# 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/36/c36zo3vuurwlz2vsilrwbwcrp4wjjakmhyv4hqisrsvpbkxge5p5.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], [2, 2], [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 = 98304
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = xindex
    x1 = (xindex // 4096) % 6
    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/ws/cwsax66rhsevxcmn5xbeuksoag7q56reoaydkjeyg7zazfngkbdy.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, [4, 4], [1, 1], [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, 4, 64, [1, 1], [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=[131072], 
    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), 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': 16, '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_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 89304
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 61
    x1 = (xindex // 61) % 61
    x2 = (xindex // 3721)
    x3 = xindex % 3721
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (64*x1) + (4096*x2)), xmask)
    tmp1 = tl.load(in_ptr0 + (1 + x0 + (64*x1) + (4096*x2)), xmask)
    tmp3 = tl.load(in_ptr0 + (2 + x0 + (64*x1) + (4096*x2)), xmask)
    tmp5 = tl.load(in_ptr0 + (3 + x0 + (64*x1) + (4096*x2)), xmask)
    tmp7 = tl.load(in_ptr0 + (64 + x0 + (64*x1) + (4096*x2)), xmask)
    tmp9 = tl.load(in_ptr0 + (65 + x0 + (64*x1) + (4096*x2)), xmask)
    tmp11 = tl.load(in_ptr0 + (66 + x0 + (64*x1) + (4096*x2)), xmask)
    tmp13 = tl.load(in_ptr0 + (67 + x0 + (64*x1) + (4096*x2)), xmask)
    tmp15 = tl.load(in_ptr0 + (128 + x0 + (64*x1) + (4096*x2)), xmask)
    tmp17 = tl.load(in_ptr0 + (129 + x0 + (64*x1) + (4096*x2)), xmask)
    tmp19 = tl.load(in_ptr0 + (130 + x0 + (64*x1) + (4096*x2)), xmask)
    tmp21 = tl.load(in_ptr0 + (131 + x0 + (64*x1) + (4096*x2)), xmask)
    tmp23 = tl.load(in_ptr0 + (192 + x0 + (64*x1) + (4096*x2)), xmask)
    tmp25 = tl.load(in_ptr0 + (193 + x0 + (64*x1) + (4096*x2)), xmask)
    tmp27 = tl.load(in_ptr0 + (194 + x0 + (64*x1) + (4096*x2)), xmask)
    tmp29 = tl.load(in_ptr0 + (195 + x0 + (64*x1) + (4096*x2)), xmask)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp8 = triton_helpers.maximum(tmp7, tmp6)
    tmp10 = triton_helpers.maximum(tmp9, tmp8)
    tmp12 = triton_helpers.maximum(tmp11, tmp10)
    tmp14 = triton_helpers.maximum(tmp13, tmp12)
    tmp16 = triton_helpers.maximum(tmp15, tmp14)
    tmp18 = triton_helpers.maximum(tmp17, tmp16)
    tmp20 = triton_helpers.maximum(tmp19, tmp18)
    tmp22 = triton_helpers.maximum(tmp21, tmp20)
    tmp24 = triton_helpers.maximum(tmp23, tmp22)
    tmp26 = triton_helpers.maximum(tmp25, tmp24)
    tmp28 = triton_helpers.maximum(tmp27, tmp26)
    tmp30 = triton_helpers.maximum(tmp29, tmp28)
    tmp31 = tmp1 > tmp0
    tmp32 = tl.full([1], 1, tl.int8)
    tmp33 = tl.full([1], 0, tl.int8)
    tmp34 = tl.where(tmp31, tmp32, tmp33)
    tmp35 = tmp3 > tmp2
    tmp36 = tl.full([1], 2, tl.int8)
    tmp37 = tl.where(tmp35, tmp36, tmp34)
    tmp38 = tmp5 > tmp4
    tmp39 = tl.full([1], 3, tl.int8)
    tmp40 = tl.where(tmp38, tmp39, tmp37)
    tmp41 = tmp7 > tmp6
    tmp42 = tl.full([1], 4, tl.int8)
    tmp43 = tl.where(tmp41, tmp42, tmp40)
    tmp44 = tmp9 > tmp8
    tmp45 = tl.full([1], 5, tl.int8)
    tmp46 = tl.where(tmp44, tmp45, tmp43)
    tmp47 = tmp11 > tmp10
    tmp48 = tl.full([1], 6, tl.int8)
    tmp49 = tl.where(tmp47, tmp48, tmp46)
    tmp50 = tmp13 > tmp12
    tmp51 = tl.full([1], 7, tl.int8)
    tmp52 = tl.where(tmp50, tmp51, tmp49)
    tmp53 = tmp15 > tmp14
    tmp54 = tl.full([1], 8, tl.int8)
    tmp55 = tl.where(tmp53, tmp54, tmp52)
    tmp56 = tmp17 > tmp16
    tmp57 = tl.full([1], 9, tl.int8)
    tmp58 = tl.where(tmp56, tmp57, tmp55)
    tmp59 = tmp19 > tmp18
    tmp60 = tl.full([1], 10, tl.int8)
    tmp61 = tl.where(tmp59, tmp60, tmp58)
    tmp62 = tmp21 > tmp20
    tmp63 = tl.full([1], 11, tl.int8)
    tmp64 = tl.where(tmp62, tmp63, tmp61)
    tmp65 = tmp23 > tmp22
    tmp66 = tl.full([1], 12, tl.int8)
    tmp67 = tl.where(tmp65, tmp66, tmp64)
    tmp68 = tmp25 > tmp24
    tmp69 = tl.full([1], 13, tl.int8)
    tmp70 = tl.where(tmp68, tmp69, tmp67)
    tmp71 = tmp27 > tmp26
    tmp72 = tl.full([1], 14, tl.int8)
    tmp73 = tl.where(tmp71, tmp72, tmp70)
    tmp74 = tmp29 > tmp28
