/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You 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 opennlp.tools.lemmatizer;

import java.io.IOException;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.HashMap;
import java.util.List;
import java.util.Map;

import opennlp.tools.ml.BeamSearch;
import opennlp.tools.ml.EventModelSequenceTrainer;
import opennlp.tools.ml.EventTrainer;
import opennlp.tools.ml.Probabilistic;
import opennlp.tools.ml.SequenceTrainer;
import opennlp.tools.ml.TrainerFactory;
import opennlp.tools.ml.TrainerFactory.TrainerType;
import opennlp.tools.ml.model.Event;
import opennlp.tools.ml.model.MaxentModel;
import opennlp.tools.ml.model.SequenceClassificationModel;
import opennlp.tools.util.ObjectStream;
import opennlp.tools.util.Sequence;
import opennlp.tools.util.SequenceValidator;
import opennlp.tools.util.StringUtil;
import opennlp.tools.util.TrainingParameters;

/**
 * A probabilistic {@link Lemmatizer} implementation.
 * <p>
 * Tries to predict the induced permutation class for each word depending on
 * its surrounding context.
 * <p>
 * Based on Grzegorz Chrupała. 2008.
 * <a href="http://grzegorz.chrupala.me/papers/phd-single.pdf">
 * Towards a Machine-Learning Architecture for Lexical Functional Grammar Parsing.
 * </a> PhD dissertation, Dublin City University
 *
 * @see Lemmatizer
 * @see Probabilistic
 */
public class LemmatizerME implements Lemmatizer, Probabilistic {

  public static final int LEMMA_NUMBER = 29;
  public static final int DEFAULT_BEAM_SIZE = 3;
  protected final int beamSize;
  private Sequence bestSequence;

  private final SequenceClassificationModel model;

  private final LemmatizerContextGenerator contextGenerator;
  private final SequenceValidator<String> sequenceValidator;

  /**
   * Initializes a {@link LemmatizerME} with the provided
   * {@link LemmatizerModel model} and a default
   * {@code beam size} of {@code 3}.
   *
   * @param model The {@link LemmatizerModel} to be used.
   */
  public LemmatizerME(LemmatizerModel model) {

    LemmatizerFactory factory = model.getFactory();
    int defaultBeamSize = LemmatizerME.DEFAULT_BEAM_SIZE;
    String beamSizeString = model.getManifestProperty(BeamSearch.BEAM_SIZE_PARAMETER);
    if (beamSizeString != null) {
      defaultBeamSize = Integer.parseInt(beamSizeString);
    }

    contextGenerator = factory.getContextGenerator();
    beamSize = defaultBeamSize;

    sequenceValidator = factory.getSequenceValidator();

    if (model.getLemmatizerSequenceModel() != null) {
      this.model = model.getLemmatizerSequenceModel();
    }
    else {
      this.model = new opennlp.tools.ml.BeamSearch(beamSize,
          (MaxentModel) model.getLemmatizerSequenceModel(), 0);
    }
  }

  @Override
  public String[] lemmatize(String[] toks, String[] tags) {
    String[] ses = predictSES(toks, tags);
    return decodeLemmas(toks, ses);
  }

  @Override
  public List<List<String>> lemmatize(List<String> toks, List<String> tags) {
    String[] tokens = toks.toArray(new String[0]);
    String[] posTags = tags.toArray(new String[0]);
    String[][] allLemmas = predictLemmas(LEMMA_NUMBER, tokens, posTags);
    List<List<String>> predictedLemmas = new ArrayList<>();
    for (String[] allLemma : allLemmas) {
      predictedLemmas.add(Arrays.asList(allLemma));
    }
    return predictedLemmas;
  }

  /**
   * Predict Short Edit Script (automatically induced lemma class).
   *
   * @param toks An array of tokens.
   * @param tags An array of postags.
   *             
   * @return An array of possible lemma classes for each token in {@code toks}.
   */
  public String[] predictSES(String[] toks, String[] tags) {
    bestSequence = model.bestSequence(toks, new Object[] {tags}, contextGenerator, sequenceValidator);
    List<String> ses = bestSequence.getOutcomes();
    return ses.toArray(new String[0]);
  }

