// Copyright (c) 2025 Huawei Technologies Co., Ltd
// All rights reserved.
//
// Licensed under the BSD 3-Clause License  (the "License");
// you may not use this file except in compliance with the License.
//
// 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 "op_plugin/OpApiInterface.h"
#include "op_plugin/utils/op_api_common.h"

const int DIMENSION_2D = 2;
const int DIMENSION_3D = 3;
const int DIM_0 = 0;
const int DIM_1 = 1;
const int DIM_2 = 2;

namespace op_api {
using npu_preparation = at_npu::native::OpPreparation;

std::tuple<at::Tensor, at::Tensor> npu_dynamic_block_quant(
    const at::Tensor& x,
    double min_scale,
    c10::string_view round_mode,
    int64_t dst_type,
    int64_t row_block_size,
    int64_t col_block_size,
    double dst_type_max) {
  at::Tensor y;
  at::Tensor scale;
  auto y_shape = op_infer::array_to_small_vector(x.sizes());
  auto scale_shape = op_infer::array_to_small_vector(x.sizes());
  if (scale_shape.size() == DIMENSION_2D) {
    scale_shape[DIM_0] = op_infer::CeilDiv(scale_shape[DIM_0], row_block_size);
    scale_shape[DIM_1] = op_infer::CeilDiv(scale_shape[DIM_1], col_block_size);
  } else if (scale_shape.size() == DIMENSION_3D) {
    scale_shape[DIM_1] = op_infer::CeilDiv(scale_shape[DIM_1], row_block_size);
    scale_shape[DIM_2] = op_infer::CeilDiv(scale_shape[DIM_2], col_block_size);
  } else {
    TORCH_CHECK(false, "x must be 2 or 3 dimensional.", OPS_ERROR(ErrCode::NOT_SUPPORT));
  }

  ASCEND_LOGI("[npu_dynamic_block_quant]: Getting aclTensor y dtype by Parameter(dst_type): %ld", dst_type);
  aclDataType y_acltype = c10_npu::GetAclDataType(dst_type);
  at::ScalarType dtype = npu_preparation::convert_to_scalar_type(y_acltype);

  y = npu_preparation::apply_tensor_without_format(y_shape, c10::dtype(dtype));
  scale = npu_preparation::apply_tensor_without_format(scale_shape, c10::dtype(c10::ScalarType::Float));

  char* round_mode_ptr = const_cast<char*>(round_mode.data());
  ASCEND_LOGI(
      "[npu_dynamic_block_quant]: Setting aclTensor y dtype to: %s",
      at_npu::native::AclDataTypeToString(y_acltype).c_str());
  TensorWrapper y_wrapper = {y, y_acltype};
  static bool npu_support_v2 = check_aclnn_kernel_available("aclnnDynamicBlockQuantV2");
  if (npu_support_v2) {
    EXEC_NPU_CMD(
        aclnnDynamicBlockQuantV2,
        x,
        min_scale,
        round_mode_ptr,
        y_acltype,
        row_block_size,
        col_block_size,
        dst_type_max,
        y_wrapper,
        scale);
  } else {
    EXEC_NPU_CMD(
        aclnnDynamicBlockQuant,
        x,
        min_scale,
        round_mode_ptr,
        y_acltype,
        row_block_size,
        col_block_size,
        y_wrapper,
        scale);
  }

  return std::tie(y, scale);
}
} // namespace op_api