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 as nn
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_cat_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = xindex // 8
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.load(in_ptr2 + 4 * x1, tmp6 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp11 = tl.load(in_ptr2 + (1 + 4 * x1), tmp6 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp12 = tmp10 + tmp11
    tmp13 = tl.load(in_ptr2 + (2 + 4 * x1), tmp6 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp14 = tmp12 + tmp13
    tmp15 = tl.load(in_ptr2 + (3 + 4 * x1), tmp6 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp16 = tmp14 + tmp15
    tmp17 = 1.0
    tmp18 = tmp16 + tmp17
    tmp19 = tmp9 / tmp18
    tmp20 = tl.full(tmp19.shape, 0.0, tmp19.dtype)
    tmp21 = tl.where(tmp6, tmp19, tmp20)
    tmp22 = tl.where(tmp4, tmp5, tmp21)
    tl.store(out_ptr0 + x2, tmp22, xmask)


@triton.jit
def triton_poi_fused_relu_1(in_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)
    tl.store(in_out_ptr0 + x0, tmp2, 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, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 8), (8, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (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)
        buf1 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(32)](primals_2, buf0, primals_1, buf1, 
            32, XBLOCK=32, num_warps=1, num_stages=1)
        del primals_1
        del primals_2
        buf2 = buf0
        del buf0
        extern_kernels.mm(buf1, reinterpret_tensor(primals_3, (8, 4), (1, 8
            ), 0), out=buf2)
        del primals_3
        buf3 = buf2
        del buf2
        triton_poi_fused_relu_1[grid(16)](buf3, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, buf3, reinterpret_tensor(primals_4,
            (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf4)
        del primals_5
    return buf4, buf1, buf3, primals_4


class SageConv(Module):
    """
    Simple Graphsage layer
    """

    def __init__(self, in_features, out_features, bias=False):
        super(SageConv, self).__init__()
        self.proj = nn.Linear(in_features * 2, out_features, bias=bias)
        self.reset_parameters()

    def reset_parameters(self):
        nn.init.normal_(self.proj.weight)
        if self.proj.bias is not None:
            nn.init.constant_(self.proj.bias, 0.0)

    def forward(self, features, adj):
        """
        Args:
            adj: can be sparse or dense matrix.
        """
        if not isinstance(adj, torch.sparse.FloatTensor):
            if len(adj.shape) == 3:
                neigh_feature = torch.bmm(adj, features) / (adj.sum(dim=1).
                    reshape((adj.shape[0], adj.shape[1], -1)) + 1)
            else:
                neigh_feature = torch.mm(adj, features) / (adj.sum(dim=1).
                    reshape(adj.shape[0], -1) + 1)
        else:
            neigh_feature = torch.spmm(adj, features) / (adj.to_dense().sum
                (dim=1).reshape(adj.shape[0], -1) + 1)
        data = torch.cat([features, neigh_feature], dim=-1)
        combined = self.proj(data)
        return combined


class SageNew(nn.Module):

    def __init__(self, nfeat, nhid, nclass, dropout):
        super(SageNew, self).__init__()
        self.sage1 = SageConv(nfeat, nhid)
        self.mlp = nn.Linear(nhid, nclass)
        self.dropout = dropout
        self.reset_parameters()

    def reset_parameters(self):
        nn.init.normal_(self.mlp.weight, std=0.05)

    def forward(self, input_0, input_1):
        primals_3 = self.sage1.proj.weight
        primals_1 = self.mlp.weight
        primals_5 = self.mlp.bias
        primals_2 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.02694