// Ceres Solver - A fast non-linear least squares minimizer
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// Author: sameeragarwal@google.com (Sameer Agarwal)

#include "ceres/partitioned_matrix_view.h"

#include <memory>
#include <random>
#include <vector>

#include "ceres/block_structure.h"
#include "ceres/casts.h"
#include "ceres/internal/eigen.h"
#include "ceres/linear_least_squares_problems.h"
#include "ceres/sparse_matrix.h"
#include "glog/logging.h"
#include "gtest/gtest.h"

namespace ceres {
namespace internal {

const double kEpsilon = 1e-14;

class PartitionedMatrixViewTest : public ::testing::Test {
 protected:
  void SetUp() final {
    std::unique_ptr<LinearLeastSquaresProblem> problem =
        CreateLinearLeastSquaresProblemFromId(2);
    CHECK(problem != nullptr);
    A_ = std::move(problem->A);

    num_cols_ = A_->num_cols();
    num_rows_ = A_->num_rows();
    num_eliminate_blocks_ = problem->num_eliminate_blocks;
    LinearSolver::Options options;
    options.elimination_groups.push_back(num_eliminate_blocks_);
    pmv_ = PartitionedMatrixViewBase::Create(
        options, *down_cast<BlockSparseMatrix*>(A_.get()));
  }

  double RandDouble() { return distribution_(prng_); }

  int num_rows_;
  int num_cols_;
  int num_eliminate_blocks_;
  std::unique_ptr<SparseMatrix> A_;
  std::unique_ptr<PartitionedMatrixViewBase> pmv_;
  std::mt19937 prng_;
  std::uniform_real_distribution<double> distribution_ =
      std::uniform_real_distribution<double>(0.0, 1.0);
};

TEST_F(PartitionedMatrixViewTest, DimensionsTest) {
  EXPECT_EQ(pmv_->num_col_blocks_e(), num_eliminate_blocks_);
  EXPECT_EQ(pmv_->num_col_blocks_f(), num_cols_ - num_eliminate_blocks_);
  EXPECT_EQ(pmv_->num_cols_e(), num_eliminate_blocks_);
  EXPECT_EQ(pmv_->num_cols_f(), num_cols_ - num_eliminate_blocks_);
  EXPECT_EQ(pmv_->num_cols(), A_->num_cols());
  EXPECT_EQ(pmv_->num_rows(), A_->num_rows());
}

TEST_F(PartitionedMatrixViewTest, RightMultiplyAndAccumulateE) {
  Vector x1(pmv_->num_cols_e());
  Vector x2(pmv_->num_cols());
  x2.setZero();

  for (int i = 0; i < pmv_->num_cols_e(); ++i) {
    x1(i) = x2(i) = RandDouble();
  }

  Vector y1 = Vector::Zero(pmv_->num_rows());
  pmv_->RightMultiplyAndAccumulateE(x1.data(), y1.data());

  Vector y2 = Vector::Zero(pmv_->num_rows());
  A_->RightMultiplyAndAccumulate(x2.data(), y2.data());

  for (int i = 0; i < pmv_->num_rows(); ++i) {
    EXPECT_NEAR(y1(i), y2(i), kEpsilon);
  }
}

TEST_F(PartitionedMatrixViewTest, RightMultiplyAndAccumulateF) {
  Vector x1(pmv_->num_cols_f());
  Vector x2 = Vector::Zero(pmv_->num_cols());

  for (int i = 0; i < pmv_->num_cols_f(); ++i) {
    x1(i) = RandDouble();
    x2(i + pmv_->num_cols_e()) = x1(i);
  }

  Vector y1 = Vector::Zero(pmv_->num_rows());
  pmv_->RightMultiplyAndAccumulateF(x1.data(), y1.data());

  Vector y2 = Vector::Zero(pmv_->num_rows());
  A_->RightMultiplyAndAccumulate(x2.data(), y2.data());

  for (int i = 0; i < pmv_->num_rows(); ++i) {
    EXPECT_NEAR(y1(i), y2(i), kEpsilon);
  }
}

