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package org.deeplearning4j.nn.modelimport.keras.layers.recurrent;

import lombok.Data;
import lombok.EqualsAndHashCode;
import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.nn.modelimport.keras.KerasLayer;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasActivationUtils;
import org.deeplearning4j.nn.api.layers.LayerConstraint;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.RNNFormat;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.FeedForwardLayer;
import org.deeplearning4j.nn.conf.layers.InputTypeUtil;
import org.deeplearning4j.nn.conf.layers.Layer;
import org.deeplearning4j.nn.conf.layers.recurrent.LastTimeStep;
import org.deeplearning4j.nn.conf.layers.recurrent.SimpleRnn;
import org.deeplearning4j.nn.conf.layers.util.MaskZeroLayer;
import org.deeplearning4j.nn.conf.layers.wrapper.BaseWrapperLayer;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasConstraintUtils;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasLayerUtils;
import org.deeplearning4j.nn.params.SimpleRnnParamInitializer;
import org.deeplearning4j.nn.weights.IWeightInit;
import org.deeplearning4j.util.TimeSeriesUtils;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.common.primitives.Pair;

import java.util.Collections;
import java.util.HashMap;
import java.util.Map;
import java.util.Set;

import static org.deeplearning4j.nn.modelimport.keras.utils.KerasInitilizationUtils.getWeightInitFromConfig;
import static org.deeplearning4j.nn.modelimport.keras.utils.KerasLayerUtils.getNOutFromConfig;

/**
 * Imports a Keras SimpleRNN layer as a DL4J SimpleRnn layer.
 *
 * @author Max Pumperla
 */
@Slf4j
@Data
@EqualsAndHashCode(callSuper = false)
public class KerasSimpleRnn extends KerasLayer {

    private final int NUM_TRAINABLE_PARAMS = 3;
    protected boolean unroll = false;
    protected boolean returnSequences;

    /**
     * Pass-through constructor from KerasLayer
     *
     * @param kerasVersion major keras version
     * @throws UnsupportedKerasConfigurationException Unsupported Keras config
     */
    public KerasSimpleRnn(Integer kerasVersion) throws UnsupportedKerasConfigurationException {
        super(kerasVersion);
    }

    /**
     * Constructor from parsed Keras layer configuration dictionary.
     *
     * @param layerConfig dictionary containing Keras layer configuration.
     * @throws InvalidKerasConfigurationException     Invalid Keras config
     * @throws UnsupportedKerasConfigurationException Unsupported Keras config
     */
    public KerasSimpleRnn(Map<String, Object> layerConfig)
            throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException {
        this(layerConfig, true,  Collections.<String, KerasLayer>emptyMap());
    }

    /**
     * Constructor from parsed Keras layer configuration dictionary.
     *
     * @param layerConfig dictionary containing Keras layer configuration.
     * @param previousLayers dictionary containing the previous layers in the topology
     * @throws InvalidKerasConfigurationException     Invalid Keras config
     * @throws UnsupportedKerasConfigurationException Unsupported Keras config
     */
    public KerasSimpleRnn(Map<String, Object> layerConfig, Map<String, ? extends KerasLayer> previousLayers)
            throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException {
        this(layerConfig, true, previousLayers);
    }

    /**
     * Constructor from parsed Keras layer configuration dictionary.
     *
     * @param layerConfig           dictionary containing Keras layer configuration.
     * @param enforceTrainingConfig whether to load Keras training configuration
     * @throws InvalidKerasConfigurationException     Invalid Keras config
     * @throws UnsupportedKerasConfigurationException Unsupported Keras config
     */
    public KerasSimpleRnn(Map<String, Object> layerConfig, boolean enforceTrainingConfig)
            throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException {
        this(layerConfig, enforceTrainingConfig, Collections.<String, KerasLayer>emptyMap());
    }


