// Copyright (c) 2025 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.

#include "paddle/phi/kernels/index_elementwise_put_kernel.h"

#include "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/funcs/index_elementwise.cu.h"
#include "paddle/phi/kernels/funcs/stride_utils.h"

namespace phi {

template <typename T, typename OffsetT = uint32_t>
void GPUIndexElementwisePutKernel(const GPUContext& dev_ctx,
                                  const DenseTensor& input,
                                  const Scalar& value,
                                  const std::vector<const DenseTensor*>& index,
                                  const std::vector<int64_t>& input_dims,
                                  const std::vector<int64_t>& input_strides,
                                  const std::vector<int64_t>& index_dims,
                                  const std::vector<int64_t>& index_strides,
                                  const int64_t slice_offset,
                                  DenseTensor* output) {
  bool is_initialized = output->initialized();
  bool is_same_place = true;
  if (is_initialized) {
    is_same_place = (input.place() == output->place());
  }
  T* output_ = dev_ctx.template Alloc<T>(output);

  if (!is_initialized || !is_same_place) {
    Copy(dev_ctx, input, dev_ctx.GetPlace(), false, output);
  }
  int64_t numel = 0;
  int64_t num_indices = 0;
  std::vector<int64_t> shape_tmp;
  std::vector<int64_t> stride_tmp;
  funcs::cal_shape_stride(index_dims, &num_indices, &shape_tmp, &stride_tmp);

  auto sizes = std::array<int64_t, 25>{};
  auto strides = std::array<int64_t, 25>{};
  for (int64_t i = 0; i < num_indices; i++) {
    sizes[i] = index_dims[i];
    strides[i] = index_strides[i];
  }

  std::array<int64_t*, 3> strides_array;
  std::vector<int64_t> desired_shape;
  std::array<std::vector<int64_t>, 3> strides_vec;

  T value_T = value.to<T>();

  funcs::IndexPutStride<3>(input_dims,
                           input_strides,
                           SizeOf(input.dtype()),
                           {},
                           {},
                           4,
                           shape_tmp,
                           stride_tmp,
                           SizeOf(index[0]->dtype()),
                           &desired_shape,
                           &strides_array,
                           &numel,
                           strides_vec);
  for (auto s : desired_shape) {
    if (s == 0) {
      return;
    }
  }
  auto offset_calc = funcs::make_offset_calculator_put<3, false, OffsetT>(
      desired_shape, strides_array);

  const int64_t N = numel;
  PADDLE_ENFORCE_EQ(true,
                    (N >= 0 && N <= std::numeric_limits<int32_t>::max()),
                    common::errors::PreconditionNotMet(
                        "the value of N should be in [0, "
                        "std::numeric_limits<int32_t>::max()]"));
  constexpr int nt = 128;
  constexpr int vt = 4;
  const dim3 block(nt);
  const dim3 grid((N + block.x * vt - 1) / (block.x * vt));
  auto stream = dev_ctx.stream();

  char* out_ptr = reinterpret_cast<char*>(output_);
  if (index.size() == 1 && index[0]->dtype() == DataType::BOOL) {
    const bool* mask_data = index[0]->data<bool>();
    funcs::index_elementwise_with_tensor_kernel<nt, vt>
        <<<grid, block, 0, stream>>>(N, [=] __device__(int64_t idx) {
          const auto offsets = offset_calc.get(idx);
          char* const out_data =
              out_ptr + static_cast<int64_t>(offsets[0]) + slice_offset;
          if (mask_data[idx]) {
            *reinterpret_cast<T*>(out_data) = value_T;
          }
        });
  } else {
    auto index_ptrs = funcs::GetIndexDataPtrs<int64_t>(index);
    funcs::index_elementwise_kernel<nt, vt, T><<<grid, block, 0, stream>>>(
        N, value_T, [=] __device__(int idx, const T value_tmp) {
          const auto offsets = offset_calc.get(idx);
          char* const out_data =
              out_ptr + static_cast<int64_t>(offsets[0]) + slice_offset;

