/*
 *  Copyright 2008-2013 NVIDIA Corporation
 *  Modifications Copyright© 2019-2025 Advanced Micro Devices, Inc. All rights reserved.
 *
 *  Licensed 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.
 */

#include <thrust/detail/config.h>

#include <thrust/random.h>
#include <thrust/scatter.h>
#include <thrust/sequence.h>
#include <thrust/shuffle.h>
#include <thrust/sort.h>

#include <algorithm>
#include <limits>
#include <map>

#include <unittest/unittest.h>

// Functions for performing statistical tests of randomness
// From NIST-Statistical-Test-Suite
// Licence:
//  "This software was developed at the National Institute of Standards and
//  Technology by employees of the Federal Government in the course of their
//  official duties. Pursuant to title 17 Section 105 of the United States Code
//  this software is not subject to copyright protection and is in the public
//  domain. The NIST Statistical Test Suite is an experimental system. NIST
//  assumes no responsibility whatsoever for its use by other parties, and makes
//  no guarantees, expressed or implied, about its quality, reliability, or any
//  other characteristic. We would appreciate acknowledgment if the software is
//  used."
class CephesFunctions
{
public:
  static double cephes_igamc(double a, double x)
  {
    double ans, ax, c, yc, r, t, y, z;
    double pk, pkm1, pkm2, qk, qkm1, qkm2;

    if ((x <= 0) || (a <= 0))
    {
      return (1.0);
    }

    if ((x < 1.0) || (x < a))
    {
      return (1.e0 - cephes_igam(a, x));
    }

    ax = a * log(x) - x - cephes_lgam(a);

    if (ax < -MAXLOG)
    {
      printf("igamc: UNDERFLOW\n");
      return 0.0;
    }
    ax = exp(ax);

    /* continued fraction */
    y    = 1.0 - a;
    z    = x + y + 1.0;
    c    = 0.0;
    pkm2 = 1.0;
    qkm2 = x;
    pkm1 = x + 1.0;
    qkm1 = z * x;
    ans  = pkm1 / qkm1;

    do
    {
      c += 1.0;
      y += 1.0;
      z += 2.0;
      yc = y * c;
      pk = pkm1 * z - pkm2 * yc;
      qk = qkm1 * z - qkm2 * yc;
      if (qk != 0)
      {
        r   = pk / qk;
        t   = fabs((ans - r) / r);
        ans = r;
      }
      else
      {
        t = 1.0;
      }
      pkm2 = pkm1;
      pkm1 = pk;
      qkm2 = qkm1;
      qkm1 = qk;
      if (fabs(pk) > big)
      {
        pkm2 *= biginv;
        pkm1 *= biginv;
        qkm2 *= biginv;
        qkm1 *= biginv;
      }
    } while (t > MACHEP);

    return ans * ax;
  }

private:
  static constexpr double rel_error = 1E-12;

  static constexpr double MACHEP = 1.11022302462515654042E-16; // 2**-53
  static constexpr double MAXLOG = 7.09782712893383996732224E2; // log(MAXNUM)
  static constexpr double MAXNUM = 1.7976931348623158E308; // 2**1024*(1-MACHEP)
  static constexpr double PI     = 3.14159265358979323846;

  static constexpr double big    = 4.503599627370496e15;
  static constexpr double biginv = 2.22044604925031308085e-16;

  static int sgngam;

  static double cephes_igam(double a, double x)
  {
    double ans, ax, c, r;

    if ((x <= 0) || (a <= 0))
    {
      return 0.0;
    }

    if ((x > 1.0) && (x > a))
    {
      return 1.e0 - cephes_igamc(a, x);
    }

    /* Compute  x**a * exp(-x) / gamma(a)  */
    ax = a * log(x) - x - cephes_lgam(a);
    if (ax < -MAXLOG)
    {
      printf("igam: UNDERFLOW\n");
      return 0.0;
    }
    ax = exp(ax);