  /**
   * Predict all possible lemmas (using a default upper bound).
   * 
   * @param numLemmas The default number of lemmas
   * @param toks An array of tokens.
   * @param tags An array of postags.
   *             
   * @return A 2-dimensional array containing all possible lemmas for each token and postag pair.
   */
  public String[][] predictLemmas(int numLemmas, String[] toks, String[] tags) {
    Sequence[] bestSequences = model.bestSequences(numLemmas, toks, new Object[] {tags},
            contextGenerator, sequenceValidator);
    String[][] allLemmas = new String[bestSequences.length][];
    for (int i = 0; i < allLemmas.length; i++) {
      List<String> ses = bestSequences[i].getOutcomes();
      String[] sesArray = ses.toArray(new String[0]);
      allLemmas[i] = decodeLemmas(toks,sesArray);
    }
    return allLemmas;
  }

  /**
   * Decodes the lemma from the word and the induced lemma class.
   *
   * @param toks An array of tokens.
   * @param preds An array of predicted lemma classes.
   *              
   * @return The array of decoded lemmas.
   */
  public static String[] decodeLemmas(String[] toks, String[] preds) {
    List<String> lemmas = new ArrayList<>();
    for (int i = 0; i < toks.length; i++) {
      String lemma = StringUtil.decodeShortestEditScript(toks[i].toLowerCase(), preds[i]);
      if (lemma.length() == 0) {
        lemma = "_";
      }
      lemmas.add(lemma);
    }
    return lemmas.toArray(new String[0]);
  }

  /**
   * Encodes the word given its lemmas.
   *
   * @param toks An array of tokens.
   * @param lemmas An array of lemmas.
   *               
   * @return The array of lemma classes.
   */
  public static String[] encodeLemmas(String[] toks, String[] lemmas) {
    List<String> sesList = new ArrayList<>();
    for (int i = 0; i < toks.length; i++) {
      String ses = StringUtil.getShortestEditScript(toks[i], lemmas[i]);
      if (ses.length() == 0) {
        ses = "_";
      }
      sesList.add(ses);
    }
    return sesList.toArray(new String[0]);
  }

  /**
   * @param sentence An array of tokens.
   * @param tags An array of postags.
   *             
   * @return Retrieves the top-k {@link Sequence sequences}.
   */
  public Sequence[] topKSequences(String[] sentence, String[] tags) {
    return model.bestSequences(DEFAULT_BEAM_SIZE, sentence,
        new Object[] { tags }, contextGenerator, sequenceValidator);
  }

  /**
   * @param sentence An array of tokens.
   * @param tags An array of postags.
   * @param minSequenceScore The minimum score to be achieved.
   *
   * @return Retrieves the top-k {@link Sequence sequences}.
   */
  public Sequence[] topKSequences(String[] sentence, String[] tags, double minSequenceScore) {
    return model.bestSequences(DEFAULT_BEAM_SIZE, sentence, new Object[] { tags }, minSequenceScore,
        contextGenerator, sequenceValidator);
  }

  /**
   * Populates the specified array with the probabilities of the last decoded sequence.
   * The sequence was determined based on the previous call to
   * {@link #lemmatize(String[], String[])}.
   * <p>
   * The specified array should be at least as large as the number of tokens in the
   * previous call to {@link #lemmatize(String[], String[])}.
   *
   * @param probs An array used to hold the probabilities of the last decoded sequence.
   */
  public void probs(double[] probs) {
    bestSequence.getProbs(probs);
  }

  /**
   * {@inheritDoc}
   * 
   * The sequence was determined based on the previous call to
   * {@link #lemmatize(String[], String[])}.
   *
   * @return An array with the same number of probabilities as tokens were sent to
   *         {@link #lemmatize(String[], String[])} when it was last called.
   */
  @Override
  public double[] probs() {
    return bestSequence.getProbs();
  }

