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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
import torch.utils.data.distributed
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_maximum_mean_mul_pow_rsqrt_0(in_out_ptr0,
    in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, 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 + x2, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr3 + x2, xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr4 + 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
    tmp9 = tl_math.abs(tmp8)
    tmp10 = tmp7 + tmp9
    tmp11 = libdevice.rsqrt(tmp10)
    tmp13 = tmp0 * tmp11
    tmp14 = tmp12 * tmp13
    tmp16 = tmp14 + tmp15
    tmp18 = triton_helpers.maximum(tmp16, tmp17)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp11, xmask)
    tl.store(out_ptr0 + (r1 + 16 * x0), tmp18, 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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1, 4, 1, 1), (4, 1, 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))
    assert_size_stride(primals_5, (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_maximum_mean_mul_pow_rsqrt_0[grid(16)](buf1,
            primals_1, primals_2, primals_3, primals_4, primals_5, buf2, 16,
            16, XBLOCK=8, num_warps=2, num_stages=1)
        del primals_2
    return buf2, primals_1, primals_3, primals_4, primals_5, buf1


class FilterResponseNormNdNew(nn.Module):

    def __init__(self, ndim, num_features, eps=1e-06, learnable_eps=False):
        """
        Input Variables:
        ----------------
            ndim: An integer indicating the number of dimensions of the expected input tensor.
            num_features: An integer indicating the number of input feature dimensions.
            eps: A scalar constant or learnable variable.
            learnable_eps: A bool value indicating whether the eps is learnable.
        """
        assert ndim in [3, 4, 5
            ], 'FilterResponseNorm only supports 3d, 4d or 5d inputs.'
        super(FilterResponseNormNdNew, self).__init__()
        shape = (1, num_features) + (1,) * (ndim - 2)
        self.eps = nn.Parameter(torch.ones(*shape) * eps)
        if not learnable_eps:
            self.eps.requires_grad_(False)
        self.gamma = nn.Parameter(torch.Tensor(*shape))
        self.beta = nn.Parameter(torch.Tensor(*shape))
        self.tau = nn.Parameter(torch.Tensor(*shape))
        self.reset_parameters()

    def reset_parameters(self):
        nn.init.ones_(self.gamma)
        nn.init.zeros_(self.beta)
        nn.init.zeros_(self.tau)

    def forward(self, input_0):
        primals_2 = self.eps
        primals_3 = self.gamma
        primals_4 = self.beta
        primals_5 = self.tau
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.020125