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 torch.nn.functional as F
from torch.nn.modules.module import Module
import torch.nn as nn
import torch.nn.modules.loss
import torch.utils.data
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_relu_threshold_backward_0(in_out_ptr0, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp3 = 0.0
    tmp4 = tmp2 <= tmp3
    tl.store(in_out_ptr0 + x0, tmp2, xmask)
    tl.store(out_ptr0 + x0, tmp4, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(primals_3, reinterpret_tensor(buf0, (4, 4, 4), (
            16, 4, 1), 0), out=buf1)
        buf2 = buf1
        del buf1
        buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(64)](buf2, buf8, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        buf3 = buf0
        del buf0
        extern_kernels.mm(reinterpret_tensor(buf2, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(primals_3, reinterpret_tensor(buf3, (4, 4, 4), (
            16, 4, 1), 0), out=buf4)
        buf5 = buf3
        del buf3
        extern_kernels.mm(reinterpret_tensor(buf2, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf5)
        buf6 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(primals_3, reinterpret_tensor(buf5, (4, 4, 4), (
            16, 4, 1), 0), out=buf6)
        buf7 = reinterpret_tensor(buf5, (4, 4, 4), (16, 4, 1), 0)
        del buf5
        extern_kernels.bmm(buf4, reinterpret_tensor(buf4, (4, 4, 4), (16, 1,
            4), 0), out=buf7)
    return buf7, buf4, buf6, reinterpret_tensor(primals_1, (16, 4), (4, 1), 0
        ), reinterpret_tensor(buf2, (16, 4), (4, 1), 0), reinterpret_tensor(
        buf4, (4, 4, 4), (16, 1, 4), 0), reinterpret_tensor(primals_3, (4, 
        4, 4), (16, 1, 4), 0), primals_5, primals_4, buf8


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

    def __init__(self, in_features, out_features, dropout=0.0, act=F.relu):
        super(GraphConvolution, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.dropout = dropout
        self.act = act
        self.linear = torch.nn.Linear(in_features, out_features, bias=False)
        self.reset_parameters()

    def reset_parameters(self):
        torch.nn.init.xavier_uniform_(self.linear.weight)

    def forward(self, input, adj):
        input = F.dropout(input, self.dropout, self.training)
        support = self.linear(input)
        output = torch.bmm(adj, support)
        output = self.act(output)
        return output

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


class InnerProductDecoder(nn.Module):
    """Decoder for using inner product for prediction."""

    def __init__(self, dropout, act=torch.sigmoid):
        super(InnerProductDecoder, self).__init__()
        self.dropout = dropout
        self.act = act

    def forward(self, z):
        z = F.dropout(z, self.dropout, training=self.training)
        adj = self.act(torch.bmm(z, torch.transpose(z, 1, 2)))
        return adj


class VGAENew(nn.Module):

    def __init__(self, input_feat_dim, hidden_dim1, output_dim, dropout):
        super(VGAENew, self).__init__()
        self.gc1 = GraphConvolution(input_feat_dim, hidden_dim1, dropout,
            act=F.relu)
        self.gc2 = GraphConvolution(hidden_dim1, output_dim, dropout, act=
            lambda x: x)
        self.gc3 = GraphConvolution(hidden_dim1, output_dim, dropout, act=
            lambda x: x)
        self.dc = InnerProductDecoder(dropout, act=lambda x: x)

    def encode(self, x, adj):
        hidden1 = self.gc1(x, adj)
        return self.gc2(hidden1, adj), self.gc3(hidden1, adj)

    def reparameterize(self, mu, logvar):
        if self.training:
            std = torch.exp(logvar)
            eps = torch.randn_like(std)
            return eps.mul(std).add_(mu)
        else:
            return mu

    def forward(self, input_0, input_1):
        primals_2 = self.gc1.linear.weight
        primals_4 = self.gc2.linear.weight
        primals_5 = self.gc3.linear.weight
        primals_1 = input_0
        primals_3 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0], output[1], output[2]


Measured latency_ms: 0.018843