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 math as tl_math
import 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_poi_fused__log_softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), 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 + x3, tmp8, xmask)


@triton.jit
def triton_per_fused_sort_1(in_ptr0, in_ptr1, out_ptr0, 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 + (64 * (r0 // 16) + r0 % 16), None)
    tmp2 = tl.load(in_ptr0 + (16 + 64 * (r0 // 16) + r0 % 16), None)
    tmp5 = tl.load(in_ptr0 + (32 + 64 * (r0 // 16) + r0 % 16), None)
    tmp8 = tl.load(in_ptr0 + (48 + 64 * (r0 // 16) + r0 % 16), None)
    tmp13 = tl.load(in_ptr1 + (64 * (r0 // 16) + r0 % 16), None)
    tmp16 = tl.load(in_ptr1 + (16 + 64 * (r0 // 16) + r0 % 16), None)
    tmp20 = tl.load(in_ptr1 + (32 + 64 * (r0 // 16) + r0 % 16), None)
    tmp24 = tl.load(in_ptr1 + (48 + 64 * (r0 // 16) + r0 % 16), None)
    tmp1 = tl_math.exp(tmp0)
    tmp3 = tl_math.exp(tmp2)
    tmp4 = tmp1 + tmp3
    tmp6 = tl_math.exp(tmp5)
    tmp7 = tmp4 + tmp6
    tmp9 = tl_math.exp(tmp8)
    tmp10 = tmp7 + tmp9
    tmp11 = tl_math.log(tmp10)
    tmp12 = tmp0 - tmp11
    tmp14 = tmp12 * tmp13
    tmp15 = tmp2 - tmp11
    tmp17 = tmp15 * tmp16
    tmp18 = tmp14 + tmp17
    tmp19 = tmp5 - tmp11
    tmp21 = tmp19 * tmp20
    tmp22 = tmp18 + tmp21
    tmp23 = tmp8 - tmp11
    tmp25 = tmp23 * tmp24
    tmp26 = tmp22 + tmp25
    tmp27 = -tmp26
    tmp28 = r0
    tmp29 = tmp28.to(tl.int16)
    tmp30 = tl.broadcast_to(tmp27, [XBLOCK, RBLOCK])
    tmp31 = tl.broadcast_to(tmp29, [XBLOCK, RBLOCK])
    tmp32, _tmp33 = triton_helpers.sort_with_index(tmp30, tmp31, None, 1,
        stable=False, descending=True)
    tl.store(out_ptr0 + tl.broadcast_to(r0, [XBLOCK, RBLOCK]), tmp32, None)


@triton.jit
def triton_poi_fused_gt_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    tmp0 = tl.load(in_ptr0 + 4)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
    tmp2 = 0.35667494393873245
    tmp3 = tmp1 > tmp2
    tl.store(out_ptr0 + tl.full([XBLOCK], 0, tl.int32), tmp3, None)


def call(args):
    arg0_1, arg1_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))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__log_softmax_0[grid(256)](arg1_1, buf0, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del arg1_1
        buf1 = empty_strided_cuda((64,), (1,), torch.float32)
        triton_per_fused_sort_1[grid(1)](buf0, arg0_1, buf1, 1, 64, XBLOCK=
            1, num_warps=2, num_stages=1)
        del arg0_1
        del buf0
        buf3 = empty_strided_cuda((), (), torch.bool)
        triton_poi_fused_gt_2[grid(1)](buf1, buf3, 1, XBLOCK=1, num_warps=1,
            num_stages=1)
    return buf1, buf3


class OhemCELoss2DNew(nn.CrossEntropyLoss):
    """2D Cross Entropy Loss with Auxilary Loss"""

    def __init__(self, n_min, thresh=0.7, ignore_index=-1):
        super(OhemCELoss2DNew, self).__init__(None, None, ignore_index,
            reduction='none')
        self.thresh = -math.log(thresh)
        self.n_min = n_min
        self.ignore_index = ignore_index

    def OhemCELoss(self, logits, labels):
        loss = super(OhemCELoss2DNew, self).forward(logits, labels).view(-1)
        loss, _ = torch.sort(loss, descending=True)
        if loss[self.n_min] > self.thresh:
            loss = loss[loss > self.thresh]
        else:
            loss = loss[:self.n_min]
        return torch.mean(loss)

    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: 0.027961