    tmp75 = tl.full([1], 15, tl.int8)
    tmp76 = tl.where(tmp74, tmp75, tmp73)
    tmp77 = tl.full([1], 4, tl.int32)
    tmp78 = tl.where((tmp76 < 0) != (tmp77 < 0), tl.where(tmp76 % tmp77 != 0, tmp76 // tmp77 - 1, tmp76 // tmp77), tmp76 // tmp77)
    tmp79 = tmp78 * tmp77
    tmp80 = tmp76 - tmp79
    tmp81 = x1
    tmp82 = tmp81 + tmp78
    tmp83 = x0
    tmp84 = tmp83 + tmp80
    tmp85 = tl.full([1], 64, tl.int64)
    tmp86 = tmp82 * tmp85
    tmp87 = tmp86 + tmp84
    tl.store(out_ptr0 + (x3 + (3744*x2)), tmp30, xmask)
    tl.store(out_ptr2 + (x4), tmp87, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/pd/cpd5pehoftbarvz3ub724yds53aewxwb2mmv2bg7lfmih66qkdwi.py
# Topologically Sorted Source Nodes: [x_3, x_4], Original ATen: [aten.convolution, aten.relu]
# Source node to ATen node mapping:
#   x_3 => convolution_1
#   x_4 => relu_1
# Graph fragment:
#   %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%getitem, %primals_4, %primals_5, [1, 1], [2, 2], [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_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=[262144], 
    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, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_relu_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_relu_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 238144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 3721) % 16
    x0 = xindex % 3721
    x4 = (xindex // 3721)
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(out_ptr0 + (x0 + (3744*x4)), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/qq/cqqxvitrybyfuwfcfybcsvehkdckwzucobzhcpmw3en2cctbjhou.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_1, [2, 2], [1, 1], [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, 61, [1, 1], [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=[262144], 
    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 = 230400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 60
    x1 = (xindex // 60) % 60
    x2 = (xindex // 3600)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (61*x1) + (3744*x2)), xmask)
    tmp1 = tl.load(in_ptr0 + (1 + x0 + (61*x1) + (3744*x2)), xmask)
    tmp7 = tl.load(in_ptr0 + (61 + x0 + (61*x1) + (3744*x2)), xmask)
    tmp12 = tl.load(in_ptr0 + (62 + x0 + (61*x1) + (3744*x2)), xmask)
    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 = x1
    tmp22 = tmp21 + tmp18
    tmp23 = x0
    tmp24 = tmp23 + tmp20
    tmp25 = tl.full([1], 61, tl.int64)
    tmp26 = tmp22 * tmp25
    tmp27 = tmp26 + tmp24
    tl.store(out_ptr0 + (x3), tmp27, xmask)
    tl.store(out_ptr1 + (x3), tmp16, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/6b/c6bha3egneow7eyxkfbjo447hqoozs3dafjrwsn4lvlwazlneymm.py
# Topologically Sorted Source Nodes: [x_7], Original ATen: [aten.relu]
# Source node to ATen node mapping:
#   x_7 => relu_2
# Graph fragment:
#   %relu_2 : [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=[262144], 
    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 = 238144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(in_out_ptr0 + (x0), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/hs/chsb7mkhhxxkwf7y6hsg3tdfkuq3dzjpttntloehuopyuyssb25p.py
# Topologically Sorted Source Nodes: [x_8], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   x_8 => convolution_2
# Graph fragment:
#   %convolution_2 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_2, %primals_6, %primals_7, [1, 1], [2, 2], [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=[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), 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 = 89304
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 3721) % 6
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x3), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ra/craec5gqxwtnaszxa2sv6dhtcntjs6zsekqcy2qnqc2g3pql2j6k.py
# Topologically Sorted Source Nodes: [x_10], Original ATen: [aten.relu]
# Source node to ATen node mapping:
#   x_10 => relu_3
# Graph fragment:
#   %relu_3 : [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 = 98304
    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/ng/cnglk7iqo3gr37e4n5s6qhnpvula3xtbud533gauvkttxcgmkwnl.py
