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
from torch.nn.modules.distance import PairwiseDistance
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_clamp_mean_norm_sub_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
    tmp0 = tl.load(in_ptr0 + 4 * r0, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * r0, None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr1 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp18 = tl.load(in_ptr0 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr1 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp27 = tl.load(in_ptr2 + 4 * r0, None, eviction_policy='evict_last')
    tmp31 = tl.load(in_ptr2 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp36 = tl.load(in_ptr2 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp41 = tl.load(in_ptr2 + (3 + 4 * r0), None, 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 = 4.0
    tmp26 = tmp24 + tmp25
    tmp28 = tmp0 - tmp27
    tmp29 = tmp28 + tmp3
    tmp30 = tmp29 * tmp29
    tmp32 = tmp6 - tmp31
    tmp33 = tmp32 + tmp3
    tmp34 = tmp33 * tmp33
    tmp35 = tmp30 + tmp34
    tmp37 = tmp12 - tmp36
    tmp38 = tmp37 + tmp3
    tmp39 = tmp38 * tmp38
    tmp40 = tmp35 + tmp39
    tmp42 = tmp18 - tmp41
    tmp43 = tmp42 + tmp3
    tmp44 = tmp43 * tmp43
    tmp45 = tmp40 + tmp44
    tmp46 = libdevice.sqrt(tmp45)
    tmp47 = tmp26 - tmp46
    tmp48 = 0.0
    tmp49 = triton_helpers.maximum(tmp47, tmp48)
    tmp50 = tl.broadcast_to(tmp49, [XBLOCK, RBLOCK])
    tmp52 = tl.sum(tmp50, 1)[:, None]
    tmp53 = 64.0
    tmp54 = tmp52 / tmp53
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp54, 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_clamp_mean_norm_sub_0[grid(1)](buf2, arg1_1,
            arg0_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 TripletLossNew(torch.nn.Module):

    def __init__(self, margin):
        super(TripletLossNew, self).__init__()
        self.margin = margin
        self.pdist = PairwiseDistance(2)

    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: 0.015981