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]


Measured latency_ms: 0.034465