#pragma once
#include "caffe2/core/context.h"
#include "caffe2/core/operator.h"
#include "caffe2/perfkernels/embedding_lookup.h"
#ifdef USE_FBGEMM
#include "fbgemm/Fbgemm.h"
#endif
#include <algorithm>
#include <functional>

namespace caffe2 {

// A templated class that implements SparseLengths[Sum,WeightedSum,Mean].
template <
    typename T, // output type
    class InputTypes, // supported input types, such as TensorTypes<float>
    bool USE_WEIGHT = false, // Whether it is SparseLengthsWeightedSum
    bool USE_MEAN = false, // Whether this is SparseLengthsMean
    bool USE_POSITIONAL_WEIGHT = false
    // USE_WEIGHT = true and USE_POSITIONAL_WEIGHT = true
    // -> SparseLengthsPositionalWeightedSum
    >
class CPUSparseLengthsReductionOp : public Operator<CPUContext> {
 public:
  USE_OPERATOR_FUNCTIONS(CPUContext);
  template <class... Args>
  explicit CPUSparseLengthsReductionOp(Args&&... args)
      : Operator<CPUContext>(std::forward<Args>(args)...) {
    static_assert(
        !(USE_WEIGHT & USE_MEAN), "Cannot both specify weight and mean.");
  }

  ~CPUSparseLengthsReductionOp() {}

  // Currently, we support float and at::Half inputs for input data type, and
  // int32_t and int64_t for the index type.

  bool RunOnDevice() override {
    return DispatchHelper<InputTypes>::call(this, Input(DATA));
  }

  template <typename InputType>
  bool DoRunWithType() {
    return DispatchHelper<TensorTypes2<int32_t, int64_t>, InputType>::call(
        this, Input(INDICES));
  }

  template <typename InputType, typename IndexType>
  bool DoRunWithType2() {
    auto& dataInput = Input(DATA);
    auto& indicesInput = Input(INDICES);
    auto& lengthsInput = Input(LENGTHS);

    const int64_t M = lengthsInput.size(0);
    const int64_t indices_size = indicesInput.numel();

    auto shape = dataInput.sizes().vec();
    shape[0] = M;
    auto* output = Output(0, shape, at::dtype<T>());
    T* out_data = output->template mutable_data<T>();

    if (indices_size == 0) {
      if (M > 0) {
        memset(out_data, 0, output->numel() * sizeof(T));
      }
      return true;
    }

    CAFFE_ENFORCE_EQ(1, indicesInput.dim(), "INDICES must be a vector");
    CAFFE_ENFORCE_EQ(1, lengthsInput.dim(), "LENGTHS must be a vector");
    const int64_t N = dataInput.size(0);
    const int D = dataInput.size_from_dim(1);

    const InputType* in_data = dataInput.template data<InputType>();
    const IndexType* indices = indicesInput.template data<IndexType>();
    const int* lengths = lengthsInput.template data<int>();
    const T* in_weight = nullptr;

    if (USE_WEIGHT) {
      // static if
      auto& weightInput = Input(WEIGHT);
      CAFFE_ENFORCE_EQ(1, weightInput.dim(), "WEIGHT must be a vector");
      if (!USE_POSITIONAL_WEIGHT) {
        CAFFE_ENFORCE_EQ(
            weightInput.numel(),
            indices_size,
            "Weight should have the same length as indices.");
      }
      in_weight = weightInput.template data<T>();
    }

