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_add_div_exp_lt_pow_relu_sqrt_sub_sum_0(in_ptr0,
    in_ptr1, in_ptr2, out_ptr2, out_ptr3, 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')
    tmp25 = tl.load(in_ptr2 + 4 * r0, None, eviction_policy='evict_last')
    tmp29 = tl.load(in_ptr2 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp34 = tl.load(in_ptr2 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp39 = tl.load(in_ptr2 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp4 = tl.full([1, 1], 0, tl.int32)
    tmp5 = triton_helpers.maximum(tmp4, tmp3)
    tmp8 = tmp6 - tmp7
    tmp9 = tmp8 * tmp8
    tmp10 = triton_helpers.maximum(tmp4, tmp9)
    tmp11 = tmp5 + tmp10
    tmp14 = tmp12 - tmp13
    tmp15 = tmp14 * tmp14
    tmp16 = triton_helpers.maximum(tmp4, tmp15)
    tmp17 = tmp11 + tmp16
    tmp20 = tmp18 - tmp19
    tmp21 = tmp20 * tmp20
    tmp22 = triton_helpers.maximum(tmp4, tmp21)
    tmp23 = tmp17 + tmp22
    tmp24 = libdevice.sqrt(tmp23)
    tmp26 = tmp0 - tmp25
    tmp27 = tmp26 * tmp26
    tmp28 = triton_helpers.maximum(tmp4, tmp27)
    tmp30 = tmp6 - tmp29
    tmp31 = tmp30 * tmp30
    tmp32 = triton_helpers.maximum(tmp4, tmp31)
    tmp33 = tmp28 + tmp32
    tmp35 = tmp12 - tmp34
    tmp36 = tmp35 * tmp35
    tmp37 = triton_helpers.maximum(tmp4, tmp36)
    tmp38 = tmp33 + tmp37
    tmp40 = tmp18 - tmp39
    tmp41 = tmp40 * tmp40
    tmp42 = triton_helpers.maximum(tmp4, tmp41)
    tmp43 = tmp38 + tmp42
    tmp44 = libdevice.sqrt(tmp43)
    tmp45 = tl_math.exp(tmp24)
    tmp46 = tl_math.exp(tmp44)
    tmp47 = tmp45 + tmp46
    tmp48 = tmp45 / tmp47
    tmp49 = tmp48 * tmp48
    tmp50 = tl.broadcast_to(tmp49, [XBLOCK, RBLOCK])
    tmp52 = tl.sum(tmp50, 1)[:, None]
    tmp53 = tmp24 < tmp44
    tmp54 = tmp53.to(tl.int64)
    tmp55 = tl.broadcast_to(tmp54, [XBLOCK, RBLOCK])
    tmp57 = tl.sum(tmp55, 1)[:, None]
    tl.store(out_ptr2 + tl.full([XBLOCK, 1], 0, tl.int32), tmp52, None)
    tl.store(out_ptr3 + tl.full([XBLOCK, 1], 0, tl.int32), tmp57, 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)
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf3 = empty_strided_cuda((), (), torch.int64)
        get_raw_stream(0)
        triton_per_fused_add_div_exp_lt_pow_relu_sqrt_sub_sum_0[grid(1)](arg0_1
            , arg1_1, arg2_1, buf2, buf3, 1, 64, XBLOCK=1, num_warps=2,
            num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
    return buf2, buf3


class softmaxtripletLossNew(nn.Module):

    def __init__(self):
        super(softmaxtripletLossNew, self).__init__()
        self.relu = nn.ReLU()

    def dist(self, a, b):
        d = a - b
        d = d ** 2
        d = self.relu(d)
        return torch.sqrt(torch.sum(d, dim=-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], output[1]


Measured latency_ms: 0.017641