# 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/ua/cuaa7l3gtv3zkrpmk7p4sxltibskglorn7ravupkeqnbzrnz7242.py
# Topologically Sorted Source Nodes: [t1], Original ATen: [aten.replication_pad2d]
# Source node to ATen node mapping:
#   t1 => _unsafe_index, _unsafe_index_1
# Graph fragment:
#   %_unsafe_index : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%primals_1, [None, None, %clamp_max, None]), kwargs = {})
#   %_unsafe_index_1 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index, [None, None, None, %clamp_max]), kwargs = {})
triton_poi_fused_replication_pad2d_0 = async_compile.triton('triton_poi_fused_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=[512], 
    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_replication_pad2d_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_replication_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 10
    x1 = (xindex // 10) % 10
    x2 = (xindex // 100)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + ((4*((3) * ((3) <= (((0) * ((0) >= ((-3) + x1)) + ((-3) + x1) * (((-3) + x1) > (0))))) + (((0) * ((0) >= ((-3) + x1)) + ((-3) + x1) * (((-3) + x1) > (0)))) * ((((0) * ((0) >= ((-3) + x1)) + ((-3) + x1) * (((-3) + x1) > (0)))) < (3)))) + (16*x2) + ((3) * ((3) <= (((0) * ((0) >= ((-3) + x0)) + ((-3) + x0) * (((-3) + x0) > (0))))) + (((0) * ((0) >= ((-3) + x0)) + ((-3) + x0) * (((-3) + x0) > (0)))) * ((((0) * ((0) >= ((-3) + x0)) + ((-3) + x0) * (((-3) + x0) > (0)))) < (3)))), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x3), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/p3/cp3tylgyuoamtlltnr7pojny2vbnvfxvhg6zp7yqctp7cuailjpi.py
# Topologically Sorted Source Nodes: [abs_1], Original ATen: [aten.abs]
# Source node to ATen node mapping:
#   abs_1 => abs_1
# Graph fragment:
#   %abs_1 : [num_users=2] = call_function[target=torch.ops.aten.abs.default](args = (%convolution,), kwargs = {})
triton_poi_fused_abs_1 = async_compile.triton('triton_poi_fused_abs_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=[512], 
    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_abs_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_abs_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = tl_math.abs(tmp0)
    tl.store(out_ptr0 + (x0), tmp1, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ze/czeo7t5ml7ntyljlxxqp7vnonbmgzmbzlqpv236dlsdhpki26mfk.py
# Topologically Sorted Source Nodes: [t3], Original ATen: [aten.avg_pool2d]
# Source node to ATen node mapping:
#   t3 => avg_pool2d
# Graph fragment:
#   %avg_pool2d : [num_users=1] = call_function[target=torch.ops.aten.avg_pool2d.default](args = (%abs_1, [3, 3], [1, 1], [1, 1]), kwargs = {})
triton_poi_fused_avg_pool2d_2 = async_compile.triton('triton_poi_fused_avg_pool2d_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=[512], 
    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_avg_pool2d_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 9, '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_avg_pool2d_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 10) % 10
    x0 = xindex % 10
    x4 = xindex
    tmp0 = (-1) + x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 10, 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 + ((-11) + x4), tmp10 & xmask, other=0.0)
    tmp12 = x0
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp13 & tmp14
    tmp16 = tmp5 & tmp15
    tmp17 = tl.load(in_ptr0 + ((-10) + x4), tmp16 & xmask, other=0.0)
    tmp18 = tmp17 + tmp11
    tmp19 = 1 + x0
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp20 & tmp21
    tmp23 = tmp5 & tmp22
    tmp24 = tl.load(in_ptr0 + ((-9) + x4), tmp23 & xmask, other=0.0)
    tmp25 = 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=0.0)
    tmp32 = tmp31 + tmp25
    tmp33 = tmp29 & tmp15
    tmp34 = tl.load(in_ptr0 + (x4), tmp33 & xmask, other=0.0)
    tmp35 = tmp34 + tmp32
    tmp36 = tmp29 & tmp22
    tmp37 = tl.load(in_ptr0 + (1 + x4), tmp36 & xmask, other=0.0)
    tmp38 = tmp37 + tmp35
    tmp39 = 1 + x1
    tmp40 = tmp39 >= tmp1
    tmp41 = tmp39 < tmp3
    tmp42 = tmp40 & tmp41
    tmp43 = tmp42 & tmp9
    tmp44 = tl.load(in_ptr0 + (9 + x4), tmp43 & xmask, other=0.0)
    tmp45 = tmp44 + tmp38
    tmp46 = tmp42 & tmp15
    tmp47 = tl.load(in_ptr0 + (10 + x4), tmp46 & xmask, other=0.0)
    tmp48 = tmp47 + tmp45
    tmp49 = tmp42 & tmp22
    tmp50 = tl.load(in_ptr0 + (11 + x4), tmp49 & xmask, other=0.0)
    tmp51 = tmp50 + tmp48
    tmp52 = 1 + ((-1)*x0) + ((-1)*x1) + (x0*x1) + (((11) * ((11) <= (2 + x0)) + (2 + x0) * ((2 + x0) < (11)))*((11) * ((11) <= (2 + x1)) + (2 + x1) * ((2 + x1) < (11)))) + ((-1)*x0*((11) * ((11) <= (2 + x1)) + (2 + x1) * ((2 + x1) < (11)))) + ((-1)*x1*((11) * ((11) <= (2 + x0)) + (2 + x0) * ((2 + x0) < (11)))) + ((11) * ((11) <= (2 + x0)) + (2 + x0) * ((2 + x0) < (11))) + ((11) * ((11) <= (2 + x1)) + (2 + x1) * ((2 + x1) < (11)))
    tmp53 = tmp51 / tmp52
    tl.store(out_ptr0 + (x4), tmp53, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/f5/cf5eo7ulbhg34r4prncpvogqmo65maeg63tlcfqg5e4v67wox6ll.py
