// Copyright (c) 2022 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 <type_traits>

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

#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/common/int_array.h"
#include "paddle/phi/common/scalar.h"
#include "paddle/phi/core/dense_tensor.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/core/tensor_utils.h"
#include "paddle/phi/kernels/empty_kernel.h"
#include "paddle/phi/kernels/expand_kernel.h"
#include "paddle/phi/kernels/funcs/eigen/common.h"
#include "paddle/phi/kernels/funcs/math_function.h"
#include "paddle/phi/kernels/funcs/slice_utils.h"
namespace phi {

template <typename T, typename Context, size_t RANK>
void SetValueImpl(const Context& dev_ctx,
                  const DenseTensor& in,
                  const DenseTensor& value,
                  const IntArray& starts,
                  const IntArray& ends,
                  const IntArray& steps,
                  const std::vector<int64_t>& axes,
                  const std::vector<int64_t>& decrease_axes,
                  const std::vector<int64_t>& none_axes,
                  DenseTensor* out) {
  auto in_dims = in.dims();
  std::vector<int64_t> starts_local = starts.GetData();
  std::vector<int64_t> ends_local = ends.GetData();
  std::vector<int64_t> steps_local = steps.GetData();
  if (starts_local.empty() && ends_local.empty() && steps_local.empty() &&
      axes.empty() && decrease_axes.empty() && none_axes.empty() &&
      value.numel() == 1) {
    ExpandKernel<T, Context>(
        dev_ctx, value, IntArray{vectorize<int64_t>(in.dims())}, out);
    return;
  }
  funcs::CheckAndUpdateSliceAttrs(
      in_dims, axes, &starts_local, &ends_local, &steps_local);
  auto slice_dims = funcs::GetSliceDims(
      in_dims, axes, starts_local, ends_local, &steps_local);
  auto decrease_slice_dims = funcs::GetDecreasedDims(slice_dims, decrease_axes);
  auto slice_dims_for_assign = decrease_slice_dims;
  if (!none_axes.empty()) {
    std::vector<int64_t> slice_dims_with_none;

    size_t none_axes_cur = 0, decrease_axes_cur = 0;
    for (int i = 0; i < slice_dims.size(); ++i) {
      while (none_axes_cur < none_axes.size() &&
             none_axes[none_axes_cur] <= i) {
        slice_dims_with_none.push_back(1);
        none_axes_cur++;
      }
      if (decrease_axes_cur < decrease_axes.size() &&
          decrease_axes[decrease_axes_cur] == i) {
        decrease_axes_cur++;
      } else {
        slice_dims_with_none.push_back(slice_dims[i]);
      }
    }
    while (none_axes_cur < none_axes.size()) {
      slice_dims_with_none.push_back(1);
      none_axes_cur++;
    }

    slice_dims_for_assign = make_ddim(slice_dims_with_none);
  }
  funcs::CheckIsDimsMatch(slice_dims_for_assign, value.dims());

  auto value_shape = vectorize<int64_t>(value.dims());

  DenseTensor value_tensor = Empty<T>(dev_ctx, IntArray{value_shape});
  value_tensor = value;
  auto it = value_shape.begin();
  while (it != value_shape.end() && *it == 1) {
    it = value_shape.erase(it);
  }
  if (value_shape.empty()) value_shape.push_back(1);
  value_tensor.Resize(value_shape);

  auto expand_shape = vectorize<int64_t>(slice_dims_for_assign);
  for (size_t i = 0; i < expand_shape.size(); i++) {
    if (expand_shape[i] == 0) expand_shape[i] = 1;
  }
  if (expand_shape.empty()) expand_shape.push_back(1);
  DenseTensor expand_tensor = Empty<T>(dev_ctx, IntArray{expand_shape});

  auto place = dev_ctx.GetPlace();
  auto& eigen_place = *dev_ctx.eigen_device();

