/*
 * 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 org.apache.lucene.sandbox.queries;

import java.io.IOException;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.HashSet;
import java.util.Iterator;
import java.util.Objects;

import org.apache.lucene.analysis.Analyzer;
import org.apache.lucene.analysis.TokenStream;
import org.apache.lucene.analysis.tokenattributes.CharTermAttribute;
import org.apache.lucene.index.IndexReader;
import org.apache.lucene.index.LeafReaderContext;
import org.apache.lucene.index.MultiTerms;
import org.apache.lucene.index.Term;
import org.apache.lucene.index.TermStates;
import org.apache.lucene.index.Terms;
import org.apache.lucene.index.TermsEnum;
import org.apache.lucene.search.BooleanClause;
import org.apache.lucene.search.BooleanQuery;
import org.apache.lucene.search.BoostAttribute;
import org.apache.lucene.search.BoostQuery;
import org.apache.lucene.search.ConstantScoreQuery;
import org.apache.lucene.search.FuzzyTermsEnum;
import org.apache.lucene.search.Query;
import org.apache.lucene.search.QueryVisitor;
import org.apache.lucene.search.TermQuery;
import org.apache.lucene.search.similarities.ClassicSimilarity;
import org.apache.lucene.search.similarities.TFIDFSimilarity;
import org.apache.lucene.util.BytesRef;
import org.apache.lucene.util.PriorityQueue;
import org.apache.lucene.util.automaton.LevenshteinAutomata;

/**
 * Fuzzifies ALL terms provided as strings and then picks the best n differentiating terms.
 * In effect this mixes the behaviour of FuzzyQuery and MoreLikeThis but with special consideration
 * of fuzzy scoring factors.
 * This generally produces good results for queries where users may provide details in a number of 
 * fields and have no knowledge of boolean query syntax and also want a degree of fuzzy matching and
 * a fast query.
 * 
 * For each source term the fuzzy variants are held in a BooleanQuery with no coord factor (because
 * we are not looking for matches on multiple variants in any one doc). Additionally, a specialized
 * TermQuery is used for variants and does not use that variant term's IDF because this would favour rarer 
 * terms eg misspellings. Instead, all variants use the same IDF ranking (the one for the source query 
 * term) and this is factored into the variant's boost. If the source query term does not exist in the
 * index the average IDF of the variants is used.
 */
public class FuzzyLikeThisQuery extends Query
{
  // TODO: generalize this query (at least it should not reuse this static sim!
  // a better way might be to convert this into multitermquery rewrite methods.
  // the rewrite method can 'average' the TermStates's term statistics (docfreq,totalTermFreq)
  // provided to TermQuery, so that the general idea is agnostic to any scoring system...
  static TFIDFSimilarity sim=new ClassicSimilarity();
  ArrayList<FieldVals> fieldVals=new ArrayList<>();
  Analyzer analyzer;

  int MAX_VARIANTS_PER_TERM=50;
  boolean ignoreTF=false;
  private int maxNumTerms;

  @Override
  public int hashCode() {
    int prime = 31;
    int result = classHash();
    result = prime * result + Objects.hashCode(analyzer);
    result = prime * result + Objects.hashCode(fieldVals);
    result = prime * result + (ignoreTF ? 1231 : 1237);
    result = prime * result + maxNumTerms;
    return result;
  }

  @Override
  public boolean equals(Object other) {
    return sameClassAs(other) &&
      equalsTo(getClass().cast(other));
  }

  private boolean equalsTo(FuzzyLikeThisQuery other) {
    return Objects.equals(analyzer, other.analyzer) &&
      Objects.equals(fieldVals, other.fieldVals) &&
      ignoreTF == other.ignoreTF &&
      maxNumTerms == other.maxNumTerms;
  }

  /**
   * 
   * @param maxNumTerms The total number of terms clauses that will appear once rewritten as a BooleanQuery
   */
  public FuzzyLikeThisQuery(int maxNumTerms, Analyzer analyzer)
  {
    this.analyzer=analyzer;
    this.maxNumTerms = maxNumTerms;
  }

  static class FieldVals
  {
    String queryString;
    String fieldName;
    int maxEdits;
    int prefixLength;
    public FieldVals(String name, int maxEdits, int length, String queryString)
    {
      fieldName = name;
      this.maxEdits = maxEdits;
      prefixLength = length;
      this.queryString = queryString;
    }

    @Override
    public int hashCode() {
      final int prime = 31;
      int result = 1;
      result = prime * result
        + ((fieldName == null) ? 0 : fieldName.hashCode());
      result = prime * result + maxEdits;
      result = prime * result + prefixLength;
      result = prime * result
        + ((queryString == null) ? 0 : queryString.hashCode());
      return result;
    }

