# 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_7/inductor_cache/fp/cfp5jrxxyxrvhcpoq5tio3p5tkhj5ugdrpyur3x4v6meatzih7jn.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_0 = async_compile.triton('triton_poi_fused_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=[16, 16], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2 + (16*y3)), xmask & ymask)
    tl.store(out_ptr0 + (y0 + (4*x2) + (64*y1)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/uw/cuwyaunsqgt6y446p46tklj3afnj5ynfwg2kxpptkdwfoiux233j.py
# Topologically Sorted Source Nodes: [out, out_1], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   out => convolution
#   out_1 => relu
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [1, 1], [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_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 = 25600
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 400
    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)
    tl.store(in_out_ptr0 + (x2), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/rf/crf4mhe3jx7leqalqrno2jhukq6yyyrwb7jxxlrjvfgbizyonfao.py
# Topologically Sorted Source Nodes: [mu, logvar, mul, std, eps, mul_1, z, out_4], Original ATen: [aten.convolution, aten.mul, aten.exp, aten.randn_like, aten.add]
# Source node to ATen node mapping:
#   eps => randn
#   logvar => convolution_3
#   mu => convolution_2
#   mul => mul
#   mul_1 => mul_1
#   out_4 => convolution_4
#   std => exp
#   z => add
# Graph fragment:
#   %convolution_2 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_1, %primals_6, %primals_7, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %convolution_3 : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_1, %primals_8, %primals_9, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%convolution_3, 0.5), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%mul,), kwargs = {})
#   %randn : [num_users=2] = call_function[target=torch.ops.aten.randn.default](args = ([4, 4, 4, 4],), kwargs = {dtype: torch.float32, device: cuda:0, pin_memory: False})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%randn, %exp), kwargs = {})
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution_2, %mul_1), kwargs = {})
#   %convolution_4 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%add, %primals_10, %primals_11, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_add_convolution_exp_mul_randn_like_2 = async_compile.triton('triton_poi_fused_add_convolution_exp_mul_randn_like_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=[16, 16], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*fp32', 8: '*fp32', 9: '*fp32', 10: 'i32', 11: '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, 5, 6, 7, 8, 9, 10, 11), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_convolution_exp_mul_randn_like_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, '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_convolution_exp_mul_randn_like_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, out_ptr1, out_ptr2, out_ptr3, out_ptr4, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (4*x2) + (64*y1)), xmask & ymask)
    tmp1 = tl.load(in_ptr1 + (y0), ymask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (y0 + (4*x2) + (64*y1)), xmask & ymask)
    tmp4 = tl.load(in_ptr3 + (y0), ymask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr4 + (x2 + (16*y3)), xmask & ymask)
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp7 = 0.5
    tmp8 = tmp5 * tmp7
    tmp9 = tl_math.exp(tmp8)
    tmp10 = tmp6 * tmp9
    tmp11 = tmp2 + tmp10
    tl.store(out_ptr0 + (x2 + (16*y3)), tmp2, xmask & ymask)
    tl.store(out_ptr1 + (x2 + (16*y3)), tmp5, xmask & ymask)
    tl.store(out_ptr2 + (y0 + (4*x2) + (64*y1)), tmp6, xmask & ymask)
    tl.store(out_ptr3 + (x2 + (16*y3)), tmp11, xmask & ymask)
    tl.store(out_ptr4 + (y0 + (4*x2) + (64*y1)), tmp11, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/ur/curff7s5hkywtnymj6ypek5datxmdsklderihqz6yrgsqpmvwflf.py
# Topologically Sorted Source Nodes: [out_8], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   out_8 => convolution_6
# Graph fragment:
#   %convolution_6 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_3, %primals_14, %primals_15, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_3 = async_compile.triton('triton_poi_fused_convolution_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=[16, 16], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 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_convolution_3', '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_convolution_3(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (4*x2) + (64*y1)), xmask & ymask)
    tmp1 = tl.load(in_ptr1 + (y0), ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + (16*y3)), tmp2, xmask & ymask)
