# 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/sr/csrxdjbtbkq5mhx4lx76hdeti625uy52jalpuc5xjwghomvl635m.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   x => relu
# Graph fragment:
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {})
#   %le_4 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_0 = async_compile.triton('triton_poi_fused_relu_threshold_backward_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=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_relu_threshold_backward_0(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 12800
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 200
    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)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + (x2), tmp4, xmask)
    tl.store(out_ptr0 + (x2), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ez/ceztobygf5essmzvewpzrqmyjudhdyx544g5o4kv24svt3edt7ho.py
# Topologically Sorted Source Nodes: [sigma, mul, z], Original ATen: [aten.exp, aten.mul, aten.add]
# Source node to ATen node mapping:
#   mul => mul
#   sigma => exp
#   z => add
# Graph fragment:
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%view_7,), 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 = (%mul, %view_5), kwargs = {})
triton_poi_fused_add_exp_mul_1 = async_compile.triton('triton_poi_fused_add_exp_mul_1', '''
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_exp_mul_1', '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_exp_mul_1(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)
    tmp4 = tl.load(in_ptr2 + (x0), xmask)
    tmp2 = tl_math.exp(tmp1)
    tmp3 = tmp0 * tmp2
    tmp5 = tmp3 + tmp4
    tl.store(out_ptr0 + (x0), tmp5, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/xu/cxumxlwfsjttpymgclelijugt7vsd7q33fi23c6apfmh2szgsxcy.py
# Topologically Sorted Source Nodes: [x_4, reconstruction], Original ATen: [aten.relu, aten.sigmoid, aten.threshold_backward]
# Source node to ATen node mapping:
#   reconstruction => sigmoid
#   x_4 => relu_4
# Graph fragment:
#   %relu_4 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_14,), kwargs = {})
#   %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%relu_4,), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_4, 0), kwargs = {})
triton_poi_fused_relu_sigmoid_threshold_backward_2 = async_compile.triton('triton_poi_fused_relu_sigmoid_threshold_backward_2', '''
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: '*i1', 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_relu_sigmoid_threshold_backward_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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_relu_sigmoid_threshold_backward_2(in_ptr0, in_ptr1, out_ptr0, out_ptr1, 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_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = tl.sigmoid(tmp4)
    tmp6 = 0.0
    tmp7 = tmp4 <= tmp6
    tl.store(out_ptr0 + (x2), tmp5, xmask)
    tl.store(out_ptr1 + (x2), 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, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15 = args
    args.clear()
    assert_size_stride(primals_1, (200, 4), (4, 1))
    assert_size_stride(primals_2, (200, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (200, 200), (200, 1))
    assert_size_stride(primals_5, (200, ), (1, ))
    assert_size_stride(primals_6, (4, 200), (200, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    assert_size_stride(primals_8, (4, 200), (200, 1))
    assert_size_stride(primals_9, (4, ), (1, ))
    assert_size_stride(primals_10, (200, 4), (4, 1))
    assert_size_stride(primals_11, (200, ), (1, ))
    assert_size_stride(primals_12, (200, 200), (200, 1))
    assert_size_stride(primals_13, (200, ), (1, ))
    assert_size_stride(primals_14, (4, 200), (200, 1))
    assert_size_stride(primals_15, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 200), (200, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 200), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 200), (3200, 800, 200, 1), 0); del buf0  # reuse
        buf19 = empty_strided_cuda((4, 4, 4, 200), (3200, 800, 200, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.relu, aten.threshold_backward]
        stream0 = get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0.run(buf1, primals_2, buf19, 12800, grid=grid(12800), stream=stream0)
        del primals_2
        buf2 = empty_strided_cuda((64, 200), (200, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 200), (200, 1), 0), reinterpret_tensor(primals_4, (200, 200), (1, 200), 0), out=buf2)
        buf3 = reinterpret_tensor(buf2, (4, 4, 4, 200), (3200, 800, 200, 1), 0); del buf2  # reuse
        buf18 = empty_strided_cuda((4, 4, 4, 200), (3200, 800, 200, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_0.run(buf3, primals_5, buf18, 12800, grid=grid(12800), stream=stream0)
        del primals_5
        buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mu], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (64, 200), (200, 1), 0), reinterpret_tensor(primals_6, (200, 4), (1, 200), 0), alpha=1, beta=1, out=buf4)
        del primals_7
        buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [logsigma], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_9, reinterpret_tensor(buf3, (64, 200), (200, 1), 0), reinterpret_tensor(primals_8, (200, 4), (1, 200), 0), alpha=1, beta=1, out=buf5)
        del primals_9
        # Topologically Sorted Source Nodes: [eps], Original ATen: [aten.randn_like]
        buf6 = torch.ops.aten.randn.default([4, 4, 4, 4], dtype=torch.float32, device=device(type='cuda', index=0), pin_memory=False)
        buf7 = buf6
        del buf6
        buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sigma, mul, z], Original ATen: [aten.exp, aten.mul, aten.add]
        triton_poi_fused_add_exp_mul_1.run(buf7, buf5, buf4, buf8, 256, grid=grid(256), stream=stream0)
        buf9 = empty_strided_cuda((64, 200), (200, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf8, (64, 4), (4, 1), 0), reinterpret_tensor(primals_10, (4, 200), (1, 4), 0), out=buf9)
        buf10 = reinterpret_tensor(buf9, (4, 4, 4, 200), (3200, 800, 200, 1), 0); del buf9  # reuse
        buf17 = empty_strided_cuda((4, 4, 4, 200), (3200, 800, 200, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_0.run(buf10, primals_11, buf17, 12800, grid=grid(12800), stream=stream0)
        del primals_11
        buf11 = empty_strided_cuda((64, 200), (200, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf10, (64, 200), (200, 1), 0), reinterpret_tensor(primals_12, (200, 200), (1, 200), 0), out=buf11)
        buf12 = reinterpret_tensor(buf11, (4, 4, 4, 200), (3200, 800, 200, 1), 0); del buf11  # reuse
        buf16 = empty_strided_cuda((4, 4, 4, 200), (3200, 800, 200, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_0.run(buf12, primals_13, buf16, 12800, grid=grid(12800), stream=stream0)
        del primals_13
        buf13 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf12, (64, 200), (200, 1), 0), reinterpret_tensor(primals_14, (200, 4), (1, 200), 0), out=buf13)
        buf14 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf15 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x_4, reconstruction], Original ATen: [aten.relu, aten.sigmoid, aten.threshold_backward]
        triton_poi_fused_relu_sigmoid_threshold_backward_2.run(buf13, primals_15, buf14, buf15, 256, grid=grid(256), stream=stream0)
        del buf13
        del primals_15
    return (buf14, reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(buf1, (64, 200), (200, 1), 0), reinterpret_tensor(buf3, (64, 200), (200, 1), 0), reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0), buf7, reinterpret_tensor(buf8, (64, 4), (4, 1), 0), reinterpret_tensor(buf10, (64, 200), (200, 1), 0), reinterpret_tensor(buf12, (64, 200), (200, 1), 0), buf14, buf15, primals_14, buf16, primals_12, buf17, primals_10, primals_8, primals_6, buf18, primals_4, buf19, )


