import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data


class SpatialCrossMapLRN(nn.Module):

    def __init__(self, local_size=1, alpha=1.0, beta=0.75, k=1,
        ACROSS_CHANNELS=True):
        super(SpatialCrossMapLRN, self).__init__()
        self.ACROSS_CHANNELS = ACROSS_CHANNELS
        if ACROSS_CHANNELS:
            self.average = nn.AvgPool3d(kernel_size=(local_size, 1, 1),
                stride=1, padding=(int((local_size - 1.0) / 2), 0, 0))
        else:
            self.average = nn.AvgPool2d(kernel_size=local_size, stride=1,
                padding=int((local_size - 1.0) / 2))
        self.alpha = alpha
        self.beta = beta
        self.k = k

    def forward(self, x):
        if self.ACROSS_CHANNELS:
            div = x.pow(2).unsqueeze(1)
            div = self.average(div).squeeze(1)
            div = div.mul(self.alpha).add(self.k).pow(self.beta)
        else:
            div = x.pow(2)
            div = self.average(div)
            div = div.mul(self.alpha).add(self.k).pow(self.beta)
        x = x.div(div)
        return x


def get_inputs():
    return [torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {}]

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
import torch.nn.parallel
import torch.optim
import torch.utils.data
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_add_div_mul_pow_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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tmp0 * tmp0
    tmp2 = 1.0
    tmp3 = tmp1 * tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tmp4 + tmp2
    tmp6 = 0.75
    tmp7 = libdevice.pow(tmp5, tmp6)
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x0, tmp8, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (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_add_div_mul_pow_0[grid(256)](arg0_1, buf0, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class SpatialCrossMapLRNNew(nn.Module):

    def __init__(self, local_size=1, alpha=1.0, beta=0.75, k=1,
        ACROSS_CHANNELS=True):
        super(SpatialCrossMapLRNNew, self).__init__()
        self.ACROSS_CHANNELS = ACROSS_CHANNELS
        if ACROSS_CHANNELS:
            self.average = nn.AvgPool3d(kernel_size=(local_size, 1, 1),
                stride=1, padding=(int((local_size - 1.0) / 2), 0, 0))
        else:
            self.average = nn.AvgPool2d(kernel_size=local_size, stride=1,
                padding=int((local_size - 1.0) / 2))
        self.alpha = alpha
        self.beta = beta
        self.k = k

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