''', 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, (400, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_2, (400, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (400, 400, 1, 1), (400, 1, 1, 1))
    assert_size_stride(primals_5, (400, ), (1, ))
    assert_size_stride(primals_6, (4, 400, 1, 1), (400, 1, 1, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    assert_size_stride(primals_8, (4, 400, 1, 1), (400, 1, 1, 1))
    assert_size_stride(primals_9, (4, ), (1, ))
    assert_size_stride(primals_10, (400, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_11, (400, ), (1, ))
    assert_size_stride(primals_12, (400, 400, 1, 1), (400, 1, 1, 1))
    assert_size_stride(primals_13, (400, ), (1, ))
    assert_size_stride(primals_14, (4, 400, 1, 1), (400, 1, 1, 1))
    assert_size_stride(primals_15, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        stream0 = get_raw_stream(0)
        triton_poi_fused_0.run(primals_3, buf0, 16, 16, grid=grid(16, 16), stream=stream0)
        del primals_3
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(buf0, primals_1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 400, 4, 4), (6400, 1, 1600, 400))
        buf2 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [out, out_1], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_1.run(buf2, primals_2, 25600, grid=grid(25600), stream=stream0)
        del primals_2
        # Topologically Sorted Source Nodes: [out_2], Original ATen: [aten.convolution]
        buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 400, 4, 4), (6400, 1, 1600, 400))
        buf4 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [out_2, out_3], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_1.run(buf4, primals_5, 25600, grid=grid(25600), stream=stream0)
        del primals_5
        # Topologically Sorted Source Nodes: [mu], Original ATen: [aten.convolution]
        buf5 = extern_kernels.convolution(buf4, primals_6, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 4, 4, 4), (64, 1, 16, 4))
        # Topologically Sorted Source Nodes: [eps], Original ATen: [aten.randn_like]
        buf9 = torch.ops.aten.randn.default([4, 4, 4, 4], dtype=torch.float32, device=device(type='cuda', index=0), pin_memory=False)
        buf10 = buf9
        del buf9
        # Topologically Sorted Source Nodes: [logvar], Original ATen: [aten.convolution]
        buf7 = extern_kernels.convolution(buf4, primals_8, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf7, (4, 4, 4, 4), (64, 1, 16, 4))
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf11 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32)
        buf12 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf13 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32)
        # Topologically Sorted Source Nodes: [mu, logvar, mul, std, eps, mul_1, z, out_4], Original ATen: [aten.convolution, aten.mul, aten.exp, aten.randn_like, aten.add]
        triton_poi_fused_add_convolution_exp_mul_randn_like_2.run(buf5, primals_7, buf7, primals_9, buf10, buf6, buf8, buf11, buf12, buf13, 16, 16, grid=grid(16, 16), stream=stream0)
        del buf10
        del buf5
        del buf7
        del primals_7
        del primals_9
        # Topologically Sorted Source Nodes: [out_4], Original ATen: [aten.convolution]
        buf14 = extern_kernels.convolution(buf13, primals_10, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf14, (4, 400, 4, 4), (6400, 1, 1600, 400))
        buf15 = buf14; del buf14  # reuse
        # Topologically Sorted Source Nodes: [out_4, out_5], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_1.run(buf15, primals_11, 25600, grid=grid(25600), stream=stream0)
        del primals_11
        # Topologically Sorted Source Nodes: [out_6], Original ATen: [aten.convolution]
        buf16 = extern_kernels.convolution(buf15, primals_12, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf16, (4, 400, 4, 4), (6400, 1, 1600, 400))
        buf17 = buf16; del buf16  # reuse
        # Topologically Sorted Source Nodes: [out_6, out_7], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_1.run(buf17, primals_13, 25600, grid=grid(25600), stream=stream0)
        del primals_13
        # Topologically Sorted Source Nodes: [out_8], Original ATen: [aten.convolution]
        buf18 = extern_kernels.convolution(buf17, primals_14, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf18, (4, 4, 4, 4), (64, 1, 16, 4))
        buf19 = reinterpret_tensor(buf13, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf13  # reuse
        # Topologically Sorted Source Nodes: [out_8], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_3.run(buf18, primals_15, buf19, 16, 16, grid=grid(16, 16), stream=stream0)
        del buf18
        del primals_15
    return (buf19, buf12, buf6, buf8, primals_1, buf0, primals_4, primals_6, primals_8, primals_10, primals_12, primals_14, buf2, buf4, buf8, buf11, buf12, buf15, buf17, )


