# 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_7/inductor_cache/4b/c4bin6v3nraanry5cu4t7z3qycurd6acdwc4l5azpezbv2erqt7w.py
# Topologically Sorted Source Nodes: [linear], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   linear => clone
# Graph fragment:
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_0 = async_compile.triton('triton_poi_fused_clone_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, 16], tile_hint=TileHint.SQUARE,
    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, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_clone_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex % 4
    x3 = (xindex // 4)
    y0 = yindex % 4
    y1 = (yindex // 4)
    x5 = xindex
    y4 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (4*x3) + (16*x2) + (64*y1)), xmask & ymask)
    tl.store(out_ptr0 + (x5 + (16*y4)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/ff/cff4pauyodt3bzn5rggprurywn64qeyzwyhkbgmdbs4wzkmbk24h.py
# Topologically Sorted Source Nodes: [mul, dotprod, max_1], Original ATen: [aten.mul, aten.sum, aten.max]
# Source node to ATen node mapping:
#   dotprod => sum_1
#   max_1 => max_1
#   mul => mul
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, %permute_2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [2], True), kwargs = {})
#   %max_1 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%sum_1, -1), kwargs = {})
triton_poi_fused_max_mul_sum_1 = async_compile.triton('triton_poi_fused_max_mul_sum_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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i64', 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, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_mul_sum_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 32, '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_mul_sum_1(in_ptr0, in_ptr1, 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_ptr1 + (x0 + (64*x1)), xmask)
    tmp3 = tl.load(in_ptr0 + (4 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (4 + x0 + (64*x1)), xmask)
    tmp7 = tl.load(in_ptr0 + (8 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (8 + x0 + (64*x1)), xmask)
    tmp11 = tl.load(in_ptr0 + (12 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + (12 + x0 + (64*x1)), xmask)
    tmp15 = tl.load(in_ptr0 + (1 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr1 + (16 + x0 + (64*x1)), xmask)
    tmp18 = tl.load(in_ptr0 + (5 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr1 + (20 + x0 + (64*x1)), xmask)
    tmp22 = tl.load(in_ptr0 + (9 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr1 + (24 + x0 + (64*x1)), xmask)
    tmp26 = tl.load(in_ptr0 + (13 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp27 = tl.load(in_ptr1 + (28 + x0 + (64*x1)), xmask)
    tmp45 = tl.load(in_ptr0 + (2 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp46 = tl.load(in_ptr1 + (32 + x0 + (64*x1)), xmask)
    tmp48 = tl.load(in_ptr0 + (6 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp49 = tl.load(in_ptr1 + (36 + x0 + (64*x1)), xmask)
    tmp52 = tl.load(in_ptr0 + (10 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp53 = tl.load(in_ptr1 + (40 + x0 + (64*x1)), xmask)
    tmp56 = tl.load(in_ptr0 + (14 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp57 = tl.load(in_ptr1 + (44 + x0 + (64*x1)), xmask)
    tmp74 = tl.load(in_ptr0 + (3 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp75 = tl.load(in_ptr1 + (48 + x0 + (64*x1)), xmask)
    tmp77 = tl.load(in_ptr0 + (7 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp78 = tl.load(in_ptr1 + (52 + x0 + (64*x1)), xmask)
    tmp81 = tl.load(in_ptr0 + (11 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp82 = tl.load(in_ptr1 + (56 + x0 + (64*x1)), xmask)
    tmp85 = tl.load(in_ptr0 + (15 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp86 = tl.load(in_ptr1 + (60 + x0 + (64*x1)), xmask)
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tmp17 = tmp15 * tmp16
    tmp20 = tmp18 * tmp19
    tmp21 = tmp17 + tmp20
    tmp24 = tmp22 * tmp23
    tmp25 = tmp21 + tmp24
    tmp28 = tmp26 * tmp27
    tmp29 = tmp25 + tmp28
    tmp30 = tmp14 > tmp29
    tmp31 = tmp14 == tmp29
    tmp32 = tmp14 != tmp14
    tmp33 = tmp29 != tmp29
    tmp34 = tmp32 > tmp33
    tmp35 = tmp30 | tmp34
    tmp36 = tmp32 & tmp33
    tmp37 = tmp31 | tmp36
    tmp38 = tl.full([1], 0, tl.int64)
    tmp39 = tl.full([1], 1, tl.int64)
    tmp40 = tmp38 < tmp39
    tmp41 = tmp37 & tmp40
    tmp42 = tmp35 | tmp41
    tmp43 = tl.where(tmp42, tmp14, tmp29)
    tmp44 = tl.where(tmp42, tmp38, tmp39)
    tmp47 = tmp45 * tmp46
    tmp50 = tmp48 * tmp49
    tmp51 = tmp47 + tmp50
    tmp54 = tmp52 * tmp53
    tmp55 = tmp51 + tmp54
    tmp58 = tmp56 * tmp57
    tmp59 = tmp55 + tmp58
    tmp60 = tmp43 > tmp59
    tmp61 = tmp43 == tmp59
    tmp62 = tmp43 != tmp43
    tmp63 = tmp59 != tmp59
    tmp64 = tmp62 > tmp63
    tmp65 = tmp60 | tmp64
    tmp66 = tmp62 & tmp63
    tmp67 = tmp61 | tmp66
    tmp68 = tl.full([1], 2, tl.int64)
    tmp69 = tmp44 < tmp68
    tmp70 = tmp67 & tmp69
    tmp71 = tmp65 | tmp70
    tmp72 = tl.where(tmp71, tmp43, tmp59)
    tmp73 = tl.where(tmp71, tmp44, tmp68)
    tmp76 = tmp74 * tmp75
    tmp79 = tmp77 * tmp78
    tmp80 = tmp76 + tmp79
    tmp83 = tmp81 * tmp82
    tmp84 = tmp80 + tmp83
    tmp87 = tmp85 * tmp86
    tmp88 = tmp84 + tmp87
    tmp89 = tmp72 > tmp88
    tmp90 = tmp72 == tmp88
    tmp91 = tmp72 != tmp72
    tmp92 = tmp88 != tmp88
    tmp93 = tmp91 > tmp92
    tmp94 = tmp89 | tmp93
    tmp95 = tmp91 & tmp92
    tmp96 = tmp90 | tmp95
    tmp97 = tl.full([1], 3, tl.int64)
    tmp98 = tmp73 < tmp97
    tmp99 = tmp96 & tmp98
    tmp100 = tmp94 | tmp99
    tmp101 = tl.where(tmp100, tmp72, tmp88)
    tmp102 = tl.where(tmp100, tmp73, tmp97)
    tl.store(out_ptr0 + (x2), tmp102, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/hh/chhmjvtnydhywuctlrzgz3hln27ndcn643agxfpeht6gnhljobu2.py
# Topologically Sorted Source Nodes: [x_max], Original ATen: [aten.index]
# Source node to ATen node mapping:
#   x_max => index
# Graph fragment:
#   %index : [num_users=1] = call_function[target=torch.ops.aten.index.Tensor](args = (%arg0_1, [%expand, %expand_1, %expand_2, %getitem_1]), kwargs = {})
triton_poi_fused_index_2 = async_compile.triton('triton_poi_fused_index_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: '*fp32', 2: '*fp32', 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, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_index_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_index_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = (xindex // 4)
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x3), xmask, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 4, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert(((0 <= tmp4) & (tmp4 < 4)) | ~(xmask), "index out of bounds: 0 <= tmp4 < 4")
    tmp6 = tl.load(in_ptr1 + (tmp4 + (4*x4)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x4), tmp6, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.clone]
        stream0 = get_raw_stream(0)
        triton_poi_fused_clone_0.run(arg0_1, buf0, 16, 16, grid=grid(16, 16), stream=stream0)
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf0, (64, 4), (4, 1), 0), reinterpret_tensor(arg1_1, (4, 4), (1, 4), 0), out=buf1)
        del arg1_1
        del buf0
        buf2 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.int64)
        # Topologically Sorted Source Nodes: [mul, dotprod, max_1], Original ATen: [aten.mul, aten.sum, aten.max]
        triton_poi_fused_max_mul_sum_1.run(arg0_1, buf1, buf2, 16, grid=grid(16), stream=stream0)
        del buf1
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_max], Original ATen: [aten.index]
        triton_poi_fused_index_2.run(buf2, arg0_1, buf3, 64, grid=grid(64), stream=stream0)
        del arg0_1
        del buf2
    return (buf3, )


