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 as nn
import torch.nn.functional as F
import torch.utils.data
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 = 20000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 5000
    x1 = xindex // 5000
    tmp0 = tl.load(in_out_ptr0 + (x0 + 5024 * x1), 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 + (x0 + 5024 * x1), tmp4, xmask)


@triton.jit
def triton_poi_fused_zero_1(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 = 0.0
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused_add_exp_mul_2(in_ptr0, in_ptr1, 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 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = tl_math.exp(tmp3)
    tmp5 = tmp0 * tmp4
    tl.store(out_ptr0 + x0, tmp5, 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,
        primals_13, primals_14, primals_15) = args
    args.clear()
    assert_size_stride(primals_1, (4, 784), (784, 1))
    assert_size_stride(primals_2, (5000, 784), (784, 1))
    assert_size_stride(primals_3, (5000,), (1,))
    assert_size_stride(primals_4, (5000, 5000), (5000, 1))
    assert_size_stride(primals_5, (5000,), (1,))
    assert_size_stride(primals_6, (20, 5000), (5000, 1))
    assert_size_stride(primals_7, (20,), (1,))
    assert_size_stride(primals_8, (20, 5000), (5000, 1))
    assert_size_stride(primals_9, (20,), (1,))
    assert_size_stride(primals_10, (5000, 20), (20, 1))
    assert_size_stride(primals_11, (5000,), (1,))
    assert_size_stride(primals_12, (5000, 5000), (5000, 1))
    assert_size_stride(primals_13, (5000,), (1,))
    assert_size_stride(primals_14, (784, 5000), (5000, 1))
    assert_size_stride(primals_15, (784,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 5000), (5024, 1), torch.float32)
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (784, 
            5000), (1, 784), 0), out=buf0)
        del primals_2
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_relu_0[grid(20000)](buf1, primals_3, 20000, XBLOCK
            =256, num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((4, 5000), (5024, 1), torch.float32)
        extern_kernels.mm(buf1, reinterpret_tensor(primals_4, (5000, 5000),
            (1, 5000), 0), out=buf2)
        buf3 = buf2
        del buf2
        triton_poi_fused_relu_0[grid(20000)](buf3, primals_5, 20000, XBLOCK
            =256, num_warps=4, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((4, 20), (20, 1), torch.float32)
        extern_kernels.addmm(primals_9, buf3, reinterpret_tensor(primals_8,
            (5000, 20), (1, 5000), 0), alpha=1, beta=1, out=buf4)
        del primals_9
        buf5 = empty_strided_cuda((4, 20), (20, 1), torch.float32)
        triton_poi_fused_zero_1[grid(80)](buf5, 80, XBLOCK=128, num_warps=4,
            num_stages=1)
        buf6 = torch.ops.aten.randn.default([4, 20], dtype=torch.float32,
            device=device(type='cuda', index=0), pin_memory=False)
        buf7 = buf6
        del buf6
        buf8 = empty_strided_cuda((4, 20), (20, 1), torch.float32)
        triton_poi_fused_add_exp_mul_2[grid(80)](buf7, buf4, buf8, 80,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf9 = empty_strided_cuda((4, 5000), (5024, 1), torch.float32)
        extern_kernels.mm(buf8, reinterpret_tensor(primals_10, (20, 5000),
            (1, 20), 0), out=buf9)
        buf10 = buf9
        del buf9
        triton_poi_fused_relu_0[grid(20000)](buf10, primals_11, 20000,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_11
        buf11 = empty_strided_cuda((4, 5000), (5024, 1), torch.float32)
        extern_kernels.mm(buf10, reinterpret_tensor(primals_12, (5000, 5000
            ), (1, 5000), 0), out=buf11)
        buf12 = buf11
        del buf11
        triton_poi_fused_relu_0[grid(20000)](buf12, primals_13, 20000,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_13
        buf13 = empty_strided_cuda((4, 784), (784, 1), torch.float32)
        extern_kernels.mm(buf12, reinterpret_tensor(primals_14, (5000, 784),
            (1, 5000), 0), out=buf13)
        buf14 = buf13
        del buf13
        triton_poi_fused_sigmoid_3[grid(3136)](buf14, primals_15, 3136,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_15
    return (buf14, buf5, buf4, primals_1, buf1, buf3, buf4, buf7, buf8,
        buf10, buf12, buf14, primals_14, primals_12, primals_10, primals_8,
        primals_4)


class VAENew(nn.Module):

    def __init__(self):
        super(VAENew, self).__init__()
        self.fc1 = nn.Linear(784, 5000)
        self.fc2 = nn.Linear(5000, 5000)
        self.fc21 = nn.Linear(5000, 20)
        self.fc22 = nn.Linear(5000, 20)
        self.fc3 = nn.Linear(20, 5000)
        self.fc32 = nn.Linear(5000, 5000)
        self.fc4 = nn.Linear(5000, 784)

    def encode(self, x):
        h1 = F.relu(self.fc1(x))
        h1 = F.relu(self.fc2(h1))
        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))
        h3 = F.relu(self.fc32(h3))
        return torch.sigmoid(self.fc4(h3))

    def forward(self, input_0):
        primals_2 = self.fc1.weight
        primals_3 = self.fc1.bias
        primals_4 = self.fc2.weight
        primals_5 = self.fc2.bias
        primals_6 = self.fc21.weight
        primals_7 = self.fc21.bias
        primals_8 = self.fc22.weight
        primals_9 = self.fc22.bias
        primals_10 = self.fc3.weight
        primals_11 = self.fc3.bias
        primals_12 = self.fc32.weight
        primals_13 = self.fc32.bias
        primals_14 = self.fc4.weight
        primals_15 = 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, primals_12, primals_13, primals_14,
            primals_15])
        return output[0], output[1], output[2]


Measured latency_ms: 0.029007