// 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 "paddle/phi/kernels/set_value_kernel.h"
#include <chrono>
#include <iostream>
#include <type_traits>
#include "paddle/phi/backends/gpu/gpu_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/math_function.h"
#include "paddle/phi/kernels/funcs/slice_utils.h"
#include "paddle/phi/kernels/strided_copy_kernel.h"
namespace phi {

template <typename T, typename Context>
void SetTensorValueKernel(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) {
  if (in.numel() == 0) {
    dev_ctx.template Alloc<T>(out);
    return;
  }

  auto in_dims = in.dims();
  auto meta = in.meta();
  std::vector<int64_t> starts_local = starts.GetData();
  std::vector<int64_t> ends_local = ends.GetData();
  std::vector<int64_t> steps_local = steps.GetData();
  funcs::CheckAndUpdateSliceAttrs(
      in_dims, axes, &starts_local, &ends_local, &steps_local);

  std::vector<int64_t> output_dims = vectorize<int64_t>(in.dims());
  std::vector<int64_t> output_stride = vectorize<int64_t>(in.strides());
  int64_t output_offset = static_cast<int64_t>(in.offset());
  for (size_t i = 0; i < axes.size(); ++i) {
    int64_t axis_size = in.dims()[axes[i]];
    if (axis_size < 0) {
      continue;
    }

    int64_t step_size = std::abs(steps_local[i]);

    auto out_dim =
        (std::abs(ends_local[i] - starts_local[i]) + step_size - 1) / step_size;
    output_offset += static_cast<int64_t>(
        starts_local[i] * output_stride[axes[i]] * SizeOf(out->dtype()));
    output_dims[axes[i]] = out_dim;
    output_stride[axes[i]] *= steps_local[i];
  }
  // generate new shape
  std::vector<int64_t> new_out_shape;
  std::vector<int64_t> new_out_stride;
  funcs::GetDecreasedDimsAndStrides(output_dims,
                                    output_stride,
                                    decrease_axes,
                                    none_axes,
                                    &new_out_shape,
                                    &new_out_stride);

  if (product(make_ddim(new_out_shape)) <= 0) {
    // 0-size tensor, no need to copy
    out->ResetHolder(in.Holder());
    out->ShareInplaceVersionCounterWith(in);
    return;
  }

  funcs::CheckIsDimsMatch(make_ddim(new_out_shape), value.dims());
  if (new_out_shape.empty()) new_out_shape.push_back(1);
  DenseTensor expand_tensor;
  if (value.numel() == 1) {
    expand_tensor = value;
    expand_tensor.Resize({1});
  } else if (product(value.dims()) == product(make_ddim(new_out_shape))) {
    expand_tensor = value;
    if (value.dims() != make_ddim(new_out_shape)) {
      expand_tensor.Resize(new_out_shape);
    }

  } else {
    auto value_dims = vectorize<int64_t>(value.dims());
    DenseTensor value_tensor = Empty<T>(dev_ctx, IntArray{value_dims});
    value_tensor = value;
    auto it = value_dims.begin();
    while (it != value_dims.end() && *it == 1) {
      it = value_dims.erase(it);
    }
    if (value_dims.empty()) value_dims.push_back(1);
    auto v_dims = make_ddim(value_dims);
    auto out_dims = make_ddim(new_out_shape);
    value_tensor.Resize(v_dims);
    if (funcs::CheckIsLastDimsMatch(v_dims, out_dims)) {
      expand_tensor = value_tensor;
    } else {
      expand_tensor = Empty<T>(dev_ctx, IntArray{new_out_shape});
      ExpandKernel<T, Context>(
          dev_ctx, value_tensor, IntArray{new_out_shape}, &expand_tensor);
    }
  }

  out->ResetHolder(in.Holder());
  out->ShareInplaceVersionCounterWith(in);
  if (starts_local.empty() && ends_local.empty() && steps_local.empty()) {
    if (expand_tensor.numel() != out->numel()) {
      ExpandKernel<T, Context>(
          dev_ctx, expand_tensor, IntArray{new_out_shape}, out);
    } else {
      Copy<Context>(dev_ctx, expand_tensor, dev_ctx.GetPlace(), false, out);
    }
  } else {
    StridedCopyKernel<T, Context>(dev_ctx,
                                  expand_tensor,
                                  new_out_shape,
                                  new_out_stride,
                                  output_offset,
                                  out);
  }
  out->set_meta(meta);
}

template <typename T, typename Context>
void SetValueKernel(const Context& dev_ctx,
                    const DenseTensor& in,
                    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, complex64>::value &&
      !std::is_same<T, complex128>::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,
                                   in,
                                   value_tensor,
                                   starts,
                                   ends,
                                   steps,
                                   axes,
                                   decrease_axes,
                                   none_axes,
                                   out);
}

}  // namespace phi

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