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.triton_helpers import libdevice, math as tl_math
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_abs_diag_embed_0(in_ptr0, out_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp3 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp0 = x0
    tmp1 = x1
    tmp2 = tmp0 == tmp1
    tmp4 = 0.0
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = tl_math.abs(tmp5)
    tl.store(out_ptr0 + x2, tmp5, xmask)
    tl.store(out_ptr1 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_diag_embed_index_put_lift_fresh_pow_1(in_out_ptr0,
    in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16
    x3 = xindex
    tmp3 = tl.load(in_ptr0 + (4 * x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (1 + 4 * x0 + 16 * x2), xmask, eviction_policy
        ='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + 4 * x0 + 16 * x2), xmask, eviction_policy
        ='evict_last')
    tmp8 = tl.load(in_ptr0 + (3 + 4 * x0 + 16 * x2), xmask, eviction_policy
        ='evict_last')
    tmp0 = x0
    tmp1 = x1
    tmp2 = tmp0 == tmp1
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp9 = tmp7 + tmp8
    tmp10 = 0.0
    tmp11 = tl.where(tmp2, tmp9, tmp10)
    tmp12 = -0.5
    tmp13 = libdevice.pow(tmp11, tmp12)
    tmp14 = float('inf')
    tmp15 = tmp13 == tmp14
    tmp16 = tl.where(tmp15, tmp10, tmp13)
    tmp17 = float('nan')
    tmp18 = tmp16 == tmp17
    tmp19 = tl.where(tmp18, tmp10, tmp16)
    tl.store(in_out_ptr0 + x3, tmp19, xmask)


@triton.jit
def triton_poi_fused_diag_embed_index_put_lift_fresh_pow_2(in_out_ptr0,
    in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16
    x3 = xindex
    tmp3 = tl.load(in_ptr0 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (4 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (8 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp8 = tl.load(in_ptr0 + (12 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp0 = x0
    tmp1 = x1
    tmp2 = tmp0 == tmp1
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp9 = tmp7 + tmp8
    tmp10 = 0.0
    tmp11 = tl.where(tmp2, tmp9, tmp10)
    tmp12 = tl.full([1], 1, tl.int32)
    tmp13 = tmp12 / tmp11
    tmp14 = float('inf')
    tmp15 = tmp13 == tmp14
    tmp16 = tl.where(tmp15, tmp10, tmp13)
    tmp17 = float('nan')
    tmp18 = tmp16 == tmp17
    tmp19 = tl.where(tmp18, tmp10, tmp16)
    tl.store(in_out_ptr0 + x3, tmp19, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4,), (1,))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_abs_diag_embed_0[grid(16)](primals_1, buf0, buf1, 
            16, XBLOCK=16, num_warps=1, num_stages=1)
        del primals_1
        buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (16, 4), (4, 1), 0),
            buf1, out=buf2)
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf4 = buf3
        del buf3
        triton_poi_fused_diag_embed_index_put_lift_fresh_pow_1[grid(64)](buf4,
            buf2, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf5 = reinterpret_tensor(buf2, (4, 4, 4), (16, 4, 1), 0)
        del buf2
        buf6 = buf5
        del buf5
        triton_poi_fused_diag_embed_index_put_lift_fresh_pow_2[grid(64)](buf6,
            primals_2, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf7 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(buf4, primals_2, out=buf7)
        buf8 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf7, (16, 4), (4, 1), 0),
            buf1, out=buf8)
        del buf1
        buf9 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf8, (4, 4, 4), (16, 4, 1), 
            0), buf6, out=buf9)
        buf10 = reinterpret_tensor(buf8, (4, 4, 4), (16, 4, 1), 0)
        del buf8
        extern_kernels.bmm(buf9, reinterpret_tensor(primals_2, (4, 4, 4), (
            16, 1, 4), 0), out=buf10)
        buf11 = buf9
        del buf9
        extern_kernels.bmm(buf10, buf4, out=buf11)
        buf12 = buf10
        del buf10
        extern_kernels.bmm(buf11, primals_3, out=buf12)
        del buf11
    return buf12, buf0, buf0, reinterpret_tensor(primals_3, (4, 4, 4), (16,
        1, 4), 0), reinterpret_tensor(buf4, (4, 4, 4), (16, 1, 4), 0
        ), primals_2, reinterpret_tensor(buf6, (4, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(buf7, (4, 16), (1, 4), 0)


class HGCNNew(nn.Module):

    def __init__(self, n_edges, in_feature, out_feature, n_agents):
        super(HGCNNew, self).__init__()
        None
        self.W_line = nn.Parameter(torch.ones(n_edges))
        self.W = None

    def forward(self, input_0, input_1):
        primals_1 = self.W_line
        primals_2 = input_0
        primals_3 = input_1
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.085893