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((200, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((200, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((200, 200), (200, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((200, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 200), (200, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, 200), (200, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((200, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((200, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((200, 200), (200, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((200, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((4, 200), (200, 1), device='cuda:0', dtype=torch.float32)
    primals_15 = 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, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15])
    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
from torch import nn
from torch.nn import functional as F
import torch.utils.data


class Decoder(nn.Module):
    """ VAE decoder """

    def __init__(self, in_channels, latent_size):
        super(Decoder, self).__init__()
        self.latent_size = latent_size
        self.in_channels = in_channels
        self.fc_dec1 = nn.Linear(latent_size, 200)
        self.fc_dec2 = nn.Linear(200, 200)
        self.fc_dec3 = nn.Linear(200, self.in_channels)

    def forward(self, x):
        x = F.relu(self.fc_dec1(x))
        x = F.relu(self.fc_dec2(x))
        x = F.relu(self.fc_dec3(x))
        reconstruction = F.sigmoid(x)
        return reconstruction


class Encoder(nn.Module):
    """ VAE encoder """

    def __init__(self, in_channels, latent_size):
        super(Encoder, self).__init__()
        self.latent_size = latent_size
        self.in_channels = in_channels
        self.fc_enc1 = nn.Linear(self.in_channels, 200)
        self.fc_enc2 = nn.Linear(200, 200)
        self.fc_mu = nn.Linear(200, latent_size)
        self.fc_logsigma = nn.Linear(200, latent_size)

