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 as nn
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_mean_mul_rsqrt_sub_var_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
    x4 = xindex
    x3 = xindex // 64
    x5 = xindex % 16
    x1 = xindex // 4 % 4
    tmp0 = tl.load(in_ptr0 + x4, xmask)
    tmp1 = tl.load(in_ptr0 + (x5 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x5 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x5 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x5 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp28 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp30 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = 4.0
    tmp9 = tmp7 / tmp8
    tmp10 = tmp0 - tmp9
    tmp11 = tmp1 - tmp9
    tmp12 = tmp11 * tmp11
    tmp13 = tmp2 - tmp9
    tmp14 = tmp13 * tmp13
    tmp15 = tmp12 + tmp14
    tmp16 = tmp4 - tmp9
    tmp17 = tmp16 * tmp16
    tmp18 = tmp15 + tmp17
    tmp19 = tmp6 - tmp9
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp22 = 3.0
    tmp23 = tmp21 / tmp22
    tmp24 = 1e-05
    tmp25 = tmp23 + tmp24
    tmp26 = libdevice.rsqrt(tmp25)
    tmp27 = tmp10 * tmp26
    tmp29 = tmp27 * tmp28
    tmp31 = tmp29 + tmp30
    tl.store(out_ptr0 + x4, tmp31, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (1, 4, 1), (4, 1, 1))
    assert_size_stride(primals_3, (1, 4, 1), (4, 1, 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_mean_mul_rsqrt_sub_var_0[grid(256)](primals_1,
            primals_2, primals_3, buf0, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del primals_2
        del primals_3
    return buf0, primals_1


class ChannelNormNew(nn.Module):

    def __init__(self, numFeatures, epsilon=1e-05, affine=True):
        super(ChannelNormNew, self).__init__()
        if affine:
            self.weight = nn.parameter.Parameter(torch.Tensor(1,
                numFeatures, 1))
            self.bias = nn.parameter.Parameter(torch.Tensor(1, numFeatures, 1))
        else:
            self.weight = None
            self.bias = None
        self.epsilon = epsilon
        self.p = 0
        self.affine = affine
        self.reset_parameters()

    def reset_parameters(self):
        if self.affine:
            torch.nn.init.ones_(self.weight)
            torch.nn.init.zeros_(self.bias)

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


Measured latency_ms: 0.01611