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_add_mul_relu_rsub_sigmoid_0(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, out_ptr1, out_ptr2, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 8 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (4 + x0 + 8 * x1), xmask)
    tmp6 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr2 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp7 = tmp5 + tmp6
    tmp8 = tl.sigmoid(tmp7)
    tmp10 = tmp8 * tmp9
    tmp11 = 1.0
    tmp12 = tmp11 - tmp8
    tmp13 = tmp12 * tmp4
    tmp14 = tmp10 + tmp13
    tl.store(out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr1 + x2, tmp8, xmask)
    tl.store(out_ptr2 + x2, tmp14, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (8, 4), (4, 1))
    assert_size_stride(primals_2, (8,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 8), (8, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 8), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_mul_relu_rsub_sigmoid_0[grid(256)](buf0,
            primals_2, primals_3, buf1, buf2, buf3, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del buf0
        del primals_2
    return buf3, primals_3, buf1, buf2


class HighwayNew(nn.Module):
    """
    Individual highway layer
    """

    def __init__(self, input_dim, activation_class=nn.ReLU):
        """
        Create a highway layer. The input is a tensor of features, the output
        is a tensor with the same dimension.

        With input $x$, return $y$:
            $g = \\sigma(W_gx+b_g)$
            $n = f(W_nx+b_n)$
            $y = gx + (1-g)n$

        Parameters:
            :param: input_dim (int): the input dimensionality
            :param: activation_class (nn.Module): the class of the
            non-linearity. Default: ReLU

        Input:
            :param: input: a float tensor with shape [batch, input_dim]

        Return:
            :return: a float tensor with shape [batch, input_dim]
        """
        super(HighwayNew, self).__init__()
        self.input_dim = input_dim
        self.layer = nn.Linear(input_dim, input_dim * 2)
        self.activation = activation_class()
        self.gate = nn.Sigmoid()
        self.layer.bias[input_dim:].data.fill_(1)
        return

    def forward(self, input_0):
        primals_1 = self.layer.weight
        primals_2 = self.layer.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.028164