//              Copyright Catch2 Authors
// Distributed under the Boost Software License, Version 1.0.
//   (See accompanying file LICENSE.txt or copy at
//        https://www.boost.org/LICENSE_1_0.txt)

// SPDX-License-Identifier: BSL-1.0
// Adapted from donated nonius code.

#include <catch2/benchmark/detail/catch_stats.hpp>

#include <catch2/internal/catch_compiler_capabilities.hpp>
#include <catch2/internal/catch_floating_point_helpers.hpp>

#include <algorithm>
#include <cassert>
#include <cmath>
#include <cstddef>
#include <numeric>
#include <random>


#if defined(CATCH_CONFIG_USE_ASYNC)
#include <future>
#endif

namespace Catch {
    namespace Benchmark {
        namespace Detail {
            namespace {

                template <typename URng, typename Estimator>
                static sample
                resample( URng& rng,
                          unsigned int resamples,
                          double const* first,
                          double const* last,
                          Estimator& estimator ) {
                    auto n = static_cast<size_t>( last - first );
                    std::uniform_int_distribution<decltype( n )> dist( 0,
                                                                       n - 1 );

                    sample out;
                    out.reserve( resamples );
                    // We allocate the vector outside the loop to avoid realloc
                    // per resample
                    std::vector<double> resampled;
                    resampled.reserve( n );
                    for ( size_t i = 0; i < resamples; ++i ) {
                        resampled.clear();
                        for ( size_t s = 0; s < n; ++s ) {
                            resampled.push_back(
                                first[static_cast<std::ptrdiff_t>(
                                    dist( rng ) )] );
                        }
                        const auto estimate =
                            estimator( resampled.data(), resampled.data() + resampled.size() );
                        out.push_back( estimate );
                    }
                    std::sort( out.begin(), out.end() );
                    return out;
                }

                static double outlier_variance( Estimate<double> mean,
                                                Estimate<double> stddev,
                                                int n ) {
                    double sb = stddev.point;
                    double mn = mean.point / n;
                    double mg_min = mn / 2.;
                    double sg = (std::min)( mg_min / 4., sb / std::sqrt( n ) );
                    double sg2 = sg * sg;
                    double sb2 = sb * sb;

                    auto c_max = [n, mn, sb2, sg2]( double x ) -> double {
                        double k = mn - x;
                        double d = k * k;
                        double nd = n * d;
                        double k0 = -n * nd;
                        double k1 = sb2 - n * sg2 + nd;
                        double det = k1 * k1 - 4 * sg2 * k0;
                        return static_cast<int>( -2. * k0 /
                                                 ( k1 + std::sqrt( det ) ) );
                    };

                    auto var_out = [n, sb2, sg2]( double c ) {
                        double nc = n - c;
                        return ( nc / n ) * ( sb2 - nc * sg2 );
                    };

                    return (std::min)( var_out( 1 ),
                                       var_out(
                                           (std::min)( c_max( 0. ),
                                                       c_max( mg_min ) ) ) ) /
                           sb2;
                }

                static double erf_inv( double x ) {
                    // Code accompanying the article "Approximating the erfinv
                    // function" in GPU Computing Gems, Volume 2
                    double w, p;

                    w = -log( ( 1.0 - x ) * ( 1.0 + x ) );

