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 import 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 = 0.25
    tmp16 = tmp14 * tmp15
    tmp17 = tl_math.exp(tmp16)
    tl.store(out_ptr0 + x3, tmp17, 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
    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 = 0.25
    tmp16 = tmp14 * tmp15
    tmp17 = tl_math.exp(tmp16)
    tmp18 = triton_helpers.maximum(tmp3, tmp5)
    tmp19 = triton_helpers.maximum(tmp18, tmp8)
    tmp20 = triton_helpers.maximum(tmp19, tmp11)
    tmp21 = tmp0 - tmp20
    tl.store(out_ptr0 + x3, tmp17, xmask)
    tl.store(out_ptr1 + x3, tmp21, xmask)


@triton.jit
def triton_per_fused__log_softmax__softmax_add_div_mul_neg_sub_sum_xlogy_2(
    in_out_ptr0, in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_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'
        )
    tmp3 = tl.load(in_ptr0 + (16 + r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (32 + r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp9 = tl.load(in_ptr0 + (48 + r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp14 = tl.load(in_ptr1 + r3, None)
    tmp19 = tl.load(in_ptr2 + r3, None)
    tmp20 = tl.load(in_ptr2 + (r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp21 = tl.load(in_ptr2 + (16 + r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp23 = tl.load(in_ptr2 + (32 + r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp25 = tl.load(in_ptr2 + (48 + r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp36 = tl.load(in_ptr3 + r3, None)
    tmp37 = tl.load(in_ptr3 + (r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp38 = tl.load(in_ptr3 + (16 + r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp40 = tl.load(in_ptr3 + (32 + r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp42 = tl.load(in_ptr3 + (48 + r0 + 64 * r2), 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 = tmp0 - tmp12
    tmp15 = tmp13 * tmp14
    tmp16 = tl.broadcast_to(tmp15, [RBLOCK])
    tmp18 = triton_helpers.promote_to_tensor(tl.sum(tmp16, 0))
    tmp22 = tmp20 + tmp21
    tmp24 = tmp22 + tmp23
    tmp26 = tmp24 + tmp25
    tmp27 = tmp19 / tmp26
    tmp28 = libdevice.isnan(tmp27).to(tl.int1)
    tmp29 = 0.0
    tmp30 = tmp27 == tmp29
    tmp31 = tl_math.log(tmp27)
    tmp32 = tmp27 * tmp31
    tmp33 = tl.where(tmp30, tmp29, tmp32)
    tmp34 = float('nan')
    tmp35 = tl.where(tmp28, tmp34, tmp33)
    tmp39 = tmp37 + tmp38
    tmp41 = tmp39 + tmp40
    tmp43 = tmp41 + tmp42
    tmp44 = tmp36 / tmp43
    tmp45 = tmp27 * tmp44
    tmp46 = tmp35 - tmp45
    tmp47 = tl.broadcast_to(tmp46, [RBLOCK])
    tmp49 = triton_helpers.promote_to_tensor(tl.sum(tmp47, 0))
    tmp50 = 0.25
    tmp51 = tmp49 * tmp50
    tmp52 = 16.0
    tmp53 = tmp51 * tmp52
    tmp54 = -tmp18
    tmp55 = 0.015625
    tmp56 = tmp54 * tmp55
    tmp57 = 4.0
    tmp58 = tmp53 * tmp57
    tmp59 = -3.0
    tmp60 = tmp56 * tmp59
    tmp61 = tmp58 + tmp60
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp53, None)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + tl.full([1], 0, tl.int32), tmp56, None)
    tl.store(out_ptr1 + tl.full([1], 0, tl.int32), tmp61, None)


def call(args):
    arg0_1, arg1_1, arg2_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))
    assert_size_stride(arg2_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)
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__log_softmax__softmax_1[grid(256)](arg1_1, buf2,
            buf5, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del arg1_1
        buf6 = empty_strided_cuda((), (), torch.float32)
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf3
        del buf3
        buf7 = buf6
        del buf6
        buf8 = empty_strided_cuda((), (), torch.float32)
        triton_per_fused__log_softmax__softmax_add_div_mul_neg_sub_sum_xlogy_2[
            grid(1)](buf4, buf7, buf5, arg2_1, buf0, buf2, buf8, 1, 256,
            num_warps=2, num_stages=1)
        del arg2_1
        del buf0
        del buf2
        del buf5
    return buf4, buf7, buf8


class DistillLossNew(nn.Module):

    def __init__(self, temperature, distillation_weight):
        super().__init__()
        self.temperature = temperature
        self.distillation_weight = distillation_weight
        self.kldiv = nn.KLDivLoss(reduction='batchmean')

    def forward(self, input_0, input_1, input_2):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        output = call([arg0_1, arg1_1, arg2_1])
        return output[0], output[1], output[2]


Measured latency_ms: 0.047904