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((400, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((400, ), (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((400, 400, 1, 1), (400, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((400, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 400, 1, 1), (400, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, 400, 1, 1), (400, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((400, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((400, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((400, 400, 1, 1), (400, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((400, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((4, 400, 1, 1), (400, 1, 1, 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
import torch.nn as nn
import torch.utils.data
import torch.nn.init as init


def kaiming_init(m):
    if isinstance(m, (nn.Linear, nn.Conv2d)):
        init.kaiming_normal(m.weight)
        if m.bias is not None:
            m.bias.data.fill_(0)
    elif isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d)):
        m.weight.data.fill_(1)
        if m.bias is not None:
            m.bias.data.fill_(0)


class VAE(nn.Module):

    def __init__(self, length, n_latent):
        super(VAE, self).__init__()
        self.length = length
        self.n_latent = n_latent
        self.C1 = nn.Conv2d(self.length, 400, kernel_size=1)
        self.C2 = nn.Conv2d(400, 400, kernel_size=1)
        self.C31 = nn.Conv2d(400, self.n_latent, kernel_size=1)
        self.C32 = nn.Conv2d(400, self.n_latent, kernel_size=1)
        self.C4 = nn.Conv2d(self.n_latent, 400, kernel_size=1)
        self.C5 = nn.Conv2d(400, 400, kernel_size=1)
        self.C6 = nn.Conv2d(400, self.length, kernel_size=1)
        self.relu = nn.ReLU(inplace=True)

    def weights_init(self):
        for module in self.modules():
            kaiming_init(module)

    def encode(self, x):
        out = self.C1(x)
        out = self.relu(out)
        out = self.C2(out)
        out = self.relu(out)
        mu = self.C31(out)
        logvar = self.C32(out)
        return mu, logvar

    def reparameterize(self, mu, logvar):
        std = torch.exp(0.5 * logvar)
        eps = torch.randn_like(std)
        return mu + eps * std

    def decode(self, z):
        out = self.C4(z)
        out = self.relu(out)
        out = self.C5(out)
        out = self.relu(out)
        out = self.C6(out)
        return out

    def forward(self, x):
        mu, logvar = self.encode(x)
        z = self.reparameterize(mu, logvar)
        return self.decode(z), z, mu, logvar


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


def get_init_inputs():
    return [[], {'length': 4, 'n_latent': 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
import torch.nn as nn
import torch.utils.data
import torch.nn.init as init
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_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 4
    y1 = yindex // 4
    tmp0 = tl.load(in_ptr0 + (x2 + 16 * y3), xmask & ymask)
    tl.store(out_ptr0 + (y0 + 4 * x2 + 64 * y1), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_convolution_relu_1(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 25600
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 400
    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)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_add_convolution_exp_mul_randn_like_2(in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, in_ptr4, out_ptr0, out_ptr1, out_ptr2, out_ptr3,
    out_ptr4, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 64 * y1), xmask & ymask)
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (y0 + 4 * x2 + 64 * y1), xmask & ymask)
    tmp4 = tl.load(in_ptr3 + y0, ymask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr4 + (x2 + 16 * y3), xmask & ymask)
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp7 = 0.5
    tmp8 = tmp5 * tmp7
    tmp9 = tl_math.exp(tmp8)
    tmp10 = tmp6 * tmp9
    tmp11 = tmp2 + tmp10
    tl.store(out_ptr0 + (x2 + 16 * y3), tmp2, xmask & ymask)
    tl.store(out_ptr1 + (x2 + 16 * y3), tmp5, xmask & ymask)
    tl.store(out_ptr2 + (y0 + 4 * x2 + 64 * y1), tmp6, xmask & ymask)
    tl.store(out_ptr3 + (x2 + 16 * y3), tmp11, xmask & ymask)
    tl.store(out_ptr4 + (y0 + 4 * x2 + 64 * y1), tmp11, xmask & ymask)


@triton.jit
def triton_poi_fused_convolution_3(in_ptr0, in_ptr1, out_ptr0, ynumel,
    xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 64 * y1), xmask & ymask)
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + 16 * y3), tmp2, xmask & ymask)