    /* power series */
    r   = a;
    c   = 1.0;
    ans = 1.0;

    do
    {
      r += 1.0;
      c *= x / r;
      ans += c;
    } while (c / ans > MACHEP);

    return ans * ax / a;
  }

  /* A[]: Stirling's formula expansion of log gamma
   * B[], C[]: log gamma function between 2 and 3
   */
  static constexpr double A[] = {
    0.000811614167470508488140545910738410384510643780,
    -0.000595061904284301438315674115386855191900394857,
    0.000793650340457716942620114419781884862459264696,
    -0.002777777777300996942672073330982129846233874559,
    0.083333333333333189929525985917280195280909538269};
  static constexpr double B[] = {
    -1378.251525691208598800585605204105377197265625,
    -38801.631513463784358464181423187255859375,
    -331612.9927388711948879063129425048828125,
    -1162370.97492762305773794651031494140625,
    -1721737.00820839661173522472381591796875,
    -853555.66424576542340219020843505859375};
  static constexpr double C[] = {
    -351.8157014365234545039129443466663360595703125,
    -17064.21066518811494461260735988616943359375,
    -220528.59055385444662533700466156005859375,
    -1139334.44367982516996562480926513671875,
    -2532523.07177582941949367523193359375,
    -2018891.4143353276886045932769775390625};

  static constexpr double MAXLGM = 2.556348e305;

  /* Logarithm of gamma function */
  static double cephes_lgam(double x)
  {
    double p, q, u, w, z;
    int i;

    sgngam = 1;

    if (x < -34.0)
    {
      q = -x;
      w = cephes_lgam(q); /* note this modifies sgngam! */
      p = floor(q);
      if (p == q)
      {
      lgsing:
        goto loverf;
      }
      i = (int) p;
      if ((i & 1) == 0)
      {
        sgngam = -1;
      }
      else
      {
        sgngam = 1;
      }
      z = q - p;
      if (z > 0.5)
      {
        p += 1.0;
        z = p - q;
      }
      z = q * sin(PI * z);
      if (z == 0.0)
      {
        goto lgsing;
      }
      /*      z = log(PI) - log( z ) - w;*/
      z = log(PI) - log(z) - w;
      return z;
    }

    if (x < 13.0)
    {
      z = 1.0;
      p = 0.0;
      u = x;
      while (u >= 3.0)
      {
        p -= 1.0;
        u = x + p;
        z *= u;
      }
      while (u < 2.0)
      {
        if (u == 0.0)
        {
          goto lgsing;
        }
        z /= u;
        p += 1.0;
        u = x + p;
      }
      if (z < 0.0)
      {
        sgngam = -1;
        z      = -z;
      }
      else
      {
        sgngam = 1;
      }
      if (u == 2.0)
      {
        return (log(z));
      }
      p -= 2.0;
      x = x + p;
      p = x * cephes_polevl(x, B, 5) / cephes_p1evl(x, C, 6);

      return log(z) + p;
    }

    if (x > MAXLGM)
    {
    loverf:
      printf("lgam: OVERFLOW\n");

      return sgngam * MAXNUM;
    }

    q = (x - 0.5) * log(x) - x + log(sqrt(2 * PI));
    if (x > 1.0e8)
    {
      return q;
    }

    p = 1.0 / (x * x);
    if (x >= 1000.0)
    {
      q += ((7.9365079365079365079365e-4 * p - 2.7777777777777777777778e-3) * p + 0.0833333333333333333333) / x;
    }
    else
    {
      q += cephes_polevl(p, A, 4) / x;
    }

    return q;
  }

  static double cephes_polevl(double x, const double* coef, int N)
  {
    const double* p = coef;
    double ans      = *p++;
    int i           = N;
    do
    {
      ans = ans * x + *p++;
    } while (--i);

    return ans;
  }

  static double cephes_p1evl(double x, const double* coef, int N)
  {
    const double* p = coef;
    double ans      = x + *p++;
    int i           = N - 1;

    do
    {
      ans = ans * x + *p++;
    } while (--i);

    return ans;
  }

  static double cephes_erf(double x)
  {
    static const double two_sqrtpi = 1.128379167095512574;
    double sum = x, term = x, xsqr = x * x;
    int j = 1;

    if (fabs(x) > 2.2)
    {
      return 1.0 - cephes_erfc(x);
    }

    do
    {
      term *= xsqr / j;
      sum -= term / (2 * j + 1);
      j++;
      term *= xsqr / j;
      sum += term / (2 * j + 1);
      j++;
    } while (fabs(term) / sum > rel_error);

    return two_sqrtpi * sum;
  }

  static double cephes_erfc(double x)
  {
    static const double one_sqrtpi = 0.564189583547756287;
    double a = 1, b = x, c = x, d = x * x + 0.5;
    double q1, q2 = b / d, n = 1.0, t;