          int64_t offset = 0;
#pragma unroll
          for (int64_t i = 0; i < num_indices; i++) {
            int64_t index =
                *reinterpret_cast<int64_t*>(index_ptrs[i] + offsets[2]);
            if (index < 0) {
              index += sizes[i];
            }
            offset += index * strides[i];
          }
          *reinterpret_cast<T*>(out_data + offset) = value_tmp;
        });
  }
}

template <typename T, typename OffsetT = uint32_t>
void GPUIndexElementwisePutWithTensorKernel(
    const GPUContext& dev_ctx,
    const DenseTensor& input,
    const DenseTensor& value,
    const std::vector<const DenseTensor*>& index,
    const std::vector<int64_t>& input_dims,
    const std::vector<int64_t>& input_strides,
    const std::vector<int64_t>& index_dims,
    const std::vector<int64_t>& index_strides,
    const int64_t slice_offset,
    DenseTensor* output) {
  bool is_initialized = output->initialized();
  bool is_same_place = true;
  if (is_initialized) {
    is_same_place = (input.place() == output->place());
  }
  T* output_ = dev_ctx.template Alloc<T>(output);

  if (!is_initialized || !is_same_place) {
    Copy(dev_ctx, input, dev_ctx.GetPlace(), false, output);
  }

  int64_t numel = 0;
  int64_t num_indices = 0;
  std::vector<int64_t> shape_tmp;
  std::vector<int64_t> stride_tmp;
  funcs::cal_shape_stride(index_dims, &num_indices, &shape_tmp, &stride_tmp);

  auto sizes = std::array<int64_t, DDim::kMaxRank + 1>{};
  auto strides = std::array<int64_t, DDim::kMaxRank + 1>{};
  for (int64_t i = 0; i < num_indices; i++) {
    sizes[i] = index_dims[i];
    strides[i] = index_strides[i];
  }
  auto index_ptrs = funcs::GetIndexDataPtrs<int64_t>(index);

  std::array<int64_t*, 3> strides_array;
  std::vector<int64_t> desired_shape;
  std::array<std::vector<int64_t>, 3> strides_vec;

  funcs::IndexPutStride<3>(input_dims,
                           input_strides,
                           SizeOf(input.dtype()),
                           vectorize<int64_t>(value.dims()),
                           vectorize<int64_t>(value.strides()),
                           SizeOf(value.dtype()),
                           shape_tmp,
                           stride_tmp,
                           SizeOf(index[0]->dtype()),
                           &desired_shape,
                           &strides_array,
                           &numel,
                           strides_vec);
  for (auto s : desired_shape) {
    if (s == 0) {
      return;
    }
  }

  auto offset_calc = funcs::make_offset_calculator_put<3, false, OffsetT>(
      desired_shape, strides_array);

  const int64_t N = numel;

  constexpr int nt = 128;
  constexpr int vt = 4;
  const dim3 block(nt);
  const dim3 grid((N + block.x * vt - 1) / (block.x * vt));
  auto stream = dev_ctx.stream();

  using dtype = funcs::OpaqueType<sizeof(T)>;

  const char* in_ptr = reinterpret_cast<const char*>(value.data<T>());
  char* out_ptr = reinterpret_cast<char*>(output_);

  funcs::index_elementwise_with_tensor_kernel<nt, vt>
      <<<grid, block, 0, stream>>>(N, [=] __device__(int64_t idx) {
        const auto offsets = offset_calc.get(idx);
        char* const out_data =
            out_ptr + static_cast<int64_t>(offsets[0]) + slice_offset;
        const char* const in_data = in_ptr + offsets[1];

        int64_t offset = 0;
#pragma unroll
        for (int64_t i = 0; i < num_indices; i++) {
          int64_t index =
              *reinterpret_cast<int64_t*>(index_ptrs[i] + offsets[2]);
          if (index < 0) {
            index += sizes[i];
          }
          offset += index * strides[i];
        }
        *reinterpret_cast<dtype*>(out_data + offset) =
            *reinterpret_cast<const dtype*>(in_data);
      });
}

