/* 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 <utility>
#include <vector>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/eigen/eigen_function.h"
#include "paddle/fluid/operators/strided_memcpy.h"

namespace paddle {
namespace operators {  // Internal

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

static std::vector<int> GetOffsets(const framework::ExecutionContext& ctx) {
  std::vector<int> res;
  int rank = ctx.Input<Tensor>("X")->dims().size();
  if (ctx.HasInput("Offsets")) {
    PADDLE_ENFORCE_EQ(ctx.Attr<std::vector<int>>("offsets").empty(), true,
                      platform::errors::InvalidArgument(
                          "Input 'Offsets' and attribute 'offsets' "
                          "should not be used at the same time for CropOp."));
    const auto* offsets_tensor = ctx.Input<Tensor>("Offsets");
    PADDLE_ENFORCE_EQ(offsets_tensor->dims().size(), 1,
                      platform::errors::InvalidArgument(
                          "The number of dimensions of input 'Offsets' for "
                          "CropOp must be 1, but the value received is %d.",
                          offsets_tensor->dims().size()));
    PADDLE_ENFORCE_EQ(
        rank, offsets_tensor->dims()[0],
        platform::errors::InvalidArgument("The number of elements (%d) for "
                                          "input 'Offsets' must be equal to "
                                          "the number of dimensions (%d) "
                                          "of the input tensor.",
                                          offsets_tensor->dims()[0], rank));
    const int* offsets_data;
    framework::Tensor cpu_tmp_tensor;
    if (platform::is_cpu_place(offsets_tensor->place())) {
      offsets_data = offsets_tensor->data<int>();
    } else {
      framework::TensorCopySync(*offsets_tensor, platform::CPUPlace(),
                                &cpu_tmp_tensor);
      offsets_data = cpu_tmp_tensor.data<int>();
    }
    res = std::vector<int>(offsets_data, offsets_data + rank);
  } else {
    res = ctx.Attr<std::vector<int>>("offsets");
    PADDLE_ENFORCE_EQ(
        rank, static_cast<int>(res.size()),
        platform::errors::InvalidArgument("The number of elements (%d) for "
                                          "input 'Offsets' must be equal to "
                                          "the number of dimensions (%d) "
                                          "of the input tensor.",
                                          res.size(), rank));
  }
  return res;
}

template <typename DeviceContext, typename T, size_t D>
void CropFunction(const framework::ExecutionContext& context) {
  auto* x = context.Input<Tensor>("X");
  auto* out = context.Output<Tensor>("Out");
  auto out_dims = out->dims();
  if (out_dims[0] == -1) {
    out_dims[0] = x->dims()[0];
  }
  out->mutable_data<T>(out_dims, context.GetPlace());
  auto x_stride = framework::stride(x->dims());
  auto offsets = GetOffsets(context);
  int64_t offset = 0;
  for (size_t i = 0; i < offsets.size(); ++i) {
    offset += (x_stride[i] * offsets[i]);
  }

  auto x_tensor = EigenTensor<T, D>::From(*x);
  auto out_tensor = EigenTensor<T, D>::From(*out);
  Eigen::DSizes<Eigen::DenseIndex, D> e_offsets;
  Eigen::DSizes<Eigen::DenseIndex, D> e_shape;
  for (size_t i = 0; i < D; ++i) {
    e_offsets[i] = offsets[i];
    e_shape[i] = out->dims()[i];
  }
  auto& place =
      *context.template device_context<DeviceContext>().eigen_device();
  EigenSlice<std::decay_t<decltype(place)>, T, D>::Eval(
      place, out_tensor, x_tensor, e_offsets, e_shape);
}

template <typename DeviceContext, typename T>
class CropKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& context) const override {
    int rank = context.Input<Tensor>("X")->dims().size();
    PADDLE_ENFORCE_GE(
        rank, 1,
        platform::errors::InvalidArgument(
            "The number of dimensions of the Input(X) for CropOp must be "
            "greater than or equal to 1, but the value received is %d.",
            rank));
    PADDLE_ENFORCE_LE(
        rank, 6,
        platform::errors::InvalidArgument(
            "The number of dimensions of the Input(X) for CropOp must be "
            "less than or equal to 6, but the value received is %d.",
            rank));
    switch (rank) {
      case 1:
        CropFunction<DeviceContext, T, 1>(context);
        break;
      case 2:
        CropFunction<DeviceContext, T, 2>(context);
        break;
      case 3:
        CropFunction<DeviceContext, T, 3>(context);
        break;
      case 4:
        CropFunction<DeviceContext, T, 4>(context);
        break;
      case 5:
        CropFunction<DeviceContext, T, 5>(context);
        break;
      case 6:
        CropFunction<DeviceContext, T, 6>(context);
        break;
    }
  }
};

template <typename DeviceContext, typename T, size_t D>
void CropGradFunction(const framework::ExecutionContext& context) {
  auto* d_x = context.Output<Tensor>(framework::GradVarName("X"));
  auto* x = context.Input<Tensor>("X");
  if (d_x != nullptr) {
    auto* d_out = context.Input<Tensor>(framework::GradVarName("Out"));
    d_x->mutable_data<T>(x->dims(), context.GetPlace());
    auto offsets = GetOffsets(context);
    Eigen::array<std::pair<int64_t, int64_t>, D> paddings;
    for (size_t i = 0; i < D; ++i) {
      paddings[i].first = offsets[i];
      paddings[i].second = d_x->dims()[i] - d_out->dims()[i] - offsets[i];
    }
    auto d_x_tensor = EigenTensor<T, D>::From(*d_x);
    auto d_out_tensor = EigenTensor<T, D>::From(*d_out);
    auto& place =
        *context.template device_context<DeviceContext>().eigen_device();
    EigenPad<std::decay_t<decltype(place)>, T, D>::Eval(
        place, d_x_tensor, d_out_tensor, paddings, static_cast<T>(0));
  }
}

template <typename DeviceContext, typename T>
class CropGradKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& context) const override {
    size_t rank =
        context.Input<Tensor>(framework::GradVarName("Out"))->dims().size();
    PADDLE_ENFORCE_GE(
        rank, 1, platform::errors::InvalidArgument(
                     "The number of dimensions of the input 'Out@GRAD' for "
                     "CropGrad must be greater than or equal "
                     "to 1, but the value received is %d.",
                     rank));
    PADDLE_ENFORCE_LE(
        rank, 6, platform::errors::InvalidArgument(
                     "The number of dimensions of the input 'Out@GRAD' for "
                     "CropGrad must be less than or equal "
                     "to 6, but the value received is %d.",
                     rank));
    switch (rank) {
      case 1:
        CropGradFunction<DeviceContext, T, 1>(context);
        break;
      case 2:
        CropGradFunction<DeviceContext, T, 2>(context);
        break;
      case 3:
        CropGradFunction<DeviceContext, T, 3>(context);
        break;
      case 4:
        CropGradFunction<DeviceContext, T, 4>(context);
        break;
      case 5:
        CropGradFunction<DeviceContext, T, 5>(context);
        break;
      case 6:
        CropGradFunction<DeviceContext, T, 6>(context);
        break;
    }
  }
};

}  // namespace operators
}  // namespace paddle
