        super(graph, name, vertexIndex, inputVertices, outputVertices);
        this.graph = graph;
        this.vertexName = name;
        this.vertexIndex = vertexIndex;
        this.inputVertices = inputVertices;
        this.outputVertices = outputVertices;
        this.layer = layer;
        this.layerPreProcessor = layerPreProcessor;
        this.outputVertex = outputVertex;

        this.inputs = new INDArray[(inputVertices != null ? inputVertices.length : 0)];
    }

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

    @Override
    public boolean isOutputVertex() {
        return outputVertex || layer instanceof BaseOutputLayer;
    }

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

    @Override
    public INDArray doForward(boolean training) {
        if (!canDoForward()) throw new IllegalStateException("Cannot do forward pass: all inputs not set");

        return layer.activate(training);
    }

    @Override
    public Pair doBackward(boolean tbptt) {
        if (!canDoBackward()) {
            throw new IllegalStateException("Cannot do backward pass: all epsilons not set");
        }

        Pair pair;
        if (tbptt && layer instanceof RecurrentLayer) {
            //Truncated BPTT for recurrent layers
            pair = ((RecurrentLayer) layer).tbpttBackpropGradient(epsilon, graph.getConfiguration().getTbpttBackLength());
        } else {
            //Normal backprop
            pair = layer.backpropGradient(epsilon);    //epsTotal may be null for OutputLayers
        }

        if (layerPreProcessor != null) {
            INDArray eps = pair.getSecond();
            eps = layerPreProcessor.backprop(eps, graph.batchSize());
            pair.setSecond(eps);
        }

        //Layers always have single activations input -> always have single epsilon output during backprop
        return new Pair<>(pair.getFirst(), new INDArray[]{pair.getSecond()});
    }

    @Override
    public void setInput(int inputNumber, INDArray input) {
        if (inputNumber > 0)
            throw new IllegalArgumentException("Invalid input number: LayerVertex instances have only 1 input (got inputNumber = " + inputNumber + ")");
        inputs[inputNumber] = input;

        INDArray currInput = inputs[0];
        if (layerPreProcessor != null) {
            currInput = layerPreProcessor.preProcess(currInput, graph.batchSize());
        }
        layer.setInput(currInput);
    }

    @Override
    public void setBackpropGradientsViewArray(INDArray backpropGradientsViewArray) {
        layer.setBackpropGradientsViewArray(backpropGradientsViewArray);
    }


    @Override
    public String toString() {
        StringBuilder sb = new StringBuilder();
        sb.append("LayerVertex(id=").append(vertexIndex).append(",name=\"").append(vertexName)
                .append("\",inputs=").append(Arrays.toString(inputVertices)).append(",outputs=").append(Arrays.toString(outputVertices))
                .append(")");
        return sb.toString();
    }

    @Override
    public boolean canDoBackward() {
        if (!isOutputVertex()) return super.canDoBackward();

        for (INDArray input : inputs) {
            if (input == null) {
                return false;
            }
        }

        if (!(layer instanceof IOutputLayer)) {
            if (epsilon == null) {
                return false;
            }
        }

        return true;
    }
}