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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@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 = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    r1 = rindex % 4
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp25 = tl.load(in_ptr1 + r1, None, eviction_policy='evict_last')
    tmp27 = tl.load(in_ptr2 + r1, None, eviction_policy='evict_last')
    tmp1 = tl.broadcast_to(tmp0, [RBLOCK])
    tmp3 = triton_helpers.promote_to_tensor(tl.sum(tmp1, 0))
    tmp5 = tl.broadcast_to(tmp1, [RBLOCK])
    tmp7 = triton_helpers.promote_to_tensor(tl.sum(tmp5, 0))
    tmp8 = tl.full([1], 256, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp1 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [RBLOCK])
    tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tmp16 = 256.0
    tmp17 = tmp3 / tmp16
    tmp18 = 255.0
    tmp19 = tmp15 / tmp18
    tmp20 = libdevice.sqrt(tmp19)
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = tmp0 - tmp17
    tmp24 = tmp23 / tmp22
    tmp26 = tmp24 * tmp25
    tmp28 = tmp26 + tmp27
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp17, None)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + tl.full([1], 0, tl.int32), tmp22, None)
    tl.store(out_ptr0 + tl.broadcast_to(r0, [RBLOCK]), tmp28, None)


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), (4, 1))
    assert_size_stride(primals_3, (1, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((), (), torch.float32)
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf1 = buf0
        del buf0
        buf5 = buf3
        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(1)](buf1, buf5,
            primals_1, primals_2, primals_3, buf6, 1, 256, num_warps=2,
            num_stages=1)
        del primals_2
        del primals_3
    return buf6, primals_1, buf1, buf5


class LayerNormalizationNew(nn.Module):

    def __init__(self, hidden_size, eps=1e-05):
        super(LayerNormalizationNew, self).__init__()
        self.eps = eps
        self.a2 = nn.Parameter(torch.ones(1, hidden_size), requires_grad=True)
        self.b2 = nn.Parameter(torch.zeros(1, hidden_size), requires_grad=True)

    def forward(self, input_0):
        primals_2 = self.a2
        primals_3 = self.b2
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.018536