code: package zemberek.core.embeddings;
  
  import java.io.BufferedReader;
  import java.io.DataInputStream;
  import java.io.DataOutputStream;
  import java.io.IOException;
  import java.io.PrintStream;
  import java.io.PrintWriter;
  import java.nio.charset.StandardCharsets;
  import java.nio.file.Files;
  import java.nio.file.Path;
  import java.util.ArrayList;
  import java.util.Collections;
  import java.util.List;
  import zemberek.core.ScoredItem;
  import zemberek.core.collections.IntVector;
  import zemberek.core.embeddings.Args.model_name;
  import zemberek.core.io.IOUtil;
  
  public class FastText {
  
    public static final int FASTTEXT_VERSION = 11;
    public static final int FASTTEXT_FILEFORMAT_MAGIC_INT32 = 793712314;
    private Args args_;
    private Dictionary dict_;
    private Model model_;
  
    FastText(Args args_, Dictionary dict_, Model model) {
      this.args_ = args_;
      this.dict_ = dict_;
      this.model_ = model;
    }
  
    public void addInputVector(Vector vec, int ind) {
      if (model_.quant_) {
        vec.addRow(model_.qwi_, ind);
      } else {
        vec.addRow(model_.wi_, ind);
      }
    }
  
    public Dictionary getDictionary() {
      return dict_;
    }
  
    public Args getArgs() {
      return args_;
    }
  
    public Matrix getInputMatrix() {
      return model_.wi_;
    }
  
    public Matrix getOutputMatrix() {
      return model_.wo_;
    }
  
    public int getWordId(String word) {
      return dict_.getId(word);
    }
  
    public int getSubwordId(String word) {
      int h = Dictionary.hash(word) % args_.bucket;
      return dict_.nwords() + h;
    }
  
    static boolean checkModel(Path in) throws IOException {
      try (DataInputStream dis = IOUtil.getDataInputStream(in)) {
        int magic = dis.readInt();
        if (magic != FASTTEXT_FILEFORMAT_MAGIC_INT32) {
          return false;
        }
        int version = dis.readInt();
        if (version != FASTTEXT_VERSION) {
          return false;
        }
      }
      return true;
    }
  
    private Vector getWordVector(String word) {
      int[] ngrams = dict_.getSubWords(word);
      Vector vec = new Vector(args_.dim);
      for (int i : ngrams) {
        vec.addRow(model_.wi_, i);
      }
      if (ngrams.length > 0) {
        vec.mul(1.0f / ngrams.length);
      }
      return vec;
    }
  
    private Vector getSubWordVector(String word) {
      int h = Dictionary.hash(word) % args_.bucket;
      h = h + dict_.nwords();
      Vector vec = new Vector(args_.dim);
      addInputVector(vec, h);
      return vec;
    }
  
    public void saveVectors(Path outFilePath) throws IOException {
      try (PrintWriter pw = new PrintWriter(outFilePath.toFile(), "utf-8")) {
        pw.println(dict_.nwords() + " " + args_.dim);
        for (int i = 0; i < dict_.nwords(); i++) {
          String word = dict_.getWord(i);
          Vector vector = getWordVector(word);
          pw.println(word + " " + vector.asString());
        }
      }
    }
  
    public void saveOutput(Path outPath) throws IOException {
  
      int n = (args_.model == model_name.supervised) ? dict_.nlabels() : dict_.nwords();
  
      try (PrintWriter pw = new PrintWriter(outPath.toFile(), "utf-8")) {
        pw.println(dict_.nwords() + " " + args_.dim);
        for (int i = 0; i < n; i++) {
          String word = (args_.model == model_name.supervised) ?
              dict_.getLabel(i) : dict_.getWord(i);
          Vector vector = new Vector(args_.dim);
          vector.addRow(model_.wo_, i);
          pw.println(word + " " + vector.asString());
        }
      }
    }
  
    private static boolean checkModel(DataInputStream dis) throws IOException {
      int magic = dis.readInt();
      if (magic != FASTTEXT_FILEFORMAT_MAGIC_INT32) {
        return false;
      }
      int version = dis.readInt();
      return version == FASTTEXT_VERSION;
    }
  
    private void signModel(DataOutputStream dos) throws IOException {
      dos.writeInt(FASTTEXT_FILEFORMAT_MAGIC_INT32);
      dos.writeInt(FASTTEXT_VERSION);
    }
  
    public void saveModel(Path outFilePath) throws IOException {
      try (DataOutputStream dos = IOUtil.getDataOutputStream(outFilePath)) {
        signModel(dos);
        args_.save(dos);
        dict_.save(dos);
        dos.writeBoolean(model_.quant_);
        if (model_.quant_) {
          model_.qwi_.save(dos);
        } else {
          model_.wi_.save(dos);
        }
        dos.writeBoolean(args_.qout);
        if (model_.quant_ && args_.qout) {
          model_.qwo_.save(dos);
        } else {
          model_.wo_.save(dos);
        }
      }
    }
  
