# 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/oh/cohhzozgklcdr3g2cpdmnac2zvbvmk53smneafef4zekz5p2kieu.py
# Topologically Sorted Source Nodes: [x, x_1], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   x => convolution
#   x_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], [1, 1], [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 = 131072
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 4096) % 8
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x3), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/xv/cxv6btckfuml6hxefmi2ilywyg4d6dpbiwh4qofpvqphr6oyxcrq.py
# Topologically Sorted Source Nodes: [max_pool2d], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   max_pool2d => _low_memory_max_pool2d_offsets_to_indices, _low_memory_max_pool2d_with_offsets, getitem
# Graph fragment:
#   %_low_memory_max_pool2d_with_offsets : [num_users=2] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%relu_1, [2, 2], [2, 2], [0, 0], [1, 1], False), kwargs = {})
#   %getitem : [num_users=2] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 0), kwargs = {})
#   %_low_memory_max_pool2d_offsets_to_indices : [num_users=2] = call_function[target=torch.ops.prims._low_memory_max_pool2d_offsets_to_indices.default](args = (%getitem_1, 2, 64, [2, 2], [0, 0]), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_1 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_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: '*i64', 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_max_pool2d_with_indices_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, '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_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 32
    x1 = (xindex // 32)
    x4 = xindex
    x2 = (xindex // 32) % 32
    tmp0 = tl.load(in_ptr0 + ((2*x0) + (128*x1)), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*x0) + (128*x1)), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (64 + (2*x0) + (128*x1)), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (65 + (2*x0) + (128*x1)), None, eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tmp17 = tl.full([1], 2, tl.int32)
    tmp18 = tl.where((tmp16 < 0) != (tmp17 < 0), tl.where(tmp16 % tmp17 != 0, tmp16 // tmp17 - 1, tmp16 // tmp17), tmp16 // tmp17)
    tmp19 = tmp18 * tmp17
    tmp20 = tmp16 - tmp19
    tmp21 = 2*x2
    tmp22 = tmp21 + tmp18
    tmp23 = 2*x0
    tmp24 = tmp23 + tmp20
    tmp25 = tl.full([1], 64, tl.int64)
    tmp26 = tmp22 * tmp25
    tmp27 = tmp26 + tmp24
    tl.store(out_ptr0 + (x4), tmp6, None)
    tl.store(out_ptr1 + (x4), tmp27, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/tk/ctkr5j63ngqpsdf5zpn24uwouxiguv7jseip5x6trqjye5gti4rn.py
# Topologically Sorted Source Nodes: [x_5, x_6], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   x_5 => convolution_2
#   x_6 => relu_2
# Graph fragment:
#   %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem, %primals_6, %primals_7, [1, 1], [1, 1], [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=[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_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 = 65536
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 1024) % 16
    tmp0 = tl.load(in_out_ptr0 + (x3), None)
    tmp1 = tl.load(in_ptr0 + (x1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x3), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/qp/cqpq5mf7sbft3bvjthjt7vxmu3sbn7sic3jloxkb73zf5gnxgntg.py
