# 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_7/inductor_cache/hh/chhgwjvcg3rsld5fc5oeyt4h7xumdk4ae6m23j7z3xtl6pqokunw.py
# Topologically Sorted Source Nodes: [input_2], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   input_2 => cat_1
# Graph fragment:
#   %cat_1 : [num_users=2] = call_function[target=torch.ops.aten.cat.default](args = ([%slice_5, %cat], 2), kwargs = {})
triton_poi_fused_cat_0 = async_compile.triton('triton_poi_fused_cat_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=[4096], 
    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_cat_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_cat_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 3072
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 16) % 12
    x0 = xindex % 16
    x2 = (xindex // 192)
    x3 = (xindex // 16)
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = 3 + ((-1)*x1)
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = tmp7 & tmp4
    tmp9 = tl.load(in_ptr0 + (x0 + (16*(3 + ((-1)*x1))) + (64*x2)), tmp8 & xmask, other=0.0)
    tmp10 = tmp5 >= tmp3
    tmp11 = tl.full([1], 8, tl.int64)
    tmp12 = tmp5 < tmp11
    tmp13 = tmp10 & tmp4
    tmp14 = tl.load(in_ptr0 + (48 + x0 + ((-16)*((-1) + ((-1)*x1))) + (64*x2)), tmp13 & xmask, other=0.0)
    tmp15 = tl.where(tmp7, tmp9, tmp14)
    tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
    tmp17 = tl.where(tmp4, tmp15, tmp16)
    tmp18 = tmp0 >= tmp3
    tmp19 = tl.full([1], 12, tl.int64)
    tmp20 = tmp0 < tmp19
    tmp21 = (-4) + x1
    tmp22 = tmp21 >= tmp1
    tmp23 = tmp21 < tmp3
    tmp24 = tmp23 & tmp18
    tmp25 = tl.load(in_ptr0 + (x0 + (16*((-4) + x1)) + (64*x2)), tmp24 & xmask, other=0.0)
    tmp26 = tmp21 >= tmp3
    tmp27 = tmp21 < tmp11
    tmp28 = tmp26 & tmp18
    tmp29 = tl.load(in_ptr0 + (48 + x0 + ((-16)*((-4) + ((-4) + x1))) + (64*x2)), tmp28 & xmask, other=0.0)
    tmp30 = tl.where(tmp23, tmp25, tmp29)
    tmp31 = tl.full(tmp30.shape, 0.0, tmp30.dtype)
    tmp32 = tl.where(tmp18, tmp30, tmp31)
    tmp33 = tl.where(tmp4, tmp17, tmp32)
    tl.store(out_ptr0 + (x0 + (32*x3)), tmp33, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/mp/cmpa4d6uejwbfkxyfjwqonvr6v264tlnaeg6twsgj2pu2e7h5l6w.py
# Topologically Sorted Source Nodes: [flip_2], Original ATen: [aten.flip]
# Source node to ATen node mapping:
#   flip_2 => rev_2
# Graph fragment:
#   %rev_2 : [num_users=1] = call_function[target=torch.ops.prims.rev.default](args = (%cat_1, [3]), kwargs = {})
triton_poi_fused_flip_1 = async_compile.triton('triton_poi_fused_flip_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=[4096], 
    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_flip_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_flip_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 3072
    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 % 16
    tmp0 = tl.load(in_ptr0 + (12 + x0 + ((-4)*x1) + (32*x2)), xmask)
    tl.store(out_ptr0 + (x3 + (32*x2)), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/oj/cojsvu7tyodme7jjt2p255kc7fsex7oemnskahx3dqtkyli6tesc.py
# Topologically Sorted Source Nodes: [input_6], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   input_6 => cat_5
# Graph fragment:
#   %cat_5 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%slice_21, %cat_4], 4), kwargs = {})
triton_poi_fused_cat_2 = async_compile.triton('triton_poi_fused_cat_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=[32768], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_2', '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_cat_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 27648
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 12
    x1 = (xindex // 12) % 12
    x2 = (xindex // 144)
    x4 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = 3 + ((-1)*x0)
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = tmp7 & tmp4
    tmp9 = x1
    tmp10 = tmp9 >= tmp1
