# 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_1/inductor_cache/ft/cftrwlullhwkxnfufrr4t7xseyg5lxqbr7donc6njagypfpftnau.py
# Topologically Sorted Source Nodes: [loss, loss_1, loss_2, loss_3], Original ATen: [aten._log_softmax]
# Source node to ATen node mapping:
#   loss => amax, sub
#   loss_1 => amax_1, sub_2
#   loss_2 => amax_2, sub_4
#   loss_3 => amax_3, sub_6
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%arg0_1, [1], True), kwargs = {})
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %amax), kwargs = {})
#   %amax_1 : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%arg0_1, [1], True), kwargs = {})
#   %sub_2 : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %amax_1), kwargs = {})
#   %amax_2 : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%arg0_1, [1], True), kwargs = {})
#   %sub_4 : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %amax_2), kwargs = {})
#   %amax_3 : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%arg0_1, [1], True), kwargs = {})
#   %sub_6 : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %amax_3), kwargs = {})
triton_poi_fused__log_softmax_0 = async_compile.triton('triton_poi_fused__log_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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__log_softmax_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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__log_softmax_0(in_ptr0, out_ptr0, out_ptr1, out_ptr2, out_ptr3, 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)
    tl.store(out_ptr1 + (x2), tmp8, xmask)
    tl.store(out_ptr2 + (x2), tmp8, xmask)
    tl.store(out_ptr3 + (x2), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/ka/ckarwoj6g5afhnyz3wbhzq4egowxjevrbzpbamjybwm4yabyx2lm.py
# Topologically Sorted Source Nodes: [loss, losses_1, loss_1, mul_1, losses_2, loss_2, mul_2, losses_3, loss_3, mul_3, losses_4, losses_5], Original ATen: [aten.nll_loss_forward, aten.add, aten.mul, aten.mean]
# Source node to ATen node mapping:
#   loss => full_default_2, full_default_3, neg, where_1
#   loss_1 => full_default_5, full_default_6, neg_1, where_3
#   loss_2 => full_default_8, full_default_9, neg_2, where_5
#   loss_3 => full_default_11, full_default_12, neg_3, where_7
#   losses_1 => mul
#   losses_2 => add_1
#   losses_3 => add_2
#   losses_4 => add_3
#   losses_5 => mean
#   mul_1 => mul_1
#   mul_2 => mul_2
#   mul_3 => mul_3
# Graph fragment:
#   %full_default_2 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([4], True), kwargs = {dtype: torch.bool, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%squeeze,), kwargs = {})
#   %full_default_3 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where_1 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%full_default_2, %neg, %full_default_3), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select, %where_1), kwargs = {})
#   %full_default_5 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([4], True), kwargs = {dtype: torch.bool, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %neg_1 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%squeeze_1,), kwargs = {})
#   %full_default_6 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where_3 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%full_default_5, %neg_1, %full_default_6), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select_1, %where_3), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {})
#   %full_default_8 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([4], True), kwargs = {dtype: torch.bool, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %neg_2 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%squeeze_2,), kwargs = {})
#   %full_default_9 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where_5 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%full_default_8, %neg_2, %full_default_9), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select_2, %where_5), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_1, %mul_2), kwargs = {})
#   %full_default_11 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([4], True), kwargs = {dtype: torch.bool, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %neg_3 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%squeeze_3,), kwargs = {})
#   %full_default_12 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where_7 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%full_default_11, %neg_3, %full_default_12), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select_3, %where_7), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_2, %mul_3), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%add_3,), kwargs = {})
triton_per_fused_add_mean_mul_nll_loss_forward_1 = async_compile.triton('triton_per_fused_add_mean_mul_nll_loss_forward_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.persistent_reduction(
