/*
 * 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", the "GNU Affero General Public License v3.0 only", 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", the "GNU Affero General Public
 * License v3.0 only", or the "Server Side Public License, v 1".
 */

package org.elasticsearch.search.aggregations.metrics;

import org.elasticsearch.common.util.Maps;
import org.elasticsearch.search.DocValueFormat;
import org.elasticsearch.search.aggregations.support.SamplingContext;

import java.util.Arrays;
import java.util.HashMap;
import java.util.Iterator;
import java.util.List;
import java.util.Map;

import static org.hamcrest.Matchers.equalTo;

public class InternalTDigestPercentilesRanksTests extends InternalPercentilesRanksTestCase<InternalTDigestPercentileRanks> {

    @Override
    protected InternalTDigestPercentileRanks createTestInstance(
        String name,
        Map<String, Object> metadata,
        boolean keyed,
        DocValueFormat format,
        double[] percents,
        double[] values,
        boolean empty
    ) {
        if (empty) {
            return new InternalTDigestPercentileRanks(name, percents, null, keyed, format, metadata);
        }
        final TDigestState state = TDigestState.createWithoutCircuitBreaking(100);
        Arrays.stream(values).forEach(state::add);

        return new InternalTDigestPercentileRanks(name, percents, state, keyed, format, metadata);
    }

    @Override
    protected void assertReduced(InternalTDigestPercentileRanks reduced, List<InternalTDigestPercentileRanks> inputs) {
        // it is hard to check the values due to the inaccuracy of the algorithm
        // the min/max values should be accurate due to the way the algo works so we can at least test those
        double min = Double.POSITIVE_INFINITY;
        double max = Double.NEGATIVE_INFINITY;
        long totalCount = 0;
        for (InternalTDigestPercentileRanks ranks : inputs) {
            if (ranks.state.centroidCount() == 0) {
                // quantiles would return NaN
                continue;
            }
            totalCount += ranks.state.size();
            min = Math.min(ranks.state.quantile(0), min);
            max = Math.max(ranks.state.quantile(1), max);
        }
        assertEquals(totalCount, reduced.state.size());
        if (totalCount > 0) {
            assertEquals(reduced.state.quantile(0), min, 0d);
            assertEquals(reduced.state.quantile(1), max, 0d);
        }
    }

    @Override
    protected boolean supportsSampling() {
        return true;
    }

    @Override
    protected void assertSampled(
        InternalTDigestPercentileRanks sampled,
        InternalTDigestPercentileRanks reduced,
        SamplingContext samplingContext
    ) {
        Iterator<Percentile> it1 = sampled.iterator();
        Iterator<Percentile> it2 = reduced.iterator();
        while (it1.hasNext() && it2.hasNext()) {
            assertThat(it1.next(), equalTo(it2.next()));
        }
    }

    @Override
    protected InternalTDigestPercentileRanks mutateInstance(InternalTDigestPercentileRanks instance) {
        String name = instance.getName();
        double[] percents = instance.keys;
        TDigestState state = instance.state;
        boolean keyed = instance.keyed;
        DocValueFormat formatter = instance.formatter();
        Map<String, Object> metadata = instance.getMetadata();
        switch (between(0, 4)) {
            case 0 -> name += randomAlphaOfLength(5);
            case 1 -> {
                percents = Arrays.copyOf(percents, percents.length + 1);
                percents[percents.length - 1] = randomDouble() * 100;
                Arrays.sort(percents);
            }
            case 2 -> {
                TDigestState newState = TDigestState.createUsingParamsFrom(state);
                newState.add(state);
                for (int i = 0; i < between(10, 100); i++) {
                    newState.add(randomDouble());
                }
                state = newState;
            }
            case 3 -> keyed = keyed == false;
            case 4 -> {
                if (metadata == null) {
                    metadata = Maps.newMapWithExpectedSize(1);
                } else {
                    metadata = new HashMap<>(instance.getMetadata());
                }
                metadata.put(randomAlphaOfLength(15), randomInt());
            }
            default -> throw new AssertionError("Illegal randomisation branch");
        }
        return new InternalTDigestPercentileRanks(name, percents, state, keyed, formatter, metadata);
    }
}