                    if ( w < 6.250000 ) {
                        w = w - 3.125000;
                        p = -3.6444120640178196996e-21;
                        p = -1.685059138182016589e-19 + p * w;
                        p = 1.2858480715256400167e-18 + p * w;
                        p = 1.115787767802518096e-17 + p * w;
                        p = -1.333171662854620906e-16 + p * w;
                        p = 2.0972767875968561637e-17 + p * w;
                        p = 6.6376381343583238325e-15 + p * w;
                        p = -4.0545662729752068639e-14 + p * w;
                        p = -8.1519341976054721522e-14 + p * w;
                        p = 2.6335093153082322977e-12 + p * w;
                        p = -1.2975133253453532498e-11 + p * w;
                        p = -5.4154120542946279317e-11 + p * w;
                        p = 1.051212273321532285e-09 + p * w;
                        p = -4.1126339803469836976e-09 + p * w;
                        p = -2.9070369957882005086e-08 + p * w;
                        p = 4.2347877827932403518e-07 + p * w;
                        p = -1.3654692000834678645e-06 + p * w;
                        p = -1.3882523362786468719e-05 + p * w;
                        p = 0.0001867342080340571352 + p * w;
                        p = -0.00074070253416626697512 + p * w;
                        p = -0.0060336708714301490533 + p * w;
                        p = 0.24015818242558961693 + p * w;
                        p = 1.6536545626831027356 + p * w;
                    } else if ( w < 16.000000 ) {
                        w = sqrt( w ) - 3.250000;
                        p = 2.2137376921775787049e-09;
                        p = 9.0756561938885390979e-08 + p * w;
                        p = -2.7517406297064545428e-07 + p * w;
                        p = 1.8239629214389227755e-08 + p * w;
                        p = 1.5027403968909827627e-06 + p * w;
                        p = -4.013867526981545969e-06 + p * w;
                        p = 2.9234449089955446044e-06 + p * w;
                        p = 1.2475304481671778723e-05 + p * w;
                        p = -4.7318229009055733981e-05 + p * w;
                        p = 6.8284851459573175448e-05 + p * w;
                        p = 2.4031110387097893999e-05 + p * w;
                        p = -0.0003550375203628474796 + p * w;
                        p = 0.00095328937973738049703 + p * w;
                        p = -0.0016882755560235047313 + p * w;
                        p = 0.0024914420961078508066 + p * w;
                        p = -0.0037512085075692412107 + p * w;
                        p = 0.005370914553590063617 + p * w;
                        p = 1.0052589676941592334 + p * w;
                        p = 3.0838856104922207635 + p * w;
                    } else {
                        w = sqrt( w ) - 5.000000;
                        p = -2.7109920616438573243e-11;
                        p = -2.5556418169965252055e-10 + p * w;
                        p = 1.5076572693500548083e-09 + p * w;
                        p = -3.7894654401267369937e-09 + p * w;
                        p = 7.6157012080783393804e-09 + p * w;
                        p = -1.4960026627149240478e-08 + p * w;
                        p = 2.9147953450901080826e-08 + p * w;
                        p = -6.7711997758452339498e-08 + p * w;
                        p = 2.2900482228026654717e-07 + p * w;
                        p = -9.9298272942317002539e-07 + p * w;
                        p = 4.5260625972231537039e-06 + p * w;
                        p = -1.9681778105531670567e-05 + p * w;
                        p = 7.5995277030017761139e-05 + p * w;
                        p = -0.00021503011930044477347 + p * w;
                        p = -0.00013871931833623122026 + p * w;
                        p = 1.0103004648645343977 + p * w;
                        p = 4.8499064014085844221 + p * w;
                    }
                    return p * x;
                }

                static double
                standard_deviation( double const* first, double const* last ) {
                    auto m = Catch::Benchmark::Detail::mean( first, last );
                    double variance =
                        std::accumulate( first,
                                         last,
                                         0.,
                                         [m]( double a, double b ) {
                                             double diff = b - m;
                                             return a + diff * diff;
                                         } ) /
                        ( last - first );
                    return std::sqrt( variance );
                }

                static sample jackknife( double ( *estimator )( double const*,
                                                                double const* ),
                                         double* first,
                                         double* last ) {
                    const auto second = first + 1;
                    sample results;
                    results.reserve( static_cast<size_t>( last - first ) );

                    for ( auto it = first; it != last; ++it ) {
                        std::iter_swap( it, first );
                        results.push_back( estimator( second, last ) );
                    }

                    return results;
                }


            } // namespace
        }     // namespace Detail
    }         // namespace Benchmark
} // namespace Catch

namespace Catch {
    namespace Benchmark {
        namespace Detail {

            double weighted_average_quantile( int k,
                                              int q,
                                              double* first,
                                              double* last ) {
                auto count = last - first;
                double idx = (count - 1) * k / static_cast<double>(q);
                int j = static_cast<int>(idx);
                double g = idx - j;
                std::nth_element(first, first + j, last);
                auto xj = first[j];
                if ( Catch::Detail::directCompare( g, 0 ) ) {
                    return xj;
                }

                auto xj1 = *std::min_element(first + (j + 1), last);
                return xj + g * (xj1 - xj);
            }

            OutlierClassification
            classify_outliers( double const* first, double const* last ) {
                std::vector<double> copy( first, last );

                auto q1 = weighted_average_quantile( 1, 4, copy.data(), copy.data() + copy.size() );
                auto q3 = weighted_average_quantile( 3, 4, copy.data(), copy.data() + copy.size() );
                auto iqr = q3 - q1;
                auto los = q1 - ( iqr * 3. );
                auto lom = q1 - ( iqr * 1.5 );
                auto him = q3 + ( iqr * 1.5 );
                auto his = q3 + ( iqr * 3. );

