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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import math
import torch.nn as nn
import torch.utils.data
import torch.nn.parallel
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_div_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
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp9 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp1 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp6
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp9
    tmp11 = tmp8 + tmp10
    tmp12 = libdevice.sqrt(tmp11)
    tmp13 = 1e-12
    tmp14 = triton_helpers.maximum(tmp12, tmp13)
    tmp15 = tmp0 / tmp14
    tl.store(out_ptr0 + x3, tmp15, xmask)


@triton.jit
def triton_poi_fused_div_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 48
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp1 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp6
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp9
    tmp11 = tmp8 + tmp10
    tmp12 = libdevice.sqrt(tmp11)
    tmp13 = 1e-12
    tmp14 = triton_helpers.maximum(tmp12, tmp13)
    tmp15 = tmp0 / tmp14
    tl.store(out_ptr0 + x2, tmp15, xmask)


@triton.jit
def triton_poi_fused_max_2(in_ptr0, out_ptr0, out_ptr1, 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 + 3 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 3 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 3 * x0), xmask, eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp5 = tmp0 > tmp1
    tmp6 = tmp0 == tmp1
    tmp7 = tmp0 != tmp0
    tmp8 = tmp1 != tmp1
    tmp9 = tmp7 > tmp8
    tmp10 = tmp5 | tmp9
    tmp11 = tmp7 & tmp8
    tmp12 = tmp6 | tmp11
    tmp13 = tl.full([1], 0, tl.int64)
    tmp14 = tl.full([1], 1, tl.int64)
    tmp15 = tmp13 < tmp14
    tmp16 = tmp12 & tmp15
    tmp17 = tmp10 | tmp16
    tmp18 = tl.where(tmp17, tmp0, tmp1)
    tmp19 = tl.where(tmp17, tmp13, tmp14)
    tmp20 = tmp18 > tmp3
    tmp21 = tmp18 == tmp3
    tmp22 = tmp18 != tmp18
    tmp23 = tmp3 != tmp3
    tmp24 = tmp22 > tmp23
    tmp25 = tmp20 | tmp24
    tmp26 = tmp22 & tmp23
    tmp27 = tmp21 | tmp26
    tmp28 = tl.full([1], 2, tl.int64)
    tmp29 = tmp19 < tmp28
    tmp30 = tmp27 & tmp29
    tmp31 = tmp25 | tmp30
    tl.where(tmp31, tmp18, tmp3)
    tmp33 = tl.where(tmp31, tmp19, tmp28)
    tl.store(out_ptr0 + x0, tmp4, xmask)
    tl.store(out_ptr1 + x0, tmp33, 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, (12, 4), (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_div_0[grid(256)](primals_1, buf0, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((12, 4), (4, 1), torch.float32)
        triton_poi_fused_div_1[grid(48)](primals_2, buf1, 48, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((64, 12), (12, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (64, 4), (4, 1), 0),
            reinterpret_tensor(buf1, (4, 12), (1, 4), 0), out=buf2)
        del buf1
        buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        buf4 = empty_strided_cuda((64, 4), (4, 1), torch.int64)
        triton_poi_fused_max_2[grid(256)](buf2, buf3, buf4, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del buf2
    return buf3, primals_2, reinterpret_tensor(buf0, (64, 4), (4, 1), 0
        ), reinterpret_tensor(buf4, (64, 4, 1), (4, 1, 1), 0)


class ArcMarginProduct_subcenterNew(nn.Module):

    def __init__(self, in_features, out_features, k=3):
        super().__init__()
        self.weight = nn.Parameter(torch.FloatTensor(out_features * k,
            in_features))
        self.reset_parameters()
        self.k = k
        self.out_features = out_features

    def reset_parameters(self):
        stdv = 1.0 / math.sqrt(self.weight.size(1))
        self.weight.data.uniform_(-stdv, stdv)

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


Measured latency_ms: 0.027206