# Topologically Sorted Source Nodes: [max_pool2d_1], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   max_pool2d_1 => _low_memory_max_pool2d_offsets_to_indices_1, _low_memory_max_pool2d_with_offsets_1
# Graph fragment:
#   %_low_memory_max_pool2d_with_offsets_1 : [num_users=2] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%relu_3, [2, 2], [2, 2], [0, 0], [1, 1], False), kwargs = {})
#   %_low_memory_max_pool2d_offsets_to_indices_1 : [num_users=2] = call_function[target=torch.ops.prims._low_memory_max_pool2d_offsets_to_indices.default](args = (%getitem_3, 2, 32, [2, 2], [0, 0]), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_3 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_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=[16384], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i64', 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, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, '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_max_pool2d_with_indices_3(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 16
    x3 = (xindex // 16)
    x1 = (xindex // 16) % 16
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + ((2*x0) + (64*x3)), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*x0) + (64*x3)), None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (32 + (2*x0) + (64*x3)), None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (33 + (2*x0) + (64*x3)), None, eviction_policy='evict_last')
    tmp2 = tmp1 > tmp0
    tmp3 = tl.full([1], 1, tl.int8)
    tmp4 = tl.full([1], 0, tl.int8)
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = triton_helpers.maximum(tmp1, tmp0)
    tmp8 = tmp7 > tmp6
    tmp9 = tl.full([1], 2, tl.int8)
    tmp10 = tl.where(tmp8, tmp9, tmp5)
    tmp11 = triton_helpers.maximum(tmp7, tmp6)
    tmp13 = tmp12 > tmp11
    tmp14 = tl.full([1], 3, tl.int8)
    tmp15 = tl.where(tmp13, tmp14, tmp10)
    tmp16 = triton_helpers.maximum(tmp12, tmp11)
    tmp17 = tl.full([1], 2, tl.int32)
    tmp18 = tl.where((tmp15 < 0) != (tmp17 < 0), tl.where(tmp15 % tmp17 != 0, tmp15 // tmp17 - 1, tmp15 // tmp17), tmp15 // tmp17)
    tmp19 = tmp18 * tmp17
    tmp20 = tmp15 - tmp19
    tmp21 = 2*x1
    tmp22 = tmp21 + tmp18
    tmp23 = 2*x0
    tmp24 = tmp23 + tmp20
    tmp25 = tl.full([1], 32, tl.int64)
    tmp26 = tmp22 * tmp25
    tmp27 = tmp26 + tmp24
    tl.store(out_ptr0 + (x4), tmp27, None)
    tl.store(out_ptr1 + (x4), tmp16, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/rd/crd6o5dwk57s3e26hulgasudtug6tb5zxdk4jbrvlvkzghm552bm.py
# Topologically Sorted Source Nodes: [x_15], Original ATen: [aten.relu]
# Source node to ATen node mapping:
#   x_15 => relu_4
# Graph fragment:
#   %relu_4 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%max_unpool2d,), kwargs = {})
triton_poi_fused_relu_4 = async_compile.triton('triton_poi_fused_relu_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=[65536], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_4', 'mutated_arg_names': ['in_out_ptr0'], '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_relu_4(in_out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 65536
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (x0), None)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(in_out_ptr0 + (x0), tmp2, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/gy/cgywnjxm7eucoifckaeatm6tlye2fyfy2h6f3pelrcjxyhttin2l.py
# Topologically Sorted Source Nodes: [x_18], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   x_18 => convolution_5
# Graph fragment:
#   %convolution_5 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_5, %primals_16, %primals_17, [1, 1], [1, 1], [1, 1], True, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_5 = async_compile.triton('triton_poi_fused_convolution_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=[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_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_convolution_5(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 32768
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 1024) % 8
    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')


