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._inductor.runtime.triton_helpers import libdevice
import torch.nn as 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_add_clamp_norm_pow_rsub_sub_0(in_ptr0, in_ptr1,
    in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    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')
    tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp13 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp18 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp19 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp26 = tl.load(in_ptr2 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp30 = tl.load(in_ptr2 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp35 = tl.load(in_ptr2 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp40 = tl.load(in_ptr2 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 - tmp1
    tmp3 = 1e-06
    tmp4 = tmp2 + tmp3
    tmp5 = tmp4 * tmp4
    tmp8 = tmp6 - tmp7
    tmp9 = tmp8 + tmp3
    tmp10 = tmp9 * tmp9
    tmp11 = tmp5 + tmp10
    tmp14 = tmp12 - tmp13
    tmp15 = tmp14 + tmp3
    tmp16 = tmp15 * tmp15
    tmp17 = tmp11 + tmp16
    tmp20 = tmp18 - tmp19
    tmp21 = tmp20 + tmp3
    tmp22 = tmp21 * tmp21
    tmp23 = tmp17 + tmp22
    tmp24 = libdevice.sqrt(tmp23)
    tmp25 = tmp24 * tmp24
    tmp27 = tmp0 - tmp26
    tmp28 = tmp27 + tmp3
    tmp29 = tmp28 * tmp28
    tmp31 = tmp6 - tmp30
    tmp32 = tmp31 + tmp3
    tmp33 = tmp32 * tmp32
    tmp34 = tmp29 + tmp33
    tmp36 = tmp12 - tmp35
    tmp37 = tmp36 + tmp3
    tmp38 = tmp37 * tmp37
    tmp39 = tmp34 + tmp38
    tmp41 = tmp18 - tmp40
    tmp42 = tmp41 + tmp3
    tmp43 = tmp42 * tmp42
    tmp44 = tmp39 + tmp43
    tmp45 = libdevice.sqrt(tmp44)
    tmp46 = 1.0
    tmp47 = tmp46 - tmp45
    tmp48 = 0.0
    tmp49 = triton_helpers.maximum(tmp47, tmp48)
    tmp50 = tmp49 * tmp49
    tmp51 = tmp25 + tmp50
    tl.store(out_ptr0 + x0, tmp51, xmask)


def call(args):
    arg0_1, arg1_1, arg2_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))
    assert_size_stride(arg2_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), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_clamp_norm_pow_rsub_sub_0[grid(64)](arg1_1,
            arg0_1, arg2_1, buf0, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
    return buf0,


class BinaryLossNew(nn.Module):
    """
    Computes contrastive loss[1, 2] twice, one time for the distance between query and positive example,
        and another for the distance between query and negative example. Both use l2-distance.
    [1] http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf, equation 4
    [2] https://gist.github.com/harveyslash/725fcc68df112980328951b3426c0e0b#file-contrastive-loss-py
    """

    def __init__(self, margin=1.0):
        """
        Args:
            margin: margin (float, optional): Default: `1.0`.
        """
        super(BinaryLossNew, self).__init__()
        self.margin = margin

    def forward(self, input_0, input_1, input_2):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        output = call([arg0_1, arg1_1, arg2_1])
        return output[0]


Measured latency_ms: 0.015658