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 import 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_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = xindex // 8
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused_add_mul_relu_rsub_sigmoid_1(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp2 = tl.load(in_ptr1 + x0, xmask)
    tmp8 = tl.load(in_ptr2 + x0, xmask)
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = tmp1 * tmp4
    tmp6 = 1.0
    tmp7 = tmp6 - tmp1
    tmp9 = tmp7 * tmp8
    tmp10 = tmp5 + tmp9
    tl.store(out_ptr0 + x0, tmp10, 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, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 8), (8, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4, 8), (8, 1))
    assert_size_stride(primals_6, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(32)](primals_1, primals_2, buf0, 32,
            XBLOCK=32, num_warps=1, num_stages=1)
        del primals_2
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_4, buf0, reinterpret_tensor(primals_3,
            (8, 4), (1, 8), 0), alpha=1, beta=1, out=buf1)
        del primals_3
        del primals_4
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_6, buf0, reinterpret_tensor(primals_5,
            (8, 4), (1, 8), 0), alpha=1, beta=1, out=buf2)
        del primals_5
        del primals_6
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_add_mul_relu_rsub_sigmoid_1[grid(16)](buf2, buf1,
            primals_1, buf3, 16, XBLOCK=16, num_warps=1, num_stages=1)
    return buf3, primals_1, buf0, buf1, buf2


class HighwayNew(nn.Module):
    """The Highway update layer from [srivastava2015]_.

    .. [srivastava2015] Srivastava, R. K., *et al.* (2015).
       `Highway Networks <http://arxiv.org/abs/1505.00387>`_.
       *arXiv*, 1505.00387.
    """

    def __init__(self, input_size: 'int', prev_input_size: 'int'):
        """Instantiate the Highway update layer.

        :param input_size: Current representation size.
        :param prev_input_size: Size of the representation obtained by the previous convolutional layer.
        """
        super().__init__()
        total_size = input_size + prev_input_size
        self.proj = nn.Linear(total_size, input_size)
        self.transform = nn.Linear(total_size, input_size)
        self.transform.bias.data.fill_(-2.0)

    def forward(self, input_0, input_1):
        primals_3 = self.proj.weight
        primals_4 = self.proj.bias
        primals_5 = self.transform.weight
        primals_6 = self.transform.bias
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]


Measured latency_ms: 0.045507