# Topologically Sorted Source Nodes: [x_11], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   x_11 => convolution_3
# Graph fragment:
#   %convolution_3 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%relu_3, %primals_8, %primals_9, [1, 1], [2, 2], [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=[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_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 = 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 = args
    args.clear()
    assert_size_stride(primals_1, (6, 3, 5, 5), (75, 25, 5, 1))
    assert_size_stride(primals_2, (6, ), (1, ))
    assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_4, (16, 6, 5, 5), (150, 25, 5, 1))
    assert_size_stride(primals_5, (16, ), (1, ))
    assert_size_stride(primals_6, (16, 6, 5, 5), (150, 25, 5, 1))
    assert_size_stride(primals_7, (6, ), (1, ))
    assert_size_stride(primals_8, (6, 3, 5, 5), (75, 25, 5, 1))
    assert_size_stride(primals_9, (3, ), (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=(2, 2), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 6, 64, 64), (24576, 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, 98304, grid=grid(98304), stream=stream0)
        del primals_2
        buf2 = empty_strided_cuda((4, 6, 61, 61), (22464, 3744, 61, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 6, 61, 61), (22326, 3721, 61, 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(buf1, buf2, buf4, 89304, grid=grid(89304), stream=stream0)
        # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.convolution]
        buf5 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1), padding=(2, 2), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 16, 61, 61), (59536, 3721, 61, 1))
        buf6 = empty_strided_cuda((4, 16, 61, 61), (59904, 3744, 61, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_3, x_4], Original ATen: [aten.convolution, aten.relu]
        triton_poi_fused_convolution_relu_2.run(buf5, primals_5, buf6, 238144, grid=grid(238144), stream=stream0)
        del buf5
        del primals_5
        buf7 = empty_strided_cuda((4, 16, 60, 60), (57600, 3600, 60, 1), torch.int64)
        buf8 = empty_strided_cuda((4, 16, 60, 60), (57600, 3600, 60, 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(buf6, buf7, buf8, 230400, grid=grid(230400), stream=stream0)
        # Topologically Sorted Source Nodes: [max_pool2d_1, x_6], Original ATen: [aten.max_pool2d_with_indices, aten.max_unpool2d]
        buf9 = torch.ops.aten.max_unpool2d.default(buf8, buf7, [61, 61])
        del buf8
        buf10 = buf9
        del buf9
        buf11 = buf10; del buf10  # reuse
        # Topologically Sorted Source Nodes: [x_7], Original ATen: [aten.relu]
        triton_poi_fused_relu_4.run(buf11, 238144, grid=grid(238144), stream=stream0)
        # Topologically Sorted Source Nodes: [x_8], Original ATen: [aten.convolution]
        buf12 = extern_kernels.convolution(buf11, primals_6, stride=(1, 1), padding=(2, 2), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 6, 61, 61), (22326, 3721, 61, 1))
        buf13 = buf12; del buf12  # reuse
        # Topologically Sorted Source Nodes: [x_8], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_5.run(buf13, primals_7, 89304, grid=grid(89304), stream=stream0)
        del primals_7
        # Topologically Sorted Source Nodes: [x_8, x_9], Original ATen: [aten.convolution, aten.max_unpool2d]
        buf14 = torch.ops.aten.max_unpool2d.default(buf13, buf4, [64, 64])
        del buf13
        buf15 = buf14
        del buf14
        buf16 = buf15; del buf15  # reuse
        # Topologically Sorted Source Nodes: [x_10], Original ATen: [aten.relu]
        triton_poi_fused_relu_6.run(buf16, 98304, grid=grid(98304), stream=stream0)
        # Topologically Sorted Source Nodes: [x_11], Original ATen: [aten.convolution]
        buf17 = extern_kernels.convolution(buf16, primals_8, stride=(1, 1), padding=(2, 2), dilation=(1, 1), transposed=True, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf17, (4, 3, 64, 64), (12288, 4096, 64, 1))
        buf18 = buf17; del buf17  # reuse
        # Topologically Sorted Source Nodes: [x_11], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_7.run(buf18, primals_9, 49152, grid=grid(49152), stream=stream0)
        del primals_9
    return (buf18, primals_1, primals_3, primals_4, primals_6, primals_8, buf1, buf2, buf4, buf6, buf7, buf11, buf16, )


