# 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_6/inductor_cache/ca/ccacj4wpx7z2m7f2urwr37plqbnkovuvco2er2l6bllg7a7ckqai.py
# Topologically Sorted Source Nodes: [padded_b], Original ATen: [aten.constant_pad_nd]
# Source node to ATen node mapping:
#   padded_b => constant_pad_nd
# Graph fragment:
#   %constant_pad_nd : [num_users=2] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%primals_3, [1, 1, 0, 1], 0.0), kwargs = {})
triton_poi_fused_constant_pad_nd_0 = async_compile.triton('triton_poi_fused_constant_pad_nd_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_constant_pad_nd_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_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 480
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 6) % 5
    x0 = xindex % 6
    x2 = (xindex // 30)
    x4 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 4, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = (-1) + x0
    tmp4 = tl.full([1], 0, tl.int64)
    tmp5 = tmp3 >= tmp4
    tmp6 = tmp3 < tmp1
    tmp7 = tmp2 & tmp5
    tmp8 = tmp7 & tmp6
    tmp9 = tl.load(in_ptr0 + ((-1) + x0 + (4*x1) + (16*x2)), tmp8 & xmask, other=0.0)
    tl.store(out_ptr0 + (x4), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/zx/czx2juz5rjbhbiirukrz6d6kx7jco6dqgpkpvvc7m75kxtwkey4h.py
# Topologically Sorted Source Nodes: [padded_c], Original ATen: [aten.constant_pad_nd]
# Source node to ATen node mapping:
#   padded_c => constant_pad_nd_1
# Graph fragment:
#   %constant_pad_nd_1 : [num_users=2] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%primals_3, [0, 1, 1, 1], 0.0), kwargs = {})
triton_poi_fused_constant_pad_nd_1 = async_compile.triton('triton_poi_fused_constant_pad_nd_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_constant_pad_nd_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_constant_pad_nd_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 480
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 5) % 6
    x0 = xindex % 5
    x2 = (xindex // 30)
    x3 = xindex
    tmp0 = (-1) + x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = x0
    tmp6 = tmp5 < tmp3
    tmp7 = tmp2 & tmp4
    tmp8 = tmp7 & tmp6
    tmp9 = tl.load(in_ptr0 + ((-4) + x0 + (4*x1) + (16*x2)), tmp8 & xmask, other=0.0)
    tl.store(out_ptr0 + (x3), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/cd/ccd5s4fpd3lxf637fdxhtl6drfxl33ps3hjs7hw2ls5lojfpirc2.py
# Topologically Sorted Source Nodes: [padded_d], Original ATen: [aten.constant_pad_nd]
# Source node to ATen node mapping:
#   padded_d => constant_pad_nd_2
# Graph fragment:
#   %constant_pad_nd_2 : [num_users=2] = call_function[target=torch.ops.aten.constant_pad_nd.default](args = (%primals_3, [0, 1, 0, 1], 0.0), kwargs = {})
triton_poi_fused_constant_pad_nd_2 = async_compile.triton('triton_poi_fused_constant_pad_nd_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_constant_pad_nd_2', '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_constant_pad_nd_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 // 5) % 5
    x0 = xindex % 5
    x2 = (xindex // 25)
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 4, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = x0
    tmp4 = tmp3 < tmp1
    tmp5 = tmp2 & tmp4
    tmp6 = tl.load(in_ptr0 + (x0 + (4*x1) + (16*x2)), tmp5 & xmask, other=0.0)
    tl.store(out_ptr0 + (x3), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/7k/c7kzycclc2im22az3rwdu4aimn2tldhwgz27hjvwwhc2sfhyjnh2.py
# Topologically Sorted Source Nodes: [stacked_2], Original ATen: [aten.stack]
# Source node to ATen node mapping:
#   stacked_2 => cat_2
# Graph fragment:
#   %cat_2 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%unsqueeze, %unsqueeze_1], 4), kwargs = {})
triton_poi_fused_stack_3 = async_compile.triton('triton_poi_fused_stack_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=[1024], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: '*fp32', 8: '*fp32', 9: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_stack_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, '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_stack_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2
    x1 = (xindex // 2) % 4
    x2 = (xindex // 8) % 8
    x5 = (xindex // 64)
    x3 = (xindex // 64) % 4
    x6 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = x1 + (4*(x2 % 2))
    tmp6 = tmp5 >= tmp1
    tmp7 = tl.full([1], 4, tl.int64)
    tmp8 = tmp5 < tmp7
    tmp9 = tmp8 & tmp4
