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_pow_sub_sum_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 % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + (x0 + 64 * x1), xmask)
    tmp4 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
    tmp5 = tl.load(in_ptr1 + (16 + x0 + 64 * x1), xmask)
    tmp9 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
    tmp10 = tl.load(in_ptr1 + (32 + x0 + 64 * x1), xmask)
    tmp14 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
    tmp15 = tl.load(in_ptr1 + (48 + x0 + 64 * x1), xmask)
    tmp20 = tl.load(in_ptr2 + (x0 + 64 * x1), xmask)
    tmp23 = tl.load(in_ptr2 + (16 + x0 + 64 * x1), xmask)
    tmp27 = tl.load(in_ptr2 + (32 + x0 + 64 * x1), xmask)
    tmp31 = tl.load(in_ptr2 + (48 + x0 + 64 * x1), xmask)
    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 = libdevice.sqrt(tmp18)
    tmp21 = tmp0 - tmp20
    tmp22 = tmp21 * tmp21
    tmp24 = tmp4 - tmp23
    tmp25 = tmp24 * tmp24
    tmp26 = tmp22 + tmp25
    tmp28 = tmp9 - tmp27
    tmp29 = tmp28 * tmp28
    tmp30 = tmp26 + tmp29
    tmp32 = tmp14 - tmp31
    tmp33 = tmp32 * tmp32
    tmp34 = tmp30 + tmp33
    tmp35 = libdevice.sqrt(tmp34)
    tmp36 = tmp19 - tmp35
    tl.store(out_ptr0 + x2, tmp36, xmask)


@triton.jit
def triton_per_fused_add_mean_mul_relu_1(in_out_ptr0, in_ptr0, in_ptr1,
    xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r2 = rindex
    r0 = rindex % 64
    tmp0 = tl.load(in_ptr0 + r2, None)
    tmp1 = tl.load(in_ptr1 + r0, None, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp2 + tmp3
    tmp5 = tl.full([1], 0, tl.int32)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tl.broadcast_to(tmp6, [RBLOCK])
    tmp9 = triton_helpers.promote_to_tensor(tl.sum(tmp7, 0))
    tmp10 = 256.0
    tmp11 = tmp9 / tmp10
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp11, None)


def call(args):
    arg0_1, arg1_1, arg2_1, arg3_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))
    assert_size_stride(arg3_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_pow_sub_sum_0[grid(64)](arg0_1, arg1_1, arg2_1,
            buf0, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1
        del buf1
        triton_per_fused_add_mean_mul_relu_1[grid(1)](buf2, arg3_1, buf0, 1,
            256, num_warps=2, num_stages=1)
        del arg3_1
        del buf0
    return buf2,


class TripletLoss_opNew(nn.Module):

    def __init__(self, margin=1.0):
        super(TripletLoss_opNew, self).__init__()
        self.margin = margin

    def forward(self, input_0, input_1, input_2, input_3):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        arg3_1 = input_3
        output = call([arg0_1, arg1_1, arg2_1, arg3_1])
        return output[0]


Measured latency_ms: 0.016483