template <typename T, typename Context>
void IndexElementwisePutKernel(const Context& dev_ctx,
                               const DenseTensor& x,
                               const std::vector<const DenseTensor*>& index,
                               const Scalar& value,
                               const std::vector<int64_t>& input_dims,
                               const std::vector<int64_t>& input_strides,
                               const std::vector<int64_t>& index_dims,
                               const std::vector<int64_t>& index_strides,
                               const int64_t slice_offset,
                               DenseTensor* out) {
  const auto& index_type = index[0]->dtype();
  PADDLE_ENFORCE_EQ(index_type == DataType::INT64 ||
                        (index_type == DataType::BOOL && index.size() == 1),
                    true,
                    common::errors::InvalidArgument(
                        "Index holds the wrong type, it holds [%s], but "
                        "desires to be [%s].",
                        index_type,
                        DataType::INT64));

  if (out->numel() == 0) return;
  if (funcs::IsInUint32Range(x.numel() * sizeof(T), out->numel() * sizeof(T))) {
    GPUIndexElementwisePutKernel<T>(dev_ctx,
                                    x,
                                    value,
                                    index,
                                    input_dims,
                                    input_strides,
                                    index_dims,
                                    index_strides,
                                    slice_offset,
                                    out);
  } else {
    GPUIndexElementwisePutKernel<T, uint64_t>(dev_ctx,
                                              x,
                                              value,
                                              index,
                                              input_dims,
                                              input_strides,
                                              index_dims,
                                              index_strides,
                                              slice_offset,
                                              out);
  }
}

template <typename T, typename Context>
void IndexElementwisePutWithTensorKernel(
    const Context& dev_ctx,
    const DenseTensor& x,
    const std::vector<const DenseTensor*>& index,
    const DenseTensor& value,
    const std::vector<int64_t>& input_dims,
    const std::vector<int64_t>& input_strides,
    const std::vector<int64_t>& index_dims,
    const std::vector<int64_t>& index_strides,
    const int64_t slice_offset,
    DenseTensor* out) {
  const auto& index_type = index[0]->dtype();
  PADDLE_ENFORCE_EQ(index_type == DataType::INT64,
                    true,
                    common::errors::InvalidArgument(
                        "Index holds the wrong type, it holds [%s], but "
                        "desires to be [%s].",
                        index_type,
                        DataType::INT64));

  if (out->numel() == 0) return;
  if (funcs::IsInUint32Range(x.numel() * sizeof(T), out->numel() * sizeof(T))) {
    GPUIndexElementwisePutWithTensorKernel<T>(dev_ctx,
                                              x,
                                              value,
                                              index,
                                              input_dims,
                                              input_strides,
                                              index_dims,
                                              index_strides,
                                              slice_offset,
                                              out);

  } else {
    GPUIndexElementwisePutWithTensorKernel<T, uint64_t>(dev_ctx,
                                                        x,
                                                        value,
                                                        index,
                                                        input_dims,
                                                        input_strides,
                                                        index_dims,
                                                        index_strides,
                                                        slice_offset,
                                                        out);
  }
}

}  // namespace phi

PD_REGISTER_KERNEL(index_elementwise_put,
                   GPU,
                   ALL_LAYOUT,
                   phi::IndexElementwisePutKernel,
                   bool,
                   float,
                   double,
                   int,
                   int8_t,
                   int64_t,
                   int16_t,
                   uint8_t,
                   phi::float16,
                   phi::bfloat16,
                   phi::complex64,
                   phi::complex128) {}

PD_REGISTER_KERNEL(index_elementwise_put_with_tensor,
                   GPU,
                   ALL_LAYOUT,
                   phi::IndexElementwisePutWithTensorKernel,
                   bool,
                   float,
                   double,
                   int,
                   int8_t,
                   int64_t,
                   int16_t,
                   uint8_t,
                   phi::float16,
                   phi::bfloat16,
                   phi::complex64,
                   phi::complex128) {}