/* Copyright (c) 2018 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. */

#pragma once
#include <string>
#include <vector>

#include "paddle/fluid/framework/op_registry.h"
#include "paddle/phi/kernels/funcs/math_function.h"

namespace paddle {
namespace operators {

enum class BoxCodeType { kEncodeCenterSize = 0, kDecodeCenterSize = 1 };

inline BoxCodeType GetBoxCodeType(const std::string &type) {
  PADDLE_ENFORCE_EQ(
      (type == "encode_center_size") || (type == "decode_center_size"),
      true,
      platform::errors::InvalidArgument(
          "The 'code_type' attribute in BoxCoder"
          " must be 'encode_center_size' or 'decode_center_size'. "
          "But received 'code_type' is %s",
          type));
  if (type == "encode_center_size") {
    return BoxCodeType::kEncodeCenterSize;
  } else {
    return BoxCodeType::kDecodeCenterSize;
  }
}

template <typename DeviceContext, typename T>
class BoxCoderKernel : public framework::OpKernel<T> {
 public:
  void EncodeCenterSize(const framework::Tensor *target_box,
                        const framework::Tensor *prior_box,
                        const framework::Tensor *prior_box_var,
                        const bool normalized,
                        const std::vector<float> variance,
                        T *output) const {
    int64_t row = target_box->dims()[0];
    int64_t col = prior_box->dims()[0];
    int64_t len = prior_box->dims()[1];

#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for collapse(2)
#endif
    for (int64_t i = 0; i < row; ++i) {
      for (int64_t j = 0; j < col; ++j) {
        auto *target_box_data = target_box->data<T>();
        auto *prior_box_data = prior_box->data<T>();
        size_t offset = i * col * len + j * len;
        T prior_box_width = prior_box_data[j * len + 2] -
                            prior_box_data[j * len] + (normalized == false);
        T prior_box_height = prior_box_data[j * len + 3] -
                             prior_box_data[j * len + 1] +
                             (normalized == false);
        T prior_box_center_x = prior_box_data[j * len] + prior_box_width / 2;
        T prior_box_center_y =
            prior_box_data[j * len + 1] + prior_box_height / 2;

        T target_box_center_x =
            (target_box_data[i * len + 2] + target_box_data[i * len]) / 2;
        T target_box_center_y =
            (target_box_data[i * len + 3] + target_box_data[i * len + 1]) / 2;
        T target_box_width = target_box_data[i * len + 2] -
                             target_box_data[i * len] + (normalized == false);
        T target_box_height = target_box_data[i * len + 3] -
                              target_box_data[i * len + 1] +
                              (normalized == false);

        output[offset] =
            (target_box_center_x - prior_box_center_x) / prior_box_width;
        output[offset + 1] =
            (target_box_center_y - prior_box_center_y) / prior_box_height;
        output[offset + 2] =
            std::log(std::fabs(target_box_width / prior_box_width));
        output[offset + 3] =
            std::log(std::fabs(target_box_height / prior_box_height));
      }
    }

    if (prior_box_var) {
      const T *prior_box_var_data = prior_box_var->data<T>();
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for collapse(3)
#endif
      for (int64_t i = 0; i < row; ++i) {
        for (int64_t j = 0; j < col; ++j) {
          for (int k = 0; k < 4; ++k) {
            size_t offset = i * col * len + j * len;
            int prior_var_offset = j * len;
            output[offset + k] /= prior_box_var_data[prior_var_offset + k];
          }
        }
      }
    } else if (!(variance.empty())) {
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for collapse(3)
#endif
      for (int64_t i = 0; i < row; ++i) {
        for (int64_t j = 0; j < col; ++j) {
          for (int k = 0; k < 4; ++k) {
            size_t offset = i * col * len + j * len;
            output[offset + k] /= static_cast<T>(variance[k]);
          }
        }
      }
    }
  }

  template <int axis, int var_size>
  void DecodeCenterSize(const framework::Tensor *target_box,
                        const framework::Tensor *prior_box,
                        const framework::Tensor *prior_box_var,
                        const bool normalized,
                        std::vector<float> variance,
                        T *output) const {
    int64_t row = target_box->dims()[0];
    int64_t col = target_box->dims()[1];
    int64_t len = target_box->dims()[2];

#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for collapse(2)
#endif
    for (int64_t i = 0; i < row; ++i) {
      for (int64_t j = 0; j < col; ++j) {
        auto *target_box_data = target_box->data<T>();
        auto *prior_box_data = prior_box->data<T>();

        T var_data[4] = {1., 1., 1., 1.};
        T *var_ptr = var_data;
        size_t offset = i * col * len + j * len;
        int prior_box_offset = axis == 0 ? j * len : i * len;

        T prior_box_width = prior_box_data[prior_box_offset + 2] -
                            prior_box_data[prior_box_offset] +
                            (normalized == false);
        T prior_box_height = prior_box_data[prior_box_offset + 3] -
                             prior_box_data[prior_box_offset + 1] +
                             (normalized == false);
        T prior_box_center_x =
            prior_box_data[prior_box_offset] + prior_box_width / 2;
        T prior_box_center_y =
            prior_box_data[prior_box_offset + 1] + prior_box_height / 2;

