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
from torch.nn.modules.loss import _Loss
from torch.optim.lr_scheduler 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__log_softmax__softmax_0(in_ptr0, out_ptr0, out_ptr1,
    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)
    tl.store(out_ptr1 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused__log_softmax__softmax_1(in_ptr0, out_ptr0, out_ptr1,
    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, tmp8, xmask)
    tl.store(out_ptr1 + x2, tmp9, xmask)


@triton.jit
def triton_red_fused__log_softmax__softmax_add_mean_mul_sub_xlogy_2(in_out_ptr0
    , in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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, :]
    _tmp34 = tl.full([XBLOCK, RBLOCK], 0, tl.float32)
    _tmp68 = 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)
        tmp20 = tl.load(in_ptr1 + (1 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp23 = tl.load(in_ptr1 + (2 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp26 = tl.load(in_ptr1 + (3 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp36 = tl.load(in_ptr2 + r2, rmask, eviction_policy='evict_first',
            other=0.0)
        tmp37 = tl.load(in_ptr2 + 4 * r1, rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp38 = tl.load(in_ptr2 + (1 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp40 = tl.load(in_ptr2 + (2 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp42 = tl.load(in_ptr2 + (3 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp51 = tl.load(in_ptr3 + r2, rmask, eviction_policy='evict_first',
            other=0.0)
        tmp52 = tl.load(in_ptr3 + 4 * r1, rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp54 = tl.load(in_ptr3 + (1 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp57 = tl.load(in_ptr3 + (2 + 4 * r1), rmask, eviction_policy=
            'evict_last', other=0.0)
        tmp60 = tl.load(in_ptr3 + (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)
        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, [XBLOCK, RBLOCK])
        tmp35 = _tmp34 + tmp33
        _tmp34 = tl.where(rmask, tmp35, _tmp34)
        tmp39 = tmp37 + tmp38
        tmp41 = tmp39 + tmp40
        tmp43 = tmp41 + tmp42
        tmp44 = tmp36 / tmp43
        tmp45 = libdevice.isnan(tmp44).to(tl.int1)
        tmp46 = tmp44 == tmp10
        tmp47 = tl_math.log(tmp44)
        tmp48 = tmp44 * tmp47
        tmp49 = tl.where(tmp46, tmp10, tmp48)
        tmp50 = tl.where(tmp45, tmp15, tmp49)
        tmp53 = tl_math.exp(tmp52)
        tmp55 = tl_math.exp(tmp54)
        tmp56 = tmp53 + tmp55
        tmp58 = tl_math.exp(tmp57)
        tmp59 = tmp56 + tmp58
        tmp61 = tl_math.exp(tmp60)
        tmp62 = tmp59 + tmp61
        tmp63 = tl_math.log(tmp62)
        tmp64 = tmp51 - tmp63
        tmp65 = tmp44 * tmp64
        tmp66 = tmp50 - tmp65
        tmp67 = tl.broadcast_to(tmp66, [XBLOCK, RBLOCK])
        tmp69 = _tmp68 + tmp67
        _tmp68 = tl.where(rmask, tmp69, _tmp68)
    tmp34 = tl.sum(_tmp34, 1)[:, None]
    tmp68 = tl.sum(_tmp68, 1)[:, None]
    tmp70 = 256.0
    tmp71 = tmp34 / tmp70
    tmp72 = tmp68 / tmp70
    tmp73 = tmp71 + tmp72
    tmp74 = 1.0
    tmp75 = tmp73 * tmp74
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp75, 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)
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__log_softmax__softmax_0[grid(256)](arg1_1, buf0,
            buf6, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del arg1_1
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__log_softmax__softmax_1[grid(256)](arg0_1, buf2,
            buf4, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del arg0_1
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf8 = buf3
        del buf3
        triton_red_fused__log_softmax__softmax_add_mean_mul_sub_xlogy_2[grid(1)
            ](buf8, buf0, buf2, buf4, buf6, 1, 256, XBLOCK=1, RBLOCK=256,
            num_warps=8, num_stages=1)
        del buf0
        del buf2
        del buf4
        del buf6
    return buf8,


class Criterion(_Loss):

    def __init__(self, alpha=1.0, name='criterion'):
        super().__init__()
        """Alpha is used to weight each loss term
        """
        self.alpha = alpha
        self.name = name

    def forward(self, input, target, weight=None, ignore_index=-1):
        """weight: sample weight
        """
        return


class SymKlCriterionNew(Criterion):

    def __init__(self, alpha=1.0, name='KL Div Criterion'):
        super().__init__()
        self.alpha = alpha
        self.name = name

    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: 0.039746