// 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/arg_min_max_kernel.h"

#include "paddle/common/ddim.h"
#include "paddle/phi/backends/xpu/xpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/core/utils/data_type.h"
#include "paddle/phi/kernels/funcs/math_function.h"

namespace phi {

// ArgMax implementation
template <typename T, typename Context>
void ArgMaxKernel(const Context& dev_ctx,
                  const DenseTensor& x,
                  const Scalar& axis,
                  bool keepdims,
                  bool flatten,
                  DataType dtype,
                  DenseTensor* out) {
  PADDLE_ENFORCE_GE(
      x.numel(),
      0,
      common::errors::InvalidArgument(
          "argmin/argmax input numel must > 0, but got %d", x.numel()));
  using XPUType = typename XPUTypeTrait<T>::Type;
  PADDLE_ENFORCE_EQ(
      (dtype == DataType::UNDEFINED || dtype == DataType::INT32 ||
       dtype == DataType::INT64),
      true,
      errors::InvalidArgument(
          "The attribute of dtype in xpu argmin/argmax must be [%s] or [%s], "
          "but received [%s]",
          DataType::INT64,
          DataType::INT32,
          dtype));
  // TODO(ZHUI): fix dtype of out
  DDim x_dims;
  int64_t axis_val = axis.to<int64_t>();
  if (flatten) {
    x_dims = make_ddim({x.numel()});
    // if flatten, the axis just as 0
    axis_val = 0;
  } else {
    x_dims = x.dims();
    if (axis_val < 0) axis_val += x_dims.size();
  }
  auto xdims_vec = vectorize<int64_t>(x_dims);
  if (dtype != DataType::INT32) {
    dev_ctx.template Alloc<int64_t>(out);
    if (x.numel() == 0) return;
    if (x.dims().size() == 0) {
      int r = xpu::constant(dev_ctx.x_context(),
                            out->data<int64_t>(),
                            x.numel(),
                            static_cast<int64_t>(0));
      PADDLE_ENFORCE_XDNN_SUCCESS(r, "constant");
      return;
    }
    int r = xpu::argmax(dev_ctx.x_context(),
                        reinterpret_cast<const XPUType*>(x.data<T>()),
                        out->data<int64_t>(),
                        xdims_vec,
                        axis_val);
    PADDLE_ENFORCE_XDNN_SUCCESS(r, "argmax");
  } else {
    dev_ctx.template Alloc<int>(out);
    if (x.numel() == 0) return;
    DenseTensor out_int64;
    out_int64.Resize(out->dims());
    dev_ctx.template Alloc<int64_t>(&out_int64);
    if (x.dims().size() == 0) {
      int r = xpu::constant(dev_ctx.x_context(),
                            out_int64.data<int64_t>(),
                            x.numel(),
                            static_cast<int64_t>(0));
      PADDLE_ENFORCE_XDNN_SUCCESS(r, "constant");
    } else {
      int r = xpu::argmax(dev_ctx.x_context(),
                          reinterpret_cast<const XPUType*>(x.data<T>()),
                          out_int64.data<int64_t>(),
                          xdims_vec,
                          axis_val);
      PADDLE_ENFORCE_XDNN_SUCCESS(r, "argmax");
    }

    int r = xpu::cast<int64_t, int>(dev_ctx.x_context(),
                                    out_int64.data<int64_t>(),
                                    out->data<int>(),
                                    out_int64.numel());
    PADDLE_ENFORCE_XDNN_SUCCESS(r, "cast");
  }
}

// ArgMin implementation
template <typename T, typename Context>
void ArgMinKernel(const Context& dev_ctx,
                  const DenseTensor& x,
                  const Scalar& axis,
                  bool keepdims,
                  bool flatten,
                  DataType dtype,
                  DenseTensor* out) {
  PADDLE_ENFORCE_GE(
      x.numel(),
      0,
      common::errors::InvalidArgument(
          "argmin/argmax input numel must > 0, but got %d", x.numel()));
  using XPUType = typename XPUTypeTrait<T>::Type;
  PADDLE_ENFORCE_EQ(
      (dtype == DataType::UNDEFINED || dtype == DataType::INT32 ||
       dtype == DataType::INT64),
      true,
      errors::InvalidArgument(
          "The attribute of dtype in xpu argmin/argmax must be [%s] or [%s], "
          "but received [%s]",
          DataType::INT64,
          DataType::INT32,
          dtype));

  DDim x_dims;
  int64_t axis_val = axis.to<int64_t>();
  if (flatten) {
    x_dims = make_ddim({x.numel()});
    // If flatten, the axis just as 0
    axis_val = 0;
  } else {
    x_dims = x.dims();
    if (axis_val < 0) axis_val += x_dims.size();
  }
  auto xdims_vec = vectorize<int64_t>(x_dims);
  if (dtype != DataType::INT32) {
    dev_ctx.template Alloc<int64_t>(out);
    if (x.numel() == 0) return;
    if (x.dims().size() == 0) {
      int r = xpu::constant(dev_ctx.x_context(),
                            out->data<int64_t>(),
                            x.numel(),
                            static_cast<int64_t>(0));
      PADDLE_ENFORCE_XDNN_SUCCESS(r, "constant");
      return;
    }
    int r = xpu::argmin(dev_ctx.x_context(),
                        reinterpret_cast<const XPUType*>(x.data<T>()),
                        out->data<int64_t>(),
                        xdims_vec,
                        axis_val);
    PADDLE_ENFORCE_XDNN_SUCCESS(r, "argmin");
  } else {
    dev_ctx.template Alloc<int>(out);
    if (x.numel() == 0) return;
    DenseTensor out_int64;
    out_int64.Resize(out->dims());
    dev_ctx.template Alloc<int64_t>(&out_int64);
    if (x.dims().size() == 0) {
      int r = xpu::constant(dev_ctx.x_context(),
                            out_int64.data<int64_t>(),
                            x.numel(),
                            static_cast<int64_t>(0));
      PADDLE_ENFORCE_XDNN_SUCCESS(r, "constant");
    } else {
      int r = xpu::argmin(dev_ctx.x_context(),
                          reinterpret_cast<const XPUType*>(x.data<T>()),
                          out_int64.data<int64_t>(),
                          xdims_vec,
                          axis_val);
      PADDLE_ENFORCE_XDNN_SUCCESS(r, "argmin");
    }

    int r = xpu::cast<int64_t, int>(dev_ctx.x_context(),
                                    out_int64.data<int64_t>(),
                                    out->data<int>(),
                                    out_int64.numel());
    PADDLE_ENFORCE_XDNN_SUCCESS(r, "cast");
  }
}

}  // namespace phi

PD_REGISTER_KERNEL(argmax,
                   XPU,
                   ALL_LAYOUT,
                   phi::ArgMaxKernel,
                   float,
                   int,
                   int64_t,
                   phi::float16,
                   phi::bfloat16) {
  kernel->OutputAt(0).SetDataType(phi::DataType::UNDEFINED);
}

PD_REGISTER_KERNEL(argmin,
                   XPU,
                   ALL_LAYOUT,
                   phi::ArgMinKernel,
                   float,
                   phi::float16,
                   phi::bfloat16) {
  kernel->OutputAt(0).SetDataType(phi::DataType::UNDEFINED);
}