// Copyright (c) 2023, Huawei Technologies.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;
constexpr int INPUT_DIMS = 2;

at::Tensor& multilabel_margin_loss_out(
    const at::Tensor & self,
    const at::Tensor & target,
    int64_t reduction,
    at::Tensor & output)
{
    DO_COMPATIBILITY(aclnnMultilabelMarginLoss, acl_op::multilabel_margin_loss_out(self, target, reduction, output));
    c10::SmallVector<int64_t, op_infer::SIZE> is_target_size;
    if (self.dim() == INPUT_DIMS && self.size(0) == 0) {
        is_target_size = {self.size(0)};
    } else {
        is_target_size = op_infer::array_to_small_vector(target.sizes());
    }
    auto is_target = npu_preparation::apply_tensor_without_format(self, is_target_size);
    return std::get<0>(op_api::multilabel_margin_loss_forward_out(self, target, reduction, output, is_target));
}

at::Tensor multilabel_margin_loss(
    const at::Tensor & self,
    const at::Tensor & target,
    int64_t reduction)
{
    return std::get<0>(at::multilabel_margin_loss_forward(self, target, reduction));
}

std::tuple<at::Tensor&, at::Tensor&> multilabel_margin_loss_forward_out(
    const at::Tensor& self,
    const at::Tensor& target,
    int64_t reduction,
    at::Tensor& output,
    at::Tensor& is_target)
{
    DO_COMPATIBILITY(aclnnMultilabelMarginLoss,
        acl_op::multilabel_margin_loss_forward_out(self, target, reduction, output, is_target));
    c10::SmallVector<int64_t, op_infer::SIZE> output_size;
    if (self.dim() == INPUT_DIMS && reduction == at::Reduction::None) {
        output_size = {self.size(0)};
    }
    c10::SmallVector<int64_t, op_infer::SIZE> is_target_size;
    if (self.dim() == INPUT_DIMS && self.size(0) == 0) {
        is_target_size = {self.size(0)};
    } else {
        is_target_size = op_infer::array_to_small_vector(target.sizes());
    }
    npu_preparation::check_tensor({self, target}, output, output, output_size);
    npu_preparation::check_tensor({self, target}, is_target, is_target, is_target_size);

    EXEC_NPU_CMD(aclnnMultilabelMarginLoss, self, target, reduction, output, is_target);
    return std::tuple<at::Tensor&, at::Tensor&>(output, is_target);
}

std::tuple<at::Tensor, at::Tensor> multilabel_margin_loss_forward(
    const at::Tensor& self,
    const at::Tensor& target,
    int64_t reduction)
{
  DO_COMPATIBILITY(aclnnMultilabelMarginLoss,
                   acl_op::multilabel_margin_loss_forward(self, target, reduction));
    c10::SmallVector<int64_t, op_infer::SIZE> output_size;
    if (self.dim() == INPUT_DIMS && reduction == at::Reduction::None) {
        output_size = {self.size(0)};
    }
    c10::SmallVector<int64_t, op_infer::SIZE> is_target_size;
    if (self.dim() == INPUT_DIMS && self.size(0) == 0) {
        is_target_size = {self.size(0)};
    } else {
        is_target_size = op_infer::array_to_small_vector(target.sizes());
    }
    auto output = npu_preparation::apply_tensor_without_format(self, output_size);
    auto is_target = npu_preparation::apply_tensor_without_format(self, is_target_size);

    op_api::multilabel_margin_loss_forward_out(self, target, reduction, output, is_target);
    return std::make_tuple(output, is_target);
}
}