# 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/33/c33l4glzlbdbibdludji3jij4pbi2id2whvicmkhdcltg4jiddkt.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.copy]
# Source node to ATen node mapping:
#   x => copy
# Graph fragment:
#   %copy : [num_users=1] = call_function[target=torch.ops.aten.copy.default](args = (%slice_5, %slice_6), kwargs = {})
#   %slice_scatter_default : [num_users=1] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%slice_tensor_1, %copy, 1, 4, 8), kwargs = {})
#   %slice_scatter_default_1 : [num_users=1] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%slice_tensor, %slice_scatter_default, 2, 4, 8), kwargs = {})
#   %slice_scatter_default_2 : [num_users=3] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%empty, %slice_scatter_default_1, 3, 4, 8), kwargs = {})
#   %slice_scatter_default_3 : [num_users=3] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%slice_scatter_default_2, %slice_15, 3, 0, 4), kwargs = {})
triton_poi_fused_copy_0 = async_compile.triton('triton_poi_fused_copy_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=[8192], 
    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_copy_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, '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_copy_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 6912
    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) % 12
    x3 = (xindex // 1728)
    x5 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 4, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = 4 + x0
    tmp4 = tmp3 >= tmp1
    tmp5 = tl.full([1], 8, tl.int64)
    tmp6 = tmp3 < tmp5
    tmp7 = tmp4 & tmp6
    tmp8 = tmp7 & tmp2
    tmp9 = x1
    tmp10 = tmp9 >= tmp1
    tmp11 = tmp9 < tmp5
    tmp12 = tmp10 & tmp11
    tmp13 = tmp12 & tmp8
    tmp14 = x2
    tmp15 = tmp14 >= tmp1
    tmp16 = tmp14 < tmp5
    tmp17 = tmp15 & tmp16
    tmp18 = tmp17 & tmp13
    tmp19 = tl.load(in_ptr0 + ((-80) + x0 + (4*x1) + (16*x2) + (64*x3)), tmp18 & xmask, other=0.0)
    tmp20 = tl.load(in_ptr1 + (4 + x5), tmp13 & xmask, other=0.0)
    tmp21 = tl.where(tmp17, tmp19, tmp20)
    tmp22 = tl.full(tmp21.shape, 0.0, tmp21.dtype)
    tmp23 = tl.where(tmp13, tmp21, tmp22)
    tmp24 = tl.load(in_ptr1 + (4 + x5), tmp8 & xmask, other=0.0)
    tmp25 = tl.where(tmp12, tmp23, tmp24)
    tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
    tmp27 = tl.where(tmp8, tmp25, tmp26)
    tmp28 = float("nan")
    tmp29 = tl.where(tmp7, tmp27, tmp28)
    tmp30 = tl.full(tmp29.shape, 0.0, tmp29.dtype)
    tmp31 = tl.where(tmp2, tmp29, tmp30)
    tmp32 = tmp0 >= tmp1
    tmp33 = tmp0 < tmp5
    tmp34 = tmp32 & tmp33
    tmp35 = tmp12 & tmp34
    tmp36 = tmp17 & tmp35
    tmp37 = tl.load(in_ptr0 + ((-84) + x0 + (4*x1) + (16*x2) + (64*x3)), tmp36 & xmask, other=0.0)
    tmp38 = tl.load(in_ptr1 + (x5), tmp35 & xmask, other=0.0)
    tmp39 = tl.where(tmp17, tmp37, tmp38)
    tmp40 = tl.full(tmp39.shape, 0.0, tmp39.dtype)
    tmp41 = tl.where(tmp35, tmp39, tmp40)
    tmp42 = tl.load(in_ptr1 + (x5), tmp34 & xmask, other=0.0)
    tmp43 = tl.where(tmp12, tmp41, tmp42)
    tmp44 = tl.full(tmp43.shape, 0.0, tmp43.dtype)
    tmp45 = tl.where(tmp34, tmp43, tmp44)
    tmp46 = tl.where(tmp34, tmp45, tmp28)
    tmp47 = tl.where(tmp2, tmp31, tmp46)
    tl.store(out_ptr0 + (x5), tmp47, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/vr/cvrirndocqm44odkw66aqriqwnqh4d7fshix6rnqjqqv3la4zz34.py
# Topologically Sorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
# Graph fragment:
#   %slice_scatter_default_4 : [num_users=3] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%slice_scatter_default_3, %slice_20, 3, 8, 12), kwargs = {})
#   %slice_scatter_default_5 : [num_users=3] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%slice_scatter_default_4, %slice_25, 2, 0, 4), kwargs = {})
#   %slice_scatter_default_6 : [num_users=3] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%slice_scatter_default_5, %slice_30, 2, 8, 12), kwargs = {})
triton_poi_fused_1 = async_compile.triton('triton_poi_fused_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=[8192], 
