# 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/ah/cahaajyq2nxdzd3h74txgiwb2xiidnp6siut4ywyx6ajl7vk3hvp.py
# Topologically Sorted Source Nodes: [max_1, x], Original ATen: [aten.max, aten.stack]
# Source node to ATen node mapping:
#   max_1 => max_1
#   x => cat
# Graph fragment:
#   %max_1 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%view, 1), kwargs = {})
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%unsqueeze, %unsqueeze_1, %unsqueeze_2, %unsqueeze_3], 1), kwargs = {})
triton_per_fused_max_stack_0 = async_compile.triton('triton_per_fused_max_stack_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.persistent_reduction(
    size_hints=[4, 16],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: '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, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_max_stack_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 1, '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}
)
@triton.jit
def triton_per_fused_max_stack_0(in_ptr0, out_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    rnumel = 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, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + ((4*((r1 // 2) % 2)) + (16*(r1 // 4)) + (64*x0) + (r1 % 2)), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, float("-inf"))
    tmp4 = triton_helpers.max2(tmp3, 1)[:, None]
    tl.store(out_ptr1 + (4*x0), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/eo/ceobo6hls3ll4owxx63ahtpijopnucn5p4qpsqmmvxjlfejrcrsw.py
# Topologically Sorted Source Nodes: [max_2, x], Original ATen: [aten.max, aten.stack]
# Source node to ATen node mapping:
#   max_2 => max_2
#   x => cat
# Graph fragment:
#   %max_2 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%view_1, 1), kwargs = {})
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%unsqueeze, %unsqueeze_1, %unsqueeze_2, %unsqueeze_3], 1), kwargs = {})
triton_per_fused_max_stack_1 = async_compile.triton('triton_per_fused_max_stack_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.persistent_reduction(
    size_hints=[4, 16],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: '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, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_max_stack_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 1, '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}
)
@triton.jit
def triton_per_fused_max_stack_1(in_ptr0, out_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    rnumel = 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, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (8 + (4*((r1 // 2) % 2)) + (16*(r1 // 4)) + (64*x0) + (r1 % 2)), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, float("-inf"))
    tmp4 = triton_helpers.max2(tmp3, 1)[:, None]
    tl.store(out_ptr1 + (4*x0), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/oy/coybmg34b4gm65jwh3ngleamgj2vns2qrk3dxswwgzq422hzqsie.py
# Topologically Sorted Source Nodes: [max_3, x], Original ATen: [aten.max, aten.stack]
# Source node to ATen node mapping:
#   max_3 => max_3
#   x => cat
# Graph fragment:
#   %max_3 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%view_2, 1), kwargs = {})
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%unsqueeze, %unsqueeze_1, %unsqueeze_2, %unsqueeze_3], 1), kwargs = {})
triton_per_fused_max_stack_2 = async_compile.triton('triton_per_fused_max_stack_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.persistent_reduction(
    size_hints=[4, 16],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: '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, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_max_stack_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 1, '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}
)
@triton.jit
def triton_per_fused_max_stack_2(in_ptr0, out_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    rnumel = 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, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (2 + (4*((r1 // 2) % 2)) + (16*(r1 // 4)) + (64*x0) + (r1 % 2)), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, float("-inf"))
    tmp4 = triton_helpers.max2(tmp3, 1)[:, None]
    tl.store(out_ptr1 + (4*x0), tmp4, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/jp/cjpadu2qlpfurhiujxmuqot7ql2a5yl6tkkp4gbieotuo2ccibi4.py
# Topologically Sorted Source Nodes: [max_4, x], Original ATen: [aten.max, aten.stack]
# Source node to ATen node mapping:
#   max_4 => max_4
#   x => cat
# Graph fragment:
#   %max_4 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%view_3, 1), kwargs = {})
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%unsqueeze, %unsqueeze_1, %unsqueeze_2, %unsqueeze_3], 1), kwargs = {})
triton_per_fused_max_stack_3 = async_compile.triton('triton_per_fused_max_stack_3', '''
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.persistent_reduction(
    size_hints=[4, 16],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: '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, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_max_stack_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 1, '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}
)
@triton.jit
def triton_per_fused_max_stack_3(in_ptr0, out_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    rnumel = 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, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (10 + (4*((r1 // 2) % 2)) + (16*(r1 // 4)) + (64*x0) + (r1 % 2)), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, float("-inf"))
    tmp4 = triton_helpers.max2(tmp3, 1)[:, None]
    tl.store(out_ptr1 + (4*x0), tmp4, 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)
        buf12 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf8 = reinterpret_tensor(buf12, (4, 1), (4, 1), 0)  # alias
        # Topologically Sorted Source Nodes: [max_1, x], Original ATen: [aten.max, aten.stack]
        stream0 = get_raw_stream(0)
        triton_per_fused_max_stack_0.run(arg0_1, buf8, 4, 16, grid=grid(4), stream=stream0)
        buf9 = reinterpret_tensor(buf12, (4, 1), (4, 1), 1)  # alias
        # Topologically Sorted Source Nodes: [max_2, x], Original ATen: [aten.max, aten.stack]
        triton_per_fused_max_stack_1.run(arg0_1, buf9, 4, 16, grid=grid(4), stream=stream0)
        buf10 = reinterpret_tensor(buf12, (4, 1), (4, 1), 2)  # alias
        # Topologically Sorted Source Nodes: [max_3, x], Original ATen: [aten.max, aten.stack]
        triton_per_fused_max_stack_2.run(arg0_1, buf10, 4, 16, grid=grid(4), stream=stream0)
        buf11 = reinterpret_tensor(buf12, (4, 1), (4, 1), 3)  # alias
        # Topologically Sorted Source Nodes: [max_4, x], Original ATen: [aten.max, aten.stack]
        triton_per_fused_max_stack_3.run(arg0_1, buf11, 4, 16, grid=grid(4), stream=stream0)
        del arg0_1
    return (buf12, )