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((6, 3, 5, 5), (75, 25, 5, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((6, ), (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((16, 6, 5, 5), (150, 25, 5, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((16, 6, 5, 5), (150, 25, 5, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((6, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((6, 3, 5, 5), (75, 25, 5, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = 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])
    return print_performance(fn, times=times, repeat=repeat)


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

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data


class Autoencoder(nn.Module):

    def __init__(self):
        super(Autoencoder, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, padding=2, kernel_size=5)
        self.maxpool1 = nn.MaxPool2d(4, stride=1, return_indices=True)
        self.conv2 = nn.Conv2d(6, 16, padding=2, kernel_size=5)
        self.maxpool2 = nn.MaxPool2d(2, stride=1, return_indices=True)
        self.unpool2 = nn.MaxUnpool2d(2, stride=1)
        self.unconv2 = nn.ConvTranspose2d(16, 6, padding=2, kernel_size=5)
        self.unpool1 = nn.MaxUnpool2d(4, stride=1)
        self.unconv1 = nn.ConvTranspose2d(6, 3, padding=2, kernel_size=5)

    def forward(self, x):
        x = self.conv1(x)
        x = F.relu(x)
        x, indices1 = self.maxpool1(x)
        x = self.conv2(x)
        x = F.relu(x)
        x, indices2 = self.maxpool2(x)
        x = self.unpool2(x, indices2)
        x = F.relu(x)
        x = self.unconv2(x)
        x = self.unpool1(x, indices1)
        x = F.relu(x)
        x = self.unconv1(x)
        return x

    def encoder(self, x):
        x = self.conv1(x)
        x = F.relu(x)
        x, indices1 = self.maxpool1(x)
        x = self.conv2(x)
        x = F.relu(x)
        x, indices2 = self.maxpool2(x)
        return x, indices1, indices2

    def decoder(self, x, indices1, indices2):
        x = self.conv1(x)
        x = F.relu(x)
        x, indices1 = self.maxpool1(x)
        x = self.conv2(x)
        x = F.relu(x)
        x, indices2 = self.maxpool2(x)
        return x, indices1, indices2