    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_1', '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_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 6912
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 12) % 12
    x0 = xindex % 12
    x3 = xindex
    tmp30 = tl.load(in_ptr0 + (x3), xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 8, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = (-4) + x1
    tmp4 = tl.full([1], 4, tl.int64)
    tmp5 = tmp3 < tmp4
    tmp6 = tmp5 & tmp2
    tmp7 = x0
    tmp8 = tmp7 >= tmp1
    tmp9 = tmp8 & tmp6
    tmp10 = tl.load(in_ptr0 + ((-4) + x3), tmp9 & xmask, other=0.0)
    tmp11 = tl.load(in_ptr0 + (x3), tmp6 & xmask, other=0.0)
    tmp12 = tl.where(tmp8, tmp10, tmp11)
    tmp13 = tl.full(tmp12.shape, 0.0, tmp12.dtype)
    tmp14 = tl.where(tmp6, tmp12, tmp13)
    tmp15 = tmp8 & tmp2
    tmp16 = tl.load(in_ptr0 + ((-52) + x3), tmp15 & xmask, other=0.0)
    tmp17 = tl.load(in_ptr0 + ((-48) + x3), tmp2 & xmask, other=0.0)
    tmp18 = tl.where(tmp8, tmp16, tmp17)
    tmp19 = tl.where(tmp5, tmp14, tmp18)
    tmp20 = tl.full(tmp19.shape, 0.0, tmp19.dtype)
    tmp21 = tl.where(tmp2, tmp19, tmp20)
    tmp22 = tmp0 < tmp4
    tmp23 = tmp8 & tmp22
    tmp24 = tl.load(in_ptr0 + (44 + x3), tmp23 & xmask, other=0.0)
    tmp25 = tl.load(in_ptr0 + (48 + x3), tmp22 & xmask, other=0.0)
    tmp26 = tl.where(tmp8, tmp24, tmp25)
    tmp27 = tl.full(tmp26.shape, 0.0, tmp26.dtype)
    tmp28 = tl.where(tmp22, tmp26, tmp27)
    tmp29 = tl.load(in_ptr0 + ((-4) + x3), tmp8 & xmask, other=0.0)
    tmp31 = tl.where(tmp8, tmp29, tmp30)
    tmp32 = tl.where(tmp22, tmp28, tmp31)
    tmp33 = tl.where(tmp2, tmp21, tmp32)
    tl.store(out_ptr0 + (x3), tmp33, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/oe/coegkhgnxe5v6e6qj7k76tureqkbihw4mfmjfmkapt6kllycc4m3.py
# Topologically Sorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
# Graph fragment:
#   %slice_scatter_default_7 : [num_users=3] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%slice_scatter_default_6, %slice_35, 1, 0, 4), kwargs = {})
#   %slice_scatter_default_8 : [num_users=1] = call_function[target=torch.ops.aten.slice_scatter.default](args = (%slice_scatter_default_7, %slice_40, 1, 8, 12), kwargs = {})
triton_poi_fused_2 = async_compile.triton('triton_poi_fused_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=[8192], 
    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_2', '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_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 6912
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 144) % 12
    x3 = xindex
    tmp14 = tl.load(in_ptr0 + (x3), xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 8, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = (-4) + x1
    tmp4 = tl.full([1], 4, tl.int64)
    tmp5 = tmp3 < tmp4
    tmp6 = tmp5 & tmp2
    tmp7 = tl.load(in_ptr0 + (x3), tmp6 & xmask, other=0.0)
    tmp8 = tl.load(in_ptr0 + ((-576) + x3), tmp2 & xmask, other=0.0)
    tmp9 = tl.where(tmp5, tmp7, tmp8)
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp2, tmp9, tmp10)
    tmp12 = tmp0 < tmp4
    tmp13 = tl.load(in_ptr0 + (576 + x3), tmp12 & xmask, other=0.0)
    tmp15 = tl.where(tmp12, tmp13, tmp14)
    tmp16 = tl.where(tmp2, tmp11, tmp15)
    tl.store(out_ptr0 + (x3), tmp16, 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))
    buf0 = empty_strided_cuda((4, 12, 12, 12), (1728, 144, 12, 1), torch.float32)
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((4, 12, 12, 12), (1728, 144, 12, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.copy]
        stream0 = get_raw_stream(0)
        triton_poi_fused_copy_0.run(arg0_1, buf0, buf1, 6912, grid=grid(6912), stream=stream0)
        del arg0_1
        buf2 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [], Original ATen: []
        triton_poi_fused_1.run(buf1, buf2, 6912, grid=grid(6912), stream=stream0)
        buf3 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [], Original ATen: []
        triton_poi_fused_2.run(buf2, buf3, 6912, grid=grid(6912), stream=stream0)
        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), (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.functional as F


