# 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_4/inductor_cache/kb/ckb5dxt7bl4nrv3xbxip5db57t6yqtx6yshfe4o5a4q6qxq6kusa.py
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.div]
# Source node to ATen node mapping:
#   out => full_default
# Graph fragment:
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([4, 4], 0.02500000037252903), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
triton_poi_fused_div_0 = async_compile.triton('triton_poi_fused_div_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: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_div_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, '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_div_0(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = 0.02500000037252903
    tl.store(out_ptr0 + (x0), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ov/covxwftg3izjg5actw2wvxpitaiwk6gdgw3gixdqaa2jdyz4zuhd.py
# Topologically Sorted Source Nodes: [out, setitem], Original ATen: [aten.div, aten.lift_fresh, aten.index_put]
# Source node to ATen node mapping:
#   out => full_default
#   setitem => full_default_1, index_put
# Graph fragment:
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([4, 4], 0.02500000037252903), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %full_default_1 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.925000011920929), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %index_put : [num_users=5] = call_function[target=torch.ops.aten.index_put_.default](args = (%full_default, [%iota_default, %arg1_1], %full_default_1), kwargs = {})
triton_poi_fused_div_index_put_lift_fresh_1 = async_compile.triton('triton_poi_fused_div_index_put_lift_fresh_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.pointwise(
    size_hints=[4], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_div_index_put_lift_fresh_1', 'mutated_arg_names': ['out_ptr0'], 'no_x_dim': False, 'num_load': 1, '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_div_index_put_lift_fresh_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = tl.full([XBLOCK], 4, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert(((0 <= tmp4) & (tmp4 < 4)) | ~(xmask), "index out of bounds: 0 <= tmp4 < 4")
    tmp6 = 0.925000011920929
    tl.store(out_ptr0 + (tmp4 + (4*x0)), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/pv/cpvkbqma42nsdzqpqpkanztnk3kwdfx7iozdpzeynueg2vowikim.py
# Topologically Sorted Source Nodes: [output], Original ATen: [aten._log_softmax]
# Source node to ATen node mapping:
#   output => amax, sub
# 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 = {})
triton_poi_fused__log_softmax_2 = async_compile.triton('triton_poi_fused__log_softmax_2', '''
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__log_softmax_2', '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__log_softmax_2(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)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/vu/cvud7ahmbxwqwxoogftz5uvfsgc76moedr4ma4x2m7tcie7qhfs3.py
# Topologically Sorted Source Nodes: [loss, output], Original ATen: [aten.xlogy, aten._log_softmax, aten.mul, aten.sub, aten.sum, aten.div]
# Source node to ATen node mapping:
#   loss => div_1, eq, full_default_2, full_default_3, isnan, log_1, mul_2, mul_3, sub_2, sum_2, where, where_1
#   output => exp, log, sub_1, sum_1
# Graph fragment:
#   %isnan : [num_users=1] = call_function[target=torch.ops.aten.isnan.default](args = (%index_put,), kwargs = {})
#   %full_default_3 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], nan), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Scalar](args = (%index_put, 0), kwargs = {})
#   %full_default_2 : [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})
#   %log_1 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%index_put,), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%index_put, %log_1), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%eq, %full_default_2, %mul_3), kwargs = {})
#   %where_1 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%isnan, %full_default_3, %where), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sum_1,), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub, %log), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%index_put, %sub_1), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%where_1, %mul_2), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%sub_2,), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_2, 4), kwargs = {})
triton_per_fused__log_softmax_div_mul_sub_sum_xlogy_3 = async_compile.triton('triton_per_fused__log_softmax_div_mul_sub_sum_xlogy_3', '''
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, 256],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: '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': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__log_softmax_div_mul_sub_sum_xlogy_3', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 6, 'num_reduction': 1, '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_per_fused__log_softmax_div_mul_sub_sum_xlogy_3(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel):
    xnumel = 1
    XBLOCK: tl.constexpr = 1
    rnumel = 256
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r0 = rindex % 16
    r3 = rindex
    r2 = (rindex // 64)
    tmp0 = tl.load(in_ptr0 + (r0), None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr1 + (r3), None)
    tmp10 = tl.load(in_ptr1 + (r0 + (64*r2)), None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + (16 + r0 + (64*r2)), None, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr1 + (32 + r0 + (64*r2)), None, eviction_policy='evict_last')
    tmp18 = tl.load(in_ptr1 + (48 + r0 + (64*r2)), None, eviction_policy='evict_last')
    tmp1 = libdevice.isnan(tmp0).to(tl.int1)
    tmp2 = 0.0
    tmp3 = tmp0 == tmp2
    tmp4 = tl_math.log(tmp0)
    tmp5 = tmp0 * tmp4
    tmp6 = tl.where(tmp3, tmp2, tmp5)
    tmp7 = float("nan")
    tmp8 = tl.where(tmp1, tmp7, tmp6)
    tmp11 = tl_math.exp(tmp10)
    tmp13 = tl_math.exp(tmp12)
    tmp14 = tmp11 + tmp13
    tmp16 = tl_math.exp(tmp15)
    tmp17 = tmp14 + tmp16
    tmp19 = tl_math.exp(tmp18)
    tmp20 = tmp17 + tmp19
    tmp21 = tl_math.log(tmp20)
    tmp22 = tmp9 - tmp21
    tmp23 = tmp0 * tmp22
    tmp24 = tmp8 - tmp23
    tmp25 = tl.broadcast_to(tmp24, [RBLOCK])
    tmp27 = triton_helpers.promote_to_tensor(tl.sum(tmp25, 0))
    tmp28 = 0.25
    tmp29 = tmp27 * tmp28
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp29, 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, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.div]
        stream0 = get_raw_stream(0)
        triton_poi_fused_div_0.run(buf0, 16, grid=grid(16), stream=stream0)
        # Topologically Sorted Source Nodes: [out, setitem], Original ATen: [aten.div, aten.lift_fresh, aten.index_put]
        triton_poi_fused_div_index_put_lift_fresh_1.run(arg1_1, buf0, 4, grid=grid(4), stream=stream0)
        del arg1_1
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [output], Original ATen: [aten._log_softmax]
        triton_poi_fused__log_softmax_2.run(arg0_1, buf2, 256, grid=grid(256), stream=stream0)
        del arg0_1
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [loss, output], Original ATen: [aten.xlogy, aten._log_softmax, aten.mul, aten.sub, aten.sum, aten.div]
        triton_per_fused__log_softmax_div_mul_sub_sum_xlogy_3.run(buf4, buf0, buf2, 1, 256, grid=grid(1), stream=stream0)
        del buf0
        del buf2
    return (buf4, )


