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.nn.modules.loss import _WeightedLoss
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 = 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
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_per_fused__log_softmax_mean_mul_neg_scatter_sum_1(in_out_ptr0,
    in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 64
    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 % 4
    r2 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + 4 * r2, None, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (1 + 4 * r2), None, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr1 + (2 + 4 * r2), None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr1 + (3 + 4 * r2), None, eviction_policy='evict_last')
    tmp1 = tl.full([1, 1], 0, tl.int64)
    tmp2 = tmp0 == tmp1
    tmp3 = 1.0
    tmp4 = 0.0
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp7 = tl_math.exp(tmp6)
    tmp9 = tl_math.exp(tmp8)
    tmp10 = tmp7 + tmp9
    tmp12 = tl_math.exp(tmp11)
    tmp13 = tmp10 + tmp12
    tmp15 = tl_math.exp(tmp14)
    tmp16 = tmp13 + tmp15
    tmp17 = tl_math.log(tmp16)
    tmp18 = tmp6 - tmp17
    tmp19 = tmp5 * tmp18
    tmp20 = tl.full([1, 1], 1, tl.int64)
    tmp21 = tmp0 == tmp20
    tmp22 = tl.where(tmp21, tmp3, tmp4)
    tmp23 = tmp8 - tmp17
    tmp24 = tmp22 * tmp23
    tmp25 = tmp19 + tmp24
    tmp26 = tl.full([1, 1], 2, tl.int64)
    tmp27 = tmp0 == tmp26
    tmp28 = tl.where(tmp27, tmp3, tmp4)
    tmp29 = tmp11 - tmp17
    tmp30 = tmp28 * tmp29
    tmp31 = tmp25 + tmp30
    tmp32 = tl.full([1, 1], 3, tl.int64)
    tmp33 = tmp0 == tmp32
    tmp34 = tl.where(tmp33, tmp3, tmp4)
    tmp35 = tmp14 - tmp17
    tmp36 = tmp34 * tmp35
    tmp37 = tmp31 + tmp36
    tmp38 = -tmp37
    tmp39 = tl.broadcast_to(tmp38, [XBLOCK, RBLOCK])
    tmp41 = tl.sum(tmp39, 1)[:, None]
    tmp42 = 64.0
    tmp43 = tmp41 / tmp42
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp43, 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, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__log_softmax_0[grid(256)](arg0_1, buf0, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf2
        del buf2
        triton_per_fused__log_softmax_mean_mul_neg_scatter_sum_1[grid(1)](buf3,
            arg1_1, buf0, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg1_1
        del buf0
    return buf3,


class SmoothCrossEntropyLossNew(_WeightedLoss):
    """
    Smooth labelling for pytorch.
    Source: https://stackoverflow.com/questions/55681502/label-smoothing-in-pytorch
    """

    def __init__(self, weight=None, reduction='mean', smoothing=0.0):
        super().__init__(weight=weight, reduction=reduction)
        self.smoothing = smoothing
        self.weight = weight
        self.reduction = reduction

    @staticmethod
    def _smooth_one_hot(targets: 'torch.Tensor', n_classes: 'int',
        smoothing=0.0):
        assert 0 <= smoothing < 1
        with torch.no_grad():
            targets = torch.empty(size=(targets.size(0), n_classes), device
                =targets.device).fill_(smoothing / (n_classes - 1)).scatter_(
                1, targets.data.unsqueeze(1), 1.0 - smoothing)
        return targets

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


Measured latency_ms: 0.027446