// 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/linspace_kernel.h"

#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/funcs/data_type_transform.h"

namespace phi {

template <typename T, typename Context>
void LinspaceKernel(const Context& dev_ctx,
                    const DenseTensor& start,
                    const DenseTensor& stop,
                    const DenseTensor& number,
                    DataType dtype,
                    DenseTensor* out) {
  int64_t num = 0;
  if (number.dtype() == DataType::INT64) {
    num = number.data<int64_t>()[0];
  } else if (number.dtype() == DataType::INT32) {
    num = number.data<int32_t>()[0];
  }
  PADDLE_ENFORCE_GE(num,
                    0,
                    common::errors::InvalidArgument(
                        "The num of linspace op should be larger "
                        "than or equal to 0, but received num is %d",
                        num));
  if (num == 0) {
    out->Resize({0});
    dev_ctx.template Alloc<T>(out);
    return;
  }
  using StepT = std::conditional_t<std::is_integral_v<T>, double, T>;
  auto start_t = funcs::TransDataType(dev_ctx, start, dtype);
  auto stop_t = funcs::TransDataType(dev_ctx, stop, dtype);

  T start_data = start_t.template data<T>()[0];
  T stop_data = stop_t.template data<T>()[0];

  out->Resize({num});
  T* out_data = dev_ctx.template Alloc<T>(out);

  if (num > 1) {
    // step should be of StepT type
    StepT step =
        (static_cast<StepT>(stop_data) - static_cast<StepT>(start_data)) /
        (num - 1);
    int half_num = num / 2;
    for (int i = 0; i < num; ++i) {
      if (i < half_num) {
        out_data[i] = static_cast<T>(start_data + step * i);
      } else {
        out_data[i] = static_cast<T>(stop_data - step * (num - i - 1));
      }
    }
  } else {
    out_data[0] = static_cast<T>(start_data);
  }
}

}  // namespace phi

PD_REGISTER_KERNEL(linspace,
                   CPU,
                   ALL_LAYOUT,
                   phi::LinspaceKernel,
                   float,
                   int32_t,
                   int64_t,
                   double) {}