# 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/dq/cdq2ys2ixsgbmto6z3gv6fap6g6tzfirrsvo2mvozuimf2d5feuy.py
# Topologically Sorted Source Nodes: [conv2d, x], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d => convolution
#   x => relu
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [2, 2], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
triton_poi_fused_convolution_relu_0 = async_compile.triton('triton_poi_fused_convolution_relu_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=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_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_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 123008
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 961) % 32
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), 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 + (x3), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/m2/cm2tzhnnikquck5qjqxil3z7xbvb4bmlv4dxtpfnobmoa2gn7kpo.py
# Topologically Sorted Source Nodes: [conv2d_1, x_1], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_1 => convolution_1
#   x_1 => relu_1
# Graph fragment:
#   %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu, %primals_4, %primals_5, [2, 2], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_1 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_1,), kwargs = {})
triton_poi_fused_convolution_relu_1 = async_compile.triton('triton_poi_fused_convolution_relu_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=[32768], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_1', '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_convolution_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 25088
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 196) % 32
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), 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 + (x3), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/yl/cylhzvm5azwoxpfzq6cmn4wrmtveszsjfaehpp6d3sldzewuqoy4.py
# Topologically Sorted Source Nodes: [conv2d_2, x_2], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   conv2d_2 => convolution_2
#   x_2 => relu_2
# Graph fragment:
#   %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_1, %primals_6, %primals_7, [2, 2], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_2 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_2,), kwargs = {})
triton_poi_fused_convolution_relu_2 = async_compile.triton('triton_poi_fused_convolution_relu_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=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_2', '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_convolution_relu_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 9216
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 36) % 64
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), 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 + (x3), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/7x/c7x644ci2kcridm32huqyaq4krk2jeobv5q45sjg2zadi4i5xifb.py
# Topologically Sorted Source Nodes: [conv2d_3, x_3], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   conv2d_3 => convolution_3
#   x_3 => relu_3
# Graph fragment:
#   %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_2, %primals_8, %primals_9, [2, 2], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %relu_3 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution_3,), kwargs = {})
#   %le_4 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_3, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_3 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_3', '''
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=[1024], 
    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_convolution_relu_threshold_backward_3', '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_convolution_relu_threshold_backward_3(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 4) % 64
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), 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 + (x3), tmp4, xmask)
    tl.store(out_ptr0 + (x3), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/5q/c5qlaxbbxxxofs4euqefep4bsfkrxrk3yorlgw53njxbvityagxo.py
# Topologically Sorted Source Nodes: [div, std, mul, z], Original ATen: [aten.div, aten.exp, aten.mul, aten.add]
# Source node to ATen node mapping:
#   div => div
#   mul => mul
#   std => exp
#   z => add
# Graph fragment:
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%slice_4, 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 = (%exp, %normal_functional), kwargs = {})
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%slice_2, %mul), kwargs = {})