    if (fabs(x) < 2.2)
    {
      return 1.0 - cephes_erf(x);
    }
    if (x < 0)
    {
      return 2.0 - cephes_erfc(-x);
    }

    do
    {
      t = a * n + b * x;
      a = b;
      b = t;
      t = c * n + d * x;
      c = d;
      d = t;
      n += 0.5;
      q1 = q2;
      q2 = b / d;
    } while (fabs(q1 - q2) / q2 > rel_error);

    return one_sqrtpi * exp(-x * x) * q2;
  }

  static double cephes_normal(double x)
  {
    double arg, result, sqrt2 = 1.414213562373095048801688724209698078569672;

    if (x > 0)
    {
      arg    = x / sqrt2;
      result = 0.5 * (1 + erf(arg));
    }
    else
    {
      arg    = -x / sqrt2;
      result = 0.5 * (1 - erf(arg));
    }

    return (result);
  }
};
int CephesFunctions::sgngam = 0;
constexpr double CephesFunctions::A[];
constexpr double CephesFunctions::B[];
constexpr double CephesFunctions::C[];

template <typename Vector>
void TestShuffleSimple()
{
  Vector data{0, 1, 2, 3, 4};
  Vector shuffled(data.begin(), data.end());
  thrust::default_random_engine g(2);
  thrust::shuffle(shuffled.begin(), shuffled.end(), g);
  thrust::sort(shuffled.begin(), shuffled.end());
  // Check all of our data is present
  // This only tests for strange conditions like duplicated elements
  ASSERT_EQUAL(shuffled, data);
}
DECLARE_VECTOR_UNITTEST(TestShuffleSimple);

template <typename Vector>
void TestShuffleCopySimple()
{
  Vector data{0, 1, 2, 3, 4};
  Vector shuffled(5);
  thrust::default_random_engine g(2);
  thrust::shuffle_copy(data.begin(), data.end(), shuffled.begin(), g);
  g.seed(2);
  thrust::shuffle(data.begin(), data.end(), g);
  ASSERT_EQUAL(shuffled, data);
}
DECLARE_VECTOR_UNITTEST(TestShuffleCopySimple);

template <typename T>
void TestHostDeviceIdentical(size_t m)
{
  thrust::host_vector<T> host_result(m);
  thrust::device_vector<T> device_result(m);
  thrust::sequence(host_result.begin(), host_result.end(), T{});
  thrust::sequence(device_result.begin(), device_result.end(), T{});

  thrust::default_random_engine host_g(183);
  thrust::default_random_engine device_g(183);

  thrust::shuffle(host_result.begin(), host_result.end(), host_g);
  thrust::shuffle(device_result.begin(), device_result.end(), device_g);

  ASSERT_EQUAL(device_result, host_result);
}
DECLARE_VARIABLE_UNITTEST(TestHostDeviceIdentical);

template <typename T>
void TestFunctionIsBijection(size_t m)
{
  thrust::default_random_engine device_g(0xD5);
  thrust::system::detail::generic::feistel_bijection device_f(m, device_g);

  const size_t total_length = device_f.nearest_power_of_two();
  if (static_cast<double>(total_length) >= static_cast<double>(std::numeric_limits<T>::max()) || m == 0)
  {
    return;
  }
  ASSERT_LEQUAL(total_length, (std::max)(m * 2, size_t(16))); // Check the rounded up size is at most double the input

  auto device_result_it = thrust::make_transform_iterator(thrust::make_counting_iterator(T(0)), device_f);

  thrust::device_vector<T> unpermuted(total_length, T(0));

  // Run a scatter, this should copy each value to the index matching is value, the result should be in ascending order
  thrust::scatter(device_result_it,
                  device_result_it + static_cast<T>(total_length), // total_length is guaranteed to fit T
                  device_result_it,
                  unpermuted.begin());

