/*
 * Copyright Elasticsearch B.V. and/or licensed to Elasticsearch B.V. under one
 * or more contributor license agreements. Licensed under the Elastic License
 * 2.0 and the Server Side Public License, v 1; you may not use this file except
 * in compliance with, at your election, the Elastic License 2.0 or the Server
 * Side Public License, v 1.
 */

package org.elasticsearch.search.aggregations.metrics;

import org.apache.lucene.document.Document;
import org.apache.lucene.document.NumericDocValuesField;
import org.apache.lucene.document.SortedNumericDocValuesField;
import org.apache.lucene.tests.index.RandomIndexWriter;
import org.apache.lucene.util.NumericUtils;
import org.elasticsearch.core.CheckedConsumer;
import org.elasticsearch.index.mapper.MappedFieldType;
import org.elasticsearch.index.mapper.NumberFieldMapper;
import org.elasticsearch.search.aggregations.AggregatorTestCase;
import org.elasticsearch.search.aggregations.support.AggregationInspectionHelper;

import java.io.IOException;
import java.util.function.Consumer;

import static java.util.Collections.singleton;

public class ExtendedStatsAggregatorTests extends AggregatorTestCase {
    private static final double TOLERANCE = 1e-5;

    // TODO: Add script test cases. Should fail with defaultValuesSourceType() commented out.

    public void testEmpty() throws IOException {
        MappedFieldType ft = new NumberFieldMapper.NumberFieldType("field", NumberFieldMapper.NumberType.LONG);
        testCase(ft, iw -> {}, stats -> {
            assertEquals(0d, stats.getCount(), 0);
            assertEquals(0d, stats.getSum(), 0);
            assertEquals(Float.NaN, stats.getAvg(), 0);
            assertEquals(Double.POSITIVE_INFINITY, stats.getMin(), 0);
            assertEquals(Double.NEGATIVE_INFINITY, stats.getMax(), 0);
            assertEquals(Double.NaN, stats.getVariance(), 0);
            assertEquals(Double.NaN, stats.getVariancePopulation(), 0);
            assertEquals(Double.NaN, stats.getVarianceSampling(), 0);
            assertEquals(Double.NaN, stats.getStdDeviation(), 0);
            assertEquals(Double.NaN, stats.getStdDeviationPopulation(), 0);
            assertEquals(Double.NaN, stats.getStdDeviationSampling(), 0);
            assertEquals(0d, stats.getSumOfSquares(), 0);
            assertFalse(AggregationInspectionHelper.hasValue(stats));
        });
    }