triton_poi_fused_add_div_exp_mul_4 = async_compile.triton('triton_poi_fused_add_div_exp_mul_4', '''
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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_div_exp_mul_4', '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_4(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 40
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 10
    x1 = (xindex // 10)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (20*x1)), xmask)
    tmp1 = tl.load(in_ptr0 + (10 + x0 + (20*x1)), xmask)
    tmp5 = tl.load(in_ptr1 + (x2), xmask)
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = tl_math.exp(tmp3)
    tmp6 = tmp4 * tmp5
    tmp7 = tmp0 + tmp6
    tl.store(out_ptr0 + (x2), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/du/cduf6krob444andnckdtxb622t46y6p2ense7shwixlcd3vtk2mf.py
# Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   x_5 => relu_4
# Graph fragment:
#   %add_tensor : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm_default, %primals_13), kwargs = {})
#   %relu_4 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%add_tensor,), kwargs = {})
#   %le_3 : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu_4, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_5 = async_compile.triton('triton_poi_fused_relu_threshold_backward_5', '''
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=[1024], 
    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_5', '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_5(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 256
    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/dk/cdkqzxyla22osetdivjgvawrv3ugrhoc4pphb4useu7rir2oxw7c.py
# Topologically Sorted Source Nodes: [x_10], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   x_10 => convolution_7
# Graph fragment:
#   %convolution_7 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_7, %primals_20, %primals_21, [2, 2], [0, 0], [1, 1], True, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_6 = async_compile.triton('triton_poi_fused_convolution_6', '''
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=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_6', '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_convolution_6(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 49152
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 4096) % 3
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, None)
''', 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, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21 = args
    args.clear()
    assert_size_stride(primals_1, (32, 3, 4, 4), (48, 16, 4, 1))
    assert_size_stride(primals_2, (32, ), (1, ))
    assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_4, (32, 32, 4, 4), (512, 16, 4, 1))
    assert_size_stride(primals_5, (32, ), (1, ))
    assert_size_stride(primals_6, (64, 32, 4, 4), (512, 16, 4, 1))
    assert_size_stride(primals_7, (64, ), (1, ))
    assert_size_stride(primals_8, (64, 64, 4, 4), (1024, 16, 4, 1))
    assert_size_stride(primals_9, (64, ), (1, ))
    assert_size_stride(primals_10, (20, 256), (256, 1))
    assert_size_stride(primals_11, (20, ), (1, ))
    assert_size_stride(primals_12, (256, 10), (10, 1))
    assert_size_stride(primals_13, (256, ), (1, ))
    assert_size_stride(primals_14, (64, 64, 4, 4), (1024, 16, 4, 1))
    assert_size_stride(primals_15, (64, ), (1, ))
    assert_size_stride(primals_16, (64, 32, 4, 4), (512, 16, 4, 1))
    assert_size_stride(primals_17, (32, ), (1, ))
    assert_size_stride(primals_18, (32, 32, 4, 4), (512, 16, 4, 1))
    assert_size_stride(primals_19, (32, ), (1, ))
    assert_size_stride(primals_20, (32, 3, 4, 4), (48, 16, 4, 1))
    assert_size_stride(primals_21, (3, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 32, 31, 31), (30752, 961, 31, 1))
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [conv2d, x], Original ATen: [aten.convolution, aten.relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_relu_0.run(buf1, primals_2, 123008, grid=grid(123008), stream=stream0)
        del primals_2
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 32, 14, 14), (6272, 196, 14, 1))
        buf3 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [conv2d_1, x_1], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_1.run(buf3, primals_5, 25088, grid=grid(25088), stream=stream0)
        del primals_5
        # Topologically Sorted Source Nodes: [conv2d_2], Original ATen: [aten.convolution]
        buf4 = extern_kernels.convolution(buf3, primals_6, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 64, 6, 6), (2304, 36, 6, 1))
