# AOT ID: ['0_inference']
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/mh/cmhoygvatdndbo2urprw34fxbqxigbkprfxpma4eso7ktldw5spf.py
# Topologically Sorted Source Nodes: [x1, pad], Original ATen: [aten.max_pool2d_with_indices, aten.replication_pad2d]
# Source node to ATen node mapping:
#   pad => _unsafe_index, _unsafe_index_1
#   x1 => _low_memory_max_pool2d_with_offsets
# Graph fragment:
#   %_low_memory_max_pool2d_with_offsets : [num_users=1] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%arg0_1, [2, 2], [2, 2], [0, 0], [1, 1], False), kwargs = {})
#   %_unsafe_index : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%getitem, [None, None, %clamp_max, None]), kwargs = {})
#   %_unsafe_index_1 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index, [None, None, None, %clamp_max_1]), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_replication_pad2d_0 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_replication_pad2d_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=[256], 
    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_max_pool2d_with_indices_replication_pad2d_0', '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_replication_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 3
    x1 = (xindex // 3) % 3
    x2 = (xindex // 9)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + ((2*((1) * ((1) <= (x0)) + (x0) * ((x0) < (1)))) + (8*((1) * ((1) <= (x1)) + (x1) * ((x1) < (1)))) + (16*x2)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (2*((1) * ((1) <= (x0)) + (x0) * ((x0) < (1)))) + (8*((1) * ((1) <= (x1)) + (x1) * ((x1) < (1)))) + (16*x2)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (4 + (2*((1) * ((1) <= (x0)) + (x0) * ((x0) < (1)))) + (8*((1) * ((1) <= (x1)) + (x1) * ((x1) < (1)))) + (16*x2)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (5 + (2*((1) * ((1) <= (x0)) + (x0) * ((x0) < (1)))) + (8*((1) * ((1) <= (x1)) + (x1) * ((x1) < (1)))) + (16*x2)), xmask, eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tl.store(out_ptr0 + (x3), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/za/czakhmsulztwf7pxr4hzejnq2bung6ciqo2vcce5jpzgjgwrpinp.py
# Topologically Sorted Source Nodes: [x1, pad, x2], Original ATen: [aten.max_pool2d_with_indices, aten.replication_pad2d]
# Source node to ATen node mapping:
#   pad => _unsafe_index, _unsafe_index_1
#   x1 => _low_memory_max_pool2d_with_offsets
#   x2 => _low_memory_max_pool2d_with_offsets_1
# Graph fragment:
#   %_low_memory_max_pool2d_with_offsets : [num_users=1] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%arg0_1, [2, 2], [2, 2], [0, 0], [1, 1], False), kwargs = {})
#   %_unsafe_index : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%getitem, [None, None, %clamp_max, None]), kwargs = {})
#   %_unsafe_index_1 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index, [None, None, None, %clamp_max_1]), kwargs = {})
#   %_low_memory_max_pool2d_with_offsets_1 : [num_users=1] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%_unsafe_index_1, [2, 2], [1, 1], [0, 0], [1, 1], False), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_replication_pad2d_1 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_replication_pad2d_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=[64], 
    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_max_pool2d_with_indices_replication_pad2d_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_replication_pad2d_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2
    x1 = (xindex // 2) % 2
    x2 = (xindex // 4)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (3*x1) + (9*x2)), xmask)
    tmp1 = tl.load(in_ptr0 + (1 + x0 + (3*x1) + (9*x2)), xmask)
    tmp3 = tl.load(in_ptr0 + (3 + x0 + (3*x1) + (9*x2)), xmask)
    tmp5 = tl.load(in_ptr0 + (4 + x0 + (3*x1) + (9*x2)), xmask)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tl.store(out_ptr0 + (x3), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/6e/c6eaxkrmxzi4itmfvx26z4gnn5t2gtll7xndhiefotqqfyotxckg.py
# Topologically Sorted Source Nodes: [x1, pad, x2, x3, add, add_1, y], Original ATen: [aten.max_pool2d_with_indices, aten.replication_pad2d, aten.add, aten.div]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   pad => _unsafe_index, _unsafe_index_1
#   x1 => _low_memory_max_pool2d_with_offsets
#   x2 => _low_memory_max_pool2d_with_offsets_1
#   x3 => _low_memory_max_pool2d_with_offsets_2
#   y => div
# Graph fragment:
#   %_low_memory_max_pool2d_with_offsets : [num_users=1] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%arg0_1, [2, 2], [2, 2], [0, 0], [1, 1], False), kwargs = {})
#   %_unsafe_index : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%getitem, [None, None, %clamp_max, None]), kwargs = {})
#   %_unsafe_index_1 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index, [None, None, None, %clamp_max_1]), kwargs = {})
#   %_low_memory_max_pool2d_with_offsets_1 : [num_users=1] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%_unsafe_index_1, [2, 2], [1, 1], [0, 0], [1, 1], False), kwargs = {})
#   %_low_memory_max_pool2d_with_offsets_2 : [num_users=1] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%getitem_2, [3, 3], [1, 1], [1, 1], [1, 1], False), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, %getitem_2), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add, %getitem_4), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%add_1, 3.0), kwargs = {})
triton_poi_fused_add_div_max_pool2d_with_indices_replication_pad2d_2 = async_compile.triton('triton_poi_fused_add_div_max_pool2d_with_indices_replication_pad2d_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_div_max_pool2d_with_indices_replication_pad2d_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 14, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_div_max_pool2d_with_indices_replication_pad2d_2(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 2) % 2
    x0 = xindex % 2
    x4 = xindex
    x3 = (xindex // 2)
    tmp52 = tl.load(in_ptr1 + ((2*x0) + (8*x3)), xmask, eviction_policy='evict_last')
    tmp53 = tl.load(in_ptr1 + (1 + (2*x0) + (8*x3)), xmask, eviction_policy='evict_last')
    tmp55 = tl.load(in_ptr1 + (4 + (2*x0) + (8*x3)), xmask, eviction_policy='evict_last')
    tmp57 = tl.load(in_ptr1 + (5 + (2*x0) + (8*x3)), xmask, eviction_policy='evict_last')
    tmp59 = tl.load(in_ptr0 + (x4), xmask)
    tmp0 = (-1) + x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 2, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = (-1) + x0
    tmp7 = tmp6 >= tmp1
    tmp8 = tmp6 < tmp3
    tmp9 = tmp7 & tmp8
    tmp10 = tmp5 & tmp9
    tmp11 = tl.load(in_ptr0 + ((-3) + x4), tmp10 & xmask, other=float("-inf"))
    tmp12 = x0
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp13 & tmp14
    tmp16 = tmp5 & tmp15
    tmp17 = tl.load(in_ptr0 + ((-2) + x4), tmp16 & xmask, other=float("-inf"))
    tmp18 = triton_helpers.maximum(tmp17, tmp11)
    tmp19 = 1 + x0
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp20 & tmp21
    tmp23 = tmp5 & tmp22
    tmp24 = tl.load(in_ptr0 + ((-1) + x4), tmp23 & xmask, other=float("-inf"))
    tmp25 = triton_helpers.maximum(tmp24, tmp18)
    tmp26 = x1
    tmp27 = tmp26 >= tmp1
    tmp28 = tmp26 < tmp3
    tmp29 = tmp27 & tmp28
    tmp30 = tmp29 & tmp9
    tmp31 = tl.load(in_ptr0 + ((-1) + x4), tmp30 & xmask, other=float("-inf"))
    tmp32 = triton_helpers.maximum(tmp31, tmp25)
    tmp33 = tmp29 & tmp15
    tmp34 = tl.load(in_ptr0 + (x4), tmp33 & xmask, other=float("-inf"))
    tmp35 = triton_helpers.maximum(tmp34, tmp32)
    tmp36 = tmp29 & tmp22
    tmp37 = tl.load(in_ptr0 + (1 + x4), tmp36 & xmask, other=float("-inf"))
    tmp38 = triton_helpers.maximum(tmp37, tmp35)
    tmp39 = 1 + x1
    tmp40 = tmp39 >= tmp1
    tmp41 = tmp39 < tmp3
    tmp42 = tmp40 & tmp41
    tmp43 = tmp42 & tmp9
    tmp44 = tl.load(in_ptr0 + (1 + x4), tmp43 & xmask, other=float("-inf"))
    tmp45 = triton_helpers.maximum(tmp44, tmp38)
    tmp46 = tmp42 & tmp15
    tmp47 = tl.load(in_ptr0 + (2 + x4), tmp46 & xmask, other=float("-inf"))
    tmp48 = triton_helpers.maximum(tmp47, tmp45)
    tmp49 = tmp42 & tmp22
    tmp50 = tl.load(in_ptr0 + (3 + x4), tmp49 & xmask, other=float("-inf"))
    tmp51 = triton_helpers.maximum(tmp50, tmp48)
    tmp54 = triton_helpers.maximum(tmp53, tmp52)
    tmp56 = triton_helpers.maximum(tmp55, tmp54)
    tmp58 = triton_helpers.maximum(tmp57, tmp56)
    tmp60 = tmp58 + tmp59
    tmp61 = tmp60 + tmp51
    tmp62 = 0.3333333333333333
    tmp63 = tmp61 * tmp62
    tl.store(in_out_ptr0 + (x4), tmp63, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 3, 3), (36, 9, 3, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x1, pad], Original ATen: [aten.max_pool2d_with_indices, aten.replication_pad2d]
        stream0 = get_raw_stream(0)
        triton_poi_fused_max_pool2d_with_indices_replication_pad2d_0.run(arg0_1, buf0, 144, grid=grid(144), stream=stream0)
        buf1 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x1, pad, x2], Original ATen: [aten.max_pool2d_with_indices, aten.replication_pad2d]
        triton_poi_fused_max_pool2d_with_indices_replication_pad2d_1.run(buf0, buf1, 64, grid=grid(64), stream=stream0)
        del buf0
        buf2 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        buf3 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [x1, pad, x2, x3, add, add_1, y], Original ATen: [aten.max_pool2d_with_indices, aten.replication_pad2d, aten.add, aten.div]
        triton_poi_fused_add_div_max_pool2d_with_indices_replication_pad2d_2.run(buf3, buf1, arg0_1, 64, grid=grid(64), stream=stream0)
        del arg0_1
        del buf1
    return (buf3, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1])
    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