# Topologically Sorted Source Nodes: [add, t4], Original ATen: [aten.add, aten.reciprocal, aten.mul]
# Source node to ATen node mapping:
#   add => add
#   t4 => mul, reciprocal
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%slice_4, 1e-10), kwargs = {})
#   %reciprocal : [num_users=2] = call_function[target=torch.ops.aten.reciprocal.default](args = (%add,), kwargs = {})
#   %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%reciprocal, 1), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%reciprocal, %reciprocal), kwargs = {})
triton_poi_fused_add_mul_reciprocal_3 = async_compile.triton('triton_poi_fused_add_mul_reciprocal_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=[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_mul_reciprocal_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_mul_reciprocal_3(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = (xindex // 4) % 4
    x2 = (xindex // 16)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (33 + x0 + (10*x1) + (100*x2)), xmask)
    tmp1 = 1e-10
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 1, tl.int32)
    tmp4 = tmp3 / tmp2
    tmp5 = 1.0
    tmp6 = tmp4 * tmp5
    tmp7 = tmp4 * tmp4
    tl.store(out_ptr0 + (x3), tmp6, xmask)
    tl.store(out_ptr1 + (x3), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/4f/c4fjhc5lwnqcd46ozbrfxuh5vqfkh5k26in5tu3l6wgl7qeh7nit.py
# Topologically Sorted Source Nodes: [pad_1], Original ATen: [aten.replication_pad2d]
# Source node to ATen node mapping:
#   pad_1 => _unsafe_index_2, _unsafe_index_3
# Graph fragment:
#   %_unsafe_index_2 : [num_users=1] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%mul, [None, None, %clamp_max_2, None]), kwargs = {})
#   %_unsafe_index_3 : [num_users=2] = call_function[target=torch.ops.aten._unsafe_index.Tensor](args = (%_unsafe_index_2, [None, None, None, %clamp_max_2]), kwargs = {})
triton_poi_fused_replication_pad2d_4 = async_compile.triton('triton_poi_fused_replication_pad2d_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=[2048], 
    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_replication_pad2d_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_replication_pad2d_4(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1296
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 18
    x1 = (xindex // 18) % 18
    x2 = (xindex // 324)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + ((4*((3) * ((3) <= (((0) * ((0) >= ((-7) + x1)) + ((-7) + x1) * (((-7) + x1) > (0))))) + (((0) * ((0) >= ((-7) + x1)) + ((-7) + x1) * (((-7) + x1) > (0)))) * ((((0) * ((0) >= ((-7) + x1)) + ((-7) + x1) * (((-7) + x1) > (0)))) < (3)))) + (16*x2) + ((3) * ((3) <= (((0) * ((0) >= ((-7) + x0)) + ((-7) + x0) * (((-7) + x0) > (0))))) + (((0) * ((0) >= ((-7) + x0)) + ((-7) + x0) * (((-7) + x0) > (0)))) * ((((0) * ((0) >= ((-7) + x0)) + ((-7) + x0) * (((-7) + x0) > (0)))) < (3)))), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x3), tmp0, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 1, 4, 4), (16, 16, 4, 1))
    assert_size_stride(primals_2, (1, 1, 3, 3), (9, 9, 3, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 10, 10), (100, 100, 10, 1), torch.float32)
        # Topologically Sorted Source Nodes: [t1], Original ATen: [aten.replication_pad2d]
        stream0 = get_raw_stream(0)
        triton_poi_fused_replication_pad2d_0.run(primals_1, buf0, 400, grid=grid(400), stream=stream0)
        del primals_1
        # Topologically Sorted Source Nodes: [t2], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 1, 10, 10), (100, 100, 10, 1))
        buf2 = empty_strided_cuda((4, 1, 10, 10), (100, 100, 10, 1), torch.float32)
        # Topologically Sorted Source Nodes: [abs_1], Original ATen: [aten.abs]
        triton_poi_fused_abs_1.run(buf1, buf2, 400, grid=grid(400), stream=stream0)
        buf3 = empty_strided_cuda((4, 1, 10, 10), (100, 400, 10, 1), torch.float32)
        # Topologically Sorted Source Nodes: [t3], Original ATen: [aten.avg_pool2d]
        triton_poi_fused_avg_pool2d_2.run(buf2, buf3, 400, grid=grid(400), stream=stream0)
        buf4 = empty_strided_cuda((4, 1, 4, 4), (16, 16, 4, 1), torch.float32)
        buf8 = empty_strided_cuda((4, 1, 4, 4), (16, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [add, t4], Original ATen: [aten.add, aten.reciprocal, aten.mul]
        triton_poi_fused_add_mul_reciprocal_3.run(buf3, buf4, buf8, 64, grid=grid(64), stream=stream0)
        del buf3
        buf5 = empty_strided_cuda((4, 1, 18, 18), (324, 324, 18, 1), torch.float32)
        # Topologically Sorted Source Nodes: [pad_1], Original ATen: [aten.replication_pad2d]
        triton_poi_fused_replication_pad2d_4.run(buf4, buf5, 1296, grid=grid(1296), stream=stream0)
        # Topologically Sorted Source Nodes: [t5], Original ATen: [aten.avg_pool2d]
        buf6 = torch.ops.aten.avg_pool2d.default(buf5, [15, 15], [1, 1], [0, 0], False, True, None)
        buf7 = buf6
        del buf6
    return (buf7, primals_2, buf0, buf1, buf2, buf4, buf5, buf8, )


