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, math as tl_math
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_abs_add_clamp_mean_pow_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)
    tmp5 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
    tmp6 = tl.load(in_ptr1 + (16 + r0 + 64 * r1), None)
    tmp11 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
    tmp12 = tl.load(in_ptr1 + (32 + r0 + 64 * r1), None)
    tmp17 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None)
    tmp18 = tl.load(in_ptr1 + (48 + r0 + 64 * r1), None)
    tmp28 = tl.load(in_ptr2 + (r0 + 64 * r1), None)
    tmp32 = tl.load(in_ptr2 + (16 + r0 + 64 * r1), None)
    tmp37 = tl.load(in_ptr2 + (32 + r0 + 64 * r1), None)
    tmp42 = tl.load(in_ptr2 + (48 + r0 + 64 * r1), None)
    tmp2 = tmp0 - tmp1
    tmp3 = tl_math.abs(tmp2)
    tmp4 = tmp3 * tmp3
    tmp7 = tmp5 - tmp6
    tmp8 = tl_math.abs(tmp7)
    tmp9 = tmp8 * tmp8
    tmp10 = tmp4 + tmp9
    tmp13 = tmp11 - tmp12
    tmp14 = tl_math.abs(tmp13)
    tmp15 = tmp14 * tmp14
    tmp16 = tmp10 + tmp15
    tmp19 = tmp17 - tmp18
    tmp20 = tl_math.abs(tmp19)
    tmp21 = tmp20 * tmp20
    tmp22 = tmp16 + tmp21
    tmp23 = 0.0001
    tmp24 = tmp22 + tmp23
    tmp25 = libdevice.sqrt(tmp24)
    tmp26 = 4.0
    tmp27 = tmp25 + tmp26
    tmp29 = tmp0 - tmp28
    tmp30 = tl_math.abs(tmp29)
    tmp31 = tmp30 * tmp30
    tmp33 = tmp5 - tmp32
    tmp34 = tl_math.abs(tmp33)
    tmp35 = tmp34 * tmp34
    tmp36 = tmp31 + tmp35
    tmp38 = tmp11 - tmp37
    tmp39 = tl_math.abs(tmp38)
    tmp40 = tmp39 * tmp39
    tmp41 = tmp36 + tmp40
    tmp43 = tmp17 - tmp42
    tmp44 = tl_math.abs(tmp43)
    tmp45 = tmp44 * tmp44
    tmp46 = tmp41 + tmp45
    tmp47 = tmp46 + tmp23
    tmp48 = libdevice.sqrt(tmp47)
    tmp49 = tmp27 - tmp48
    tmp50 = 0.0
    tmp51 = triton_helpers.maximum(tmp49, tmp50)
    tmp52 = tl.broadcast_to(tmp51, [XBLOCK, RBLOCK])
    tmp54 = tl.sum(tmp52, 1)[:, None]
    tmp55 = 64.0
    tmp56 = tmp54 / tmp55
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp56, 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_abs_add_clamp_mean_pow_sub_sum_0[grid(1)](buf2,
            arg0_1, arg1_1, arg2_1, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
    return buf2,


class PairwiseDistance(nn.Module):
    """class for calculating distance

    Arguments:
        nn {[type]} -- [description]
    """

    def __init__(self, smooth=0.0001):
        """Initializer

        Arguments:
            smooth {int} -- [description]
        """
        super(PairwiseDistance, self).__init__()
        self.smooth = smooth

    def forward(self, x1, x2):
        """x1, x2 represent input data

        Arguments:
            x1 {[type]} -- [description]
            x2 {[type]} -- [description]

        Returns:
            [type] -- [description]
        """
        assert x1.size() == x2.size()
        diff = torch.abs(x1 - x2)
        out = torch.pow(diff, 2).sum(dim=1)
        return torch.pow(out + self.smooth, 0.5)


class TripletMarginLossNew(nn.Module):
    """Triplet loss

    Arguments:
        nn {[type]} -- [description]
    """

    def __init__(self, margin):
        super(TripletMarginLossNew, self).__init__()
        self.margin = margin
        self.pdist = PairwiseDistance()

    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: 0.015337