class CircPad(torch.nn.Module):

    def __init__(self, pad_size):
        super(CircPad, self).__init__()
        if type(pad_size) == tuple:
            self.padding = pad_size
        else:
            self.padding = tuple(pad_size for i in range(6))

    def forward(self, x):
        x = F.pad(x, self.padding, mode='circular')
        return x

    def __repr__(self):
        return f'{type(self).__name__}(pad_size={self.padding})'


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


def get_init_inputs():
    return [[], {'pad_size': 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
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_copy_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 6912
    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 % 12
    x3 = xindex // 1728
    x5 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 4, tl.int64)
    tmp2 = tmp0 < tmp1
    tmp3 = 4 + x0
    tmp4 = tmp3 >= tmp1
    tmp5 = tl.full([1], 8, tl.int64)
    tmp6 = tmp3 < tmp5
    tmp7 = tmp4 & tmp6
    tmp8 = tmp7 & tmp2
    tmp9 = x1
    tmp10 = tmp9 >= tmp1
    tmp11 = tmp9 < tmp5
    tmp12 = tmp10 & tmp11
    tmp13 = tmp12 & tmp8
    tmp14 = x2
    tmp15 = tmp14 >= tmp1
    tmp16 = tmp14 < tmp5
    tmp17 = tmp15 & tmp16
    tmp18 = tmp17 & tmp13
    tmp19 = tl.load(in_ptr0 + (-80 + x0 + 4 * x1 + 16 * x2 + 64 * x3), 
        tmp18 & xmask, other=0.0)
    tmp20 = tl.load(in_ptr1 + (4 + x5), tmp13 & xmask, other=0.0)
    tmp21 = tl.where(tmp17, tmp19, tmp20)
    tmp22 = tl.full(tmp21.shape, 0.0, tmp21.dtype)
    tmp23 = tl.where(tmp13, tmp21, tmp22)
    tmp24 = tl.load(in_ptr1 + (4 + x5), tmp8 & xmask, other=0.0)
    tmp25 = tl.where(tmp12, tmp23, tmp24)
    tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
    tmp27 = tl.where(tmp8, tmp25, tmp26)
    tmp28 = float('nan')
    tmp29 = tl.where(tmp7, tmp27, tmp28)
    tmp30 = tl.full(tmp29.shape, 0.0, tmp29.dtype)
    tmp31 = tl.where(tmp2, tmp29, tmp30)
    tmp32 = tmp0 >= tmp1
    tmp33 = tmp0 < tmp5
    tmp34 = tmp32 & tmp33
    tmp35 = tmp12 & tmp34
    tmp36 = tmp17 & tmp35
    tmp37 = tl.load(in_ptr0 + (-84 + x0 + 4 * x1 + 16 * x2 + 64 * x3), 
        tmp36 & xmask, other=0.0)
    tmp38 = tl.load(in_ptr1 + x5, tmp35 & xmask, other=0.0)
    tmp39 = tl.where(tmp17, tmp37, tmp38)
    tmp40 = tl.full(tmp39.shape, 0.0, tmp39.dtype)
    tmp41 = tl.where(tmp35, tmp39, tmp40)
    tmp42 = tl.load(in_ptr1 + x5, tmp34 & xmask, other=0.0)
    tmp43 = tl.where(tmp12, tmp41, tmp42)
    tmp44 = tl.full(tmp43.shape, 0.0, tmp43.dtype)
    tmp45 = tl.where(tmp34, tmp43, tmp44)
    tmp46 = tl.where(tmp34, tmp45, tmp28)
    tmp47 = tl.where(tmp2, tmp31, tmp46)
    tl.store(out_ptr0 + x5, tmp47, xmask)


