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#pragma once

#include <c10/util/irange.h>
#include <torch/optim/optimizer.h>
#include <torch/serialize/archive.h>
#include <torch/types.h>
#include <cstddef>
#include <cstdint>
#include <deque>
#include <string>
#include <vector>

namespace torch {
namespace optim {
namespace detail {
// Utility function to save state
template <typename DerivedOptimizerParamState>
void serialize(
    serialize::OutputArchive& archive,
    const ska::flat_hash_map<void*, std::unique_ptr<OptimizerParamState>>&
        state) {
  for (const auto& item : state) {
    serialize::OutputArchive param_state_archive(archive.compilation_unit());
    std::string tensorimpl_key =
        std::to_string(reinterpret_cast<size_t>(item.first));
    const DerivedOptimizerParamState& curr_state =
        static_cast<const DerivedOptimizerParamState&>(*(item.second.get()));
    curr_state.serialize(param_state_archive);
    archive.write(tensorimpl_key, param_state_archive);
  }
}

// Utility function to load state
template <typename DerivedOptimizerParamState>
void serialize(
    serialize::InputArchive& archive,
    ska::flat_hash_map<void*, std::unique_ptr<OptimizerParamState>>& state) {
  std::vector<std::string> tensorimpl_keys = archive.keys();
  for (const std::string& tensorimpl_key : tensorimpl_keys) {
    serialize::InputArchive param_state_archive;
    archive.read(tensorimpl_key, param_state_archive);
    DerivedOptimizerParamState param_state;
    param_state.serialize(param_state_archive);
    state[reinterpret_cast<void*>(std::stoull(tensorimpl_key))] =
        std::make_unique<DerivedOptimizerParamState>(param_state);
  }
}

// Utility function to save param_groups
template <typename DerivedOptimizerParamOptions>
void serialize(
    serialize::OutputArchive& archive,
    const std::vector<OptimizerParamGroup>& param_groups) {
  archive.write(
      "param_groups/size",
      torch::tensor(static_cast<int64_t>(param_groups.size())));
  for (const auto i : c10::irange(param_groups.size())) {
    serialize::OutputArchive param_group_archive(archive.compilation_unit());
    std::vector<Tensor> params = param_groups[i].params();
    param_group_archive.write(
        "params/size", torch::tensor(static_cast<int64_t>(params.size())));
    for (const auto index : c10::irange(params.size())) {
      param_group_archive.write(
          "params/" + std::to_string(index),
          IValue(std::to_string(
              reinterpret_cast<size_t>(params[index].unsafeGetTensorImpl()))));
    }
    const DerivedOptimizerParamOptions& param_group_options =
        static_cast<const DerivedOptimizerParamOptions&>(
            param_groups[i].options());
    serialize::OutputArchive param_group_options_archive(
        param_group_archive.compilation_unit());
    param_group_options.serialize(param_group_options_archive);
    param_group_archive.write("options", param_group_options_archive);
    archive.write("param_groups/" + std::to_string(i), param_group_archive);
  }
}

// Utility function to load param_groups
// We take as input vector of pair of string and unique_ptr to optimizer options
// so that we can retain the state for each param by using the old tensor impl
// keys (saved during serialization) and map the new tensor impl keys to the
// correct state for each param
template <typename DerivedOptimizerParamOptions>
void serialize(
    serialize::InputArchive& archive,
    std::vector<
        std::pair<std::vector<std::string>, std::unique_ptr<OptimizerOptions>>>&
        param_groups) {
  torch::Tensor param_groups_size_tensor;
  archive.read("param_groups/size", param_groups_size_tensor);
  const int64_t param_groups_size = param_groups_size_tensor.item<int64_t>();
  for (const auto i : c10::irange(param_groups_size)) {
    serialize::InputArchive param_group_archive;
    archive.read("param_groups/" + std::to_string(i), param_group_archive);
    torch::Tensor size_tensor;
    param_group_archive.read("params/size", size_tensor);
    const int64_t size = size_tensor.item<int64_t>();
    std::vector<std::string> params;
    for (const auto index : c10::irange(size)) {
      IValue ivalue;
      param_group_archive.read("params/" + std::to_string(index), ivalue);
      std::string element = ivalue.toStringRef();
      params.emplace_back(element);
    }
    serialize::InputArchive param_group_options_archive;
    param_group_archive.read("options", param_group_options_archive);
    DerivedOptimizerParamOptions param_group_options(0);
    param_group_options.serialize(param_group_options_archive);
    param_groups.emplace_back(std::make_pair(
        params,
        std::make_unique<DerivedOptimizerParamOptions>(param_group_options)));
  }
}
} // namespace detail