  Copy(dev_ctx, in, place, false, out);
  ExpandKernel<T, Context>(
      dev_ctx, value_tensor, IntArray{expand_shape}, &expand_tensor);
  expand_tensor.Resize(slice_dims);

  auto out_e = EigenTensor<T, RANK>::From(*out);
  auto value_e = EigenTensor<T, RANK>::From(expand_tensor);

  auto starts_indices = Eigen::DSizes<int64_t, RANK>();
  auto ends_indices = Eigen::DSizes<int64_t, RANK>();
  auto strides_indices = Eigen::DSizes<int64_t, RANK>();

  for (size_t i = 0; i < RANK; ++i) {
    starts_indices[i] = 0;
    ends_indices[i] = slice_dims[i];
    strides_indices[i] = 1;
  }
  for (size_t i = 0; i < axes.size(); i++) {
    int axis_index = axes[i];
    starts_indices[axis_index] = starts_local[i];
    ends_indices[axis_index] = ends_local[i];
    strides_indices[axis_index] = steps_local[i];
    if (starts_local[i] ==
        ends_local[i]) {  // slice is empty, data will not be changed
      return;
    }
  }

  out_e.stridedSlice(starts_indices, ends_indices, strides_indices)
      .device(eigen_place) = value_e;
}

template <typename T, typename Context>
void SetTensorValueKernel(const Context& dev_ctx,
                          const DenseTensor& x,
                          const DenseTensor& value,
                          const IntArray& starts,
                          const IntArray& ends,
                          const IntArray& steps,
                          const std::vector<int64_t>& axes,
                          const std::vector<int64_t>& decrease_axes,
                          const std::vector<int64_t>& none_axes,
                          DenseTensor* out) {
  if (x.numel() == 0) {
    dev_ctx.template Alloc<T>(out);
    return;
  }

  const int rank = x.dims().size();

  switch (rank) {
#define CASE_RANK(__RK)                           \
  case __RK:                                      \
    SetValueImpl<T, Context, __RK>(dev_ctx,       \
                                   x,             \
                                   value,         \
                                   starts,        \
                                   ends,          \
                                   steps,         \
                                   axes,          \
                                   decrease_axes, \
                                   none_axes,     \
                                   out);          \
    break;
    CASE_RANK(1)
    CASE_RANK(2)
    CASE_RANK(3)
    CASE_RANK(4)
    CASE_RANK(5)
    CASE_RANK(6)
#undef CASE_RANK
    default:
      PADDLE_THROW(errors::InvalidArgument(
          "The rank of input should be less than 7, but received %d.", rank));
  }
}

template <typename T, typename Context>
void SetValueKernel(const Context& dev_ctx,
                    const DenseTensor& x,
                    const IntArray& starts,
                    const IntArray& ends,
                    const IntArray& steps,
                    const std::vector<int64_t>& axes,
                    const std::vector<int64_t>& decrease_axes,
                    const std::vector<int64_t>& none_axes,
                    const std::vector<int64_t>& shape,
                    const std::vector<Scalar>& values,
                    DenseTensor* out) {
  std::vector<T> assign_values;
  assign_values.reserve(values.size());
  for (const auto& val : values) {
    assign_values.push_back(val.to<T>());
  }

  bool is_full_set_one_value = false;
  std::vector<int64_t> starts_local = starts.GetData();
  std::vector<int64_t> ends_local = ends.GetData();
  std::vector<int64_t> steps_local = steps.GetData();
  if (starts_local.empty() && ends_local.empty() && steps_local.empty() &&
      shape.size() == 1 && shape[0] == 1 && assign_values.size() == 1) {
    is_full_set_one_value = true;
  }
  if (is_full_set_one_value && std::is_same<T, float>::value) {
    dev_ctx.template Alloc<T>(out);
    funcs::set_constant(dev_ctx, out, static_cast<float>(assign_values[0]));
    return;
  }

  DenseTensor value_tensor = Empty<T>(dev_ctx, shape);
  TensorFromVector(assign_values, dev_ctx, &value_tensor);
  value_tensor.Resize(shape);

  SetTensorValueKernel<T, Context>(dev_ctx,
                                   x,
                                   value_tensor,
                                   starts,
                                   ends,
                                   steps,
                                   axes,
                                   decrease_axes,
                                   none_axes,
                                   out);
}

}  // namespace phi

PD_REGISTER_KERNEL(set_value,
                   CPU,
                   ALL_LAYOUT,
                   phi::SetValueKernel,
                   float,
                   double,
                   int,
                   int64_t,
                   bool,
                   int16_t,
                   uint8_t,
                   int8_t,
                   phi::float16,
                   phi::bfloat16,
                   phi::complex64,
                   phi::complex128) {}
PD_REGISTER_KERNEL(set_value_with_tensor,
                   CPU,
                   ALL_LAYOUT,
                   phi::SetTensorValueKernel,
                   float,
                   double,
                   int,
                   int64_t,
                   bool,
                   int16_t,
                   uint8_t,
                   int8_t,
                   phi::bfloat16,
                   phi::float16,
                   phi::complex64,
                   phi::complex128) {}