/* Copyright (c) 2019 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 "lite/backends/arm/math/reduce_max.h"
#include "lite/backends/arm/math/funcs.h"
#include "lite/core/tensor.h"

namespace paddle {
namespace lite {
namespace arm {
namespace math {

template <>
void reduce_n<float>(const float* src,
                     float* dst,
                     int num_in,
                     int channel_in,
                     int height_in,
                     int width_in) {
  int hw_size = height_in * width_in;
  int chw_size = channel_in * hw_size;
  int data_index, src_index, src_index0;
  for (int c = 0; c < channel_in; ++c) {
    for (int h = 0; h < height_in; ++h) {
      for (int w = 0; w < width_in; ++w) {
        data_index = c * hw_size + h * width_in + w;
        dst[data_index] = src[data_index];
        for (int n = 1; n < num_in; ++n) {
          src_index = n * chw_size + data_index;
          dst[data_index] = dst[data_index] > src[src_index] ? dst[data_index]
                                                             : src[src_index];
        }
      }
    }
  }
}

template <>
void reduce_first_of_three<float>(
    const float* src, float* dst, int first_in, int second_in, int third_in) {
  for (int i = 0; i < second_in; i++) {
    for (int j = 0; j < third_in; j++) {
      dst[i * third_in + j] = src[i * third_in + j];
      for (int k = 1; k < first_in; k++) {
        dst[i * third_in + j] =
            src[k * second_in * third_in + i * third_in + j] >
                    dst[i * third_in + j]
                ? src[k * second_in * third_in + i * third_in + j]
                : dst[i * third_in + j];
      }
    }
  }
}

template <>
void reduce_second_of_three<float>(
    const float* src, float* dst, int first_in, int second_in, int third_in) {
  for (int i = 0; i < first_in; i++) {
    for (int j = 0; j < third_in; j++) {
      dst[i * third_in + j] = src[i * second_in * third_in + j];
      for (int k = 1; k < second_in; k++) {
        dst[i * third_in + j] =
            src[i * second_in * third_in + third_in * k + j] >
                    dst[i * third_in + j]
                ? src[i * second_in * third_in + third_in * k + j]
                : dst[i * third_in + j];
      }
    }
  }
}

template <>
void reduce_third_of_three<float>(
    const float* src, float* dst, int first_in, int second_in, int third_in) {
  for (int i = 0; i < first_in; i++) {
    for (int j = 0; j < second_in; j++) {
      dst[i * second_in + j] = src[i * second_in * third_in + j * second_in];
      for (int k = 0; k < third_in; k++) {
        dst[i * second_in + j] =
            src[i * second_in * third_in + j * second_in + k] >
                    dst[i * second_in + j]
                ? src[i * second_in * third_in + j * second_in + k]
                : dst[i * second_in + j];
      }
    }
  }
}

template <>
void reduce_all_of_three<float>(
    const float* src, float* dst, int first_in, int second_in, int third_in) {
  float max = src[0];
  int total_element = first_in * second_in * third_in;
  for (int i = 0; i < total_element; i++) {
    max = src[i] > max ? src[i] : max;
  }
  dst[0] = max;
}

template <>
void reduce_c<float>(const float* src,
                     float* dst,
                     int num_in,
                     int channel_in,
                     int height_in,
                     int width_in) {
  int hw_size = height_in * width_in;
  int chw_size = hw_size * channel_in;
  int data_index, src_index0, src_index;
  for (int n = 0; n < num_in; ++n) {
    for (int h = 0; h < height_in; ++h) {
      for (int w = 0; w < width_in; ++w) {
        data_index = n * hw_size + h * width_in + w;
        src_index0 = n * chw_size + h * width_in + w;
        dst[data_index] = src[src_index0];
        for (int c = 1; c < channel_in; ++c) {
          src_index = src_index0 + c * hw_size;
          dst[data_index] = dst[data_index] > src[src_index] ? dst[data_index]
                                                             : src[src_index];
        }
      }
    }
  }
}