# kernel path: runs/run_shard_4/inductor_cache/k3/ck3it6pguxbprqavq7jtvx7zudf7ymudhezrol335pggcqczxj7m.py
# Topologically Sorted Source Nodes: [x_20], Original ATen: [aten.relu]
# Source node to ATen node mapping:
#   x_20 => relu_6
# Graph fragment:
#   %relu_6 : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%max_unpool2d_1,), kwargs = {})
triton_poi_fused_relu_6 = async_compile.triton('triton_poi_fused_relu_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=[131072], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_6', 'mutated_arg_names': ['in_out_ptr0'], '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_relu_6(in_out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 131072
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (x0), None)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(in_out_ptr0 + (x0), tmp2, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/hk/chkbtn34xrzxxbrali3tmhhvipkxe6tbpkdaom3gw5eqe2btyuvf.py
# Topologically Sorted Source Nodes: [x_23], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   x_23 => convolution_7
# Graph fragment:
#   %convolution_7 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_7, %primals_20, %primals_21, [1, 1], [1, 1], [1, 1], True, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_7 = async_compile.triton('triton_poi_fused_convolution_7', '''
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_7', '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_7(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16384
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (x0), None)
    tmp1 = tl.load(in_ptr0 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tl.store(in_out_ptr0 + (x0), tmp3, 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, (8, 1, 3, 3), (9, 9, 3, 1))
    assert_size_stride(primals_2, (8, ), (1, ))
    assert_size_stride(primals_3, (4, 1, 64, 64), (4096, 4096, 64, 1))
    assert_size_stride(primals_4, (8, 8, 3, 3), (72, 9, 3, 1))
    assert_size_stride(primals_5, (8, ), (1, ))
    assert_size_stride(primals_6, (16, 8, 3, 3), (72, 9, 3, 1))
    assert_size_stride(primals_7, (16, ), (1, ))
    assert_size_stride(primals_8, (16, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_9, (16, ), (1, ))
    assert_size_stride(primals_10, (3, 4096), (4096, 1))
    assert_size_stride(primals_11, (3, ), (1, ))
    assert_size_stride(primals_12, (4096, 3), (3, 1))
    assert_size_stride(primals_13, (4096, ), (1, ))
    assert_size_stride(primals_14, (16, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_15, (16, ), (1, ))
    assert_size_stride(primals_16, (16, 8, 3, 3), (72, 9, 3, 1))
    assert_size_stride(primals_17, (8, ), (1, ))
    assert_size_stride(primals_18, (8, 8, 3, 3), (72, 9, 3, 1))
    assert_size_stride(primals_19, (8, ), (1, ))
    assert_size_stride(primals_20, (8, 1, 3, 3), (9, 9, 3, 1))
    assert_size_stride(primals_21, (1, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 8, 64, 64), (32768, 4096, 64, 1))
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [x, x_1], Original ATen: [aten.convolution, aten.relu]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_relu_0.run(buf1, primals_2, 131072, grid=grid(131072), stream=stream0)
        del primals_2
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 8, 64, 64), (32768, 4096, 64, 1))
        buf3 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [x_2, x_3], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_0.run(buf3, primals_5, 131072, grid=grid(131072), stream=stream0)
        del primals_5
        buf4 = empty_strided_cuda((4, 8, 32, 32), (8192, 1024, 32, 1), torch.float32)
        buf5 = empty_strided_cuda((4, 8, 32, 32), (8192, 1024, 32, 1), torch.int64)
        # Topologically Sorted Source Nodes: [max_pool2d], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_1.run(buf3, buf4, buf5, 32768, grid=grid(32768), stream=stream0)
        # Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.convolution]
        buf6 = extern_kernels.convolution(buf4, primals_6, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 16, 32, 32), (16384, 1024, 32, 1))
        buf7 = buf6; del buf6  # reuse
        # Topologically Sorted Source Nodes: [x_5, x_6], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_2.run(buf7, primals_7, 65536, grid=grid(65536), stream=stream0)
        del primals_7
        # Topologically Sorted Source Nodes: [x_7], Original ATen: [aten.convolution]
