// 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.

#pragma once

#include <cstring>
#include <random>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <vector>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/mixed_vector.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/memory/memcpy.h"

namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using SelectedRows = framework::SelectedRows;
using LoDTensor = framework::LoDTensor;
#if defined(PADDLE_WITH_CUDA)
template <typename T>
using Vector = framework::Vector<T>;
#else
template <typename T>
using Vector = framework::CPUVector<T>;
#endif

template <typename T>
class FilterByInstagKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& context) const override {
    // X1 is global FC output
    // Dim [batch size, embedding size]
    auto* x1 = context.Input<LoDTensor>("Ins");
    bool is_x1_lod = context.Attr<bool>("is_lod");
    // X2 is ins tag list
    // LoD [[0, Sum(ins1), Sum(ins1, ins2), ... ]]
    auto* x2 = context.Input<LoDTensor>("Ins_tag");
    // X3 is local fc tag list
    // LoD [[0, Sum(fc1), Sum(fc1, fc2) ...]]
    auto* x3 = context.Input<Tensor>("Filter_tag");

    std::unordered_set<int64_t> filter_tag;
    auto* x3_data = x3->data<int64_t>();
    size_t len = x3->dims()[0];
    for (size_t i = 0; i < len; i++) {
      filter_tag.insert(x3_data[i]);
    }

    // expected auto = const int64_t
    auto* x2_data = x2->data<int64_t>();
    // e.g get [0, 1, 2, 3, ...]
    auto x2_lods = x2->lod()[0];
    Vector<size_t> x1_lods(1, 0);
    if (!is_x1_lod) {
      for (int i = 0; i < x1->dims()[0]; i++) {
        x1_lods.push_back(i + 1);
      }
    } else {
      x1_lods = context.Input<LoDTensor>("Ins")->lod()[0];
    }
    std::unordered_map<int64_t, int64_t> mmap_aux;
    Vector<size_t> out_lods(1, 0);
    for (size_t i = 0; i < x2_lods.size() - 1; i++) {
      for (size_t j = x2_lods[i]; j < x2_lods[i + 1]; j++) {
        if (filter_tag.find(x2_data[j]) != filter_tag.end()) {
          size_t batch_len = x1_lods[i + 1] - x1_lods[i];
          mmap_aux[out_lods.back()] = x1_lods[i];
          out_lods.push_back(out_lods.back() + batch_len);
          break;
        }
      }
    }
    // set output value
    // for those whose ins been dropout, set 0 for whole lines.
    // otherwise, copy whole line
    // Dim [local fc count, batch size, embedding size]
    LoDTensor* out = context.Output<LoDTensor>("Out");
    LoDTensor* map = context.Output<LoDTensor>("IndexMap");
    LoDTensor* loss_weight = context.Output<LoDTensor>("LossWeight");
    // expected auto = const T
    auto* x1_data = x1->data<T>();
    // expected auto = T
    size_t x1_embed_size = x1->dims()[1];
    if (out_lods.size() - 1 > 0) {
      out->Resize(framework::make_ddim(
          {(int64_t)out_lods.back(), (int64_t)x1_embed_size}));
      map->Resize(framework::make_ddim({(int64_t)out_lods.size() - 1, 3}));
      loss_weight->Resize(
          framework::make_ddim({(int64_t)out_lods.size() - 1, 1}));
    } else {
      out->Resize(framework::make_ddim({1, (int64_t)x1_embed_size}));
      map->Resize(framework::make_ddim({1, 3}));
      loss_weight->Resize(framework::make_ddim({1, 1}));
    }
    auto* out_data = out->mutable_data<T>(context.GetPlace());
    auto* map_data = map->mutable_data<int64_t>(context.GetPlace());
    auto* loss_weight_data =
        loss_weight->mutable_data<float>(context.GetPlace());
    if (out_lods.size() - 1 > 0) {
      Vector<size_t> map_lods;
      for (size_t i = 0; i < out_lods.size() - 1; i++) {
        map_data[i * 3] = (int64_t)out_lods[i];
        map_data[i * 3 + 1] = mmap_aux[map_data[i * 3]];
        map_data[i * 3 + 2] = out_lods[i + 1] - out_lods[i];
        map_lods.push_back(i);
      }
      map_lods.push_back(out_lods.size() - 1);
      std::vector<Vector<size_t>> map_lod_info;
      map_lod_info.push_back(map_lods);

      map->set_lod(map_lod_info);
      loss_weight->set_lod(map_lod_info);
      std::vector<Vector<size_t>> out_lod_info;
      out_lod_info.push_back(out_lods);
      out->set_lod(out_lod_info);
      memset(out_data, 0, out->numel() * sizeof(T));
      for (int i = 0; i < loss_weight->numel(); i++) {
        loss_weight_data[i] = 1;
      }

      for (size_t i = 0; i < out_lods.size() - 1; i++) {
        size_t pos = out_lods[i];
        for (int k = map_data[i * 3 + 1];
             k < map_data[i * 3 + 1] + map_data[i * 3 + 2]; k++) {
          memcpy(out_data + pos * x1_embed_size, x1_data + k * x1_embed_size,
                 x1_embed_size * sizeof(T));
          ++pos;
        }
      }
    } else {
      Vector<size_t> map_lods;
      map_data[0] = 0;
      map_data[1] = 1;
      map_data[2] = 1;
      map_lods.push_back(0);
      map_lods.push_back(1);
      out_lods.push_back(1);
      std::vector<Vector<size_t>> map_lod_info;
      map_lod_info.push_back(map_lods);
      map->set_lod(map_lod_info);
      loss_weight->set_lod(map_lod_info);
      std::vector<Vector<size_t>> out_lod_info;
      out_lod_info.push_back(out_lods);
      out->set_lod(out_lod_info);
      memset(out_data, 0, out->numel() * sizeof(T));
      loss_weight_data[0] = 0;
    }
  }
};

template <typename T>
class FilterByInstagGradKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& context) const override {
    auto* output_grad = context.Input<LoDTensor>(framework::GradVarName("Out"));
    auto* x1_grad = context.Output<LoDTensor>(framework::GradVarName("Ins"));
    auto* loss_weight = context.Input<LoDTensor>("LossWeight");
    auto* mmap = context.Input<LoDTensor>("IndexMap");
    auto* x1 = context.Input<LoDTensor>("Ins");
    x1_grad->set_lod(context.Input<LoDTensor>("Ins")->lod());
    x1_grad->Resize(x1->dims());
    auto mmap_data = mmap->data<int64_t>();
    // expected auto = T
    auto* output_grad_data = output_grad->data<T>();

    auto* loss_weight_data = loss_weight->data<float>();
    // expected auto = T
    auto* x1_grad_data = x1_grad->mutable_data<T>(context.GetPlace());
    memset(x1_grad_data, 0, x1->dims()[0] * x1->dims()[1] * sizeof(T));
    if (loss_weight->numel() != 1 || loss_weight_data[0] != 0) {
      auto output_dims = output_grad->dims();
      for (int i = 0; i < mmap->dims()[0]; i++) {
        int src_ln = mmap_data[i * 3], dst_ln = mmap_data[i * 3 + 1];
        int line_cnt = mmap_data[i * 3 + 2];
        for (int l = 0; l < line_cnt; l++) {
          for (int j = 0; j < output_dims[1]; j++) {
            x1_grad_data[(dst_ln + l) * output_dims[1] + j] =
                output_grad_data[(src_ln + l) * output_dims[1] + j];
          }
        }
      }
    }
  }
};
}  // namespace operators
}  // namespace paddle
