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 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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_div_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp1 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp6
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp9
    tmp11 = tmp8 + tmp10
    tmp12 = libdevice.sqrt(tmp11)
    tmp13 = 1e-12
    tmp14 = triton_helpers.maximum(tmp12, tmp13)
    tmp15 = tmp0 / tmp14
    tl.store(out_ptr0 + x2, tmp15, xmask)


@triton.jit
def triton_per_fused__log_softmax_arange_exp_mul_nll_loss_forward_1(in_out_ptr0
    , in_ptr0, in_ptr1, 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 + 4 * r0, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * r0, None, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr0 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr1 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp2 = tl_math.exp(tmp1)
    tmp3 = tmp0 * tmp2
    tmp6 = tl_math.exp(tmp5)
    tmp7 = tmp4 * tmp6
    tmp8 = triton_helpers.maximum(tmp3, tmp7)
    tmp11 = tl_math.exp(tmp10)
    tmp12 = tmp9 * tmp11
    tmp13 = triton_helpers.maximum(tmp8, tmp12)
    tmp16 = tl_math.exp(tmp15)
    tmp17 = tmp14 * tmp16
    tmp18 = triton_helpers.maximum(tmp13, tmp17)
    tmp19 = tmp3 - tmp18
    tmp20 = tl_math.exp(tmp19)
    tmp21 = tmp7 - tmp18
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tmp20 + tmp22
    tmp24 = tmp12 - tmp18
    tmp25 = tl_math.exp(tmp24)
    tmp26 = tmp23 + tmp25
    tmp27 = tmp17 - tmp18
    tmp28 = tl_math.exp(tmp27)
    tmp29 = tmp26 + tmp28
    tmp30 = r0
    tmp31 = tl.full([1, 1], -100, tl.int64)
    tmp32 = tmp30 != tmp31
    tmp33 = tl.full([1, 1], 0, tl.int64)
    tmp34 = tl.where(tmp32, tmp30, tmp33)
    tmp35 = tl.load(in_ptr0 + (tmp34 + 4 * r0), None, eviction_policy=
        'evict_last')
    tmp36 = tl.load(in_ptr1 + (tmp34 + 4 * r0), None, eviction_policy=
        'evict_last')
    tmp37 = tl_math.exp(tmp36)
    tmp38 = tmp35 * tmp37
    tmp39 = tmp38 - tmp18
    tmp40 = tl_math.log(tmp29)
    tmp41 = tmp39 - tmp40
    tmp42 = -tmp41
    tmp43 = 0.0
    tmp44 = tl.where(tmp32, tmp42, tmp43)
    tmp45 = tl.broadcast_to(tmp44, [XBLOCK, RBLOCK])
    tmp47 = tl.sum(tmp45, 1)[:, None]
    tmp48 = tmp32.to(tl.int64)
    tmp49 = tl.broadcast_to(tmp48, [XBLOCK, RBLOCK])
    tmp51 = tl.sum(tmp49, 1)[:, None]
    tmp52 = tmp51.to(tl.float32)
    tmp53 = tmp47 / tmp52
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp53, None)


def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    assert_size_stride(arg1_1, (4, 4), (4, 1))
    assert_size_stride(arg2_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)
        get_raw_stream(0)
        triton_poi_fused_div_0[grid(16)](arg0_1, buf0, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del arg0_1
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_div_0[grid(16)](arg1_1, buf1, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del arg1_1
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf0, reinterpret_tensor(buf1, (4, 4), (1, 4), 0),
            out=buf2)
        del buf0
        del buf1
        buf5 = empty_strided_cuda((), (), torch.float32)
        buf7 = buf5
        del buf5
        triton_per_fused__log_softmax_arange_exp_mul_nll_loss_forward_1[grid(1)
            ](buf7, buf2, arg2_1, 1, 4, XBLOCK=1, num_warps=2, num_stages=1)
        del arg2_1
        del buf2
    return buf7,


class NTXentNew(nn.Module):

    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: 0.047097