/* Copyright (c) 2016 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 <vector>

#include <boost/preprocessor/arithmetic/div.hpp>
#include <boost/preprocessor/arithmetic/mod.hpp>
#include <boost/preprocessor/comparison/greater.hpp>
#include <boost/preprocessor/comparison/greater_equal.hpp>
#include <boost/preprocessor/control/if.hpp>
#include <boost/preprocessor/repetition/repeat.hpp>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"

#define MAX_RANK_SUPPORTED 6

#define EXPAND_TEMPLATE(z, n, data) \
  case n + 1: {                     \
    Expand<n + 1>(context);         \
    break;                          \
  }
#define REP_EXPAND_TEMPLATE(n) BOOST_PP_REPEAT(n, EXPAND_TEMPLATE, ~)
#define COND(n)                                               \
  BOOST_PP_GREATER_EQUAL(BOOST_PP_DIV(n, MAX_RANK_SUPPORTED), \
                         BOOST_PP_MOD(n, MAX_RANK_SUPPORTED))
#define EXPAND_GRAD_CASE(n)                                        \
  case n: {                                                        \
    ExpandBackward<n>(context, reshape_dims_vec, reduce_dims_vec); \
    break;                                                         \
  }
#define EXPAND_GRAD_TEMPLATE(z, n, data) \
  BOOST_PP_IF(COND(n), EXPAND_GRAD_CASE(n), )
#define REP_EXPAND_GRAD_TEMPLATE(n) BOOST_PP_REPEAT(n, EXPAND_GRAD_TEMPLATE, ~)

namespace paddle {
namespace operators {
inline std::vector<int> get_expand_times(
    const framework::ExecutionContext& ctx) {
  auto list_expand_times_tensor =
      ctx.MultiInput<framework::Tensor>("expand_times_tensor");
  if (list_expand_times_tensor.size() > 0) {
    // get tensor from
    std::vector<int> vec_epxand_times;
    for (size_t i = 0; i < list_expand_times_tensor.size(); ++i) {
      auto tensor = list_expand_times_tensor[i];
      if (platform::is_gpu_place(tensor->place())) {
        framework::Tensor temp;
        TensorCopySync(*tensor, platform::CPUPlace(), &temp);
        vec_epxand_times.push_back(*temp.data<int32_t>());
      } else {
        vec_epxand_times.push_back(*tensor->data<int32_t>());
      }
    }

    return vec_epxand_times;
  } else {
    return ctx.Attr<std::vector<int>>("expand_times");
  }
}

using Tensor = framework::Tensor;
template <typename T, int MajorType = Eigen::RowMajor,
          typename IndexType = Eigen::DenseIndex>
using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
template <typename T, size_t D, int MajorType = Eigen::RowMajor,
          typename IndexType = Eigen::DenseIndex>
using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;

template <typename DeviceContext, typename T>
class ExpandKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& context) const override {
    auto rank = context.Input<Tensor>("X")->dims().size();
    switch (rank) {
      REP_EXPAND_TEMPLATE(MAX_RANK_SUPPORTED)
      default:
        PADDLE_ENFORCE(false,
                       "Only support tensor with rank being between 1 and 6.");
    }
  }

 protected:
  template <int Rank>
  void Expand(const framework::ExecutionContext& context) const {
    auto* in0 = context.Input<Tensor>("X");

    auto in_dims = in0->dims();
    auto expand_times = get_expand_times(context);
    auto* out0 = context.Output<Tensor>("Out");
    Eigen::DSizes<int, Rank> bcast_dims;
    for (size_t i = 0; i < expand_times.size(); ++i) {
      bcast_dims[i] = expand_times[i];
    }

    framework::DDim out_dims(in_dims);
    for (size_t i = 0; i < expand_times.size(); ++i) {
      out_dims[i] *= expand_times[i];
    }

    out0->Resize(out_dims);
    auto x = EigenTensor<T, Rank>::From(*in0);
    out0->mutable_data<T>(context.GetPlace());
    auto y = EigenTensor<T, Rank>::From(*out0);
    auto& place =
        *context.template device_context<DeviceContext>().eigen_device();
    y.device(place) = x.broadcast(bcast_dims);
  }
};

