# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_7/inductor_cache/wx/cwxwvlntewdrqi2r4caciy5ht4jdvafnhtiqncr4lo4aegcb4imz.py
# Topologically Sorted Source Nodes: [y_hat], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   y_hat => amax, exp, sub_4
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%arg2_1, [-1], True), kwargs = {})
#   %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg2_1, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_4,), kwargs = {})
triton_poi_fused__softmax_0 = async_compile.triton('triton_poi_fused__softmax_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@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)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/5j/c5j4v74hrnpb5w2o3vp5sb24ogno4lmlssll6w3sne2nub3lg3qe.py
# Topologically Sorted Source Nodes: [zeros_like, mul, svm_targets, mul_1, projection_dist, margin, svm_loss, n_plus, add, add_1, n_plus_rate, mul_2, sub_2, n_minus, add_2, n_minus_rate, sub_3, mul_3, y_cv, y_hat, log, mul_4, sub_4, sub_5, log_1, mul_5, add_4, mean_1, platt_loss, add_5], Original ATen: [aten.zeros_like, aten.mul, aten.sub, aten.rsub, aten.maximum, aten.mean, aten.sum, aten.add, aten.div, aten.reciprocal, aten._softmax, aten.log]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   add_2 => add_2
#   add_4 => add_4
#   add_5 => add_5
#   log => log
#   log_1 => log_1
#   margin => maximum
#   mean_1 => mean_1
#   mul => mul
#   mul_1 => mul_1
#   mul_2 => mul_3
#   mul_3 => mul_4
#   mul_4 => mul_5
#   mul_5 => mul_6
#   n_minus => sum_2
#   n_minus_rate => mul_2, reciprocal
#   n_plus => sum_1
#   n_plus_rate => div
#   platt_loss => mul_7
#   projection_dist => sub_1
#   sub_2 => sub_2
#   sub_3 => sub_3
#   sub_4 => sub_5
#   sub_5 => sub_6
#   svm_loss => mean
#   svm_targets => sub
#   y_cv => add_3
#   y_hat => div_1, sum_3
#   zeros_like => full_default
# Graph fragment:
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([4, 4, 4, 4], 0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, 2), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul, 1), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %arg1_1), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %mul_1), kwargs = {})
#   %maximum : [num_users=1] = call_function[target=torch.ops.aten.maximum.default](args = (%full_default, %sub_1), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%maximum,), kwargs = {})
#   %sum_1 : [num_users=2] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%arg0_1, [0]), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, 1.0), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, 2.0), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%add, %add_1), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div, %arg0_1), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1.0, %arg0_1), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%sub_2, [0]), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_2, 2.0), kwargs = {})
#   %reciprocal : [num_users=1] = call_function[target=torch.ops.aten.reciprocal.default](args = (%add_2,), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%reciprocal, 1.0), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg0_1), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_2, %sub_3), kwargs = {})
#   %add_3 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_3, %mul_4), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
#   %div_1 : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_3), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%div_1,), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_3, %log), kwargs = {})
#   %sub_5 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %add_3), kwargs = {})
#   %sub_6 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %div_1), kwargs = {})
#   %log_1 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sub_6,), kwargs = {})
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_5, %log_1), kwargs = {})
#   %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_5, %mul_6), kwargs = {})
#   %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%add_4,), kwargs = {})
#   %mul_7 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mean_1, -1), kwargs = {})
#   %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mean, %mul_7), kwargs = {})
triton_red_fused__softmax_add_div_log_maximum_mean_mul_reciprocal_rsub_sub_sum_zeros_like_1 = async_compile.triton('triton_red_fused__softmax_add_div_log_maximum_mean_mul_reciprocal_rsub_sub_sum_zeros_like_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.reduction(
    size_hints=[1, 256],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {4: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 5), equal_to_1=(4,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_red_fused__softmax_add_div_log_maximum_mean_mul_reciprocal_rsub_sub_sum_zeros_like_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 11, 'num_reduction': 2, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@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):
    xnumel = 1
    rnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = 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)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

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)
        # Topologically Sorted Source Nodes: [y_hat], Original ATen: [aten._softmax]
        stream0 = get_raw_stream(0)
        triton_poi_fused__softmax_0.run(arg2_1, buf2, 256, grid=grid(256), stream=stream0)
        del arg2_1
        buf0 = empty_strided_cuda((), (), torch.float32)
        buf5 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [zeros_like, mul, svm_targets, mul_1, projection_dist, margin, svm_loss, n_plus, add, add_1, n_plus_rate, mul_2, sub_2, n_minus, add_2, n_minus_rate, sub_3, mul_3, y_cv, y_hat, log, mul_4, sub_4, sub_5, log_1, mul_5, add_4, mean_1, platt_loss, add_5], Original ATen: [aten.zeros_like, aten.mul, aten.sub, aten.rsub, aten.maximum, aten.mean, aten.sum, aten.add, aten.div, aten.reciprocal, aten._softmax, aten.log]
        triton_red_fused__softmax_add_div_log_maximum_mean_mul_reciprocal_rsub_sub_sum_zeros_like_1.run(buf5, arg0_1, arg1_1, buf2, 1, 256, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
        del buf2
    return (buf5, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg2_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1, arg2_1])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import torch


class SvmProbsLoss(torch.nn.Module):

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

    def forward(self, decisions, logits, targets, multi_label=False):
        y = targets.float()
        svm_targets = y * 2 - 1
        projection_dist = 1 - svm_targets * decisions
        margin = torch.max(torch.zeros_like(projection_dist), projection_dist)
        svm_loss = margin.mean()
        n_plus = torch.sum(y, dim=0)
        n_minus = torch.sum(1.0 - y, dim=0)
        n_plus_rate = (n_plus + 1.0) / (n_plus + 2.0)
        n_minus_rate = 1.0 / (n_minus + 2.0)
        y_cv = n_plus_rate * y + n_minus_rate * (1 - y)
        y_hat = torch.sigmoid(logits) if multi_label else torch.softmax(logits,
            dim=-1)
        platt_loss = -1 * torch.mean(y_cv * torch.log(y_hat) + (1 - y_cv) *
            torch.log(1 - y_hat))
        return svm_loss + platt_loss


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), torch.rand(
        [4, 4, 4, 4])]


def get_init_inputs():
    return [[], {}]

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]
