# 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/le/clewmq2oyakpojeemfsrrjq5tneb2unj5om75r32lnu3wfwo4lbd.py
# Topologically Sorted Source Nodes: [logits], Original ATen: [aten._log_softmax]
# Source node to ATen node mapping:
#   logits => 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_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: '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_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__log_softmax_0(in_ptr0, out_ptr0, 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)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/u4/cu4kfpb4bm4o7xwrvdjhv3pjm46tbhctvi7mgrront5rs3v44s3v.py
# Topologically Sorted Source Nodes: [logits, scatter_, mul, sum_1, neg, truediv], Original ATen: [aten._log_softmax, aten.scatter, aten.mul, aten.sum, aten.neg, aten.div]
# Source node to ATen node mapping:
#   logits => exp, log, sub_1, sum_1
#   mul => mul
#   neg => neg
#   scatter_ => scatter_upon_const_tensor
#   sum_1 => sum_2
#   truediv => div
# Graph fragment:
#   %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 = {})
#   %scatter_upon_const_tensor : [num_users=1] = call_function[target=torch._inductor.fx_passes.post_grad.scatter_upon_const_tensor](args = (), kwargs = {shape: [4, 4], background_val: 1.3333333333333333, dtype: torch.float32, dim: 1, selector: %unsqueeze, val: -0.33333333333333326})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, %scatter_upon_const_tensor), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%mul,), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%sum_2,), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%neg, 4), kwargs = {})
triton_per_fused__log_softmax_div_mul_neg_scatter_sum_1 = async_compile.triton('triton_per_fused__log_softmax_div_mul_neg_scatter_sum_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, 16],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i64', 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_neg_scatter_sum_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, '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_neg_scatter_sum_1(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    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)
    r2 = rindex
    r1 = (rindex // 4)
    r0 = rindex % 4
    tmp0 = tl.load(in_ptr0 + (r2), None)
    tmp1 = tl.load(in_ptr0 + (4*r1), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + (4*r1)), None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + (4*r1)), None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + (4*r1)), None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr1 + (r1), 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 = tmp0 - tmp12
    tmp15 = r0
    tmp16 = tmp14 == tmp15
    tmp17 = -0.33333333333333326
    tmp18 = 1.3333333333333333
    tmp19 = tl.where(tmp16, tmp17, tmp18)
    tmp20 = tmp13 * tmp19
    tmp21 = tl.broadcast_to(tmp20, [XBLOCK, RBLOCK])
    tmp23 = tl.sum(tmp21, 1)[:, None]
    tmp24 = -tmp23
    tmp25 = 0.25
    tmp26 = tmp24 * tmp25
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp26, 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, ), (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: [logits], Original ATen: [aten._log_softmax]
        stream0 = get_raw_stream(0)
        triton_poi_fused__log_softmax_0.run(arg0_1, buf0, 16, grid=grid(16), stream=stream0)
        del arg0_1
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [logits, scatter_, mul, sum_1, neg, truediv], Original ATen: [aten._log_softmax, aten.scatter, aten.mul, aten.sum, aten.neg, aten.div]
        triton_per_fused__log_softmax_div_mul_neg_scatter_sum_1.run(buf2, buf0, arg1_1, 1, 16, grid=grid(1), stream=stream0)
        del arg1_1
        del buf0
    return (buf2, )


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, ), (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
import torch.nn.functional as F
import torch.utils.data


def one_hot(val: 'torch.LongTensor', num: 'int', num_first: 'bool'=False
    ) ->torch.FloatTensor:
    """
    Overview:
        Convert a ``torch.LongTensor`` to one hot encoding.
        This implementation can be slightly faster than ``torch.nn.functional.one_hot``
    Arguments:
        - val (:obj:`torch.LongTensor`): each element contains the state to be encoded, the range should be [0, num-1]
        - num (:obj:`int`): number of states of the one hot encoding
        - num_first (:obj:`bool`): If ``num_first`` is False, the one hot encoding is added as the last; \\
            Otherwise as the first dimension.
    Returns:
        - one_hot (:obj:`torch.FloatTensor`)
    Example:
        >>> one_hot(2*torch.ones([2,2]).long(),3)
        tensor([[[0., 0., 1.],
                 [0., 0., 1.]],
                [[0., 0., 1.],
                 [0., 0., 1.]]])
        >>> one_hot(2*torch.ones([2,2]).long(),3,num_first=True)
        tensor([[[0., 0.], [1., 0.]],
                [[0., 1.], [0., 0.]],
                [[1., 0.], [0., 1.]]])
    """
    assert isinstance(val, torch.Tensor), type(val)
    assert val.dtype == torch.long
    assert len(val.shape) >= 1
    old_shape = val.shape
    val_reshape = val.reshape(-1, 1)
    ret = torch.zeros(val_reshape.shape[0], num, device=val.device)
    index_neg_one = torch.eq(val_reshape, -1).long()
    if index_neg_one.sum() != 0:
        val_reshape = torch.where(val_reshape != -1, val_reshape, torch.
            zeros(val_reshape.shape, device=val.device).long())
    try:
        ret.scatter_(1, val_reshape, 1)
        if index_neg_one.sum() != 0:
            ret = ret * (1 - index_neg_one)
    except RuntimeError:
        raise RuntimeError('value: {}\nnum: {}\t:val_shape: {}\n'.format(
            val_reshape, num, val_reshape.shape))
    if num_first:
        return ret.permute(1, 0).reshape(num, *old_shape)
    else:
        return ret.reshape(*old_shape, num)


