// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2022 Google Inc. All rights reserved.
// http://ceres-solver.org/
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// Author: sameeragarwal@google.com (Sameer Agarwal)

#include "ceres/dense_qr.h"

#include <memory>
#include <numeric>
#include <string>
#include <tuple>
#include <vector>

#include "Eigen/Dense"
#include "ceres/internal/eigen.h"
#include "ceres/linear_solver.h"
#include "glog/logging.h"
#include "gmock/gmock.h"
#include "gtest/gtest.h"

namespace ceres::internal {

using Param = DenseLinearAlgebraLibraryType;

namespace {

std::string ParamInfoToString(testing::TestParamInfo<Param> info) {
  return DenseLinearAlgebraLibraryTypeToString(info.param);
}

}  // namespace

class DenseQRTest : public ::testing::TestWithParam<Param> {};

TEST_P(DenseQRTest, FactorAndSolve) {
  // TODO(sameeragarwal): Convert these tests into type parameterized tests so
  // that we can test the single and double precision solvers.

  using Scalar = double;
  using MatrixType = Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic>;
  using VectorType = Eigen::Matrix<Scalar, Eigen::Dynamic, 1>;

  LinearSolver::Options options;
  ContextImpl context;
#ifndef CERES_NO_CUDA
  options.context = &context;
  std::string error;
  CHECK(context.InitCuda(&error)) << error;
#endif  // CERES_NO_CUDA
  options.dense_linear_algebra_library_type = GetParam();
  const double kEpsilon = std::numeric_limits<double>::epsilon() * 1.5e4;
  std::unique_ptr<DenseQR> dense_qr = DenseQR::Create(options);

  const int kNumTrials = 10;
  const int kMinNumCols = 1;
  const int kMaxNumCols = 10;
  const int kMinRowsFactor = 1;
  const int kMaxRowsFactor = 3;
  for (int num_cols = kMinNumCols; num_cols < kMaxNumCols; ++num_cols) {
    for (int num_rows = kMinRowsFactor * num_cols;
         num_rows < kMaxRowsFactor * num_cols;
         ++num_rows) {
      for (int trial = 0; trial < kNumTrials; ++trial) {
        MatrixType lhs = MatrixType::Random(num_rows, num_cols);
        Vector x = VectorType::Random(num_cols);
        Vector rhs = lhs * x;
        Vector actual = Vector::Random(num_cols);
        LinearSolver::Summary summary;
        summary.termination_type = dense_qr->FactorAndSolve(num_rows,
                                                            num_cols,
                                                            lhs.data(),
                                                            rhs.data(),
                                                            actual.data(),
                                                            &summary.message);
        ASSERT_EQ(summary.termination_type,
                  LinearSolverTerminationType::SUCCESS);
        ASSERT_NEAR((x - actual).norm() / x.norm(), 0.0, kEpsilon)
            << "\nexpected: " << x.transpose()
            << "\nactual  : " << actual.transpose();
      }
    }
  }
}

namespace {

// NOTE: preprocessor directives in a macro are not standard conforming
decltype(auto) MakeValues() {
  return ::testing::Values(EIGEN
#ifndef CERES_NO_LAPACK
                           ,
                           LAPACK
#endif
#ifndef CERES_NO_CUDA
                           ,
                           CUDA
#endif
  );
}

}  // namespace

INSTANTIATE_TEST_SUITE_P(_, DenseQRTest, MakeValues(), ParamInfoToString);

}  // namespace ceres::internal