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
from math import sqrt
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_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
    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)
    tl.store(out_ptr0 + x0, tmp30, xmask)


@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 336
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 21
    x1 = xindex // 21
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + x1, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 5, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (2 * ((-1 + x0) % 2) + 8 * ((-1 + x0) // 2 % 
        2) + 16 * x1), tmp9 & xmask, eviction_policy='evict_last', other=0.0)
    tmp11 = tl.load(in_ptr1 + (1 + 2 * ((-1 + x0) % 2) + 8 * ((-1 + x0) // 
        2 % 2) + 16 * x1), tmp9 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp12 = triton_helpers.maximum(tmp11, tmp10)
    tmp13 = tl.load(in_ptr1 + (4 + 2 * ((-1 + x0) % 2) + 8 * ((-1 + x0) // 
        2 % 2) + 16 * x1), tmp9 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp14 = triton_helpers.maximum(tmp13, tmp12)
    tmp15 = tl.load(in_ptr1 + (5 + 2 * ((-1 + x0) % 2) + 8 * ((-1 + x0) // 
        2 % 2) + 16 * x1), tmp9 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp16 = triton_helpers.maximum(tmp15, tmp14)
    tmp17 = tl.full(tmp16.shape, 0.0, tmp16.dtype)
    tmp18 = tl.where(tmp9, tmp16, tmp17)
    tmp19 = tmp0 >= tmp7
    tl.full([1], 21, tl.int64)
    tmp22 = tl.load(in_ptr1 + (16 * x1 + (-5 + x0) % 16), tmp19 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp23 = tl.where(tmp9, tmp18, tmp22)
    tmp24 = tl.where(tmp4, tmp5, tmp23)
    tl.store(out_ptr0 + x2, tmp24, 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, 1, 1), torch.float32)
        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)
        buf1 = empty_strided_cuda((4, 4, 21), (84, 21, 1), torch.float32)
        triton_poi_fused_cat_1[grid(336)](buf0, arg0_1, buf1, 336, XBLOCK=
            128, num_warps=4, num_stages=1)
        del arg0_1
        del buf0
    return buf1,


class SpatialPyramidPoolingNew(nn.Module):
    """Generate fixed length representation regardless of image dimensions

    Based on the paper "Spatial Pyramid Pooling in Deep Convolutional Networks
    for Visual Recognition" (https://arxiv.org/pdf/1406.4729.pdf)

    :param [int] num_pools: Number of pools to split each input feature map into.
        Each element must be a perfect square in order to equally divide the
        pools across the feature map. Default corresponds to the original
        paper's implementation
    :param str mode: Specifies the type of pooling, either max or avg
    """

    def __init__(self, num_pools=[1, 4, 16], mode='max'):
        super(SpatialPyramidPoolingNew, self).__init__()
        self.name = 'SpatialPyramidPooling'
        if mode == 'max':
            pool_func = nn.AdaptiveMaxPool2d
        elif mode == 'avg':
            pool_func = nn.AdaptiveAvgPool2d
        else:
            raise NotImplementedError(
                f"Unknown pooling mode '{mode}', expected 'max' or 'avg'")
        self.pools = nn.ModuleList([])
        for p in num_pools:
            side_length = sqrt(p)
            if not side_length.is_integer():
                raise ValueError(f'Bin size {p} is not a perfect square')
            self.pools.append(pool_func(int(side_length)))

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


Measured latency_ms: 0.027653