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
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__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
    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
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_red_fused__softmax_add_div_log_maximum_mean_mul_reciprocal_rsub_sub_sum_zeros_like_1(
    in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, rnumel, XBLOCK: tl.
    constexpr, RBLOCK: tl.constexpr):
    rnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rbase = tl.arange(0, RBLOCK)[None, :]
    _tmp11 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    _tmp55 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r0 = rindex
        r1 = rindex % 64
        r4 = rindex // 4
        tmp0 = tl.load(in_ptr0 + r0, rmask, eviction_policy='evict_first',
            other=0.0)
        tmp5 = tl.load(in_ptr1 + r0, rmask, eviction_policy='evict_first',
            other=0.0)
        tmp13 = tl.load(in_ptr0 + r1, rmask, eviction_policy='evict_last',
            other=0.0)
        tmp14 = tl.load(in_ptr0 + (64 + r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp16 = tl.load(in_ptr0 + (128 + r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp18 = tl.load(in_ptr0 + (192 + r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp38 = tl.load(in_ptr2 + r0, rmask, eviction_policy='evict_first',
            other=0.0)
        tmp39 = tl.load(in_ptr2 + 4 * r4, rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp40 = tl.load(in_ptr2 + (1 + 4 * r4), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp42 = tl.load(in_ptr2 + (2 + 4 * r4), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp44 = tl.load(in_ptr2 + (3 + 4 * r4), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp1 = 2.0
        tmp2 = tmp0 * tmp1
        tmp3 = 1.0
        tmp4 = tmp2 - tmp3
        tmp6 = tmp4 * tmp5
        tmp7 = tmp3 - tmp6
        tmp8 = 0.0
        tmp9 = triton_helpers.maximum(tmp8, tmp7)
        tmp10 = tl.broadcast_to(tmp9, [XBLOCK, RBLOCK])
        tmp12 = _tmp11 + tmp10
        _tmp11 = tl.where(rmask, tmp12, _tmp11)
        tmp15 = tmp13 + tmp14
        tmp17 = tmp15 + tmp16
        tmp19 = tmp17 + tmp18
        tmp20 = tmp19 + tmp3
        tmp21 = tmp19 + tmp1
        tmp22 = tmp20 / tmp21
        tmp23 = tmp22 * tmp0
        tmp24 = tmp3 - tmp13
        tmp25 = tmp3 - tmp14
        tmp26 = tmp24 + tmp25
        tmp27 = tmp3 - tmp16
        tmp28 = tmp26 + tmp27
        tmp29 = tmp3 - tmp18
        tmp30 = tmp28 + tmp29
        tmp31 = tmp30 + tmp1
        tmp32 = tl.full([1, 1], 1, tl.int32)
        tmp33 = tmp32 / tmp31
        tmp34 = tmp33 * tmp3
        tmp35 = tmp3 - tmp0
        tmp36 = tmp34 * tmp35
        tmp37 = tmp23 + tmp36
        tmp41 = tmp39 + tmp40
        tmp43 = tmp41 + tmp42
        tmp45 = tmp43 + tmp44
        tmp46 = tmp38 / tmp45
        tmp47 = tl_math.log(tmp46)
        tmp48 = tmp37 * tmp47
        tmp49 = tmp3 - tmp37
        tmp50 = tmp3 - tmp46
        tmp51 = tl_math.log(tmp50)
        tmp52 = tmp49 * tmp51
        tmp53 = tmp48 + tmp52
        tmp54 = tl.broadcast_to(tmp53, [XBLOCK, RBLOCK])
        tmp56 = _tmp55 + tmp54
        _tmp55 = tl.where(rmask, tmp56, _tmp55)
    tmp11 = tl.sum(_tmp11, 1)[:, None]
    tmp55 = tl.sum(_tmp55, 1)[:, None]
    tmp57 = 256.0
    tmp58 = tmp11 / tmp57
    tmp59 = tmp55 / tmp57
    tmp60 = -1.0
    tmp61 = tmp59 * tmp60
    tmp62 = tmp58 + tmp61
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp62, None)


def call(args):
    arg0_1, arg1_1, arg2_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))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_0[grid(256)](arg2_1, buf2, 256, XBLOCK=
            256, num_warps=4, num_stages=1)
        del arg2_1
        buf0 = empty_strided_cuda((), (), torch.float32)
        buf5 = buf0
        del buf0
        triton_red_fused__softmax_add_div_log_maximum_mean_mul_reciprocal_rsub_sub_sum_zeros_like_1[
            grid(1)](buf5, arg0_1, arg1_1, buf2, 1, 256, XBLOCK=1, RBLOCK=
            256, num_warps=8, num_stages=1)
        del arg0_1
        del arg1_1
        del buf2
    return buf5,


class SvmProbsLossNew(torch.nn.Module):

    def __init__(self):
        super(SvmProbsLossNew, self).__init__()

    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: 0.02857