// Note: These functions are all called `serialize()` so they can be called
// inside a template where the archive type is a template type and can thus be
// passed such that the appropriate overload is selected.

void serialize(
    serialize::OutputArchive& archive,
    const std::string& key,
    const int64_t& value);

void serialize(
    serialize::InputArchive& archive,
    const std::string& key,
    int64_t& value);

void serialize(
    serialize::OutputArchive& archive,
    const std::string& key,
    const std::vector<int64_t>& steps);

void serialize(
    serialize::InputArchive& archive,
    const std::string& key,
    std::vector<int64_t>& steps);

// Utility function to save state and param_groups
template <
    typename DerivedOptimizerParamState,
    typename DerivedOptimizerParamOptions>
void serialize(serialize::OutputArchive& archive, const Optimizer& optimizer) {
  archive.write("pytorch_version", IValue("1.5.0"));
  serialize::OutputArchive state_archive(archive.compilation_unit());
  detail::serialize<DerivedOptimizerParamState>(
      state_archive, optimizer.state());
  archive.write("state", state_archive);

  serialize::OutputArchive param_groups_archive(archive.compilation_unit());
  detail::serialize<DerivedOptimizerParamOptions>(
      param_groups_archive, optimizer.param_groups());
  archive.write("param_groups", param_groups_archive);
}

// Utility function to load state and param_groups and update state
template <
    typename DerivedOptimizerParamState,
    typename DerivedOptimizerParamOptions>
void serialize(serialize::InputArchive& archive, Optimizer& optimizer) {
  IValue pytorch_version;
  archive.read("pytorch_version", pytorch_version);
  TORCH_INTERNAL_ASSERT(pytorch_version.toStringRef() == "1.5.0");
  serialize::InputArchive state_archive;
  archive.read("state", state_archive);
  ska::flat_hash_map<void*, std::unique_ptr<OptimizerParamState>> saved_state;
  detail::serialize<DerivedOptimizerParamState>(state_archive, saved_state);

  serialize::InputArchive param_groups_archive;
  archive.read("param_groups", param_groups_archive);
  std::vector<
      std::pair<std::vector<std::string>, std::unique_ptr<OptimizerOptions>>>
      saved_param_groups;
  detail::serialize<DerivedOptimizerParamOptions>(
      param_groups_archive, saved_param_groups);

  // update state and optimizer options
  TORCH_CHECK(
      saved_param_groups.size() == optimizer.param_groups().size(),
      "loaded state dict has a different number of parameter groups");
  for (const auto i : c10::irange(saved_param_groups.size())) {
    std::vector<std::string> param_group_old_keys = saved_param_groups[i].first;
    std::vector<Tensor> params = optimizer.param_groups()[i].params();
    TORCH_CHECK(
        param_group_old_keys.size() == params.size(),
        "loaded state dict contains a parameter group that has a different size than the optimizer's parameter group");

    for (const auto idx : c10::irange(params.size())) {
      auto param_group_old_key =
          reinterpret_cast<void*>(std::stoull(param_group_old_keys[idx]));
      if (saved_state.find(param_group_old_key) != saved_state.end()) {
        optimizer.state()[params[idx].unsafeGetTensorImpl()] =
            std::move(saved_state[param_group_old_key]);
      }
    }