        buf8 = extern_kernels.convolution(buf7, primals_8, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 16, 32, 32), (16384, 1024, 32, 1))
        buf9 = buf8; del buf8  # reuse
        # Topologically Sorted Source Nodes: [x_7, x_8], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_2.run(buf9, primals_9, 65536, grid=grid(65536), stream=stream0)
        del primals_9
        buf10 = empty_strided_cuda((4, 16, 16, 16), (4096, 256, 16, 1), torch.int64)
        buf11 = empty_strided_cuda((4, 16, 16, 16), (4096, 256, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [max_pool2d_1], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_3.run(buf9, buf10, buf11, 16384, grid=grid(16384), stream=stream0)
        buf12 = empty_strided_cuda((4, 3), (3, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_11], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_11, reinterpret_tensor(buf11, (4, 4096), (4096, 1), 0), reinterpret_tensor(primals_10, (4096, 3), (1, 4096), 0), alpha=1, beta=1, out=buf12)
        del primals_11
        buf13 = empty_strided_cuda((4, 4096), (4096, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_12], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_13, buf12, reinterpret_tensor(primals_12, (3, 4096), (1, 3), 0), alpha=1, beta=1, out=buf13)
        del primals_13
        # Topologically Sorted Source Nodes: [x_14], Original ATen: [aten.max_unpool2d]
        buf14 = torch.ops.aten.max_unpool2d.default(reinterpret_tensor(buf13, (4, 16, 16, 16), (4096, 256, 16, 1), 0), buf10, [32, 32])
        del buf13
        buf15 = buf14
        del buf14
        buf16 = buf15; del buf15  # reuse
        # Topologically Sorted Source Nodes: [x_15], Original ATen: [aten.relu]
        triton_poi_fused_relu_4.run(buf16, 65536, grid=grid(65536), stream=stream0)
        # Topologically Sorted Source Nodes: [x_16], Original ATen: [aten.convolution]
        buf17 = extern_kernels.convolution(buf16, primals_14, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf17, (4, 16, 32, 32), (16384, 1024, 32, 1))
        buf18 = buf17; del buf17  # reuse
        # Topologically Sorted Source Nodes: [x_16, x_17], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_2.run(buf18, primals_15, 65536, grid=grid(65536), stream=stream0)
        del primals_15
        # Topologically Sorted Source Nodes: [x_18], Original ATen: [aten.convolution]
        buf19 = extern_kernels.convolution(buf18, primals_16, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf19, (4, 8, 32, 32), (8192, 1024, 32, 1))
        buf20 = buf19; del buf19  # reuse
        # Topologically Sorted Source Nodes: [x_18], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_5.run(buf20, primals_17, 32768, grid=grid(32768), stream=stream0)
        del primals_17
        # Topologically Sorted Source Nodes: [x_18, x_19], Original ATen: [aten.convolution, aten.max_unpool2d]
        buf21 = torch.ops.aten.max_unpool2d.default(buf20, buf5, [64, 64])
        del buf20
        buf22 = buf21
        del buf21
        buf23 = buf22; del buf22  # reuse
        # Topologically Sorted Source Nodes: [x_20], Original ATen: [aten.relu]
        triton_poi_fused_relu_6.run(buf23, 131072, grid=grid(131072), stream=stream0)
        # Topologically Sorted Source Nodes: [x_21], Original ATen: [aten.convolution]
        buf24 = extern_kernels.convolution(buf23, primals_18, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf24, (4, 8, 64, 64), (32768, 4096, 64, 1))
        buf25 = buf24; del buf24  # reuse
        # Topologically Sorted Source Nodes: [x_21, x_22], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_0.run(buf25, primals_19, 131072, grid=grid(131072), stream=stream0)
        del primals_19
        # Topologically Sorted Source Nodes: [x_23], Original ATen: [aten.convolution]
        buf26 = extern_kernels.convolution(buf25, primals_20, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf26, (4, 1, 64, 64), (4096, 4096, 64, 1))
        buf27 = buf26; del buf26  # reuse
        # Topologically Sorted Source Nodes: [x_23], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_7.run(buf27, primals_21, 16384, grid=grid(16384), stream=stream0)
        del primals_21
    return (buf27, primals_1, primals_3, primals_4, primals_6, primals_8, primals_14, primals_16, primals_18, primals_20, buf1, buf3, buf4, buf5, buf7, buf9, buf10, reinterpret_tensor(buf11, (4, 4096), (4096, 1), 0), buf12, buf16, buf18, buf23, buf25, primals_12, primals_10, )


