# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_4/inductor_cache/eb/ceb5n3ngnbtecolkfq76e577yj7jgiajgbg2es3d3ftje6u4ouia.py
# Topologically Sorted Source Nodes: [max_pool2d], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   max_pool2d => _low_memory_max_pool2d_with_offsets
# Graph fragment:
#   %_low_memory_max_pool2d_with_offsets : [num_users=1] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%arg0_1, [4, 4], [4, 4], [0, 0], [1, 1], False), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_0 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 16, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_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)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ag/caged2v3ghesufrt2xaas6sdgsryky6qbfqxwx2eodbdhhnn4p7l.py
# Topologically Sorted Source Nodes: [max_pool2d_2], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   max_pool2d_2 => _low_memory_max_pool2d_with_offsets_2
# Graph fragment:
#   %_low_memory_max_pool2d_with_offsets_2 : [num_users=1] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%arg0_1, [2, 2], [2, 2], [1, 1], [1, 1], False), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_1 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_max_pool2d_with_indices_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 3) % 3
    x0 = xindex % 3
    x2 = (xindex // 9)
    x4 = xindex
    tmp0 = (-1) + (2*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) + (2*x0)
    tmp7 = tmp6 >= tmp1
    tmp8 = tmp6 < tmp3
    tmp9 = tmp7 & tmp8
    tmp10 = tmp5 & tmp9
    tmp11 = tl.load(in_ptr0 + ((-5) + (2*x0) + (8*x1) + (16*x2)), tmp10 & xmask, eviction_policy='evict_last', other=float("-inf"))
    tmp12 = 2*x0
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp13 & tmp14
    tmp16 = tmp5 & tmp15
    tmp17 = tl.load(in_ptr0 + ((-4) + (2*x0) + (8*x1) + (16*x2)), tmp16 & xmask, eviction_policy='evict_last', other=float("-inf"))
    tmp18 = triton_helpers.maximum(tmp17, tmp11)
    tmp19 = 2*x1
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp20 & tmp21
    tmp23 = tmp22 & tmp9
    tmp24 = tl.load(in_ptr0 + ((-1) + (2*x0) + (8*x1) + (16*x2)), tmp23 & xmask, eviction_policy='evict_last', other=float("-inf"))
    tmp25 = triton_helpers.maximum(tmp24, tmp18)
    tmp26 = tmp22 & tmp15
    tmp27 = tl.load(in_ptr0 + ((2*x0) + (8*x1) + (16*x2)), tmp26 & xmask, eviction_policy='evict_last', other=float("-inf"))
    tmp28 = triton_helpers.maximum(tmp27, tmp25)
    tl.store(out_ptr0 + (x4), tmp28, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/j5/cj5wkl65czizsr4nzr4kzsxgyhcgrezmthc2buavxem2hznh5cig.py
# Topologically Sorted Source Nodes: [res_2], Original ATen: [aten.cat]
# Source node to ATen node mapping:
#   res_2 => cat_2
# Graph fragment:
#   %cat_2 : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%cat_1, %view_3], 1), kwargs = {})
triton_poi_fused_cat_2 = async_compile.triton('triton_poi_fused_cat_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[512], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_cat_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 7, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_cat_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 480
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 120
    x1 = (xindex // 120)
    x2 = xindex
    tmp0 = x0
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 56, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.full([1], 20, tl.int64)
    tmp6 = tmp0 < tmp5
    tmp7 = tmp6 & tmp4
    tmp8 = tl.full([1], 4, tl.int64)
    tmp9 = tmp0 < tmp8
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + ((4*x1) + x0), tmp10 & xmask, eviction_policy='evict_last', other=0.0)
    tmp12 = tmp0 >= tmp8
    tmp13 = tmp12 & tmp7
    tmp14 = tl.load(in_ptr1 + ((2*(((-4) + x0) % 2)) + (8*((((-4) + x0) // 2) % 8)) + (64*x1)), tmp13 & xmask, eviction_policy='evict_last', other=0.0)
    tmp15 = tl.load(in_ptr1 + (1 + (2*(((-4) + x0) % 2)) + (8*((((-4) + x0) // 2) % 8)) + (64*x1)), tmp13 & xmask, eviction_policy='evict_last', other=0.0)
    tmp16 = triton_helpers.maximum(tmp15, tmp14)
    tmp17 = tl.load(in_ptr1 + (4 + (2*(((-4) + x0) % 2)) + (8*((((-4) + x0) // 2) % 8)) + (64*x1)), tmp13 & xmask, eviction_policy='evict_last', other=0.0)
    tmp18 = triton_helpers.maximum(tmp17, tmp16)
    tmp19 = tl.load(in_ptr1 + (5 + (2*(((-4) + x0) % 2)) + (8*((((-4) + x0) // 2) % 8)) + (64*x1)), tmp13 & xmask, eviction_policy='evict_last', other=0.0)
    tmp20 = triton_helpers.maximum(tmp19, tmp18)
    tmp21 = tl.full(tmp20.shape, 0.0, tmp20.dtype)
    tmp22 = tl.where(tmp13, tmp20, tmp21)
    tmp23 = tl.where(tmp9, tmp11, tmp22)
    tmp24 = tl.full(tmp23.shape, 0.0, tmp23.dtype)
    tmp25 = tl.where(tmp7, tmp23, tmp24)
    tmp26 = tmp0 >= tmp5
    tmp27 = tmp26 & tmp4
    tmp28 = tl.load(in_ptr2 + ((36*x1) + ((-20) + x0)), tmp27 & xmask, eviction_policy='evict_last', other=0.0)
    tmp29 = tl.where(tmp6, tmp25, tmp28)
    tmp30 = tl.full(tmp29.shape, 0.0, tmp29.dtype)
    tmp31 = tl.where(tmp4, tmp29, tmp30)
    tmp32 = tmp0 >= tmp3
    tmp33 = tl.full([1], 120, tl.int64)
    tmp34 = tmp0 < tmp33
    tmp35 = tl.load(in_ptr1 + ((64*x1) + (((-56) + x0) % 64)), tmp32 & xmask, eviction_policy='evict_last', other=0.0)
    tmp36 = tl.where(tmp4, tmp31, tmp35)
    tl.store(out_ptr0 + (x2), tmp36, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

