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
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_poi_fused_mean_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 16 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (4 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp3 = tl.load(in_ptr0 + (8 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp5 = tl.load(in_ptr0 + (12 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp9 = tl.load(in_ptr0 + (1 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp10 = tl.load(in_ptr0 + (5 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp12 = tl.load(in_ptr0 + (9 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp14 = tl.load(in_ptr0 + (13 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp18 = tl.load(in_ptr0 + (2 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp19 = tl.load(in_ptr0 + (6 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp21 = tl.load(in_ptr0 + (10 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp23 = tl.load(in_ptr0 + (14 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp27 = tl.load(in_ptr0 + (3 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp28 = tl.load(in_ptr0 + (7 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp30 = tl.load(in_ptr0 + (11 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp32 = tl.load(in_ptr0 + (15 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp11 = tmp9 + tmp10
    tmp13 = tmp11 + tmp12
    tmp15 = tmp13 + tmp14
    tmp16 = tmp15 / tmp7
    tmp17 = tmp8 + tmp16
    tmp20 = tmp18 + tmp19
    tmp22 = tmp20 + tmp21
    tmp24 = tmp22 + tmp23
    tmp25 = tmp24 / tmp7
    tmp26 = tmp17 + tmp25
    tmp29 = tmp27 + tmp28
    tmp31 = tmp29 + tmp30
    tmp33 = tmp31 + tmp32
    tmp34 = tmp33 / tmp7
    tmp35 = tmp26 + tmp34
    tmp36 = tmp35 / tmp7
    tl.store(out_ptr0 + x0, tmp36, xmask)


@triton.jit
def triton_poi_fused_add_div_1(in_ptr0, in_ptr1, 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 // 16
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = 1e-06
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 / tmp3
    tl.store(out_ptr0 + x2, tmp4, 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((16,), (1,), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mean_0[grid(16)](arg0_1, buf0, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_div_1[grid(256)](arg0_1, buf0, buf1, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
        del buf0
    return reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0),


class CumulativeMagSpectralNormNew(nn.Module):

    def __init__(self, cumulative=False, use_mid_freq_mu=False):
        """

        Args:
            cumulative: 是否采用累积的方式计算 mu
            use_mid_freq_mu: 仅采用中心频率的 mu 来代替全局 mu

        Notes:
            先算均值再累加 等同于 先累加再算均值

        """
        super().__init__()
        self.eps = 1e-06
        self.cumulative = cumulative
        self.use_mid_freq_mu = use_mid_freq_mu

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


Measured latency_ms: 0.027996