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((8, 1, 3, 3), (9, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((8, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 1, 64, 64), (4096, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((8, 8, 3, 3), (72, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((8, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((16, 8, 3, 3), (72, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((16, 16, 3, 3), (144, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((3, 4096), (4096, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((3, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((4096, 3), (3, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((4096, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((16, 16, 3, 3), (144, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_15 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_16 = rand_strided((16, 8, 3, 3), (72, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_17 = rand_strided((8, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_18 = rand_strided((8, 8, 3, 3), (72, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_19 = rand_strided((8, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_20 = rand_strided((8, 1, 3, 3), (9, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_21 = rand_strided((1, ), (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


class Autoencoder(torch.nn.Module):

    def __init__(self):
        super().__init__()
        self.conv1 = torch.nn.Conv2d(1, 8, 3, padding=1)
        self.conv2 = torch.nn.Conv2d(8, 8, 3, padding=1)
        self.conv3 = torch.nn.Conv2d(8, 16, 3, padding=1)
        self.conv4 = torch.nn.Conv2d(16, 16, 3, padding=1)
        self.lc1 = torch.nn.Linear(16 * 16 * 16, 3)
        self.lc2 = torch.nn.Linear(3, 16 * 16 * 16)
        self.trans1 = torch.nn.ConvTranspose2d(16, 16, 3, padding=1)
        self.trans2 = torch.nn.ConvTranspose2d(16, 8, 3, padding=1)
        self.trans3 = torch.nn.ConvTranspose2d(8, 8, 3, padding=1)
        self.trans4 = torch.nn.ConvTranspose2d(8, 1, 3, padding=1)
        self.mp = torch.nn.MaxPool2d(2, return_indices=True)
        self.up = torch.nn.MaxUnpool2d(2)
        self.relu = torch.nn.ReLU()

    def encoder(self, x):
        x = self.conv1(x)
        x = self.relu(x)
        x = self.conv2(x)
        x = self.relu(x)
        s1 = x.size()
        x, ind1 = self.mp(x)
        x = self.conv3(x)
        x = self.relu(x)
        x = self.conv4(x)
        x = self.relu(x)
        s2 = x.size()
        x, ind2 = self.mp(x)
        x = x.view(int(x.size()[0]), -1)
        x = self.lc1(x)
        return x, ind1, s1, ind2, s2

    def decoder(self, x, ind1, s1, ind2, s2):
        x = self.lc2(x)
        x = x.view(int(x.size()[0]), 16, 16, 16)
        x = self.up(x, ind2, output_size=s2)
        x = self.relu(x)
        x = self.trans1(x)
        x = self.relu(x)
        x = self.trans2(x)
        x = self.up(x, ind1, output_size=s1)
        x = self.relu(x)
        x = self.trans3(x)
        x = self.relu(x)
        x = self.trans4(x)
        return x

    def forward(self, x):
        x, ind1, s1, ind2, s2 = self.encoder(x)
        output = self.decoder(x, ind1, s1, ind2, s2)
        return output


def get_inputs():
    return [torch.rand([4, 1, 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
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):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = xindex // 4096 % 8
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x3, tmp4, None)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_1(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 32
    x1 = xindex // 32
    x4 = xindex
    x2 = xindex // 32 % 32
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 128 * x1), None, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 128 * x1), None, eviction_policy
        ='evict_last')
    tmp3 = tl.load(in_ptr0 + (64 + 2 * x0 + 128 * x1), None,
        eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (65 + 2 * x0 + 128 * x1), None,
        eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp7 = tmp1 > tmp0
    tmp8 = tl.full([1], 1, tl.int8)
    tmp9 = tl.full([1], 0, tl.int8)
    tmp10 = tl.where(tmp7, tmp8, tmp9)
    tmp11 = tmp3 > tmp2
    tmp12 = tl.full([1], 2, tl.int8)
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tmp14 = tmp5 > tmp4
    tmp15 = tl.full([1], 3, tl.int8)
    tmp16 = tl.where(tmp14, tmp15, tmp13)
    tmp17 = tl.full([1], 2, tl.int32)
    tmp18 = tl.where((tmp16 < 0) != (tmp17 < 0), tl.where(tmp16 % tmp17 != 
        0, tmp16 // tmp17 - 1, tmp16 // tmp17), tmp16 // tmp17)
    tmp19 = tmp18 * tmp17
    tmp20 = tmp16 - tmp19
    tmp21 = 2 * x2
    tmp22 = tmp21 + tmp18
    tmp23 = 2 * x0
    tmp24 = tmp23 + tmp20
    tmp25 = tl.full([1], 64, tl.int64)
    tmp26 = tmp22 * tmp25
    tmp27 = tmp26 + tmp24
    tl.store(out_ptr0 + x4, tmp6, None)
    tl.store(out_ptr1 + x4, tmp27, None)


