// Copyright 2024 Tencent
// SPDX-License-Identifier: BSD-3-Clause

#include "pass_level2.h"

namespace pnnx {

class F_conv_transpose3d : public GraphRewriterPass
{
public:
    const char* match_pattern_graph() const
    {
        return R"PNNXIR(7767517
15 14
pnnx.Input              input_0     0 1 input
pnnx.Input              input_1     0 1 weight
pnnx.Input              input_2     0 1 bias
prim::Constant          op_0        0 1 stride value=%stride
prim::Constant          op_1        0 1 padding value=%padding
prim::Constant          op_2        0 1 dilation value=%dilation
prim::Constant          op_3        0 1 transposed value=True
prim::Constant          op_4        0 1 output_padding value=%output_padding
prim::Constant          op_5        0 1 groups value=%groups
prim::Constant          op_6        0 1 benchmark value=*
prim::Constant          op_7        0 1 deterministic value=*
prim::Constant          op_8        0 1 cudnn_enabled value=*
prim::Constant          op_9        0 1 allow_tf32 value=*
aten::_convolution      op_10       13 1 input weight bias stride padding dilation transposed output_padding groups benchmark deterministic cudnn_enabled allow_tf32 out
pnnx.Output             output      1 0 out
)PNNXIR";
    }

    const char* type_str() const
    {
        return "F.conv_transpose3d";
    }

    bool match(const std::map<std::string, Parameter>& captured_params) const
    {
        return captured_params.at("stride").type == 5 && captured_params.at("stride").ai.size() == 3;
    }
};

REGISTER_GLOBAL_PNNX_GRAPH_REWRITER_PASS(F_conv_transpose3d, 140)

class F_conv_transpose3d_bias : public GraphRewriterPass
{
public:
    const char* match_pattern_graph() const
    {
        return R"PNNXIR(7767517
6 5
pnnx.Input              input_0     0 1 input
pnnx.Input              input_1     0 1 weight
F.conv_transpose3d      op_0        2 1 input weight a bias=None stride=%stride padding=%padding dilation=%dilation output_padding=%output_padding groups=%groups
pnnx.Attribute          op_1        0 1 bias @data=(1,%out_channels,1,1,1)f32
aten::add               op_2        2 1 a bias out
pnnx.Output             output      1 0 out
)PNNXIR";
    }

    const char* replace_pattern_graph() const
    {
        return R"PNNXIR(7767517
5 4
pnnx.Input              input_0     0 1 input
pnnx.Input              input_1     0 1 weight
pnnx.Attribute          bias        0 1 bias @data=%op_1.data
F.conv_transpose3d      conv        3 1 input weight bias out stride=%stride padding=%padding dilation=%dilation output_padding=%output_padding groups=%groups
pnnx.Output             output      1 0 out
)PNNXIR";
    }

    void write(const std::map<std::string, Operator*>& ops, const std::map<std::string, Parameter>& captured_params, const std::map<std::string, Attribute>& captured_attrs) const
    {
        GraphRewriterPass::write(ops, captured_params, captured_attrs);

        Operator* op_conv = ops.at("conv");

        op_conv->inputnames.resize(3);
        op_conv->inputnames[0] = "input";
        op_conv->inputnames[1] = "weight";
        op_conv->inputnames[2] = "bias";

        const int out_channels = captured_params.at("out_channels").i;

        Operator* op_bias = ops.at("bias");
        // fix bias shape
        op_bias->attrs["data"].shape = std::vector<int>{out_channels};
        op_bias->outputs[0]->shape = std::vector<int>{out_channels};
    }
};