TEST_F(PartitionedMatrixViewTest, LeftMultiplyAndAccumulate) {
  Vector x = Vector::Zero(pmv_->num_rows());
  for (int i = 0; i < pmv_->num_rows(); ++i) {
    x(i) = RandDouble();
  }

  Vector y = Vector::Zero(pmv_->num_cols());
  Vector y1 = Vector::Zero(pmv_->num_cols_e());
  Vector y2 = Vector::Zero(pmv_->num_cols_f());

  A_->LeftMultiplyAndAccumulate(x.data(), y.data());
  pmv_->LeftMultiplyAndAccumulateE(x.data(), y1.data());
  pmv_->LeftMultiplyAndAccumulateF(x.data(), y2.data());

  for (int i = 0; i < pmv_->num_cols(); ++i) {
    EXPECT_NEAR(y(i),
                (i < pmv_->num_cols_e()) ? y1(i) : y2(i - pmv_->num_cols_e()),
                kEpsilon);
  }
}

TEST_F(PartitionedMatrixViewTest, BlockDiagonalEtE) {
  std::unique_ptr<BlockSparseMatrix> block_diagonal_ee(
      pmv_->CreateBlockDiagonalEtE());
  const CompressedRowBlockStructure* bs = block_diagonal_ee->block_structure();

  EXPECT_EQ(block_diagonal_ee->num_rows(), 2);
  EXPECT_EQ(block_diagonal_ee->num_cols(), 2);
  EXPECT_EQ(bs->cols.size(), 2);
  EXPECT_EQ(bs->rows.size(), 2);

  EXPECT_NEAR(block_diagonal_ee->values()[0], 10.0, kEpsilon);
  EXPECT_NEAR(block_diagonal_ee->values()[1], 155.0, kEpsilon);
}

TEST_F(PartitionedMatrixViewTest, BlockDiagonalFtF) {
  std::unique_ptr<BlockSparseMatrix> block_diagonal_ff(
      pmv_->CreateBlockDiagonalFtF());
  const CompressedRowBlockStructure* bs = block_diagonal_ff->block_structure();

  EXPECT_EQ(block_diagonal_ff->num_rows(), 3);
  EXPECT_EQ(block_diagonal_ff->num_cols(), 3);
  EXPECT_EQ(bs->cols.size(), 3);
  EXPECT_EQ(bs->rows.size(), 3);
  EXPECT_NEAR(block_diagonal_ff->values()[0], 70.0, kEpsilon);
  EXPECT_NEAR(block_diagonal_ff->values()[1], 17.0, kEpsilon);
  EXPECT_NEAR(block_diagonal_ff->values()[2], 37.0, kEpsilon);
}

const int kMaxNumThreads = 8;
class PartitionedMatrixViewParallelTest : public ::testing::TestWithParam<int> {
 protected:
  static const int kNumProblems = 3;
  void SetUp() final {
    int problem_ids[kNumProblems] = {2, 4, 6};
    for (int i = 0; i < kNumProblems; ++i) {
      auto problem = CreateLinearLeastSquaresProblemFromId(problem_ids[i]);
      CHECK(problem != nullptr);
      SetUpMatrix(i, problem.get());
    }

    context_.EnsureMinimumThreads(kMaxNumThreads);
  }

  void SetUpMatrix(int id, LinearLeastSquaresProblem* problem) {
    A_[id] = std::move(problem->A);
    auto& A = A_[id];
    auto block_sparse = down_cast<BlockSparseMatrix*>(A.get());
    block_sparse->AddTransposeBlockStructure();

    num_cols_[id] = A->num_cols();
    num_rows_[id] = A->num_rows();
    num_eliminate_blocks_[id] = problem->num_eliminate_blocks;
    LinearSolver::Options options;
    options.elimination_groups.push_back(num_eliminate_blocks_[id]);
    pmv_[id] = PartitionedMatrixViewBase::Create(options, *block_sparse);
  }

  double RandDouble() { return distribution_(prng_); }

  ContextImpl context_;
  int num_rows_[kNumProblems];
  int num_cols_[kNumProblems];
  int num_eliminate_blocks_[kNumProblems];
  std::unique_ptr<SparseMatrix> A_[kNumProblems];
  std::unique_ptr<PartitionedMatrixViewBase> pmv_[kNumProblems];
  std::mt19937 prng_;
  std::uniform_real_distribution<double> distribution_ =
      std::uniform_real_distribution<double>(0.0, 1.0);
};

