/* 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.
==============================================================================*/

#include "tensorflow/lite/delegates/gpu/common/memory_management/internal.h"

namespace tflite {
namespace gpu {

bool IsCoveringObject(const uint2& first_object, const uint2& second_object) {
  return first_object.x >= second_object.x && first_object.y >= second_object.y;
}

bool IsCoveringObject(const uint3& first_object, const uint3& second_object) {
  return first_object.x >= second_object.x &&
         first_object.y >= second_object.y && first_object.z >= second_object.z;
}

size_t AbsDiffInElements(const uint2& first_size, const uint2& second_size) {
  const size_t first_elements_cnt = first_size.y * first_size.x;
  const size_t second_elements_cnt = second_size.y * second_size.x;
  return first_elements_cnt >= second_elements_cnt
             ? first_elements_cnt - second_elements_cnt
             : second_elements_cnt - first_elements_cnt;
}

size_t AbsDiffInElements(const uint3& first_size, const uint3& second_size) {
  const size_t first_elements_cnt = first_size.z * first_size.y * first_size.x;
  const size_t second_elements_cnt =
      second_size.z * second_size.y * second_size.x;
  return first_elements_cnt >= second_elements_cnt
             ? first_elements_cnt - second_elements_cnt
             : second_elements_cnt - first_elements_cnt;
}

std::vector<TaskProfile> CalculateTaskProfiles(
    const std::vector<TensorUsageRecord<size_t>>& usage_records) {
  TaskId num_tasks = 0;
  for (size_t i = 0; i < usage_records.size(); ++i) {
    num_tasks = std::max(num_tasks, usage_records[i].last_task + 1);
  }
  std::vector<TaskProfile> task_profiles(num_tasks);
  for (size_t rec_id = 0; rec_id < usage_records.size(); ++rec_id) {
    // Each tensor usage record must be added to profile of every task between
    // its first_task and last_task.
    for (TaskId task_id = usage_records[rec_id].first_task;
         task_id <= usage_records[rec_id].last_task; ++task_id) {
      task_profiles[task_id].emplace_back(&usage_records[rec_id], rec_id);
    }
  }
  // Records in each TaskProfile must be sorted in non-increasing order of
  // corresponding tensors sizes.
  for (auto& task_profile : task_profiles) {
    std::sort(task_profile.begin(), task_profile.end(), CompareBySize);
  }
  return task_profiles;
}

std::vector<size_t> CalculatePositionalMaximums(
    const std::vector<TensorUsageRecord<size_t>>& usage_records) {
  std::vector<TaskProfile> task_profiles = CalculateTaskProfiles(usage_records);
  std::vector<size_t> positional_max;
  for (const auto& task_profile : task_profiles) {
    // Update positional_max with values of current TaskProfile.
    size_t i = 0;
    for (; i < task_profile.size() && i < positional_max.size(); ++i) {
      positional_max[i] = std::max(positional_max[i],
                                   task_profile[i].usage_record->tensor_size);
    }
    // If current task_profile has more records, than there are in
    // positional_max, we should append new elements into positional_max.
    for (; i < task_profile.size(); ++i) {
      positional_max.push_back(task_profile[i].usage_record->tensor_size);
    }
  }
  return positional_max;
}

}  // namespace gpu
}  // namespace tflite