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, (400, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_2, (400,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (400, 400, 1, 1), (400, 1, 1, 1))
    assert_size_stride(primals_5, (400,), (1,))
    assert_size_stride(primals_6, (4, 400, 1, 1), (400, 1, 1, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 400, 1, 1), (400, 1, 1, 1))
    assert_size_stride(primals_9, (4,), (1,))
    assert_size_stride(primals_10, (400, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_11, (400,), (1,))
    assert_size_stride(primals_12, (400, 400, 1, 1), (400, 1, 1, 1))
    assert_size_stride(primals_13, (400,), (1,))
    assert_size_stride(primals_14, (4, 400, 1, 1), (400, 1, 1, 1))
    assert_size_stride(primals_15, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(16, 16)](primals_3, buf0, 16, 16, XBLOCK=16,
            YBLOCK=16, num_warps=4, num_stages=1)
        del primals_3
        buf1 = extern_kernels.convolution(buf0, primals_1, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 400, 4, 4), (6400, 1, 1600, 400))
        buf2 = buf1
        del buf1
        triton_poi_fused_convolution_relu_1[grid(25600)](buf2, primals_2, 
            25600, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 400, 4, 4), (6400, 1, 1600, 400))
        buf4 = buf3
        del buf3
        triton_poi_fused_convolution_relu_1[grid(25600)](buf4, primals_5, 
            25600, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_5
        buf5 = extern_kernels.convolution(buf4, primals_6, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 4, 4, 4), (64, 1, 16, 4))
        buf9 = torch.ops.aten.randn.default([4, 4, 4, 4], dtype=torch.
            float32, device=device(type='cuda', index=0), pin_memory=False)
        buf10 = buf9
        del buf9
        buf7 = extern_kernels.convolution(buf4, primals_8, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf7, (4, 4, 4, 4), (64, 1, 16, 4))
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf11 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32)
        buf12 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf13 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32)
        triton_poi_fused_add_convolution_exp_mul_randn_like_2[grid(16, 16)](
            buf5, primals_7, buf7, primals_9, buf10, buf6, buf8, buf11,
            buf12, buf13, 16, 16, XBLOCK=16, YBLOCK=16, num_warps=4,
            num_stages=1)
        del buf10
        del buf5
        del buf7
        del primals_7
        del primals_9
        buf14 = extern_kernels.convolution(buf13, primals_10, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf14, (4, 400, 4, 4), (6400, 1, 1600, 400))
        buf15 = buf14
        del buf14
        triton_poi_fused_convolution_relu_1[grid(25600)](buf15, primals_11,
            25600, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_11
        buf16 = extern_kernels.convolution(buf15, primals_12, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf16, (4, 400, 4, 4), (6400, 1, 1600, 400))
        buf17 = buf16
        del buf16
        triton_poi_fused_convolution_relu_1[grid(25600)](buf17, primals_13,
            25600, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_13
        buf18 = extern_kernels.convolution(buf17, primals_14, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf18, (4, 4, 4, 4), (64, 1, 16, 4))
        buf19 = reinterpret_tensor(buf13, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf13
        triton_poi_fused_convolution_3[grid(16, 16)](buf18, primals_15,
            buf19, 16, 16, XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1)
        del buf18
        del primals_15
    return (buf19, buf12, buf6, buf8, primals_1, buf0, primals_4, primals_6,
        primals_8, primals_10, primals_12, primals_14, buf2, buf4, buf8,
        buf11, buf12, buf15, buf17)


def kaiming_init(m):
    if isinstance(m, (nn.Linear, nn.Conv2d)):
        init.kaiming_normal(m.weight)
        if m.bias is not None:
            m.bias.data.fill_(0)
    elif isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d)):
        m.weight.data.fill_(1)
        if m.bias is not None:
            m.bias.data.fill_(0)


class VAENew(nn.Module):

    def __init__(self, length, n_latent):
        super(VAENew, self).__init__()
        self.length = length
        self.n_latent = n_latent
        self.C1 = nn.Conv2d(self.length, 400, kernel_size=1)
        self.C2 = nn.Conv2d(400, 400, kernel_size=1)
        self.C31 = nn.Conv2d(400, self.n_latent, kernel_size=1)
        self.C32 = nn.Conv2d(400, self.n_latent, kernel_size=1)
        self.C4 = nn.Conv2d(self.n_latent, 400, kernel_size=1)
        self.C5 = nn.Conv2d(400, 400, kernel_size=1)
        self.C6 = nn.Conv2d(400, self.length, kernel_size=1)
        self.relu = nn.ReLU(inplace=True)

    def weights_init(self):
        for module in self.modules():
            kaiming_init(module)

    def encode(self, x):
        out = self.C1(x)
        out = self.relu(out)
        out = self.C2(out)
        out = self.relu(out)
        mu = self.C31(out)
        logvar = self.C32(out)
        return mu, logvar

    def reparameterize(self, mu, logvar):
        std = torch.exp(0.5 * logvar)
        eps = torch.randn_like(std)
        return mu + eps * std

    def decode(self, z):
        out = self.C4(z)
        out = self.relu(out)
        out = self.C5(out)
        out = self.relu(out)
        out = self.C6(out)
        return out

    def forward(self, input_0):
        primals_1 = self.C1.weight
        primals_2 = self.C1.bias
        primals_4 = self.C2.weight
        primals_5 = self.C2.bias
        primals_6 = self.C31.weight
        primals_7 = self.C31.bias
        primals_8 = self.C32.weight
        primals_9 = self.C32.bias
        primals_10 = self.C4.weight
        primals_11 = self.C4.bias
        primals_12 = self.C5.weight
        primals_13 = self.C5.bias
        primals_14 = self.C6.weight
        primals_15 = self.C6.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], output[3]