class stack_pool(nn.Module):

    def __init__(self):
        super(stack_pool, self).__init__()
        self.pool2 = nn.MaxPool2d(2, stride=2)
        self.pool2s1 = nn.MaxPool2d(2, stride=1)
        self.pool3s1 = nn.MaxPool2d(3, stride=1, padding=1)
        self.padding = nn.ReplicationPad2d((0, 1, 0, 1))

    def forward(self, x):
        x1 = self.pool2(x)
        x2 = self.pool2s1(self.padding(x1))
        x3 = self.pool3s1(x2)
        y = (x1 + x2 + x3) / 3.0
        return y


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


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

import torch
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
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_max_pool2d_with_indices_replication_pad2d_0(in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 3
    x1 = xindex // 3 % 3
    x2 = xindex // 9
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * (1 * (1 <= x0) + x0 * (x0 < 1)) + 8 * (1 *
        (1 <= x1) + x1 * (x1 < 1)) + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * (1 * (1 <= x0) + x0 * (x0 < 1)) + 8 *
        (1 * (1 <= x1) + x1 * (x1 < 1)) + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (4 + 2 * (1 * (1 <= x0) + x0 * (x0 < 1)) + 8 *
        (1 * (1 <= x1) + x1 * (x1 < 1)) + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp5 = tl.load(in_ptr0 + (5 + 2 * (1 * (1 <= x0) + x0 * (x0 < 1)) + 8 *
        (1 * (1 <= x1) + x1 * (x1 < 1)) + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tl.store(out_ptr0 + x3, tmp6, xmask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_replication_pad2d_1(in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2
    x1 = xindex // 2 % 2
    x2 = xindex // 4
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 3 * x1 + 9 * x2), xmask)
    tmp1 = tl.load(in_ptr0 + (1 + x0 + 3 * x1 + 9 * x2), xmask)
    tmp3 = tl.load(in_ptr0 + (3 + x0 + 3 * x1 + 9 * x2), xmask)
    tmp5 = tl.load(in_ptr0 + (4 + x0 + 3 * x1 + 9 * x2), xmask)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tl.store(out_ptr0 + x3, tmp6, xmask)


