// Copyright (c) 2020 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 "paddle/fluid/framework/op_registry.h"

namespace paddle {
namespace operators {

using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
using DDim = framework::DDim;

template <typename T, typename IndexT = int>
void IndexSelectInner(const framework::ExecutionContext& context,
                      const LoDTensor& input, const LoDTensor& index,
                      LoDTensor* output, int dim) {
  auto input_dim = input.dims();
  auto input_dim_size = input_dim.size();
  auto output_dim = output->dims();

  auto slice_size = 1;
  for (auto i = dim + 1; i < input_dim_size; i++) {
    slice_size *= input_dim[i];
  }

  auto input_width = slice_size * input_dim[dim];
  auto output_width = slice_size * output_dim[dim];

  auto outer_nums = 1;
  for (auto i = 0; i < dim; i++) {
    outer_nums *= input_dim[i];
  }

  auto index_size = index.dims()[0];

  std::vector<T> input_vec;
  std::vector<IndexT> index_vec;
  TensorToVector(input, context.device_context(), &input_vec);
  TensorToVector(index, context.device_context(), &index_vec);
  std::vector<T> out_vec(output->numel());

  VLOG(3) << "Index_Select_Debug; outer_nums: " << outer_nums
          << "; slice_size: " << slice_size << "; input_width: " << input_width
          << "; output_width: " << output_width
          << "; index_size: " << index_size;

  for (auto i = 0; i < outer_nums; i++) {
    auto input_start_offset = i * input_width;
    auto output_start_offset = i * output_width;

    for (auto j = 0; j < index_size; j++) {
      IndexT index_value = index_vec[j];
      for (auto k = 0; k < slice_size; k++) {
        out_vec[output_start_offset + j * slice_size + k] =
            input_vec[input_start_offset + index_value * slice_size + k];
      }
    }
  }
  output->mutable_data<T>(context.GetPlace());
  framework::TensorFromVector(out_vec, context.device_context(), output);
  output->Resize(output_dim);
}

template <typename DeviceContext, typename T>
class IndexSelectKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& context) const override {
    auto* inputs_var = context.InputVar("X");
    auto* index_var = context.InputVar("Index");
    auto* output_var = context.OutputVar("Out");

    auto& inputs = inputs_var->Get<LoDTensor>();
    auto& index = index_var->Get<LoDTensor>();
    auto* output = output_var->GetMutable<framework::LoDTensor>();

    int dim = context.Attr<int>("dim");
    if (dim < 0) {
      dim += inputs.dims().size();
    }

    const auto& index_type = index.type();
    bool index_type_match = index_type == framework::proto::VarType::INT32 ||
                            index_type == framework::proto::VarType::INT64;
    PADDLE_ENFORCE_EQ(index_type_match, true,
                      platform::errors::InvalidArgument(
                          "Input(Index) holds the wrong type, it holds %s, but "
                          "desires to be %s or %s",
                          paddle::framework::DataTypeToString(index_type),
                          paddle::framework::DataTypeToString(
                              framework::proto::VarType::INT32),
                          paddle::framework::DataTypeToString(
                              framework::proto::VarType::INT64)));

    if (index_type == framework::proto::VarType::INT32) {
      IndexSelectInner<T, int>(context, inputs, index, output, dim);
    } else if (index_type == framework::proto::VarType::INT64) {
      IndexSelectInner<T, int64_t>(context, inputs, index, output, dim);
    }
  }
};

template <typename T, typename IndexT = int>
void IndexSelectGradInner(const framework::ExecutionContext& context,
                          const LoDTensor& out_grad, const LoDTensor& index,
                          LoDTensor* x_grad, int dim) {
  std::vector<T> input_vec;
  std::vector<IndexT> index_vec;
  TensorToVector(out_grad, context.device_context(), &input_vec);
  TensorToVector(index, context.device_context(), &index_vec);

  auto input_dim = out_grad.dims();
  auto input_dim_size = input_dim.size();
  auto output_dim = x_grad->dims();
  std::vector<T> out_vec(x_grad->numel(), 0);

  auto slice_size = 1;
  for (auto i = dim + 1; i < input_dim_size; i++) {
    slice_size *= input_dim[i];
  }

  auto input_width = slice_size * input_dim[dim];
  auto output_width = slice_size * output_dim[dim];

  auto outer_nums = 1;
  for (auto i = 0; i < dim; i++) {
    outer_nums *= input_dim[i];
  }

  auto index_size = index.dims()[0];
  VLOG(3) << "Index_Select_Grad_Debug; outer_nums: " << outer_nums
          << "; slice_size: " << slice_size << "; input_width: " << input_width
          << "; output_width: " << output_width
          << "; index_size: " << index_size;

  for (auto i = 0; i < outer_nums; i++) {
    auto input_start_offset = i * input_width;
    auto output_start_offset = i * output_width;

    for (auto j = 0; j < index_size; j++) {
      IndexT index_value = index_vec[j];
      for (auto k = 0; k < slice_size; k++) {
        out_vec[output_start_offset + index_value * slice_size + k] +=
            input_vec[input_start_offset + j * slice_size + k];
      }
    }
  }
  x_grad->mutable_data<T>(context.GetPlace());
  framework::TensorFromVector(out_vec, context.device_context(), x_grad);
  x_grad->Resize(output_dim);
}

template <typename DeviceContext, typename T>
class IndexSelectGradKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& context) const override {
    auto* index_var = context.InputVar("Index");
    auto* x_grad_var = context.OutputVar(framework::GradVarName("X"));
    auto* out_grad_var = context.InputVar(framework::GradVarName("Out"));

    auto& index = index_var->Get<LoDTensor>();
    auto& out_grad = out_grad_var->Get<LoDTensor>();
    auto* x_grad = x_grad_var->GetMutable<framework::LoDTensor>();
    int dim = context.Attr<int>("dim");
    if (dim < 0) {
      dim += out_grad.dims().size();
    }

    const auto& index_type = index.type();
    bool index_type_match = index_type == framework::proto::VarType::INT32 ||
                            index_type == framework::proto::VarType::INT64;
    PADDLE_ENFORCE_EQ(index_type_match, true,
                      platform::errors::InvalidArgument(
                          "Input(Index) holds the wrong type, it holds %s, but "
                          "desires to be %s or %s",
                          paddle::framework::DataTypeToString(index_type),
                          paddle::framework::DataTypeToString(
                              framework::proto::VarType::INT32),
                          paddle::framework::DataTypeToString(
                              framework::proto::VarType::INT64)));

    if (index_type == framework::proto::VarType::INT32) {
      IndexSelectGradInner<T, int>(context, out_grad, index, x_grad, dim);
    } else if (index_type == framework::proto::VarType::INT64) {
      IndexSelectGradInner<T, int64_t>(context, out_grad, index, x_grad, dim);
    }
  }
};

}  // namespace operators
}  // namespace paddle