    tmp11 = tmp9 < tmp3
    tmp12 = tmp11 & tmp8
    tmp13 = tl.load(in_ptr0 + (12 + ((-4)*x1) + (32*x2) + (3 + ((-1)*x0))), tmp12 & xmask, eviction_policy='evict_last', other=0.0)
    tmp14 = tmp9 >= tmp3
    tmp15 = tl.full([1], 12, tl.int64)
    tmp16 = tmp9 < tmp15
    tmp17 = tmp14 & tmp8
    tmp18 = tl.load(in_ptr0 + ((4*((-4) + x1)) + (32*x2) + (3 + ((-1)*x0))), tmp17 & xmask, eviction_policy='evict_last', other=0.0)
    tmp19 = tl.where(tmp11, tmp13, tmp18)
    tmp20 = tl.full(tmp19.shape, 0.0, tmp19.dtype)
    tmp21 = tl.where(tmp8, tmp19, tmp20)
    tmp22 = tmp5 >= tmp3
    tmp23 = tl.full([1], 8, tl.int64)
    tmp24 = tmp5 < tmp23
    tmp25 = tmp22 & tmp4
    tmp26 = tmp11 & tmp25
    tmp27 = tl.load(in_ptr0 + (15 + ((-1)*((-1) + ((-1)*x0))) + ((-4)*x1) + (32*x2)), tmp26 & xmask, eviction_policy='evict_last', other=0.0)
    tmp28 = tmp14 & tmp25
    tmp29 = tl.load(in_ptr0 + (3 + ((-1)*((-1) + ((-1)*x0))) + (4*((-4) + x1)) + (32*x2)), tmp28 & xmask, eviction_policy='evict_last', other=0.0)
    tmp30 = tl.where(tmp11, tmp27, tmp29)
    tmp31 = tl.full(tmp30.shape, 0.0, tmp30.dtype)
    tmp32 = tl.where(tmp25, tmp30, tmp31)
    tmp33 = tl.where(tmp7, tmp21, tmp32)
    tmp34 = tl.full(tmp33.shape, 0.0, tmp33.dtype)
    tmp35 = tl.where(tmp4, tmp33, tmp34)
    tmp36 = tmp0 >= tmp3
    tmp37 = tmp0 < tmp15
    tmp38 = (-4) + x0
    tmp39 = tmp38 >= tmp1
    tmp40 = tmp38 < tmp3
    tmp41 = tmp40 & tmp36
    tmp42 = tmp11 & tmp41
    tmp43 = tl.load(in_ptr0 + (12 + ((-4)*x1) + (32*x2) + ((-4) + x0)), tmp42 & xmask, eviction_policy='evict_last', other=0.0)
    tmp44 = tmp14 & tmp41
    tmp45 = tl.load(in_ptr0 + ((4*((-4) + x1)) + (32*x2) + ((-4) + x0)), tmp44 & xmask, eviction_policy='evict_last', other=0.0)
    tmp46 = tl.where(tmp11, tmp43, tmp45)
    tmp47 = tl.full(tmp46.shape, 0.0, tmp46.dtype)
    tmp48 = tl.where(tmp41, tmp46, tmp47)
    tmp49 = tmp38 >= tmp3
    tmp50 = tmp38 < tmp23
    tmp51 = tmp49 & tmp36
    tmp52 = tmp11 & tmp51
    tmp53 = tl.load(in_ptr0 + (15 + ((-1)*((-4) + ((-4) + x0))) + ((-4)*x1) + (32*x2)), tmp52 & xmask, eviction_policy='evict_last', other=0.0)
    tmp54 = tmp14 & tmp51
    tmp55 = tl.load(in_ptr0 + (3 + ((-1)*((-4) + ((-4) + x0))) + (4*((-4) + x1)) + (32*x2)), tmp54 & xmask, eviction_policy='evict_last', other=0.0)
    tmp56 = tl.where(tmp11, tmp53, tmp55)
    tmp57 = tl.full(tmp56.shape, 0.0, tmp56.dtype)
    tmp58 = tl.where(tmp51, tmp56, tmp57)
    tmp59 = tl.where(tmp40, tmp48, tmp58)
    tmp60 = tl.full(tmp59.shape, 0.0, tmp59.dtype)
    tmp61 = tl.where(tmp36, tmp59, tmp60)
    tmp62 = tl.where(tmp4, tmp35, tmp61)
    tl.store(out_ptr0 + (x4), tmp62, 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, 4), (256, 64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf2 = empty_strided_cuda((4, 4, 12, 8, 4), (1536, 384, 32, 4, 1), torch.float32)
        buf0 = reinterpret_tensor(buf2, (4, 4, 12, 4, 4), (1536, 384, 32, 4, 1), 0)  # alias
        # Topologically Sorted Source Nodes: [input_2], Original ATen: [aten.cat]
        stream0 = get_raw_stream(0)
        triton_poi_fused_cat_0.run(arg0_1, buf0, 3072, grid=grid(3072), stream=stream0)
        del arg0_1
        buf1 = reinterpret_tensor(buf2, (4, 4, 12, 4, 4), (1536, 384, 32, 4, 1), 16)  # alias
        # Topologically Sorted Source Nodes: [flip_2], Original ATen: [aten.flip]
        triton_poi_fused_flip_1.run(buf0, buf1, 3072, grid=grid(3072), stream=stream0)
        buf3 = empty_strided_cuda((4, 4, 12, 12, 12), (6912, 1728, 144, 12, 1), torch.float32)
        # Topologically Sorted Source Nodes: [input_6], Original ATen: [aten.cat]
        triton_poi_fused_cat_2.run(buf2, buf3, 27648, grid=grid(27648), stream=stream0)
        del buf0
        del buf1
        del buf2
    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, 4), (256, 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.utils.data
import torch
import torch.nn as nn


