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
import torch.nn.functional as F
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_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 4
    x2 = xindex // 16
    tmp0 = tl.load(in_ptr0 + (64 + x3), xmask)
    tmp3 = tl.load(in_ptr0 + (64 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp5 = tl.load(in_ptr0 + (68 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp8 = tl.load(in_ptr0 + (72 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp11 = tl.load(in_ptr0 + (76 + x0 + 16 * 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
    tl.store(out_ptr0 + x3, tmp16, xmask)


@triton.jit
def triton_poi_fused__log_softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 4
    x2 = xindex // 16
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (4 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (8 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp9 = tl.load(in_ptr0 + (12 + x0 + 16 * x2), xmask, 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
    tl.store(out_ptr0 + x3, tmp13, xmask)


@triton.jit
def triton_per_fused__log_softmax__softmax_add_div_mse_loss_mul_sub_sum_xlogy_3(
    in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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
    r4 = 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')
    tmp16 = tl.load(in_ptr1 + r3, None)
    tmp17 = tl.load(in_ptr1 + (r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp19 = tl.load(in_ptr1 + (16 + r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp22 = tl.load(in_ptr1 + (32 + r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp25 = tl.load(in_ptr1 + (48 + r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp36 = tl.load(in_ptr2 + r3, None)
    tmp37 = tl.load(in_ptr2 + (r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp38 = tl.load(in_ptr2 + (16 + r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp40 = tl.load(in_ptr2 + (32 + r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp42 = tl.load(in_ptr2 + (48 + r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp53 = tl.load(in_ptr3 + r4, None, eviction_policy='evict_last')
    tmp2 = tmp1 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp6
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp9
    tmp11 = tmp8 + tmp10
    tmp12 = libdevice.sqrt(tmp11)
    tmp13 = 1e-12
    tmp14 = triton_helpers.maximum(tmp12, tmp13)
    tmp15 = tmp0 / tmp14
    tmp18 = tmp17 * tmp17
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp23 = tmp22 * tmp22
    tmp24 = tmp21 + tmp23
    tmp26 = tmp25 * tmp25
    tmp27 = tmp24 + tmp26
    tmp28 = libdevice.sqrt(tmp27)
    tmp29 = triton_helpers.maximum(tmp28, tmp13)
    tmp30 = tmp16 / tmp29
    tmp31 = tmp15 - tmp30
    tmp32 = tmp31 * tmp31
    tmp33 = tl.broadcast_to(tmp32, [RBLOCK])
    tmp35 = triton_helpers.promote_to_tensor(tl.sum(tmp33, 0))
    tmp39 = tmp37 + tmp38
    tmp41 = tmp39 + tmp40
    tmp43 = tmp41 + tmp42
    tmp44 = tmp36 / tmp43
    tmp45 = libdevice.isnan(tmp44).to(tl.int1)
    tmp46 = 0.0
    tmp47 = tmp44 == tmp46
    tmp48 = tl_math.log(tmp44)
    tmp49 = tmp44 * tmp48
    tmp50 = tl.where(tmp47, tmp46, tmp49)
    tmp51 = float('nan')
    tmp52 = tl.where(tmp45, tmp51, tmp50)
    tmp54 = tmp44 * tmp53
    tmp55 = tmp52 - tmp54
    tmp56 = tl.broadcast_to(tmp55, [RBLOCK])
    tmp58 = triton_helpers.promote_to_tensor(tl.sum(tmp56, 0))
    tmp59 = 16.0
    tmp60 = tmp58 * tmp59
    tmp61 = 0.25
    tmp62 = tmp60 * tmp61
    tmp63 = 1.0
    tmp64 = tmp62 * tmp63
    tmp65 = tmp35 + tmp64
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp65, None)


def call(args):
    arg0_1, arg1_1, arg2_1, arg3_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))
    assert_size_stride(arg3_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_0[grid(256)](arg3_1, buf2, 256, XBLOCK=
            128, num_warps=4, num_stages=1)
        del arg3_1
        buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_1[grid(64)](arg2_1, buf4, 64, XBLOCK=64, num_warps
            =1, num_stages=1)
        del arg2_1
        buf5 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__log_softmax_2[grid(64)](buf4, buf5, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del buf4
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf7 = buf1
        del buf1
        triton_per_fused__log_softmax__softmax_add_div_mse_loss_mul_sub_sum_xlogy_3[
            grid(1)](buf7, arg0_1, arg1_1, buf2, buf5, 1, 256, num_warps=2,
            num_stages=1)
        del arg0_1
        del arg1_1
        del buf2
        del buf5
    return buf7,


class KDTH(nn.Module):
    """KD with a Teacher Head auxiliary loss"""

    def __init__(self, T=4):
        super(KDTH, self).__init__()
        self.T = T

    def forward(self, y_s, y_t):
        y_s_th = y_s[1]
        y_s = y_s[0]
        p_t = F.softmax(y_t / self.T, dim=1)
        p_s_th = F.log_softmax(y_s_th / self.T, dim=1)
        loss_th = F.kl_div(p_s_th, p_t, size_average=False
            ) * self.T ** 2 / y_s.shape[0]
        return loss_th


class KDE(nn.Module):
    """KD on embeddings - KDE"""

    def __init__(self):
        super(KDE, self).__init__()

    def forward(self, embedding_s, embedding_t):
        inputs_embed = F.normalize(embedding_s, p=2.0, dim=1)
        targets_embed = F.normalize(embedding_t, p=2.0, dim=1)
        loss_kde = nn.MSELoss(reduction='sum')(inputs_embed, targets_embed)
        return loss_kde


class KDETHNew(nn.Module):
    """Combination of KDE and TH"""

    def __init__(self, T=4, th_weight=1.0):
        super(KDETHNew, self).__init__()
        self.kde = KDE()
        self.kdth = KDTH(T=T)
        self.th_weight = th_weight

    def forward(self, input_0, input_1, input_2, input_3):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        arg3_1 = input_3
        output = call([arg0_1, arg1_1, arg2_1, arg3_1])
        return output[0]


Measured latency_ms: 0.015157