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 math as tl_math
import math
import torch.nn.functional as F
import torch.nn as nn
from torch.nn.parameter import Parameter
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_cat_relu_0(in_out_ptr0, 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
    x2 = xindex
    x0 = xindex % 4
    x1 = 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)
    tl.store(out_ptr0 + (x0 + 12 * x1), tmp4, xmask)


@triton.jit
def triton_poi_fused_add_cat_relu_1(in_out_ptr0, 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
    x2 = xindex
    x0 = xindex % 4
    x1 = 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)
    tl.store(out_ptr0 + (x0 + 12 * x1), tmp4, xmask)


@triton.jit
def triton_poi_fused__log_softmax_2(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
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused__log_softmax_3(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
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp2 = tl_math.exp(tmp1)
    tmp4 = tl_math.exp(tmp3)
    tmp5 = tmp2 + tmp4
    tmp7 = tl_math.exp(tmp6)
    tmp8 = tmp5 + tmp7
    tmp10 = tl_math.exp(tmp9)
    tmp11 = tmp8 + tmp10
    tmp12 = tl_math.log(tmp11)
    tmp13 = tmp0 - tmp12
    tl.store(out_ptr0 + x2, tmp13, 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, 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,), (1,))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4, 12), (12, 1))
    assert_size_stride(primals_10, (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_2, primals_1, out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_3, buf0, out=buf1)
        buf2 = buf1
        del buf1
        buf10 = empty_strided_cuda((4, 12), (12, 1), torch.float32)
        buf8 = reinterpret_tensor(buf10, (4, 4), (12, 1), 0)
        get_raw_stream(0)
        triton_poi_fused_add_cat_relu_0[grid(16)](buf2, primals_4, buf8, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_4
        buf3 = buf0
        del buf0
        extern_kernels.mm(buf2, primals_5, out=buf3)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_3, buf3, out=buf4)
        buf5 = buf4
        del buf4
        buf9 = reinterpret_tensor(buf10, (4, 4), (12, 1), 4)
        triton_poi_fused_add_cat_relu_1[grid(16)](buf5, primals_6, buf9, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_6
        buf6 = buf3
        del buf3
        extern_kernels.mm(buf5, primals_7, out=buf6)
        buf7 = reinterpret_tensor(buf10, (4, 4), (12, 1), 8)
        extern_kernels.addmm(primals_8, primals_3, buf6, alpha=1, beta=1,
            out=buf7)
        del primals_8
        buf11 = buf6
        del buf6
        extern_kernels.addmm(primals_10, buf10, reinterpret_tensor(
            primals_9, (12, 4), (1, 12), 0), alpha=1, beta=1, out=buf11)
        del primals_10
        buf12 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused__log_softmax_2[grid(16)](buf11, buf12, 16, XBLOCK=
            16, num_warps=1, num_stages=1)
        buf13 = buf11
        del buf11
        triton_poi_fused__log_softmax_3[grid(16)](buf12, buf13, 16, XBLOCK=
            16, num_warps=1, num_stages=1)
        del buf12
    return buf13, buf2, buf5, buf10, buf13, primals_9, reinterpret_tensor(
        primals_3, (4, 4), (1, 4), 0), reinterpret_tensor(primals_7, (4, 4),
        (1, 4), 0), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0
        ), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0)


class GraphConvolution(nn.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 GCNSyntheticNew(nn.Module):
    """
    3-layer GCN used in GNN Explainer synthetic tasks, including
    """

    def __init__(self, nfeat, nhid, nout, nclass, dropout):
        super(GCNSyntheticNew, self).__init__()
        self.gc1 = GraphConvolution(nfeat, nhid)
        self.gc2 = GraphConvolution(nhid, nhid)
        self.gc3 = GraphConvolution(nhid, nout)
        self.lin = nn.Linear(nhid + nhid + nout, nclass)
        self.dropout = dropout

    def loss(self, pred, label):
        return F.nll_loss(pred, label)

    def forward(self, input_0, input_1):
        primals_1 = self.gc1.weight
        primals_4 = self.gc1.bias
        primals_2 = self.gc2.weight
        primals_6 = self.gc2.bias
        primals_3 = self.gc3.weight
        primals_8 = self.gc3.bias
        primals_9 = self.lin.weight
        primals_10 = self.lin.bias
        primals_5 = input_0
        primals_7 = 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: 0.10043