template <>
void reduce_h<float>(const float* src,
                     float* dst,
                     int num_in,
                     int channel_in,
                     int height_in,
                     int width_in) {
  int cw_size = channel_in * width_in;
  int chw_size = cw_size * height_in;
  int hw_size = height_in * width_in;
  int data_index, src_index, src_index0;
  for (int n = 0; n < num_in; ++n) {
    for (int c = 0; c < channel_in; ++c) {
      for (int w = 0; w < width_in; ++w) {
        data_index = n * cw_size + c * width_in + w;
        src_index0 = n * chw_size + c * hw_size + w;
        dst[data_index] = src[src_index0];
        for (int h = 1; h < height_in; ++h) {
          src_index = src_index0 + h * width_in;
          dst[data_index] = dst[data_index] > src[src_index] ? dst[data_index]
                                                             : src[src_index];
        }
      }
    }
  }
}

template <>
void reduce_w<float>(const float* src,
                     float* dst,
                     int num_in,
                     int channel_in,
                     int height_in,
                     int width_in) {
  int ch_size = channel_in * height_in;
  int hw_size = height_in * width_in;
  int chw_size = ch_size * width_in;
  int data_index = 0;
  int src_index0 = 0;
  int src_index = 0;
  for (int n = 0; n < num_in; ++n) {
    for (int c = 0; c < channel_in; ++c) {
      for (int h = 0; h < height_in; ++h) {
        data_index = n * ch_size + c * height_in + h;
        src_index0 = n * chw_size + c * hw_size + h * width_in;
        dst[data_index] = src[src_index0];
        for (int w = 1; w < width_in; ++w) {
          src_index = src_index0 + w;
          dst[data_index] = dst[data_index] > src[src_index] ? dst[data_index]
                                                             : src[src_index];
        }
      }
    }
  }
}

template <>
void reduce_all<float>(const float* src,
                       float* dst,
                       int num_in,
                       int channel_in,
                       int height_in,
                       int width_in) {
  float max = src[0];
  int src_index;
  int n_id, c_id;
  for (int n = 0; n < num_in; ++n) {
    n_id = n * channel_in * height_in * width_in;
    for (int c = 0; c < channel_in; ++c) {
      c_id = c * height_in * width_in;
      for (int h = 0; h < height_in; ++h) {
        for (int w = 0; w < width_in; ++w) {
          src_index = n_id + c_id + h * width_in + w;
          max = src[src_index] > max ? src[src_index] : max;
        }
      }
    }
  }
  dst[0] = max;
}

template <>
void reduce_nc<float>(const float* src,
                      float* dst,
                      int num_in,
                      int channel_in,
                      int height_in,
                      int width_in) {
  // reduce n first.
  DDimLite ddimA({1, channel_in, height_in, width_in});
  lite::Tensor tensor_tmp;
  tensor_tmp.Resize(ddimA);
  float* tmp_out = tensor_tmp.mutable_data<float>();
  reduce_n(src, tmp_out, num_in, channel_in, height_in, width_in);
  reduce_c(tmp_out, dst, 1, channel_in, height_in, width_in);
}

template <>
void reduce_ch<float>(const float* src,
                      float* dst,
                      int num_in,
                      int channel_in,
                      int height_in,
                      int width_in) {
  // reduce c first
  DDimLite ddimA({num_in, 1, height_in, width_in});
  lite::Tensor tensor_tmp;
  tensor_tmp.Resize(ddimA);
  float* tmp_out = tensor_tmp.mutable_data<float>();
  reduce_c(src, tmp_out, num_in, channel_in, height_in, width_in);
  reduce_h(tmp_out, dst, num_in, 1, height_in, width_in);
}

template <>
void reduce_hw<float>(const float* src,
                      float* dst,
                      int num_in,
                      int channel_in,
                      int height_in,
                      int width_in) {
  // reduce h first
  DDimLite ddimA({num_in, channel_in, 1, width_in});
  lite::Tensor tensor_tmp;
  tensor_tmp.Resize(ddimA);
  float* tmp_out = tensor_tmp.mutable_data<float>();
  reduce_h(src, tmp_out, num_in, channel_in, height_in, width_in);
  reduce_w(tmp_out, dst, num_in, channel_in, 1, width_in);
}

}  // namespace math
}  // namespace arm
}  // namespace lite
}  // namespace paddle
