// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2022 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
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// modification, are permitted provided that the following conditions are met:
//
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//   this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright notice,
//   this list of conditions and the following disclaimer in the documentation
//   and/or other materials provided with the distribution.
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//   used to endorse or promote products derived from this software without
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// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// Author: joydeepb@cs.utexas.edu (Joydeep Biswas)

#include <string>

#include "ceres/dense_cholesky.h"
#include "ceres/internal/eigen.h"

#include "glog/logging.h"
#include "gtest/gtest.h"

using std::string;

namespace ceres {
namespace internal {

#ifndef CERES_NO_CUDA

TEST(DenseCholeskyCudaSolverOld, InvalidOptionOnCreate) {
  LinearSolver::Options options;
  auto dense_cuda_solver = CUDADenseCholesky32Bit::Create(options);
  EXPECT_EQ(dense_cuda_solver, nullptr);
}

// Tests the CUDA Cholesky solver with a simple 4x4 matrix.
TEST(DenseCholeskyCudaSolverOld, Cholesky4x4Matrix) {
  Eigen::Matrix4d A;
  A <<  4,  12, -16, 0,
       12,  37, -43, 0,
      -16, -43,  98, 0,
        0,   0,   0, 1;
  const Eigen::Vector4d b = Eigen::Vector4d::Ones();
  LinearSolver::Options options;
  options.dense_linear_algebra_library_type = CUDA;
  auto dense_cuda_solver = CUDADenseCholesky32Bit::Create(options);
  ASSERT_NE(dense_cuda_solver, nullptr);
  string error_string;
  ASSERT_EQ(dense_cuda_solver->Factorize(A.cols(),
                                        A.data(),
                                        &error_string),
            LinearSolverTerminationType::LINEAR_SOLVER_SUCCESS);
  Eigen::Vector4d x = Eigen::Vector4d::Zero();
  ASSERT_EQ(dense_cuda_solver->Solve(b.data(), x.data(), &error_string),
            LinearSolverTerminationType::LINEAR_SOLVER_SUCCESS);
  EXPECT_NEAR(x(0), 113.75 / 3.0, std::numeric_limits<double>::epsilon() * 10);
  EXPECT_NEAR(x(1), -31.0 / 3.0, std::numeric_limits<double>::epsilon() * 10);
  EXPECT_NEAR(x(2), 5.0 / 3.0, std::numeric_limits<double>::epsilon() * 10);
  EXPECT_NEAR(x(3), 1.0000, std::numeric_limits<double>::epsilon() * 10);
}

TEST(DenseCholeskyCudaSolverOld, SingularMatrix) {
  Eigen::Matrix3d A;
  A <<  1, 0, 0,
        0, 1, 0,
        0, 0, 0;
  const Eigen::Vector3d b = Eigen::Vector3d::Ones();
  LinearSolver::Options options;
  options.dense_linear_algebra_library_type = CUDA;
  auto dense_cuda_solver = CUDADenseCholesky32Bit::Create(options);
  ASSERT_NE(dense_cuda_solver, nullptr);
  string error_string;
  ASSERT_EQ(dense_cuda_solver->Factorize(A.cols(),
                                        A.data(),
                                        &error_string),
            LinearSolverTerminationType::LINEAR_SOLVER_FAILURE);
}

TEST(DenseCholeskyCudaSolverOld, NegativeMatrix) {
  Eigen::Matrix3d A;
  A <<  1, 0, 0,
        0, 1, 0,
        0, 0, -1;
  const Eigen::Vector3d b = Eigen::Vector3d::Ones();
  LinearSolver::Options options;
  options.dense_linear_algebra_library_type = CUDA;
  auto dense_cuda_solver = CUDADenseCholesky32Bit::Create(options);
  ASSERT_NE(dense_cuda_solver, nullptr);
  string error_string;
  ASSERT_EQ(dense_cuda_solver->Factorize(A.cols(),
                                        A.data(),
                                        &error_string),
            LinearSolverTerminationType::LINEAR_SOLVER_FAILURE);
}

TEST(DenseCholeskyCudaSolverOld, MustFactorizeBeforeSolve) {
  const Eigen::Vector3d b = Eigen::Vector3d::Ones();
  LinearSolver::Options options;
  options.dense_linear_algebra_library_type = CUDA;
  auto dense_cuda_solver = CUDADenseCholesky32Bit::Create(options);
  ASSERT_NE(dense_cuda_solver, nullptr);
  string error_string;
  ASSERT_EQ(dense_cuda_solver->Solve(b.data(), nullptr, &error_string),
            LinearSolverTerminationType::LINEAR_SOLVER_FATAL_ERROR);
}

#ifndef CERES_CUDA_NO_64BIT_SOLVER_API
// TODO(sameeragarwal): Convert these tests into templated tests so that we use
// the exact same code for both versions of the test.