    /**
     * Constructor from parsed Keras layer configuration dictionary.
     *
     * @param layerConfig           dictionary containing Keras layer configuration
     * @param enforceTrainingConfig whether to enforce training-related configuration options
     * @param previousLayers dictionary containing the previous layers in the topology
     * @throws InvalidKerasConfigurationException     Invalid Keras config
     * @throws UnsupportedKerasConfigurationException Unsupported Keras config
     */
    public KerasSimpleRnn(Map<String, Object> layerConfig, boolean enforceTrainingConfig,
                          Map<String, ? extends KerasLayer> previousLayers)
            throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException {
        super(layerConfig, enforceTrainingConfig);

        IWeightInit init = getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_INIT(),
                enforceTrainingConfig, conf, kerasMajorVersion);

        IWeightInit recurrentInit = getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_INNER_INIT(),
                enforceTrainingConfig, conf, kerasMajorVersion);

        Map<String, Object> innerConfig = KerasLayerUtils.getInnerLayerConfigFromConfig(layerConfig, conf);
        this.returnSequences = (Boolean) innerConfig.get(conf.getLAYER_FIELD_RETURN_SEQUENCES());

        KerasRnnUtils.getRecurrentDropout(conf, layerConfig);
        this.unroll = KerasRnnUtils.getUnrollRecurrentLayer(conf, layerConfig);

        Pair<Boolean, Double> maskingConfig = KerasLayerUtils.getMaskingConfiguration(inboundLayerNames, previousLayers);

        LayerConstraint biasConstraint = KerasConstraintUtils.getConstraintsFromConfig(
                layerConfig, conf.getLAYER_FIELD_B_CONSTRAINT(), conf, kerasMajorVersion);
        LayerConstraint weightConstraint = KerasConstraintUtils.getConstraintsFromConfig(
                layerConfig, conf.getLAYER_FIELD_W_CONSTRAINT(), conf, kerasMajorVersion);
        LayerConstraint recurrentConstraint = KerasConstraintUtils.getConstraintsFromConfig(
                layerConfig, conf.getLAYER_FIELD_RECURRENT_CONSTRAINT(), conf, kerasMajorVersion);

        SimpleRnn.Builder builder = new SimpleRnn.Builder()
                .name(this.layerName)
                .nOut(getNOutFromConfig(layerConfig, conf))
                .dropOut(this.dropout)
                .activation(KerasActivationUtils.getIActivationFromConfig(layerConfig, conf))
                .weightInit(init)
                .weightInitRecurrent(recurrentInit)
                .biasInit(0.0)
                .l1(this.weightL1Regularization)
                .l2(this.weightL2Regularization).dataFormat(RNNFormat.NWC);
        Integer nIn = KerasLayerUtils.getNInFromInputDim(layerConfig, conf);
        if(nIn != null)
            builder.setNIn(nIn);
        if (biasConstraint != null)
            builder.constrainBias(biasConstraint);
        if (weightConstraint != null)
            builder.constrainInputWeights(weightConstraint);
        if (recurrentConstraint != null)
            builder.constrainRecurrent(recurrentConstraint);

        this.layer = builder.build();
        if (!returnSequences) {
            this.layer = new LastTimeStep(this.layer);
        }
        if (maskingConfig.getFirst()) {
            this.layer = new MaskZeroLayer(this.layer, maskingConfig.getSecond());
        }
    }

    /**
     * Get DL4J SimpleRnn layer.
     *
     * @return SimpleRnn Layer
     */
    public Layer getSimpleRnnLayer() {
        return this.layer;
    }

    /**
     * Get layer output type.
     *
     * @param inputType Array of InputTypes
     * @return output type as InputType
     * @throws InvalidKerasConfigurationException Invalid Keras config
     */
    @Override
    public InputType getOutputType(InputType... inputType) throws InvalidKerasConfigurationException {
        if (inputType.length > 1)
            throw new InvalidKerasConfigurationException(
                    "Keras SimpleRnn layer accepts only one input (received " + inputType.length + ")");
        InputPreProcessor preProcessor = getInputPreprocessor(inputType);
        if (preProcessor != null)
            return preProcessor.getOutputType(inputType[0]);
        else
            return this.getSimpleRnnLayer().getOutputType(-1, inputType[0]);
    }