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
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@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 % 6
    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_ptr2,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 89304
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 61
    x1 = xindex // 61 % 61
    x2 = xindex // 3721
    x3 = xindex % 3721
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1 + 4096 * x2), xmask)
    tmp1 = tl.load(in_ptr0 + (1 + x0 + 64 * x1 + 4096 * x2), xmask)
    tmp3 = tl.load(in_ptr0 + (2 + x0 + 64 * x1 + 4096 * x2), xmask)
    tmp5 = tl.load(in_ptr0 + (3 + x0 + 64 * x1 + 4096 * x2), xmask)
    tmp7 = tl.load(in_ptr0 + (64 + x0 + 64 * x1 + 4096 * x2), xmask)
    tmp9 = tl.load(in_ptr0 + (65 + x0 + 64 * x1 + 4096 * x2), xmask)
    tmp11 = tl.load(in_ptr0 + (66 + x0 + 64 * x1 + 4096 * x2), xmask)
    tmp13 = tl.load(in_ptr0 + (67 + x0 + 64 * x1 + 4096 * x2), xmask)
    tmp15 = tl.load(in_ptr0 + (128 + x0 + 64 * x1 + 4096 * x2), xmask)
    tmp17 = tl.load(in_ptr0 + (129 + x0 + 64 * x1 + 4096 * x2), xmask)
    tmp19 = tl.load(in_ptr0 + (130 + x0 + 64 * x1 + 4096 * x2), xmask)
    tmp21 = tl.load(in_ptr0 + (131 + x0 + 64 * x1 + 4096 * x2), xmask)
    tmp23 = tl.load(in_ptr0 + (192 + x0 + 64 * x1 + 4096 * x2), xmask)
    tmp25 = tl.load(in_ptr0 + (193 + x0 + 64 * x1 + 4096 * x2), xmask)
    tmp27 = tl.load(in_ptr0 + (194 + x0 + 64 * x1 + 4096 * x2), xmask)
    tmp29 = tl.load(in_ptr0 + (195 + x0 + 64 * x1 + 4096 * x2), xmask)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp8 = triton_helpers.maximum(tmp7, tmp6)
    tmp10 = triton_helpers.maximum(tmp9, tmp8)
    tmp12 = triton_helpers.maximum(tmp11, tmp10)
    tmp14 = triton_helpers.maximum(tmp13, tmp12)
    tmp16 = triton_helpers.maximum(tmp15, tmp14)
    tmp18 = triton_helpers.maximum(tmp17, tmp16)
    tmp20 = triton_helpers.maximum(tmp19, tmp18)
    tmp22 = triton_helpers.maximum(tmp21, tmp20)
    tmp24 = triton_helpers.maximum(tmp23, tmp22)
    tmp26 = triton_helpers.maximum(tmp25, tmp24)
    tmp28 = triton_helpers.maximum(tmp27, tmp26)
    tmp30 = triton_helpers.maximum(tmp29, tmp28)
    tmp31 = tmp1 > tmp0
    tmp32 = tl.full([1], 1, tl.int8)
    tmp33 = tl.full([1], 0, tl.int8)
    tmp34 = tl.where(tmp31, tmp32, tmp33)
    tmp35 = tmp3 > tmp2
    tmp36 = tl.full([1], 2, tl.int8)
    tmp37 = tl.where(tmp35, tmp36, tmp34)
    tmp38 = tmp5 > tmp4
    tmp39 = tl.full([1], 3, tl.int8)
    tmp40 = tl.where(tmp38, tmp39, tmp37)
    tmp41 = tmp7 > tmp6
    tmp42 = tl.full([1], 4, tl.int8)
    tmp43 = tl.where(tmp41, tmp42, tmp40)
    tmp44 = tmp9 > tmp8
    tmp45 = tl.full([1], 5, tl.int8)
    tmp46 = tl.where(tmp44, tmp45, tmp43)