@triton.jit
def triton_poi_fused_add_div_max_pool2d_with_indices_replication_pad2d_2(
    in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 2 % 2
    x0 = xindex % 2
    x4 = xindex
    x3 = xindex // 2
    tmp52 = tl.load(in_ptr1 + (2 * x0 + 8 * x3), xmask, eviction_policy=
        'evict_last')
    tmp53 = tl.load(in_ptr1 + (1 + 2 * x0 + 8 * x3), xmask, eviction_policy
        ='evict_last')
    tmp55 = tl.load(in_ptr1 + (4 + 2 * x0 + 8 * x3), xmask, eviction_policy
        ='evict_last')
    tmp57 = tl.load(in_ptr1 + (5 + 2 * x0 + 8 * x3), xmask, eviction_policy
        ='evict_last')
    tmp59 = tl.load(in_ptr0 + x4, xmask)
    tmp0 = -1 + x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 2, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tmp2 & tmp4
    tmp6 = -1 + x0
    tmp7 = tmp6 >= tmp1
    tmp8 = tmp6 < tmp3
    tmp9 = tmp7 & tmp8
    tmp10 = tmp5 & tmp9
    tmp11 = tl.load(in_ptr0 + (-3 + x4), tmp10 & xmask, other=float('-inf'))
    tmp12 = x0
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp13 & tmp14
    tmp16 = tmp5 & tmp15
    tmp17 = tl.load(in_ptr0 + (-2 + x4), tmp16 & xmask, other=float('-inf'))
    tmp18 = triton_helpers.maximum(tmp17, tmp11)
    tmp19 = 1 + x0
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp20 & tmp21
    tmp23 = tmp5 & tmp22
    tmp24 = tl.load(in_ptr0 + (-1 + x4), tmp23 & xmask, other=float('-inf'))
    tmp25 = triton_helpers.maximum(tmp24, tmp18)
    tmp26 = x1
    tmp27 = tmp26 >= tmp1
    tmp28 = tmp26 < tmp3
    tmp29 = tmp27 & tmp28
    tmp30 = tmp29 & tmp9
    tmp31 = tl.load(in_ptr0 + (-1 + x4), tmp30 & xmask, other=float('-inf'))
    tmp32 = triton_helpers.maximum(tmp31, tmp25)
    tmp33 = tmp29 & tmp15
    tmp34 = tl.load(in_ptr0 + x4, tmp33 & xmask, other=float('-inf'))
    tmp35 = triton_helpers.maximum(tmp34, tmp32)
    tmp36 = tmp29 & tmp22
    tmp37 = tl.load(in_ptr0 + (1 + x4), tmp36 & xmask, other=float('-inf'))
    tmp38 = triton_helpers.maximum(tmp37, tmp35)
    tmp39 = 1 + x1
    tmp40 = tmp39 >= tmp1
    tmp41 = tmp39 < tmp3
    tmp42 = tmp40 & tmp41
    tmp43 = tmp42 & tmp9
    tmp44 = tl.load(in_ptr0 + (1 + x4), tmp43 & xmask, other=float('-inf'))
    tmp45 = triton_helpers.maximum(tmp44, tmp38)
    tmp46 = tmp42 & tmp15
    tmp47 = tl.load(in_ptr0 + (2 + x4), tmp46 & xmask, other=float('-inf'))
    tmp48 = triton_helpers.maximum(tmp47, tmp45)
    tmp49 = tmp42 & tmp22
    tmp50 = tl.load(in_ptr0 + (3 + x4), tmp49 & xmask, other=float('-inf'))
    tmp51 = triton_helpers.maximum(tmp50, tmp48)
    tmp54 = triton_helpers.maximum(tmp53, tmp52)
    tmp56 = triton_helpers.maximum(tmp55, tmp54)
    tmp58 = triton_helpers.maximum(tmp57, tmp56)
    tmp60 = tmp58 + tmp59
    tmp61 = tmp60 + tmp51
    tmp62 = 0.3333333333333333
    tmp63 = tmp61 * tmp62
    tl.store(in_out_ptr0 + x4, tmp63, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 3, 3), (36, 9, 3, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_max_pool2d_with_indices_replication_pad2d_0[grid(144)
            ](arg0_1, buf0, 144, XBLOCK=128, num_warps=4, num_stages=1)
        buf1 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        triton_poi_fused_max_pool2d_with_indices_replication_pad2d_1[grid(64)](
            buf0, buf1, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf0
        buf2 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        buf3 = buf2
        del buf2
        triton_poi_fused_add_div_max_pool2d_with_indices_replication_pad2d_2[
            grid(64)](buf3, buf1, arg0_1, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del arg0_1
        del buf1
    return buf3,


class stack_poolNew(nn.Module):

    def __init__(self):
        super(stack_poolNew, self).__init__()
        self.pool2 = nn.MaxPool2d(2, stride=2)
        self.pool2s1 = nn.MaxPool2d(2, stride=1)
        self.pool3s1 = nn.MaxPool2d(3, stride=1, padding=1)
        self.padding = nn.ReplicationPad2d((0, 1, 0, 1))

    def forward(self, input_0):
        arg0_1 = input_0
        output = call([arg0_1])
        return output[0]
