// Copyright (c) 2024 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 <numeric>  // std::iota

#include "paddle/phi/common/memory_utils.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/core/mixed_vector.h"
#include "paddle/phi/kernels/funcs/eigen/common.h"
#include "paddle/phi/kernels/funcs/math_function.h"

namespace phi {

template <typename Context, typename T>
struct SequenceExpandFunctor {
  void operator()(const Context& dev_ctx,
                  const DenseTensor& x,
                  const Vector<size_t>& x_lod,   /*expand source lod*/
                  const Vector<size_t>& ref_lod, /*expand referenced lod*/
                  DenseTensor* out);
};

template <typename Context, typename T>
struct SequenceExpandGradFunctor {
  void operator()(const Context& dev_ctx,
                  const DenseTensor& dout,
                  const Vector<size_t>& x_lod,   /*expand source lod*/
                  const Vector<size_t>& ref_lod, /*expand referenced lod*/
                  DenseTensor* dx);
};

template <typename T, typename Context>
void SequenceExpandKernel(const Context& dev_ctx,
                          const DenseTensor& x_in,
                          const DenseTensor& y_in,
                          int ref_level,
                          DenseTensor* out) {
  // From InferShape
  const auto& x_dims = x_in.dims();
  auto out_dims = x_dims;
  auto& x_lod = x_in.lod();
  auto& y_lod = y_in.lod();

  PADDLE_ENFORCE_LE(x_lod.size(),
                    1UL,
                    common::errors::InvalidArgument(
                        "Level of Input(X)'s lod should not be "
                        "greater than 1. But received: lod level %u.",
                        x_lod.size()));
  PADDLE_ENFORCE_GT(y_lod.size(),
                    0UL,
                    common::errors::InvalidArgument(
                        "Level of Input(Y)'s lod should be greater than 0. But "
                        "received: lod level %u.",
                        y_lod.size()));
  PADDLE_ENFORCE_EQ(
      ref_level == -1 ||
          (ref_level >= 0 && ref_level < static_cast<int>(y_lod.size())),
      true,
      common::errors::InvalidArgument(
          "Invalid `ref_level`, which should be either equal to -1 "
          "or in [0, %d), but received `ref_level` = %u.",
          y_lod.size(),
          ref_level));

  if (ref_level == -1) ref_level = static_cast<int>(y_lod.size() - 1);

  if (!x_lod.empty()) {
    PADDLE_ENFORCE_EQ(
        x_lod[0].size(),
        y_lod[ref_level].size(),
        common::errors::InvalidArgument(
            "Level number of Input(X)'s lod could be 0. Otherwise "
            "size of Input(X)'s first level lod should be equal to "
            "size of Input(Y)'s referred level lod. But received: "
            "Input(X).lod[0].size() = %u, Input(Y).lod[%d].size() = "
            "%u",
            x_lod[0].size(),
            ref_level,
            y_lod[ref_level].size()));
  } else {
    PADDLE_ENFORCE_EQ(x_dims[0],
                      static_cast<int64_t>(y_lod[ref_level].size()) - 1,
                      common::errors::InvalidArgument(
                          "When Input(X)'s lod is null, the dims[0] of "
                          "Input(X) should match the "
                          "size of Input(Y)'s referred level lod. But received "
                          "Input(X): input rank %u, input shape [%s]; received "
                          "Input(Y).lod[%d].size() - 1 = %d.",
                          x_dims.size(),
                          x_dims,
                          ref_level,
                          static_cast<int64_t>(y_lod[ref_level].size()) - 1));
  }

  int64_t out_first_dim = 0;
  if (y_lod[ref_level].size() <= 1) {
    out_first_dim = x_dims[0];
  } else {
    for (size_t i = 1; i < y_lod[ref_level].size(); ++i) {
      int x_seq_len = 1;
      if (x_lod.size() == 1) {
        x_seq_len = static_cast<int>(x_lod[0][i] - x_lod[0][i - 1]);
      }
      out_first_dim += static_cast<int64_t>(
          (y_lod[ref_level][i] - y_lod[ref_level][i - 1]) * x_seq_len);
    }
  }
  out_dims[0] = out_first_dim;
  out->Resize(out_dims);