    /**
     * Returns number of trainable parameters in layer.
     *
     * @return number of trainable parameters (12)
     */
    @Override
    public int getNumParams() {
        return NUM_TRAINABLE_PARAMS;
    }

    /**
     * Gets appropriate DL4J InputPreProcessor for given InputTypes.
     *
     * @param inputType Array of InputTypes
     * @return DL4J InputPreProcessor
     * @throws InvalidKerasConfigurationException Invalid Keras configuration exception
     * @see org.deeplearning4j.nn.conf.InputPreProcessor
     */
    @Override
    public InputPreProcessor getInputPreprocessor(InputType... inputType) throws InvalidKerasConfigurationException {
        if (inputType.length > 1)
            throw new InvalidKerasConfigurationException(
                    "Keras SimpleRnn layer accepts only one input (received " + inputType.length + ")");

        RNNFormat f = TimeSeriesUtils.getFormatFromRnnLayer(layer);
        return InputTypeUtil.getPreprocessorForInputTypeRnnLayers(inputType[0], f, layerName);
    }

    /**
     * Get whether SimpleRnn layer should be unrolled (for truncated BPTT).
     *
     * @return whether RNN should be unrolled (boolean)
     */
    public boolean getUnroll() {
        return this.unroll;
    }


    /**
     * Set weights for layer.
     *
     * @param weights Simple RNN weights
     * @throws InvalidKerasConfigurationException Invalid Keras configuration exception
     */
    @Override
    public void setWeights(Map<String, INDArray> weights) throws InvalidKerasConfigurationException {
        this.weights = new HashMap<>();


        INDArray W;
        if (weights.containsKey(conf.getKERAS_PARAM_NAME_W()))
            W = weights.get(conf.getKERAS_PARAM_NAME_W());
        else
            throw new InvalidKerasConfigurationException(
                    "Keras SimpleRNN layer does not contain parameter " + conf.getKERAS_PARAM_NAME_W());
        this.weights.put(SimpleRnnParamInitializer.WEIGHT_KEY, W);


        INDArray RW;
        if (weights.containsKey(conf.getKERAS_PARAM_NAME_RW()))
            RW = weights.get(conf.getKERAS_PARAM_NAME_RW());
        else
            throw new InvalidKerasConfigurationException(
                    "Keras SimpleRNN layer does not contain parameter " + conf.getKERAS_PARAM_NAME_RW());
        this.weights.put(SimpleRnnParamInitializer.RECURRENT_WEIGHT_KEY, RW);


        INDArray b;
        if (weights.containsKey(conf.getKERAS_PARAM_NAME_B()))
            b = weights.get(conf.getKERAS_PARAM_NAME_B());
        else
            throw new InvalidKerasConfigurationException(
                    "Keras SimpleRNN layer does not contain parameter " + conf.getKERAS_PARAM_NAME_B());
        this.weights.put(SimpleRnnParamInitializer.BIAS_KEY, b);


        if (weights.size() > NUM_TRAINABLE_PARAMS) {
            Set<String> paramNames = weights.keySet();
            paramNames.remove(conf.getKERAS_PARAM_NAME_B());
            paramNames.remove(conf.getKERAS_PARAM_NAME_W());
            paramNames.remove(conf.getKERAS_PARAM_NAME_RW());
            String unknownParamNames = paramNames.toString();
            log.warn("Attemping to set weights for unknown parameters: "
                    + unknownParamNames.substring(1, unknownParamNames.length() - 1));
        }

        FeedForwardLayer ffl;
        if(this.layer instanceof BaseWrapperLayer){
            BaseWrapperLayer bwl = (BaseWrapperLayer)this.layer;
            ffl = (FeedForwardLayer)bwl.getUnderlying();
        } else {
            ffl = (FeedForwardLayer) this.layer;
        }
        if(ffl.getNIn() != W.rows()){
            //Workaround/hack for ambiguous input shapes (nIn inference) for some RNN models (using NCW format but not recorded in config)
            //We can reliably infer nIn from the shape of the weights array however
            ffl.setNIn(W.rows());
        }
    }

}