TEST_P(PartitionedMatrixViewParallelTest, RightMultiplyAndAccumulateEParallel) {
  const int kNumThreads = GetParam();
  for (int p = 0; p < kNumProblems; ++p) {
    auto& pmv = pmv_[p];
    auto& A = A_[p];

    Vector x1(pmv->num_cols_e());
    Vector x2(pmv->num_cols());
    x2.setZero();

    for (int i = 0; i < pmv->num_cols_e(); ++i) {
      x1(i) = x2(i) = RandDouble();
    }

    Vector y1 = Vector::Zero(pmv->num_rows());
    pmv->RightMultiplyAndAccumulateE(
        x1.data(), y1.data(), &context_, kNumThreads);

    Vector y2 = Vector::Zero(pmv->num_rows());
    A->RightMultiplyAndAccumulate(x2.data(), y2.data());

    for (int i = 0; i < pmv->num_rows(); ++i) {
      EXPECT_NEAR(y1(i), y2(i), kEpsilon);
    }
  }
}

TEST_P(PartitionedMatrixViewParallelTest, RightMultiplyAndAccumulateFParallel) {
  const int kNumThreads = GetParam();
  for (int p = 0; p < kNumProblems; ++p) {
    auto& pmv = pmv_[p];
    auto& A = A_[p];
    Vector x1(pmv->num_cols_f());
    Vector x2(pmv->num_cols());
    x2.setZero();

    for (int i = 0; i < pmv->num_cols_f(); ++i) {
      x1(i) = x2(i + pmv->num_cols_e()) = RandDouble();
    }

    Vector y1 = Vector::Zero(pmv->num_rows());
    pmv->RightMultiplyAndAccumulateF(
        x1.data(), y1.data(), &context_, kNumThreads);

    Vector y2 = Vector::Zero(pmv->num_rows());
    A->RightMultiplyAndAccumulate(x2.data(), y2.data());

    for (int i = 0; i < pmv->num_rows(); ++i) {
      EXPECT_NEAR(y1(i), y2(i), kEpsilon);
    }
  }
}

TEST_P(PartitionedMatrixViewParallelTest, LeftMultiplyAndAccumulateParallel) {
  const int kNumThreads = GetParam();
  for (int p = 0; p < kNumProblems; ++p) {
    auto& pmv = pmv_[p];
    auto& A = A_[p];
    Vector x = Vector::Zero(pmv->num_rows());
    for (int i = 0; i < pmv->num_rows(); ++i) {
      x(i) = RandDouble();
    }
    Vector x_pre = x;

    Vector y = Vector::Zero(pmv->num_cols());
    Vector y1 = Vector::Zero(pmv->num_cols_e());
    Vector y2 = Vector::Zero(pmv->num_cols_f());

    A->LeftMultiplyAndAccumulate(x.data(), y.data());
    pmv->LeftMultiplyAndAccumulateE(
        x.data(), y1.data(), &context_, kNumThreads);
    pmv->LeftMultiplyAndAccumulateF(
        x.data(), y2.data(), &context_, kNumThreads);

    for (int i = 0; i < pmv->num_cols(); ++i) {
      EXPECT_NEAR(y(i),
                  (i < pmv->num_cols_e()) ? y1(i) : y2(i - pmv->num_cols_e()),
                  kEpsilon);
    }
  }
}

INSTANTIATE_TEST_SUITE_P(ParallelProducts,
                         PartitionedMatrixViewParallelTest,
                         ::testing::Values(1, 2, 4, 8),
                         ::testing::PrintToStringParamName());

}  // namespace internal
}  // namespace ceres