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, ), (1, ), device='cuda:0', dtype=torch.int64)
    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
import torch.nn as nn


class SmooothLabelCELoss(nn.Module):

    def __init__(self, smooth=0.1, use_uniform=False, reduction='mean'):
        super(SmooothLabelCELoss, self).__init__()
        self.smooth_coef = smooth
        self.smooth_std = 0.5
        self.reduction = reduction
        self.use_uniform = use_uniform
        self.intervals = 5
        self._loss = nn.KLDivLoss(reduction='batchmean')
        self.norm = nn.LogSoftmax(dim=1)

    def get_closest_number(self, y, C, num):
        assert num <= C
        half = num // 2
        if y + half < C:
            left = max(y - half, 0)
            right = left + num
        else:
            right = C
            left = right - num
        return left, right

    def center_smooth_label(self, target, C):
        B = target.shape[0]
        I = torch.empty(size=(B, self.intervals), device=target.device,
            dtype=torch.int64)
        for b in range(B):
            left, right = self.get_closest_number(target[b].item(), C, self
                .intervals)
            I[b, :] = torch.arange(left, right)
        softed = torch.zeros((B, C), dtype=torch.float32, device=target.device)
        use_uniform = self.use_uniform
        if use_uniform:
            softed.scatter_(1, I, self.smooth_coef * 1.0 / self.intervals)
            softed[torch.arange(0, B), target
                ] = 1 - self.smooth_coef + self.smooth_coef * 1.0 / self.intervals
        else:
            src = torch.exp(-self.smooth_std * (I - target.unsqueeze(dim=1)
                ) ** 2)
            softed.scatter_(1, I, src)
            softed = softed / softed.sum(1, keepdim=True)
        return softed

    def global_smooth_label(self, target, C):
        B = target.shape[0]
        if C is None:
            C = target.max() + 1
        out = torch.ones(B, C, device=target.device
            ) * self.smooth_coef * 1.0 / C
        out[torch.arange(0, B), target
            ] = 1 - self.smooth_coef + self.smooth_coef * 1.0 / C
        return out

    def forward(self, output, label):
        output = self.norm(output)
        C = output.shape[1]
        soft_label = self.global_smooth_label(label, C)
        loss = self._loss(output, soft_label)
        return loss


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), torch.ones([4], dtype=torch.int64)]