    size_hints=[1, 4],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32', 7: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {6: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=(6,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_mean_mul_nll_loss_forward_1', 'mutated_arg_names': ['in_out_ptr1'], 'no_x_dim': False, 'num_load': 20, 'num_reduction': 1, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_per_fused_add_mean_mul_nll_loss_forward_1(in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 4
    RBLOCK: tl.constexpr = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = 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')
    tmp3 = tl.load(in_ptr1 + (1 + (4*r0)), None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (2 + (4*r0)), None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr1 + (3 + (4*r0)), None, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr0 + (1 + (4*r0)), None, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr2 + (1 + (4*r0)), None, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr2 + (4*r0), None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr2 + (2 + (4*r0)), None, eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr2 + (3 + (4*r0)), None, eviction_policy='evict_last')
    tmp37 = tl.load(in_ptr0 + (2 + (4*r0)), None, eviction_policy='evict_last')
    tmp38 = tl.load(in_ptr3 + (2 + (4*r0)), None, eviction_policy='evict_last')
    tmp39 = tl.load(in_ptr3 + (4*r0), None, eviction_policy='evict_last')
    tmp41 = tl.load(in_ptr3 + (1 + (4*r0)), None, eviction_policy='evict_last')
    tmp46 = tl.load(in_ptr3 + (3 + (4*r0)), None, eviction_policy='evict_last')
    tmp55 = tl.load(in_ptr0 + (3 + (4*r0)), None, eviction_policy='evict_last')
    tmp56 = tl.load(in_ptr4 + (3 + (4*r0)), None, eviction_policy='evict_last')
    tmp57 = tl.load(in_ptr4 + (4*r0), None, eviction_policy='evict_last')
    tmp59 = tl.load(in_ptr4 + (1 + (4*r0)), None, eviction_policy='evict_last')
    tmp62 = tl.load(in_ptr4 + (2 + (4*r0)), None, eviction_policy='evict_last')
    tmp2 = tl_math.exp(tmp1)
    tmp4 = tl_math.exp(tmp3)
    tmp5 = tmp2 + tmp4
    tmp7 = tl_math.exp(tmp6)
    tmp8 = tmp5 + tmp7
    tmp10 = tl_math.exp(tmp9)
    tmp11 = tmp8 + tmp10
    tmp12 = tl_math.log(tmp11)
    tmp13 = tmp1 - tmp12
    tmp14 = -tmp13
    tmp15 = tl.full([1, 1], True, tl.int1)
    tmp16 = 0.0
    tmp17 = tl.where(tmp15, tmp14, tmp16)
    tmp18 = tmp0 * tmp17
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tl_math.exp(tmp20)
    tmp24 = tmp22 + tmp23
    tmp26 = tl_math.exp(tmp25)
    tmp27 = tmp24 + tmp26
    tmp29 = tl_math.exp(tmp28)
    tmp30 = tmp27 + tmp29
    tmp31 = tl_math.log(tmp30)
    tmp32 = tmp20 - tmp31
    tmp33 = -tmp32
    tmp34 = tl.where(tmp15, tmp33, tmp16)
    tmp35 = tmp19 * tmp34
    tmp36 = tmp18 + tmp35
    tmp40 = tl_math.exp(tmp39)
    tmp42 = tl_math.exp(tmp41)
    tmp43 = tmp40 + tmp42
    tmp44 = tl_math.exp(tmp38)
    tmp45 = tmp43 + tmp44
    tmp47 = tl_math.exp(tmp46)
    tmp48 = tmp45 + tmp47
    tmp49 = tl_math.log(tmp48)
    tmp50 = tmp38 - tmp49
    tmp51 = -tmp50
    tmp52 = tl.where(tmp15, tmp51, tmp16)
    tmp53 = tmp37 * tmp52
    tmp54 = tmp36 + tmp53
    tmp58 = tl_math.exp(tmp57)
    tmp60 = tl_math.exp(tmp59)
    tmp61 = tmp58 + tmp60
    tmp63 = tl_math.exp(tmp62)
    tmp64 = tmp61 + tmp63
    tmp65 = tl_math.exp(tmp56)
    tmp66 = tmp64 + tmp65
    tmp67 = tl_math.log(tmp66)
    tmp68 = tmp56 - tmp67
    tmp69 = -tmp68
    tmp70 = tl.where(tmp15, tmp69, tmp16)
    tmp71 = tmp55 * tmp70
    tmp72 = tmp54 + tmp71
    tmp73 = tl.broadcast_to(tmp72, [XBLOCK, RBLOCK])
    tmp75 = tl.sum(tmp73, 1)[:, None]
    tmp76 = 4.0
    tmp77 = tmp75 / tmp76
    tl.debug_barrier()
    tl.store(in_out_ptr1 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp77, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

