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 import nn
from typing import Optional
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_add_div_mul_pow_reciprocal_sub_sum_0(in_out_ptr0,
    in_ptr0, in_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
    tmp8 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
    tmp12 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp5 = tmp4 - tmp1
    tmp6 = tmp5 * tmp5
    tmp7 = tmp3 + tmp6
    tmp9 = tmp8 - tmp1
    tmp10 = tmp9 * tmp9
    tmp11 = tmp7 + tmp10
    tmp13 = tmp12 - tmp1
    tmp14 = tmp13 * tmp13
    tmp15 = tmp11 + tmp14
    tmp16 = 1.0
    tmp17 = tmp15 * tmp16
    tmp18 = tmp17 + tmp16
    tmp19 = tl.full([1], 1, tl.int32)
    tmp20 = tmp19 / tmp18
    tmp21 = tmp20 * tmp16
    tmp22 = tmp21 / tmp21
    tl.store(in_out_ptr0 + x2, tmp22, 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, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
        buf1 = reinterpret_tensor(buf0, (4, 1, 4, 4), (16, 16, 4, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_add_div_mul_pow_reciprocal_sub_sum_0[grid(64)](buf1,
            primals_1, primals_2, 64, XBLOCK=64, num_warps=1, num_stages=1)
    return buf1, primals_1, primals_2


class ClusterAssignmentNew(nn.Module):

    def __init__(self, n_classes: 'int', enc_shape: 'int', alpha: 'float'=
        1.0, cluster_centers: 'Optional[torch.Tensor]'=None) ->None:
        """
        Module to handle the soft assignment, for a description see in 3.1.1. in Xie/Girshick/Farhadi,
        where the Student's t-distribution is used to measure similarity between feature vector and each
        cluster centroid.
        :param n_classes: number of clusters
        :param enc_shape: embedding dimension of feature vectors
        :param alpha: parameter representing the degrees of freedom in the t-distribution, default 1.0
        :param cluster_centers: clusters centers to initialise, if None then use Xavier uniform
        """
        super().__init__()
        self.enc_shape = enc_shape
        self.n_classes = n_classes
        self.alpha = alpha
        if cluster_centers is None:
            initial_cluster_centers = torch.zeros(self.n_classes, self.
                enc_shape, dtype=torch.float)
            nn.init.xavier_uniform_(initial_cluster_centers)
        else:
            initial_cluster_centers = cluster_centers
        self.cluster_centers = nn.Parameter(initial_cluster_centers)

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


Measured latency_ms: 0.015683