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
from torch.nn import 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 = 8
    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_sigmoid_backward_2(in_out_ptr0, in_ptr0,
    out_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)
    tmp4 = 1.0
    tmp5 = tmp4 - tmp3
    tmp6 = tmp3 * tmp5
    tl.store(in_out_ptr0 + x2, tmp3, xmask)
    tl.store(out_ptr0 + x2, tmp6, 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, (2, 400), (400, 1))
    assert_size_stride(primals_5, (2,), (1,))
    assert_size_stride(primals_6, (2, 400), (400, 1))
    assert_size_stride(primals_7, (2,), (1,))
    assert_size_stride(primals_8, (400, 2), (2, 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, 2), (2, 1), torch.float32)
        extern_kernels.addmm(primals_5, buf1, reinterpret_tensor(primals_4,
            (400, 2), (1, 400), 0), alpha=1, beta=1, out=buf2)
        del primals_5
        buf3 = empty_strided_cuda((4, 2), (2, 1), torch.float32)
        extern_kernels.addmm(primals_7, buf1, reinterpret_tensor(primals_6,
            (400, 2), (1, 400), 0), alpha=1, beta=1, out=buf3)
        del primals_7
        buf4 = torch.ops.aten.randn.default([4, 2], dtype=torch.float32,
            device=device(type='cuda', index=0), pin_memory=False)
        buf5 = buf4
        del buf4
        buf6 = empty_strided_cuda((4, 2), (2, 1), torch.float32)
        triton_poi_fused_add_exp_mul_1[grid(8)](buf2, buf5, buf3, buf6, 8,
            XBLOCK=8, num_warps=1, num_stages=1)
        buf7 = empty_strided_cuda((4, 400), (400, 1), torch.float32)
        extern_kernels.mm(buf6, reinterpret_tensor(primals_8, (2, 400), (1,
            2), 0), out=buf7)
        buf8 = buf7
        del buf7
        triton_poi_fused_relu_0[grid(1600)](buf8, primals_9, 1600, XBLOCK=
            128, 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
        buf11 = empty_strided_cuda((4, 784), (784, 1), torch.float32)
        triton_poi_fused_sigmoid_sigmoid_backward_2[grid(3136)](buf10,
            primals_11, buf11, 3136, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_11
    return (reinterpret_tensor(buf10, (4, 1, 28, 28), (784, 784, 28, 1), 0),
        buf2, buf3, primals_1, buf1, buf3, buf5, buf6, buf8, buf11,
        primals_10, primals_8, primals_6, primals_4)


class BaseVAE(nn.Module):
    """
    Base abstract class for the Variational Autoencoders
    """

    def __init__(self, channels=1, width=28, height=28, z_dim=2):
        """
        Constructor

        Parameters:
            channels - The number of channels for the image
            width - The width of the image in pixels
            height - The height of the image in pixels
            z_dim - The dimension of the latent space
        """
        super(BaseVAE, self).__init__()
        self.channels = channels
        self.width = width
        self.height = height
        self.z_dim = z_dim

    def getNbChannels(self):
        """
        Returns the number of channels of the handled images
        """
        return self.channels

    def getWidth(self):
        """
        Returns the width of the handled images in pixels
        """
        return self.width

    def getHeight(self):
        """
        Returns the height of the handled images in pixels
        """
        return self.height

    def getZDim(self):
        """
        Returns the dimension of the latent space of the VAE
        """
        return self.z_dim

    def flatten(self, x):
        """
        Can be used to flatten the output image. This method will only handle
        images of the original size specified for the network
        """
        return x.view(-1, self.channels * self.height * self.width)

    def unflatten(self, x):
        """
        Can be used to unflatten an image handled by the network. This method
        will only handle images of the original size specified for the network
        """
        return x.view(-1, self.channels, self.height, self.width)


class FCVAENew(BaseVAE):
    """
    Fully connected Variational Autoencoder
    """

    def __init__(self, channels=1, width=28, height=28, z_dim=2):
        super(FCVAENew, self).__init__(channels, width, height, z_dim)
        self.fc1 = nn.Linear(self.channels * self.width * self.height, 400)
        self.fc21 = nn.Linear(400, self.z_dim)
        self.fc22 = nn.Linear(400, self.z_dim)
        self.fc3 = nn.Linear(self.z_dim, 400)
        self.fc4 = nn.Linear(400, self.channels * self.width * self.height)

    def encode(self, x):
        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):
        h3 = F.relu(self.fc3(z))
        return torch.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: 0.107899