// Copyright (c) 2023 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.
// You may obtain a copy of the License at
//
// https://opensource.org/licenses/BSD-3-Clause
//
// 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/AclOpsInterface.h"
#include "op_plugin/OpApiInterface.h"
#include "op_plugin/utils/op_api_common.h"

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

#if VERSION_BETWEEN(V2R1, VERSION_NEWEST)
std::tuple<at::Tensor, at::Tensor> _prelu_kernel_backward(
    const at::Tensor& grad_output,
    const at::Tensor& self,
    const at::Tensor& weight)
{
    DO_COMPATIBILITY(aclnnPreluBackward, acl_op::_prelu_kernel_backward(grad_output, self, weight));
    c10::SmallVector<int64_t, N> weight_shape = op_infer::array_to_small_vector(weight.sizes());
    at::Tensor reshape_weight = weight.reshape({-1});

    at::Tensor grad_input = npu_preparation::apply_tensor_without_format(grad_output);
    at::Tensor grad_weight = npu_preparation::apply_tensor_without_format(reshape_weight);
    EXEC_NPU_CMD(aclnnPreluBackward, grad_output, self, reshape_weight, grad_input, grad_weight);
    grad_weight = grad_weight.reshape(weight_shape);
    return std::tie<at::Tensor, at::Tensor>(grad_input, grad_weight);
}
#endif
}