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((4, 1, 4, 4), (16, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((1, 1, 3, 3), (9, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2])
    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 HILL(nn.Module):

    def __init__(self, img_size):
        super(HILL, self).__init__()
        self.img_size = img_size
        self.pad_3 = nn.ReplicationPad2d(3)
        self.pad = nn.ReplicationPad2d(7)
        self.conv1 = nn.Conv2d(1, 1, 3, 1, padding=1, bias=False)
        self.avepool1 = nn.AvgPool2d(3, stride=1, padding=1)
        self.avepool2 = nn.AvgPool2d(15, stride=1)
        self.eps = 1e-10
        self.res()

    def res(self):
        self.conv1.weight.data = torch.tensor([[-1, 2, -1], [2, -4, 2], [-1,
            2, -1]], dtype=torch.float).view(1, 1, 3, 3)

    def forward(self, x):
        t1 = self.pad_3(x)
        t2 = self.conv1(t1)
        t3 = self.avepool1(torch.abs(t2))
        t4 = 1 / (t3[:, :, 3:self.img_size + 3, 3:self.img_size + 3] + self.eps
            )
        t5 = self.avepool2(self.pad(t4))
        return t5


def get_inputs():
    return [torch.rand([4, 1, 4, 4])]


def get_init_inputs():
    return [[], {'img_size': 4}]