        buf5 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [conv2d_2, x_2], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_2.run(buf5, primals_7, 9216, grid=grid(9216), stream=stream0)
        del primals_7
        # Topologically Sorted Source Nodes: [conv2d_3], Original ATen: [aten.convolution]
        buf6 = extern_kernels.convolution(buf5, primals_8, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 64, 2, 2), (256, 4, 2, 1))
        buf7 = buf6; del buf6  # reuse
        buf24 = empty_strided_cuda((4, 64, 2, 2), (256, 4, 2, 1), torch.bool)
        # Topologically Sorted Source Nodes: [conv2d_3, x_3], Original ATen: [aten.convolution, aten.relu, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_3.run(buf7, primals_9, buf24, 1024, grid=grid(1024), stream=stream0)
        del primals_9
        buf8 = empty_strided_cuda((4, 20), (20, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_11, reinterpret_tensor(buf7, (4, 256), (256, 1), 0), reinterpret_tensor(primals_10, (256, 20), (1, 256), 0), alpha=1, beta=1, out=buf8)
        del primals_11
        buf9 = empty_strided_cuda((4, 10), (10, 1), torch.float32)
        # Topologically Sorted Source Nodes: [eps], Original ATen: [aten.normal_functional]
        buf10 = torch.ops.aten.normal_functional.default(buf9)
        buf11 = buf10
        del buf10
        buf12 = buf9; del buf9  # reuse
        # Topologically Sorted Source Nodes: [div, std, mul, z], Original ATen: [aten.div, aten.exp, aten.mul, aten.add]
        triton_poi_fused_add_div_exp_mul_4.run(buf8, buf11, buf12, 40, grid=grid(40), stream=stream0)
        buf13 = empty_strided_cuda((4, 256), (256, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(buf12, reinterpret_tensor(primals_12, (10, 256), (1, 10), 0), out=buf13)
        buf14 = buf13; del buf13  # reuse
        buf23 = empty_strided_cuda((4, 256), (256, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_5.run(buf14, primals_13, buf23, 1024, grid=grid(1024), stream=stream0)
        del primals_13
        # Topologically Sorted Source Nodes: [conv_transpose2d], Original ATen: [aten.convolution]
        buf15 = extern_kernels.convolution(reinterpret_tensor(buf14, (4, 64, 2, 2), (256, 4, 2, 1), 0), primals_14, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf15, (4, 64, 6, 6), (2304, 36, 6, 1))
        buf16 = buf15; del buf15  # reuse
        # Topologically Sorted Source Nodes: [conv_transpose2d, x_7], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_2.run(buf16, primals_15, 9216, grid=grid(9216), stream=stream0)
        del primals_15
        # Topologically Sorted Source Nodes: [conv_transpose2d_1], Original ATen: [aten.convolution]
        buf17 = extern_kernels.convolution(buf16, primals_16, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf17, (4, 32, 14, 14), (6272, 196, 14, 1))
        buf18 = buf17; del buf17  # reuse
        # Topologically Sorted Source Nodes: [conv_transpose2d_1, x_8], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_1.run(buf18, primals_17, 25088, grid=grid(25088), stream=stream0)
        del primals_17
        # Topologically Sorted Source Nodes: [conv_transpose2d_2], Original ATen: [aten.convolution]
        buf19 = extern_kernels.convolution(buf18, primals_18, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=True, output_padding=(1, 1), groups=1, bias=None)
        assert_size_stride(buf19, (4, 32, 31, 31), (30752, 961, 31, 1))
        buf20 = buf19; del buf19  # reuse
        # Topologically Sorted Source Nodes: [conv_transpose2d_2, x_9], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_0.run(buf20, primals_19, 123008, grid=grid(123008), stream=stream0)
        del primals_19
        # Topologically Sorted Source Nodes: [x_10], Original ATen: [aten.convolution]
        buf21 = extern_kernels.convolution(buf20, primals_20, stride=(2, 2), padding=(0, 0), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf21, (4, 3, 64, 64), (12288, 4096, 64, 1))
        buf22 = buf21; del buf21  # reuse
        # Topologically Sorted Source Nodes: [x_10], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_6.run(buf22, primals_21, 49152, grid=grid(49152), stream=stream0)
        del primals_21
    return (buf22, reinterpret_tensor(buf8, (4, 10), (20, 1), 0), reinterpret_tensor(buf8, (4, 10), (20, 1), 10), buf8, primals_1, primals_3, primals_4, primals_6, primals_8, primals_14, primals_16, primals_18, primals_20, buf1, buf3, buf5, reinterpret_tensor(buf7, (4, 256), (256, 1), 0), reinterpret_tensor(buf8, (4, 10), (20, 1), 10), buf11, buf12, reinterpret_tensor(buf14, (4, 64, 2, 2), (256, 4, 2, 1), 0), buf16, buf18, buf20, buf23, primals_12, primals_10, buf24, )


