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.nn as nn
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
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp3 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp5 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp8 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp11 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp4 = tmp3 * tmp1
    tmp6 = tmp5 * tmp1
    tmp7 = triton_helpers.maximum(tmp4, tmp6)
    tmp9 = tmp8 * tmp1
    tmp10 = triton_helpers.maximum(tmp7, tmp9)
    tmp12 = tmp11 * tmp1
    tmp13 = triton_helpers.maximum(tmp10, tmp12)
    tmp14 = tmp2 - tmp13
    tmp15 = tmp14 * tmp1
    tmp16 = tl_math.exp(tmp15)
    tl.store(out_ptr0 + x3, tmp16, xmask)


@triton.jit
def triton_poi_fused_1(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)
    tmp3 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp5 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp8 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp11 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp4 = tmp3 * tmp1
    tmp6 = tmp5 * tmp1
    tmp7 = triton_helpers.maximum(tmp4, tmp6)
    tmp9 = tmp8 * tmp1
    tmp10 = triton_helpers.maximum(tmp7, tmp9)
    tmp12 = tmp11 * tmp1
    tmp13 = triton_helpers.maximum(tmp10, tmp12)
    tmp14 = tmp2 - tmp13
    tmp15 = tmp14 * tmp1
    tl.store(out_ptr0 + x3, tmp15, xmask)


@triton.jit
def triton_per_fused__log_softmax__softmax_div_mul_sub_sum_xlogy_2(in_out_ptr0,
    in_ptr0, in_ptr1, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r3 = rindex
    r0 = rindex % 16
    r2 = rindex // 64
    tmp0 = tl.load(in_ptr0 + r3, None)
    tmp1 = tl.load(in_ptr0 + (r0 + 64 * r2), None, eviction_policy='evict_last'
        )
    tmp2 = tl.load(in_ptr0 + (16 + r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp17 = tl.load(in_ptr1 + r3, None)
    tmp18 = tl.load(in_ptr1 + (r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp20 = tl.load(in_ptr1 + (16 + r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp23 = tl.load(in_ptr1 + (32 + r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp26 = tl.load(in_ptr1 + (48 + r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    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)
    tmp19 = tl_math.exp(tmp18)
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp19 + tmp21
    tmp24 = tl_math.exp(tmp23)
    tmp25 = tmp22 + tmp24
    tmp27 = tl_math.exp(tmp26)
    tmp28 = tmp25 + tmp27
    tmp29 = tl_math.log(tmp28)
    tmp30 = tmp17 - tmp29
    tmp31 = tmp8 * tmp30
    tmp32 = tmp16 - tmp31
    tmp33 = tl.broadcast_to(tmp32, [RBLOCK])
    tmp35 = triton_helpers.promote_to_tensor(tl.sum(tmp33, 0))
    tmp36 = 0.25
    tmp37 = tmp35 * tmp36
    tmp38 = 1.0
    tmp39 = tmp37 * tmp38
    tmp40 = tmp39 * tmp38
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp40, 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)](arg0_1, buf0, 256, XBLOCK=
            256, num_warps=4, num_stages=1)
        del arg0_1
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_1[grid(256)](arg1_1, buf2, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del arg1_1
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf3
        del buf3
        triton_per_fused__log_softmax__softmax_div_mul_sub_sum_xlogy_2[grid(1)
            ](buf4, buf0, buf2, 1, 256, num_warps=2, num_stages=1)
        del buf0
        del buf2
    return buf4,


class KLDivergenceNew(nn.Module):
    """A measure of how one probability distribution Q is different from a
    second, reference probability distribution P.

    Args:
        tau (float): Temperature coefficient. Defaults to 1.0.
        reduction (str): Specifies the reduction to apply to the loss:
            ``'none'`` | ``'batchmean'`` | ``'sum'`` | ``'mean'``.
            ``'none'``: no reduction will be applied,
            ``'batchmean'``: the sum of the output will be divided by
                the batchsize,
            ``'sum'``: the output will be summed,
            ``'mean'``: the output will be divided by the number of
                elements in the output.
            Default: ``'batchmean'``
        loss_weight (float): Weight of loss. Defaults to 1.0.
    """

    def __init__(self, tau=1.0, reduction='batchmean', loss_weight=1.0):
        super(KLDivergenceNew, self).__init__()
        self.tau = tau
        self.loss_weight = loss_weight
        accept_reduction = {'none', 'batchmean', 'sum', 'mean'}
        assert reduction in accept_reduction, f'KLDivergence supports reduction {accept_reduction}, but gets {reduction}.'
        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: 0.039892