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.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_replication_pad2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 10
    x1 = xindex // 10 % 10
    x2 = xindex // 100
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (4 * (3 * (3 <= 0 * (0 >= -3 + x1) + (-3 + x1) *
        (-3 + x1 > 0)) + (0 * (0 >= -3 + x1) + (-3 + x1) * (-3 + x1 > 0)) *
        (0 * (0 >= -3 + x1) + (-3 + x1) * (-3 + x1 > 0) < 3)) + 16 * x2 + (
        3 * (3 <= 0 * (0 >= -3 + x0) + (-3 + x0) * (-3 + x0 > 0)) + (0 * (0 >=
        -3 + x0) + (-3 + x0) * (-3 + x0 > 0)) * (0 * (0 >= -3 + x0) + (-3 +
        x0) * (-3 + x0 > 0) < 3))), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_poi_fused_abs_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl_math.abs(tmp0)
    tl.store(out_ptr0 + x0, tmp1, xmask)


@triton.jit
def triton_poi_fused_avg_pool2d_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 10 % 10
    x0 = xindex % 10
    x4 = xindex
    tmp0 = -1 + x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 10, 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 + (-11 + x4), tmp10 & xmask, other=0.0)
    tmp12 = x0
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp13 & tmp14
    tmp16 = tmp5 & tmp15
    tmp17 = tl.load(in_ptr0 + (-10 + x4), tmp16 & xmask, other=0.0)
    tmp18 = tmp17 + tmp11
    tmp19 = 1 + x0
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp20 & tmp21
    tmp23 = tmp5 & tmp22
    tmp24 = tl.load(in_ptr0 + (-9 + x4), tmp23 & xmask, other=0.0)
    tmp25 = 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=0.0)
    tmp32 = tmp31 + tmp25
    tmp33 = tmp29 & tmp15
    tmp34 = tl.load(in_ptr0 + x4, tmp33 & xmask, other=0.0)
    tmp35 = tmp34 + tmp32
    tmp36 = tmp29 & tmp22
    tmp37 = tl.load(in_ptr0 + (1 + x4), tmp36 & xmask, other=0.0)
    tmp38 = tmp37 + tmp35
    tmp39 = 1 + x1
    tmp40 = tmp39 >= tmp1
    tmp41 = tmp39 < tmp3
    tmp42 = tmp40 & tmp41
    tmp43 = tmp42 & tmp9
    tmp44 = tl.load(in_ptr0 + (9 + x4), tmp43 & xmask, other=0.0)
    tmp45 = tmp44 + tmp38
    tmp46 = tmp42 & tmp15
    tmp47 = tl.load(in_ptr0 + (10 + x4), tmp46 & xmask, other=0.0)
    tmp48 = tmp47 + tmp45
    tmp49 = tmp42 & tmp22
    tmp50 = tl.load(in_ptr0 + (11 + x4), tmp49 & xmask, other=0.0)
    tmp51 = tmp50 + tmp48
    tmp52 = 1 + -1 * x0 + -1 * x1 + x0 * x1 + (11 * (11 <= 2 + x0) + (2 +
        x0) * (2 + x0 < 11)) * (11 * (11 <= 2 + x1) + (2 + x1) * (2 + x1 < 11)
        ) + -1 * x0 * (11 * (11 <= 2 + x1) + (2 + x1) * (2 + x1 < 11)
        ) + -1 * x1 * (11 * (11 <= 2 + x0) + (2 + x0) * (2 + x0 < 11)) + (
        11 * (11 <= 2 + x0) + (2 + x0) * (2 + x0 < 11)) + (11 * (11 <= 2 +
        x1) + (2 + x1) * (2 + x1 < 11))
    tmp53 = tmp51 / tmp52
    tl.store(out_ptr0 + x4, tmp53, xmask)


