import torch
from torch.nn import functional as F
from torch import nn
import torch.nn


class VAE(nn.Module):

    def __init__(self, in_ch, out_ch, hidden_ch=128):
        super(VAE, self).__init__()
        self.in_ch = in_ch
        self.out_ch = out_ch
        self.fc1 = nn.Linear(in_ch, hidden_ch)
        self.fc21 = nn.Linear(hidden_ch, 20)
        self.fc22 = nn.Linear(hidden_ch, 20)
        self.fc3 = nn.Linear(20, hidden_ch)
        self.fc4 = nn.Linear(hidden_ch, out_ch)

    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, x):
        mu, logvar = self.encode(x.view(-1, self.in_ch))
        z = self.reparameterize(mu, logvar)
        return self.decode(z), mu, logvar


def get_inputs():
    return [torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {'in_ch': 4, 'out_ch': 4}]

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
import torch.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
    ):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, 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, None)


@triton.jit
def triton_poi_fused_add_exp_mul_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 1280
    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 = 256
    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.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, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (128, 4), (4, 1))
    assert_size_stride(primals_3, (128,), (1,))
    assert_size_stride(primals_4, (20, 128), (128, 1))
    assert_size_stride(primals_5, (20,), (1,))
    assert_size_stride(primals_6, (20, 128), (128, 1))
    assert_size_stride(primals_7, (20,), (1,))
    assert_size_stride(primals_8, (128, 20), (20, 1))
    assert_size_stride(primals_9, (128,), (1,))
    assert_size_stride(primals_10, (4, 128), (128, 1))
    assert_size_stride(primals_11, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 128), (128, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 128), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_relu_0[grid(8192)](buf1, primals_3, 8192, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((64, 20), (20, 1), torch.float32)
        extern_kernels.addmm(primals_5, buf1, reinterpret_tensor(primals_4,
            (128, 20), (1, 128), 0), alpha=1, beta=1, out=buf2)
        del primals_5
        buf3 = empty_strided_cuda((64, 20), (20, 1), torch.float32)
        extern_kernels.addmm(primals_7, buf1, reinterpret_tensor(primals_6,
            (128, 20), (1, 128), 0), alpha=1, beta=1, out=buf3)
        del primals_7
        buf4 = torch.ops.aten.randn.default([64, 20], dtype=torch.float32,
            device=device(type='cuda', index=0), pin_memory=False)
        buf5 = buf4
        del buf4
        buf6 = empty_strided_cuda((64, 20), (20, 1), torch.float32)
        triton_poi_fused_add_exp_mul_1[grid(1280)](buf2, buf5, buf3, buf6, 
            1280, XBLOCK=256, num_warps=4, num_stages=1)
        buf7 = empty_strided_cuda((64, 128), (128, 1), torch.float32)
        extern_kernels.mm(buf6, reinterpret_tensor(primals_8, (20, 128), (1,
            20), 0), out=buf7)
        buf8 = buf7
        del buf7
        triton_poi_fused_relu_0[grid(8192)](buf8, primals_9, 8192, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_9
        buf9 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf8, reinterpret_tensor(primals_10, (128, 4), (1,
            128), 0), out=buf9)
        buf10 = buf9
        del buf9
        triton_poi_fused_sigmoid_2[grid(256)](buf10, primals_11, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_11
    return (buf10, buf2, buf3, reinterpret_tensor(primals_1, (64, 4), (4, 1
        ), 0), buf1, buf3, buf5, buf6, buf8, buf10, primals_10, primals_8,
        primals_6, primals_4)


class VAENew(nn.Module):

    def __init__(self, in_ch, out_ch, hidden_ch=128):
        super(VAENew, self).__init__()
        self.in_ch = in_ch
        self.out_ch = out_ch
        self.fc1 = nn.Linear(in_ch, hidden_ch)
        self.fc21 = nn.Linear(hidden_ch, 20)
        self.fc22 = nn.Linear(hidden_ch, 20)
        self.fc3 = nn.Linear(20, hidden_ch)
        self.fc4 = nn.Linear(hidden_ch, out_ch)

    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]