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((32, 3, 4, 4), (48, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 3, 64, 64), (12288, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((32, 32, 4, 4), (512, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((64, 32, 4, 4), (512, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((64, 64, 4, 4), (1024, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((20, 256), (256, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((20, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((256, 10), (10, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((256, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((64, 64, 4, 4), (1024, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_15 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_16 = rand_strided((64, 32, 4, 4), (512, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_17 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_18 = rand_strided((32, 32, 4, 4), (512, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_19 = rand_strided((32, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_20 = rand_strided((32, 3, 4, 4), (48, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_21 = rand_strided((3, ), (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, primals_16, primals_17, primals_18, primals_19, primals_20, primals_21])
    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


def reparametrize(mu, logvar):
    std = logvar.div(2).exp()
    eps = std.data.new(std.size()).normal_()
    return mu + std * eps


class Encoder_H(nn.Module):

    def __init__(self, input_shape=(64, 64), z_dim=10, nc=3, padding=1):
        super(Encoder_H, self).__init__()
        self.conv2d_1 = nn.Conv2d(nc, 32, 4, 2, padding)
        self.conv2d_2 = nn.Conv2d(32, 32, 4, 2, padding)
        self.conv2d_3 = nn.Conv2d(32, 64, 4, 2, padding)
        self.conv2d_4 = nn.Conv2d(64, 64, 4, 2, padding)
        self.flatten_shape, self.dconv_size = self._get_conv_output(input_shape
            , nc)
        self.linear = nn.Linear(self.flatten_shape, z_dim * 2)

    def _get_conv_output(self, shape, nc):
        bs = 1
        dummy_x = torch.empty(bs, nc, *shape)
        x, dconv_size = self._forward_features(dummy_x)
        flatten_shape = x.flatten(1).size(1)
        return flatten_shape, dconv_size

    def _forward_features(self, x):
        size0 = x.shape[1:]
        x = torch.relu(self.conv2d_1(x))
        size1 = x.shape[1:]
        x = torch.relu(self.conv2d_2(x))
        size2 = x.shape[1:]
        x = torch.relu(self.conv2d_3(x))
        size3 = x.shape[1:]
        x = torch.relu(self.conv2d_4(x))
        size4 = x.shape[1:]
        return x, [size0, size1, size2, size3, size4]

    def forward(self, x):
        x = torch.relu(self.conv2d_1(x))
        x = torch.relu(self.conv2d_2(x))
        x = torch.relu(self.conv2d_3(x))
        x = torch.relu(self.conv2d_4(x))
        x = self.linear(x.flatten(1))
        return x


class Decoder_H(nn.Module):

    def __init__(self, output_shape, z_dim=10, nc=3, padding=1):
        super(Decoder_H, self).__init__()
        self.output_shape = output_shape
        flatten_shape = output_shape[-1][0] * output_shape[-1][1
            ] * output_shape[-1][2]
        self.linear = nn.Linear(z_dim, flatten_shape)
        self.conv2d_1 = nn.ConvTranspose2d(64, 64, 4, 2, padding)
        self.conv2d_2 = nn.ConvTranspose2d(64, 32, 4, 2, padding)
        self.conv2d_3 = nn.ConvTranspose2d(32, 32, 4, 2, padding)
        self.conv2d_4 = nn.ConvTranspose2d(32, nc, 4, 2, padding)

    def _forward_features(self, x):
        x = torch.relu(self.conv2d_1(x, self.output_shape[3][1:]))
        x = torch.relu(self.conv2d_2(x, self.output_shape[2][1:]))
        x = torch.relu(self.conv2d_3(x, self.output_shape[1][1:]))
        x = self.conv2d_4(x, self.output_shape[0][1:])
        return x

    def forward(self, x):
        x = torch.relu(self.linear(x))
        x = x.view(-1, *self.output_shape[4])
        x = self._forward_features(x)
        return x


class BetaVAE_H(nn.Module):

    def __init__(self, input_shape=(64, 64), z_dim=10, nc=3, padding=0,
        activation=nn.Identity()):
        super(BetaVAE_H, self).__init__()
        self.z_dim = z_dim
        self.activation = activation
        self.encoder = Encoder_H(input_shape=input_shape, nc=nc, z_dim=
            z_dim, padding=padding)
        self.decoder = Decoder_H(self.encoder.dconv_size, nc=nc, z_dim=
            z_dim, padding=padding)

    def forward(self, x):
        distributions = self.encoder(x)
        mu = distributions[:, :self.z_dim]
        logvar = distributions[:, self.z_dim:]
        z = reparametrize(mu, logvar)
        x_recon = self.decoder(z)
        return self.activation(x_recon), mu, logvar


def get_inputs():
    return [torch.rand([4, 3, 64, 64])]


def get_init_inputs():
    return [[], {}]