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)
        # Topologically Sorted Source Nodes: [max_pool2d], Original ATen: [aten.max_pool2d_with_indices]
        stream0 = get_raw_stream(0)
        triton_poi_fused_max_pool2d_with_indices_0.run(arg0_1, buf0, 16, grid=grid(16), stream=stream0)
        buf1 = empty_strided_cuda((4, 4, 3, 3), (36, 9, 3, 1), torch.float32)
        # Topologically Sorted Source Nodes: [max_pool2d_2], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_1.run(arg0_1, buf1, 144, grid=grid(144), stream=stream0)
        buf2 = empty_strided_cuda((4, 120), (120, 1), torch.float32)
        # Topologically Sorted Source Nodes: [res_2], Original ATen: [aten.cat]
        triton_poi_fused_cat_2.run(buf0, arg0_1, buf1, buf2, 480, grid=grid(480), stream=stream0)
        del arg0_1
        del buf0
        del buf1
    return (buf2, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import torch
from math import floor
from math import ceil
import torch.nn as nn
import torch.nn.functional as F


class SpatialPyramidPooling2d(nn.Module):
    """apply spatial pyramid pooling over a 4d input(a mini-batch of 2d inputs
    with additional channel dimension) as described in the paper
    'Spatial Pyramid Pooling in deep convolutional Networks for visual recognition'
    Args:
        num_level:
        pool_type: max_pool, avg_pool, Default:max_pool
    By the way, the target output size is num_grid:
        num_grid = 0
        for i in range num_level:
            num_grid += (i + 1) * (i + 1)
        num_grid = num_grid * channels # channels is the channel dimension of input data
    examples:
        >>> input = torch.randn((1,3,32,32), dtype=torch.float32)
        >>> net = torch.nn.Sequential(nn.Conv2d(in_channels=3,out_channels=32,kernel_size=3,stride=1),\\
                                      nn.ReLU(),\\
                                      SpatialPyramidPooling2d(num_level=2,pool_type='avg_pool'),\\
                                      nn.Linear(32 * (1*1 + 2*2), 10))
        >>> output = net(input)
    """

