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 import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_index_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 2
    x0 = xindex % 2
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 10, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert((0 <= tmp4) & (tmp4 < 10) | ~xmask,
        'index out of bounds: 0 <= tmp4 < 10')
    tmp6 = tl.load(in_ptr1 + (x0 + 2 * tmp4), xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_per_fused_clamp_div_pow_sub_sum_1(in_out_ptr0, in_ptr0, in_ptr1,
    xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 128
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r2 = rindex
    r0 = rindex % 8
    tmp0 = tl.load(in_ptr0 + r2, None)
    tmp1 = tl.load(in_ptr1 + r0, None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp4 = 1e-12
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp6 = 1000000000000.0
    tmp7 = triton_helpers.minimum(tmp5, tmp6)
    tmp8 = tl.broadcast_to(tmp7, [XBLOCK, RBLOCK])
    tmp10 = tl.sum(tmp8, 1)[:, None]
    tmp11 = 0.25
    tmp12 = tmp10 * tmp11
    tmp13 = 0.5
    tmp14 = tmp12 * tmp13
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp14, None)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 2), (32, 8, 2, 1))
    assert_size_stride(primals_2, (10, 2), (2, 1))
    assert_size_stride(primals_3, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 2), (2, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_index_0[grid(8)](primals_3, primals_2, buf0, 8,
            XBLOCK=8, num_warps=1, num_stages=1)
        del primals_2
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1
        del buf1
        triton_per_fused_clamp_div_pow_sub_sum_1[grid(1)](buf2, primals_1,
            buf0, 1, 128, XBLOCK=1, num_warps=2, num_stages=1)
    return buf2, primals_1, primals_3, buf0


class CenterLossNew(nn.Module):
    """Center loss.

    Reference:
    Wen et al. A Discriminative Feature Learning Approach for Deep Face Recognition. ECCV 2016.

    Args:
        num_classes (int): number of classes.
        feat_dim (int): feature dimension.
    """

    def __init__(self, num_classes=10, feat_dim=2):
        super(CenterLossNew, self).__init__()
        self.num_classes = num_classes
        self.feat_dim = feat_dim
        if torch.cuda.is_available():
            self.centers = nn.Parameter(torch.randn(self.num_classes, self.
                feat_dim))
        else:
            self.centers = nn.Parameter(torch.randn(self.num_classes, self.
                feat_dim))

    def forward_old(self, x, labels):
        """
        Args:
            x: feature matrix with shape (batch_size, feat_dim).
            labels: ground truth labels with shape (batch_size).
        """
        batch_size = x.size(0)
        distmat = torch.pow(x, 2).sum(dim=1, keepdim=True).expand(batch_size,
            self.num_classes) + torch.pow(self.centers, 2).sum(dim=1,
            keepdim=True).expand(self.num_classes, batch_size).t()
        distmat.addmm_(x, self.centers.t(), beta=1, alpha=-2)
        classes = torch.arange(self.num_classes).long()
        if torch.cuda.is_available():
            classes = classes
        labels = labels.unsqueeze(1).expand(batch_size, self.num_classes)
        mask = labels.eq(classes.expand(batch_size, self.num_classes))
        dist = distmat * mask.float()
        loss = dist.clamp(min=1e-12, max=1000000000000.0).sum(
            ) / batch_size / self.feat_dim
        return loss

    def forward(self, input_0, input_1):
        primals_2 = self.centers
        primals_1 = input_0
        primals_3 = input_1
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.02855