    tmp47 = tmp11 > tmp10
    tmp48 = tl.full([1], 6, tl.int8)
    tmp49 = tl.where(tmp47, tmp48, tmp46)
    tmp50 = tmp13 > tmp12
    tmp51 = tl.full([1], 7, tl.int8)
    tmp52 = tl.where(tmp50, tmp51, tmp49)
    tmp53 = tmp15 > tmp14
    tmp54 = tl.full([1], 8, tl.int8)
    tmp55 = tl.where(tmp53, tmp54, tmp52)
    tmp56 = tmp17 > tmp16
    tmp57 = tl.full([1], 9, tl.int8)
    tmp58 = tl.where(tmp56, tmp57, tmp55)
    tmp59 = tmp19 > tmp18
    tmp60 = tl.full([1], 10, tl.int8)
    tmp61 = tl.where(tmp59, tmp60, tmp58)
    tmp62 = tmp21 > tmp20
    tmp63 = tl.full([1], 11, tl.int8)
    tmp64 = tl.where(tmp62, tmp63, tmp61)
    tmp65 = tmp23 > tmp22
    tmp66 = tl.full([1], 12, tl.int8)
    tmp67 = tl.where(tmp65, tmp66, tmp64)
    tmp68 = tmp25 > tmp24
    tmp69 = tl.full([1], 13, tl.int8)
    tmp70 = tl.where(tmp68, tmp69, tmp67)
    tmp71 = tmp27 > tmp26
    tmp72 = tl.full([1], 14, tl.int8)
    tmp73 = tl.where(tmp71, tmp72, tmp70)
    tmp74 = tmp29 > tmp28
    tmp75 = tl.full([1], 15, tl.int8)
    tmp76 = tl.where(tmp74, tmp75, tmp73)
    tmp77 = tl.full([1], 4, tl.int32)
    tmp78 = tl.where((tmp76 < 0) != (tmp77 < 0), tl.where(tmp76 % tmp77 != 
        0, tmp76 // tmp77 - 1, tmp76 // tmp77), tmp76 // tmp77)
    tmp79 = tmp78 * tmp77
    tmp80 = tmp76 - tmp79
    tmp81 = x1
    tmp82 = tmp81 + tmp78
    tmp83 = x0
    tmp84 = tmp83 + tmp80
    tmp85 = tl.full([1], 64, tl.int64)
    tmp86 = tmp82 * tmp85
    tmp87 = tmp86 + tmp84
    tl.store(out_ptr0 + (x3 + 3744 * x2), tmp30, xmask)
    tl.store(out_ptr2 + x4, tmp87, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_2(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 238144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 3721 % 16
    x0 = xindex % 3721
    x4 = xindex // 3721
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(out_ptr0 + (x0 + 3744 * x4), tmp4, xmask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_3(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 230400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 60
    x1 = xindex // 60 % 60
    x2 = xindex // 3600
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 61 * x1 + 3744 * x2), xmask)
    tmp1 = tl.load(in_ptr0 + (1 + x0 + 61 * x1 + 3744 * x2), xmask)
    tmp7 = tl.load(in_ptr0 + (61 + x0 + 61 * x1 + 3744 * x2), xmask)
    tmp12 = tl.load(in_ptr0 + (62 + x0 + 61 * x1 + 3744 * x2), xmask)
    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 = x1
    tmp22 = tmp21 + tmp18
    tmp23 = x0
    tmp24 = tmp23 + tmp20
    tmp25 = tl.full([1], 61, tl.int64)
    tmp26 = tmp22 * tmp25
    tmp27 = tmp26 + tmp24
    tl.store(out_ptr0 + x3, tmp27, xmask)
    tl.store(out_ptr1 + x3, tmp16, xmask)


