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
import torch.nn as nn
from torch.nn.parameter import Parameter
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_sub_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = 4.0
    tmp9 = tmp7 / tmp8
    tmp10 = tmp0 - tmp9
    tl.store(out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused_add_div_mul_pow_1(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
    x2 = xindex
    x1 = xindex // 4
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp18 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp1 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp6
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp9
    tmp11 = tmp8 + tmp10
    tmp12 = 4.0
    tmp13 = tmp11 / tmp12
    tmp14 = 1e-12
    tmp15 = tmp13 + tmp14
    tmp16 = tmp15 * tmp15
    tmp17 = tmp0 / tmp16
    tmp19 = tmp17 * tmp18
    tmp21 = tmp19 + tmp20
    tl.store(out_ptr0 + x2, tmp21, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4,), (1,))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 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_sub_0[grid(256)](primals_3, buf0, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_div_mul_pow_1[grid(256)](buf0, primals_2,
            primals_1, buf1, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del buf0
        del primals_1
        del primals_2
    return buf1, primals_3


class LayerNormNew(nn.Module):

    def __init__(self, input_dim, cond_dim=0, center=True, scale=True,
        epsilon=None, conditional=False, hidden_units=None,
        hidden_activation='linear', hidden_initializer='xaiver', **kwargs):
        super(LayerNormNew, self).__init__()
        """
        input_dim: inputs.shape[-1]
        cond_dim: cond.shape[-1]
        """
        self.center = center
        self.scale = scale
        self.conditional = conditional
        self.hidden_units = hidden_units
        self.hidden_initializer = hidden_initializer
        self.epsilon = epsilon or 1e-12
        self.input_dim = input_dim
        self.cond_dim = cond_dim
        if self.center:
            self.beta = Parameter(torch.zeros(input_dim))
        if self.scale:
            self.gamma = Parameter(torch.ones(input_dim))
        if self.conditional:
            if self.hidden_units is not None:
                self.hidden_dense = nn.Linear(in_features=self.cond_dim,
                    out_features=self.hidden_units, bias=False)
            if self.center:
                self.beta_dense = nn.Linear(in_features=self.cond_dim,
                    out_features=input_dim, bias=False)
            if self.scale:
                self.gamma_dense = nn.Linear(in_features=self.cond_dim,
                    out_features=input_dim, bias=False)
        self.initialize_weights()

    def initialize_weights(self):
        if self.conditional:
            if self.hidden_units is not None:
                if self.hidden_initializer == 'normal':
                    torch.nn.init.normal(self.hidden_dense.weight)
                elif self.hidden_initializer == 'xavier':
                    torch.nn.init.xavier_uniform_(self.hidden_dense.weight)
            if self.center:
                torch.nn.init.constant_(self.beta_dense.weight, 0)
            if self.scale:
                torch.nn.init.constant_(self.gamma_dense.weight, 0)

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


Measured latency_ms: 0.027937