class ReflectionPad3d(nn.Module):

    def __init__(self, padding):
        super(ReflectionPad3d, self).__init__()
        self.padding = padding
        if isinstance(padding, int):
            self.padding = (padding,) * 6

    def forward(self, input):
        """
        Arguments
            :param input: tensor of shape :math:`(N, C_{	ext{in}}, H, [W, D]))`
        Returns
            :return: tensor of shape :math:`(N, C_{	ext{in}}, [D + 2 * self.padding[0],
                     H + 2 * self.padding[1]], W + 2 * self.padding[2]))`
        """
        input = torch.cat([input, input.flip([2])[:, :, 0:self.padding[-1]]
            ], dim=2)
        input = torch.cat([input.flip([2])[:, :, -self.padding[-2]:], input
            ], dim=2)
        if len(self.padding) > 2:
            input = torch.cat([input, input.flip([3])[:, :, :, 0:self.
                padding[-3]]], dim=3)
            input = torch.cat([input.flip([3])[:, :, :, -self.padding[-4]:],
                input], dim=3)
        if len(self.padding) > 4:
            input = torch.cat([input, input.flip([4])[:, :, :, :, 0:self.
                padding[-5]]], dim=4)
            input = torch.cat([input.flip([4])[:, :, :, :, -self.padding[-6
                ]:], input], dim=4)
        return input


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


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

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
import torch.utils.data
import torch
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_cat_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 3072
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16 % 12
    x0 = xindex % 16
    x2 = xindex // 192
    x3 = xindex // 16
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = 3 + -1 * x1
    tmp7 = tmp5 < tmp3
    tmp8 = tmp7 & tmp4
    tmp9 = tl.load(in_ptr0 + (x0 + 16 * (3 + -1 * x1) + 64 * x2), tmp8 &
        xmask, other=0.0)
    tmp10 = tmp5 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp13 = tmp10 & tmp4
    tmp14 = tl.load(in_ptr0 + (48 + x0 + -16 * (-1 + -1 * x1) + 64 * x2), 
        tmp13 & xmask, other=0.0)
    tmp15 = tl.where(tmp7, tmp9, tmp14)
    tmp16 = tl.full(tmp15.shape, 0.0, tmp15.dtype)
    tmp17 = tl.where(tmp4, tmp15, tmp16)
    tmp18 = tmp0 >= tmp3
    tl.full([1], 12, tl.int64)
    tmp21 = -4 + x1
    tmp23 = tmp21 < tmp3
    tmp24 = tmp23 & tmp18
    tmp25 = tl.load(in_ptr0 + (x0 + 16 * (-4 + x1) + 64 * x2), tmp24 &
        xmask, other=0.0)
    tmp26 = tmp21 >= tmp3
    tmp28 = tmp26 & tmp18
    tmp29 = tl.load(in_ptr0 + (48 + x0 + -16 * (-4 + (-4 + x1)) + 64 * x2),
        tmp28 & xmask, other=0.0)
    tmp30 = tl.where(tmp23, tmp25, tmp29)
    tmp31 = tl.full(tmp30.shape, 0.0, tmp30.dtype)
    tmp32 = tl.where(tmp18, tmp30, tmp31)
    tmp33 = tl.where(tmp4, tmp17, tmp32)
    tl.store(out_ptr0 + (x0 + 32 * x3), tmp33, xmask)


@triton.jit
def triton_poi_fused_flip_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 3072
    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 % 16
    tmp0 = tl.load(in_ptr0 + (12 + x0 + -4 * x1 + 32 * x2), xmask)
    tl.store(out_ptr0 + (x3 + 32 * x2), tmp0, xmask)


