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 import triton_helpers
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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 1600
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 400
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_sigmoid_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 3136
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 784
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.sigmoid(tmp2)
    tl.store(in_out_ptr0 + x2, tmp3, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11) = args
    args.clear()
    assert_size_stride(primals_1, (4, 784), (784, 1))
    assert_size_stride(primals_2, (400, 784), (784, 1))
    assert_size_stride(primals_3, (400,), (1,))
    assert_size_stride(primals_4, (20, 400), (400, 1))
    assert_size_stride(primals_5, (20,), (1,))
    assert_size_stride(primals_6, (20, 400), (400, 1))
    assert_size_stride(primals_7, (20,), (1,))
    assert_size_stride(primals_8, (400, 20), (20, 1))
    assert_size_stride(primals_9, (400,), (1,))
    assert_size_stride(primals_10, (784, 400), (400, 1))
    assert_size_stride(primals_11, (784,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 400), (400, 1), torch.float32)
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (784, 
            400), (1, 784), 0), out=buf0)
        del primals_2
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_relu_0[grid(1600)](buf1, primals_3, 1600, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((4, 20), (20, 1), torch.float32)
        extern_kernels.addmm(primals_5, buf1, reinterpret_tensor(primals_4,
            (400, 20), (1, 400), 0), alpha=1, beta=1, out=buf2)
        del primals_5
        buf3 = empty_strided_cuda((4, 20), (20, 1), torch.float32)
        extern_kernels.addmm(primals_7, buf1, reinterpret_tensor(primals_6,
            (400, 20), (1, 400), 0), alpha=1, beta=1, out=buf3)
        del primals_7
        buf4 = empty_strided_cuda((4, 400), (400, 1), torch.float32)
        extern_kernels.mm(buf2, reinterpret_tensor(primals_8, (20, 400), (1,
            20), 0), out=buf4)
        buf5 = buf4
        del buf4
        triton_poi_fused_relu_0[grid(1600)](buf5, primals_9, 1600, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_9
        buf6 = empty_strided_cuda((4, 784), (784, 1), torch.float32)
        extern_kernels.mm(buf5, reinterpret_tensor(primals_10, (400, 784),
            (1, 400), 0), out=buf6)
        buf7 = buf6
        del buf6
        triton_poi_fused_sigmoid_1[grid(3136)](buf7, primals_11, 3136,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_11
    return (buf7, buf2, buf3, primals_1, buf1, buf2, buf5, buf7, primals_10,
        primals_8, primals_6, primals_4)


class VAENew(nn.Module):

    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(784, 400)
        self.fc21 = nn.Linear(400, 20)
        self.fc22 = nn.Linear(400, 20)
        self.fc3 = nn.Linear(20, 400)
        self.fc4 = nn.Linear(400, 784)

    def encode(self, x):
        h1 = F.relu(self.fc1(x))
        return self.fc21(h1), self.fc22(h1)

    def reparameterize(self, mu, logvar):
        if self.training:
            std = torch.exp(0.5 * logvar)
            eps = torch.randn_like(std)
            return eps.mul(std).add_(mu)
        else:
            return mu

    def decode(self, z):
        h3 = F.relu(self.fc3(z))
        return F.sigmoid(self.fc4(h3))

    def forward(self, input_0):
        primals_2 = self.fc1.weight
        primals_3 = self.fc1.bias
        primals_4 = self.fc21.weight
        primals_5 = self.fc21.bias
        primals_6 = self.fc22.weight
        primals_7 = self.fc22.bias
        primals_8 = self.fc3.weight
        primals_9 = self.fc3.bias
        primals_10 = self.fc4.weight
        primals_11 = self.fc4.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11])
        return output[0], output[1], output[2]


Measured latency_ms: 0.070917