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
import torch.utils.data
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_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 3152
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 788
    x1 = xindex // 788
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 784, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (784 * x1 + x0), tmp4 & xmask, eviction_policy
        ='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 788, tl.int64)
    tmp9 = tl.load(in_ptr1 + (4 * x1 + (-784 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    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_cat_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 8
    x1 = xindex // 8
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_poi_fused_sigmoid_3(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, primals_12
        ) = args
    args.clear()
    assert_size_stride(primals_1, (4, 784), (784, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 788), (788, 1))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4, 8), (8, 1))
    assert_size_stride(primals_10, (4,), (1,))
    assert_size_stride(primals_11, (784, 4), (4, 1))
    assert_size_stride(primals_12, (784,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 788), (788, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(3152)](primals_1, primals_2, buf0, 3152,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf0, reinterpret_tensor(primals_3, (788, 4), (1,
            788), 0), out=buf1)
        del primals_3
        buf2 = buf1
        del buf1
        triton_poi_fused_relu_1[grid(16)](buf2, primals_4, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_4
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_6, buf2, reinterpret_tensor(primals_5,
            (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf3)
        del primals_6
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_8, buf2, reinterpret_tensor(primals_7,
            (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf4)
        del primals_8
        buf5 = empty_strided_cuda((4, 8), (8, 1), torch.float32)
        triton_poi_fused_cat_2[grid(32)](buf3, primals_2, buf5, 32, XBLOCK=
            32, num_warps=1, num_stages=1)
        del primals_2
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf5, reinterpret_tensor(primals_9, (8, 4), (1, 8
            ), 0), out=buf6)
        buf7 = buf6
        del buf6
        triton_poi_fused_relu_1[grid(16)](buf7, primals_10, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_10
        buf8 = empty_strided_cuda((4, 784), (784, 1), torch.float32)
        extern_kernels.mm(buf7, reinterpret_tensor(primals_11, (4, 784), (1,
            4), 0), out=buf8)
        buf9 = buf8
        del buf8
        triton_poi_fused_sigmoid_3[grid(3136)](buf9, primals_12, 3136,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_12
    return (buf9, buf3, buf4, buf0, buf2, buf5, buf7, buf9, primals_11,
        primals_9, primals_7, primals_5)


class CVAENew(nn.Module):

    def __init__(self, conditional_size, hidden_size, latent_size):
        super(CVAENew, self).__init__()
        self.fc1 = nn.Linear(28 * 28 + conditional_size, hidden_size)
        self.fc21 = nn.Linear(hidden_size, latent_size)
        self.fc22 = nn.Linear(hidden_size, latent_size)
        self.fc3 = nn.Linear(latent_size + conditional_size, hidden_size)
        self.fc4 = nn.Linear(hidden_size, 28 * 28)
        self.relu = nn.ReLU()
        self.sigmoid = nn.Sigmoid()

    def encode(self, x, c):
        inputs = torch.cat([x, c], 1)
        h1 = self.relu(self.fc1(inputs))
        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, c):
        inputs = torch.cat([z, c], 1)
        h3 = self.relu(self.fc3(inputs))
        return self.sigmoid(self.fc4(h3))

    def forward(self, input_0, input_1):
        primals_3 = self.fc1.weight
        primals_4 = self.fc1.bias
        primals_2 = self.fc21.weight
        primals_6 = self.fc21.bias
        primals_5 = self.fc22.weight
        primals_8 = self.fc22.bias
        primals_9 = self.fc3.weight
        primals_10 = self.fc3.bias
        primals_11 = self.fc4.weight
        primals_12 = self.fc4.bias
        primals_1 = input_0
        primals_7 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12])
        return output[0], output[1], output[2]


Measured latency_ms: 0.100117