    def __init__(self, num_level, pool_type='max_pool'):
        super(SpatialPyramidPooling2d, self).__init__()
        self.num_level = num_level
        self.pool_type = pool_type

    def forward(self, x):
        N, _C, H, W = x.size()
        for i in range(self.num_level):
            level = i + 1
            kernel_size = ceil(H / level), ceil(W / level)
            stride = ceil(H / level), ceil(W / level)
            padding = floor((kernel_size[0] * level - H + 1) / 2), floor((
                kernel_size[1] * level - W + 1) / 2)
            if self.pool_type == 'max_pool':
                tensor = F.max_pool2d(x, kernel_size=kernel_size, stride=
                    stride, padding=padding).view(N, -1)
            else:
                tensor = F.avg_pool2d(x, kernel_size=kernel_size, stride=
                    stride, padding=padding).view(N, -1)
            if i == 0:
                res = tensor
            else:
                res = torch.cat((res, tensor), 1)
        return res

    def __repr__(self):
        return self.__class__.__name__ + '(' + 'num_level = ' + str(self.
            num_level) + ', pool_type = ' + str(self.pool_type) + ')'


def get_inputs():
    return [torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {'num_level': 4}]

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
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_max_pool2d_with_indices_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_max_pool2d_with_indices_1(in_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 144
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 3 % 3
    x0 = xindex % 3
    x2 = xindex // 9
    x4 = xindex
    tmp0 = -1 + 2 * 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 + 2 * x0
    tmp7 = tmp6 >= tmp1
    tmp8 = tmp6 < tmp3
    tmp9 = tmp7 & tmp8
    tmp10 = tmp5 & tmp9
    tmp11 = tl.load(in_ptr0 + (-5 + 2 * x0 + 8 * x1 + 16 * x2), tmp10 &
        xmask, eviction_policy='evict_last', other=float('-inf'))
    tmp12 = 2 * x0
    tmp13 = tmp12 >= tmp1
    tmp14 = tmp12 < tmp3
    tmp15 = tmp13 & tmp14
    tmp16 = tmp5 & tmp15
    tmp17 = tl.load(in_ptr0 + (-4 + 2 * x0 + 8 * x1 + 16 * x2), tmp16 &
        xmask, eviction_policy='evict_last', other=float('-inf'))
    tmp18 = triton_helpers.maximum(tmp17, tmp11)
    tmp19 = 2 * x1
    tmp20 = tmp19 >= tmp1
    tmp21 = tmp19 < tmp3
    tmp22 = tmp20 & tmp21
    tmp23 = tmp22 & tmp9
    tmp24 = tl.load(in_ptr0 + (-1 + 2 * x0 + 8 * x1 + 16 * x2), tmp23 &
        xmask, eviction_policy='evict_last', other=float('-inf'))
    tmp25 = triton_helpers.maximum(tmp24, tmp18)
    tmp26 = tmp22 & tmp15
    tmp27 = tl.load(in_ptr0 + (2 * x0 + 8 * x1 + 16 * x2), tmp26 & xmask,
        eviction_policy='evict_last', other=float('-inf'))
    tmp28 = triton_helpers.maximum(tmp27, tmp25)
    tl.store(out_ptr0 + x4, tmp28, xmask)


