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
import torch.nn as nn
from torch.nn.modules.loss import *
from torch.nn.modules import *
from torch.optim import *
from torch.optim.lr_scheduler import *
import torch.distributed
import torch.multiprocessing
import torch.backends
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__log_softmax_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')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_per_fused_arange_nll_loss_forward_1(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
    tmp6 = tl.load(in_ptr0 + 4 * r0, None, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr0 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp0 = r0
    tmp1 = tl.full([1, 1], -100, tl.int64)
    tmp2 = tmp0 != tmp1
    tmp3 = tl.full([1, 1], 0, tl.int64)
    tmp4 = tl.where(tmp2, tmp0, tmp3)
    tmp5 = tl.load(in_ptr0 + (tmp4 + 4 * r0), None, eviction_policy=
        'evict_last')
    tmp7 = tl_math.exp(tmp6)
    tmp9 = tl_math.exp(tmp8)
    tmp10 = tmp7 + tmp9
    tmp12 = tl_math.exp(tmp11)
    tmp13 = tmp10 + tmp12
    tmp15 = tl_math.exp(tmp14)
    tmp16 = tmp13 + tmp15
    tmp17 = tl_math.log(tmp16)
    tmp18 = tmp5 - tmp17
    tmp19 = -tmp18
    tmp20 = 0.0
    tmp21 = tl.where(tmp2, tmp19, tmp20)
    tmp22 = tl.broadcast_to(tmp21, [XBLOCK, RBLOCK])
    tmp24 = tl.sum(tmp22, 1)[:, None]
    tmp25 = tmp2.to(tl.int64)
    tmp26 = tl.broadcast_to(tmp25, [XBLOCK, RBLOCK])
    tmp28 = tl.sum(tmp26, 1)[:, None]
    tmp29 = tmp28.to(tl.float32)
    tmp30 = tmp24 / tmp29
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp30, 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((1, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(arg0_1, (1, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(arg1_1, (1, 4, 4), (0, 1, 4), 0), out=buf0)
        del arg0_1
        del arg1_1
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__log_softmax_0[grid(16)](buf0, buf1, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del buf0
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf2
        del buf2
        triton_per_fused_arange_nll_loss_forward_1[grid(1)](buf4, buf1, 1, 
            4, XBLOCK=1, num_warps=2, num_stages=1)
        del buf1
    return buf4,


class ContrastivePairwiseEmbeddingLossNew(nn.Module):
    """ContrastivePairwiseEmbeddingLoss – proof of concept criterion.

    Still work in progress.

    @TODO: Docs. Contribution is welcome.
    """

    def __init__(self, margin=1.0, reduction='mean'):
        """
        Args:
            margin: margin parameter
            reduction: criterion reduction type
        """
        super().__init__()
        self.margin = margin
        self.reduction = reduction or 'none'

    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: 0.028924