/*
 * 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.
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package org.apache.solr.ltr;

import java.io.IOException;
import java.util.ArrayList;
import java.util.Collections;
import java.util.HashMap;
import java.util.LinkedHashMap;
import java.util.List;
import java.util.Map;

import org.apache.lucene.document.Document;
import org.apache.lucene.document.Field;
import org.apache.lucene.document.FloatDocValuesField;
import org.apache.lucene.index.IndexReader;
import org.apache.lucene.index.LeafReaderContext;
import org.apache.lucene.index.RandomIndexWriter;
import org.apache.lucene.index.ReaderUtil;
import org.apache.lucene.index.Term;
import org.apache.lucene.search.BooleanClause;
import org.apache.lucene.search.BooleanQuery;
import org.apache.lucene.search.IndexSearcher;
import org.apache.lucene.search.ScoreMode;
import org.apache.lucene.search.Scorer;
import org.apache.lucene.search.TermQuery;
import org.apache.lucene.search.TopDocs;
import org.apache.lucene.search.Weight;
import org.apache.lucene.store.Directory;
import org.apache.solr.SolrTestCase;
import org.apache.solr.core.SolrResourceLoader;
import org.apache.solr.ltr.feature.Feature;
import org.apache.solr.ltr.feature.ValueFeature;
import org.apache.solr.ltr.model.LTRScoringModel;
import org.apache.solr.ltr.model.ModelException;
import org.apache.solr.ltr.model.TestLinearModel;
import org.apache.solr.ltr.norm.IdentityNormalizer;
import org.apache.solr.ltr.norm.Normalizer;
import org.apache.solr.ltr.norm.NormalizerException;
import org.junit.Test;

public class TestLTRScoringQuery extends SolrTestCase {

  public final static SolrResourceLoader solrResourceLoader = new SolrResourceLoader();

  private IndexSearcher getSearcher(IndexReader r) {
    final IndexSearcher searcher = newSearcher(r, false, false);
    return searcher;
  }

  private static List<Feature> makeFeatures(int[] featureIds) {
    final List<Feature> features = new ArrayList<>();
    for (final int i : featureIds) {
      Map<String,Object> params = new HashMap<String,Object>();
      params.put("value", i);
      final Feature f = Feature.getInstance(solrResourceLoader,
          ValueFeature.class.getName(),
          "f" + i, params);
      f.setIndex(i);
      features.add(f);
    }
    return features;
  }

  private static List<Feature> makeFilterFeatures(int[] featureIds) {
    final List<Feature> features = new ArrayList<>();
    for (final int i : featureIds) {
      Map<String,Object> params = new HashMap<String,Object>();
      params.put("value", i);
      final Feature f = Feature.getInstance(solrResourceLoader,
          ValueFeature.class.getName(),
          "f" + i, params);
      f.setIndex(i);
      features.add(f);
    }
    return features;
  }

  private LTRScoringQuery.ModelWeight performQuery(TopDocs hits,
      IndexSearcher searcher, int docid, LTRScoringQuery model) throws IOException,
      ModelException {
    final List<LeafReaderContext> leafContexts = searcher.getTopReaderContext()
        .leaves();
    final int n = ReaderUtil.subIndex(hits.scoreDocs[0].doc, leafContexts);
    final LeafReaderContext context = leafContexts.get(n);
    final int deBasedDoc = hits.scoreDocs[0].doc - context.docBase;

    final Weight weight = searcher.createWeight(searcher.rewrite(model), ScoreMode.COMPLETE, 1);
    final Scorer scorer = weight.scorer(context);

    // rerank using the field final-score
    scorer.iterator().advance(deBasedDoc);
    scorer.score();

    // assertEquals(42.0f, score, 0.0001);
    // assertTrue(weight instanceof AssertingWeight);
    // (AssertingIndexSearcher)
    assertTrue(weight instanceof LTRScoringQuery.ModelWeight);
    final LTRScoringQuery.ModelWeight modelWeight = (LTRScoringQuery.ModelWeight) weight;
    return modelWeight;