    def forward(self, x):
        x = F.relu(self.fc_enc1(x))
        x = F.relu(self.fc_enc2(x))
        mu = self.fc_mu(x)
        logsigma = self.fc_logsigma(x)
        return mu, logsigma


class VAE(nn.Module):
    """ Variational Autoencoder """

    def __init__(self, in_channels, latent_size):
        super(VAE, self).__init__()
        self.encoder = Encoder(in_channels, latent_size)
        self.decoder = Decoder(in_channels, latent_size)

    def forward(self, x):
        mu, logsigma = self.encoder(x)
        sigma = logsigma.exp()
        eps = torch.randn_like(sigma)
        z = eps.mul(sigma).add_(mu)
        recon_x = self.decoder(z)
        return recon_x, mu, logsigma


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


def get_init_inputs():
    return [[], {'in_channels': 4, 'latent_size': 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 import nn
from torch.nn import 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_threshold_backward_0(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 12800
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 200
    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)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_add_exp_mul_1(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)
    tmp4 = tl.load(in_ptr2 + x0, xmask)
    tmp2 = tl_math.exp(tmp1)
    tmp3 = tmp0 * tmp2
    tmp5 = tmp3 + tmp4
    tl.store(out_ptr0 + x0, tmp5, xmask)


@triton.jit
def triton_poi_fused_relu_sigmoid_threshold_backward_2(in_ptr0, in_ptr1,
    out_ptr0, out_ptr1, 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_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = tl.sigmoid(tmp4)
    tmp6 = 0.0
    tmp7 = tmp4 <= tmp6
    tl.store(out_ptr0 + x2, tmp5, xmask)
    tl.store(out_ptr1 + x2, tmp7, 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, (200, 4), (4, 1))
    assert_size_stride(primals_2, (200,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (200, 200), (200, 1))
    assert_size_stride(primals_5, (200,), (1,))
    assert_size_stride(primals_6, (4, 200), (200, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 200), (200, 1))
    assert_size_stride(primals_9, (4,), (1,))
    assert_size_stride(primals_10, (200, 4), (4, 1))
    assert_size_stride(primals_11, (200,), (1,))
    assert_size_stride(primals_12, (200, 200), (200, 1))
    assert_size_stride(primals_13, (200,), (1,))
    assert_size_stride(primals_14, (4, 200), (200, 1))
    assert_size_stride(primals_15, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 200), (200, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 200), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4, 200), (3200, 800, 200, 1), 0)
        del buf0
        buf19 = empty_strided_cuda((4, 4, 4, 200), (3200, 800, 200, 1),
            torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(12800)](buf1,
            primals_2, buf19, 12800, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((64, 200), (200, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf1, (64, 200), (200, 1), 0),
            reinterpret_tensor(primals_4, (200, 200), (1, 200), 0), out=buf2)
        buf3 = reinterpret_tensor(buf2, (4, 4, 4, 200), (3200, 800, 200, 1), 0)
        del buf2
        buf18 = empty_strided_cuda((4, 4, 4, 200), (3200, 800, 200, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_0[grid(12800)](buf3,
            primals_5, buf18, 12800, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (64, 200),
            (200, 1), 0), reinterpret_tensor(primals_6, (200, 4), (1, 200),
            0), alpha=1, beta=1, out=buf4)
        del primals_7
        buf5 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_9, reinterpret_tensor(buf3, (64, 200),
            (200, 1), 0), reinterpret_tensor(primals_8, (200, 4), (1, 200),
            0), alpha=1, beta=1, out=buf5)
        del primals_9
        buf6 = torch.ops.aten.randn.default([4, 4, 4, 4], dtype=torch.
            float32, device=device(type='cuda', index=0), pin_memory=False)
        buf7 = buf6
        del buf6
        buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_exp_mul_1[grid(256)](buf7, buf5, buf4, buf8, 
            256, XBLOCK=128, num_warps=4, num_stages=1)
        buf9 = empty_strided_cuda((64, 200), (200, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf8, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_10, (4, 200), (1, 4), 0), out=buf9)
        buf10 = reinterpret_tensor(buf9, (4, 4, 4, 200), (3200, 800, 200, 1), 0
            )
        del buf9
        buf17 = empty_strided_cuda((4, 4, 4, 200), (3200, 800, 200, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_0[grid(12800)](buf10,
            primals_11, buf17, 12800, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_11
        buf11 = empty_strided_cuda((64, 200), (200, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf10, (64, 200), (200, 1), 0),
            reinterpret_tensor(primals_12, (200, 200), (1, 200), 0), out=buf11)
        buf12 = reinterpret_tensor(buf11, (4, 4, 4, 200), (3200, 800, 200, 
            1), 0)
        del buf11
        buf16 = empty_strided_cuda((4, 4, 4, 200), (3200, 800, 200, 1),
            torch.bool)
        triton_poi_fused_relu_threshold_backward_0[grid(12800)](buf12,
            primals_13, buf16, 12800, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_13
        buf13 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf12, (64, 200), (200, 1), 0),
            reinterpret_tensor(primals_14, (200, 4), (1, 200), 0), out=buf13)
        buf14 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf15 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_relu_sigmoid_threshold_backward_2[grid(256)](buf13,
            primals_15, buf14, buf15, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del buf13
        del primals_15
    return (buf14, reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0),
        reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
        reinterpret_tensor(buf1, (64, 200), (200, 1), 0),
        reinterpret_tensor(buf3, (64, 200), (200, 1), 0),
        reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0), buf7,
        reinterpret_tensor(buf8, (64, 4), (4, 1), 0), reinterpret_tensor(
        buf10, (64, 200), (200, 1), 0), reinterpret_tensor(buf12, (64, 200),
        (200, 1), 0), buf14, buf15, primals_14, buf16, primals_12, buf17,
        primals_10, primals_8, primals_6, buf18, primals_4, buf19)


