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
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_poi_fused_mean_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_mean_mul_pow_sqrt_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-10
    tmp15 = tmp13 + tmp14
    tmp16 = libdevice.sqrt(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, 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, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mean_sub_0[grid(256)](primals_1, buf0, 256, XBLOCK
            =256, 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_mean_mul_pow_sqrt_1[grid(256)](buf0,
            primals_2, primals_3, buf1, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del buf0
        del primals_2
        del primals_3
    return buf1, primals_1


class LayerNormalizationNew(nn.Module):

    def __init__(self, normal_shape, gamma=True, beta=True, epsilon=1e-10):
        """Layer normalization layer

        See: [Layer Normalization](https://arxiv.org/pdf/1607.06450.pdf)

        :param normal_shape: The shape of the input tensor or the last dimension of the input tensor.
        :param gamma: Add a scale parameter if it is True.
        :param beta: Add an offset parameter if it is True.
        :param epsilon: Epsilon for calculating variance.
        """
        super(LayerNormalizationNew, self).__init__()
        if isinstance(normal_shape, int):
            normal_shape = normal_shape,
        else:
            normal_shape = normal_shape[-1],
        self.normal_shape = torch.Size(normal_shape)
        self.epsilon = epsilon
        if gamma:
            self.gamma = nn.Parameter(torch.Tensor(*normal_shape))
        else:
            self.register_parameter('gamma', None)
        if beta:
            self.beta = nn.Parameter(torch.Tensor(*normal_shape))
        else:
            self.register_parameter('beta', None)
        self.reset_parameters()

    def reset_parameters(self):
        if self.gamma is not None:
            self.gamma.data.fill_(1)
        if self.beta is not None:
            self.beta.data.zero_()

    def extra_repr(self):
        return 'normal_shape={}, gamma={}, beta={}, epsilon={}'.format(self
            .normal_shape, self.gamma is not None, self.beta is not None,
            self.epsilon)

    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: 0.02812