/* Copyright 2019 The TensorFlow 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 TENSORFLOW_LITE_EXPERIMENTAL_MICRO_MEMORY_PLANNER_LINEAR_MEMORY_PLANNER_H_
#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_MEMORY_PLANNER_LINEAR_MEMORY_PLANNER_H_

#include "tensorflow/lite/experimental/micro/memory_planner/memory_planner.h"

namespace tflite {

// The simplest possible memory planner that just lays out all buffers at
// increasing offsets without trying to reuse memory.
class LinearMemoryPlanner : public MemoryPlanner {
 public:
  LinearMemoryPlanner();
  ~LinearMemoryPlanner() override;

  TfLiteStatus AddBuffer(tflite::ErrorReporter* error_reporter, int size,
                         int first_time_used, int last_time_used) override;

  int GetMaximumMemorySize() override;
  int GetBufferCount() override;
  TfLiteStatus GetOffsetForBuffer(tflite::ErrorReporter* error_reporter,
                                  int buffer_index, int* offset) override;

 private:
  static constexpr int kMaxBufferCount = 1024;
  int buffer_offsets_[kMaxBufferCount];
  int current_buffer_count_;
  int next_free_offset_;
};

}  // namespace tflite

#endif  // TENSORFLOW_LITE_EXPERIMENTAL_MICRO_MEMORY_PLANNER_LINEAR_MEMORY_PLANNER_H_
