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
from torch._inductor.runtime import triton_helpers
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_add_avg_pool2d_max_pool2d_with_indices_0(in_out_ptr0,
    in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 16 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp3 = tl.load(in_ptr0 + (2 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp5 = tl.load(in_ptr0 + (3 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp7 = tl.load(in_ptr0 + (4 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp9 = tl.load(in_ptr0 + (5 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp11 = tl.load(in_ptr0 + (6 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp13 = tl.load(in_ptr0 + (7 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp15 = tl.load(in_ptr0 + (8 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp17 = tl.load(in_ptr0 + (9 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp19 = tl.load(in_ptr0 + (10 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp21 = tl.load(in_ptr0 + (11 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp23 = tl.load(in_ptr0 + (12 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp25 = tl.load(in_ptr0 + (13 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp27 = tl.load(in_ptr0 + (14 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp29 = tl.load(in_ptr0 + (15 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp8 = triton_helpers.maximum(tmp7, tmp6)
    tmp10 = triton_helpers.maximum(tmp9, tmp8)
    tmp12 = triton_helpers.maximum(tmp11, tmp10)
    tmp14 = triton_helpers.maximum(tmp13, tmp12)
    tmp16 = triton_helpers.maximum(tmp15, tmp14)
    tmp18 = triton_helpers.maximum(tmp17, tmp16)
    tmp20 = triton_helpers.maximum(tmp19, tmp18)
    tmp22 = triton_helpers.maximum(tmp21, tmp20)
    tmp24 = triton_helpers.maximum(tmp23, tmp22)
    tmp26 = triton_helpers.maximum(tmp25, tmp24)
    tmp28 = triton_helpers.maximum(tmp27, tmp26)
    tmp30 = triton_helpers.maximum(tmp29, tmp28)
    tmp31 = tmp1 + tmp0
    tmp32 = tmp3 + tmp31
    tmp33 = tmp5 + tmp32
    tmp34 = tmp7 + tmp33
    tmp35 = tmp9 + tmp34
    tmp36 = tmp11 + tmp35
    tmp37 = tmp13 + tmp36
    tmp38 = tmp15 + tmp37
    tmp39 = tmp17 + tmp38
    tmp40 = tmp19 + tmp39
    tmp41 = tmp21 + tmp40
    tmp42 = tmp23 + tmp41
    tmp43 = tmp25 + tmp42
    tmp44 = tmp27 + tmp43
    tmp45 = tmp29 + tmp44
    tmp46 = 0.0625
    tmp47 = tmp45 * tmp46
    tmp48 = tmp30 + tmp47
    tl.store(in_out_ptr0 + x0, tmp48, 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, 1, 1), (4, 1, 16, 16), torch.float32)
        buf2 = reinterpret_tensor(buf0, (4, 4, 1, 1), (4, 1, 1, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_add_avg_pool2d_max_pool2d_with_indices_0[grid(16)](
            buf2, arg0_1, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del arg0_1
    return buf2,


class _MixPool2dNew(torch.nn.Module):

    def __init__(self, kernel_size, stride, padding=0, ceil_mode=False):
        super(_MixPool2dNew, self).__init__()
        self.max_pool = torch.nn.MaxPool2d(kernel_size, stride, padding,
            ceil_mode=ceil_mode)
        self.avg_pool = torch.nn.AvgPool2d(kernel_size, stride, padding,
            ceil_mode=ceil_mode)

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


Measured latency_ms: 0.014122