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
from typing import Tuple
import torch.nn.functional
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_div_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    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')
    tmp4 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp1 * tmp1
    tmp3 = tmp2 * tmp2
    tmp5 = tmp4 * tmp4
    tmp6 = tmp5 * tmp5
    tmp7 = tmp3 + tmp6
    tmp9 = tmp8 * tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp7 + tmp10
    tmp13 = tmp12 * tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tmp11 + tmp14
    tmp16 = 0.25
    tmp17 = libdevice.pow(tmp15, tmp16)
    tmp18 = 0.0
    tmp19 = triton_helpers.maximum(tmp17, tmp18)
    tmp20 = tmp0 / tmp19
    tl.store(out_ptr0 + x2, tmp20, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4, 4), (256, 64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_div_0[grid(1024)](arg0_1, buf0, 1024, XBLOCK=256,
            num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class NormalizeNew(torch.nn.Module):
    """Normalize a tensor time series with mean and standard deviation.
    Given mean: ``(mean[1],...,mean[n])`` and std: ``(std[1],..,std[n])`` for ``n``
    channels, this transform will normalize each channel of the input
    ``torch.*Tensor`` i.e.,
    ``output[channel] = (input[channel] - mean[channel]) / std[channel]``

    .. note::
        This transform acts out of place, i.e., it does not mutate the input tensor.

    Args:
        mean (tuple): Sequence of means for each channel.
        std (tuple): Sequence of standard deviations for each channel.
        inplace(bool,optional): Bool to make this operation in-place.

    """

    def __init__(self, mean: 'Tuple[float]', std: 'Tuple[float]', inplace:
        'bool'=False):
        super(NormalizeNew, self).__init__()
        self.mean = mean
        self.std = std
        self.inplace = inplace

    def __repr__(self):
        return self.__class__.__name__ + '(mean={0}, std={1})'.format(self.
            mean, self.std)

    def forward(self, input_0):
        arg0_1 = input_0
        output = call([arg0_1])
        return output[0]


Measured latency_ms: 0.014162