                OutlierClassification o;
                for ( ; first != last; ++first ) {
                    const double t = *first;
                    if ( t < los ) {
                        ++o.low_severe;
                    } else if ( t < lom ) {
                        ++o.low_mild;
                    } else if ( t > his ) {
                        ++o.high_severe;
                    } else if ( t > him ) {
                        ++o.high_mild;
                    }
                    ++o.samples_seen;
                }
                return o;
            }

            double mean( double const* first, double const* last ) {
                auto count = last - first;
                double sum = 0.;
                while (first != last) {
                    sum += *first;
                    ++first;
                }
                return sum / static_cast<double>(count);
            }

            double normal_cdf( double x ) {
                return std::erfc( -x / std::sqrt( 2.0 ) ) / 2.0;
            }

            double erfc_inv(double x) {
                return erf_inv(1.0 - x);
            }

            double normal_quantile(double p) {
                static const double ROOT_TWO = std::sqrt(2.0);

                double result = 0.0;
                assert(p >= 0 && p <= 1);
                if (p < 0 || p > 1) {
                    return result;
                }

                result = -erfc_inv(2.0 * p);
                // result *= normal distribution standard deviation (1.0) * sqrt(2)
                result *= /*sd * */ ROOT_TWO;
                // result += normal disttribution mean (0)
                return result;
            }

            Estimate<double>
            bootstrap( double confidence_level,
                       double* first,
                       double* last,
                       sample const& resample,
                       double ( *estimator )( double const*, double const* ) ) {
                auto n_samples = last - first;

                double point = estimator( first, last );
                // Degenerate case with a single sample
                if ( n_samples == 1 )
                    return { point, point, point, confidence_level };

                sample jack = jackknife( estimator, first, last );
                double jack_mean =
                    mean( jack.data(), jack.data() + jack.size() );
                double sum_squares = 0, sum_cubes = 0;
                for ( double x : jack ) {
                    auto difference = jack_mean - x;
                    auto square = difference * difference;
                    auto cube = square * difference;
                    sum_squares += square;
                    sum_cubes += cube;
                }

                double accel = sum_cubes / ( 6 * std::pow( sum_squares, 1.5 ) );
                long n = static_cast<long>( resample.size() );
                double prob_n =
                    std::count_if( resample.begin(),
                                   resample.end(),
                                   [point]( double x ) { return x < point; } ) /
                    static_cast<double>( n );
                // degenerate case with uniform samples
                if ( Catch::Detail::directCompare( prob_n, 0. ) ) {
                    return { point, point, point, confidence_level };
                }

                double bias = normal_quantile( prob_n );
                double z1 = normal_quantile( ( 1. - confidence_level ) / 2. );

                auto cumn = [n]( double x ) -> long {
                    return std::lround( normal_cdf( x ) *
                                        static_cast<double>( n ) );
                };
                auto a = [bias, accel]( double b ) {
                    return bias + b / ( 1. - accel * b );
                };
                double b1 = bias + z1;
                double b2 = bias - z1;
                double a1 = a( b1 );
                double a2 = a( b2 );
                auto lo = static_cast<size_t>( (std::max)( cumn( a1 ), 0l ) );
                auto hi =
                    static_cast<size_t>( (std::min)( cumn( a2 ), n - 1 ) );

                return { point, resample[lo], resample[hi], confidence_level };
            }

            bootstrap_analysis analyse_samples(double confidence_level,
                                               unsigned int n_resamples,
                                               double* first,
                                               double* last) {
                CATCH_INTERNAL_START_WARNINGS_SUPPRESSION
                CATCH_INTERNAL_SUPPRESS_GLOBALS_WARNINGS
                static std::random_device entropy;
                CATCH_INTERNAL_STOP_WARNINGS_SUPPRESSION

                auto n = static_cast<int>(last - first); // seriously, one can't use integral types without hell in C++

                auto mean = &Detail::mean;
                auto stddev = &standard_deviation;

#if defined(CATCH_CONFIG_USE_ASYNC)
                auto Estimate = [=](double(*f)(double const*, double const*)) {
                    auto seed = entropy();
                    return std::async(std::launch::async, [=] {
                        std::mt19937 rng(seed);
                        auto resampled = resample(rng, n_resamples, first, last, f);
                        return bootstrap(confidence_level, first, last, resampled, f);
                    });
                };

                auto mean_future = Estimate(mean);
                auto stddev_future = Estimate(stddev);

                auto mean_estimate = mean_future.get();
                auto stddev_estimate = stddev_future.get();
#else
                auto Estimate = [=](double(*f)(double const* , double const*)) {
                    auto seed = entropy();
                    std::mt19937 rng(seed);
                    auto resampled = resample(rng, n_resamples, first, last, f);
                    return bootstrap(confidence_level, first, last, resampled, f);
                };

                auto mean_estimate = Estimate(mean);
                auto stddev_estimate = Estimate(stddev);
#endif // CATCH_USE_ASYNC

                double outlier_variance = Detail::outlier_variance(mean_estimate, stddev_estimate, n);

                return { mean_estimate, stddev_estimate, outlier_variance };
            }
        } // namespace Detail
    } // namespace Benchmark
} // namespace Catch