@triton.jit
def triton_poi_fused_cat_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 27648
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 12
    x1 = xindex // 12 % 12
    x2 = xindex // 144
    x4 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = 3 + -1 * x0
    tmp7 = tmp5 < tmp3
    tmp8 = tmp7 & tmp4
    tmp9 = x1
    tmp11 = tmp9 < tmp3
    tmp12 = tmp11 & tmp8
    tmp13 = tl.load(in_ptr0 + (12 + -4 * x1 + 32 * x2 + (3 + -1 * x0)), 
        tmp12 & xmask, eviction_policy='evict_last', other=0.0)
    tmp14 = tmp9 >= tmp3
    tl.full([1], 12, tl.int64)
    tmp17 = tmp14 & tmp8
    tmp18 = tl.load(in_ptr0 + (4 * (-4 + x1) + 32 * x2 + (3 + -1 * x0)), 
        tmp17 & xmask, eviction_policy='evict_last', other=0.0)
    tmp19 = tl.where(tmp11, tmp13, tmp18)
    tmp20 = tl.full(tmp19.shape, 0.0, tmp19.dtype)
    tmp21 = tl.where(tmp8, tmp19, tmp20)
    tmp22 = tmp5 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp25 = tmp22 & tmp4
    tmp26 = tmp11 & tmp25
    tmp27 = tl.load(in_ptr0 + (15 + -1 * (-1 + -1 * x0) + -4 * x1 + 32 * x2
        ), tmp26 & xmask, eviction_policy='evict_last', other=0.0)
    tmp28 = tmp14 & tmp25
    tmp29 = tl.load(in_ptr0 + (3 + -1 * (-1 + -1 * x0) + 4 * (-4 + x1) + 32 *
        x2), tmp28 & xmask, eviction_policy='evict_last', other=0.0)
    tmp30 = tl.where(tmp11, tmp27, tmp29)
    tmp31 = tl.full(tmp30.shape, 0.0, tmp30.dtype)
    tmp32 = tl.where(tmp25, tmp30, tmp31)
    tmp33 = tl.where(tmp7, tmp21, tmp32)
    tmp34 = tl.full(tmp33.shape, 0.0, tmp33.dtype)
    tmp35 = tl.where(tmp4, tmp33, tmp34)
    tmp36 = tmp0 >= tmp3
    tmp38 = -4 + x0
    tmp40 = tmp38 < tmp3
    tmp41 = tmp40 & tmp36
    tmp42 = tmp11 & tmp41
    tmp43 = tl.load(in_ptr0 + (12 + -4 * x1 + 32 * x2 + (-4 + x0)), tmp42 &
        xmask, eviction_policy='evict_last', other=0.0)
    tmp44 = tmp14 & tmp41
    tmp45 = tl.load(in_ptr0 + (4 * (-4 + x1) + 32 * x2 + (-4 + x0)), tmp44 &
        xmask, eviction_policy='evict_last', other=0.0)
    tmp46 = tl.where(tmp11, tmp43, tmp45)
    tmp47 = tl.full(tmp46.shape, 0.0, tmp46.dtype)
    tmp48 = tl.where(tmp41, tmp46, tmp47)
    tmp49 = tmp38 >= tmp3
    tmp51 = tmp49 & tmp36
    tmp52 = tmp11 & tmp51
    tmp53 = tl.load(in_ptr0 + (15 + -1 * (-4 + (-4 + x0)) + -4 * x1 + 32 *
        x2), tmp52 & xmask, eviction_policy='evict_last', other=0.0)
    tmp54 = tmp14 & tmp51
    tmp55 = tl.load(in_ptr0 + (3 + -1 * (-4 + (-4 + x0)) + 4 * (-4 + x1) + 
        32 * x2), tmp54 & xmask, eviction_policy='evict_last', other=0.0)
    tmp56 = tl.where(tmp11, tmp53, tmp55)
    tmp57 = tl.full(tmp56.shape, 0.0, tmp56.dtype)
    tmp58 = tl.where(tmp51, tmp56, tmp57)
    tmp59 = tl.where(tmp40, tmp48, tmp58)
    tmp60 = tl.full(tmp59.shape, 0.0, tmp59.dtype)
    tmp61 = tl.where(tmp36, tmp59, tmp60)
    tmp62 = tl.where(tmp4, tmp35, tmp61)
    tl.store(out_ptr0 + x4, tmp62, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4, 4), (256, 64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf2 = empty_strided_cuda((4, 4, 12, 8, 4), (1536, 384, 32, 4, 1),
            torch.float32)
        buf0 = reinterpret_tensor(buf2, (4, 4, 12, 4, 4), (1536, 384, 32, 4,
            1), 0)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(3072)](arg0_1, buf0, 3072, XBLOCK=256,
            num_warps=4, num_stages=1)
        del arg0_1
        buf1 = reinterpret_tensor(buf2, (4, 4, 12, 4, 4), (1536, 384, 32, 4,
            1), 16)
        triton_poi_fused_flip_1[grid(3072)](buf0, buf1, 3072, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf3 = empty_strided_cuda((4, 4, 12, 12, 12), (6912, 1728, 144, 12,
            1), torch.float32)
        triton_poi_fused_cat_2[grid(27648)](buf2, buf3, 27648, XBLOCK=128,
            num_warps=4, num_stages=1)
        del buf0
        del buf1
        del buf2
    return buf3,


class ReflectionPad3dNew(nn.Module):

    def __init__(self, padding):
        super(ReflectionPad3dNew, self).__init__()
        self.padding = padding
        if isinstance(padding, int):
            self.padding = (padding,) * 6

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