  
    public static FastText load(Path path) throws IOException {
      try (DataInputStream dis = IOUtil.getDataInputStream(path)) {
        if (!checkModel(dis)) {
          throw new IllegalStateException("Model file has wrong file format.");
        }
        return load(dis);
      }
    }
  
    static FastText load(DataInputStream dis) throws IOException {
      Args args_ = Args.load(dis);
      Dictionary dict_ = Dictionary.load(dis, args_);
      Model model_ = Model.load(dis, args_);
      if (args_.model == Args.model_name.supervised) {
        model_.setTargetCounts(dict_.getCounts(Dictionary.TYPE_LABEL));
      } else {
        model_.setTargetCounts(dict_.getCounts(Dictionary.TYPE_WORD));
      }
      return new FastText(args_, dict_, model_);
    }
  
    public FastText quantize(Path path, Args qargs) throws IOException {
      try (DataInputStream dis = IOUtil.getDataInputStream(path)) {
        if (!checkModel(dis)) {
          throw new IllegalStateException("Model file has wrong file format.");
        }
        return quantize(dis, qargs);
      }
    }
  
    // selects ids of highest L2Norm valued embeddings.
    // Returns (word - subword) indexes.
    int[] selectEmbeddings(int cutoff) {
  
      Matrix input_ = model_.wi_;
      List<L2NormData> normIndexes = new ArrayList<>(input_.m_);
      int eosid = dict_.getId(Dictionary.EOS); // we want to retain EOS
      for (int i = 0; i < input_.m_; i++) {
        if (i == eosid) {
          continue;
        }
        normIndexes.add(new L2NormData(i, input_.l2NormRow(i)));
      }
      normIndexes.sort((a, b) -> Float.compare(b.l2Norm, a.l2Norm));
  
      int[] result = new int[cutoff];
      for (int i = 0; i < cutoff - 1; i++) {
        result[i] = normIndexes.get(i).index;
      }
      // add EOS.
      result[cutoff - 1] = eosid;
      return result;
    }
  
    public List<String> getLabels() {
      return getDictionary().getLabels();
    }
  
    static class L2NormData {
  
      final int index;
      final float l2Norm;
  
      public L2NormData(int index, float l2Norm) {
        this.index = index;
        this.l2Norm = l2Norm;
      }
    }
  
    FastText quantize(DataInputStream dis, Args qargs) throws IOException {
      Args args_ = Args.load(dis);
      if (args_.model != model_name.supervised) {
        throw new IllegalArgumentException("Only supervised models can be quantized.");
      }
      Dictionary dict_ = Dictionary.load(dis, args_);
      Model model_ = Model.load(dis, args_);
  
      args_.qout = qargs.qout;
  
      Matrix input = model_.wi_;
      if (qargs.cutoff > 0 && qargs.cutoff < input.m_) {
        int[] idx = selectEmbeddings(qargs.cutoff);
        idx = dict_.prune(idx);
        Matrix newInput = new Matrix(idx.length, args_.dim);
        for (int i = 0; i < idx.length; i++) {
          for (int j = 0; j < args_.dim; j++) {
            newInput.set(i, j, input.at(idx[i], j));
          }
        }
        model_.wi_ = newInput;
        // TODO: add retraining. It was hard because of the design differences
      }
  
      QMatrix qwi_ = new QMatrix(model_.wi_, qargs.dsub, qargs.qnorm);
  
      QMatrix qwo_ = model_.qwo_;
      if (qargs.qout) {
        qwo_ = new QMatrix(model_.wo_, 2, qargs.qnorm);
      }
  
      model_ = new Model(model_.wi_, model_.wo_, args_, 0);
      model_.quant_ = true;
      model_.setQuantizePointer(qwi_, qwo_, args_.qout);
  
      if (args_.model == Args.model_name.supervised) {
        model_.setTargetCounts(dict_.getCounts(Dictionary.TYPE_LABEL));
      } else {
        model_.setTargetCounts(dict_.getCounts(Dictionary.TYPE_WORD));
      }
      return new FastText(args_, dict_, model_);
    }
  
    public EvaluationResult test(Path in, int k) throws IOException {
      return test(in, k, -100f);
    }
  
    public static class EvaluationResult {
  
      public final float precision;
      public final float recall;
      public final int k;
      public final int numberOfExamples;
  
      public EvaluationResult(
          float precision,
          float recall,
          int k,
          int numberOfExamples) {
        this.precision = precision;
        this.recall = recall;
        this.k = k;
        this.numberOfExamples = numberOfExamples;
      }
  
      public void printTo(PrintStream stream) {
        stream.println(toString());
      }
  