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
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as 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_convolution_relu_0(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 123008
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 961 % 32
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, 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 + x3, tmp4, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_1(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 25088
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 196 % 32
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, 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 + x3, tmp4, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_2(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 9216
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 36 % 64
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, 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 + x3, tmp4, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_3(in_out_ptr0,
    in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4 % 64
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, 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 + x3, tmp4, xmask)
    tl.store(out_ptr0 + x3, tmp6, xmask)


@triton.jit
def triton_poi_fused_add_div_exp_mul_4(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 40
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 10
    x1 = xindex // 10
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 20 * x1), xmask)
    tmp1 = tl.load(in_ptr0 + (10 + x0 + 20 * x1), xmask)
    tmp5 = tl.load(in_ptr1 + x2, xmask)
    tmp2 = 0.5
    tmp3 = tmp1 * tmp2
    tmp4 = tl_math.exp(tmp3)
    tmp6 = tmp4 * tmp5
    tmp7 = tmp0 + tmp6
    tl.store(out_ptr0 + x2, tmp7, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_5(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 256
    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_convolution_6(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)
    x3 = xindex
    x1 = xindex // 4096 % 3
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, None)


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, primals_16, primals_17,
        primals_18, primals_19, primals_20, primals_21) = args
    args.clear()
    assert_size_stride(primals_1, (32, 3, 4, 4), (48, 16, 4, 1))
    assert_size_stride(primals_2, (32,), (1,))
    assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_4, (32, 32, 4, 4), (512, 16, 4, 1))
    assert_size_stride(primals_5, (32,), (1,))
    assert_size_stride(primals_6, (64, 32, 4, 4), (512, 16, 4, 1))
    assert_size_stride(primals_7, (64,), (1,))
    assert_size_stride(primals_8, (64, 64, 4, 4), (1024, 16, 4, 1))
    assert_size_stride(primals_9, (64,), (1,))
    assert_size_stride(primals_10, (20, 256), (256, 1))
    assert_size_stride(primals_11, (20,), (1,))
    assert_size_stride(primals_12, (256, 10), (10, 1))
    assert_size_stride(primals_13, (256,), (1,))
    assert_size_stride(primals_14, (64, 64, 4, 4), (1024, 16, 4, 1))
    assert_size_stride(primals_15, (64,), (1,))
    assert_size_stride(primals_16, (64, 32, 4, 4), (512, 16, 4, 1))
    assert_size_stride(primals_17, (32,), (1,))
    assert_size_stride(primals_18, (32, 32, 4, 4), (512, 16, 4, 1))
    assert_size_stride(primals_19, (32,), (1,))
    assert_size_stride(primals_20, (32, 3, 4, 4), (48, 16, 4, 1))
    assert_size_stride(primals_21, (3,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(2, 
            2), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 32, 31, 31), (30752, 961, 31, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_0[grid(123008)](buf1, primals_2, 
            123008, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_2
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 32, 14, 14), (6272, 196, 14, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_relu_1[grid(25088)](buf3, primals_5, 
            25088, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_5
        buf4 = extern_kernels.convolution(buf3, primals_6, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 64, 6, 6), (2304, 36, 6, 1))
        buf5 = buf4
        del buf4
        triton_poi_fused_convolution_relu_2[grid(9216)](buf5, primals_7, 
            9216, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_7