    public void testRandomDoubles() throws IOException {
        MappedFieldType ft = new NumberFieldMapper.NumberFieldType("field", NumberFieldMapper.NumberType.DOUBLE);
        final ExtendedSimpleStatsAggregator expected = new ExtendedSimpleStatsAggregator();
        testCase(ft, iw -> {
            int numDocs = randomIntBetween(10, 50);
            for (int i = 0; i < numDocs; i++) {
                Document doc = new Document();
                int numValues = randomIntBetween(1, 5);
                for (int j = 0; j < numValues; j++) {
                    double value = randomDoubleBetween(-100d, 100d, true);
                    long valueAsLong = NumericUtils.doubleToSortableLong(value);
                    doc.add(new SortedNumericDocValuesField("field", valueAsLong));
                    expected.add(value);
                }
                iw.addDocument(doc);
            }
        }, stats -> {
            assertEquals(expected.count, stats.getCount(), 0);
            assertEquals(expected.sum, stats.getSum(), TOLERANCE);
            assertEquals(expected.min, stats.getMin(), 0);
            assertEquals(expected.max, stats.getMax(), 0);
            assertEquals(expected.sum / expected.count, stats.getAvg(), TOLERANCE);
            assertEquals(expected.sumOfSqrs, stats.getSumOfSquares(), TOLERANCE);
            assertEquals(expected.stdDev(), stats.getStdDeviation(), TOLERANCE);
            assertEquals(expected.stdDevPopulation(), stats.getStdDeviationPopulation(), TOLERANCE);
            assertEquals(expected.stdDevSampling(), stats.getStdDeviationSampling(), TOLERANCE);
            assertEquals(expected.variance(), stats.getVariance(), TOLERANCE);
            assertEquals(expected.variancePopulation(), stats.getVariancePopulation(), TOLERANCE);
            assertEquals(expected.varianceSampling(), stats.getVarianceSampling(), TOLERANCE);
            assertEquals(
                expected.stdDevBound(ExtendedStats.Bounds.LOWER, stats.getSigma()),
                stats.getStdDeviationBound(ExtendedStats.Bounds.LOWER),
                TOLERANCE
            );
            assertEquals(
                expected.stdDevBound(ExtendedStats.Bounds.UPPER, stats.getSigma()),
                stats.getStdDeviationBound(ExtendedStats.Bounds.UPPER),
                TOLERANCE
            );
            assertEquals(
                expected.stdDevBound(ExtendedStats.Bounds.LOWER_POPULATION, stats.getSigma()),
                stats.getStdDeviationBound(ExtendedStats.Bounds.LOWER_POPULATION),
                TOLERANCE
            );
            assertEquals(
                expected.stdDevBound(ExtendedStats.Bounds.UPPER_POPULATION, stats.getSigma()),
                stats.getStdDeviationBound(ExtendedStats.Bounds.UPPER_POPULATION),
                TOLERANCE
            );
            assertEquals(
                expected.stdDevBound(ExtendedStats.Bounds.LOWER_SAMPLING, stats.getSigma()),
                stats.getStdDeviationBound(ExtendedStats.Bounds.LOWER_SAMPLING),
                TOLERANCE
            );
            assertEquals(
                expected.stdDevBound(ExtendedStats.Bounds.UPPER_SAMPLING, stats.getSigma()),
                stats.getStdDeviationBound(ExtendedStats.Bounds.UPPER_SAMPLING),
                TOLERANCE
            );
            assertTrue(AggregationInspectionHelper.hasValue(stats));
        });
    }

    /**
     * Testcase for https://github.com/elastic/elasticsearch/issues/37303
     */
    public void testVarianceNonNegative() throws IOException {
        MappedFieldType ft = new NumberFieldMapper.NumberFieldType("field", NumberFieldMapper.NumberType.DOUBLE);
        final ExtendedSimpleStatsAggregator expected = new ExtendedSimpleStatsAggregator();
        testCase(ft, iw -> {
            int numDocs = 3;
            for (int i = 0; i < numDocs; i++) {
                Document doc = new Document();
                double value = 49.95d;
                long valueAsLong = NumericUtils.doubleToSortableLong(value);
                doc.add(new SortedNumericDocValuesField("field", valueAsLong));
                expected.add(value);
                iw.addDocument(doc);
            }
        }, stats -> {
            // since the value(49.95) is a constant, variance should be 0
            assertEquals(0.0d, stats.getVariance(), TOLERANCE);
            assertEquals(0.0d, stats.getVariancePopulation(), TOLERANCE);
            assertEquals(0.0d, stats.getVarianceSampling(), TOLERANCE);
            assertEquals(0.0d, stats.getStdDeviation(), TOLERANCE);
            assertEquals(0.0d, stats.getStdDeviationPopulation(), TOLERANCE);
            assertEquals(0.0d, stats.getStdDeviationSampling(), TOLERANCE);
        });
    }

