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.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_avg_pool2d_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 4
    x0 = xindex % 4
    x6 = xindex
    x3 = xindex // 64
    x7 = xindex % 64
    tmp0 = -1 + x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, 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 + (-5 + x6), tmp10 & xmask, other=0.0)
    tmp12 = x0
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp13 & tmp14
    tmp16 = tmp5 & tmp15
    tmp17 = tl.load(in_ptr0 + (-4 + x6), 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 + (-3 + x6), 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 + x6), tmp30 & xmask, other=0.0)
    tmp32 = tmp31 + tmp25
    tmp33 = tmp29 & tmp15
    tmp34 = tl.load(in_ptr0 + x6, tmp33 & xmask, other=0.0)
    tmp35 = tmp34 + tmp32
    tmp36 = tmp29 & tmp22
    tmp37 = tl.load(in_ptr0 + (1 + x6), 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 + (3 + x6), tmp43 & xmask, other=0.0)
    tmp45 = tmp44 + tmp38
    tmp46 = tmp42 & tmp15
    tmp47 = tl.load(in_ptr0 + (4 + x6), tmp46 & xmask, other=0.0)
    tmp48 = tmp47 + tmp45
    tmp49 = tmp42 & tmp22
    tmp50 = tl.load(in_ptr0 + (5 + x6), tmp49 & xmask, other=0.0)
    tmp51 = tmp50 + tmp48
    tmp52 = 1 + -1 * x0 + -1 * x1 + x0 * x1 + (5 * (5 <= 2 + x0) + (2 + x0) *
        (2 + x0 < 5)) * (5 * (5 <= 2 + x1) + (2 + x1) * (2 + x1 < 5)
        ) + -1 * x0 * (5 * (5 <= 2 + x1) + (2 + x1) * (2 + x1 < 5)
        ) + -1 * x1 * (5 * (5 <= 2 + x0) + (2 + x0) * (2 + x0 < 5)) + (5 *
        (5 <= 2 + x0) + (2 + x0) * (2 + x0 < 5)) + (5 * (5 <= 2 + x1) + (2 +
        x1) * (2 + x1 < 5))
    tmp53 = tmp51 / tmp52
    tl.store(out_ptr0 + (x7 + 128 * x3), tmp53, xmask)


@triton.jit
def triton_poi_fused_cat_1(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 32
    x1 = xindex // 32
    tmp0 = 0.0
    tl.store(out_ptr0 + (x0 + 128 * x1), tmp0, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf3 = empty_strided_cuda((4, 8, 4, 4), (128, 16, 4, 1), torch.float32)
        buf0 = reinterpret_tensor(buf3, (4, 4, 4, 4), (128, 16, 4, 1), 32)
        get_raw_stream(0)
        triton_poi_fused_avg_pool2d_0[grid(256)](arg0_1, buf0, 256, XBLOCK=
            128, num_warps=4, num_stages=1)
        del arg0_1
        buf1 = reinterpret_tensor(buf3, (4, 2, 4, 4), (128, 16, 4, 1), 0)
        triton_poi_fused_cat_1[grid(128)](buf1, 128, XBLOCK=128, num_warps=
            4, num_stages=1)
        buf2 = reinterpret_tensor(buf3, (4, 2, 4, 4), (128, 16, 4, 1), 96)
        triton_poi_fused_cat_1[grid(128)](buf2, 128, XBLOCK=128, num_warps=
            4, num_stages=1)
    return buf3,


class RevPaddingLayerNew(nn.Module):

    def __init__(self, stride):
        super().__init__()
        self.pool = nn.AvgPool2d(kernel_size=3, stride=stride, padding=1)

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


Measured latency_ms: 0.036531