        T target_box_center_x = 0, target_box_center_y = 0;
        T target_box_width = 0, target_box_height = 0;
        int prior_var_offset = axis == 0 ? j * len : i * len;
        if (var_size == 2) {
          std::memcpy(var_ptr,
                      prior_box_var->data<T>() + prior_var_offset,
                      4 * sizeof(T));
        } else if (var_size == 1) {
          var_ptr = reinterpret_cast<T *>(variance.data());
        }
        T box_var_x = *var_ptr;
        T box_var_y = *(var_ptr + 1);
        T box_var_w = *(var_ptr + 2);
        T box_var_h = *(var_ptr + 3);

        target_box_center_x =
            box_var_x * target_box_data[offset] * prior_box_width +
            prior_box_center_x;
        target_box_center_y =
            box_var_y * target_box_data[offset + 1] * prior_box_height +
            prior_box_center_y;
        target_box_width =
            std::exp(box_var_w * target_box_data[offset + 2]) * prior_box_width;
        target_box_height = std::exp(box_var_h * target_box_data[offset + 3]) *
                            prior_box_height;

        output[offset] = target_box_center_x - target_box_width / 2;
        output[offset + 1] = target_box_center_y - target_box_height / 2;
        output[offset + 2] =
            target_box_center_x + target_box_width / 2 - (normalized == false);
        output[offset + 3] =
            target_box_center_y + target_box_height / 2 - (normalized == false);
      }
    }
  }

  void Compute(const framework::ExecutionContext &context) const override {
    auto *prior_box = context.Input<framework::Tensor>("PriorBox");
    auto *prior_box_var = context.Input<framework::Tensor>("PriorBoxVar");
    auto *target_box = context.Input<framework::LoDTensor>("TargetBox");
    auto *output_box = context.Output<framework::Tensor>("OutputBox");
    std::vector<float> variance = context.Attr<std::vector<float>>("variance");
    const int axis = context.Attr<int>("axis");
    if (target_box->lod().size()) {
      PADDLE_ENFORCE_EQ(target_box->lod().size(),
                        1UL,
                        platform::errors::InvalidArgument(
                            "Input(TargetBox) of BoxCoder operator "
                            "supports LoD with only one level. But received "
                            "level = %d",
                            target_box->lod().size()));
    }
    if (prior_box_var) {
      PADDLE_ENFORCE_EQ(variance.empty(),
                        true,
                        platform::errors::InvalidArgument(
                            "Input 'PriorBoxVar' and attribute 'variance' "
                            "of BoxCoder operator should not be used at the "
                            "same time."));
    }
    if (!(variance.empty())) {
      PADDLE_ENFORCE_EQ(static_cast<int>(variance.size()),
                        4,
                        platform::errors::InvalidArgument(
                            "Size of attribute 'variance' of BoxCoder "
                            "operator should be 4. But received "
                            "size = %d",
                            variance.size()));
    }
    auto code_type = GetBoxCodeType(context.Attr<std::string>("code_type"));
    bool normalized = context.Attr<bool>("box_normalized");

    auto row = target_box->dims()[0];
    auto col = prior_box->dims()[0];
    if (code_type == BoxCodeType::kDecodeCenterSize) {
      col = target_box->dims()[1];
    }
    auto len = prior_box->dims()[1];

    output_box->mutable_data<T>({row, col, len}, context.GetPlace());

    T *output = output_box->data<T>();
    if (code_type == BoxCodeType::kEncodeCenterSize) {
      EncodeCenterSize(
          target_box, prior_box, prior_box_var, normalized, variance, output);
    } else if (code_type == BoxCodeType::kDecodeCenterSize) {
      if (prior_box_var) {
        if (axis == 0) {
          DecodeCenterSize<0, 2>(target_box,
                                 prior_box,
                                 prior_box_var,
                                 normalized,
                                 variance,
                                 output);
        } else {
          DecodeCenterSize<1, 2>(target_box,
                                 prior_box,
                                 prior_box_var,
                                 normalized,
                                 variance,
                                 output);
        }
      } else if (!(variance.empty())) {
        if (axis == 0) {
          DecodeCenterSize<0, 1>(target_box,
                                 prior_box,
                                 prior_box_var,
                                 normalized,
                                 variance,
                                 output);
        } else {
          DecodeCenterSize<1, 1>(target_box,
                                 prior_box,
                                 prior_box_var,
                                 normalized,
                                 variance,
                                 output);
        }
      } else {
        if (axis == 0) {
          DecodeCenterSize<0, 0>(target_box,
                                 prior_box,
                                 prior_box_var,
                                 normalized,
                                 variance,
                                 output);
        } else {
          DecodeCenterSize<1, 0>(target_box,
                                 prior_box,
                                 prior_box_var,
                                 normalized,
                                 variance,
                                 output);
        }
      }
    }
  }
};

}  // namespace operators
}  // namespace paddle
