//   Copyright (c) 2020 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.
//
#ifndef CACHED_GAUSSIAN_SAMPLING_H
#define CACHED_GAUSSIAN_SAMPLING_H

#include <random>
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#include "sampling_method.h"
#include "utils.h"
#include <glog/logging.h>

namespace DeepES{

class CachedGaussianSampling: public SamplingMethod {

public:
  CachedGaussianSampling();

  ~CachedGaussianSampling();

  /*Initialize the sampling algorithm given the config with the protobuf format.
   *DeepES library uses only one configuration file for all sampling algorithms.
    A defalut configuration file can be found at: . // TODO: where?
    Usally you won't have to modify the configuration items of other algorithms
    if you are not using them.
   */
  bool load_config(const DeepESConfig& config);

  /*@brief generate Gaussian noise and the related key.
   *
   *@Args:
   *     key: a unique key associated with the sampled noise.
   *     noise: a pointer pointed to the memory that stores the noise
   *     size: the number of float to be sampled.
   *
   *@return:
   *     success: generate Gaussian successfully or not.
   */
  bool sampling(int* key, float* noise, int64_t size);

  /*@brief reconstruct the Gaussion noise given the key.
   * This function is often used for updating the neuron network parameters in the offline environment.
   *
   *@Args:
   *     key: a unique key associated with the sampled noise.
   *     noise: a pointer pointed to the memory that stores the noise
   *     size: the number of float to be sampled.
   *
   *@return:
   *     success: reconstruct Gaussian successfully or not.
   */
  bool resampling(int key, float* noise, int64_t size);

private:
  float _std;
  int _cache_size;
  float* _noise_cache = nullptr;

  bool _create_noise_cache();
};

}

#endif