@triton.jit
def triton_poi_fused_convolution_relu_2(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 // 1024 % 16
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x3, tmp4, None)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_3(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex % 16
    x3 = xindex // 16
    x1 = xindex // 16 % 16
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 64 * x3), None, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 64 * x3), None, eviction_policy=
        'evict_last')
    tmp7 = tl.load(in_ptr0 + (32 + 2 * x0 + 64 * x3), None, eviction_policy
        ='evict_last')
    tmp12 = tl.load(in_ptr0 + (33 + 2 * x0 + 64 * x3), None,
        eviction_policy='evict_last')
    tmp2 = tmp1 > tmp0
    tmp3 = tl.full([1], 1, tl.int8)
    tmp4 = tl.full([1], 0, tl.int8)
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = triton_helpers.maximum(tmp1, tmp0)
    tmp8 = tmp7 > tmp6
    tmp9 = tl.full([1], 2, tl.int8)
    tmp10 = tl.where(tmp8, tmp9, tmp5)
    tmp11 = triton_helpers.maximum(tmp7, tmp6)
    tmp13 = tmp12 > tmp11
    tmp14 = tl.full([1], 3, tl.int8)
    tmp15 = tl.where(tmp13, tmp14, tmp10)
    tmp16 = triton_helpers.maximum(tmp12, tmp11)
    tmp17 = tl.full([1], 2, tl.int32)
    tmp18 = tl.where((tmp15 < 0) != (tmp17 < 0), tl.where(tmp15 % tmp17 != 
        0, tmp15 // tmp17 - 1, tmp15 // tmp17), tmp15 // tmp17)
    tmp19 = tmp18 * tmp17
    tmp20 = tmp15 - tmp19
    tmp21 = 2 * x1
    tmp22 = tmp21 + tmp18
    tmp23 = 2 * x0
    tmp24 = tmp23 + tmp20
    tmp25 = tl.full([1], 32, tl.int64)
    tmp26 = tmp22 * tmp25
    tmp27 = tmp26 + tmp24
    tl.store(out_ptr0 + x4, tmp27, None)
    tl.store(out_ptr1 + x4, tmp16, None)


@triton.jit
def triton_poi_fused_relu_4(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, None)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(in_out_ptr0 + x0, tmp2, None)


@triton.jit
def triton_poi_fused_convolution_5(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 // 1024 % 8
    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)


@triton.jit
def triton_poi_fused_relu_6(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, None)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(in_out_ptr0 + x0, tmp2, None)


