import torch
import torch.nn as nn


class VarianceNorm2d(nn.Module):

    def __init__(self, num_features, bias=False):
        super().__init__()
        self.num_features = num_features
        self.bias = bias
        self.alpha = nn.Parameter(torch.zeros(num_features))
        self.alpha.data.normal_(1, 0.02)

    def forward(self, x):
        vars = torch.var(x, dim=(2, 3), keepdim=True)
        h = x / torch.sqrt(vars + 1e-05)
        out = self.alpha.view(-1, self.num_features, 1, 1) * h
        return out


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


def get_init_inputs():
    return [[], {'num_features': 4}]

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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_per_fused_add_div_mul_sqrt_var_0(in_out_ptr0, in_ptr0, in_ptr1,
    out_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    x2 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp22 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tl.where(xmask, tmp1, 0)
    tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp6 = tl.where(xmask, tmp4, 0)
    tmp7 = tl.sum(tmp6, 1)[:, None]
    tmp8 = tl.full([XBLOCK, 1], 16, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp1 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [XBLOCK, RBLOCK])
    tmp15 = tl.where(xmask, tmp13, 0)
    tmp16 = tl.sum(tmp15, 1)[:, None]
    tmp17 = 15.0
    tmp18 = tmp16 / tmp17
    tmp19 = 1e-05
    tmp20 = tmp18 + tmp19
    tmp21 = libdevice.sqrt(tmp20)
    tmp23 = tmp0 / tmp21
    tmp24 = tmp22 * tmp23
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp21, xmask)
    tl.store(out_ptr0 + (r1 + 16 * x0), tmp24, xmask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        buf3 = reinterpret_tensor(buf1, (4, 4, 1, 1), (4, 1, 1, 1), 0)
        del buf1
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused_add_div_mul_sqrt_var_0[grid(16)](buf3, primals_1,
            primals_2, buf4, 16, 16, XBLOCK=8, num_warps=2, num_stages=1)
        del primals_2
    return buf4, primals_1, buf3


class VarianceNorm2dNew(nn.Module):

    def __init__(self, num_features, bias=False):
        super().__init__()
        self.num_features = num_features
        self.bias = bias
        self.alpha = nn.Parameter(torch.zeros(num_features))
        self.alpha.data.normal_(1, 0.02)

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