TEST(DenseCholeskyCudaSolverNew, InvalidOptionOnCreate) {
  LinearSolver::Options options;
  auto dense_cuda_solver = CUDADenseCholesky64Bit::Create(options);
  EXPECT_EQ(dense_cuda_solver, nullptr);
}

TEST(DenseCholeskyCudaSolverNew, Cholesky4x4Matrix) {
  Eigen::Matrix4d A;
  A <<  4,  12, -16, 0,
       12,  37, -43, 0,
      -16, -43,  98, 0,
        0,   0,   0, 1;
  const Eigen::Vector4d b = Eigen::Vector4d::Ones();
  LinearSolver::Options options;
  options.dense_linear_algebra_library_type = CUDA;
  auto dense_cuda_solver = CUDADenseCholesky64Bit::Create(options);
  ASSERT_NE(dense_cuda_solver, nullptr);
  string error_string;
  ASSERT_EQ(dense_cuda_solver->Factorize(A.cols(),
                                        A.data(),
                                        &error_string),
            LinearSolverTerminationType::LINEAR_SOLVER_SUCCESS);
  Eigen::Vector4d x = Eigen::Vector4d::Zero();
  ASSERT_EQ(dense_cuda_solver->Solve(b.data(), x.data(), &error_string),
            LinearSolverTerminationType::LINEAR_SOLVER_SUCCESS);
  EXPECT_NEAR(x(0), 113.75 / 3.0, std::numeric_limits<double>::epsilon() * 10);
  EXPECT_NEAR(x(1), -31.0 / 3.0, std::numeric_limits<double>::epsilon() * 10);
  EXPECT_NEAR(x(2), 5.0 / 3.0, std::numeric_limits<double>::epsilon() * 10);
  EXPECT_NEAR(x(3), 1.0000, std::numeric_limits<double>::epsilon() * 10);
}

TEST(DenseCholeskyCudaSolverNew, SingularMatrix) {
  Eigen::Matrix3d A;
  A <<  1, 0, 0,
        0, 1, 0,
        0, 0, 0;
  const Eigen::Vector3d b = Eigen::Vector3d::Ones();
  LinearSolver::Options options;
  options.dense_linear_algebra_library_type = CUDA;
  auto dense_cuda_solver = CUDADenseCholesky64Bit::Create(options);
  ASSERT_NE(dense_cuda_solver, nullptr);
  string error_string;
  ASSERT_EQ(dense_cuda_solver->Factorize(A.cols(),
                                        A.data(),
                                        &error_string),
            LinearSolverTerminationType::LINEAR_SOLVER_FAILURE);
}

TEST(DenseCholeskyCudaSolverNew, NegativeMatrix) {
  Eigen::Matrix3d A;
  A <<  1, 0, 0,
        0, 1, 0,
        0, 0, -1;
  const Eigen::Vector3d b = Eigen::Vector3d::Ones();
  LinearSolver::Options options;
  options.dense_linear_algebra_library_type = CUDA;
  auto dense_cuda_solver = CUDADenseCholesky64Bit::Create(options);
  ASSERT_NE(dense_cuda_solver, nullptr);
  string error_string;
  ASSERT_EQ(dense_cuda_solver->Factorize(A.cols(),
                                        A.data(),
                                        &error_string),
            LinearSolverTerminationType::LINEAR_SOLVER_FAILURE);
}

TEST(DenseCholeskyCudaSolverNew, MustFactorizeBeforeSolve) {
  const Eigen::Vector3d b = Eigen::Vector3d::Ones();
  LinearSolver::Options options;
  options.dense_linear_algebra_library_type = CUDA;
  auto dense_cuda_solver = CUDADenseCholesky64Bit::Create(options);
  ASSERT_NE(dense_cuda_solver, nullptr);
  string error_string;
  ASSERT_EQ(dense_cuda_solver->Solve(b.data(), nullptr, &error_string),
            LinearSolverTerminationType::LINEAR_SOLVER_FATAL_ERROR);
}
#endif  // CERES_CUDA_NO_64BIT_SOLVER_API

#endif  // CERES_NO_CUDA

}  // namespace internal
}  // namespace ceres