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
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms.functional as F
import torch.nn.functional as F
from torch import optim as optim
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_adaptive_max_pool2d_0(in_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + 16 * x2, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 16 * x2), xmask, eviction_policy='evict_last'
        )
    tmp3 = tl.load(in_ptr0 + (2 + 16 * x2), xmask, eviction_policy='evict_last'
        )
    tmp5 = tl.load(in_ptr0 + (3 + 16 * x2), xmask, eviction_policy='evict_last'
        )
    tmp7 = tl.load(in_ptr0 + (4 + 16 * x2), xmask, eviction_policy='evict_last'
        )
    tmp9 = tl.load(in_ptr0 + (5 + 16 * x2), xmask, eviction_policy='evict_last'
        )
    tmp11 = tl.load(in_ptr0 + (6 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp13 = tl.load(in_ptr0 + (7 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp15 = tl.load(in_ptr0 + (8 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp17 = tl.load(in_ptr0 + (9 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp19 = tl.load(in_ptr0 + (10 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp21 = tl.load(in_ptr0 + (11 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp23 = tl.load(in_ptr0 + (12 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp25 = tl.load(in_ptr0 + (13 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp27 = tl.load(in_ptr0 + (14 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp29 = tl.load(in_ptr0 + (15 + 16 * x2), 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)
    tl.store(out_ptr0 + (x0 + 8 * x1), tmp30, xmask)


@triton.jit
def triton_per_fused_mean_1(in_ptr0, out_ptr1, xnumel, rnumel, XBLOCK: tl.
    constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    x2 = xindex % 4
    x3 = xindex // 4
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp5 = 16.0
    tmp6 = tmp4 / tmp5
    tl.store(out_ptr1 + (x2 + 8 * x3), tmp6, 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, 1, 1), (8, 1, 1, 1), torch.float32)
        buf0 = reinterpret_tensor(buf3, (4, 4, 1, 1), (8, 1, 1, 1), 4)
        get_raw_stream(0)
        triton_poi_fused_adaptive_max_pool2d_0[grid(16)](arg0_1, buf0, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        buf2 = reinterpret_tensor(buf3, (4, 4, 1, 1), (8, 1, 1, 1), 0)
        triton_per_fused_mean_1[grid(16)](arg0_1, buf2, 16, 16, XBLOCK=1,
            num_warps=2, num_stages=1)
        del arg0_1
    return buf3,


def adaptive_catavgmax_pool2d(x, output_size=1):
    x_avg = F.adaptive_avg_pool2d(x, output_size)
    x_max = F.adaptive_max_pool2d(x, output_size)
    return torch.cat((x_avg, x_max), 1)


class AdaptiveCatAvgMaxPool2dNew(nn.Module):

    def __init__(self, output_size=1):
        super(AdaptiveCatAvgMaxPool2dNew, self).__init__()
        self.output_size = output_size

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


Measured latency_ms: 0.015093