    auto& saved_options = reinterpret_cast<DerivedOptimizerParamOptions&>(
        *saved_param_groups[i].second);
    auto& current_options = reinterpret_cast<DerivedOptimizerParamOptions&>(
        optimizer.param_groups()[i].options());
    current_options = saved_options;
  }
}

template <typename BufferContainer>
void serialize(
    serialize::OutputArchive& archive,
    const std::string& key,
    const BufferContainer& buffers) {
  archive.write(
      key + "/size", torch::tensor(static_cast<int64_t>(buffers.size())));
  for (const auto index : c10::irange(buffers.size())) {
    archive.write(
        key + "/" + std::to_string(index), buffers[index], /*is_buffer=*/true);
  }
}

template <typename BufferContainer>
void serialize(
    serialize::InputArchive& archive,
    const std::string& key,
    BufferContainer& buffers) {
  buffers.clear();
  torch::Tensor size_tensor;
  archive.read(key + "/size", size_tensor);
  const size_t size = size_tensor.item<int64_t>();
  for (const auto index : c10::irange(size)) {
    buffers.emplace_back();
    archive.read(
        key + "/" + std::to_string(index), buffers.back(), /*is_buffer=*/true);
  }
}

template <typename T>
c10::List<T> deque_to_list(const std::deque<T>& dq) {
  c10::List<T> list;
  list.reserve(dq.size());
  for (const auto& e : dq) {
    list.emplace_back(e);
  }
  return list;
}

template <typename T>
std::deque<T> list_to_deque(const c10::List<T>& list) {
  std::deque<T> dq;
  for (const auto& e : list) {
    dq.emplace_back(e);
  }
  return dq;
}

#define _TORCH_OPTIM_SERIALIZE(name) \
  torch::optim::serialize(archive, #name, self.name)

#define _TORCH_OPTIM_SERIALIZE_WITH_TEMPLATE_ARG(OptimizerName)               \
  torch::optim::serialize<OptimizerName##ParamState, OptimizerName##Options>( \
      archive, self)

#define _TORCH_OPTIM_SERIALIZE_TORCH_ARG(name)           \
  {                                                      \
    auto ivalue = torch::IValue(name());                 \
    /* do not serialize if name is an undefined tensor*/ \
    if (!(ivalue.isTensor() &&                           \
          ivalue.unsafeToTensorImpl() ==                 \
              at::UndefinedTensorImpl::singleton())) {   \
      archive.write(#name, ivalue);                      \
    }                                                    \
  }

#define _TORCH_OPTIM_SERIALIZE_TORCH_ARG_DEQUE(name)           \
  {                                                            \
    c10::IValue ivalue = torch::IValue(deque_to_list(name())); \
    archive.write(#name, ivalue);                              \
  }

#define _TORCH_OPTIM_DESERIALIZE_TORCH_ARG(T, name)                   \
  {                                                                   \
    c10::IValue ivalue;                                               \
    bool exists = archive.try_read(#name, ivalue);                    \
    if (exists) {                                                     \
      name(ivalue.to<T>());                                           \
    } else {                                                          \
      bool is_tensor_type = std::is_base_of<torch::Tensor, T>::value; \
      TORCH_INTERNAL_ASSERT(is_tensor_type);                          \
    }                                                                 \
  }

#define _TORCH_OPTIM_DESERIALIZE_TORCH_ARG_OPTIONAL(T, name) \
  {                                                          \
    c10::IValue ivalue;                                      \
    bool exists = archive.try_read(#name, ivalue);           \
    if (exists) {                                            \
      name(ivalue.toOptional<T>());                          \
    }                                                        \
  }

#define _TORCH_OPTIM_DESERIALIZE_TORCH_ARG_DEQUE(T, name) \
  {                                                       \
    c10::IValue ivalue;                                   \
    archive.read(#name, ivalue);                          \
    auto list = ivalue.to<c10::List<T::value_type>>();    \
    name(list_to_deque(list));                            \
  }

} // namespace optim
} // namespace torch

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