  // Check every index is in the result, if any are missing then the function was not a bijection over [0,m)
  ASSERT_EQUAL(true, thrust::equal(unpermuted.begin(), unpermuted.end(), thrust::make_counting_iterator(T(0))));
}
DECLARE_INTEGRAL_VARIABLE_UNITTEST(TestFunctionIsBijection);

void TestBijectionLength()
{
  thrust::default_random_engine g(0xD5);

  uint64_t m = 31;
  thrust::system::detail::generic::feistel_bijection f(m, g);
  ASSERT_EQUAL(f.nearest_power_of_two(), uint64_t(32));

  m = 32;
  f = thrust::system::detail::generic::feistel_bijection(m, g);
  ASSERT_EQUAL(f.nearest_power_of_two(), uint64_t(32));

  m = 1;
  f = thrust::system::detail::generic::feistel_bijection(m, g);
  ASSERT_EQUAL(f.nearest_power_of_two(), uint64_t(16));
}
DECLARE_UNITTEST(TestBijectionLength);

// Individual input keys should be permuted to output locations with uniform
// probability. Perform chi-squared test with confidence 99.9%.
template <typename Vector>
void TestShuffleKeyPosition()
{
  using T            = typename Vector::value_type;
  size_t m           = 20;
  size_t num_samples = 100;
  thrust::host_vector<size_t> index_sum(m, 0);
  thrust::host_vector<T> sequence(m);
  thrust::sequence(sequence.begin(), sequence.end(), T(0));

  thrust::default_random_engine g(0xD5);
  for (size_t i = 0; i < num_samples; i++)
  {
    Vector shuffled(sequence.begin(), sequence.end());
    thrust::shuffle(shuffled.begin(), shuffled.end(), g);
    thrust::host_vector<T> tmp(shuffled.begin(), shuffled.end());

    for (auto j = 0ull; j < m; j++)
    {
      index_sum[tmp[j]] += j;
    }
  }

  double expected_average_position = static_cast<double>(m - 1) / 2;
  double chi_squared               = 0.0;
  for (auto j = 0ull; j < m; j++)
  {
    double average_position = static_cast<double>(index_sum[j]) / num_samples;
    chi_squared += std::pow(expected_average_position - average_position, 2) / expected_average_position;
  }
  // Tabulated chi-squared critical value for m-1=19 degrees of freedom
  // and 99.9% confidence
  double confidence_threshold = 43.82;
  ASSERT_LESS(chi_squared, confidence_threshold);
}
DECLARE_INTEGRAL_VECTOR_UNITTEST(TestShuffleKeyPosition);

struct vector_compare
{
  template <typename VectorT>
  bool operator()(const VectorT& a, const VectorT& b) const
  {
    for (auto i = 0ull; i < a.size(); i++)
    {
      if (a[i] < b[i])
      {
        return true;
      }
      if (a[i] > b[i])
      {
        return false;
      }
    }
    return false;
  }
};

// Brute force check permutations are uniformly distributed on small input
// Uses a chi-squared test indicating 99% confidence the output is uniformly
// random
template <typename Vector>
void TestShuffleUniformPermutation()
{
  using T = typename Vector::value_type;

  size_t m                  = 5;
  size_t num_samples        = 1000;
  size_t total_permutations = 1 * 2 * 3 * 4 * 5;
  std::map<thrust::host_vector<T>, size_t, vector_compare> permutation_counts;
  Vector sequence(m);
  thrust::sequence(sequence.begin(), sequence.end(), T(0));
  thrust::default_random_engine g(0xD5);
  for (auto i = 0ull; i < num_samples; i++)
  {
    thrust::shuffle(sequence.begin(), sequence.end(), g);
    thrust::host_vector<T> tmp(sequence.begin(), sequence.end());
    permutation_counts[tmp]++;
  }

  ASSERT_EQUAL(permutation_counts.size(), total_permutations);

  double chi_squared    = 0.0;
  double expected_count = static_cast<double>(num_samples) / total_permutations;
  for (auto kv : permutation_counts)
  {
    chi_squared += std::pow(expected_count - kv.second, 2) / expected_count;
  }
  double p_score = CephesFunctions::cephes_igamc((double) (total_permutations - 1) / 2.0, chi_squared / 2.0);
  ASSERT_GREATER(p_score, 0.01);
}
DECLARE_VECTOR_UNITTEST(TestShuffleUniformPermutation);

template <typename Vector>
void TestShuffleEvenSpacingBetweenOccurances()
{
  using T                     = typename Vector::value_type;
  const uint64_t shuffle_size = 10;
  const uint64_t num_samples  = 1000;

