import torch
from torch._inductor.select_algorithm import extern_kernels
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_poi_fused_add_div_mean_mul_pow_sqrt_0(in_ptr0, in_ptr1, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp2 = tl.load(in_ptr1 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp3 = tmp2 * tmp2
    tmp5 = tmp4 * tmp4
    tmp6 = tmp3 + tmp5
    tmp8 = tmp7 * tmp7
    tmp9 = tmp6 + tmp8
    tmp11 = tmp10 * tmp10
    tmp12 = tmp9 + tmp11
    tmp13 = 4.0
    tmp14 = tmp12 / tmp13
    tmp15 = libdevice.sqrt(tmp14)
    tmp16 = 1e-08
    tmp17 = tmp15 + tmp16
    tmp18 = tmp1 / tmp17
    tmp19 = tmp0 * tmp18
    tl.store(out_ptr0 + x2, tmp19, 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, 4), (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, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_div_mean_mul_pow_sqrt_0[grid(256)](primals_2,
            primals_1, buf0, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_4, reinterpret_tensor(buf0, (64, 4), (
            4, 1), 0), reinterpret_tensor(primals_3, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf1)
        del primals_4
    return reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), primals_1, reinterpret_tensor(buf0, (64, 4), (4, 1), 0), primals_3


class RMSNorm(nn.Module):
    """An implementation of RMS Normalization.

    # https://catalyst-team.github.io/catalyst/_modules/catalyst/contrib/nn/modules/rms_norm.html#RMSNorm
    """

    def __init__(self, dimension: 'int', epsilon: 'float'=1e-08, is_bias:
        'bool'=False):
        """
        Args:
            dimension (int): the dimension of the layer output to normalize
            epsilon (float): an epsilon to prevent dividing by zero
                in case the layer has zero variance. (default = 1e-8)
            is_bias (bool): a boolean value whether to include bias term
                while normalization
        """
        super().__init__()
        self.dimension = dimension
        self.epsilon = epsilon
        self.is_bias = is_bias
        self.scale = nn.Parameter(torch.ones(self.dimension))
        if self.is_bias:
            self.bias = nn.Parameter(torch.zeros(self.dimension))

    def forward(self, x: 'torch.Tensor') ->torch.Tensor:
        x_std = torch.sqrt(torch.mean(x ** 2, -1, keepdim=True))
        x_norm = x / (x_std + self.epsilon)
        if self.is_bias:
            return self.scale * x_norm + self.bias
        return self.scale * x_norm


class LightHeadNew(nn.Module):

    def __init__(self, in_features, num_classes):
        super().__init__()
        self.norm = RMSNorm(in_features)
        self.fc = nn.Linear(in_features, num_classes)

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


Measured latency_ms: 0.027215