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 math
import torch


class BinaryChunk(torch.nn.Module):

    def __init__(self, nCls, isLogit=False, pooling='max', chunk_dim=-1):
        super(BinaryChunk, self).__init__()
        self.nClass = nCls
        self.nChunk = int(math.ceil(math.log2(self.nClass)))
        self.pooling = pooling
        self.isLogit = isLogit

    def __repr__(self):
        main_str = super(BinaryChunk, self).__repr__()
        if self.isLogit:
            main_str += '_logit'
        main_str += (
            f'_nChunk{self.nChunk}_cls[{self.nClass}]_pool[{self.pooling}]')
        return main_str

    def chunk_poll(self, ck, nSamp):
        x2 = ck.contiguous().view(nSamp, -1)
        if self.pooling == 'max':
            x3 = torch.max(x2, 1)
            return x3.values
        else:
            x3 = torch.mean(x2, 1)
            return x3

    def forward(self, x):
        nSamp = x.shape[0]
        x_max = []
        for ck in x.chunk(self.nChunk, -1):
            if self.isLogit:
                x_max.append(self.chunk_poll(ck, nSamp))
            else:
                for xx in ck.chunk(2, -2):
                    x2 = xx.contiguous().view(nSamp, -1)
                    if self.pooling == 'max':
                        x3 = torch.max(x2, 1)
                        x_max.append(x3.values)
                    else:
                        x3 = torch.mean(x2, 1)
                        x_max.append(x3)
        x = torch.stack(x_max, 1)
        return x


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


def get_init_inputs():
    return [[], {'nCls': 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 math
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_per_fused_max_stack_0(in_ptr0, out_ptr1, xnumel, rnumel, XBLOCK:
    tl.constexpr):
    xnumel = 4
    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
    tmp0 = tl.load(in_ptr0 + (4 * (r1 // 2 % 2) + 16 * (r1 // 4) + 64 * x0 +
        r1 % 2), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, float('-inf'))
    tmp4 = triton_helpers.max2(tmp3, 1)[:, None]
    tl.store(out_ptr1 + 4 * x0, tmp4, xmask)


