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 torch.nn as nn
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_div_stack_0(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
    x1 = xindex // 16
    x0 = xindex % 16
    x2 = xindex
    tmp41 = tl.load(in_ptr1 + x2, xmask)
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1), tmp4 & xmask, other=0.0)
    tmp6 = tl.load(in_ptr0 + (64 + x0 + 16 * x1), tmp4 & xmask, other=0.0)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.full(tmp7.shape, 0.0, tmp7.dtype)
    tmp9 = tl.where(tmp4, tmp7, tmp8)
    tmp10 = tmp0 >= tmp3
    tmp11 = tl.full([1], 8, tl.int64)
    tmp12 = tmp0 < tmp11
    tmp13 = tmp10 & tmp12
    tmp14 = tl.load(in_ptr0 + (x0 + 16 * (-4 + x1)), tmp13 & xmask, other=0.0)
    tmp15 = tl.load(in_ptr0 + (128 + x0 + 16 * (-4 + x1)), tmp13 & xmask,
        other=0.0)
    tmp16 = tmp14 + tmp15
    tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
    tmp18 = tl.where(tmp13, tmp16, tmp17)
    tmp19 = tmp0 >= tmp11
    tmp20 = tl.full([1], 12, tl.int64)
    tmp21 = tmp0 < tmp20
    tmp22 = tmp19 & tmp21
    tmp23 = tl.load(in_ptr0 + (64 + x0 + 16 * (-8 + x1)), tmp22 & xmask,
        other=0.0)
    tmp24 = tl.load(in_ptr0 + (192 + x0 + 16 * (-8 + x1)), tmp22 & xmask,
        other=0.0)
    tmp25 = tmp23 + tmp24
    tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
    tmp27 = tl.where(tmp22, tmp25, tmp26)
    tmp28 = tmp0 >= tmp20
    tl.full([1], 16, tl.int64)
    tmp31 = tl.load(in_ptr0 + (128 + x0 + 16 * (-12 + x1)), tmp28 & xmask,
        other=0.0)
    tmp32 = tl.load(in_ptr0 + (192 + x0 + 16 * (-12 + x1)), tmp28 & xmask,
        other=0.0)
    tmp33 = tmp31 + tmp32
    tmp34 = tl.full(tmp33.shape, 0.0, tmp33.dtype)
    tmp35 = tl.where(tmp28, tmp33, tmp34)
    tmp36 = tl.where(tmp22, tmp27, tmp35)
    tmp37 = tl.where(tmp13, tmp18, tmp36)
    tmp38 = tl.where(tmp4, tmp9, tmp37)
    tmp39 = 0.5
    tmp40 = tmp38 * tmp39
    tmp42 = tmp40 + tmp41
    tl.store(in_out_ptr0 + x2, tmp42, xmask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        buf2 = reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf1
        get_raw_stream(0)
        triton_poi_fused_add_div_stack_0[grid(256)](buf2, buf0, primals_2, 
            256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf0
    return buf2, reinterpret_tensor(primals_2, (64, 4), (4, 1), 0)


class TemporalRelationNew(nn.Module):

    def __init__(self, feat_dim, time_window=1):
        super(TemporalRelationNew, self).__init__()
        self.time_window = time_window
        self.feat_dim = feat_dim
        self.WT = nn.Linear(self.feat_dim, self.feat_dim, bias=False)

    def forward(self, input_0):
        primals_1 = self.WT.weight
        primals_2 = input_0
        output = call([primals_1, primals_2])
        return output[0]


Measured latency_ms: 0.023981