import torch
from torch._inductor.select_algorithm import extern_kernels
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 math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_per_fused_add_diagonal_copy_logsumexp_mean_neg_0(in_out_ptr0,
    in_ptr0, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 4
    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 + 5 * r0, None, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + 4 * r0, None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.sum(tmp1, 1)[:, None]
    tmp6 = triton_helpers.maximum(tmp4, tmp5)
    tmp8 = triton_helpers.maximum(tmp6, tmp7)
    tmp10 = triton_helpers.maximum(tmp8, tmp9)
    tmp11 = tl_math.abs(tmp10)
    tmp12 = float('inf')
    tmp13 = tmp11 == tmp12
    tmp14 = 0.0
    tmp15 = tl.where(tmp13, tmp14, tmp10)
    tmp16 = tmp4 - tmp15
    tmp17 = tl_math.exp(tmp16)
    tmp18 = tmp5 - tmp15
    tmp19 = tl_math.exp(tmp18)
    tmp20 = tmp17 + tmp19
    tmp21 = tmp7 - tmp15
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tmp20 + tmp22
    tmp24 = tmp9 - tmp15
    tmp25 = tl_math.exp(tmp24)
    tmp26 = tmp23 + tmp25
    tmp27 = tl_math.log(tmp26)
    tmp28 = tmp27 + tmp15
    tmp29 = tl.broadcast_to(tmp28, [XBLOCK, RBLOCK])
    tmp31 = tl.sum(tmp29, 1)[:, None]
    tmp32 = 4.0
    tmp33 = tmp3 / tmp32
    tmp34 = -tmp33
    tmp35 = tmp31 / tmp32
    tmp36 = tmp34 + tmp35
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp36, None)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    assert_size_stride(arg1_1, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(arg1_1, reinterpret_tensor(arg0_1, (4, 4), (1, 4),
            0), out=buf0)
        del arg0_1
        del arg1_1
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf1
        del buf1
        get_raw_stream(0)
        triton_per_fused_add_diagonal_copy_logsumexp_mean_neg_0[grid(1)](buf3,
            buf0, 1, 4, XBLOCK=1, num_warps=2, num_stages=1)
        del buf0
    return buf3,


class NPairsLossNew(nn.Module):

    def __init__(self, name):
        super(NPairsLossNew, self).__init__()
        self.name = name

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


Measured latency_ms: 0.021285