// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2015 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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// Author: sameeragarwal@google.com (Sameer Agarwal)

#include "ceres/compressed_col_sparse_matrix_utils.h"

#include <algorithm>
#include <vector>

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

namespace ceres::internal {

void CompressedColumnScalarMatrixToBlockMatrix(
    const int* scalar_rows,
    const int* scalar_cols,
    const std::vector<Block>& row_blocks,
    const std::vector<Block>& col_blocks,
    std::vector<int>* block_rows,
    std::vector<int>* block_cols) {
  CHECK(block_rows != nullptr);
  CHECK(block_cols != nullptr);
  block_rows->clear();
  block_cols->clear();
  const int num_col_blocks = col_blocks.size();

  // This loop extracts the block sparsity of the scalar sparse matrix
  // It does so by iterating over the columns, but only considering
  // the columns corresponding to the first element of each column
  // block. Within each column, the inner loop iterates over the rows,
  // and detects the presence of a row block by checking for the
  // presence of a non-zero entry corresponding to its first element.
  block_cols->push_back(0);
  int c = 0;
  for (int col_block = 0; col_block < num_col_blocks; ++col_block) {
    int column_size = 0;
    for (int idx = scalar_cols[c]; idx < scalar_cols[c + 1]; ++idx) {
      auto it = std::lower_bound(row_blocks.begin(),
                                 row_blocks.end(),
                                 scalar_rows[idx],
                                 [](const Block& block, double value) {
                                   return block.position < value;
                                 });
      // Since we are using lower_bound, it will return the row id where the row
      // block starts. For everything but the first row of the block, where
      // these values will be the same, we can skip, as we only need the first
      // row to detect the presence of the block.
      //
      // For rows all but the first row in the last row block, lower_bound will
      // return row_blocks_.end(), but those can be skipped like the rows in
      // other row blocks too.
      if (it == row_blocks.end() || it->position != scalar_rows[idx]) {
        continue;
      }

      block_rows->push_back(it - row_blocks.begin());
      ++column_size;
    }
    block_cols->push_back(block_cols->back() + column_size);
    c += col_blocks[col_block].size;
  }
}

void BlockOrderingToScalarOrdering(const std::vector<Block>& blocks,
                                   const std::vector<int>& block_ordering,
                                   std::vector<int>* scalar_ordering) {
  CHECK_EQ(blocks.size(), block_ordering.size());
  const int num_blocks = blocks.size();
  scalar_ordering->resize(NumScalarEntries(blocks));
  int cursor = 0;
  for (int i = 0; i < num_blocks; ++i) {
    const int block_id = block_ordering[i];
    const int block_size = blocks[block_id].size;
    int block_position = blocks[block_id].position;
    for (int j = 0; j < block_size; ++j) {
      (*scalar_ordering)[cursor++] = block_position++;
    }
  }
}
}  // namespace ceres::internal