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/*-
 *
 *  * Copyright 2017 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.layers.recurrent;

import java.util.Map;
import org.deeplearning4j.berkeley.Pair;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.gradient.Gradient;
import org.nd4j.linalg.activations.IActivation;
import org.nd4j.linalg.api.ndarray.INDArray;

/**
 * Helper for the recurrent LSTM layer (no peephole connections).
 *
 * @author saudet
 */
public interface LSTMHelper {
    boolean checkSupported(IActivation gateActivationFn, IActivation activationFn, boolean hasPeepholeConnections);

    Pair backpropGradient(final NeuralNetConfiguration conf,
                    final IActivation gateActivationFn, final INDArray input, final INDArray recurrentWeights, //Shape: [hiddenLayerSize,4*hiddenLayerSize+3]; order: [wI,wF,wO,wG,wFF,wOO,wGG]
                    final INDArray inputWeights, //Shape: [n^(L-1),4*hiddenLayerSize]; order: [wi,wf,wo,wg]
                    final INDArray epsilon, final boolean truncatedBPTT, final int tbpttBackwardLength,
                    final FwdPassReturn fwdPass, final boolean forwards, final String inputWeightKey,
                    final String recurrentWeightKey, final String biasWeightKey,
                    final Map gradientViews, INDArray maskArray, //Input mask: should only be used with bidirectional RNNs + variable length
                    final boolean hasPeepholeConnections);            //True for GravesLSTM, false for LSTM

    FwdPassReturn activate(final Layer layer, final NeuralNetConfiguration conf,
                    final IActivation gateActivationFn, //Activation function for the gates - sigmoid or hard sigmoid (must be found in range 0 to 1)
                    final INDArray input, final INDArray recurrentWeights, //Shape: [hiddenLayerSize,4*hiddenLayerSize+3]; order: [wI,wF,wO,wG,wFF,wOO,wGG]
                    final INDArray inputWeights, //Shape: [n^(L-1),4*hiddenLayerSize]; order: [wi,wf,wo,wg]
                    final INDArray biases, //Shape: [4,hiddenLayerSize]; order: [bi,bf,bo,bg]^T
                    final boolean training, final INDArray prevOutputActivations,
                    final INDArray prevMemCellState, boolean forBackprop, boolean forwards,
                    final String inputWeightKey, INDArray maskArray, //Input mask: should only be used with bidirectional RNNs + variable length
                    final boolean hasPeepholeConnections);            //True for GravesLSTM, false for LSTM
}