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]


Measured latency_ms: 0.037766