    tmp10 = tl.load(in_ptr0 + ((4*(x2 // 2)) + (16*x5) + (x1 + (4*(x2 % 2)))), tmp9 & xmask, eviction_policy='evict_last', other=0.0)
    tmp11 = tl.load(in_ptr1 + (x3), tmp9 & xmask, eviction_policy='evict_last', other=0.0)
    tmp12 = tmp10 + tmp11
    tmp13 = tl.full(tmp12.shape, 0.0, tmp12.dtype)
    tmp14 = tl.where(tmp9, tmp12, tmp13)
    tmp15 = tmp5 >= tmp7
    tmp16 = tl.full([1], 8, tl.int64)
    tmp17 = tmp5 < tmp16
    tmp18 = tmp15 & tmp4
    tmp19 = tl.load(in_ptr2 + ((4*(x2 // 2)) + (16*x5) + ((-4) + x1 + (4*(x2 % 2)))), tmp18 & xmask, eviction_policy='evict_last', other=0.0)
    tmp20 = tl.load(in_ptr3 + (x3), tmp18 & xmask, eviction_policy='evict_last', other=0.0)
    tmp21 = tmp19 + tmp20
    tmp22 = tl.full(tmp21.shape, 0.0, tmp21.dtype)
    tmp23 = tl.where(tmp18, tmp21, tmp22)
    tmp24 = tl.where(tmp8, tmp14, tmp23)
    tmp25 = tl.full(tmp24.shape, 0.0, tmp24.dtype)
    tmp26 = tl.where(tmp4, tmp24, tmp25)
    tmp27 = tmp0 >= tmp3
    tmp28 = tl.full([1], 2, tl.int64)
    tmp29 = tmp0 < tmp28
    tmp30 = tmp8 & tmp27
    tmp31 = tl.load(in_ptr4 + ((4*(x2 // 2)) + (16*x5) + (x1 + (4*(x2 % 2)))), tmp30 & xmask, eviction_policy='evict_last', other=0.0)
    tmp32 = tl.load(in_ptr5 + (x3), tmp30 & xmask, eviction_policy='evict_last', other=0.0)
    tmp33 = tmp31 + tmp32
    tmp34 = tl.full(tmp33.shape, 0.0, tmp33.dtype)
    tmp35 = tl.where(tmp30, tmp33, tmp34)
    tmp36 = tmp15 & tmp27
    tmp37 = tl.load(in_ptr6 + ((4*(x2 // 2)) + (16*x5) + ((-4) + x1 + (4*(x2 % 2)))), tmp36 & xmask, eviction_policy='evict_last', other=0.0)
    tmp38 = tl.load(in_ptr7 + (x3), tmp36 & xmask, eviction_policy='evict_last', other=0.0)
    tmp39 = tmp37 + tmp38
    tmp40 = tl.full(tmp39.shape, 0.0, tmp39.dtype)
    tmp41 = tl.where(tmp36, tmp39, tmp40)
    tmp42 = tl.where(tmp8, tmp35, tmp41)
    tmp43 = tl.full(tmp42.shape, 0.0, tmp42.dtype)
    tmp44 = tl.where(tmp27, tmp42, tmp43)
    tmp45 = tl.where(tmp4, tmp26, tmp44)
    tl.store(out_ptr0 + (x6), tmp45, xmask)
''', 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, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 2, 3), (24, 6, 3, 1))
    assert_size_stride(primals_5, (4, ), (1, ))
    assert_size_stride(primals_6, (4, 4, 3, 2), (24, 6, 2, 1))
    assert_size_stride(primals_7, (4, ), (1, ))
    assert_size_stride(primals_8, (4, 4, 2, 2), (16, 4, 2, 1))
    assert_size_stride(primals_9, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [out_a], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 1), padding=(1, 1), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
        buf1 = empty_strided_cuda((4, 4, 5, 6), (120, 30, 6, 1), torch.float32)
        # Topologically Sorted Source Nodes: [padded_b], Original ATen: [aten.constant_pad_nd]
        stream0 = get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_0.run(primals_3, buf1, 480, grid=grid(480), stream=stream0)
        # Topologically Sorted Source Nodes: [out_b], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 4, 4), (64, 16, 4, 1))
        buf3 = empty_strided_cuda((4, 4, 6, 5), (120, 30, 5, 1), torch.float32)
        # Topologically Sorted Source Nodes: [padded_c], Original ATen: [aten.constant_pad_nd]
        triton_poi_fused_constant_pad_nd_1.run(primals_3, buf3, 480, grid=grid(480), stream=stream0)
        # Topologically Sorted Source Nodes: [out_c], Original ATen: [aten.convolution]
        buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 4, 4, 4), (64, 16, 4, 1))
        buf5 = empty_strided_cuda((4, 4, 5, 5), (100, 25, 5, 1), torch.float32)
        # Topologically Sorted Source Nodes: [padded_d], Original ATen: [aten.constant_pad_nd]
        triton_poi_fused_constant_pad_nd_2.run(primals_3, buf5, 400, grid=grid(400), stream=stream0)
        # Topologically Sorted Source Nodes: [out_d], Original ATen: [aten.convolution]
        buf6 = extern_kernels.convolution(buf5, primals_8, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 4, 4, 4), (64, 16, 4, 1))
        buf7 = empty_strided_cuda((4, 4, 8, 4, 2), (256, 64, 8, 2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [stacked_2], Original ATen: [aten.stack]
        triton_poi_fused_stack_3.run(buf0, primals_2, buf2, primals_5, buf4, primals_7, buf6, primals_9, buf7, 1024, grid=grid(1024), stream=stream0)
        del buf0
        del buf2
        del buf4
        del buf6
        del primals_2
        del primals_5
        del primals_7
        del primals_9
    return (reinterpret_tensor(buf7, (4, 4, 8, 8), (256, 64, 8, 1), 0), primals_1, primals_3, primals_4, primals_6, primals_8, buf1, buf3, buf5, )