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((4, 4), (4, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [loss, loss_1, loss_2, loss_3], Original ATen: [aten._log_softmax]
        stream0 = get_raw_stream(0)
        triton_poi_fused__log_softmax_0.run(arg0_1, buf0, buf1, buf3, buf4, 16, grid=grid(16), stream=stream0)
        del arg0_1
        buf6 = empty_strided_cuda((), (), torch.float32)
        buf7 = buf6; del buf6  # reuse
        # Topologically Sorted Source Nodes: [loss, losses_1, loss_1, mul_1, losses_2, loss_2, mul_2, losses_3, loss_3, mul_3, losses_4, losses_5], Original ATen: [aten.nll_loss_forward, aten.add, aten.mul, aten.mean]
        triton_per_fused_add_mean_mul_nll_loss_forward_1.run(buf7, arg1_1, buf0, buf1, buf3, buf4, 1, 4, grid=grid(1), stream=stream0)
        del arg1_1
        del buf0
        del buf1
        del buf3
        del buf4
    return (buf7, )


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, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_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
from torch import Tensor
from torch.backends import cudnn as cudnn
from torch import nn as nn
from torch.nn import functional as F
from torch.nn import init as init
from typing import List


class SoftCrossEntropyLoss(nn.Module):
    """Calculate the CrossEntropyLoss with soft targets.

    :param weight: Weight to assign to each of the classes. Default: None
    :type weight: list of float
    :param reduction: The way to reduce the losses: 'none' | 'mean' | 'sum'.
        'none': no reduction,
        'mean': the mean of the losses,
        'sum': the sum of the losses.
    :type reduction: str
    """

    def __init__(self, weight: 'List[float]'=None, reduction: 'str'='mean'):
        super().__init__()
        if weight is None:
            self.weight = None
        else:
            self.register_buffer('weight', torch.Tensor(weight))
        self.reduction = reduction

    def forward(self, input: 'Tensor', target: 'Tensor') ->Tensor:
        """Calculate the loss.

        :param input: prediction logits
        :param target: target probabilities
        :return: loss
        """
        n, k = input.shape
        losses = input.new_zeros(n)
        for i in range(k):
            cls_idx = input.new_full((n,), i, dtype=torch.long)
            loss = F.cross_entropy(input, cls_idx, reduction='none')
            if self.weight is not None:
                loss = loss * self.weight[i]
            losses += target[:, i].float() * loss
        if self.reduction == 'mean':
            losses = losses.mean()
        elif self.reduction == 'sum':
            losses = losses.sum()
        elif self.reduction != 'none':
            raise ValueError(f'Unrecognized reduction: {self.reduction}')
        return losses


def get_inputs():
    return [torch.rand([4, 4]), torch.rand([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
from torch.backends import cudnn as cudnn
from torch import nn as nn
from torch.nn import init as init
from typing import List
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, out_ptr1, out_ptr2,
    out_ptr3, 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)
    tl.store(out_ptr1 + x2, tmp8, xmask)
    tl.store(out_ptr2 + x2, tmp8, xmask)
    tl.store(out_ptr3 + x2, tmp8, xmask)


