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.triton_helpers import math as tl_math
import torch.nn as nn
import torch.nn.functional
import torch.nn.parallel
import torch.utils.data
import torch.optim
import torch.utils.data.distributed
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_per_fused_add_exp_mean_mul_pow_sub_sum_0(in_out_ptr0, in_ptr0,
    in_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex % 16
    r1 = rindex // 16
    tmp0 = tl.load(in_ptr0 + (r0 + 64 * r1), None)
    tmp2 = tl.load(in_ptr1 + (r0 + 64 * r1), None)
    tmp10 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
    tmp12 = tl.load(in_ptr1 + (16 + r0 + 64 * r1), None)
    tmp19 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
    tmp21 = tl.load(in_ptr1 + (32 + r0 + 64 * r1), None)
    tmp28 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None)
    tmp30 = tl.load(in_ptr1 + (48 + r0 + 64 * r1), None)
    tmp1 = tmp0 * tmp0
    tmp3 = tl_math.exp(tmp2)
    tmp4 = tmp1 + tmp3
    tmp5 = tmp4 - tmp2
    tmp6 = 1.0
    tmp7 = tmp5 - tmp6
    tmp8 = 0.5
    tmp9 = tmp7 * tmp8
    tmp11 = tmp10 * tmp10
    tmp13 = tl_math.exp(tmp12)
    tmp14 = tmp11 + tmp13
    tmp15 = tmp14 - tmp12
    tmp16 = tmp15 - tmp6
    tmp17 = tmp16 * tmp8
    tmp18 = tmp9 + tmp17
    tmp20 = tmp19 * tmp19
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tmp20 + tmp22
    tmp24 = tmp23 - tmp21
    tmp25 = tmp24 - tmp6
    tmp26 = tmp25 * tmp8
    tmp27 = tmp18 + tmp26
    tmp29 = tmp28 * tmp28
    tmp31 = tl_math.exp(tmp30)
    tmp32 = tmp29 + tmp31
    tmp33 = tmp32 - tmp30
    tmp34 = tmp33 - tmp6
    tmp35 = tmp34 * tmp8
    tmp36 = tmp27 + tmp35
    tmp37 = tl.broadcast_to(tmp36, [XBLOCK, RBLOCK])
    tmp39 = tl.sum(tmp37, 1)[:, None]
    tmp40 = 64.0
    tmp41 = tmp39 / tmp40
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp41, 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)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1
        del buf1
        get_raw_stream(0)
        triton_per_fused_add_exp_mean_mul_pow_sub_sum_0[grid(1)](buf2,
            arg0_1, arg1_1, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf2,


class VAE_Kl_LossNew(nn.Module):

    def __init__(self, if_print=False):
        super(VAE_Kl_LossNew, self).__init__()
        self.if_print = if_print

    def standard_KL_loss(self, means, variances):
        loss_KL = torch.mean(torch.sum(0.5 * (means ** 2 + torch.exp(
            variances) - variances - 1), dim=1))
        return loss_KL

    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: 0.015319