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.nn.functional as F
import torch.nn.parallel
import torch.optim
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 = 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_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
    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')
    tmp15 = tl.load(in_ptr0 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr0 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr0 + (3 + 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 = tmp0 * tmp13
    tmp16 = tmp3 - tmp12
    tmp17 = tmp15 * tmp16
    tmp18 = tmp14 + tmp17
    tmp20 = tmp6 - tmp12
    tmp21 = tmp19 * tmp20
    tmp22 = tmp18 + tmp21
    tmp24 = tmp9 - tmp12
    tmp25 = tmp23 * tmp24
    tmp26 = tmp22 + tmp25
    tmp27 = -tmp26
    tmp28 = tl.broadcast_to(tmp27, [XBLOCK, RBLOCK])
    tmp30 = tl.sum(tmp28, 1)[:, None]
    tmp31 = 64.0
    tmp32 = tmp30 / tmp31
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp32, 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, 4, 4, 4), (64, 16, 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
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1
        del buf1
        triton_per_fused__log_softmax_mean_mul_neg_sum_1[grid(1)](buf2,
            arg1_1, buf0, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg1_1
        del buf0
    return buf2,


def _is_long(x):
    return isinstance(x, torch.LongTensor) or isinstance(x, torch.LongTensor)


def onehot(indexes, N=None, ignore_index=None):
    """
    Creates a one-representation of indexes with N possible entries
    if N is not specified, it will suit the maximum index appearing.
    indexes is a long-tensor of indexes
    ignore_index will be zero in onehot representation
    """
    if N is None:
        N = indexes.max() + 1
    sz = list(indexes.size())
    output = indexes.new().byte().resize_(*sz, N).zero_()
    output.scatter_(-1, indexes.unsqueeze(-1), 1)
    if ignore_index is not None and ignore_index >= 0:
        output.masked_fill_(indexes.eq(ignore_index).unsqueeze(-1), 0)
    return output


def smoothing(out, y, smooth_eps):
    num_classes = out.shape[1]
    if smooth_eps == 0:
        return y
    my = onehot(y, num_classes)
    true_class, false_class = 1.0 - smooth_eps * num_classes / (num_classes - 1
        ), smooth_eps / (num_classes - 1)
    my = my * true_class + torch.ones_like(my) * false_class
    return my


def cross_entropy(logits, target, weight=None, ignore_index=-100, reduction
    ='mean', smooth_eps=0.0):
    """cross entropy loss with support for target distributions"""
    with torch.no_grad():
        if smooth_eps > 0:
            target = smoothing(logits, target, smooth_eps)
    if _is_long(target):
        return F.cross_entropy(logits, target, weight, ignore_index=
            ignore_index, reduction=reduction)
    masked_indices = None
    logits.size(-1)
    if _is_long(target) and ignore_index >= 0:
        masked_indices = target.eq(ignore_index)
    lsm = F.log_softmax(logits, dim=-1)
    if weight is not None:
        lsm = lsm * weight.unsqueeze(0)
    loss = -(target * lsm).sum(-1)
    if masked_indices is not None:
        loss.masked_fill_(masked_indices, 0)
    if reduction == 'sum':
        loss = loss.sum()
    elif reduction == 'mean':
        if masked_indices is None:
            loss = loss.mean()
        else:
            loss = loss.sum() / float(loss.size(0) - masked_indices.sum())
    return loss


class CrossEntropyLossNew(nn.CrossEntropyLoss):
    """CrossEntropyLoss - with ability to recieve distrbution as targets and built-in label smoothing"""

    def __init__(self, weight=None, ignore_index=-100, reduction='mean',
        smooth_eps=0.0):
        super(CrossEntropyLossNew, self).__init__(weight=weight,
            ignore_index=ignore_index, reduction=reduction)
        self.smooth_eps = smooth_eps

    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: 0.027678