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 torch import 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_mean_pow_sqrt_sub_0(in_out_ptr0, in_out_ptr1,
    in_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)
    r2 = rindex
    x0 = xindex % 4
    x1 = xindex // 4
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * r2 + 64 * x1), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp5 = 16.0
    tmp6 = tmp4 / tmp5
    tmp7 = tmp0 - tmp6
    tmp8 = tmp7 * tmp7
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK])
    tmp11 = tl.where(xmask, tmp9, 0)
    tmp12 = tl.sum(tmp11, 1)[:, None]
    tmp13 = tmp12 / tmp5
    tmp14 = 1e-05
    tmp15 = tmp13 + tmp14
    tmp16 = libdevice.sqrt(tmp15)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x3, tmp6, xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + x3, tmp16, xmask)


@triton.jit
def triton_poi_fused_add_div_mul_sub_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    in_ptr4, 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
    x0 = xindex % 4
    x3 = xindex // 64
    x2 = xindex // 16 % 4
    tmp0 = tl.load(in_ptr0 + x4, xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + 4 * x3), xmask, eviction_policy='evict_last'
        )
    tmp3 = tl.load(in_ptr2 + (x0 + 4 * x3), xmask, eviction_policy='evict_last'
        )
    tmp5 = tl.load(in_ptr3 + x2, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x2, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 / tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tl.store(out_ptr0 + x4, tmp8, 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, 1, 4), (4, 16, 16, 1), torch.float32)
        buf1 = reinterpret_tensor(buf0, (4, 1, 1, 4), (4, 4, 4, 1), 0)
        del buf0
        buf2 = empty_strided_cuda((4, 1, 1, 4), (4, 16, 16, 1), torch.float32)
        buf3 = reinterpret_tensor(buf2, (4, 1, 1, 4), (4, 4, 4, 1), 0)
        del buf2
        get_raw_stream(0)
        triton_per_fused_add_mean_pow_sqrt_sub_0[grid(16)](buf1, buf3,
            primals_1, 16, 16, XBLOCK=8, num_warps=2, num_stages=1)
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_div_mul_sub_1[grid(256)](primals_1, buf1, buf3,
            primals_2, primals_3, buf4, 256, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_2
        del primals_3
    return buf4, primals_1, buf1, buf3


class LayerNormNew(nn.Module):

    def __init__(self, num_channels, eps=1e-05):
        super().__init__()
        self.num_channels = num_channels
        self.eps = eps
        self.weight = nn.Parameter(torch.Tensor(num_channels))
        self.bias = nn.Parameter(torch.Tensor(num_channels))
        self.reset_parameters()

    def reset_parameters(self):
        nn.init.ones_(self.weight)
        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: 0.033018