  }

  @Test
  public void testLTRScoringQueryEquality() throws ModelException {
    final List<Feature> features = makeFeatures(new int[] {0, 1, 2});
    final List<Normalizer> norms =
        new ArrayList<Normalizer>(
            Collections.nCopies(features.size(),IdentityNormalizer.INSTANCE));
    final List<Feature> allFeatures = makeFeatures(
        new int[] {0, 1, 2, 3, 4, 5, 6, 7, 8, 9});
    final Map<String,Object> modelParams = TestLinearModel.makeFeatureWeights(features);

    final LTRScoringModel algorithm1 = TestLinearModel.createLinearModel(
        "testModelName",
        features, norms, "testStoreName", allFeatures, modelParams);

    final LTRScoringQuery m0 = new LTRScoringQuery(algorithm1);

    final HashMap<String,String[]> externalFeatureInfo = new HashMap<>();
    externalFeatureInfo.put("queryIntent", new String[] {"company"});
    externalFeatureInfo.put("user_query", new String[] {"abc"});
    final LTRScoringQuery m1 = new LTRScoringQuery(algorithm1, externalFeatureInfo, false, null);

    final HashMap<String,String[]> externalFeatureInfo2 = new HashMap<>();
    externalFeatureInfo2.put("user_query", new String[] {"abc"});
    externalFeatureInfo2.put("queryIntent", new String[] {"company"});
    int totalPoolThreads = 10, numThreadsPerRequest = 10;
    LTRThreadModule threadManager = new LTRThreadModule(totalPoolThreads, numThreadsPerRequest);
    final LTRScoringQuery m2 = new LTRScoringQuery(algorithm1, externalFeatureInfo2, false, threadManager);


    // Models with same algorithm and efis, just in different order should be the same
    assertEquals(m1, m2);
    assertEquals(m1.hashCode(), m2.hashCode());

    // Models with same algorithm, but different efi content should not match
    assertFalse(m1.equals(m0));
    assertFalse(m1.hashCode() == m0.hashCode());


    final LTRScoringModel algorithm2 = TestLinearModel.createLinearModel(
        "testModelName2",
        features, norms, "testStoreName", allFeatures, modelParams);
    final LTRScoringQuery m3 = new LTRScoringQuery(algorithm2);

    assertFalse(m1.equals(m3));
    assertFalse(m1.hashCode() == m3.hashCode());

    final LTRScoringModel algorithm3 = TestLinearModel.createLinearModel(
        "testModelName",
        features, norms, "testStoreName3", allFeatures, modelParams);
    final LTRScoringQuery m4 = new LTRScoringQuery(algorithm3);

    assertFalse(m1.equals(m4));
    assertFalse(m1.hashCode() == m4.hashCode());
  }


  @Test
  public void testLTRScoringQuery() throws IOException, ModelException {
    final Directory dir = newDirectory();
    final RandomIndexWriter w = new RandomIndexWriter(random(), dir);

    Document doc = new Document();
    doc.add(newStringField("id", "0", Field.Store.YES));
    doc.add(newTextField("field", "wizard the the the the the oz",
        Field.Store.NO));
    doc.add(new FloatDocValuesField("final-score", 1.0f));

    w.addDocument(doc);
    doc = new Document();
    doc.add(newStringField("id", "1", Field.Store.YES));
    // 1 extra token, but wizard and oz are close;
    doc.add(newTextField("field", "wizard oz the the the the the the",
        Field.Store.NO));
    doc.add(new FloatDocValuesField("final-score", 2.0f));
    w.addDocument(doc);

    final IndexReader r = w.getReader();
    w.close();

    // Do ordinary BooleanQuery:
    final BooleanQuery.Builder bqBuilder = new BooleanQuery.Builder();
    bqBuilder.add(new TermQuery(new Term("field", "wizard")), BooleanClause.Occur.SHOULD);
    bqBuilder.add(new TermQuery(new Term("field", "oz")), BooleanClause.Occur.SHOULD);
    final IndexSearcher searcher = getSearcher(r);
    // first run the standard query
    final TopDocs hits = searcher.search(bqBuilder.build(), 10);
    assertEquals(2, hits.totalHits.value);
    assertEquals("0", searcher.doc(hits.scoreDocs[0].doc).get("id"));
    assertEquals("1", searcher.doc(hits.scoreDocs[1].doc).get("id"));