  thrust::host_vector<T> h_results;
  Vector sequence(shuffle_size);
  thrust::sequence(sequence.begin(), sequence.end(), 0);
  thrust::default_random_engine g(0xD6);
  for (auto i = 0ull; i < num_samples; i++)
  {
    thrust::shuffle(sequence.begin(), sequence.end(), g);
    thrust::host_vector<T> tmp(sequence.begin(), sequence.end());
    h_results.insert(h_results.end(), sequence.begin(), sequence.end());
  }

  std::vector<std::vector<std::vector<uint64_t>>> distance_between(
    num_samples, std::vector<std::vector<uint64_t>>(num_samples, std::vector<uint64_t>(shuffle_size, 0)));

  for (uint64_t sample = 0; sample < num_samples; sample++)
  {
    for (uint64_t i = 0; i < shuffle_size - 1; i++)
    {
      for (uint64_t j = 1; j < shuffle_size - i; j++)
      {
        T val_1 = h_results[sample * shuffle_size + i];
        T val_2 = h_results[sample * shuffle_size + i + j];
        distance_between[val_1][val_2][j]++;
        distance_between[val_2][val_1][shuffle_size - j]++;
      }
    }
  }

  const double expected_occurances = (double) num_samples / (shuffle_size - 1);
  for (uint64_t val_1 = 0; val_1 < shuffle_size; val_1++)
  {
    for (uint64_t val_2 = val_1 + 1; val_2 < shuffle_size; val_2++)
    {
      double chi_squared = 0.0;
      auto& distances    = distance_between[val_1][val_2];
      for (uint64_t i = 1; i < shuffle_size; i++)
      {
        chi_squared += std::pow((double) distances[i] - expected_occurances, 2) / expected_occurances;
      }

      double p_score = CephesFunctions::cephes_igamc((double) (shuffle_size - 2) / 2.0, chi_squared / 2.0);
      ASSERT_GREATER(p_score, 0.01);
    }
  }
}
DECLARE_INTEGRAL_VECTOR_UNITTEST(TestShuffleEvenSpacingBetweenOccurances);

template <typename Vector>
void TestShuffleEvenDistribution()
{
  using T                        = typename Vector::value_type;
  const uint64_t shuffle_sizes[] = {10, 100, 500};
  thrust::default_random_engine g(0xD5);
  for (auto shuffle_size : shuffle_sizes)
  {
    if (shuffle_size > (uint64_t) std::numeric_limits<T>::max())
    {
      continue;
    }
    const uint64_t num_samples = shuffle_size == 500 ? 1000 : 200;

    std::vector<uint64_t> counts(shuffle_size * shuffle_size, 0);
    Vector sequence(shuffle_size);
    for (auto i = 0ull; i < num_samples; i++)
    {
      thrust::sequence(sequence.begin(), sequence.end(), 0);
      thrust::shuffle(sequence.begin(), sequence.end(), g);
      thrust::host_vector<T> tmp(sequence.begin(), sequence.end());
      for (uint64_t j = 0; j < shuffle_size; j++)
      {
        assert(j < tmp.size());
        counts.at(j * shuffle_size + tmp[j])++;
      }
    }

    const double expected_occurances = (double) num_samples / shuffle_size;
    for (uint64_t i = 0; i < shuffle_size; i++)
    {
      double chi_squared_pos = 0.0;
      double chi_squared_num = 0.0;
      for (uint64_t j = 0; j < shuffle_size; j++)
      {
        auto count_pos = counts.at(i * shuffle_size + j);
        auto count_num = counts.at(j * shuffle_size + i);
        chi_squared_pos += std::pow((double) count_pos - expected_occurances, 2) / expected_occurances;
        chi_squared_num += std::pow((double) count_num - expected_occurances, 2) / expected_occurances;
      }

      double p_score_pos = CephesFunctions::cephes_igamc((double) (shuffle_size - 1) / 2.0, chi_squared_pos / 2.0);
      ASSERT_GREATER(p_score_pos, 0.001 / (double) shuffle_size);

      double p_score_num = CephesFunctions::cephes_igamc((double) (shuffle_size - 1) / 2.0, chi_squared_num / 2.0);
      ASSERT_GREATER(p_score_num, 0.001 / (double) shuffle_size);
    }
  }
}
DECLARE_INTEGRAL_VECTOR_UNITTEST(TestShuffleEvenDistribution);
