// 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/utils/OpAdapter.h"

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

std::tuple<at::Tensor, at::Tensor> batch_norm_reduce(const at::Tensor& self, double eps)
{
    TORCH_CHECK(self.dim() > 1, "The dim input tensor [self] must more than 1." + OPS_ERROR(ErrCode::PARAM));
    auto output_size = {self.size(1)};
    at::Tensor sum = npu_preparation::apply_tensor(output_size, self.options().dtype(at::kFloat), self);
    at::Tensor square_sum = npu_preparation::apply_tensor(output_size, self.options().dtype(at::kFloat), self);

    at::Tensor self_copy = self;
    if (self.scalar_type() != at::kFloat) {
        self_copy = at_npu::native::custom_ops::_npu_dtype_cast(self_copy, at::kFloat);
    }

    at_npu::native::OpCommand cmd;
    cmd.Name("BNTrainingReduce")
        .Input(self_copy)
        .Output(sum)
        .Output(square_sum)
        .Run();

    return std::tie(sum, square_sum);
}
} // namespace acl_op