import torch
import torch.nn
import torch.onnx


class HuggingfaceFastGelu(torch.nn.Module):

    def forward(self, x):
        return 0.5 * x * (1.0 + torch.tanh(x * 0.7978845608 * (1.0 + 
            0.044715 * x * x)))


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


def get_init_inputs():
    return [[], {}]

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
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn
import torch.onnx
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_add_mul_tanh_0(in_ptr0, 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)
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp3 = 0.7978845608
    tmp4 = tmp0 * tmp3
    tmp5 = 0.044715
    tmp6 = tmp0 * tmp5
    tmp7 = tmp6 * tmp0
    tmp8 = 1.0
    tmp9 = tmp7 + tmp8
    tmp10 = tmp4 * tmp9
    tmp11 = libdevice.tanh(tmp10)
    tmp12 = tmp11 + tmp8
    tmp13 = tmp2 * tmp12
    tl.store(out_ptr0 + x0, tmp13, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_mul_tanh_0[grid(256)](arg0_1, buf0, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class HuggingfaceFastGeluNew(torch.nn.Module):

    def forward(self, input_0):
        arg0_1 = input_0
        output = call([arg0_1])
        return output[0]