@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 6912
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 12 % 12
    x0 = xindex % 12
    x3 = xindex
    tmp30 = tl.load(in_ptr0 + x3, xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 8, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = -4 + x1
    tmp4 = tl.full([1], 4, tl.int64)
    tmp5 = tmp3 < tmp4
    tmp6 = tmp5 & tmp2
    tmp7 = x0
    tmp8 = tmp7 >= tmp1
    tmp9 = tmp8 & tmp6
    tmp10 = tl.load(in_ptr0 + (-4 + x3), tmp9 & xmask, other=0.0)
    tmp11 = tl.load(in_ptr0 + x3, tmp6 & xmask, other=0.0)
    tmp12 = tl.where(tmp8, tmp10, tmp11)
    tmp13 = tl.full(tmp12.shape, 0.0, tmp12.dtype)
    tmp14 = tl.where(tmp6, tmp12, tmp13)
    tmp15 = tmp8 & tmp2
    tmp16 = tl.load(in_ptr0 + (-52 + x3), tmp15 & xmask, other=0.0)
    tmp17 = tl.load(in_ptr0 + (-48 + x3), tmp2 & xmask, other=0.0)
    tmp18 = tl.where(tmp8, tmp16, tmp17)
    tmp19 = tl.where(tmp5, tmp14, tmp18)
    tmp20 = tl.full(tmp19.shape, 0.0, tmp19.dtype)
    tmp21 = tl.where(tmp2, tmp19, tmp20)
    tmp22 = tmp0 < tmp4
    tmp23 = tmp8 & tmp22
    tmp24 = tl.load(in_ptr0 + (44 + x3), tmp23 & xmask, other=0.0)
    tmp25 = tl.load(in_ptr0 + (48 + x3), tmp22 & xmask, other=0.0)
    tmp26 = tl.where(tmp8, tmp24, tmp25)
    tmp27 = tl.full(tmp26.shape, 0.0, tmp26.dtype)
    tmp28 = tl.where(tmp22, tmp26, tmp27)
    tmp29 = tl.load(in_ptr0 + (-4 + x3), tmp8 & xmask, other=0.0)
    tmp31 = tl.where(tmp8, tmp29, tmp30)
    tmp32 = tl.where(tmp22, tmp28, tmp31)
    tmp33 = tl.where(tmp2, tmp21, tmp32)
    tl.store(out_ptr0 + x3, tmp33, xmask)


@triton.jit
def triton_poi_fused_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 6912
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 144 % 12
    x3 = xindex
    tmp14 = tl.load(in_ptr0 + x3, xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 8, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = -4 + x1
    tmp4 = tl.full([1], 4, tl.int64)
    tmp5 = tmp3 < tmp4
    tmp6 = tmp5 & tmp2
    tmp7 = tl.load(in_ptr0 + x3, tmp6 & xmask, other=0.0)
    tmp8 = tl.load(in_ptr0 + (-576 + x3), tmp2 & xmask, other=0.0)
    tmp9 = tl.where(tmp5, tmp7, tmp8)
    tmp10 = tl.full(tmp9.shape, 0.0, tmp9.dtype)
    tmp11 = tl.where(tmp2, tmp9, tmp10)
    tmp12 = tmp0 < tmp4
    tmp13 = tl.load(in_ptr0 + (576 + x3), tmp12 & xmask, other=0.0)
    tmp15 = tl.where(tmp12, tmp13, tmp14)
    tmp16 = tl.where(tmp2, tmp11, tmp15)
    tl.store(out_ptr0 + x3, tmp16, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    buf0 = empty_strided_cuda((4, 12, 12, 12), (1728, 144, 12, 1), torch.
        float32)
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((4, 12, 12, 12), (1728, 144, 12, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_copy_0[grid(6912)](arg0_1, buf0, buf1, 6912,
            XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
        buf2 = buf0
        del buf0
        triton_poi_fused_1[grid(6912)](buf1, buf2, 6912, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf3 = buf1
        del buf1
        triton_poi_fused_2[grid(6912)](buf2, buf3, 6912, XBLOCK=128,
            num_warps=4, num_stages=1)
        del buf2
    return buf3,


class CircPadNew(torch.nn.Module):

    def __init__(self, pad_size):
        super(CircPadNew, self).__init__()
        if type(pad_size) == tuple:
            self.padding = pad_size
        else:
            self.padding = tuple(pad_size for i in range(6))

    def __repr__(self):
        return f'{type(self).__name__}(pad_size={self.padding})'

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