import torch
from torch import device
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._inductor.runtime.triton_helpers import math as tl_math
import torch.nn.functional as F
from torch import nn
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_add_exp_mul_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 80
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask)
    tmp2 = tl.load(in_ptr2 + x0, xmask)
    tmp3 = 0.5
    tmp4 = tmp2 * tmp3
    tmp5 = tl_math.exp(tmp4)
    tmp6 = tmp1 * tmp5
    tmp7 = tmp0 + tmp6
    tl.store(out_ptr0 + x0, tmp7, xmask)


@triton.jit
def triton_poi_fused_sigmoid_2(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=
            256, 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 = torch.ops.aten.randn.default([4, 20], dtype=torch.float32,
            device=device(type='cuda', index=0), pin_memory=False)
        buf5 = buf4
        del buf4
        buf6 = empty_strided_cuda((4, 20), (20, 1), torch.float32)
        triton_poi_fused_add_exp_mul_1[grid(80)](buf2, buf5, buf3, buf6, 80,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf7 = empty_strided_cuda((4, 400), (400, 1), torch.float32)
        extern_kernels.mm(buf6, reinterpret_tensor(primals_8, (20, 400), (1,
            20), 0), out=buf7)
        buf8 = buf7
        del buf7
        triton_poi_fused_relu_0[grid(1600)](buf8, primals_9, 1600, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_9
        buf9 = empty_strided_cuda((4, 784), (784, 1), torch.float32)
        extern_kernels.mm(buf8, reinterpret_tensor(primals_10, (400, 784),
            (1, 400), 0), out=buf9)
        buf10 = buf9
        del buf9
        triton_poi_fused_sigmoid_2[grid(3136)](buf10, primals_11, 3136,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_11
    return (buf10, buf2, buf3, primals_1, buf1, buf3, buf5, buf6, buf8,
        buf10, primals_10, primals_8, primals_6, primals_4)


class VAENew(nn.Module):
    """A classic VAE.

    Params
    ------
    input_dim : int
        The size of the (flattened) image vector 
    latent_dim : int
        The size of the latent memory    
    """

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

    def encode(self, x):
        """Encode a torch tensor (batch_size, input_size)"""
        x = x.view(-1, self.input_dim)
        h1 = F.relu(self.fc1(x))
        return self.fc21(h1), self.fc22(h1)

    def _reparameterize(self, mu, logvar):
        std = torch.exp(0.5 * logvar)
        eps = torch.randn_like(std)
        return mu + eps * std

    def decode(self, z):
        """Expand a latent memory, to input_size."""
        h3 = F.relu(self.fc3(z))
        return torch.sigmoid(self.fc4(h3))

    def sample(self, n, device=None):
        """Use noise to sample n images from latent space."""
        with torch.no_grad():
            x = torch.randn(n, self.latent_dim)
            x = x
            samples = self.decode(x)
            return samples

    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: 0.103481