        buf6 = extern_kernels.convolution(buf5, primals_8, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 64, 2, 2), (256, 4, 2, 1))
        buf7 = buf6
        del buf6
        buf24 = empty_strided_cuda((4, 64, 2, 2), (256, 4, 2, 1), torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_3[grid(1024)](buf7
            , primals_9, buf24, 1024, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_9
        buf8 = empty_strided_cuda((4, 20), (20, 1), torch.float32)
        extern_kernels.addmm(primals_11, reinterpret_tensor(buf7, (4, 256),
            (256, 1), 0), reinterpret_tensor(primals_10, (256, 20), (1, 256
            ), 0), alpha=1, beta=1, out=buf8)
        del primals_11
        buf9 = empty_strided_cuda((4, 10), (10, 1), torch.float32)
        buf10 = torch.ops.aten.normal_functional.default(buf9)
        buf11 = buf10
        del buf10
        buf12 = buf9
        del buf9
        triton_poi_fused_add_div_exp_mul_4[grid(40)](buf8, buf11, buf12, 40,
            XBLOCK=64, num_warps=1, num_stages=1)
        buf13 = empty_strided_cuda((4, 256), (256, 1), torch.float32)
        extern_kernels.mm(buf12, reinterpret_tensor(primals_12, (10, 256),
            (1, 10), 0), out=buf13)
        buf14 = buf13
        del buf13
        buf23 = empty_strided_cuda((4, 256), (256, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_5[grid(1024)](buf14,
            primals_13, buf23, 1024, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_13
        buf15 = extern_kernels.convolution(reinterpret_tensor(buf14, (4, 64,
            2, 2), (256, 4, 2, 1), 0), primals_14, stride=(2, 2), padding=(
            0, 0), dilation=(1, 1), transposed=True, output_padding=(0, 0),
            groups=1, bias=None)
        assert_size_stride(buf15, (4, 64, 6, 6), (2304, 36, 6, 1))
        buf16 = buf15
        del buf15
        triton_poi_fused_convolution_relu_2[grid(9216)](buf16, primals_15, 
            9216, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_15
        buf17 = extern_kernels.convolution(buf16, primals_16, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf17, (4, 32, 14, 14), (6272, 196, 14, 1))
        buf18 = buf17
        del buf17
        triton_poi_fused_convolution_relu_1[grid(25088)](buf18, primals_17,
            25088, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_17
        buf19 = extern_kernels.convolution(buf18, primals_18, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=True,
            output_padding=(1, 1), groups=1, bias=None)
        assert_size_stride(buf19, (4, 32, 31, 31), (30752, 961, 31, 1))
        buf20 = buf19
        del buf19
        triton_poi_fused_convolution_relu_0[grid(123008)](buf20, primals_19,
            123008, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_19
        buf21 = extern_kernels.convolution(buf20, primals_20, stride=(2, 2),
            padding=(0, 0), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf21, (4, 3, 64, 64), (12288, 4096, 64, 1))
        buf22 = buf21
        del buf21
        triton_poi_fused_convolution_6[grid(49152)](buf22, primals_21, 
            49152, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_21
    return (buf22, reinterpret_tensor(buf8, (4, 10), (20, 1), 0),
        reinterpret_tensor(buf8, (4, 10), (20, 1), 10), buf8, primals_1,
        primals_3, primals_4, primals_6, primals_8, primals_14, primals_16,
        primals_18, primals_20, buf1, buf3, buf5, reinterpret_tensor(buf7,
        (4, 256), (256, 1), 0), reinterpret_tensor(buf8, (4, 10), (20, 1), 
        10), buf11, buf12, reinterpret_tensor(buf14, (4, 64, 2, 2), (256, 4,
        2, 1), 0), buf16, buf18, buf20, buf23, primals_12, primals_10, buf24)


def reparametrize(mu, logvar):
    std = logvar.div(2).exp()
    eps = std.data.new(std.size()).normal_()
    return mu + std * eps


class Encoder_H(nn.Module):

    def __init__(self, input_shape=(64, 64), z_dim=10, nc=3, padding=1):
        super(Encoder_H, self).__init__()
        self.conv2d_1 = nn.Conv2d(nc, 32, 4, 2, padding)
        self.conv2d_2 = nn.Conv2d(32, 32, 4, 2, padding)
        self.conv2d_3 = nn.Conv2d(32, 64, 4, 2, padding)
        self.conv2d_4 = nn.Conv2d(64, 64, 4, 2, padding)
        self.flatten_shape, self.dconv_size = self._get_conv_output(input_shape
            , nc)
        self.linear = nn.Linear(self.flatten_shape, z_dim * 2)

