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

#include "ceres/block_random_access_sparse_matrix.h"

#include <algorithm>
#include <memory>
#include <set>
#include <utility>
#include <vector>

#include "ceres/internal/export.h"
#include "ceres/triplet_sparse_matrix.h"
#include "ceres/types.h"
#include "glog/logging.h"

namespace ceres {
namespace internal {

using std::make_pair;
using std::pair;
using std::set;
using std::vector;

BlockRandomAccessSparseMatrix::BlockRandomAccessSparseMatrix(
    const vector<int>& blocks, const set<pair<int, int>>& block_pairs)
    : kMaxRowBlocks(10 * 1000 * 1000), blocks_(blocks) {
  CHECK_LT(blocks.size(), kMaxRowBlocks);

  // Build the row/column layout vector and count the number of scalar
  // rows/columns.
  int num_cols = 0;
  block_positions_.reserve(blocks_.size());
  for (int block_size : blocks_) {
    block_positions_.push_back(num_cols);
    num_cols += block_size;
  }

  // Count the number of scalar non-zero entries and build the layout
  // object for looking into the values array of the
  // TripletSparseMatrix.
  int num_nonzeros = 0;
  for (const auto& block_pair : block_pairs) {
    const int row_block_size = blocks_[block_pair.first];
    const int col_block_size = blocks_[block_pair.second];
    num_nonzeros += row_block_size * col_block_size;
  }

  VLOG(1) << "Matrix Size [" << num_cols << "," << num_cols << "] "
          << num_nonzeros;

  tsm_ =
      std::make_unique<TripletSparseMatrix>(num_cols, num_cols, num_nonzeros);
  tsm_->set_num_nonzeros(num_nonzeros);
  int* rows = tsm_->mutable_rows();
  int* cols = tsm_->mutable_cols();
  double* values = tsm_->mutable_values();

  int pos = 0;
  for (const auto& block_pair : block_pairs) {
    const int row_block_size = blocks_[block_pair.first];
    const int col_block_size = blocks_[block_pair.second];
    cell_values_.emplace_back(block_pair, values + pos);
    layout_[IntPairToLong(block_pair.first, block_pair.second)] =
        new CellInfo(values + pos);
    pos += row_block_size * col_block_size;
  }

  // Fill the sparsity pattern of the underlying matrix.
  for (const auto& block_pair : block_pairs) {
    const int row_block_id = block_pair.first;
    const int col_block_id = block_pair.second;
    const int row_block_size = blocks_[row_block_id];
    const int col_block_size = blocks_[col_block_id];
    int pos =
        layout_[IntPairToLong(row_block_id, col_block_id)]->values - values;
    for (int r = 0; r < row_block_size; ++r) {
      for (int c = 0; c < col_block_size; ++c, ++pos) {
        rows[pos] = block_positions_[row_block_id] + r;
        cols[pos] = block_positions_[col_block_id] + c;
        values[pos] = 1.0;
        DCHECK_LT(rows[pos], tsm_->num_rows());
        DCHECK_LT(cols[pos], tsm_->num_rows());
      }
    }
  }
}

// Assume that the user does not hold any locks on any cell blocks
// when they are calling SetZero.
BlockRandomAccessSparseMatrix::~BlockRandomAccessSparseMatrix() {
  for (const auto& entry : layout_) {
    delete entry.second;
  }
}

CellInfo* BlockRandomAccessSparseMatrix::GetCell(int row_block_id,
                                                 int col_block_id,
                                                 int* row,
                                                 int* col,
                                                 int* row_stride,
                                                 int* col_stride) {
  const LayoutType::iterator it =
      layout_.find(IntPairToLong(row_block_id, col_block_id));
  if (it == layout_.end()) {
    return nullptr;
  }

  // Each cell is stored contiguously as its own little dense matrix.
  *row = 0;
  *col = 0;
  *row_stride = blocks_[row_block_id];
  *col_stride = blocks_[col_block_id];
  return it->second;
}

// Assume that the user does not hold any locks on any cell blocks
// when they are calling SetZero.
void BlockRandomAccessSparseMatrix::SetZero() {
  if (tsm_->num_nonzeros()) {
    VectorRef(tsm_->mutable_values(), tsm_->num_nonzeros()).setZero();
  }
}

void BlockRandomAccessSparseMatrix::SymmetricRightMultiply(const double* x,
                                                           double* y) const {
  for (const auto& cell_position_and_data : cell_values_) {
    const int row = cell_position_and_data.first.first;
    const int row_block_size = blocks_[row];
    const int row_block_pos = block_positions_[row];

    const int col = cell_position_and_data.first.second;
    const int col_block_size = blocks_[col];
    const int col_block_pos = block_positions_[col];

    MatrixVectorMultiply<Eigen::Dynamic, Eigen::Dynamic, 1>(
        cell_position_and_data.second,
        row_block_size,
        col_block_size,
        x + col_block_pos,
        y + row_block_pos);

    // Since the matrix is symmetric, but only the upper triangular
    // part is stored, if the block being accessed is not a diagonal
    // block, then use the same block to do the corresponding lower
    // triangular multiply also.
    if (row != col) {
      MatrixTransposeVectorMultiply<Eigen::Dynamic, Eigen::Dynamic, 1>(
          cell_position_and_data.second,
          row_block_size,
          col_block_size,
          x + row_block_pos,
          y + col_block_pos);
    }
  }
}

}  // namespace internal
}  // namespace ceres