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
import torch.nn as nn
import torch.jit
import torch.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_add_div_mean_mul_pow_relu_sqrt_sub_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)
    tmp11 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
    tmp14 = 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
    tmp8 = 1e-16
    tmp9 = tmp7 + tmp8
    tmp10 = libdevice.sqrt(tmp9)
    tmp12 = tmp0 / tmp10
    tmp13 = tmp11 * tmp12
    tmp15 = tmp13 + tmp14
    tmp17 = tmp15 - tmp16
    tmp18 = tl.full([1, 1], 0, tl.int32)
    tmp19 = triton_helpers.maximum(tmp18, tmp17)
    tmp20 = tmp19 + tmp16
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp10, xmask)
    tl.store(out_ptr0 + (r1 + 16 * x0), tmp20, 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, (4,), (1,))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused_add_div_mean_mul_pow_relu_sqrt_sub_0[grid(16)](buf1,
            primals_1, primals_2, primals_3, primals_4, buf2, 16, 16,
            XBLOCK=1, num_warps=2, num_stages=1)
    return (buf2, primals_1, primals_2, primals_3, primals_4,
        reinterpret_tensor(buf1, (4, 4, 1, 1), (4, 1, 1, 1), 0))


class FilterResponseNormNew(nn.Module):

    def __init__(self, in_size, eps=1e-16):
        super().__init__()
        self.eps = eps
        self.in_size = in_size
        self.register_parameter('scale', nn.Parameter(torch.ones(in_size,
            dtype=torch.float)))
        self.register_parameter('bias', nn.Parameter(torch.zeros(in_size,
            dtype=torch.float)))
        self.register_parameter('threshold', nn.Parameter(torch.zeros(
            in_size, dtype=torch.float)))

    def forward(self, input_0):
        primals_2 = self.scale
        primals_3 = self.bias
        primals_4 = self.threshold
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]


Measured latency_ms: 0.028223