    def _get_conv_output(self, shape, nc):
        bs = 1
        dummy_x = torch.empty(bs, nc, *shape)
        x, dconv_size = self._forward_features(dummy_x)
        flatten_shape = x.flatten(1).size(1)
        return flatten_shape, dconv_size

    def _forward_features(self, x):
        size0 = x.shape[1:]
        x = torch.relu(self.conv2d_1(x))
        size1 = x.shape[1:]
        x = torch.relu(self.conv2d_2(x))
        size2 = x.shape[1:]
        x = torch.relu(self.conv2d_3(x))
        size3 = x.shape[1:]
        x = torch.relu(self.conv2d_4(x))
        size4 = x.shape[1:]
        return x, [size0, size1, size2, size3, size4]

    def forward(self, x):
        x = torch.relu(self.conv2d_1(x))
        x = torch.relu(self.conv2d_2(x))
        x = torch.relu(self.conv2d_3(x))
        x = torch.relu(self.conv2d_4(x))
        x = self.linear(x.flatten(1))
        return x


class Decoder_H(nn.Module):

    def __init__(self, output_shape, z_dim=10, nc=3, padding=1):
        super(Decoder_H, self).__init__()
        self.output_shape = output_shape
        flatten_shape = output_shape[-1][0] * output_shape[-1][1
            ] * output_shape[-1][2]
        self.linear = nn.Linear(z_dim, flatten_shape)
        self.conv2d_1 = nn.ConvTranspose2d(64, 64, 4, 2, padding)
        self.conv2d_2 = nn.ConvTranspose2d(64, 32, 4, 2, padding)
        self.conv2d_3 = nn.ConvTranspose2d(32, 32, 4, 2, padding)
        self.conv2d_4 = nn.ConvTranspose2d(32, nc, 4, 2, padding)

    def _forward_features(self, x):
        x = torch.relu(self.conv2d_1(x, self.output_shape[3][1:]))
        x = torch.relu(self.conv2d_2(x, self.output_shape[2][1:]))
        x = torch.relu(self.conv2d_3(x, self.output_shape[1][1:]))
        x = self.conv2d_4(x, self.output_shape[0][1:])
        return x

    def forward(self, x):
        x = torch.relu(self.linear(x))
        x = x.view(-1, *self.output_shape[4])
        x = self._forward_features(x)
        return x


class BetaVAE_HNew(nn.Module):

    def __init__(self, input_shape=(64, 64), z_dim=10, nc=3, padding=0,
        activation=nn.Identity()):
        super(BetaVAE_HNew, self).__init__()
        self.z_dim = z_dim
        self.activation = activation
        self.encoder = Encoder_H(input_shape=input_shape, nc=nc, z_dim=
            z_dim, padding=padding)
        self.decoder = Decoder_H(self.encoder.dconv_size, nc=nc, z_dim=
            z_dim, padding=padding)

    def forward(self, input_0):
        primals_1 = self.encoder.conv2d_1.weight
        primals_2 = self.encoder.conv2d_1.bias
        primals_4 = self.encoder.conv2d_2.weight
        primals_5 = self.encoder.conv2d_2.bias
        primals_6 = self.encoder.conv2d_3.weight
        primals_7 = self.encoder.conv2d_3.bias
        primals_8 = self.encoder.conv2d_4.weight
        primals_9 = self.encoder.conv2d_4.bias
        primals_10 = self.encoder.linear.weight
        primals_11 = self.encoder.linear.bias
        primals_12 = self.decoder.linear.weight
        primals_13 = self.decoder.linear.bias
        primals_14 = self.decoder.conv2d_1.weight
        primals_15 = self.decoder.conv2d_1.bias
        primals_16 = self.decoder.conv2d_2.weight
        primals_17 = self.decoder.conv2d_2.bias
        primals_18 = self.decoder.conv2d_3.weight
        primals_19 = self.decoder.conv2d_3.bias
        primals_20 = self.decoder.conv2d_4.weight
        primals_21 = self.decoder.conv2d_4.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, primals_16, primals_17, primals_18, primals_19,
            primals_20, primals_21])
        return output[0], output[1], output[2]