    List<Feature> features = makeFeatures(new int[] {0, 1, 2});
    final List<Feature> allFeatures = makeFeatures(new int[] {0, 1, 2, 3, 4, 5,
        6, 7, 8, 9});
    List<Normalizer> norms =
        new ArrayList<Normalizer>(
            Collections.nCopies(features.size(),IdentityNormalizer.INSTANCE));
    LTRScoringModel ltrScoringModel = TestLinearModel.createLinearModel("test",
        features, norms, "test", allFeatures,
        TestLinearModel.makeFeatureWeights(features));

    LTRScoringQuery.ModelWeight modelWeight = performQuery(hits, searcher,
        hits.scoreDocs[0].doc, new LTRScoringQuery(ltrScoringModel));
    assertEquals(3, modelWeight.getModelFeatureValuesNormalized().length);

    for (int i = 0; i < 3; i++) {
      assertEquals(i, modelWeight.getModelFeatureValuesNormalized()[i], 0.0001);
    }
    int[] posVals = new int[] {0, 1, 2};
    int pos = 0;
    for (LTRScoringQuery.FeatureInfo fInfo:modelWeight.getFeaturesInfo()) {
        if (fInfo == null){
          continue;
        }
        assertEquals(posVals[pos], fInfo.getValue(), 0.0001);
        assertEquals("f"+posVals[pos], fInfo.getName());
        pos++;
    }

    final int[] mixPositions = new int[] {8, 2, 4, 9, 0};
    features = makeFeatures(mixPositions);
    norms =
        new ArrayList<Normalizer>(
            Collections.nCopies(features.size(),IdentityNormalizer.INSTANCE));
    ltrScoringModel = TestLinearModel.createLinearModel("test",
        features, norms, "test", allFeatures, TestLinearModel.makeFeatureWeights(features));

    modelWeight = performQuery(hits, searcher, hits.scoreDocs[0].doc,
        new LTRScoringQuery(ltrScoringModel));
    assertEquals(mixPositions.length,
        modelWeight.getModelFeatureWeights().length);

    for (int i = 0; i < mixPositions.length; i++) {
      assertEquals(mixPositions[i],
          modelWeight.getModelFeatureValuesNormalized()[i], 0.0001);
    }

    final ModelException expectedModelException = new ModelException("no features declared for model test");
    final int[] noPositions = new int[] {};
    features = makeFeatures(noPositions);
    norms =
        new ArrayList<Normalizer>(
            Collections.nCopies(features.size(),IdentityNormalizer.INSTANCE));
    try {
      ltrScoringModel = TestLinearModel.createLinearModel("test",
          features, norms, "test", allFeatures, TestLinearModel.makeFeatureWeights(features));
      fail("unexpectedly got here instead of catching "+expectedModelException);
      modelWeight = performQuery(hits, searcher, hits.scoreDocs[0].doc,
          new LTRScoringQuery(ltrScoringModel));
      assertEquals(0, modelWeight.getModelFeatureWeights().length);
    } catch (ModelException actualModelException) {
      assertEquals(expectedModelException.toString(), actualModelException.toString());
    }

    // test normalizers
    features = makeFilterFeatures(mixPositions);
    final Normalizer norm = new Normalizer() {

      @Override
      public float normalize(float value) {
        return 42.42f;
      }

      @Override
      public LinkedHashMap<String,Object> paramsToMap() {
        return null;
      }

      @Override
      protected void validate() throws NormalizerException {
      }

    };
    norms =
        new ArrayList<Normalizer>(
            Collections.nCopies(features.size(),norm));
    final LTRScoringModel normMeta = TestLinearModel.createLinearModel("test",
        features, norms, "test", allFeatures,
        TestLinearModel.makeFeatureWeights(features));

    modelWeight = performQuery(hits, searcher, hits.scoreDocs[0].doc,
        new LTRScoringQuery(normMeta));
    normMeta.normalizeFeaturesInPlace(modelWeight.getModelFeatureValuesNormalized());
    assertEquals(mixPositions.length,
        modelWeight.getModelFeatureWeights().length);
    for (int i = 0; i < mixPositions.length; i++) {
      assertEquals(42.42f, modelWeight.getModelFeatureValuesNormalized()[i], 0.0001);
    }
    r.close();
    dir.close();

  }

}
