// ATTENTION: The code in this file is highly EXPERIMENTAL.
// Adventurous users should note that the APIs will probably change.

#pragma once

// Before:
//   X is a tensor with shape=[1, 1, 2, 3, 1, 5, 1]
//   Y = Squeeze(X, axes=[1, 4]) -> shape=[1, 2, 3, 5, 1]
//   Z = Squeeze(Y, axes=[0, 4]) -> shape=[2, 3, 5]
// After:
//   Z = Squeeze(X, axes=[0, 1, 4, 6])

#include "onnx/optimizer/passes/optimize_pass.h"

namespace ONNX_NAMESPACE {
namespace optimization {

struct FuseConsecutiveSqueezes final : public OptimizePass {
  explicit FuseConsecutiveSqueezes()
      : OptimizePass("fuse_consecutive_squeezes", API_TYPE::IR) {}

  // returns a vector `ret` such that squeeze by `ret` is equivalent
  // to squeeze by `axes_1` and then by `axes_2`
  std::vector<int64_t> compose_squeezes(
      const std::vector<int64_t>& axes_1,
      const std::vector<int64_t>& axes_2) {
    std::vector<int64_t> ret;
    ret.reserve(axes_1.size() + axes_2.size());

    std::vector<int64_t> sorted_axes_1(axes_1.begin(), axes_1.end());
    std::sort(sorted_axes_1.begin(), sorted_axes_1.end());
    std::copy(
        sorted_axes_1.begin(), sorted_axes_1.end(), std::back_inserter(ret));

    for (int64_t i : axes_2) {
      for (auto iter = sorted_axes_1.begin(); iter != sorted_axes_1.end();
           ++iter) {
        // if current axis 1 - prev_num is bigger than axis 2
        // put axis 2 + prev_num as new axis
        int64_t prev_num = std::distance(sorted_axes_1.begin(), iter);
        if (*iter - prev_num > i) {
          ret.push_back(i + prev_num);
          break;
        }
        // if no current axis 1 - prev_num is bigger than axis 2
        // put axis 2 + prev_num + 1 as new axis
        if (std::next(iter) == sorted_axes_1.end()) {
          ret.push_back(i + prev_num + 1);
        }
      }
    }
    std::sort(ret.begin(), ret.end());
    return ret;
  }

  void fuse_consecutive_squeezes(Graph& graph) {
    for (auto it = graph.begin(); it != graph.end(); ++it) {
      auto* n = *it;
      DescendOnGraphAttributes(
          n, [this](Graph& g) { fuse_consecutive_squeezes(g); });
      if (n->kind() == kSqueeze && n->input()->node()->kind() == kSqueeze) {
        auto orig_input = n->input();
        n->is_(
            kaxes,
            compose_squeezes(orig_input->node()->is(kaxes), n->is(kaxes)));
        n->replaceInput(0, orig_input->node()->input());
        if (orig_input->uses().size() == 0) {
          orig_input->node()->destroy();
        }
      }
    }
  }

  void optimize(Graph& graph) override {
    fuse_consecutive_squeezes(graph);
  }
};

} // namespace optimization
} // namespace ONNX_NAMESPACE
