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
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_per_fused_add_mean_pow_relu_rsub_sub_sum_0(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 64
    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)
    r0 = rindex % 16
    r1 = rindex // 16
    tmp0 = tl.load(in_ptr0 + (r0 + 64 * r1), None)
    tmp1 = tl.load(in_ptr1 + (r0 + 64 * r1), None)
    tmp4 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
    tmp5 = tl.load(in_ptr1 + (16 + r0 + 64 * r1), None)
    tmp9 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
    tmp10 = tl.load(in_ptr1 + (32 + r0 + 64 * r1), None)
    tmp14 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None)
    tmp15 = tl.load(in_ptr1 + (48 + r0 + 64 * r1), None)
    tmp23 = tl.load(in_ptr2 + (r0 + 64 * r1), None)
    tmp26 = tl.load(in_ptr2 + (16 + r0 + 64 * r1), None)
    tmp30 = tl.load(in_ptr2 + (32 + r0 + 64 * r1), None)
    tmp34 = tl.load(in_ptr2 + (48 + r0 + 64 * r1), None)
    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 = 3.0
    tmp20 = tmp19 - tmp18
    tmp21 = tl.full([1, 1], 0, tl.int32)
    tmp22 = triton_helpers.maximum(tmp21, tmp20)
    tmp24 = tmp0 - tmp23
    tmp25 = tmp24 * tmp24
    tmp27 = tmp4 - tmp26
    tmp28 = tmp27 * tmp27
    tmp29 = tmp25 + tmp28
    tmp31 = tmp9 - tmp30
    tmp32 = tmp31 * tmp31
    tmp33 = tmp29 + tmp32
    tmp35 = tmp14 - tmp34
    tmp36 = tmp35 * tmp35
    tmp37 = tmp33 + tmp36
    tmp38 = 1.0
    tmp39 = tmp37 - tmp38
    tmp40 = triton_helpers.maximum(tmp21, tmp39)
    tmp41 = tmp22 + tmp40
    tmp42 = tl.broadcast_to(tmp41, [XBLOCK, RBLOCK])
    tmp44 = tl.sum(tmp42, 1)[:, None]
    tmp45 = 64.0
    tmp46 = tmp44 / tmp45
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp46, None)


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)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1
        del buf1
        get_raw_stream(0)
        triton_per_fused_add_mean_pow_relu_rsub_sub_sum_0[grid(1)](buf2,
            arg0_1, arg2_1, arg1_1, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
    return buf2,


class TripletLossDoubleMarginNew(nn.Module):
    """
    Triplet Loss with positive and negative margins, following the work of [1]
    
    References
    ----------
    [1] Ho, K., Keuper, J., Pfreundt, F. J., & Keuper, M. (2021, January). 
    Learning embeddings for image clustering: An empirical study of triplet loss approaches. 
    In 2020 25th International Conference on Pattern Recognition (ICPR) (pp. 87-94). IEEE.
    """

    def __init__(self, pos_margin=1.0, neg_margin=3.0):
        """
        Constructor of the loss.

        Parameters
        ----------
        pos_margin : float, optional
            Margin for positive examples. The default is 1.0.
        neg_margin : float, optional
            Margin for negative examples. The default is 3.0.

        Returns
        -------
        None.

        """
        super(TripletLossDoubleMarginNew, self).__init__()
        self.pos_margin = pos_margin
        self.neg_margin = neg_margin

    def calc_euclidean(self, x1, x2):
        return (x1 - x2).pow(2).sum(1)

    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: 0.015939