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 *  * 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.
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 *  *  See the NOTICE file distributed with this work for additional
 *  *  information regarding copyright ownership.
 *  * 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
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package org.deeplearning4j.nn.modelimport.keras.layers.embeddings;

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.api.layers.LayerConstraint;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.EmbeddingLayer;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasConstraintUtils;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasInitilizationUtils;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasLayerUtils;
import org.deeplearning4j.nn.params.DefaultParamInitializer;
import org.deeplearning4j.nn.weights.IWeightInit;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;

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

/**
 * Imports an Embedding layer from Keras.
 *
 * @author dave@skymind.io
 */
@Slf4j
@Data
@EqualsAndHashCode(callSuper = false)
public class Keras2DEmbedding extends KerasLayer {

    private final int NUM_TRAINABLE_PARAMS = 1;
    private boolean zeroMasking;
    private int inputDim;
    private int inputLength;
    private boolean inferInputLength;


    /**
     * Pass through constructor for unit tests
     *
     * @throws UnsupportedKerasConfigurationException Unsupported Keras config
     */
    public Keras2DEmbedding() throws UnsupportedKerasConfigurationException {
    }

    /**
     * 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 Keras2DEmbedding(Map<String, Object> layerConfig)
            throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException {
        this(layerConfig, true);
    }

    /**
     * Constructor from parsed Keras layer configuration dictionary.
     *
     * @param layerConfig           dictionary containing Keras layer configuration
     * @param enforceTrainingConfig whether to enforce training-related configuration options
     * @throws InvalidKerasConfigurationException     Invalid Keras config
     * @throws UnsupportedKerasConfigurationException Unsupported Keras config
     */
    public Keras2DEmbedding(Map<String, Object> layerConfig, boolean enforceTrainingConfig)
            throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException {
        super(layerConfig, enforceTrainingConfig);

        this.inputDim = getInputDimFromConfig(layerConfig);
        this.inputLength = getInputLengthFromConfig(layerConfig);
        this.inferInputLength = this.inputLength == 0;
        if (this.inferInputLength)
            this.inputLength = 1; // set dummy value, so shape inference works

        this.zeroMasking = KerasLayerUtils.getZeroMaskingFromConfig(layerConfig, conf);
        if (zeroMasking)
            log.warn("Masking in keras and DL4J work differently. We do not completely support mask_zero flag " +
                    "on Embedding layers. Zero Masking for the Embedding layer only works with unidirectional LSTM for now."
                    + " If you want to have this behaviour for your imported model " +
                    "in DL4J, apply masking as a pre-processing step to your input." +
                    "See https://deeplearning4j.konduit.ai/models/recurrent#masking-one-to-many-many-to-one-and-sequence-classification for more on this.");

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

        LayerConstraint embeddingConstraint = KerasConstraintUtils.getConstraintsFromConfig(
                layerConfig, conf.getLAYER_FIELD_EMBEDDINGS_CONSTRAINT(), conf, kerasMajorVersion);
        int nOutFromConfig = KerasLayerUtils.getNOutFromConfig(layerConfig, conf);
        EmbeddingLayer.Builder builder = new EmbeddingLayer.Builder()
                .name(this.layerName)
                .nIn(inputDim)
                .nOut(nOutFromConfig)
                .dropOut(this.dropout).activation(Activation.IDENTITY)
                .weightInit(init)
                .biasInit(0.0)
                .l1(this.weightL1Regularization)
                .l2(this.weightL2Regularization)
                .hasBias(false);
        if (embeddingConstraint != null)
            builder.constrainWeights(embeddingConstraint);
        this.layer = builder.build();

        this.inputShape = new int[]{inputDim,1};
    }

    /**
     * Get DL4J Embedding Sequence layer.
     *
     * @return Embedding Sequence layer
     */
    public EmbeddingLayer getEmbeddingLayer() {
        return (EmbeddingLayer) 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 {
        /* Check whether layer requires a preprocessor for this InputType. */
        InputPreProcessor preprocessor = getInputPreprocessor(inputType[0]);
        if (preprocessor != null) {
            return this.getEmbeddingLayer().getOutputType(-1, preprocessor.getOutputType(inputType[0]));
        }
        return this.getEmbeddingLayer().getOutputType(-1, inputType[0]);
    }

