// 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;

namespace {
int64_t adaptive_avg_pool2d_backward_safe_size(const at::Tensor &self)
{
    c10::SmallVector<int64_t, N> dims = {-2, -1};

    int64_t size = 1;
    if (self.sizes().empty()) {
        return size;
    }

    for (int64_t ndim : dims) {
        ndim = op_plugin::utils::make_warp_dim(ndim, self.sizes().size());
        size *= self.sizes()[ndim];
    }

    return size;
}

at::Tensor &adaptive_avg_pool2d_backward_out_nocheck(at::Tensor &result, const at::Tensor &grad_output,
                                                     const at::Tensor &self)
{
    TORCH_CHECK(grad_output.dim() >= 2, "The grad_output should be at least 2D"
        + OPS_ERROR(ErrCode::PARAM));
    if (grad_output.size(grad_output.dim() - 2) == 1 && grad_output.size(grad_output.dim() - 1) == 1) {
        result.fill_(1.0 / adaptive_avg_pool2d_backward_safe_size(self));
        result.mul_(grad_output);
    } else {
        at_npu::native::OpCommand cmd;
        cmd.Name("AdaptiveAvgPool2dGrad")
            .Input(grad_output)
            .Output(result)
            .Attr("orig_input_shape", self.sizes())
            .Run();
    }
    return result;
}
} // namespace

at::Tensor _adaptive_avg_pool2d_backward(const at::Tensor &grad_output, const at::Tensor &self)
{
    at::Tensor result = npu_preparation::apply_tensor(self);
    adaptive_avg_pool2d_backward_out_nocheck(result, grad_output, self);
    return result;
}
} // namespace acl_op