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 libdevice, math as tl_math
import torch.utils.data
import torch.nn as nn
from sklearn import *
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__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
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_red_fused__softmax_log_mean_mul_sub_xlogy_1(in_out_ptr0, in_ptr0,
    in_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr, RBLOCK: tl.constexpr):
    rnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rbase = tl.arange(0, RBLOCK)[None, :]
    _tmp30 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex
        r1 = rindex // 4
        tmp0 = tl.load(in_ptr0 + r2, rmask, eviction_policy='evict_first',
            other=0.0)
        tmp1 = tl.load(in_ptr0 + 4 * r1, rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp2 = tl.load(in_ptr0 + (1 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp4 = tl.load(in_ptr0 + (2 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp6 = tl.load(in_ptr0 + (3 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp17 = tl.load(in_ptr1 + r2, rmask, eviction_policy='evict_first',
            other=0.0)
        tmp18 = tl.load(in_ptr1 + 4 * r1, rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp19 = tl.load(in_ptr1 + (1 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp21 = tl.load(in_ptr1 + (2 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp23 = tl.load(in_ptr1 + (3 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp3 = tmp1 + tmp2
        tmp5 = tmp3 + tmp4
        tmp7 = tmp5 + tmp6
        tmp8 = tmp0 / tmp7
        tmp9 = libdevice.isnan(tmp8).to(tl.int1)
        tmp10 = 0.0
        tmp11 = tmp8 == tmp10
        tmp12 = tl_math.log(tmp8)
        tmp13 = tmp8 * tmp12
        tmp14 = tl.where(tmp11, tmp10, tmp13)
        tmp15 = float('nan')
        tmp16 = tl.where(tmp9, tmp15, tmp14)
        tmp20 = tmp18 + tmp19
        tmp22 = tmp20 + tmp21
        tmp24 = tmp22 + tmp23
        tmp25 = tmp17 / tmp24
        tmp26 = tl_math.log(tmp25)
        tmp27 = tmp8 * tmp26
        tmp28 = tmp16 - tmp27
        tmp29 = tl.broadcast_to(tmp28, [XBLOCK, RBLOCK])
        tmp31 = _tmp30 + tmp29
        _tmp30 = tl.where(rmask, tmp31, _tmp30)
    tmp30 = tl.sum(_tmp30, 1)[:, None]
    tmp32 = 256.0
    tmp33 = tmp30 / tmp32
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp33, 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__softmax_0[grid(256)](arg1_1, buf0, 256, XBLOCK=
            128, num_warps=4, num_stages=1)
        del arg1_1
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_0[grid(256)](arg0_1, buf2, 256, XBLOCK=
            128, num_warps=4, num_stages=1)
        del arg0_1
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf3
        del buf3
        triton_red_fused__softmax_log_mean_mul_sub_xlogy_1[grid(1)](buf4,
            buf0, buf2, 1, 256, XBLOCK=1, RBLOCK=256, num_warps=8, num_stages=1
            )
        del buf0
        del buf2
    return buf4,


class KLLossNew(nn.Module):
    """
        This criterion is a implemenation of Focal Loss, which is proposed in
        Focal Loss for Dense Object Detection.

            Loss(x, class) = - \\alpha (1-softmax(x)[class])^gamma \\log(softmax(x)[class])

        The losses are averaged across observations for each minibatch.

        Args:
            alpha(1D Tensor, Variable) : the scalar factor for this criterion
            gamma(float, double) : gamma > 0; reduces the relative loss for well-classiﬁed examples (p > .5),
                                   putting more focus on hard, misclassiﬁed examples
            size_average(bool): By default, the losses are averaged over observations for each minibatch.
                                However, if the field size_average is set to False, the losses are
                                instead summed for each minibatch.

    """

    def __init__(self, margin=0.0, size_average=None, reduce=None,
        reduction='mean'):
        super(KLLossNew, self).__init__()

    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: 0.014024