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 import Module
from torch.nn.modules.module import Module
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
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
    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_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 % 16
    r1 = rindex // 16
    tmp0 = tl.load(in_ptr0 + (r0 + 64 * r1), None)
    tmp2 = tl.load(in_ptr1 + (r0 + 64 * r1), None)
    tmp4 = tl.load(in_ptr1 + (16 + r0 + 64 * r1), None)
    tmp7 = tl.load(in_ptr1 + (32 + r0 + 64 * r1), None)
    tmp10 = tl.load(in_ptr1 + (48 + r0 + 64 * r1), None)
    tmp16 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
    tmp21 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
    tmp26 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None)
    tmp1 = -tmp0
    tmp3 = tl_math.exp(tmp2)
    tmp5 = tl_math.exp(tmp4)
    tmp6 = tmp3 + tmp5
    tmp8 = tl_math.exp(tmp7)
    tmp9 = tmp6 + tmp8
    tmp11 = tl_math.exp(tmp10)
    tmp12 = tmp9 + tmp11
    tmp13 = tl_math.log(tmp12)
    tmp14 = tmp2 - tmp13
    tmp15 = tmp1 * tmp14
    tmp17 = -tmp16
    tmp18 = tmp4 - tmp13
    tmp19 = tmp17 * tmp18
    tmp20 = tmp15 + tmp19
    tmp22 = -tmp21
    tmp23 = tmp7 - tmp13
    tmp24 = tmp22 * tmp23
    tmp25 = tmp20 + tmp24
    tmp27 = -tmp26
    tmp28 = tmp10 - tmp13
    tmp29 = tmp27 * tmp28
    tmp30 = tmp25 + tmp29
    tmp31 = tl.broadcast_to(tmp30, [XBLOCK, RBLOCK])
    tmp33 = tl.sum(tmp31, 1)[:, None]
    tmp34 = 64.0
    tmp35 = tmp33 / tmp34
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp35, 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)](arg1_1, buf0, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del arg1_1
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf3 = buf2
        del buf2
        triton_per_fused__log_softmax_mean_mul_neg_sum_1[grid(1)](buf3,
            arg0_1, buf0, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del buf0
    return buf3,


def cross_entropy(input, target, size_average=True):
    """ Cross entropy that accepts soft targets
    Args:
         pred: predictions for neural network
         targets: targets, can be soft
         size_average: if false, sum is returned instead of mean

    Examples::

        input = torch.FloatTensor([[1.1, 2.8, 1.3], [1.1, 2.1, 4.8]])
        input = torch.autograd.Variable(out, requires_grad=True)

        target = torch.FloatTensor([[0.05, 0.9, 0.05], [0.05, 0.05, 0.9]])
        target = torch.autograd.Variable(y1)
        loss = cross_entropy(input, target)
        loss.backward()
    """
    logsoftmax = torch.nn.LogSoftmax(dim=1)
    if size_average:
        return torch.mean(torch.sum(-target * logsoftmax(input), dim=1))
    else:
        return torch.sum(torch.sum(-target * logsoftmax(input), dim=1))


class CutMixCrossEntropyLossNew(Module):

    def __init__(self, size_average=True):
        super().__init__()
        self.size_average = size_average

    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: 0.027907