import torch
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.cuda
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_cat_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 8
    x0 = xindex % 4
    x2 = xindex // 32
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 4 * x1 + 16 * x2), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 4 * (-4 + x1) + 16 * x2), tmp6 & xmask,
        other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tmp11 = tl.load(in_ptr2 + (x0 + 4 * (-4 + x1) + 16 * x2), tmp6 & xmask,
        other=0.0)
    tmp12 = tl.where(tmp4, tmp5, tmp11)
    tl.store(out_ptr0 + x3, tmp10, xmask)
    tl.store(out_ptr1 + x3, tmp12, xmask)


@triton.jit
def triton_per_fused_mv_1(in_ptr0, in_ptr1, out_ptr0, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (4 * r1 + 32 * (x0 // 4) + x0 % 4), xmask,
        other=0.0)
    tmp1 = tl.load(in_ptr1 + r1, None, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.where(xmask, tmp3, 0)
    tmp6 = tl.sum(tmp5, 1)[:, None]
    tl.store(out_ptr0 + x0, tmp6, xmask)


@triton.jit
def triton_poi_fused_add_mul_rsub_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp7 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp2 = 1.0
    tmp3 = tmp2 - tmp1
    tmp4 = -1e+30
    tmp5 = tmp3 * tmp4
    tmp6 = tmp0 + tmp5
    tmp8 = tmp7 + tmp5
    tl.store(out_ptr0 + x2, tmp6, xmask)
    tl.store(out_ptr1 + x2, tmp8, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (8,), (1,))
    assert_size_stride(primals_5, (8,), (1,))
    assert_size_stride(primals_6, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 8, 4), (32, 4, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 8, 4), (32, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(128)](primals_1, primals_2, primals_3,
            buf0, buf1, 128, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        del primals_2
        del primals_3
        buf2 = empty_strided_cuda((16,), (1,), torch.float32)
        triton_per_fused_mv_1[grid(16)](buf0, primals_4, buf2, 16, 8,
            XBLOCK=8, num_warps=2, num_stages=1)
        del primals_4
        buf3 = empty_strided_cuda((16,), (1,), torch.float32)
        triton_per_fused_mv_1[grid(16)](buf1, primals_5, buf3, 16, 8,
            XBLOCK=8, num_warps=2, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_mul_rsub_2[grid(64)](buf2, primals_6, buf3,
            buf4, buf5, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf2
        del buf3
        del primals_6
    return buf4, buf5, buf0, buf1


def mask_logits(target, mask):
    mask = mask.type(torch.float32)
    return target + -1e+30 * (1 - mask)


class OutputLayerNew(nn.Module):

    def __init__(self, hidden_size):
        super(OutputLayerNew, self).__init__()
        self.weight1 = torch.empty(hidden_size * 2, 1)
        self.weight2 = torch.empty(hidden_size * 2, 1)
        nn.init.xavier_uniform_(self.weight1)
        nn.init.xavier_uniform_(self.weight2)
        self.weight1 = nn.Parameter(self.weight1.squeeze(), requires_grad=True)
        self.weight2 = nn.Parameter(self.weight2.squeeze(), requires_grad=True)

    def forward(self, input_0, input_1, input_2, input_3):
        primals_4 = self.weight1
        primals_5 = self.weight2
        primals_1 = input_0
        primals_2 = input_1
        primals_3 = input_2
        primals_6 = input_3
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0], output[1]


Measured latency_ms: 0.035888