    @Override
    public boolean equals(Object obj) {
      if (this == obj)
        return true;
      if (obj == null)
        return false;
      if (getClass() != obj.getClass())
        return false;
      FieldVals other = (FieldVals) obj;
      if (fieldName == null) {
        if (other.fieldName != null)
          return false;
      } else if (!fieldName.equals(other.fieldName))
        return false;
      if (maxEdits != other.maxEdits) {
        return false;
      }
      if (prefixLength != other.prefixLength)
        return false;
      if (queryString == null) {
        if (other.queryString != null)
          return false;
      } else if (!queryString.equals(other.queryString))
        return false;
      return true;
    }
    


  }
    
  /**
   * Adds user input for "fuzzification" 
   * @param queryString The string which will be parsed by the analyzer and for which fuzzy variants will be parsed
   * @param minSimilarity The minimum similarity of the term variants; must be 0, 1 or 2 (see FuzzyTermsEnum)
   * @param prefixLength Length of required common prefix on variant terms (see FuzzyTermsEnum)
   */
  public void addTerms(String queryString, String fieldName,float minSimilarity, int prefixLength) 
  {
    int maxEdits = (int) minSimilarity;
    if (maxEdits != minSimilarity || maxEdits < 0 || maxEdits > LevenshteinAutomata.MAXIMUM_SUPPORTED_DISTANCE) {
      throw new IllegalArgumentException("minSimilarity must integer value between 0 and " + LevenshteinAutomata.MAXIMUM_SUPPORTED_DISTANCE + ", inclusive; got " + minSimilarity);
    }
    fieldVals.add(new FieldVals(fieldName,maxEdits,prefixLength,queryString));
  }


  private void addTerms(IndexReader reader, FieldVals f, ScoreTermQueue q) throws IOException {
    if (f.queryString == null) return;
    final Terms terms = MultiTerms.getTerms(reader, f.fieldName);
    if (terms == null) {
      return;
    }
    try (TokenStream ts = analyzer.tokenStream(f.fieldName, f.queryString)) {
      CharTermAttribute termAtt = ts.addAttribute(CharTermAttribute.class);

      int corpusNumDocs = reader.numDocs();
      HashSet<String> processedTerms = new HashSet<>();
      ts.reset();
      while (ts.incrementToken()) {
        String term = termAtt.toString();
        if (!processedTerms.contains(term)) {
          processedTerms.add(term);
          ScoreTermQueue variantsQ = new ScoreTermQueue(MAX_VARIANTS_PER_TERM); //maxNum variants considered for any one term
          float minScore = 0;
          Term startTerm = new Term(f.fieldName, term);
          FuzzyTermsEnum fe = new FuzzyTermsEnum(terms, startTerm, f.maxEdits, f.prefixLength, true);
          //store the df so all variants use same idf
          int df = reader.docFreq(startTerm);
          int numVariants = 0;
          int totalVariantDocFreqs = 0;
          BytesRef possibleMatch;
          BoostAttribute boostAtt =
            fe.attributes().addAttribute(BoostAttribute.class);
          while ((possibleMatch = fe.next()) != null) {
            numVariants++;
            totalVariantDocFreqs += fe.docFreq();
            float score = boostAtt.getBoost();
            if (variantsQ.size() < MAX_VARIANTS_PER_TERM || score > minScore) {
              ScoreTerm st = new ScoreTerm(new Term(startTerm.field(), BytesRef.deepCopyOf(possibleMatch)), score, startTerm);
              variantsQ.insertWithOverflow(st);
              minScore = variantsQ.top().score; // maintain minScore
            }
            fe.setMaxNonCompetitiveBoost(variantsQ.size() >= MAX_VARIANTS_PER_TERM ? minScore : Float.NEGATIVE_INFINITY);
          }

          if (numVariants > 0) {
            int avgDf = totalVariantDocFreqs / numVariants;
            if (df == 0)//no direct match we can use as df for all variants
              {
                df = avgDf; //use avg df of all variants
              }