class LabelSmoothCELoss(nn.Module):
    """
    Overview:
        Label smooth cross entropy loss.
    Interfaces:
        forward
    """

    def __init__(self, ratio: 'float') ->None:
        super().__init__()
        self.ratio = ratio

    def forward(self, logits: 'torch.Tensor', labels: 'torch.LongTensor'
        ) ->torch.Tensor:
        """
        Overview:
            Calculate label smooth cross entropy loss.
        Arguments:
            - logits (:obj:`torch.Tensor`): Predicted logits.
            - labels (:obj:`torch.LongTensor`): Ground truth.
        Returns:
            - loss (:obj:`torch.Tensor`): Calculated loss.
        """
        B, N = logits.shape
        val = float(self.ratio) / (N - 1)
        one_hot = torch.full_like(logits, val)
        one_hot.scatter_(1, labels.unsqueeze(1), 1 - val)
        logits = F.log_softmax(logits, dim=1)
        return -torch.sum(logits * one_hot.detach()) / B


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


def get_init_inputs():
    return [[], {'ratio': 4}]

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 torch.nn as nn
import torch.utils.data
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 = 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)


@triton.jit
def triton_per_fused__log_softmax_div_mul_neg_scatter_sum_1(in_out_ptr0,
    in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 16
    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)
    r2 = rindex
    r1 = rindex // 4
    r0 = rindex % 4
    tmp0 = tl.load(in_ptr0 + r2, None)
    tmp1 = tl.load(in_ptr0 + 4 * r1, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * r1), None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + 4 * r1), None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + 4 * r1), None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr1 + r1, 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 = tmp0 - tmp12
    tmp15 = r0
    tmp16 = tmp14 == tmp15
    tmp17 = -0.33333333333333326
    tmp18 = 1.3333333333333333
    tmp19 = tl.where(tmp16, tmp17, tmp18)
    tmp20 = tmp13 * tmp19
    tmp21 = tl.broadcast_to(tmp20, [XBLOCK, RBLOCK])
    tmp23 = tl.sum(tmp21, 1)[:, None]
    tmp24 = -tmp23
    tmp25 = 0.25
    tmp26 = tmp24 * tmp25
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp26, 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,), (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__log_softmax_0[grid(16)](arg0_1, buf0, 16, XBLOCK=
            16, num_warps=1, num_stages=1)
        del arg0_1
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1
        del buf1
        triton_per_fused__log_softmax_div_mul_neg_scatter_sum_1[grid(1)](buf2,
            buf0, arg1_1, 1, 16, XBLOCK=1, num_warps=2, num_stages=1)
        del arg1_1
        del buf0
    return buf2,


def one_hot(val: 'torch.LongTensor', num: 'int', num_first: 'bool'=False
    ) ->torch.FloatTensor:
    """
    Overview:
        Convert a ``torch.LongTensor`` to one hot encoding.
        This implementation can be slightly faster than ``torch.nn.functional.one_hot``
    Arguments:
        - val (:obj:`torch.LongTensor`): each element contains the state to be encoded, the range should be [0, num-1]
        - num (:obj:`int`): number of states of the one hot encoding
        - num_first (:obj:`bool`): If ``num_first`` is False, the one hot encoding is added as the last; \\
            Otherwise as the first dimension.
    Returns:
        - one_hot (:obj:`torch.FloatTensor`)
    Example:
        >>> one_hot(2*torch.ones([2,2]).long(),3)
        tensor([[[0., 0., 1.],
                 [0., 0., 1.]],
                [[0., 0., 1.],
                 [0., 0., 1.]]])
        >>> one_hot(2*torch.ones([2,2]).long(),3,num_first=True)
        tensor([[[0., 0.], [1., 0.]],
                [[0., 1.], [0., 0.]],
                [[1., 0.], [0., 1.]]])
    """
    assert isinstance(val, torch.Tensor), type(val)
    assert val.dtype == torch.long
    assert len(val.shape) >= 1
    old_shape = val.shape
    val_reshape = val.reshape(-1, 1)
    ret = torch.zeros(val_reshape.shape[0], num, device=val.device)
    index_neg_one = torch.eq(val_reshape, -1).long()
    if index_neg_one.sum() != 0:
        val_reshape = torch.where(val_reshape != -1, val_reshape, torch.
            zeros(val_reshape.shape, device=val.device).long())
    try:
        ret.scatter_(1, val_reshape, 1)
        if index_neg_one.sum() != 0:
            ret = ret * (1 - index_neg_one)
    except RuntimeError:
        raise RuntimeError('value: {}\nnum: {}\t:val_shape: {}\n'.format(
            val_reshape, num, val_reshape.shape))
    if num_first:
        return ret.permute(1, 0).reshape(num, *old_shape)
    else:
        return ret.reshape(*old_shape, num)


class LabelSmoothCELossNew(nn.Module):
    """
    Overview:
        Label smooth cross entropy loss.
    Interfaces:
        forward
    """

    def __init__(self, ratio: 'float') ->None:
        super().__init__()
        self.ratio = ratio

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