@triton.jit
def triton_poi_fused_cat_2(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 480
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 120
    x1 = xindex // 120
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 56, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.full([1], 20, tl.int64)
    tmp6 = tmp0 < tmp5
    tmp7 = tmp6 & tmp4
    tmp8 = tl.full([1], 4, tl.int64)
    tmp9 = tmp0 < tmp8
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + (4 * x1 + x0), tmp10 & xmask, eviction_policy
        ='evict_last', other=0.0)
    tmp12 = tmp0 >= tmp8
    tmp13 = tmp12 & tmp7
    tmp14 = tl.load(in_ptr1 + (2 * ((-4 + x0) % 2) + 8 * ((-4 + x0) // 2 % 
        8) + 64 * x1), tmp13 & xmask, eviction_policy='evict_last', other=0.0)
    tmp15 = tl.load(in_ptr1 + (1 + 2 * ((-4 + x0) % 2) + 8 * ((-4 + x0) // 
        2 % 8) + 64 * x1), tmp13 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp16 = triton_helpers.maximum(tmp15, tmp14)
    tmp17 = tl.load(in_ptr1 + (4 + 2 * ((-4 + x0) % 2) + 8 * ((-4 + x0) // 
        2 % 8) + 64 * x1), tmp13 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp18 = triton_helpers.maximum(tmp17, tmp16)
    tmp19 = tl.load(in_ptr1 + (5 + 2 * ((-4 + x0) % 2) + 8 * ((-4 + x0) // 
        2 % 8) + 64 * x1), tmp13 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp20 = triton_helpers.maximum(tmp19, tmp18)
    tmp21 = tl.full(tmp20.shape, 0.0, tmp20.dtype)
    tmp22 = tl.where(tmp13, tmp20, tmp21)
    tmp23 = tl.where(tmp9, tmp11, tmp22)
    tmp24 = tl.full(tmp23.shape, 0.0, tmp23.dtype)
    tmp25 = tl.where(tmp7, tmp23, tmp24)
    tmp26 = tmp0 >= tmp5
    tmp27 = tmp26 & tmp4
    tmp28 = tl.load(in_ptr2 + (36 * x1 + (-20 + x0)), tmp27 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp29 = tl.where(tmp6, tmp25, tmp28)
    tmp30 = tl.full(tmp29.shape, 0.0, tmp29.dtype)
    tmp31 = tl.where(tmp4, tmp29, tmp30)
    tmp32 = tmp0 >= tmp3
    tl.full([1], 120, tl.int64)
    tmp35 = tl.load(in_ptr1 + (64 * x1 + (-56 + x0) % 64), tmp32 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp36 = tl.where(tmp4, tmp31, tmp35)
    tl.store(out_ptr0 + x2, tmp36, 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_max_pool2d_with_indices_0[grid(16)](arg0_1, buf0, 
            16, XBLOCK=16, num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((4, 4, 3, 3), (36, 9, 3, 1), torch.float32)
        triton_poi_fused_max_pool2d_with_indices_1[grid(144)](arg0_1, buf1,
            144, XBLOCK=256, num_warps=4, num_stages=1)
        buf2 = empty_strided_cuda((4, 120), (120, 1), torch.float32)
        triton_poi_fused_cat_2[grid(480)](buf0, arg0_1, buf1, buf2, 480,
            XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
        del buf0
        del buf1
    return buf2,


class SpatialPyramidPooling2dNew(nn.Module):
    """apply spatial pyramid pooling over a 4d input(a mini-batch of 2d inputs
    with additional channel dimension) as described in the paper
    'Spatial Pyramid Pooling in deep convolutional Networks for visual recognition'
    Args:
        num_level:
        pool_type: max_pool, avg_pool, Default:max_pool
    By the way, the target output size is num_grid:
        num_grid = 0
        for i in range num_level:
            num_grid += (i + 1) * (i + 1)
        num_grid = num_grid * channels # channels is the channel dimension of input data
    examples:
        >>> input = torch.randn((1,3,32,32), dtype=torch.float32)
        >>> net = torch.nn.Sequential(nn.Conv2d(in_channels=3,out_channels=32,kernel_size=3,stride=1),\\
                                      nn.ReLU(),\\
                                      SpatialPyramidPooling2d(num_level=2,pool_type='avg_pool'),\\
                                      nn.Linear(32 * (1*1 + 2*2), 10))
        >>> output = net(input)
    """

    def __init__(self, num_level, pool_type='max_pool'):
        super(SpatialPyramidPooling2dNew, self).__init__()
        self.num_level = num_level
        self.pool_type = pool_type

    def __repr__(self):
        return self.__class__.__name__ + '(' + 'num_level = ' + str(self.
            num_level) + ', pool_type = ' + str(self.pool_type) + ')'

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