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


@triton.jit
def triton_poi_fused_avg_pool2d_sub_0(in_out_ptr0, in_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
    x3 = xindex
    tmp54 = tl.load(in_ptr0 + x3, xmask)
    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 + x3), 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 + x3), 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 + x3), 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 + x3), tmp30 & xmask, other=0.0)
    tmp32 = tmp31 + tmp25
    tmp33 = tmp29 & tmp15
    tmp34 = tl.load(in_ptr0 + x3, tmp33 & xmask, other=0.0)
    tmp35 = tmp34 + tmp32
    tmp36 = tmp29 & tmp22
    tmp37 = tl.load(in_ptr0 + (1 + x3), 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 + x3), tmp43 & xmask, other=0.0)
    tmp45 = tmp44 + tmp38
    tmp46 = tmp42 & tmp15
    tmp47 = tl.load(in_ptr0 + (4 + x3), tmp46 & xmask, other=0.0)
    tmp48 = tmp47 + tmp45
    tmp49 = tmp42 & tmp22
    tmp50 = tl.load(in_ptr0 + (5 + x3), tmp49 & xmask, other=0.0)
    tmp51 = tmp50 + tmp48
    tmp52 = (0 * (0 >= -1 + x0) + (-1 + x0) * (-1 + x0 > 0)) * (0 * (0 >= -
        1 + x1) + (-1 + x1) * (-1 + x1 > 0)) + (4 * (4 <= 2 + x0) + (2 + x0
        ) * (2 + x0 < 4)) * (4 * (4 <= 2 + x1) + (2 + x1) * (2 + x1 < 4)
        ) + -1 * (0 * (0 >= -1 + x0) + (-1 + x0) * (-1 + x0 > 0)) * (4 * (4 <=
        2 + x1) + (2 + x1) * (2 + x1 < 4)) + -1 * (0 * (0 >= -1 + x1) + (-1 +
        x1) * (-1 + x1 > 0)) * (4 * (4 <= 2 + x0) + (2 + x0) * (2 + x0 < 4))
    tmp53 = tmp51 / tmp52
    tmp55 = tmp53 - tmp54
    tl.store(in_out_ptr0 + x3, tmp55, 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)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_avg_pool2d_sub_0[grid(256)](buf1, arg0_1, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
    return buf1,


class PoolingNew(nn.Module):
    """
    Implementation of pooling for PoolFormer
    --pool_size: pooling size
    """

    def __init__(self, pool_size=3):
        super().__init__()
        self.pool = nn.AvgPool2d(pool_size, stride=1, padding=pool_size // 
            2, count_include_pad=False)

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


Measured latency_ms: 0.027562