  auto* x = &x_in;
  PADDLE_ENFORCE_EQ(
      y_lod.empty(),
      false,
      common::errors::InvalidArgument(
          "Input(Y) DenseTensor of SequenceExpandOp does not contain "
          "LoD information."));

  if (ref_level == -1) ref_level = y_lod.size() - 1;

  dev_ctx.template Alloc<T>(out);

  if (y_lod[ref_level].size() <= 1) {
    Copy(dev_ctx, *x, dev_ctx.GetPlace(), false, out);
    return;
  }

  // x lod level is at most 1.
  Vector<size_t> out_lod;
  if (x_lod.size() == 1) {
    out_lod.push_back(0);
    int out_offset = 0;
    for (size_t i = 1; i < y_lod[ref_level].size(); ++i) {
      int repeat_num = y_lod[ref_level][i] - y_lod[ref_level][i - 1];
      int x_start = x_lod[0][i - 1];
      int x_end = x_lod[0][i];
      int x_seq_len = x_end - x_start;
      for (int j = 0; j < repeat_num; ++j) {
        out_lod.push_back(out_lod.back() + x_seq_len);
        out_offset++;
      }
    }
    // write lod to out if x has lod
    auto& ref_lod = *out->mutable_lod();
    ref_lod[0] = out_lod;
  }
  Vector<size_t> ref_x_lod;
  if (x->lod().size() == 1) {
    ref_x_lod = x->lod()[0];
  } else {
    // x_lod doesn't has lod, use fake x lod, level = 0
    ref_x_lod.resize(x->dims()[0] + 1);
    std::iota(ref_x_lod.begin(), ref_x_lod.end(), 0);
  }
  SequenceExpandFunctor<Context, T> functor;
  functor(dev_ctx, *x, ref_x_lod, y_lod[ref_level], out);
}

template <typename T, typename Context>
void SequenceExpandGradKernel(const Context& dev_ctx,
                              const DenseTensor& x_in,
                              const DenseTensor& y_in,
                              const DenseTensor& out_grad,
                              int ref_level,
                              DenseTensor* x_grad) {
  auto* g_out = &out_grad;
  auto* x = &x_in;
  auto* y = &y_in;
  auto* g_x = x_grad;

  dev_ctx.template Alloc<T>(g_x);
  g_x->set_lod(x->lod());

  funcs::SetConstant<Context, T> set_zero;
  set_zero(dev_ctx, g_x, static_cast<T>(0));

  auto& y_lod = y->lod();
  if (ref_level == -1) ref_level = y_lod.size() - 1;
  // just copy the gradient
  if (y_lod[ref_level].size() <= 1) {
    Copy(dev_ctx, *g_out, dev_ctx.GetPlace(), false, g_x);
    return;
  }

  Vector<size_t> ref_x_lod;
  Vector<size_t> ref_lod = y_lod[ref_level];
  if (x->lod().size() == 1) {
    ref_x_lod = x->lod()[0];
  } else {
    // x_lod doesn't has lod, use fake x lod, level = 0
    ref_x_lod.resize(x->dims()[0] + 1);
    std::iota(ref_x_lod.begin(), ref_x_lod.end(), 0);
  }
  SequenceExpandGradFunctor<Context, T> functor;
  functor(dev_ctx, *g_out, ref_x_lod, ref_lod, g_x);
}

// for GPU kernel
inline void GetOutputOffset(const Vector<size_t>& x_lod,
                            const Vector<size_t>& ref_lod,
                            Vector<size_t>* out_offset) {
  size_t offset = 0;
  int lod_size = static_cast<int>(x_lod.size());
  for (int i = 0; i < static_cast<int>(x_lod.size()); ++i) {
    (*out_offset)[i] = offset;
    if (i < lod_size - 1) {
      offset += (ref_lod[i + 1] - ref_lod[i]) * (x_lod[i + 1] - x_lod[i]);
    }
  }
}

}  // namespace phi