#ifdef USE_FBGEMM
    // If this is the first call or block size has changed (should never
    // happen actually), generate a kernel.
    if (D != last_block_size) {
      last_block_size = D;
      if (std::is_same<InputType, float>::value) {
        if (std::is_same<IndexType, std::int32_t>::value) {
          kernel_fp32_i32_ =
              fbgemm::GenerateEmbeddingSpMDM<float, std::int32_t>(
                  D,
                  USE_WEIGHT,
                  USE_MEAN,
                  /*prefetch distance*/ 16,
                  USE_POSITIONAL_WEIGHT,
                  /*use_offsets*/ false);
        } else {
          CAFFE_ENFORCE((std::is_same<IndexType, std::int64_t>::value));
          kernel_fp32_i64_ =
              fbgemm::GenerateEmbeddingSpMDM<float, std::int64_t>(
                  D,
                  USE_WEIGHT,
                  USE_MEAN,
                  /*prefetch distance*/ 16,
                  USE_POSITIONAL_WEIGHT,
                  /*use_offsets*/ false);
        }
      } else {
        CAFFE_ENFORCE((std::is_same<InputType, at::Half>::value));
        if (std::is_same<IndexType, std::int32_t>::value) {
          kernel_fp16_i32_ =
              fbgemm::GenerateEmbeddingSpMDM<fbgemm::float16, std::int32_t>(
                  D,
                  USE_WEIGHT,
                  USE_MEAN,
                  /*prefetch distance*/ 16,
                  USE_POSITIONAL_WEIGHT,
                  /*use_offsets*/ false);
        } else {
          CAFFE_ENFORCE((std::is_same<IndexType, std::int64_t>::value));
          kernel_fp16_i64_ =
              fbgemm::GenerateEmbeddingSpMDM<fbgemm::float16, std::int64_t>(
                  D,
                  USE_WEIGHT,
                  USE_MEAN,
                  /*prefetch distance*/ 16,
                  USE_POSITIONAL_WEIGHT,
                  /*use_offsets*/ false);
        }
      }
    }

    bool success;
    if (std::is_same<InputType, float>::value) {
      if (std::is_same<IndexType, std::int32_t>::value) {
        success = kernel_fp32_i32_(
            M,
            indices_size,
            N,
            reinterpret_cast<const float*>(in_data),
            indicesInput.template data<std::int32_t>(),
            lengths,
            in_weight,
            out_data);
      } else {
        success = kernel_fp32_i64_(
            M,
            indices_size,
            N,
            reinterpret_cast<const float*>(in_data),
            indicesInput.template data<std::int64_t>(),
            lengths,
            in_weight,
            out_data);
      }
    } else {
      if (std::is_same<IndexType, std::int32_t>::value) {
        success = kernel_fp16_i32_(
            M,
            indices_size,
            N,
            reinterpret_cast<const fbgemm::float16*>(in_data),
            indicesInput.template data<std::int32_t>(),
            lengths,
            in_weight,
            out_data);
      } else {
        success = kernel_fp16_i64_(
            M,
            indices_size,
            N,
            reinterpret_cast<const fbgemm::float16*>(in_data),
            indicesInput.template data<std::int64_t>(),
            lengths,
            in_weight,
            out_data);
      }
    }

    if (success) {
      return true;
    }

    int64_t current = 0;
    for (int m = 0; m < M; ++m) {
      for (int i = 0; i < lengths[m]; ++i) {
        CAFFE_ENFORCE_LT(
            current,
            indices_size,
            "Your input seems to be incorrect: the sum of lengths values "
            "should be the size of the indices tensor, but it appears not.");
        IndexType idx = indices[current];
        CAFFE_ENFORCE(
            0 <= idx && idx < N,
            "Index ",
            current,
            " is out of bounds: ",
            idx,
            ", range 0 to ",
            N);
        ++current;
      }
    }
    CAFFE_ENFORCE_EQ(
        current,
        indices_size,
        "Your input seems to be incorrect: the sum of lengths values should be "
        "the size of the indices tensor, but it appears not.");

    return false;
#endif

    // delegate work to perfkernel that branches based on architecture
    EmbeddingLookup<IndexType, InputType, T, USE_POSITIONAL_WEIGHT>(
        D,
        M,
        indices_size,
        N,
        in_data,
        indices,
        lengths,
        in_weight,
        nullptr, // scale_bias field is only used in SparseLengths8BitsRowwiseOp
        USE_MEAN,
        out_data);
    return true;
  }

  enum {
    DATA = 0, // Data input.
    WEIGHT = 1, // Weight input used in SparseLengthsWeightedSum
    INDICES = 1 + USE_WEIGHT, // 1 in SparseLengths[Sum,Mean] and
                              // 2 in SparseLengthsWeightedSum
    LENGTHS = 2 + USE_WEIGHT, // 2 in SparseLengths[Sum, Mean],
                              // 3 in SparseLengthsWeightedSum
  };

