// 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.
// You may obtain a copy of the License at
//
// 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;

std::tuple<at::Tensor, at::Tensor> batch_norm_reduce(const at::Tensor& self, double eps)
{
    DO_COMPATIBILITY(aclnnBatchNormReduce, acl_op::batch_norm_reduce(self, eps));
    TORCH_CHECK(self.dim() > 1, "The dim input tensor [self] must more than 1." + OPS_ERROR(ErrCode::PARAM));
    int64_t c_value;
    int64_t self_npu_format = npu_preparation::get_tensor_npu_format(self);
    if (self_npu_format == ACL_FORMAT_NHWC || self_npu_format == ACL_FORMAT_NDHWC) {
        c_value = self.size(-1);
    } else {
        c_value = self.size(1);
    }
    auto output_size = {c_value};
    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);
    }
    EXEC_NPU_CMD(aclnnBatchNormReduce, self_copy, sum, square_sum);
    return std::make_tuple(sum, square_sum);
}
}