@triton.jit
def triton_per_fused_max_stack_1(in_ptr0, out_ptr1, xnumel, rnumel, XBLOCK:
    tl.constexpr):
    xnumel = 4
    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
    tmp0 = tl.load(in_ptr0 + (8 + 4 * (r1 // 2 % 2) + 16 * (r1 // 4) + 64 *
        x0 + r1 % 2), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, float('-inf'))
    tmp4 = triton_helpers.max2(tmp3, 1)[:, None]
    tl.store(out_ptr1 + 4 * x0, tmp4, xmask)


@triton.jit
def triton_per_fused_max_stack_2(in_ptr0, out_ptr1, xnumel, rnumel, XBLOCK:
    tl.constexpr):
    xnumel = 4
    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
    tmp0 = tl.load(in_ptr0 + (2 + 4 * (r1 // 2 % 2) + 16 * (r1 // 4) + 64 *
        x0 + r1 % 2), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, float('-inf'))
    tmp4 = triton_helpers.max2(tmp3, 1)[:, None]
    tl.store(out_ptr1 + 4 * x0, tmp4, xmask)


@triton.jit
def triton_per_fused_max_stack_3(in_ptr0, out_ptr1, xnumel, rnumel, XBLOCK:
    tl.constexpr):
    xnumel = 4
    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
    tmp0 = tl.load(in_ptr0 + (10 + 4 * (r1 // 2 % 2) + 16 * (r1 // 4) + 64 *
        x0 + r1 % 2), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, float('-inf'))
    tmp4 = triton_helpers.max2(tmp3, 1)[:, None]
    tl.store(out_ptr1 + 4 * x0, tmp4, 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)
        buf12 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf8 = reinterpret_tensor(buf12, (4, 1), (4, 1), 0)
        get_raw_stream(0)
        triton_per_fused_max_stack_0[grid(4)](arg0_1, buf8, 4, 16, XBLOCK=1,
            num_warps=2, num_stages=1)
        buf9 = reinterpret_tensor(buf12, (4, 1), (4, 1), 1)
        triton_per_fused_max_stack_1[grid(4)](arg0_1, buf9, 4, 16, XBLOCK=1,
            num_warps=2, num_stages=1)
        buf10 = reinterpret_tensor(buf12, (4, 1), (4, 1), 2)
        triton_per_fused_max_stack_2[grid(4)](arg0_1, buf10, 4, 16, XBLOCK=
            1, num_warps=2, num_stages=1)
        buf11 = reinterpret_tensor(buf12, (4, 1), (4, 1), 3)
        triton_per_fused_max_stack_3[grid(4)](arg0_1, buf11, 4, 16, XBLOCK=
            1, num_warps=2, num_stages=1)
        del arg0_1
    return buf12,


class BinaryChunkNew(torch.nn.Module):

    def __init__(self, nCls, isLogit=False, pooling='max', chunk_dim=-1):
        super(BinaryChunkNew, self).__init__()
        self.nClass = nCls
        self.nChunk = int(math.ceil(math.log2(self.nClass)))
        self.pooling = pooling
        self.isLogit = isLogit

    def __repr__(self):
        main_str = super(BinaryChunkNew, self).__repr__()
        if self.isLogit:
            main_str += '_logit'
        main_str += (
            f'_nChunk{self.nChunk}_cls[{self.nClass}]_pool[{self.pooling}]')
        return main_str

    def chunk_poll(self, ck, nSamp):
        x2 = ck.contiguous().view(nSamp, -1)
        if self.pooling == 'max':
            x3 = torch.max(x2, 1)
            return x3.values
        else:
            x3 = torch.mean(x2, 1)
            return x3

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