import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Module
import math
import torch.nn as nn
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_add_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 80
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 20
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_add_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 48
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 12
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_add_relu_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10) = args
    args.clear()
    assert_size_stride(primals_1, (4, 20), (20, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (20,), (1,))
    assert_size_stride(primals_5, (20, 12), (12, 1))
    assert_size_stride(primals_6, (12,), (1,))
    assert_size_stride(primals_7, (12, 4), (4, 1))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4, 4), (4, 1))
    assert_size_stride(primals_10, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 20), (20, 1), torch.float32)
        extern_kernels.mm(primals_2, primals_1, out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 20), (20, 1), torch.float32)
        extern_kernels.mm(primals_3, buf0, out=buf1)
        del buf0
        buf2 = buf1
        del buf1
        get_raw_stream(0)
        triton_poi_fused_add_relu_0[grid(80)](buf2, primals_4, 80, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_4
        buf3 = empty_strided_cuda((4, 12), (12, 1), torch.float32)
        extern_kernels.mm(buf2, primals_5, out=buf3)
        buf4 = empty_strided_cuda((4, 12), (12, 1), torch.float32)
        extern_kernels.mm(primals_3, buf3, out=buf4)
        del buf3
        buf5 = buf4
        del buf4
        triton_poi_fused_add_relu_1[grid(48)](buf5, primals_6, 48, XBLOCK=
            64, num_warps=1, num_stages=1)
        del primals_6
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf5, primals_7, out=buf6)
        buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_3, buf6, out=buf7)
        buf8 = buf7
        del buf7
        triton_poi_fused_add_relu_2[grid(16)](buf8, primals_8, 16, XBLOCK=
            16, num_warps=1, num_stages=1)
        del primals_8
        buf9 = buf6
        del buf6
        extern_kernels.mm(buf8, primals_9, out=buf9)
        buf10 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_10, primals_3, buf9, alpha=1, beta=1,
            out=buf10)
        del buf9
        del primals_10
    return buf10, buf2, buf5, buf8, reinterpret_tensor(primals_3, (4, 4), (
        1, 4), 0), reinterpret_tensor(primals_9, (4, 4), (1, 4), 0
        ), reinterpret_tensor(primals_7, (4, 12), (1, 4), 0
        ), reinterpret_tensor(primals_5, (12, 20), (1, 12), 0
        ), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0)


class GraphConvolution(Module):
    """
    Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
    """

    def __init__(self, in_features, out_features, bias=True):
        super(GraphConvolution, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.weight = Parameter(torch.FloatTensor(in_features, out_features))
        if bias:
            self.bias = Parameter(torch.FloatTensor(out_features))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters()

    def reset_parameters(self):
        stdv = 1.0 / math.sqrt(self.weight.size(1))
        self.weight.data.uniform_(-stdv, stdv)
        if self.bias is not None:
            self.bias.data.uniform_(-stdv, stdv)

    def forward(self, input, adj):
        support = torch.mm(input, self.weight)
        output = torch.spmm(adj, support)
        if self.bias is not None:
            return output + self.bias
        else:
            return output

    def __repr__(self):
        return self.__class__.__name__ + ' (' + str(self.in_features
            ) + ' -> ' + str(self.out_features) + ')'


class EmbedGCNNew(nn.Module):

    def __init__(self, n_feat, n_hid, n_embed):
        super(EmbedGCNNew, self).__init__()
        self.gc1 = GraphConvolution(n_feat, 5 * n_hid)
        self.gc2 = GraphConvolution(5 * n_hid, 3 * n_hid)
        self.gc3 = GraphConvolution(3 * n_hid, n_hid)
        self.gc6 = GraphConvolution(n_hid, n_embed)

    def forward(self, input_0, input_1):
        primals_1 = self.gc1.weight
        primals_4 = self.gc1.bias
        primals_5 = self.gc2.weight
        primals_6 = self.gc2.bias
        primals_7 = self.gc3.weight
        primals_8 = self.gc3.bias
        primals_2 = self.gc6.weight
        primals_10 = self.gc6.bias
        primals_3 = input_0
        primals_9 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9, primals_10])
        return output[0]


Measured latency_ms: 0.050415