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, 4, 3, 3), (36, 9, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4, 2, 3), (24, 6, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4, 3, 2), (24, 6, 2, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, 4, 2, 2), (16, 4, 2, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9])
    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.functional as F
import torch.nn as nn


def getIncomingShape(incoming):
    size = incoming.size()
    return [size[0], size[1], size[2], size[3]]


def interleave(tensors, axis):
    old_shape = getIncomingShape(tensors[0])[1:]
    new_shape = [-1] + old_shape
    new_shape[axis] *= len(tensors)
    stacked = torch.stack(tensors, axis + 1)
    reshaped = stacked.view(new_shape)
    return reshaped


class UnpoolingAsConvolution(nn.Module):

    def __init__(self, in_kernels, out_kernels):
        super(UnpoolingAsConvolution, self).__init__()
        self.conv_A = nn.Conv2d(in_kernels, out_kernels, kernel_size=(3, 3),
            stride=1, padding=1)
        self.conv_B = nn.Conv2d(in_kernels, out_kernels, kernel_size=(2, 3),
            stride=1, padding=0)
        self.conv_C = nn.Conv2d(in_kernels, out_kernels, kernel_size=(3, 2),
            stride=1, padding=0)
        self.conv_D = nn.Conv2d(in_kernels, out_kernels, kernel_size=(2, 2),
            stride=1, padding=0)

    def forward(self, x):
        out_a = self.conv_A(x)
        padded_b = F.pad(x, (1, 1, 0, 1))
        out_b = self.conv_B(padded_b)
        padded_c = F.pad(x, (0, 1, 1, 1))
        out_c = self.conv_C(padded_c)
        padded_d = F.pad(x, (0, 1, 0, 1))
        out_d = self.conv_D(padded_d)
        out_left = interleave([out_a, out_b], axis=2)
        out_right = interleave([out_c, out_d], axis=2)
        out = interleave([out_left, out_right], axis=3)
        return out


