// Tencent is pleased to support the open source community by making ncnn available.
//
// Copyright (C) 2021 THL A29 Limited, a Tencent company. 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 "pass_ncnn.h"

namespace pnnx {

namespace ncnn {

class nn_Conv1d : public GraphRewriterPass
{
public:
    const char* match_pattern_graph() const
    {
        return R"PNNXIR(7767517
3 2
pnnx.Input              input       0 1 input
nn.Conv1d               op_0        1 1 input out in_channels=%in_channels out_channels=%out_channels kernel_size=%kernel_size stride=%stride padding_mode=%padding_mode padding=%padding dilation=%dilation groups=1 bias=%bias @weight @bias
pnnx.Output             output      1 0 out
)PNNXIR";
    }

    const char* type_str() const
    {
        return "Convolution1D";
    }

    const char* name_str() const
    {
        return "conv1d";
    }

    void write(Operator* op, const std::map<std::string, Parameter>& captured_params, const std::map<std::string, Attribute>& captured_attrs) const
    {
        std::string padding_mode = captured_params.at("padding_mode").s;
        if (padding_mode != "zeros")
        {
            fprintf(stderr, "unsupported padding_mode %s\n", padding_mode.c_str());
        }

        op->params["0"] = captured_params.at("out_channels");
        op->params["1"] = captured_params.at("kernel_size").ai[0];
        op->params["2"] = captured_params.at("dilation").ai[0];
        op->params["3"] = captured_params.at("stride").ai[0];
        if (captured_params.at("padding").type == 4)
        {
            if (captured_params.at("padding").s == "same")
                op->params["4"] = -233;
            else if (captured_params.at("padding").s == "valid")
                op->params["4"] = 0;
        }
        else
        {
            op->params["4"] = captured_params.at("padding").ai[0];
        }
        op->params["5"] = captured_params.at("bias").b ? 1 : 0;
        op->params["6"] = (int)(captured_attrs.at("op_0.weight").data.size() / sizeof(float));

        op->attrs["0"] = Attribute();
        op->attrs["0"].data = {0, 0, 0, 0};
        op->attrs["1"] = captured_attrs.at("op_0.weight");
        if (captured_params.at("bias").b)
            op->attrs["2"] = captured_attrs.at("op_0.bias");
    }
};

class nn_Conv1d_1 : public GraphRewriterPass
{
public:
    const char* match_pattern_graph() const
    {
        return R"PNNXIR(7767517
3 2
pnnx.Input              input       0 1 input
nn.Conv1d               op_0        1 1 input out in_channels=%in_channels out_channels=%out_channels kernel_size=%kernel_size stride=%stride padding_mode=%padding_mode padding=%padding dilation=%dilation groups=%groups bias=%bias @weight @bias
pnnx.Output             output      1 0 out
)PNNXIR";
    }

    const char* type_str() const
    {
        return "ConvolutionDepthWise1D";
    }

    const char* name_str() const
    {
        return "convdw1d";
    }

    void write(Operator* op, const std::map<std::string, Parameter>& captured_params, const std::map<std::string, Attribute>& captured_attrs) const
    {
        std::string padding_mode = captured_params.at("padding_mode").s;
        if (padding_mode != "zeros")
        {
            fprintf(stderr, "unsupported padding_mode %s\n", padding_mode.c_str());
        }

        op->params["0"] = captured_params.at("out_channels");
        op->params["1"] = captured_params.at("kernel_size").ai[0];
        op->params["2"] = captured_params.at("dilation").ai[0];
        op->params["3"] = captured_params.at("stride").ai[0];
        if (captured_params.at("padding").type == 4)
        {
            if (captured_params.at("padding").s == "same")
                op->params["4"] = -233;
            else if (captured_params.at("padding").s == "valid")
                op->params["4"] = 0;
        }
        else
        {
            op->params["4"] = captured_params.at("padding").ai[0];
        }
        op->params["5"] = captured_params.at("bias").b ? 1 : 0;
        op->params["6"] = (int)(captured_attrs.at("op_0.weight").data.size() / sizeof(float));
        op->params["7"] = captured_params.at("groups");

        op->attrs["0"] = Attribute();
        op->attrs["0"].data = {0, 0, 0, 0};
        op->attrs["1"] = captured_attrs.at("op_0.weight");
        if (captured_params.at("bias").b)
            op->attrs["2"] = captured_attrs.at("op_0.bias");
    }
};

REGISTER_GLOBAL_PNNX_NCNN_GRAPH_REWRITER_PASS(nn_Conv1d, 20)
REGISTER_GLOBAL_PNNX_NCNN_GRAPH_REWRITER_PASS(nn_Conv1d_1, 21)

} // namespace ncnn

} // namespace pnnx