@triton.jit
def triton_poi_fused_add_mul_reciprocal_3(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (33 + x0 + 10 * x1 + 100 * x2), xmask)
    tmp1 = 1e-10
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 1, tl.int32)
    tmp4 = tmp3 / tmp2
    tmp5 = 1.0
    tmp6 = tmp4 * tmp5
    tmp7 = tmp4 * tmp4
    tl.store(out_ptr0 + x3, tmp6, xmask)
    tl.store(out_ptr1 + x3, tmp7, xmask)


@triton.jit
def triton_poi_fused_replication_pad2d_4(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 1296
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 18
    x1 = xindex // 18 % 18
    x2 = xindex // 324
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (4 * (3 * (3 <= 0 * (0 >= -7 + x1) + (-7 + x1) *
        (-7 + x1 > 0)) + (0 * (0 >= -7 + x1) + (-7 + x1) * (-7 + x1 > 0)) *
        (0 * (0 >= -7 + x1) + (-7 + x1) * (-7 + x1 > 0) < 3)) + 16 * x2 + (
        3 * (3 <= 0 * (0 >= -7 + x0) + (-7 + x0) * (-7 + x0 > 0)) + (0 * (0 >=
        -7 + x0) + (-7 + x0) * (-7 + x0 > 0)) * (0 * (0 >= -7 + x0) + (-7 +
        x0) * (-7 + x0 > 0) < 3))), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + x3, tmp0, xmask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 1, 4, 4), (16, 16, 4, 1))
    assert_size_stride(primals_2, (1, 1, 3, 3), (9, 9, 3, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 10, 10), (100, 100, 10, 1), torch.
            float32)
        get_raw_stream(0)
        triton_poi_fused_replication_pad2d_0[grid(400)](primals_1, buf0, 
            400, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 1, 10, 10), (100, 100, 10, 1))
        buf2 = empty_strided_cuda((4, 1, 10, 10), (100, 100, 10, 1), torch.
            float32)
        triton_poi_fused_abs_1[grid(400)](buf1, buf2, 400, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf3 = empty_strided_cuda((4, 1, 10, 10), (100, 400, 10, 1), torch.
            float32)
        triton_poi_fused_avg_pool2d_2[grid(400)](buf2, buf3, 400, XBLOCK=
            128, num_warps=4, num_stages=1)
        buf4 = empty_strided_cuda((4, 1, 4, 4), (16, 16, 4, 1), torch.float32)
        buf8 = empty_strided_cuda((4, 1, 4, 4), (16, 16, 4, 1), torch.float32)
        triton_poi_fused_add_mul_reciprocal_3[grid(64)](buf3, buf4, buf8, 
            64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf3
        buf5 = empty_strided_cuda((4, 1, 18, 18), (324, 324, 18, 1), torch.
            float32)
        triton_poi_fused_replication_pad2d_4[grid(1296)](buf4, buf5, 1296,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf6 = torch.ops.aten.avg_pool2d.default(buf5, [15, 15], [1, 1], [0,
            0], False, True, None)
        buf7 = buf6
        del buf6
    return buf7, primals_2, buf0, buf1, buf2, buf4, buf5, buf8


class HILLNew(nn.Module):

    def __init__(self, img_size):
        super(HILLNew, self).__init__()
        self.img_size = img_size
        self.pad_3 = nn.ReplicationPad2d(3)
        self.pad = nn.ReplicationPad2d(7)
        self.conv1 = nn.Conv2d(1, 1, 3, 1, padding=1, bias=False)
        self.avepool1 = nn.AvgPool2d(3, stride=1, padding=1)
        self.avepool2 = nn.AvgPool2d(15, stride=1)
        self.eps = 1e-10
        self.res()

    def res(self):
        self.conv1.weight.data = torch.tensor([[-1, 2, -1], [2, -4, 2], [-1,
            2, -1]], dtype=torch.float).view(1, 1, 3, 3)

    def forward(self, input_0):
        primals_2 = self.conv1.weight
        primals_1 = input_0
        output = call([primals_1, primals_2])
        return output[0]