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


def get_init_inputs():
    return [[], {'in_kernels': 4, 'out_kernels': 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
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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_constant_pad_nd_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 480
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 6 % 5
    x0 = xindex % 6
    x2 = xindex // 30
    x4 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 4, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = -1 + x0
    tmp4 = tl.full([1], 0, tl.int64)
    tmp5 = tmp3 >= tmp4
    tmp6 = tmp3 < tmp1
    tmp7 = tmp2 & tmp5
    tmp8 = tmp7 & tmp6
    tmp9 = tl.load(in_ptr0 + (-1 + x0 + 4 * x1 + 16 * x2), tmp8 & xmask,
        other=0.0)
    tl.store(out_ptr0 + x4, tmp9, xmask)


@triton.jit
def triton_poi_fused_constant_pad_nd_1(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 480
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 5 % 6
    x0 = xindex % 5
    x2 = xindex // 30
    x3 = xindex
    tmp0 = -1 + x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = x0
    tmp6 = tmp5 < tmp3
    tmp7 = tmp2 & tmp4
    tmp8 = tmp7 & tmp6
    tmp9 = tl.load(in_ptr0 + (-4 + x0 + 4 * x1 + 16 * x2), tmp8 & xmask,
        other=0.0)
    tl.store(out_ptr0 + x3, tmp9, xmask)


@triton.jit
def triton_poi_fused_constant_pad_nd_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 // 5 % 5
    x0 = xindex % 5
    x2 = xindex // 25
    x3 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 4, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = x0
    tmp4 = tmp3 < tmp1
    tmp5 = tmp2 & tmp4
    tmp6 = tl.load(in_ptr0 + (x0 + 4 * x1 + 16 * x2), tmp5 & xmask, other=0.0)
    tl.store(out_ptr0 + x3, tmp6, xmask)


@triton.jit
def triton_poi_fused_stack_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4,
    in_ptr5, in_ptr6, in_ptr7, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2
    x1 = xindex // 2 % 4
    x2 = xindex // 8 % 8
    x5 = xindex // 64
    x3 = xindex // 64 % 4
    x6 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = x1 + 4 * (x2 % 2)
    tmp7 = tl.full([1], 4, tl.int64)
    tmp8 = tmp5 < tmp7
    tmp9 = tmp8 & tmp4
    tmp10 = tl.load(in_ptr0 + (4 * (x2 // 2) + 16 * x5 + (x1 + 4 * (x2 % 2)
        )), tmp9 & xmask, eviction_policy='evict_last', other=0.0)
    tmp11 = tl.load(in_ptr1 + x3, tmp9 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp12 = tmp10 + tmp11
    tmp13 = tl.full(tmp12.shape, 0.0, tmp12.dtype)
    tmp14 = tl.where(tmp9, tmp12, tmp13)
    tmp15 = tmp5 >= tmp7
    tl.full([1], 8, tl.int64)
    tmp18 = tmp15 & tmp4
    tmp19 = tl.load(in_ptr2 + (4 * (x2 // 2) + 16 * x5 + (-4 + x1 + 4 * (x2 %
        2))), tmp18 & xmask, eviction_policy='evict_last', other=0.0)
    tmp20 = tl.load(in_ptr3 + x3, tmp18 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp21 = tmp19 + tmp20
    tmp22 = tl.full(tmp21.shape, 0.0, tmp21.dtype)
    tmp23 = tl.where(tmp18, tmp21, tmp22)
    tmp24 = tl.where(tmp8, tmp14, tmp23)
    tmp25 = tl.full(tmp24.shape, 0.0, tmp24.dtype)
    tmp26 = tl.where(tmp4, tmp24, tmp25)
    tmp27 = tmp0 >= tmp3
    tl.full([1], 2, tl.int64)
    tmp30 = tmp8 & tmp27
    tmp31 = tl.load(in_ptr4 + (4 * (x2 // 2) + 16 * x5 + (x1 + 4 * (x2 % 2)
        )), tmp30 & xmask, eviction_policy='evict_last', other=0.0)
    tmp32 = tl.load(in_ptr5 + x3, tmp30 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp33 = tmp31 + tmp32
    tmp34 = tl.full(tmp33.shape, 0.0, tmp33.dtype)
    tmp35 = tl.where(tmp30, tmp33, tmp34)
    tmp36 = tmp15 & tmp27
    tmp37 = tl.load(in_ptr6 + (4 * (x2 // 2) + 16 * x5 + (-4 + x1 + 4 * (x2 %
        2))), tmp36 & xmask, eviction_policy='evict_last', other=0.0)
    tmp38 = tl.load(in_ptr7 + x3, tmp36 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp39 = tmp37 + tmp38
    tmp40 = tl.full(tmp39.shape, 0.0, tmp39.dtype)
    tmp41 = tl.where(tmp36, tmp39, tmp40)
    tmp42 = tl.where(tmp8, tmp35, tmp41)
    tmp43 = tl.full(tmp42.shape, 0.0, tmp42.dtype)
    tmp44 = tl.where(tmp27, tmp42, tmp43)
    tmp45 = tl.where(tmp4, tmp26, tmp44)
    tl.store(out_ptr0 + x6, tmp45, xmask)