#ifdef USE_FBGEMM
 private:
  std::int64_t last_block_size{-1};
  fbgemm::EmbeddingSpMDMKernelSignature<float, std::int32_t>::Type
      kernel_fp32_i32_;
  fbgemm::EmbeddingSpMDMKernelSignature<float, std::int64_t>::Type
      kernel_fp32_i64_;
  fbgemm::EmbeddingSpMDMKernelSignature<fbgemm::float16, std::int32_t>::Type
      kernel_fp16_i32_;
  fbgemm::EmbeddingSpMDMKernelSignature<fbgemm::float16, std::int64_t>::Type
      kernel_fp16_i64_;
#endif
};

template <typename T, class Context, class Engine = DefaultEngine>
class TTSparseLengthsSumOp final : public Operator<Context> {
 public:
  USE_OPERATOR_CONTEXT_FUNCTIONS;
  template <class... Args>
  explicit TTSparseLengthsSumOp(Args&&... args)
      : Operator<Context>(std::forward<Args>(args)...),
        factor_i(this->template GetRepeatedArgument<int>(
            "factor_i",
            vector<int>{1, 1, 1})),
        factor_j(this->template GetRepeatedArgument<int>(
            "factor_j",
            vector<int>{1, 1, 1})),
        ranks(this->template GetRepeatedArgument<int>(
            "ranks",
            vector<int>{1, 1, 1, 1})),
        emb_size(this->template GetSingleArgument<int>("emb_size", 64)) {
    // cumprod of i, used for index slice
    l_cumprod.push_back(1);
    for (size_t i = 1; i < factor_i.size(); ++i) {
      l_cumprod.push_back(l_cumprod[i - 1] * factor_i[i - 1]);
    }
  }

  ~TTSparseLengthsSumOp() {}

  void Ind2Sub(int64_t* out_factor_index, const int64_t* indices, int len) {
    // TODO: vectorization
    auto N = factor_i.size();
    for (int j = 0; j < len; j++) {
      auto idx = indices[j];
      for (int i = N; i > 0; i--) {
        out_factor_index[j * N + i - 1] = idx / l_cumprod[i - 1];
        idx = idx % l_cumprod[i - 1];
      }
    }
  }

  bool GetSlice(
      std::vector<std::vector<T>>& tgt_slice,
      const T* core,
      const vector<int64_t>& ind_slice,
      int bs,
      int idx) {
    // implement the functinality index_select(core, 1, ind_slice)
    auto num_of_elements = ranks[idx] * factor_j[idx] * ranks[idx + 1];
    for (int i = 0; i < bs; i++) {
      memcpy(
          tgt_slice[i].data(),
          core + ind_slice[i] * num_of_elements,
          num_of_elements * sizeof(T));
    }
    return true;
  }

  // ind: it stores the index to each tensor core
  // bs: the number of indices
  // GatherAllRows uses two steps to calculate the lengthsum functionality: 1) it uses tensor train
  // to calculate the embedding for each index. 2) it sums the embedding for each bag.
  // In Step 1), it batches all the indices together. Specifically, for every index, it uses the pre-computed
  // ind of each tensor core to extract the corresponding slice of the core. Then it does gemm operation
  // sequentially on the slices to produce the embedding result for each index.
  // In Step 2), it takes the embedding computed in step 1) and apply the sum operation for each bag.
  bool GatherAllRows(
      int64_t* ind,
      int bs,
      int x_len,
      vector<const T*> cores,
      int segments,
      const int* lengths,
      T* out_data) {
    // compute the largest memory consumption of intermediate result
    // TODO: dynamic allocation size: cur_rows*factor_j[i]*ranks[i+1]
    // and also explore the contiguous memory storage for res and int_res
    int max_rank = *max_element(ranks.begin(), ranks.end());
    std::vector<std::vector<T>> res(bs, std::vector<T>(emb_size * max_rank, 0));
    std::vector<std::vector<T>> int_res(
        bs, std::vector<T>(emb_size * max_rank, 0));

    // Store the matrix A
    vector<T*> Y_ptr(bs);
    // Store the intermediate result in each layer
    vector<T*> Z_ptr(bs);