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.nn import Module
from torch.nn import Parameter
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_pow_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 + x0, xmask)
    tmp1 = tmp0 * tmp0
    tl.store(out_ptr0 + x0, tmp1, xmask)


@triton.jit
def triton_poi_fused_mul_pow_sub_sum_1(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp14 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp16 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp1 = tmp0 * tmp0
    tmp3 = tmp1 - tmp2
    tmp5 = tmp4 * tmp4
    tmp7 = tmp5 - tmp6
    tmp8 = tmp3 + tmp7
    tmp10 = tmp9 * tmp9
    tmp12 = tmp10 - tmp11
    tmp13 = tmp8 + tmp12
    tmp15 = tmp14 * tmp14
    tmp17 = tmp15 - tmp16
    tmp18 = tmp13 + tmp17
    tmp19 = 0.5
    tmp20 = tmp18 * tmp19
    tl.store(out_ptr0 + x0, tmp20, xmask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (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, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_pow_0[grid(16)](primals_1, buf0, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_1, primals_2, out=buf1)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_pow_0[grid(16)](primals_2, buf2, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf0, buf2, out=buf3)
        del buf2
        buf4 = empty_strided_cuda((4,), (1,), torch.float32)
        triton_poi_fused_mul_pow_sub_sum_1[grid(4)](buf1, buf3, buf4, 4,
            XBLOCK=4, num_warps=1, num_stages=1)
        del buf3
    return reinterpret_tensor(buf4, (4, 1), (1, 1), 0
        ), primals_2, buf1, reinterpret_tensor(buf0, (4, 4), (1, 4), 0
        ), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0)


class SecondOrderInteractionNew(Module):
    """
    Factorized parameters for the Second Order Interactions

    Parameters
    ----------
    n_features: int
        Length of the input vector.
    n_factors: int, optional
        Number of factors of the factorized parameters
    """

    def __init__(self, n_features, n_factors):
        super(SecondOrderInteractionNew, self).__init__()
        self.batch_size = None
        self.n_features = n_features
        self.n_factors = n_factors
        self.v = Parameter(torch.Tensor(self.n_features, self.n_factors))
        self.v.data.uniform_(-0.01, 0.01)

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


Measured latency_ms: 0.053609