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, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 2, 3), (24, 6, 3, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4, 3, 2), (24, 6, 2, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 4, 2, 2), (16, 4, 2, 1))
    assert_size_stride(primals_9, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 4, 4), (64, 16, 4, 1))
        buf1 = empty_strided_cuda((4, 4, 5, 6), (120, 30, 6, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_constant_pad_nd_0[grid(480)](primals_3, buf1, 480,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 4, 4), (64, 16, 4, 1))
        buf3 = empty_strided_cuda((4, 4, 6, 5), (120, 30, 5, 1), torch.float32)
        triton_poi_fused_constant_pad_nd_1[grid(480)](primals_3, buf3, 480,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf4 = extern_kernels.convolution(buf3, primals_6, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 4, 4, 4), (64, 16, 4, 1))
        buf5 = empty_strided_cuda((4, 4, 5, 5), (100, 25, 5, 1), torch.float32)
        triton_poi_fused_constant_pad_nd_2[grid(400)](primals_3, buf5, 400,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf6 = extern_kernels.convolution(buf5, primals_8, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 4, 4, 4), (64, 16, 4, 1))
        buf7 = empty_strided_cuda((4, 4, 8, 4, 2), (256, 64, 8, 2, 1),
            torch.float32)
        triton_poi_fused_stack_3[grid(1024)](buf0, primals_2, buf2,
            primals_5, buf4, primals_7, buf6, primals_9, buf7, 1024, XBLOCK
            =128, num_warps=4, num_stages=1)
        del buf0
        del buf2
        del buf4
        del buf6
        del primals_2
        del primals_5
        del primals_7
        del primals_9
    return reinterpret_tensor(buf7, (4, 4, 8, 8), (256, 64, 8, 1), 0
        ), primals_1, primals_3, primals_4, primals_6, primals_8, buf1, buf3, buf5


def getIncomingShape(incoming):
    size = incoming.size()
    return [size[0], size[1], size[2], size[3]]


def interleave(tensors, axis):
    old_shape = getIncomingShape(tensors[0])[1:]
    new_shape = [-1] + old_shape
    new_shape[axis] *= len(tensors)
    stacked = torch.stack(tensors, axis + 1)
    reshaped = stacked.view(new_shape)
    return reshaped


class UnpoolingAsConvolutionNew(nn.Module):

    def __init__(self, in_kernels, out_kernels):
        super(UnpoolingAsConvolutionNew, self).__init__()
        self.conv_A = nn.Conv2d(in_kernels, out_kernels, kernel_size=(3, 3),
            stride=1, padding=1)
        self.conv_B = nn.Conv2d(in_kernels, out_kernels, kernel_size=(2, 3),
            stride=1, padding=0)
        self.conv_C = nn.Conv2d(in_kernels, out_kernels, kernel_size=(3, 2),
            stride=1, padding=0)
        self.conv_D = nn.Conv2d(in_kernels, out_kernels, kernel_size=(2, 2),
            stride=1, padding=0)

    def forward(self, input_0):
        primals_1 = self.conv_A.weight
        primals_2 = self.conv_A.bias
        primals_4 = self.conv_B.weight
        primals_5 = self.conv_B.bias
        primals_6 = self.conv_C.weight
        primals_7 = self.conv_C.bias
        primals_8 = self.conv_D.weight
        primals_9 = self.conv_D.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]
