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 abc import abstractmethod
from typing import Union
import torch.utils.data
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_neg_pow_sub_sum_0(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 // 4
    x0 = xindex % 4
    x2 = xindex // 16
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x3, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (4 * x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (1 + 4 * x3), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (1 + 4 * x0 + 16 * x2), xmask, eviction_policy
        ='evict_last')
    tmp9 = tl.load(in_ptr0 + (2 + 4 * x3), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (2 + 4 * x0 + 16 * x2), xmask,
        eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr0 + (3 + 4 * x3), xmask, eviction_policy='evict_last'
        )
    tmp15 = tl.load(in_ptr1 + (3 + 4 * x0 + 16 * x2), xmask,
        eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp6 = tmp4 - tmp5
    tmp7 = tmp6 * tmp6
    tmp8 = tmp3 + tmp7
    tmp11 = tmp9 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tmp8 + tmp12
    tmp16 = tmp14 - tmp15
    tmp17 = tmp16 * tmp16
    tmp18 = tmp13 + tmp17
    tmp19 = -tmp18
    tl.store(out_ptr0 + x4, tmp19, xmask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 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_neg_pow_sub_sum_0[grid(256)](arg0_1, arg1_1, buf0,
            256, XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
        del arg1_1
    return buf0,


class Similarity(nn.Module):
    """Base class for similarity functions."""

    @abstractmethod
    def forward(self, x: 'torch.Tensor', y: 'torch.Tensor') ->torch.Tensor:
        """
        Compute pair-wise similarities.

        :param x: shape: (*, n, d)
            The first vectors.
        :param y: shape: (*, m, d)
            The second vectors.

        :return: shape: (*, n, m)
            The similarity values.
        """
        raise NotImplementedError

    def one_to_one(self, x: 'torch.Tensor', y: 'torch.Tensor') ->torch.Tensor:
        """
        Compute batched one-to-one similarities.

        :param x: shape: (*, d)
            The first vectors.
        :param y: shape: (*, d)
            The second vectors.

        :return: shape: (*)
            The similarity values.
        """
        return self(x.unsqueeze(dim=-2), y.unsqueeze(dim=-2)).squeeze(dim=-1
            ).squeeze(dim=-1)


class NegativePowerNormSimilarityNew(Similarity):
    """Negative power norm: -\\|x - y\\|_p^p."""

    def __init__(self, p: 'Union[int, float]'=2):
        """
        Initialize the similarity.

        :param p:
            The parameter p for the p-norm.
        """
        super().__init__()
        self.p = p

    def forward(self, input_0, input_1):
        arg0_1 = input_0
        arg1_1 = input_1
        output = call([arg0_1, arg1_1])
        return output[0]


Measured latency_ms: 0.015004