@triton.jit
def triton_poi_fused_convolution_7(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)
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, None)
    tmp1 = tl.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tl.store(in_out_ptr0 + x0, tmp3, 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, (8, 1, 3, 3), (9, 9, 3, 1))
    assert_size_stride(primals_2, (8,), (1,))
    assert_size_stride(primals_3, (4, 1, 64, 64), (4096, 4096, 64, 1))
    assert_size_stride(primals_4, (8, 8, 3, 3), (72, 9, 3, 1))
    assert_size_stride(primals_5, (8,), (1,))
    assert_size_stride(primals_6, (16, 8, 3, 3), (72, 9, 3, 1))
    assert_size_stride(primals_7, (16,), (1,))
    assert_size_stride(primals_8, (16, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_9, (16,), (1,))
    assert_size_stride(primals_10, (3, 4096), (4096, 1))
    assert_size_stride(primals_11, (3,), (1,))
    assert_size_stride(primals_12, (4096, 3), (3, 1))
    assert_size_stride(primals_13, (4096,), (1,))
    assert_size_stride(primals_14, (16, 16, 3, 3), (144, 9, 3, 1))
    assert_size_stride(primals_15, (16,), (1,))
    assert_size_stride(primals_16, (16, 8, 3, 3), (72, 9, 3, 1))
    assert_size_stride(primals_17, (8,), (1,))
    assert_size_stride(primals_18, (8, 8, 3, 3), (72, 9, 3, 1))
    assert_size_stride(primals_19, (8,), (1,))
    assert_size_stride(primals_20, (8, 1, 3, 3), (9, 9, 3, 1))
    assert_size_stride(primals_21, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 8, 64, 64), (32768, 4096, 64, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_0[grid(131072)](buf1, primals_2, 
            131072, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_2
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 8, 64, 64), (32768, 4096, 64, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_relu_0[grid(131072)](buf3, primals_5, 
            131072, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((4, 8, 32, 32), (8192, 1024, 32, 1),
            torch.float32)
        buf5 = empty_strided_cuda((4, 8, 32, 32), (8192, 1024, 32, 1),
            torch.int64)
        triton_poi_fused_max_pool2d_with_indices_1[grid(32768)](buf3, buf4,
            buf5, 32768, XBLOCK=256, num_warps=4, num_stages=1)
        buf6 = extern_kernels.convolution(buf4, primals_6, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 16, 32, 32), (16384, 1024, 32, 1))
        buf7 = buf6
        del buf6
        triton_poi_fused_convolution_relu_2[grid(65536)](buf7, primals_7, 
            65536, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_7
        buf8 = extern_kernels.convolution(buf7, primals_8, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 16, 32, 32), (16384, 1024, 32, 1))
        buf9 = buf8
        del buf8
        triton_poi_fused_convolution_relu_2[grid(65536)](buf9, primals_9, 
            65536, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_9
        buf10 = empty_strided_cuda((4, 16, 16, 16), (4096, 256, 16, 1),
            torch.int64)
        buf11 = empty_strided_cuda((4, 16, 16, 16), (4096, 256, 16, 1),
            torch.float32)
        triton_poi_fused_max_pool2d_with_indices_3[grid(16384)](buf9, buf10,
            buf11, 16384, XBLOCK=256, num_warps=4, num_stages=1)
        buf12 = empty_strided_cuda((4, 3), (3, 1), torch.float32)
        extern_kernels.addmm(primals_11, reinterpret_tensor(buf11, (4, 4096
            ), (4096, 1), 0), reinterpret_tensor(primals_10, (4096, 3), (1,
            4096), 0), alpha=1, beta=1, out=buf12)
        del primals_11
        buf13 = empty_strided_cuda((4, 4096), (4096, 1), torch.float32)
        extern_kernels.addmm(primals_13, buf12, reinterpret_tensor(
            primals_12, (3, 4096), (1, 3), 0), alpha=1, beta=1, out=buf13)
        del primals_13
        buf14 = torch.ops.aten.max_unpool2d.default(reinterpret_tensor(
            buf13, (4, 16, 16, 16), (4096, 256, 16, 1), 0), buf10, [32, 32])
        del buf13
        buf15 = buf14
        del buf14
        buf16 = buf15
        del buf15
        triton_poi_fused_relu_4[grid(65536)](buf16, 65536, XBLOCK=512,
            num_warps=4, num_stages=1)
        buf17 = extern_kernels.convolution(buf16, primals_14, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf17, (4, 16, 32, 32), (16384, 1024, 32, 1))
        buf18 = buf17
        del buf17
        triton_poi_fused_convolution_relu_2[grid(65536)](buf18, primals_15,
            65536, XBLOCK=512, num_warps=4, num_stages=1)
        del primals_15
        buf19 = extern_kernels.convolution(buf18, primals_16, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf19, (4, 8, 32, 32), (8192, 1024, 32, 1))
        buf20 = buf19
        del buf19
        triton_poi_fused_convolution_5[grid(32768)](buf20, primals_17, 
            32768, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_17
        buf21 = torch.ops.aten.max_unpool2d.default(buf20, buf5, [64, 64])
        del buf20
        buf22 = buf21
        del buf21
        buf23 = buf22
        del buf22
        triton_poi_fused_relu_6[grid(131072)](buf23, 131072, XBLOCK=1024,
            num_warps=4, num_stages=1)
        buf24 = extern_kernels.convolution(buf23, primals_18, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf24, (4, 8, 64, 64), (32768, 4096, 64, 1))
        buf25 = buf24
        del buf24
        triton_poi_fused_convolution_relu_0[grid(131072)](buf25, primals_19,
            131072, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_19
        buf26 = extern_kernels.convolution(buf25, primals_20, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf26, (4, 1, 64, 64), (4096, 4096, 64, 1))
        buf27 = buf26
        del buf26
        triton_poi_fused_convolution_7[grid(16384)](buf27, primals_21, 
            16384, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_21
    return (buf27, primals_1, primals_3, primals_4, primals_6, primals_8,
        primals_14, primals_16, primals_18, primals_20, buf1, buf3, buf4,
        buf5, buf7, buf9, buf10, reinterpret_tensor(buf11, (4, 4096), (4096,
        1), 0), buf12, buf16, buf18, buf23, buf25, primals_12, primals_10)