    /**
     * Returns number of trainable parameters in layer.
     *
     * @return number of trainable parameters (1)
     */
    @Override
    public int getNumParams() {
        return NUM_TRAINABLE_PARAMS;
    }

    /**
     * Set weights for layer.
     *
     * @param weights Embedding layer weights
     */
    @Override
    public void setWeights(Map<String, INDArray> weights) throws InvalidKerasConfigurationException {
        this.weights = new HashMap<>();
        // TODO: "embeddings" is incorrectly read as "s" for some applications
        if (weights.containsKey("s")) {
            INDArray kernel = weights.get("s");
            weights.remove("s");
            weights.put(conf.getLAYER_FIELD_EMBEDDING_WEIGHTS(), kernel);
        }

        if (!weights.containsKey(conf.getLAYER_FIELD_EMBEDDING_WEIGHTS()))
            throw new InvalidKerasConfigurationException(
                    "Parameter " + conf.getLAYER_FIELD_EMBEDDING_WEIGHTS() + " does not exist in weights");
        INDArray kernel = weights.get(conf.getLAYER_FIELD_EMBEDDING_WEIGHTS());
        if (this.zeroMasking) {
            kernel.putRow(0, Nd4j.zeros(kernel.columns()));
        }
        this.weights.put(DefaultParamInitializer.WEIGHT_KEY, kernel);

        if (weights.size() > 2) {
            Set<String> paramNames = weights.keySet();
            paramNames.remove(conf.getLAYER_FIELD_EMBEDDING_WEIGHTS());
            String unknownParamNames = paramNames.toString();
            log.warn("Attempting to set weights for unknown parameters: "
                    + unknownParamNames.substring(1, unknownParamNames.length() - 1));
        }
    }

    /**
     * Get Keras input length from Keras layer configuration. In Keras input_length, if present, denotes
     * the number of indices to embed per mini-batch, i.e. input will be of shape (mb, input_length)
     * and (mb, 1) else.
     *
     * @param layerConfig dictionary containing Keras layer configuration
     * @return input length as int
     */
    private int getInputLengthFromConfig(Map<String, Object> layerConfig) throws InvalidKerasConfigurationException {
        Map<String, Object> innerConfig = KerasLayerUtils.getInnerLayerConfigFromConfig(layerConfig, conf);
        if (!innerConfig.containsKey(conf.getLAYER_FIELD_INPUT_LENGTH()))
            throw new InvalidKerasConfigurationException(
                    "Keras Embedding layer config missing " + conf.getLAYER_FIELD_INPUT_LENGTH() + " field");
        if (innerConfig.get(conf.getLAYER_FIELD_INPUT_LENGTH()) == null) {
            return 0;
        } else {
            return (int) innerConfig.get(conf.getLAYER_FIELD_INPUT_LENGTH());
        }
    }

    /**
     * Get Keras input dimension from Keras layer configuration.
     *
     * @param layerConfig dictionary containing Keras layer configuration
     * @return input dim as int
     */
    private int getInputDimFromConfig(Map<String, Object> layerConfig) throws InvalidKerasConfigurationException {
        Map<String, Object> innerConfig = KerasLayerUtils.getInnerLayerConfigFromConfig(layerConfig, conf);
        if (!innerConfig.containsKey(conf.getLAYER_FIELD_INPUT_DIM()))
            throw new InvalidKerasConfigurationException(
                    "Keras Embedding layer config missing " + conf.getLAYER_FIELD_INPUT_DIM() + " field");
        return (int) innerConfig.get(conf.getLAYER_FIELD_INPUT_DIM());
    }
}
