// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2023 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
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//
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// Author: sameeragarwal@google.com (Sameer Agarwal)

#include <memory>

#include "Eigen/Cholesky"
#include "ceres/block_sparse_matrix.h"
#include "ceres/casts.h"
#include "ceres/context_impl.h"
#include "ceres/linear_least_squares_problems.h"
#include "ceres/linear_solver.h"
#include "ceres/triplet_sparse_matrix.h"
#include "ceres/types.h"
#include "gtest/gtest.h"

namespace ceres::internal {

// TODO(sameeragarwal): These tests needs to be re-written, since
// SparseNormalCholeskySolver is a composition of two classes now,
// InnerProductComputer and SparseCholesky.
//
// So the test should exercise the composition, rather than the
// numerics of the solver, which are well covered by tests for those
// classes.
class SparseNormalCholeskySolverTest : public ::testing::Test {
 protected:
  void SetUp() final {
    std::unique_ptr<LinearLeastSquaresProblem> problem =
        CreateLinearLeastSquaresProblemFromId(2);

    ASSERT_TRUE(problem != nullptr);
    A_.reset(down_cast<BlockSparseMatrix*>(problem->A.release()));
    b_ = std::move(problem->b);
    D_ = std::move(problem->D);
  }

  void TestSolver(const LinearSolver::Options& options, double* D) {
    Matrix dense_A;
    A_->ToDenseMatrix(&dense_A);
    Matrix lhs = dense_A.transpose() * dense_A;
    if (D != nullptr) {
      lhs += (ConstVectorRef(D, A_->num_cols()).array() *
              ConstVectorRef(D, A_->num_cols()).array())
                 .matrix()
                 .asDiagonal();
    }

    Vector rhs(A_->num_cols());
    rhs.setZero();
    A_->LeftMultiplyAndAccumulate(b_.get(), rhs.data());
    Vector expected_solution = lhs.llt().solve(rhs);

    std::unique_ptr<LinearSolver> solver(LinearSolver::Create(options));
    LinearSolver::PerSolveOptions per_solve_options;
    per_solve_options.D = D;
    Vector actual_solution(A_->num_cols());
    LinearSolver::Summary summary;
    summary = solver->Solve(
        A_.get(), b_.get(), per_solve_options, actual_solution.data());

    EXPECT_EQ(summary.termination_type, LinearSolverTerminationType::SUCCESS);

    const double eps = options.use_mixed_precision_solves ? 2e-6 : 1e-8;
    for (int i = 0; i < A_->num_cols(); ++i) {
      EXPECT_NEAR(expected_solution(i), actual_solution(i), eps)
          << "\nExpected: " << expected_solution.transpose()
          << "\nActual: " << actual_solution.transpose();
    }
  }

  void TestSolver(const LinearSolver::Options& options) {
    TestSolver(options, nullptr);
    TestSolver(options, D_.get());
  }

  std::unique_ptr<BlockSparseMatrix> A_;
  std::unique_ptr<double[]> b_;
  std::unique_ptr<double[]> D_;
};

#ifndef CERES_NO_SUITESPARSE
TEST_F(SparseNormalCholeskySolverTest,
       SparseNormalCholeskyUsingSuiteSparsePreOrdering) {
  LinearSolver::Options options;
  options.sparse_linear_algebra_library_type = SUITE_SPARSE;
  options.type = SPARSE_NORMAL_CHOLESKY;
  options.ordering_type = OrderingType::NATURAL;
  ContextImpl context;
  options.context = &context;
  TestSolver(options);
}

TEST_F(SparseNormalCholeskySolverTest,
       SparseNormalCholeskyUsingSuiteSparsePostOrdering) {
  LinearSolver::Options options;
  options.sparse_linear_algebra_library_type = SUITE_SPARSE;
  options.type = SPARSE_NORMAL_CHOLESKY;
  options.ordering_type = OrderingType::AMD;
  ContextImpl context;
  options.context = &context;
  TestSolver(options);
}
#endif

#ifndef CERES_NO_ACCELERATE_SPARSE
TEST_F(SparseNormalCholeskySolverTest,
       SparseNormalCholeskyUsingAccelerateSparsePreOrdering) {
  LinearSolver::Options options;
  options.sparse_linear_algebra_library_type = ACCELERATE_SPARSE;
  options.type = SPARSE_NORMAL_CHOLESKY;
  options.ordering_type = OrderingType::NATURAL;
  ContextImpl context;
  options.context = &context;
  TestSolver(options);
}

TEST_F(SparseNormalCholeskySolverTest,
       SparseNormalCholeskyUsingAcceleratePostOrdering) {
  LinearSolver::Options options;
  options.sparse_linear_algebra_library_type = ACCELERATE_SPARSE;
  options.type = SPARSE_NORMAL_CHOLESKY;
  options.ordering_type = OrderingType::AMD;
  ContextImpl context;
  options.context = &context;
  TestSolver(options);
}
#endif

#ifdef CERES_USE_EIGEN_SPARSE
TEST_F(SparseNormalCholeskySolverTest,
       SparseNormalCholeskyUsingEigenPreOrdering) {
  LinearSolver::Options options;
  options.sparse_linear_algebra_library_type = EIGEN_SPARSE;
  options.type = SPARSE_NORMAL_CHOLESKY;
  options.ordering_type = OrderingType::NATURAL;
  ContextImpl context;
  options.context = &context;
  TestSolver(options);
}

TEST_F(SparseNormalCholeskySolverTest,
       SparseNormalCholeskyUsingEigenPostOrdering) {
  LinearSolver::Options options;
  options.sparse_linear_algebra_library_type = EIGEN_SPARSE;
  options.type = SPARSE_NORMAL_CHOLESKY;
  options.ordering_type = OrderingType::AMD;
  ContextImpl context;
  options.context = &context;
  TestSolver(options);
}
#endif  // CERES_USE_EIGEN_SPARSE

#ifndef CERES_NO_CUDSS
TEST_F(SparseNormalCholeskySolverTest, SparseNormalCholeskyUsingCuDSSSingle) {
  LinearSolver::Options options;
  options.sparse_linear_algebra_library_type = CUDA_SPARSE;
  options.type = SPARSE_NORMAL_CHOLESKY;
  options.ordering_type = OrderingType::AMD;
  options.use_mixed_precision_solves = true;
  ContextImpl context;
  options.context = &context;
  std::string error;
  CHECK(context.InitCuda(&error)) << error;
  TestSolver(options);
}

TEST_F(SparseNormalCholeskySolverTest, SparseNormalCholeskyUsingCuDSSDouble) {
  LinearSolver::Options options;
  options.sparse_linear_algebra_library_type = CUDA_SPARSE;
  options.type = SPARSE_NORMAL_CHOLESKY;
  options.ordering_type = OrderingType::AMD;
  ContextImpl context;
  options.context = &context;
  std::string error;
  CHECK(context.InitCuda(&error)) << error;
  TestSolver(options);
}
#endif  // CERES_USE_EIGEN_SPARSE

}  // namespace ceres::internal