  /**
   * Starts a training of a {@link LemmatizerModel} with the given parameters.
   *
   * @param languageCode The ISO conform language code.
   * @param samples The {@link ObjectStream} of {@link LemmaSample} used as input for training.
   * @param params The {@link TrainingParameters} for the context of the training.
   * @param factory The {@link LemmatizerFactory} for creating related objects defined
   *                via {@code params}.
   *
   * @return A valid, trained {@link LemmatizerModel} instance.
   * @throws IOException Thrown if IO errors occurred.
   */
  public static LemmatizerModel train(String languageCode, ObjectStream<LemmaSample> samples,
                                      TrainingParameters params, LemmatizerFactory factory)
          throws IOException {

    LemmatizerContextGenerator contextGenerator = factory.getContextGenerator();
    Map<String, String> manifestInfoEntries = new HashMap<>();
    TrainerType trainerType = TrainerFactory.getTrainerType(params);

    MaxentModel lemmatizerModel = null;
    SequenceClassificationModel seqLemmatizerModel = null;
    if (TrainerType.EVENT_MODEL_TRAINER.equals(trainerType)) {
      ObjectStream<Event> es = new LemmaSampleEventStream(samples, contextGenerator);

      EventTrainer<TrainingParameters> trainer =
          TrainerFactory.getEventTrainer(params, manifestInfoEntries);
      lemmatizerModel = trainer.train(es);
    }
    else if (TrainerType.EVENT_MODEL_SEQUENCE_TRAINER.equals(trainerType)) {
      LemmaSampleSequenceStream ss = new LemmaSampleSequenceStream(samples, contextGenerator);
      EventModelSequenceTrainer<LemmaSample, TrainingParameters> trainer =
          TrainerFactory.getEventModelSequenceTrainer(params, manifestInfoEntries);
      lemmatizerModel = trainer.train(ss);
    }
    else if (TrainerType.SEQUENCE_TRAINER.equals(trainerType)) {
      SequenceTrainer<TrainingParameters> trainer = TrainerFactory.getSequenceModelTrainer(
              params, manifestInfoEntries);

      // TODO: This will probably cause issue, since the feature generator uses the outcomes array
      LemmaSampleSequenceStream ss = new LemmaSampleSequenceStream(samples, contextGenerator);
      seqLemmatizerModel = trainer.train(ss);
    }
    else {
      throw new IllegalArgumentException("Trainer type is not supported: " + trainerType);
    }

    int beamSize = params.getIntParameter(BeamSearch.BEAM_SIZE_PARAMETER,
            LemmatizerME.DEFAULT_BEAM_SIZE);
    if (lemmatizerModel != null) {
      return new LemmatizerModel(languageCode, lemmatizerModel, beamSize, manifestInfoEntries, factory);
    }
    else {
      return new LemmatizerModel(languageCode, seqLemmatizerModel, manifestInfoEntries, factory);
    }
  }

  /**
   * @param sentence An array of tokens.
   * @param tags An array of postags.
   *
   * @return Retrieves the top-k {@link Sequence lemma classes}.
   */
  public Sequence[] topKLemmaClasses(String[] sentence, String[] tags) {
    return model.bestSequences(DEFAULT_BEAM_SIZE, sentence,
        new Object[] { tags }, contextGenerator, sequenceValidator);
  }

  /**
   * @param sentence An array of tokens.
   * @param tags An array of postags.
   * @param minSequenceScore The minimum score to be achieved.
   *
   * @return Retrieves the top-k {@link Sequence lemma classes}.
   */
  public Sequence[] topKLemmaClasses(String[] sentence, String[] tags, double minSequenceScore) {
    return model.bestSequences(DEFAULT_BEAM_SIZE, sentence, new Object[] { tags }, minSequenceScore,
        contextGenerator, sequenceValidator);
  }
}
