# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_4/inductor_cache/2q/c2quadsftlq5nrg5tvyhve5hvqdfzowpmbi3ggojqyhfneakzg62.py
# Topologically Sorted Source Nodes: [truediv, std, mul, z], Original ATen: [aten.div, aten.exp, aten.mul, aten.add]
# Source node to ATen node mapping:
#   mul => mul
#   std => exp
#   truediv => div
#   z => add
# Graph fragment:
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%view_3, 2), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%div,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%randn, %exp), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_1, %mul), kwargs = {})
triton_poi_fused_add_div_exp_mul_0 = async_compile.triton('triton_poi_fused_add_div_exp_mul_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_div_exp_mul_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_div_exp_mul_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    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)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4, ), (1, ))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mu], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_3, reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del primals_2
        del primals_3
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [log_var], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf1)
        del primals_4
        del primals_5
        # Topologically Sorted Source Nodes: [eps], Original ATen: [aten.randn_like]
        buf2 = torch.ops.aten.randn.default([4, 4, 4, 4], dtype=torch.float32, device=device(type='cuda', index=0), pin_memory=False)
        buf3 = buf2
        del buf2
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [truediv, std, mul, z], Original ATen: [aten.div, aten.exp, aten.mul, aten.add]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_div_exp_mul_0.run(buf0, buf3, buf1, buf4, 256, grid=grid(256), stream=stream0)
        buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [_theta], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf4, (64, 4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf5)
        del primals_7
        buf6 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [hid], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_9, buf5, reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf6)
        del primals_9
    return (reinterpret_tensor(buf6, (4, 4, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0), buf3, reinterpret_tensor(buf4, (64, 4), (4, 1), 0), buf5, primals_8, primals_6, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

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


class VAE(nn.Module):

    def __init__(self, encode_dims, decode_dims, dropout=0.0):
        super(VAE, self).__init__()
        self.encoder = nn.ModuleDict({f'enc_{i}': nn.Linear(encode_dims[i],
            encode_dims[i + 1]) for i in range(len(encode_dims) - 2)})
        self.fc_mu = nn.Linear(encode_dims[-2], encode_dims[-1])
        self.fc_logvar = nn.Linear(encode_dims[-2], encode_dims[-1])
        self.decoder = nn.ModuleDict({f'dec_{i}': nn.Linear(decode_dims[i],
            decode_dims[i + 1]) for i in range(len(decode_dims) - 1)})
        self.latent_dim = encode_dims[-1]
        self.dropout = nn.Dropout(p=dropout)
        self.fc1 = nn.Linear(encode_dims[-1], encode_dims[-1])

    def encode(self, x):
        hid = x
        for i, layer in self.encoder.items():
            hid = F.relu(self.dropout(layer(hid)))
        mu, log_var = self.fc_mu(hid), self.fc_logvar(hid)
        return mu, log_var

    def inference(self, x):
        _mu, _log_var = self.encode(x)
        theta = torch.softmax(x, dim=1)
        return theta

    def reparameterize(self, mu, log_var):
        std = torch.exp(log_var / 2)
        eps = torch.randn_like(std)
        z = mu + eps * std
        return z

    def decode(self, z):
        hid = z
        for i, (_, layer) in enumerate(self.decoder.items()):
            hid = layer(hid)
            if i < len(self.decoder) - 1:
                hid = F.relu(self.dropout(hid))
        return hid

    def forward(self, x, collate_fn=None):
        mu, log_var = self.encode(x)
        _theta = self.reparameterize(mu, log_var)
        _theta = self.fc1(_theta)
        if collate_fn is not None:
            theta = collate_fn(_theta)
        else:
            theta = _theta
        x_reconst = self.decode(theta)
        return x_reconst, mu, log_var


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


def get_init_inputs():
    return [[], {'encode_dims': [4, 4], 'decode_dims': [4, 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.triton_helpers import math as tl_math
import torch.nn as nn
import torch.nn.functional as F
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_add_div_exp_mul_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    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)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_3, reinterpret_tensor(primals_1, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf0)
        del primals_2
        del primals_3
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(primals_1, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0
            ), alpha=1, beta=1, out=buf1)
        del primals_4
        del primals_5
        buf2 = torch.ops.aten.randn.default([4, 4, 4, 4], dtype=torch.
            float32, device=device(type='cuda', index=0), pin_memory=False)
        buf3 = buf2
        del buf2
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_div_exp_mul_0[grid(256)](buf0, buf3, buf1,
            buf4, 256, XBLOCK=128, num_warps=4, num_stages=1)
        buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf4, (64, 4), (
            4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf5)
        del primals_7
        buf6 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_9, buf5, reinterpret_tensor(primals_8,
            (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf6)
        del primals_9
    return reinterpret_tensor(buf6, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), reinterpret_tensor(primals_1, (64, 4), (4, 1), 0
        ), reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), buf3, reinterpret_tensor(buf4, (64, 4), (4, 1), 0
        ), buf5, primals_8, primals_6


class VAENew(nn.Module):

    def __init__(self, encode_dims, decode_dims, dropout=0.0):
        super(VAENew, self).__init__()
        self.encoder = nn.ModuleDict({f'enc_{i}': nn.Linear(encode_dims[i],
            encode_dims[i + 1]) for i in range(len(encode_dims) - 2)})
        self.fc_mu = nn.Linear(encode_dims[-2], encode_dims[-1])
        self.fc_logvar = nn.Linear(encode_dims[-2], encode_dims[-1])
        self.decoder = nn.ModuleDict({f'dec_{i}': nn.Linear(decode_dims[i],
            decode_dims[i + 1]) for i in range(len(decode_dims) - 1)})
        self.latent_dim = encode_dims[-1]
        self.dropout = nn.Dropout(p=dropout)
        self.fc1 = nn.Linear(encode_dims[-1], encode_dims[-1])

    def encode(self, x):
        hid = x
        for i, layer in self.encoder.items():
            hid = F.relu(self.dropout(layer(hid)))
        mu, log_var = self.fc_mu(hid), self.fc_logvar(hid)
        return mu, log_var

    def inference(self, x):
        _mu, _log_var = self.encode(x)
        theta = torch.softmax(x, dim=1)
        return theta

    def reparameterize(self, mu, log_var):
        std = torch.exp(log_var / 2)
        eps = torch.randn_like(std)
        z = mu + eps * std
        return z

    def decode(self, z):
        hid = z
        for i, (_, layer) in enumerate(self.decoder.items()):
            hid = layer(hid)
            if i < len(self.decoder) - 1:
                hid = F.relu(self.dropout(hid))
        return hid

    def forward(self, input_0):
        primals_2 = self.fc_mu.weight
        primals_3 = self.fc_mu.bias
        primals_4 = self.fc_logvar.weight
        primals_5 = self.fc_logvar.bias
        primals_6 = self.decoder.dec_0.weight
        primals_7 = self.decoder.dec_0.bias
        primals_8 = self.fc1.weight
        primals_9 = self.fc1.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])
        return output[0], output[1], output[2]
