// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// 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 <bmcompiler_if.h>
#include "lite/kernels/npu/bridges/registry.h"
#include "lite/kernels/bm/bridges/graph.h"
#include "lite/kernels/bm/bridges/utility.h"

namespace paddle {
namespace lite {
namespace subgraph {
namespace bm {

int PoolConverter(void* ctx, OpLite* op, KernelBase* kernel) {
    CHECK(ctx != nullptr);
    CHECK(op != nullptr);
    auto graph = static_cast<Graph*>(ctx);
    auto scope = op->scope();
    auto op_info = op->op_info();
    auto op_type = op_info->Type();
    auto unique_op_name = lite::subgraph::bm::UniqueName(op_type);
    // input
    auto x_var_name = op_info->Input("X").front();
    auto x = scope->FindVar(x_var_name)->GetMutable<lite::Tensor>();
    auto x_dims = x->dims();
    const int64_t* x_shape_data =
              const_cast<const int64_t*>(&x_dims.data()[0]);
    std::vector<int32_t> i_x_shape_data(x_dims.size());
    for (size_t i = 0; i < x_dims.size(); i++) {
        i_x_shape_data[i] = static_cast<int>(x_shape_data[i]);
    }
    // output
    int32_t *shape[1];
    int32_t dim[1];
    const char *name[1];
    auto output_var_name = op_info->Output("Out").front();
    auto output = scope->FindVar(output_var_name)->GetMutable<lite::Tensor>();
    auto output_dims = output->dims();
    const int64_t* output_shape_data =
              const_cast<const int64_t*>(&output_dims.data()[0]);
    std::vector<int32_t> i_output_shape_data(output_dims.size());
    for (size_t i = 0; i < output_dims.size(); i++) {
        i_output_shape_data[i] = static_cast<int>(output_shape_data[i]);
    }
    shape[0] = &i_output_shape_data[0];
    name[0] = static_cast<const char*>(output_var_name.c_str());
    dim[0] = output_dims.size();
    auto pooling_type = op_info->GetAttr<std::string>("pooling_type");
    CHECK(pooling_type == "max" || pooling_type == "avg");
    auto ksize = op_info->GetAttr<std::vector<int>>("ksize");
    auto paddings = op_info->GetAttr<std::vector<int>>("paddings");
    auto strides = op_info->GetAttr<std::vector<int>>("strides");
    auto global_pooling = op_info->GetAttr<bool>("global_pooling");
    auto ceil_mode = op_info->GetAttr<bool>("ceil_mode");
    bool average_exclusive = false;
    if (pooling_type == "avg") {
        average_exclusive = op_info->GetAttr<bool>("exclusive");
    }
    add_pooling_layer(graph->GetCompilerHandle(),
        const_cast<const int*>(&i_x_shape_data[0]),
        x_dims.size(),
        static_cast<const char*>(x_var_name.c_str()),
        1,
        shape,
        dim,
        name,
        ksize[0],
        ksize[1],
        paddings[0],
        paddings[0],
        paddings[1],
        paddings[1],
        strides[0],
        strides[1],
        (ksize[0] > 1 && ksize[1] > 1) && pooling_type == "max" ? 0 : 1,
        static_cast<int>(average_exclusive),
        static_cast<int>(global_pooling),
        static_cast<int>(ceil_mode),
        static_cast<const char*>(unique_op_name.c_str()),
        nullptr);
    graph->AddNode(output_var_name);
    return SUCCESS;
}

}  // namespace bm
}  // namespace subgraph
}  // namespace lite
}  // namespace paddle
REGISTER_SUBGRAPH_BRIDGE(pool2d, kBM,
    paddle::lite::subgraph::bm::PoolConverter);