@triton.jit
def triton_per_fused_add_mean_mul_nll_loss_forward_1(in_out_ptr1, in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, in_ptr4, 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')
    tmp3 = tl.load(in_ptr1 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr1 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr0 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr2 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr2 + 4 * r0, None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr2 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr2 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp37 = tl.load(in_ptr0 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp38 = tl.load(in_ptr3 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp39 = tl.load(in_ptr3 + 4 * r0, None, eviction_policy='evict_last')
    tmp41 = tl.load(in_ptr3 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp46 = tl.load(in_ptr3 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp55 = tl.load(in_ptr0 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp56 = tl.load(in_ptr4 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp57 = tl.load(in_ptr4 + 4 * r0, None, eviction_policy='evict_last')
    tmp59 = tl.load(in_ptr4 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp62 = tl.load(in_ptr4 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp2 = tl_math.exp(tmp1)
    tmp4 = tl_math.exp(tmp3)
    tmp5 = tmp2 + tmp4
    tmp7 = tl_math.exp(tmp6)
    tmp8 = tmp5 + tmp7
    tmp10 = tl_math.exp(tmp9)
    tmp11 = tmp8 + tmp10
    tmp12 = tl_math.log(tmp11)
    tmp13 = tmp1 - tmp12
    tmp14 = -tmp13
    tmp15 = tl.full([1, 1], True, tl.int1)
    tmp16 = 0.0
    tmp17 = tl.where(tmp15, tmp14, tmp16)
    tmp18 = tmp0 * tmp17
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tl_math.exp(tmp20)
    tmp24 = tmp22 + tmp23
    tmp26 = tl_math.exp(tmp25)
    tmp27 = tmp24 + tmp26
    tmp29 = tl_math.exp(tmp28)
    tmp30 = tmp27 + tmp29
    tmp31 = tl_math.log(tmp30)
    tmp32 = tmp20 - tmp31
    tmp33 = -tmp32
    tmp34 = tl.where(tmp15, tmp33, tmp16)
    tmp35 = tmp19 * tmp34
    tmp36 = tmp18 + tmp35
    tmp40 = tl_math.exp(tmp39)
    tmp42 = tl_math.exp(tmp41)
    tmp43 = tmp40 + tmp42
    tmp44 = tl_math.exp(tmp38)
    tmp45 = tmp43 + tmp44
    tmp47 = tl_math.exp(tmp46)
    tmp48 = tmp45 + tmp47
    tmp49 = tl_math.log(tmp48)
    tmp50 = tmp38 - tmp49
    tmp51 = -tmp50
    tmp52 = tl.where(tmp15, tmp51, tmp16)
    tmp53 = tmp37 * tmp52
    tmp54 = tmp36 + tmp53
    tmp58 = tl_math.exp(tmp57)
    tmp60 = tl_math.exp(tmp59)
    tmp61 = tmp58 + tmp60
    tmp63 = tl_math.exp(tmp62)
    tmp64 = tmp61 + tmp63
    tmp65 = tl_math.exp(tmp56)
    tmp66 = tmp64 + tmp65
    tmp67 = tl_math.log(tmp66)
    tmp68 = tmp56 - tmp67
    tmp69 = -tmp68
    tmp70 = tl.where(tmp15, tmp69, tmp16)
    tmp71 = tmp55 * tmp70
    tmp72 = tmp54 + tmp71
    tmp73 = tl.broadcast_to(tmp72, [XBLOCK, RBLOCK])
    tmp75 = tl.sum(tmp73, 1)[:, None]
    tmp76 = 4.0
    tmp77 = tmp75 / tmp76
    tl.debug_barrier()
    tl.store(in_out_ptr1 + tl.full([XBLOCK, 1], 0, tl.int32), tmp77, 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((4, 4), (4, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__log_softmax_0[grid(16)](arg0_1, buf0, buf1, buf3,
            buf4, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del arg0_1
        buf6 = empty_strided_cuda((), (), torch.float32)
        buf7 = buf6
        del buf6
        triton_per_fused_add_mean_mul_nll_loss_forward_1[grid(1)](buf7,
            arg1_1, buf0, buf1, buf3, buf4, 1, 4, XBLOCK=1, num_warps=2,
            num_stages=1)
        del arg1_1
        del buf0
        del buf1
        del buf3
        del buf4
    return buf7,


class SoftCrossEntropyLossNew(nn.Module):
    """Calculate the CrossEntropyLoss with soft targets.

    :param weight: Weight to assign to each of the classes. Default: None
    :type weight: list of float
    :param reduction: The way to reduce the losses: 'none' | 'mean' | 'sum'.
        'none': no reduction,
        'mean': the mean of the losses,
        'sum': the sum of the losses.
    :type reduction: str
    """

    def __init__(self, weight: 'List[float]'=None, reduction: 'str'='mean'):
        super().__init__()
        if weight is None:
            self.weight = None
        else:
            self.register_buffer('weight', torch.Tensor(weight))
        self.reduction = reduction

    def forward(self, input_0, input_1):
        arg0_1 = input_0
        arg1_1 = input_1
        output = call([arg0_1, arg1_1])
        return output[0]