class AutoencoderNew(torch.nn.Module):

    def __init__(self):
        super().__init__()
        self.conv1 = torch.nn.Conv2d(1, 8, 3, padding=1)
        self.conv2 = torch.nn.Conv2d(8, 8, 3, padding=1)
        self.conv3 = torch.nn.Conv2d(8, 16, 3, padding=1)
        self.conv4 = torch.nn.Conv2d(16, 16, 3, padding=1)
        self.lc1 = torch.nn.Linear(16 * 16 * 16, 3)
        self.lc2 = torch.nn.Linear(3, 16 * 16 * 16)
        self.trans1 = torch.nn.ConvTranspose2d(16, 16, 3, padding=1)
        self.trans2 = torch.nn.ConvTranspose2d(16, 8, 3, padding=1)
        self.trans3 = torch.nn.ConvTranspose2d(8, 8, 3, padding=1)
        self.trans4 = torch.nn.ConvTranspose2d(8, 1, 3, padding=1)
        self.mp = torch.nn.MaxPool2d(2, return_indices=True)
        self.up = torch.nn.MaxUnpool2d(2)
        self.relu = torch.nn.ReLU()

    def encoder(self, x):
        x = self.conv1(x)
        x = self.relu(x)
        x = self.conv2(x)
        x = self.relu(x)
        s1 = x.size()
        x, ind1 = self.mp(x)
        x = self.conv3(x)
        x = self.relu(x)
        x = self.conv4(x)
        x = self.relu(x)
        s2 = x.size()
        x, ind2 = self.mp(x)
        x = x.view(int(x.size()[0]), -1)
        x = self.lc1(x)
        return x, ind1, s1, ind2, s2

    def decoder(self, x, ind1, s1, ind2, s2):
        x = self.lc2(x)
        x = x.view(int(x.size()[0]), 16, 16, 16)
        x = self.up(x, ind2, output_size=s2)
        x = self.relu(x)
        x = self.trans1(x)
        x = self.relu(x)
        x = self.trans2(x)
        x = self.up(x, ind1, output_size=s1)
        x = self.relu(x)
        x = self.trans3(x)
        x = self.relu(x)
        x = self.trans4(x)
        return x

    def forward(self, input_0):
        primals_1 = self.conv1.weight
        primals_2 = self.conv1.bias
        primals_4 = self.conv2.weight
        primals_5 = self.conv2.bias
        primals_6 = self.conv3.weight
        primals_7 = self.conv3.bias
        primals_8 = self.conv4.weight
        primals_9 = self.conv4.bias
        primals_10 = self.lc1.weight
        primals_11 = self.lc1.bias
        primals_12 = self.lc2.weight
        primals_13 = self.lc2.bias
        primals_14 = self.trans1.weight
        primals_15 = self.trans1.bias
        primals_16 = self.trans2.weight
        primals_17 = self.trans2.bias
        primals_18 = self.trans3.weight
        primals_19 = self.trans3.bias
        primals_20 = self.trans4.weight
        primals_21 = self.trans4.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]
