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
import math
import torch.utils.data
from torch.nn 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_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
    x3 = xindex
    x1 = xindex // 4 % 4
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = x1
    tmp2 = x0
    tmp3 = tmp1 == tmp2
    tmp4 = 1.0
    tmp5 = 0.0
    tmp6 = tl.where(tmp3, tmp4, tmp5)
    tmp7 = tmp0 + tmp6
    tl.store(out_ptr0 + x3, tmp7, xmask)


@triton.jit
def triton_poi_fused_clone_1(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_div_sum_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_out_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 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(in_out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused_add_clamp_min_linalg_vector_norm_3(in_ptr0, in_ptr1,
    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_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp5 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + 1)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
    tmp11 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp12 = tl.load(in_ptr1 + 2)
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK])
    tmp17 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp18 = tl.load(in_ptr1 + 3)
    tmp19 = tl.broadcast_to(tmp18, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = tmp3 * tmp3
    tmp8 = tmp5 + tmp7
    tmp9 = tmp8 * tmp8
    tmp10 = tmp4 + tmp9
    tmp14 = tmp11 + tmp13
    tmp15 = tmp14 * tmp14
    tmp16 = tmp10 + tmp15
    tmp20 = tmp17 + tmp19
    tmp21 = tmp20 * tmp20
    tmp22 = tmp16 + tmp21
    tmp23 = libdevice.sqrt(tmp22)
    tmp24 = 1e-12
    tmp25 = triton_helpers.maximum(tmp23, tmp24)
    tl.store(out_ptr0 + x0, tmp25, xmask)


@triton.jit
def triton_poi_fused_add_div_4(in_ptr0, in_ptr1, in_ptr2, out_ptr0, 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
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 / tmp3
    tl.store(out_ptr0 + x2, tmp4, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (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_add_0[grid(256)](primals_2, buf0, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_clone_1[grid(256)](primals_1, buf1, 256, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_1
        buf2 = 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(buf1, (16, 4, 4), (16, 4, 1), 0), out=buf2)
        buf3 = reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf2
        triton_poi_fused_div_sum_2[grid(256)](buf3, buf0, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf4 = reinterpret_tensor(buf0, (64, 4), (4, 1), 0)
        del buf0
        extern_kernels.mm(reinterpret_tensor(buf3, (64, 4), (4, 1), 0),
            primals_3, out=buf4)
        del primals_3
        buf5 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        triton_poi_fused_add_clamp_min_linalg_vector_norm_3[grid(64)](buf4,
            primals_4, buf5, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf6 = buf1
        del buf1
        triton_poi_fused_add_div_4[grid(256)](buf4, primals_4, buf5, buf6, 
            256, XBLOCK=256, num_warps=4, num_stages=1)
        del buf5
    return buf6, primals_4, buf4, reinterpret_tensor(buf3, (4, 64), (1, 4), 0)


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


class DenseSAGEConvNew(torch.nn.Module):
    """See :class:`torch_geometric.nn.conv.sage_conv.SAGEConv`.

    :rtype: :class:`Tensor`
    """

    def __init__(self, in_channels, out_channels, normalize=True, bias=True):
        super(DenseSAGEConvNew, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.normalize = normalize
        self.weight = Parameter(torch.Tensor(self.in_channels, out_channels))
        if bias:
            self.bias = Parameter(torch.Tensor(out_channels))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters()

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

    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.bias
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]


Measured latency_ms: 0.043434