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
import math
from torch.nn import Parameter
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_clone_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 64
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_add_1(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 256
    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 + x2, xmask)
    tmp2 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tl.store(in_out_ptr0 + x2, 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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 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, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(256)](primals_2, buf0, 256, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_2
        buf1 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf0, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(primals_1, (16, 4, 4), (16, 4, 1), 0),
            out=buf1)
        buf2 = reinterpret_tensor(buf0, (64, 4), (4, 1), 0)
        del buf0
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 4), (4, 1), 0),
            primals_3, out=buf2)
        del primals_3
        buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf3)
        del primals_4
        buf4 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf2
        triton_poi_fused_add_1[grid(256)](buf4, buf3, primals_5, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del buf3
        del primals_5
    return buf4, reinterpret_tensor(primals_1, (64, 4), (4, 1), 0
        ), reinterpret_tensor(buf1, (4, 64), (1, 4), 0)


def uniform(size, tensor):
    bound = 1.0 / math.sqrt(size)
    if tensor is not None:
        tensor.data.uniform_(-bound, bound)


class DenseGraphConvNew(torch.nn.Module):
    """See :class:`torch_geometric.nn.conv.GraphConv`.
    """

    def __init__(self, in_channels, out_channels, aggr='add', bias=True):
        assert aggr in ['add', 'mean', 'max']
        super(DenseGraphConvNew, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.aggr = aggr
        self.weight = Parameter(torch.Tensor(in_channels, out_channels))
        self.lin = torch.nn.Linear(in_channels, out_channels, bias=bias)
        self.reset_parameters()

    def reset_parameters(self):
        uniform(self.in_channels, self.weight)
        self.lin.reset_parameters()

    def __repr__(self):
        return '{}({}, {})'.format(self.__class__.__name__, self.
            in_channels, self.out_channels)

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


Measured latency_ms: 0.049195