/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */

#include "optimizer.h"
#include <glog/logging.h>
#include <cstdlib>
#include <cstring>
#include <string>

#include "parameter_optimizer.h"

using paddle::optimizer::ParameterOptimizer;
using paddle::optimizer::Tensor;

template <paddle_element_type VALUE>
struct EnumToType {};

template <class T>
struct TypeToEnum {};

#define MATCH_ENUM_TYPE(TYPE, ENUM)                 \
  template <>                                       \
  struct TypeToEnum<TYPE> {                         \
    static paddle_element_type v() { return ENUM; } \
    static constexpr TYPE value = ENUM;             \
  };                                                \
  template <>                                       \
  struct EnumToType<ENUM> {                         \
    typedef TYPE Type;                              \
  }

MATCH_ENUM_TYPE(int32_t, PADDLE_ELEMENT_TYPE_INT32);
MATCH_ENUM_TYPE(uint32_t, PADDLE_ELEMENT_TYPE_UINT32);
MATCH_ENUM_TYPE(int64_t, PADDLE_ELEMENT_TYPE_INT64);
MATCH_ENUM_TYPE(uint64_t, PADDLE_ELEMENT_TYPE_UINT64);
MATCH_ENUM_TYPE(float, PADDLE_ELEMENT_TYPE_FLOAT32);
MATCH_ENUM_TYPE(double, PADDLE_ELEMENT_TYPE_FLOAT64);

struct paddle_optimizer {
  paddle::optimizer::ParameterOptimizer* impl;
};

paddle_optimizer* paddle_create_optimizer(const unsigned char* config_proto,
                                          const int config_proto_len,
                                          const paddle_element_type data_type,
                                          void* param_buffer,
                                          int num_bytes,
                                          const char* state,
                                          const int state_len) {
  paddle_optimizer* optimizer = new paddle_optimizer;
  std::string config(config_proto, config_proto + config_proto_len);
  Tensor* parameter = new Tensor(reinterpret_cast<float*>(param_buffer),
                                 num_bytes / sizeof(float));
  optimizer->impl = ParameterOptimizer::Create(config, parameter);
  if (state != nullptr) {
    std::string s(state, state + state_len);
    optimizer->impl->DeserializeState(s);
  }
  return optimizer;
}

int paddle_release_optimizer(paddle_optimizer* o) {
  if (o != nullptr) delete o->impl;
  return PADDLE_SUCCESS;
}

int paddle_update_parameter(paddle_optimizer* o,
                            const paddle_element_type data_type,
                            const void* grad_buffer,
                            int num_bytes) {
  // TOOD(zhihong): datatype not work. need to add the runtime datatype
  auto grad_type = reinterpret_cast<const float*>(grad_buffer);
  Tensor* gradient =
      new Tensor(const_cast<float*>(grad_type), num_bytes / sizeof(float));
  o->impl->Update(gradient);
  return PADDLE_SUCCESS;
}

int paddle_optimizer_get_weights(paddle_optimizer* o, void** param_buffer) {
  int param_size = 0;
  *param_buffer = (void*)o->impl->get_weight(&param_size);
  return param_size;
}

int paddle_optimizer_get_state(paddle_optimizer* o, const char** state) {
  std::string s = o->impl->SerializeState();
  int state_len = s.size();

  if (state_len > 0) {
    *state = (char*)std::malloc(state_len);
    std::memcpy((void*)*state, (const void*)s.c_str(), state_len);
  }

  return state_len;
}