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, math as tl_math
import torch.nn as 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_per_fused_abs_add_mean_mul_pow_rsqrt_0(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 16
    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
    x2 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp8 = tl.load(in_ptr1 + 0)
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK, 1])
    tmp13 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr3 + x2, xmask, eviction_policy='evict_last')
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.where(xmask, tmp2, 0)
    tmp5 = tl.sum(tmp4, 1)[:, None]
    tmp6 = 16.0
    tmp7 = tmp5 / tmp6
    tmp10 = tl_math.abs(tmp9)
    tmp11 = tmp7 + tmp10
    tmp12 = libdevice.rsqrt(tmp11)
    tmp14 = tmp0 * tmp12
    tmp15 = tmp13 * tmp14
    tmp17 = tmp15 + tmp16
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp12, xmask)
    tl.store(out_ptr0 + (r1 + 16 * x0), tmp17, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1,), (1,))
    assert_size_stride(primals_3, (1, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_4, (1, 4, 1, 1), (4, 1, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        buf1 = reinterpret_tensor(buf0, (4, 4, 1, 1), (4, 1, 1, 1), 0)
        del buf0
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused_abs_add_mean_mul_pow_rsqrt_0[grid(16)](buf1,
            primals_1, primals_2, primals_3, primals_4, buf2, 16, 16,
            XBLOCK=8, num_warps=2, num_stages=1)
        del primals_4
    return buf2, primals_1, primals_2, primals_3, buf1


class FRN_selfNew(nn.Module):

    def __init__(self, num_features, eps=1e-05, is_eps_learnable=True):
        super(FRN_selfNew, self).__init__()
        self.num_features = num_features
        self.init_eps = eps
        self.is_eps_learnable = is_eps_learnable
        self.gamma = nn.Parameter(torch.Tensor(1, num_features, 1, 1),
            requires_grad=True)
        self.beta = nn.Parameter(torch.Tensor(1, num_features, 1, 1),
            requires_grad=True)
        self.eps = nn.Parameter(torch.Tensor(1), requires_grad=True)
        self.reset_parameters()

    def reset_parameters(self):
        nn.init.ones_(self.gamma)
        nn.init.zeros_(self.beta)
        if self.is_eps_learnable:
            nn.init.constant_(self.eps, self.init_eps)

    def extra_repr(self):
        return 'num_features={num_features}, eps={init_eps}'.format(**self.
            __dict__)

    def forward(self, input_0):
        primals_3 = self.gamma
        primals_4 = self.beta
        primals_2 = self.eps
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]


Measured latency_ms: 0.019841