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
from torchvision.transforms import *
import torch.nn
import torch
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_add_div_mean_mul_std_sub_0(in_out_ptr0, in_out_ptr1,
    in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    RBLOCK: tl.constexpr = 64
    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
    r3 = rindex // 16
    tmp0 = tl.load(in_ptr0 + (r1 + 64 * x0), xmask, other=0.0)
    tmp26 = tl.load(in_ptr1 + r3, None, eviction_policy='evict_last')
    tmp30 = tl.load(in_ptr2 + r3, None, eviction_policy='evict_last')
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp6 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp8 = tl.where(xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = tl.full([XBLOCK, 1], 64, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp1 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.where(xmask, tmp15, 0)
    tmp18 = tl.sum(tmp17, 1)[:, None]
    tmp19 = 64.0
    tmp20 = tmp4 / tmp19
    tmp21 = 63.0
    tmp22 = tmp18 / tmp21
    tmp23 = libdevice.sqrt(tmp22)
    tmp24 = 1e-05
    tmp25 = tmp23 + tmp24
    tmp27 = tmp0 - tmp20
    tmp28 = tmp27 / tmp25
    tmp29 = tmp26 * tmp28
    tmp31 = tmp29 + tmp30
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp20, xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + x0, tmp25, xmask)
    tl.store(out_ptr0 + (r1 + 64 * x0), 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, (4,), (1,))
    assert_size_stride(primals_3, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4,), (1,), torch.float32)
        buf3 = empty_strided_cuda((4,), (1,), torch.float32)
        buf1 = buf0
        del buf0
        buf5 = reinterpret_tensor(buf3, (4, 1, 1, 1), (1, 1, 1, 1), 0)
        del buf3
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused_add_div_mean_mul_std_sub_0[grid(4)](buf1, buf5,
            primals_1, primals_2, primals_3, buf6, 4, 64, XBLOCK=1,
            num_warps=2, num_stages=1)
        del primals_2
        del primals_3
    return buf6, primals_1, reinterpret_tensor(buf1, (4, 1, 1, 1), (1, 1, 1,
        1), 0), buf5


class LayerNormConv2dNew(nn.Module):

    def __init__(self, num_features, eps=1e-05, affine=True):
        super().__init__()
        self.num_features = num_features
        self.affine = affine
        self.eps = eps
        if self.affine:
            self.gamma = nn.Parameter(torch.Tensor(num_features).uniform_())
            self.beta = nn.Parameter(torch.zeros(num_features))

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


Measured latency_ms: 0.033355