/*!
 * Copyright 2016-2019 XGBoost contributors
 */
#ifndef XGBOOST_TESTS_CPP_HELPERS_H_
#define XGBOOST_TESTS_CPP_HELPERS_H_

#include <iostream>
#include <fstream>
#include <cstdio>
#include <string>
#include <vector>
#include <sys/stat.h>
#include <sys/types.h>

#include <gtest/gtest.h>

#include <dmlc/filesystem.h>
#include <xgboost/base.h>
#include <xgboost/json.h>
#include <xgboost/generic_parameters.h>

#include "../../src/common/common.h"
#include "../../src/gbm/gbtree_model.h"

#if defined(__CUDACC__)
#define DeclareUnifiedTest(name) GPU ## name
#else
#define DeclareUnifiedTest(name) name
#endif

#if defined(__CUDACC__)
#define GPUIDX 0
#else
#define GPUIDX -1
#endif

namespace xgboost {
class ObjFunction;
class Metric;
struct LearnerModelParam;
class GradientBooster;
}

bool FileExists(const std::string& filename);

int64_t GetFileSize(const std::string& filename);

void CreateSimpleTestData(const std::string& filename);

void CreateBigTestData(const std::string& filename, size_t n_entries);

void CheckObjFunction(std::unique_ptr<xgboost::ObjFunction> const& obj,
                      std::vector<xgboost::bst_float> preds,
                      std::vector<xgboost::bst_float> labels,
                      std::vector<xgboost::bst_float> weights,
                      std::vector<xgboost::bst_float> out_grad,
                      std::vector<xgboost::bst_float> out_hess);

xgboost::Json CheckConfigReloadImpl(xgboost::Configurable* const configurable,
                                    std::string name);

template <typename T>
xgboost::Json CheckConfigReload(std::unique_ptr<T> const& configurable,
                                std::string name = "") {
  return CheckConfigReloadImpl(dynamic_cast<xgboost::Configurable*>(configurable.get()),
                               name);
}

void CheckRankingObjFunction(std::unique_ptr<xgboost::ObjFunction> const& obj,
                             std::vector<xgboost::bst_float> preds,
                             std::vector<xgboost::bst_float> labels,
                             std::vector<xgboost::bst_float> weights,
                             std::vector<xgboost::bst_uint> groups,
                             std::vector<xgboost::bst_float> out_grad,
                             std::vector<xgboost::bst_float> out_hess);

xgboost::bst_float GetMetricEval(
  xgboost::Metric * metric,
  xgboost::HostDeviceVector<xgboost::bst_float> preds,
  std::vector<xgboost::bst_float> labels,
  std::vector<xgboost::bst_float> weights = std::vector<xgboost::bst_float>(),
  std::vector<xgboost::bst_uint> groups = std::vector<xgboost::bst_uint>());

namespace xgboost {
bool IsNear(std::vector<xgboost::bst_float>::const_iterator _beg1,
            std::vector<xgboost::bst_float>::const_iterator _end1,
            std::vector<xgboost::bst_float>::const_iterator _beg2);

/*!
 * \brief Linear congruential generator.
 *
 * The distribution defined in std is not portable. Given the same seed, it
 * migth produce different outputs on different platforms or with different
 * compilers.  The SimpleLCG implemented here is to make sure all tests are
 * reproducible.
 */
class SimpleLCG {
 private:
  using StateType = int64_t;
  static StateType constexpr default_init_ = 3;
  static StateType constexpr default_alpha_ = 61;
  static StateType constexpr max_value_ = ((StateType)1 << 32) - 1;

  StateType state_;
  StateType const alpha_;
  StateType const mod_;

  StateType const seed_;

 public:
  SimpleLCG() : state_{default_init_},
                alpha_{default_alpha_}, mod_{max_value_}, seed_{state_}{}
  /*!
   * \brief Initialize SimpleLCG.
   *
   * \param state  Initial state, can also be considered as seed. If set to
   *               zero, SimpleLCG will use internal default value.
   * \param alpha  multiplier
   * \param mod    modulo
   */
  SimpleLCG(StateType state,
            StateType alpha=default_alpha_, StateType mod=max_value_)
      : state_{state == 0 ? default_init_ : state},
        alpha_{alpha}, mod_{mod} , seed_{state} {}

  StateType operator()();
  StateType Min() const;
  StateType Max() const;
};

template <typename ResultT>
class SimpleRealUniformDistribution {
 private:
  ResultT const lower;
  ResultT const upper;

  /*! \brief Over-simplified version of std::generate_canonical. */
  template <size_t Bits, typename GeneratorT>
  ResultT GenerateCanonical(GeneratorT* rng) const {
    static_assert(std::is_floating_point<ResultT>::value,
                  "Result type must be floating point.");
    long double const r = (static_cast<long double>(rng->Max())
                           - static_cast<long double>(rng->Min())) + 1.0L;
    auto const log2r = static_cast<size_t>(std::log(r) / std::log(2.0L));
    size_t m = std::max<size_t>(1UL, (Bits + log2r - 1UL) / log2r);
    ResultT sum_value = 0, r_k = 1;

    for (size_t k = m; k != 0; --k) {
      sum_value += ResultT((*rng)() - rng->Min()) * r_k;
      r_k *= r;
    }

