import torch
from torch._inductor.select_algorithm import extern_kernels
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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_add_exp_mul_0(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 64
    x1 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + (64 + x0 + 128 * x1), xmask)
    tmp6 = tl.load(in_ptr1 + (x0 + 128 * x1), xmask)
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = tl_math.exp(tmp3)
    tmp5 = tmp0 * tmp4
    tmp7 = tmp5 + tmp6
    tl.store(out_ptr0 + x2, tmp7, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (8, 4), (4, 1))
    assert_size_stride(primals_2, (8,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 8), (8, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 8), (1, 4), 0
            ), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 2, 4, 8), (64, 32, 8, 1), torch.float32)
        buf2 = torch.ops.aten.normal_functional.default(buf1)
        buf3 = buf2
        del buf2
        buf4 = buf1
        del buf1
        get_raw_stream(0)
        triton_poi_fused_add_exp_mul_0[grid(256)](buf3, buf0, buf4, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
    return buf4, reinterpret_tensor(buf0, (4, 2, 4, 8), (128, 32, 8, 1), 0
        ), reinterpret_tensor(buf0, (4, 2, 4, 8), (128, 32, 8, 1), 64
        ), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), reinterpret_tensor(buf0, (4, 2, 4, 8), (128, 32, 8, 1), 64), buf3


class DisentangledAELatentNew(torch.nn.Module):
    """Dense Dientangled Latent Layer between encoder and decoder"""

    def __init__(self, hidden_size: 'int', latent_size: 'int', dropout: 'float'
        ):
        super(DisentangledAELatentNew, self).__init__()
        self.latent_size = latent_size
        self.hidden_size = hidden_size
        self.dropout = dropout
        self.latent = torch.nn.Linear(self.hidden_size, self.latent_size * 2)

    @staticmethod
    def reparameterize(mu, logvar, training=True):
        if training:
            std = logvar.mul(0.5).exp_()
            eps = std.data.new(std.size()).normal_()
            return eps.mul(std).add_(mu)
        return mu

    def forward(self, input_0):
        primals_1 = self.latent.weight
        primals_2 = self.latent.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0], output[1], output[2]


Measured latency_ms: 0.038583