REGISTER_GLOBAL_PNNX_GRAPH_REWRITER_PASS(F_conv_transpose3d_bias, 141)

class F_conv_transpose3d_onnx : public GraphRewriterPass
{
public:
    const char* match_pattern_graph() const
    {
        return R"PNNXIR(7767517
5 4
pnnx.Input              input_0     0 1 input
pnnx.Input              input_1     0 1 weight
pnnx.Input              input_2     0 1 bias
ConvTranspose           op_0        3 1 input weight bias out %*=%*
pnnx.Output             output      1 0 out
)PNNXIR";
    }

    const char* type_str() const
    {
        return "F.conv_transpose3d";
    }

    bool match(const std::map<std::string, Parameter>& captured_params) const
    {
        if (captured_params.find("op_0.kernel_shape") != captured_params.end())
        {
            if (captured_params.at("op_0.kernel_shape").type != 5 || captured_params.at("op_0.kernel_shape").ai.size() != 3)
                return false;
        }

        if (captured_params.find("op_0.dilations") != captured_params.end())
        {
            if (captured_params.at("op_0.dilations").type != 5 || captured_params.at("op_0.dilations").ai.size() != 3)
                return false;
        }

        if (captured_params.find("op_0.strides") != captured_params.end())
        {
            if (captured_params.at("op_0.strides").type != 5 || captured_params.at("op_0.strides").ai.size() != 3)
                return false;
        }

        if (captured_params.find("op_0.output_padding") != captured_params.end())
        {
            if (captured_params.at("op_0.output_padding").type != 5 || captured_params.at("op_0.output_padding").ai.size() != 3)
                return false;
        }

        if (captured_params.find("op_0.pads") != captured_params.end())
        {
            if (captured_params.at("op_0.pads").type != 5 || captured_params.at("op_0.pads").ai.size() != 6)
                return false;

            const std::vector<int>& pads = captured_params.at("op_0.pads").ai;
            if (pads[0] != pads[3] || pads[1] != pads[4] || pads[2] != pads[5])
                return false;
        }

        return true;
    }

    void write(Operator* op, const std::map<std::string, Parameter>& captured_params) const
    {
        if (captured_params.find("op_0.dilations") != captured_params.end())
        {
            op->params["dilation"] = captured_params.at("op_0.dilations");
        }
        else
        {
            op->params["dilation"] = {1, 1, 1};
        }

        if (captured_params.find("op_0.strides") != captured_params.end())
        {
            op->params["stride"] = captured_params.at("op_0.strides");
        }
        else
        {
            op->params["stride"] = {1, 1, 1};
        }

        if (captured_params.find("op_0.output_padding") != captured_params.end())
        {
            op->params["output_padding"] = captured_params.at("op_0.output_padding");
        }
        else
        {
            op->params["output_padding"] = {0, 0, 0};
        }

        if (captured_params.find("op_0.pads") != captured_params.end())
        {
            const std::vector<int>& pads = captured_params.at("op_0.pads").ai;
            op->params["padding"] = {pads[0], pads[1], pads[2]};
        }
        else
        {
            op->params["padding"] = {0, 0, 0};
        }

        if (captured_params.find("op_0.group") != captured_params.end())
        {
            op->params["groups"] = captured_params.at("op_0.group");
        }
        else
        {
            op->params["groups"] = 1;
        }
    }
};

REGISTER_GLOBAL_PNNX_GRAPH_REWRITER_PASS(F_conv_transpose3d_onnx, 140)

class F_conv_transpose3d_onnx_1 : public F_conv_transpose3d_onnx
{
public:
    const char* match_pattern_graph() const
    {
        return R"PNNXIR(7767517
4 3
pnnx.Input              input_0     0 1 input
pnnx.Input              input_1     0 1 weight
ConvTranspose           op_0        2 1 input weight out %*=%*
pnnx.Output             output      1 0 out
)PNNXIR";
    }

    void write(Operator* op, const std::map<std::string, Parameter>& captured_params) const
    {
        F_conv_transpose3d_onnx::write(op, captured_params);

        op->params["bias"] = Parameter();
    }
};

REGISTER_GLOBAL_PNNX_GRAPH_REWRITER_PASS(F_conv_transpose3d_onnx_1, 140)

} // namespace pnnx