    ResultT res = sum_value / r_k;
    return res;
  }

 public:
  SimpleRealUniformDistribution(ResultT l, ResultT u) :
      lower{l}, upper{u} {}

  template <typename GeneratorT>
  ResultT operator()(GeneratorT* rng) const {
    ResultT tmp = GenerateCanonical<std::numeric_limits<ResultT>::digits,
                                    GeneratorT>(rng);
    return (tmp * (upper - lower)) + lower;
  }
};

// Generate in-memory random data without using DMatrix.
class RandomDataGenerator {
  bst_row_t rows_;
  size_t cols_;
  float sparsity_;

  float lower_;
  float upper_;

  int32_t device_;
  int32_t seed_;

  Json ArrayInterfaceImpl(HostDeviceVector<float> *storage, size_t rows,
                          size_t cols) const;

 public:
  RandomDataGenerator(bst_row_t rows, size_t cols, float sparsity)
      : rows_{rows}, cols_{cols}, sparsity_{sparsity}, lower_{0.0f}, upper_{1.0f},
        device_{-1}, seed_{0} {}

  RandomDataGenerator &Lower(float v) {
    lower_ = v;
    return *this;
  }
  RandomDataGenerator& Upper(float v) {
    upper_ = v;
    return *this;
  }
  RandomDataGenerator& Device(int32_t d) {
    device_ = d;
    return *this;
  }
  RandomDataGenerator& Seed(int32_t s) {
    seed_ = s;
    return *this;
  }

  void GenerateDense(HostDeviceVector<float>* out) const;
  std::string GenerateArrayInterface(HostDeviceVector<float>* storage) const;
  std::string GenerateColumnarArrayInterface(
      std::vector<HostDeviceVector<float>> *data) const;
  void GenerateCSR(HostDeviceVector<float>* value, HostDeviceVector<bst_row_t>* row_ptr,
                   HostDeviceVector<bst_feature_t>* columns) const;

  std::shared_ptr<DMatrix> GenerateDMatix(bool with_label = false,
                                          bool float_label = true,
                                          size_t classes = 1) const;
};

std::unique_ptr<DMatrix> CreateSparsePageDMatrix(
    size_t n_entries, size_t page_size, std::string tmp_file);

/**
 * \fn std::unique_ptr<DMatrix> CreateSparsePageDMatrixWithRC(size_t n_rows, size_t n_cols,
 *                                                            size_t page_size);
 *
 * \brief Creates dmatrix with some records, each record containing random number of
 *        features in [1, n_cols]
 *
 * \param n_rows      Number of records to create.
 * \param n_cols      Max number of features within that record.
 * \param page_size   Sparse page size for the pages within the dmatrix. If page size is 0
 *                    then the entire dmatrix is resident in memory; else, multiple sparse pages
 *                    of page size are created and backed to disk, which would have to be
 *                    streamed in at point of use.
 * \param deterministic The content inside the dmatrix is constant for this configuration, if true;
 *                      else, the content changes every time this method is invoked
 *
 * \return The new dmatrix.
 */
std::unique_ptr<DMatrix> CreateSparsePageDMatrixWithRC(
    size_t n_rows, size_t n_cols, size_t page_size, bool deterministic,
    const dmlc::TemporaryDirectory& tempdir = dmlc::TemporaryDirectory());

gbm::GBTreeModel CreateTestModel(LearnerModelParam const* param, size_t n_classes = 1);

std::unique_ptr<GradientBooster> CreateTrainedGBM(
    std::string name, Args kwargs, size_t kRows, size_t kCols,
    LearnerModelParam const* learner_model_param,
    GenericParameter const* generic_param);

inline GenericParameter CreateEmptyGenericParam(int gpu_id) {
  xgboost::GenericParameter tparam;
  std::vector<std::pair<std::string, std::string>> args {
    {"gpu_id", std::to_string(gpu_id)}};
  tparam.Init(args);
  return tparam;
}

inline HostDeviceVector<GradientPair> GenerateRandomGradients(const size_t n_rows,
                                                              float lower= 0.0f, float upper = 1.0f) {
  xgboost::SimpleLCG gen;
  xgboost::SimpleRealUniformDistribution<bst_float> dist(lower, upper);
  std::vector<GradientPair> h_gpair(n_rows);
  for (auto &gpair : h_gpair) {
    bst_float grad = dist(&gen);
    bst_float hess = dist(&gen);
    gpair = GradientPair(grad, hess);
  }
  HostDeviceVector<GradientPair> gpair(h_gpair);
  return gpair;
}
}  // namespace xgboost
#endif