    public void testRandomLongs() throws IOException {
        MappedFieldType ft = new NumberFieldMapper.NumberFieldType("field", NumberFieldMapper.NumberType.LONG);
        final ExtendedSimpleStatsAggregator expected = new ExtendedSimpleStatsAggregator();
        testCase(ft, iw -> {
            int numDocs = randomIntBetween(10, 50);
            for (int i = 0; i < numDocs; i++) {
                Document doc = new Document();
                int numValues = randomIntBetween(1, 5);
                for (int j = 0; j < numValues; j++) {
                    long value = randomIntBetween(-100, 100);
                    doc.add(new SortedNumericDocValuesField("field", value));
                    expected.add(value);
                }
                iw.addDocument(doc);
            }
        }, stats -> {
            assertEquals(expected.count, stats.getCount(), 0);
            assertEquals(expected.sum, stats.getSum(), TOLERANCE);
            assertEquals(expected.min, stats.getMin(), 0);
            assertEquals(expected.max, stats.getMax(), 0);
            assertEquals(expected.sum / expected.count, stats.getAvg(), TOLERANCE);
            assertEquals(expected.sumOfSqrs, stats.getSumOfSquares(), TOLERANCE);
            assertEquals(expected.stdDev(), stats.getStdDeviation(), TOLERANCE);
            assertEquals(expected.stdDevPopulation(), stats.getStdDeviationPopulation(), TOLERANCE);
            assertEquals(expected.stdDevSampling(), stats.getStdDeviationSampling(), TOLERANCE);
            assertEquals(expected.variance(), stats.getVariance(), TOLERANCE);
            assertEquals(expected.variancePopulation(), stats.getVariancePopulation(), TOLERANCE);
            assertEquals(expected.varianceSampling(), stats.getVarianceSampling(), TOLERANCE);
            assertEquals(
                expected.stdDevBound(ExtendedStats.Bounds.LOWER, stats.getSigma()),
                stats.getStdDeviationBound(ExtendedStats.Bounds.LOWER),
                TOLERANCE
            );
            assertEquals(
                expected.stdDevBound(ExtendedStats.Bounds.UPPER, stats.getSigma()),
                stats.getStdDeviationBound(ExtendedStats.Bounds.UPPER),
                TOLERANCE
            );
            assertEquals(
                expected.stdDevBound(ExtendedStats.Bounds.LOWER_POPULATION, stats.getSigma()),
                stats.getStdDeviationBound(ExtendedStats.Bounds.LOWER_POPULATION),
                TOLERANCE
            );
            assertEquals(
                expected.stdDevBound(ExtendedStats.Bounds.UPPER_POPULATION, stats.getSigma()),
                stats.getStdDeviationBound(ExtendedStats.Bounds.UPPER_POPULATION),
                TOLERANCE
            );
            assertEquals(
                expected.stdDevBound(ExtendedStats.Bounds.LOWER_SAMPLING, stats.getSigma()),
                stats.getStdDeviationBound(ExtendedStats.Bounds.LOWER_SAMPLING),
                TOLERANCE
            );
            assertEquals(
                expected.stdDevBound(ExtendedStats.Bounds.UPPER_SAMPLING, stats.getSigma()),
                stats.getStdDeviationBound(ExtendedStats.Bounds.UPPER_SAMPLING),
                TOLERANCE
            );
            assertTrue(AggregationInspectionHelper.hasValue(stats));
        });
    }

    public void testSummationAccuracy() throws IOException {
        double[] values = new double[] { 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.9, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7 };
        verifyStatsOfDoubles(values, 13.5, 16.21, 0d);

        // Summing up an array which contains NaN and infinities and expect a result same as naive summation
        int n = randomIntBetween(5, 10);
        values = new double[n];
        double sum = 0;
        double sumOfSqrs = 0;
        for (int i = 0; i < n; i++) {
            values[i] = frequently()
                ? randomFrom(Double.NaN, Double.NEGATIVE_INFINITY, Double.POSITIVE_INFINITY)
                : randomDoubleBetween(Double.MIN_VALUE, Double.MAX_VALUE, true);
            sum += values[i];
            sumOfSqrs += values[i] * values[i];
        }
        verifyStatsOfDoubles(values, sum, sumOfSqrs, TOLERANCE);