class Decoder(nn.Module):
    """ VAE decoder """

    def __init__(self, in_channels, latent_size):
        super(Decoder, self).__init__()
        self.latent_size = latent_size
        self.in_channels = in_channels
        self.fc_dec1 = nn.Linear(latent_size, 200)
        self.fc_dec2 = nn.Linear(200, 200)
        self.fc_dec3 = nn.Linear(200, self.in_channels)

    def forward(self, x):
        x = F.relu(self.fc_dec1(x))
        x = F.relu(self.fc_dec2(x))
        x = F.relu(self.fc_dec3(x))
        reconstruction = F.sigmoid(x)
        return reconstruction


class Encoder(nn.Module):
    """ VAE encoder """

    def __init__(self, in_channels, latent_size):
        super(Encoder, self).__init__()
        self.latent_size = latent_size
        self.in_channels = in_channels
        self.fc_enc1 = nn.Linear(self.in_channels, 200)
        self.fc_enc2 = nn.Linear(200, 200)
        self.fc_mu = nn.Linear(200, latent_size)
        self.fc_logsigma = nn.Linear(200, latent_size)

    def forward(self, x):
        x = F.relu(self.fc_enc1(x))
        x = F.relu(self.fc_enc2(x))
        mu = self.fc_mu(x)
        logsigma = self.fc_logsigma(x)
        return mu, logsigma


class VAENew(nn.Module):
    """ Variational Autoencoder """

    def __init__(self, in_channels, latent_size):
        super(VAENew, self).__init__()
        self.encoder = Encoder(in_channels, latent_size)
        self.decoder = Decoder(in_channels, latent_size)

    def forward(self, input_0):
        primals_1 = self.encoder.fc_enc1.weight
        primals_2 = self.encoder.fc_enc1.bias
        primals_4 = self.encoder.fc_enc2.weight
        primals_5 = self.encoder.fc_enc2.bias
        primals_6 = self.encoder.fc_mu.weight
        primals_7 = self.encoder.fc_mu.bias
        primals_8 = self.encoder.fc_logsigma.weight
        primals_9 = self.encoder.fc_logsigma.bias
        primals_10 = self.decoder.fc_dec1.weight
        primals_11 = self.decoder.fc_dec1.bias
        primals_12 = self.decoder.fc_dec2.weight
        primals_13 = self.decoder.fc_dec2.bias
        primals_14 = self.decoder.fc_dec3.weight
        primals_15 = self.decoder.fc_dec3.bias
        primals_3 = 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]
