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._inductor.runtime.triton_helpers import libdevice
from torch.nn import Module
import torch.nn.functional as F
from torch.nn.modules.module import Module
from torch.nn.parameter import Parameter
import torch.nn as nn
import torch.nn.modules.loss
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_elu_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 1.0
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.expm1(tmp4)
    tmp6 = tmp5 * tmp3
    tmp7 = tl.where(tmp2, tmp4, tmp6)
    tl.store(out_ptr0 + x0, tmp7, xmask)


@triton.jit
def triton_poi_fused_elu_linalg_vector_norm_1(in_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp25 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 1.0
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.expm1(tmp4)
    tmp6 = tmp5 * tmp3
    tmp7 = tl.where(tmp2, tmp4, tmp6)
    tmp8 = tmp7 * tmp7
    tmp10 = tmp9 > tmp1
    tmp11 = tmp9 * tmp3
    tmp12 = libdevice.expm1(tmp11)
    tmp13 = tmp12 * tmp3
    tmp14 = tl.where(tmp10, tmp11, tmp13)
    tmp15 = tmp14 * tmp14
    tmp16 = tmp8 + tmp15
    tmp18 = tmp17 > tmp1
    tmp19 = tmp17 * tmp3
    tmp20 = libdevice.expm1(tmp19)
    tmp21 = tmp20 * tmp3
    tmp22 = tl.where(tmp18, tmp19, tmp21)
    tmp23 = tmp22 * tmp22
    tmp24 = tmp16 + tmp23
    tmp26 = tmp25 > tmp1
    tmp27 = tmp25 * tmp3
    tmp28 = libdevice.expm1(tmp27)
    tmp29 = tmp28 * tmp3
    tmp30 = tl.where(tmp26, tmp27, tmp29)
    tmp31 = tmp30 * tmp30
    tmp32 = tmp24 + tmp31
    tl.store(out_ptr0 + x0, tmp32, xmask)


@triton.jit
def triton_poi_fused_div_elu_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp8 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 1.0
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.expm1(tmp4)
    tmp6 = tmp5 * tmp3
    tmp7 = tl.where(tmp2, tmp4, tmp6)
    tmp9 = libdevice.sqrt(tmp8)
    tmp10 = 1e-12
    tmp11 = triton_helpers.maximum(tmp9, tmp10)
    tmp12 = tmp7 / tmp11
    tl.store(out_ptr0 + x2, tmp12, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_3(in_out_ptr0, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_1, primals_2, out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_3, buf0, out=buf1)
        buf2 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_elu_0[grid(16)](buf1, buf2, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf2, primals_4, out=buf3)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_3, buf3, out=buf4)
        buf5 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        triton_poi_fused_elu_linalg_vector_norm_1[grid(4)](buf4, buf5, 4,
            XBLOCK=4, num_warps=1, num_stages=1)
        buf6 = buf3
        del buf3
        triton_poi_fused_div_elu_2[grid(16)](buf4, buf5, buf6, 16, XBLOCK=
            16, num_warps=1, num_stages=1)
        del buf5
        buf7 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf6, reinterpret_tensor(buf6, (4, 4), (1, 4), 0),
            out=buf7)
        buf8 = buf7
        del buf7
        buf9 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_3[grid(16)](buf8, buf9, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
    return buf8, buf6, buf1, buf4, buf6, buf9, reinterpret_tensor(primals_3,
        (4, 4), (1, 4), 0), reinterpret_tensor(buf2, (4, 4), (1, 4), 0
        ), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0
        ), reinterpret_tensor(primals_1, (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, 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.weight = Parameter(torch.FloatTensor(in_features, out_features))
        self.reset_parameters()

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

    def forward(self, input, adj):
        input = F.dropout(input, self.dropout, self.training)
        support = torch.mm(input, self.weight)
        output = torch.spmm(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.mm(z, z.t()))
        return adj


class GCNModelAENew(nn.Module):

    def __init__(self, input_feat_dim, hidden_dim1, hidden_dim2, dropout):
        super(GCNModelAENew, self).__init__()
        self.gc1 = GraphConvolution(input_feat_dim, hidden_dim1, dropout,
            act=F.elu)
        self.gc2 = GraphConvolution(hidden_dim1, hidden_dim2, dropout, act=
            F.elu)
        self.dc = InnerProductDecoder(dropout, act=F.relu)

    def encode(self, x, adj, edge_weight=None):
        hidden1 = self.gc1(x, adj)
        return self.gc2(hidden1, adj)

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


Measured latency_ms: 0.077501