@triton.jit
def triton_poi_fused_relu_4(in_out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 238144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(in_out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_convolution_5(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 89304
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 3721 % 6
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


@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)
    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) = args
    args.clear()
    assert_size_stride(primals_1, (6, 3, 5, 5), (75, 25, 5, 1))
    assert_size_stride(primals_2, (6,), (1,))
    assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 4096, 64, 1))
    assert_size_stride(primals_4, (16, 6, 5, 5), (150, 25, 5, 1))
    assert_size_stride(primals_5, (16,), (1,))
    assert_size_stride(primals_6, (16, 6, 5, 5), (150, 25, 5, 1))
    assert_size_stride(primals_7, (6,), (1,))
    assert_size_stride(primals_8, (6, 3, 5, 5), (75, 25, 5, 1))
    assert_size_stride(primals_9, (3,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(2, 2), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 6, 64, 64), (24576, 4096, 64, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_relu_0[grid(98304)](buf1, primals_2, 
            98304, XBLOCK=512, num_warps=8, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((4, 6, 61, 61), (22464, 3744, 61, 1),
            torch.float32)
        buf4 = empty_strided_cuda((4, 6, 61, 61), (22326, 3721, 61, 1),
            torch.int64)
        triton_poi_fused_max_pool2d_with_indices_1[grid(89304)](buf1, buf2,
            buf4, 89304, XBLOCK=512, num_warps=8, num_stages=1)
        buf5 = extern_kernels.convolution(buf2, primals_4, stride=(1, 1),
            padding=(2, 2), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf5, (4, 16, 61, 61), (59536, 3721, 61, 1))
        buf6 = empty_strided_cuda((4, 16, 61, 61), (59904, 3744, 61, 1),
            torch.float32)
        triton_poi_fused_convolution_relu_2[grid(238144)](buf5, primals_5,
            buf6, 238144, XBLOCK=512, num_warps=8, num_stages=1)
        del buf5
        del primals_5
        buf7 = empty_strided_cuda((4, 16, 60, 60), (57600, 3600, 60, 1),
            torch.int64)
        buf8 = empty_strided_cuda((4, 16, 60, 60), (57600, 3600, 60, 1),
            torch.float32)
        triton_poi_fused_max_pool2d_with_indices_3[grid(230400)](buf6, buf7,
            buf8, 230400, XBLOCK=512, num_warps=8, num_stages=1)
        buf9 = torch.ops.aten.max_unpool2d.default(buf8, buf7, [61, 61])
        del buf8
        buf10 = buf9
        del buf9
        buf11 = buf10
        del buf10
        triton_poi_fused_relu_4[grid(238144)](buf11, 238144, XBLOCK=1024,
            num_warps=4, num_stages=1)
        buf12 = extern_kernels.convolution(buf11, primals_6, stride=(1, 1),
            padding=(2, 2), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf12, (4, 6, 61, 61), (22326, 3721, 61, 1))
        buf13 = buf12
        del buf12
        triton_poi_fused_convolution_5[grid(89304)](buf13, primals_7, 89304,
            XBLOCK=512, num_warps=8, num_stages=1)
        del primals_7
        buf14 = torch.ops.aten.max_unpool2d.default(buf13, buf4, [64, 64])
        del buf13
        buf15 = buf14
        del buf14
        buf16 = buf15
        del buf15
        triton_poi_fused_relu_6[grid(98304)](buf16, 98304, XBLOCK=1024,
            num_warps=4, num_stages=1)
        buf17 = extern_kernels.convolution(buf16, primals_8, stride=(1, 1),
            padding=(2, 2), dilation=(1, 1), transposed=True,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf17, (4, 3, 64, 64), (12288, 4096, 64, 1))
        buf18 = buf17
        del buf17
        triton_poi_fused_convolution_7[grid(49152)](buf18, primals_9, 49152,
            XBLOCK=512, num_warps=4, num_stages=1)
        del primals_9
    return (buf18, primals_1, primals_3, primals_4, primals_6, primals_8,
        buf1, buf2, buf4, buf6, buf7, buf11, buf16)


class AutoencoderNew(nn.Module):

    def __init__(self):
        super(AutoencoderNew, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, padding=2, kernel_size=5)
        self.maxpool1 = nn.MaxPool2d(4, stride=1, return_indices=True)
        self.conv2 = nn.Conv2d(6, 16, padding=2, kernel_size=5)
        self.maxpool2 = nn.MaxPool2d(2, stride=1, return_indices=True)
        self.unpool2 = nn.MaxUnpool2d(2, stride=1)
        self.unconv2 = nn.ConvTranspose2d(16, 6, padding=2, kernel_size=5)
        self.unpool1 = nn.MaxUnpool2d(4, stride=1)
        self.unconv1 = nn.ConvTranspose2d(6, 3, padding=2, kernel_size=5)

    def encoder(self, x):
        x = self.conv1(x)
        x = F.relu(x)
        x, indices1 = self.maxpool1(x)
        x = self.conv2(x)
        x = F.relu(x)
        x, indices2 = self.maxpool2(x)
        return x, indices1, indices2

    def decoder(self, x, indices1, indices2):
        x = self.conv1(x)
        x = F.relu(x)
        x, indices1 = self.maxpool1(x)
        x = self.conv2(x)
        x = F.relu(x)
        x, indices2 = self.maxpool2(x)
        return x, indices1, indices2

    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.unconv2.weight
        primals_7 = self.unconv2.bias
        primals_8 = self.unconv1.weight
        primals_9 = self.unconv1.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])
        return output[0]