template <typename DeviceContext, typename T>
class ExpandGradKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& context) const override {
    auto* in0 = context.Input<Tensor>("X");
    // auto& expand_times = context.Attr<std::vector<int>>("expand_times");
    auto expand_times = get_expand_times(context);
    auto x_dims = in0->dims();
    // 1. reshape_dims_vec is the broadcast parameter. For each dimension i,
    //    if expand_times[i] > 1 and x_dims[i] > 1, i will be splitted to two
    //    dimensions [expand_times[i], x_dims[i]].
    // 2. reduce_dims_vec is the dimension parameter to compute gradients. For
    //    each dimension expanded, the gradients should be summed to original
    //    size.
    std::vector<int> reshape_dims_vec;
    std::vector<int> reduce_dims_vec;
    for (size_t i = 0; i < expand_times.size(); ++i) {
      if (expand_times[i] == 1) {
        reshape_dims_vec.push_back(x_dims[i]);
      } else {
        if (x_dims[i] == 1) {
          reduce_dims_vec.push_back(reshape_dims_vec.size());
          reshape_dims_vec.push_back(expand_times[i]);
        } else {
          reduce_dims_vec.push_back(reshape_dims_vec.size());
          reshape_dims_vec.push_back(expand_times[i]);
          reshape_dims_vec.push_back(x_dims[i]);
        }
      }
    }

    int dims = reshape_dims_vec.size() * MAX_RANK_SUPPORTED +
               reduce_dims_vec.size() - MAX_RANK_SUPPORTED - 1;
    // no need reduce, just copy
    if (reduce_dims_vec.size() == 0) {
      auto* in0 = context.Input<Tensor>(framework::GradVarName("Out"));
      auto* out0 = context.Output<Tensor>(framework::GradVarName("X"));
      out0->mutable_data<T>(context.GetPlace());
      framework::TensorCopy(*in0, context.GetPlace(), context.device_context(),
                            out0);
    } else {
      switch (dims) {
        REP_EXPAND_GRAD_TEMPLATE(72)
        default:
          PADDLE_ENFORCE(
              false, "Only support tensor with rank being between 1 and 6.");
      }
    }
  }

 protected:
  template <int Dims>
  void ExpandBackward(const framework::ExecutionContext& context,
                      const std::vector<int>& reshape_dims_vec,
                      const std::vector<int>& reduce_dims_vec) const {
    size_t reshape_size = Dims / MAX_RANK_SUPPORTED + 1;
    size_t reduce_size = Dims % MAX_RANK_SUPPORTED + 1;
    PADDLE_ENFORCE_EQ(reshape_size, reshape_dims_vec.size(),
                      "Inconsistent size between template Dims and "
                      "reshape dimensions.");
    PADDLE_ENFORCE_EQ(reduce_size, reduce_dims_vec.size(),
                      "Inconsistent size between template Dims and "
                      "reduce dimensions.");
    auto* in0 = context.Input<Tensor>(framework::GradVarName("Out"));
    auto* out0 = context.Output<Tensor>(framework::GradVarName("X"));
    out0->mutable_data<T>(context.GetPlace());
    auto x_grad = EigenVector<T>::Flatten(*out0);
    Eigen::DSizes<int, Dims / MAX_RANK_SUPPORTED + 1> reshape_dims;
    for (size_t i = 0; i < reshape_size; ++i) {
      reshape_dims[i] = reshape_dims_vec[i];
    }
    Eigen::DSizes<int, Dims % MAX_RANK_SUPPORTED + 1> reduce_dims;
    for (size_t i = 0; i < reduce_size; ++i) {
      reduce_dims[i] = reduce_dims_vec[i];
    }
    auto out_grad = EigenVector<T>::Flatten(*in0);
    x_grad.device(
        *context.template device_context<DeviceContext>().eigen_device()) =
        out_grad.reshape(reshape_dims)
            .sum(reduce_dims)
            .reshape(x_grad.dimensions());
  }
};

}  // namespace operators
}  // namespace paddle