// 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 {

at::Tensor& index_add_out(
    const at::Tensor& self,
    int64_t dim,
    const at::Tensor& index,
    const at::Tensor& source,
    const at::Scalar& alpha,
    at::Tensor& result) {
    DO_COMPATIBILITY(aclnnIndexAdd, acl_op::index_add_out(self, dim, index, source, alpha, result));
    auto self_sizes = self.sizes().vec();
    auto source_sizes = source.sizes().vec();
    if (source.dim() != 0 && self.dim() != 0) {
        auto wrapped_dim = at::maybe_wrap_dim(dim, self.dim());
        self_sizes.erase(self_sizes.begin() + wrapped_dim);
        source_sizes.erase(source_sizes.begin() + wrapped_dim);
    }
    TORCH_CHECK(self_sizes == source_sizes,
                "source tensor shape must match self tensor shape, excluding the specified dimension. Got self.shape = ",
                self.sizes(),
                " source.shape = ",
                source.sizes(),
                OPS_ERROR(ErrCode::PARAM));
    at_npu::native::OpPreparation::check_tensor({self, index, source},
                                                result,
                                                result.scalar_type(),
                                                self.sizes());
    if (!result.is_same(self)) {
        result.copy_(self);
    }
    EXEC_NPU_CMD(aclnnIndexAdd, result, dim, index, source, alpha, result);
    return result;
}

at::Tensor index_add(
    const at::Tensor& self,
    int64_t dim,
    const at::Tensor& index,
    const at::Tensor& source,
    const at::Scalar& alpha) {
    DO_COMPATIBILITY(aclnnIndexAdd, acl_op::index_add(self, dim, index, source, alpha));
    auto self_sizes = self.sizes().vec();
    auto source_sizes = source.sizes().vec();
    if (source.dim() != 0 && self.dim() != 0) {
        auto wrapped_dim = at::maybe_wrap_dim(dim, self.dim());
        self_sizes.erase(self_sizes.begin() + wrapped_dim);
        source_sizes.erase(source_sizes.begin() + wrapped_dim);
    }
    TORCH_CHECK(self_sizes == source_sizes,
                "source tensor shape must match self tensor shape, excluding the specified dimension. Got self.shape = ",
                self.sizes(),
                " source.shape = ",
                source.sizes(),
                OPS_ERROR(ErrCode::PARAM));
    at::Tensor result = at_npu::native::OpPreparation::apply_tensor_without_format(self.sizes(), self.options());
    EXEC_NPU_CMD(aclnnIndexAdd, result.copy_(self), dim, index, source, alpha, result.copy_(self));
    return result;
}

at::Tensor index_add(
    const at::Tensor& self,
    at::Dimname dim,
    const at::Tensor& index,
    const at::Tensor& source,
    const at::Scalar& alpha)
{
    return op_api::index_add(self, dimname_to_position(self, dim), index, source, alpha);
}

}