      @Override
      public String toString() {
        return String.format(
            "P@%d: %.3f  R@%d: %.3f F@%d %.3f  Number of examples = %d",
            k, precision,
            k, recall,
            k, (2 * precision * recall) / (precision + recall),
            numberOfExamples);
      }
    }
  
    public EvaluationResult test(Path in, int k, float threshold) throws IOException {
      int nexamples = 0, nlabels = 0;
      float precision = 0.0f;
      String lineStr;
      BufferedReader reader = Files.newBufferedReader(in, StandardCharsets.UTF_8);
      while ((lineStr = reader.readLine()) != null) {
        IntVector words = new IntVector(), labels = new IntVector();
        dict_.getLine(lineStr, words, labels);
        if (labels.size() > 0 && words.size() > 0) {
          List<Model.FloatIntPair> modelPredictions = model_.predict(words.copyOf(), threshold, k);
          for (Model.FloatIntPair pair : modelPredictions) {
            if (labels.contains(pair.second)) {
              precision += 1.0f;
            }
          }
          nexamples++;
          nlabels += labels.size();
        }
      }
      return new EvaluationResult(
          precision / (k * nexamples),
          precision / nlabels,
          k,
          nexamples);
    }
  
    public void test(Path in, int k, float threshold, Meter meter) throws IOException {
      String lineStr;
      BufferedReader reader = Files.newBufferedReader(in, StandardCharsets.UTF_8);
      while ((lineStr = reader.readLine()) != null) {
        IntVector words = new IntVector(), labels = new IntVector();
        dict_.getLine(lineStr, words, labels);
        if (labels.size() > 0 && words.size() > 0) {
          List<Model.FloatIntPair> modelPredictions = model_.predict(words.copyOf(), threshold, k);
          meter.log(labels, modelPredictions);
        }
      }
    }
  
    Vector textVectors(List<String> paragraph) {
      Vector vec = new Vector(args_.dim);
      for (String s : paragraph) {
        IntVector line = new IntVector();
        dict_.getLine(s, line, model_.getRng());
        if (line.size() == 0) {
          continue;
        }
        dict_.addWordNgramHashes(line, args_.wordNgrams);
        for (int i : line.copyOf()) {
          vec.addRow(model_.wi_, i);
        }
        vec.mul((float) (1.0 / line.size()));
      }
      return vec;
    }
  
    public float[] sentenceVector(String s) {
      Vector svec = new Vector(args_.dim);
  
      if (args_.model == model_name.supervised) {
        IntVector line = new IntVector();
        dict_.getLine(s, line, model_.getRng());
        for (int i : line.copyOf()) {
          addInputVector(svec, i);
        }
        if(line.size()>0) {
          svec.mul(1f/line.size());
        }
        return svec.getData();
      }
  
      IntVector line = new IntVector();
      dict_.getLine(s, line, model_.getRng());
      dict_.addWordNgramHashes(line, args_.wordNgrams);
      if (line.size() == 0) {
        return svec.getData();
      }
  
      int count = 0;
      for (int i : line.copyOf()) {
  
        Vector vec = getWordVector(dict_.getWord(i));
        float norm = vec.norm();
  
        if (norm > 0) {
          vec.mul(1f / norm);
          svec.addVector(vec);
          count++;
        }
      }
      if (count > 0) {
        svec.mul(1f / count);
      }
      return svec.getData();
    }
  
    Vector getSentenceVector(String s) {
      Vector svec = new Vector(args_.dim);
      if (args_.model == model_name.supervised) {
        IntVector line = new IntVector(), labels = new IntVector();
        dict_.getLine(s, line, labels);
        for (int i = 0; i < line.size(); i++) {
          addInputVector(svec, line.get(i));
        }
        if (!line.isempty()) {
          svec.mul(1.0f / line.size());
        }
      } else {
        String[] tokens = s.split("\\s+");
        int count = 0;
        for (String token : tokens) {
          if (token.length() == 0) {
            continue;
          }
          Vector vec = getWordVector(token);
          float norm = vec.norm();
          if (norm > 0) {
            vec.mul(1.0f / norm);
            svec.addVector(vec);
            count++;
          }
        }
        if (count > 0) {
          svec.mul(1.0f / count);
        }
      }
      return svec;
    }
  
  
    public List<ScoredItem<String>> predict(String line, int k) {
      return predict(line, k, -100f);
    }
  
    public List<ScoredItem<String>> predict(String line, int k, float threshold) {
      IntVector words = new IntVector();
      IntVector labels = new IntVector();
      dict_.getLine(line, words, labels);
      if (words.isempty()) {
        return Collections.emptyList();
      }
      List<Model.FloatIntPair> modelPredictions =
          model_.predict(words.copyOf(), threshold, k);
      List<ScoredItem<String>> result = new ArrayList<>(modelPredictions.size());
      for (Model.FloatIntPair pair : modelPredictions) {
        result.add(new ScoredItem<>(dict_.getLabel(pair.second), pair.first));
      }
      return result;
    }
  
  }
  