            // take the top variants (scored by edit distance) and reset the score
            // to include an IDF factor then add to the global queue for ranking
            // overall top query terms
            int size = variantsQ.size();
            for (int i = 0; i < size; i++) {
              ScoreTerm st = variantsQ.pop();
              st.score = (st.score * st.score) * sim.idf(df, corpusNumDocs);
              q.insertWithOverflow(st);
            }
          }
        }
      }
      ts.end();
    }
  }

  private Query newTermQuery(IndexReader reader, Term term) throws IOException {
    if (ignoreTF) {
      return new ConstantScoreQuery(new TermQuery(term));
    } else {
      // we build an artificial TermStates that will give an overall df and ttf
      // equal to 1
      TermStates context = new TermStates(reader.getContext());
      for (LeafReaderContext leafContext : reader.leaves()) {
        Terms terms = leafContext.reader().terms(term.field());
        if (terms != null) {
          TermsEnum termsEnum = terms.iterator();
          if (termsEnum.seekExact(term.bytes())) {
            int freq = 1 - context.docFreq(); // we want the total df and ttf to be 1
            context.register(termsEnum.termState(), leafContext.ord, freq, freq);
          }
        }
      }
      return new TermQuery(term, context);
    }
  }

  @Override
  public void visit(QueryVisitor visitor) {
    visitor.visitLeaf(this);
  }

  @Override
  public Query rewrite(IndexReader reader) throws IOException
  {
    ScoreTermQueue q = new ScoreTermQueue(maxNumTerms);
    //load up the list of possible terms
    for (FieldVals f : fieldVals) {
      addTerms(reader, f, q);
    }
        
    BooleanQuery.Builder bq = new BooleanQuery.Builder();
        
    //create BooleanQueries to hold the variants for each token/field pair and ensure it
    // has no coord factor
    //Step 1: sort the termqueries by term/field
    HashMap<Term,ArrayList<ScoreTerm>> variantQueries=new HashMap<>();
    int size = q.size();
    for(int i = 0; i < size; i++)
      {
        ScoreTerm st = q.pop();
        ArrayList<ScoreTerm> l= variantQueries.get(st.fuzziedSourceTerm);
        if(l==null)
          {
            l=new ArrayList<>();
            variantQueries.put(st.fuzziedSourceTerm,l);
          }
        l.add(st);
      }
    //Step 2: Organize the sorted termqueries into zero-coord scoring boolean queries
    for (Iterator<ArrayList<ScoreTerm>> iter = variantQueries.values().iterator(); iter.hasNext();)
      {
        ArrayList<ScoreTerm> variants = iter.next();
        if(variants.size()==1)
          {
            //optimize where only one selected variant
            ScoreTerm st= variants.get(0);
            Query tq = newTermQuery(reader, st.term);
            // set the boost to a mix of IDF and score
            bq.add(new BoostQuery(tq, st.score), BooleanClause.Occur.SHOULD); 
          }
        else
          {
            BooleanQuery.Builder termVariants=new BooleanQuery.Builder();
            for (Iterator<ScoreTerm> iterator2 = variants.iterator(); iterator2
                   .hasNext();)
              {
                ScoreTerm st = iterator2.next();
                // found a match
                Query tq = newTermQuery(reader, st.term);
                // set the boost using the ScoreTerm's score
                termVariants.add(new BoostQuery(tq, st.score), BooleanClause.Occur.SHOULD);          // add to query                    
              }
            bq.add(termVariants.build(), BooleanClause.Occur.SHOULD);          // add to query
          }
      }
    //TODO possible alternative step 3 - organize above booleans into a new layer of field-based
    // booleans with a minimum-should-match of NumFields-1?
    return bq.build();
  }

  //Holds info for a fuzzy term variant - initially score is set to edit distance (for ranking best
  // term variants) then is reset with IDF for use in ranking against all other
  // terms/fields
  private static class ScoreTerm{
    public Term term;
    public float score;
    Term fuzziedSourceTerm;
        
    public ScoreTerm(Term term, float score, Term fuzziedSourceTerm){
      this.term = term;
      this.score = score;
      this.fuzziedSourceTerm=fuzziedSourceTerm;
    }
  }
      
  private static class ScoreTermQueue extends PriorityQueue<ScoreTerm> {        
    public ScoreTermQueue(int size){
      super(size);
    }
        
    /* (non-Javadoc)
     * @see org.apache.lucene.util.PriorityQueue#lessThan(java.lang.Object, java.lang.Object)
     */
    @Override
    protected boolean lessThan(ScoreTerm termA, ScoreTerm termB) {
      if (termA.score== termB.score)
        return termA.term.compareTo(termB.term) > 0;
      else
        return termA.score < termB.score;
    }
        
  }    
      
  /* (non-Javadoc)
   * @see org.apache.lucene.search.Query#toString(java.lang.String)
   */
  @Override
  public String toString(String field)
  {
    return null;
  }


  public boolean isIgnoreTF()
  {
    return ignoreTF;
  }


  public void setIgnoreTF(boolean ignoreTF)
  {
    this.ignoreTF = ignoreTF;
  }
    
}