// Ceres Solver - A fast non-linear least squares minimizer
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// http://ceres-solver.org/
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// Authors: sameeragarwal@google.com (Sameer Agarwal)

#include <memory>
#include <random>

#include "Eigen/Dense"
#include "benchmark/benchmark.h"
#include "ceres/block_jacobi_preconditioner.h"
#include "ceres/block_sparse_matrix.h"
#include "ceres/fake_bundle_adjustment_jacobian.h"
#include "ceres/internal/config.h"
#include "ceres/internal/eigen.h"

namespace ceres::internal {

constexpr int kNumCameras = 1000;
constexpr int kNumPoints = 10000;
constexpr int kCameraSize = 6;
constexpr int kPointSize = 3;
constexpr double kVisibility = 0.1;

constexpr int kNumRowBlocks = 100000;
constexpr int kNumColBlocks = 10000;
constexpr int kMinRowBlockSize = 1;
constexpr int kMaxRowBlockSize = 5;
constexpr int kMinColBlockSize = 1;
constexpr int kMaxColBlockSize = 15;
constexpr double kBlockDensity = 5.0 / kNumColBlocks;

static void BM_BlockSparseJacobiPreconditionerBA(benchmark::State& state) {
  std::mt19937 prng;
  auto jacobian = CreateFakeBundleAdjustmentJacobian(
      kNumCameras, kNumPoints, kCameraSize, kPointSize, kVisibility, prng);
  BlockSparseJacobiPreconditioner p(*jacobian);
  Vector d = Vector::Ones(jacobian->num_cols());
  for (auto _ : state) {
    p.Update(*jacobian, d.data());
  }
}

BENCHMARK(BM_BlockSparseJacobiPreconditionerBA);

static void BM_BlockCRSJacobiPreconditionerBA(benchmark::State& state) {
  std::mt19937 prng;
  auto jacobian = CreateFakeBundleAdjustmentJacobian(
      kNumCameras, kNumPoints, kCameraSize, kPointSize, kVisibility, prng);

  CompressedRowSparseMatrix jacobian_crs(
      jacobian->num_rows(), jacobian->num_cols(), jacobian->num_nonzeros());
  jacobian->ToCompressedRowSparseMatrix(&jacobian_crs);
  BlockCRSJacobiPreconditioner p(jacobian_crs);
  Vector d = Vector::Ones(jacobian_crs.num_cols());
  for (auto _ : state) {
    p.Update(jacobian_crs, d.data());
  }
}

BENCHMARK(BM_BlockCRSJacobiPreconditionerBA);

static void BM_BlockSparseJacobiPreconditionerUnstructured(
    benchmark::State& state) {
  BlockSparseMatrix::RandomMatrixOptions options;
  options.num_row_blocks = kNumRowBlocks;
  options.num_col_blocks = kNumColBlocks;
  options.min_row_block_size = kMinRowBlockSize;
  options.min_col_block_size = kMinColBlockSize;
  options.max_row_block_size = kMaxRowBlockSize;
  options.max_col_block_size = kMaxColBlockSize;
  options.block_density = kBlockDensity;
  std::mt19937 prng;

  auto jacobian = BlockSparseMatrix::CreateRandomMatrix(options, prng);
  BlockSparseJacobiPreconditioner p(*jacobian);
  Vector d = Vector::Ones(jacobian->num_cols());
  for (auto _ : state) {
    p.Update(*jacobian, d.data());
  }
}

BENCHMARK(BM_BlockSparseJacobiPreconditionerUnstructured);

static void BM_BlockCRSJacobiPreconditionerUnstructured(
    benchmark::State& state) {
  BlockSparseMatrix::RandomMatrixOptions options;
  options.num_row_blocks = kNumRowBlocks;
  options.num_col_blocks = kNumColBlocks;
  options.min_row_block_size = kMinRowBlockSize;
  options.min_col_block_size = kMinColBlockSize;
  options.max_row_block_size = kMaxRowBlockSize;
  options.max_col_block_size = kMaxColBlockSize;
  options.block_density = kBlockDensity;
  std::mt19937 prng;

  auto jacobian = BlockSparseMatrix::CreateRandomMatrix(options, prng);
  CompressedRowSparseMatrix jacobian_crs(
      jacobian->num_rows(), jacobian->num_cols(), jacobian->num_nonzeros());
  jacobian->ToCompressedRowSparseMatrix(&jacobian_crs);
  BlockCRSJacobiPreconditioner p(jacobian_crs);
  Vector d = Vector::Ones(jacobian_crs.num_cols());
  for (auto _ : state) {
    p.Update(jacobian_crs, d.data());
  }
}
BENCHMARK(BM_BlockCRSJacobiPreconditionerUnstructured);

}  // namespace ceres::internal

BENCHMARK_MAIN();