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_mean_mul_sqrt_var_0(in_out_ptr0, in_out_ptr1,
    in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    RBLOCK: tl.constexpr = 64
    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
    r3 = rindex // 16
    tmp0 = tl.load(in_ptr0 + (r1 + 64 * x0), xmask, other=0.0)
    tmp25 = tl.load(in_ptr1 + r3, None, eviction_policy='evict_last')
    tmp29 = tl.load(in_ptr2 + r3, None, eviction_policy='evict_last')
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = 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], 64, 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]
    tmp18 = tl.sum(tmp3, 1)[:, None]
    tmp19 = 64.0
    tmp20 = tmp18 / tmp19
    tmp21 = tmp16 / tmp19
    tmp22 = 1e-05
    tmp23 = tmp21 + tmp22
    tmp24 = libdevice.sqrt(tmp23)
    tmp26 = tmp0 - tmp20
    tmp27 = tmp26 / tmp24
    tmp28 = tmp25 * tmp27
    tmp30 = tmp28 + tmp29
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp20, xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + x0, tmp24, xmask)
    tl.store(out_ptr0 + (r1 + 64 * x0), tmp30, 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)
        buf1 = empty_strided_cuda((4, 1, 1, 1, 1), (1, 4, 4, 4, 4), torch.
            float32)
        buf3 = empty_strided_cuda((4, 1, 1, 1, 1), (1, 4, 4, 4, 4), torch.
            float32)
        buf4 = reinterpret_tensor(buf3, (4, 1, 1, 1, 1), (1, 1, 1, 1, 1), 0)
        del buf3
        buf5 = reinterpret_tensor(buf1, (4, 1, 1, 1, 1), (1, 1, 1, 1, 1), 0)
        del buf1
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused_add_mean_mul_sqrt_var_0[grid(4)](buf4, buf5,
            primals_1, primals_2, primals_3, buf6, 4, 64, XBLOCK=1,
            num_warps=2, num_stages=1)
        del primals_2
        del primals_3
    return buf6, primals_1, buf4, buf5


class GroupNorm2DNew(nn.Module):

    def __init__(self, num_channels, num_groups=4, epsilon=1e-05):
        super(GroupNorm2DNew, self).__init__()
        self.num_channels = num_channels
        self.num_groups = num_channels // 4
        self.epsilon = epsilon
        self.gamma = nn.Parameter(torch.ones(num_channels))
        self.beta = nn.Parameter(torch.zeros(num_channels))

    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: 0.021939