import torch
from torch import nn
import torch.nn.functional as F


class HighwayNetwork(nn.Module):

    def __init__(self, size):
        super().__init__()
        self.W1 = nn.Linear(size, size)
        self.W2 = nn.Linear(size, size)
        self.W1.bias.data.fill_(0.0)

    def forward(self, x):
        x1 = self.W1(x)
        x2 = self.W2(x)
        g = torch.sigmoid(x2)
        y = g * F.relu(x1) + (1.0 - g) * x
        return y


def get_inputs():
    return [torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {'size': 4}]

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, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(primals_3, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf1)
        del primals_4
        del primals_5
        buf2 = 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)](buf1, buf0,
            primals_3, buf2, 256, XBLOCK=128, num_warps=4, num_stages=1)
    return buf2, primals_3, buf0, buf1


class HighwayNetworkNew(nn.Module):

    def __init__(self, size):
        super().__init__()
        self.W1 = nn.Linear(size, size)
        self.W2 = nn.Linear(size, size)
        self.W1.bias.data.fill_(0.0)

    def forward(self, input_0):
        primals_1 = self.W1.weight
        primals_2 = self.W1.bias
        primals_4 = self.W2.weight
        primals_5 = self.W2.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]
