import torch
from torch._inductor.select_algorithm import extern_kernels
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_std_0(in_ptr0, 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 % 16
    r2 = rindex // 16
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0 + 64 * r2), xmask, other=0.0)
    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], 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]
    tl.store(out_ptr0 + x0, tmp16, 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
    x3 = xindex
    x1 = xindex // 16 % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = 63.0
    tmp3 = tmp1 / tmp2
    tmp4 = libdevice.sqrt(tmp3)
    tmp5 = 1e-15
    tmp6 = tmp4 + tmp5
    tmp7 = tmp0 / tmp6
    tl.store(out_ptr0 + x3, tmp7, xmask)


@triton.jit
def triton_poi_fused_div_2(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = 0.0625
    tmp2 = tmp0 * tmp1
    tl.store(in_out_ptr0 + x0, tmp2, 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)
        buf1 = empty_strided_cuda((4,), (1,), torch.float32)
        get_raw_stream(0)
        triton_per_fused_std_0[grid(4)](arg0_1, buf1, 4, 64, XBLOCK=1,
            num_warps=2, num_stages=1)
        buf3 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
        triton_poi_fused_add_div_1[grid(256)](arg0_1, buf1, buf3, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
        del buf1
        buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(buf3, reinterpret_tensor(buf3, (4, 16, 4), (64, 
            1, 16), 0), out=buf4)
        del buf3
        buf5 = buf4
        del buf4
        triton_poi_fused_div_2[grid(64)](buf5, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
    return buf5,


def normalize_by_stddev(tensor):
    """
    divides channel-wise by standard deviation of channel
    """
    channels = tensor.shape[1]
    stddev = tensor.std(dim=(0, 2)).view(1, channels, 1) + 1e-15
    return tensor.div(stddev)


class NormalizedGramMatrixNew(nn.Module):
    """
    I have found that normalizing the tensor before calculating the gram matrices leads to better convergence.
    """

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


Measured latency_ms: 0.019245