        // Summing up some big double values and expect infinity result
        n = randomIntBetween(5, 10);
        double[] largeValues = new double[n];
        for (int i = 0; i < n; i++) {
            largeValues[i] = Double.MAX_VALUE;
        }
        verifyStatsOfDoubles(largeValues, Double.POSITIVE_INFINITY, Double.POSITIVE_INFINITY, 0d);

        for (int i = 0; i < n; i++) {
            largeValues[i] = -Double.MAX_VALUE;
        }
        verifyStatsOfDoubles(largeValues, Double.NEGATIVE_INFINITY, Double.POSITIVE_INFINITY, 0d);
    }

    private void verifyStatsOfDoubles(double[] values, double expectedSum, double expectedSumOfSqrs, double delta) throws IOException {
        final String fieldName = "field";
        MappedFieldType ft = new NumberFieldMapper.NumberFieldType(fieldName, NumberFieldMapper.NumberType.DOUBLE);
        double max = Double.NEGATIVE_INFINITY;
        double min = Double.POSITIVE_INFINITY;
        for (double value : values) {
            max = Math.max(max, value);
            min = Math.min(min, value);
        }
        double expectedMax = max;
        double expectedMin = min;
        testCase(ft, iw -> {
            for (double value : values) {
                iw.addDocument(singleton(new NumericDocValuesField(fieldName, NumericUtils.doubleToSortableLong(value))));
            }
        }, stats -> {
            assertEquals(values.length, stats.getCount());
            assertEquals(expectedSum / values.length, stats.getAvg(), delta);
            assertEquals(expectedSum, stats.getSum(), delta);
            assertEquals(expectedSumOfSqrs, stats.getSumOfSquares(), delta);
            assertEquals(expectedMax, stats.getMax(), 0d);
            assertEquals(expectedMin, stats.getMin(), 0d);
        });
    }

    public void testCase(
        MappedFieldType ft,
        CheckedConsumer<RandomIndexWriter, IOException> buildIndex,
        Consumer<InternalExtendedStats> verify
    ) throws IOException {
        ExtendedStatsAggregationBuilder aggBuilder = new ExtendedStatsAggregationBuilder("my_agg").field("field")
            .sigma(randomDoubleBetween(0, 10, true));

        testCase(buildIndex, verify, new AggTestConfig(aggBuilder, ft));
    }

    static class ExtendedSimpleStatsAggregator extends StatsAggregatorTests.SimpleStatsAggregator {
        double sumOfSqrs = 0;

        void add(double value) {
            super.add(value);
            sumOfSqrs += (value * value);
        }

        double stdDev() {
            return Math.sqrt(variance());
        }

        double stdDevPopulation() {
            return Math.sqrt(variancePopulation());
        }

        double stdDevSampling() {
            return Math.sqrt(varianceSampling());
        }

        double stdDevBound(ExtendedStats.Bounds bounds, double sigma) {
            return switch (bounds) {
                case UPPER -> (sum / count) + (Math.sqrt(variance()) * sigma);
                case UPPER_POPULATION -> (sum / count) + (Math.sqrt(variancePopulation()) * sigma);
                case UPPER_SAMPLING -> (sum / count) + (Math.sqrt(varianceSampling()) * sigma);
                case LOWER -> (sum / count) - (Math.sqrt(variance()) * sigma);
                case LOWER_POPULATION -> (sum / count) - (Math.sqrt(variancePopulation()) * sigma);
                case LOWER_SAMPLING -> (sum / count) - (Math.sqrt(varianceSampling()) * sigma);
            };
        }

        double variance() {
            return variancePopulation();
        }

        double variancePopulation() {
            double variance = (sumOfSqrs - ((sum * sum) / count)) / count;
            return variance < 0 ? 0 : variance;
        }

        double varianceSampling() {
            double variance = (sumOfSqrs - ((sum * sum) / count)) / (count - 1);
            return variance < 0 ? 0 : variance;
        }
    }
}