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 libdevice, math as tl_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_div_0(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = 0.02500000037252903
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused_div_index_put_lift_fresh_1(in_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.full([XBLOCK], 4, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert((0 <= tmp4) & (tmp4 < 4) | ~xmask,
        'index out of bounds: 0 <= tmp4 < 4')
    tmp6 = 0.925000011920929
    tl.store(out_ptr0 + (tmp4 + 4 * x0), tmp6, xmask)


@triton.jit
def triton_poi_fused__log_softmax_2(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__log_softmax_div_mul_sub_sum_xlogy_3(in_out_ptr0,
    in_ptr0, in_ptr1, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r0 = rindex % 16
    r3 = rindex
    r2 = rindex // 64
    tmp0 = tl.load(in_ptr0 + r0, None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr1 + r3, None)
    tmp10 = tl.load(in_ptr1 + (r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp12 = tl.load(in_ptr1 + (16 + r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp15 = tl.load(in_ptr1 + (32 + r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp18 = tl.load(in_ptr1 + (48 + r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp1 = libdevice.isnan(tmp0).to(tl.int1)
    tmp2 = 0.0
    tmp3 = tmp0 == tmp2
    tmp4 = tl_math.log(tmp0)
    tmp5 = tmp0 * tmp4
    tmp6 = tl.where(tmp3, tmp2, tmp5)
    tmp7 = float('nan')
    tmp8 = tl.where(tmp1, tmp7, tmp6)
    tmp11 = tl_math.exp(tmp10)
    tmp13 = tl_math.exp(tmp12)
    tmp14 = tmp11 + tmp13
    tmp16 = tl_math.exp(tmp15)
    tmp17 = tmp14 + tmp16
    tmp19 = tl_math.exp(tmp18)
    tmp20 = tmp17 + tmp19
    tmp21 = tl_math.log(tmp20)
    tmp22 = tmp9 - tmp21
    tmp23 = tmp0 * tmp22
    tmp24 = tmp8 - tmp23
    tmp25 = tl.broadcast_to(tmp24, [RBLOCK])
    tmp27 = triton_helpers.promote_to_tensor(tl.sum(tmp25, 0))
    tmp28 = 0.25
    tmp29 = tmp27 * tmp28
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp29, 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,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_div_0[grid(16)](buf0, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        triton_poi_fused_div_index_put_lift_fresh_1[grid(4)](arg1_1, buf0, 
            4, XBLOCK=4, num_warps=1, num_stages=1)
        del arg1_1
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__log_softmax_2[grid(256)](arg0_1, buf2, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del arg0_1
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf3
        del buf3
        triton_per_fused__log_softmax_div_mul_sub_sum_xlogy_3[grid(1)](buf4,
            buf0, buf2, 1, 256, num_warps=2, num_stages=1)
        del buf0
        del buf2
    return buf4,


class SmooothLabelCELossNew(nn.Module):

    def __init__(self, smooth=0.1, use_uniform=False, reduction='mean'):
        super(SmooothLabelCELossNew, self).__init__()
        self.smooth_coef = smooth
        self.smooth_std = 0.5
        self.reduction = reduction
        self.use_uniform = use_uniform
        self.intervals = 5
        self._loss = nn.KLDivLoss(reduction='batchmean')
        self.norm = nn.LogSoftmax(dim=1)

    def get_closest_number(self, y, C, num):
        assert num <= C
        half = num // 2
        if y + half < C:
            left = max(y - half, 0)
            right = left + num
        else:
            right = C
            left = right - num
        return left, right

    def center_smooth_label(self, target, C):
        B = target.shape[0]
        I = torch.empty(size=(B, self.intervals), device=target.device,
            dtype=torch.int64)
        for b in range(B):
            left, right = self.get_closest_number(target[b].item(), C, self
                .intervals)
            I[b, :] = torch.arange(left, right)
        softed = torch.zeros((B, C), dtype=torch.float32, device=target.device)
        use_uniform = self.use_uniform
        if use_uniform:
            softed.scatter_(1, I, self.smooth_coef * 1.0 / self.intervals)
            softed[torch.arange(0, B), target
                ] = 1 - self.smooth_coef + self.smooth_coef * 1.0 / self.intervals
        else:
            src = torch.exp(-self.smooth_std * (I - target.unsqueeze(dim=1)
                ) ** 2)
            softed.scatter_(1, I, src)
            softed = softed / softed.sum(1, keepdim=True)
        return softed

    def global_smooth_label(self, target, C):
        B = target.shape[0]
        if C is None:
            C = target.max() + 1
        out = torch.ones(B, C, device=target.device
            ) * self.smooth_coef * 1.0 / C
        out[torch.arange(0, B), target
            ] = 1 - self.smooth_coef + self.smooth_coef * 1.0 / C
        return out

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