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)
    arg1_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_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
import torch.nn as nn
from itertools import product as product


class VNMaxPool(nn.Module):

    def __init__(self, in_channels):
        super(VNMaxPool, self).__init__()
        self.map_to_dir = nn.Linear(in_channels, in_channels, bias=False)

    def forward(self, x):
        """
        x: point features of shape [B, N_feat, 3, N_samples, ...]
        """
        d = self.map_to_dir(x.transpose(1, -1)).transpose(1, -1)
        dotprod = (x * d).sum(2, keepdims=True)
        idx = dotprod.max(dim=-1, keepdim=False)[1]
        index_tuple = torch.meshgrid([torch.arange(j) for j in x.size()[:-1]]
            ) + (idx,)
        x_max = x[index_tuple]
        return x_max


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


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

import torch
from torch._inductor.select_algorithm import extern_kernels
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
import torch.nn as nn
from itertools import product as product
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_clone_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex % 4
    x3 = xindex // 4
    y0 = yindex % 4
    y1 = yindex // 4
    x5 = xindex
    y4 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x3 + 16 * x2 + 64 * y1), xmask & ymask)
    tl.store(out_ptr0 + (x5 + 16 * y4), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_max_mul_sum_1(in_ptr0, in_ptr1, 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_ptr1 + (x0 + 64 * x1), xmask)
    tmp3 = tl.load(in_ptr0 + (4 + 16 * x2), xmask, eviction_policy='evict_last'
        )
    tmp4 = tl.load(in_ptr1 + (4 + x0 + 64 * x1), xmask)
    tmp7 = tl.load(in_ptr0 + (8 + 16 * x2), xmask, eviction_policy='evict_last'
        )
    tmp8 = tl.load(in_ptr1 + (8 + x0 + 64 * x1), xmask)
    tmp11 = tl.load(in_ptr0 + (12 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp12 = tl.load(in_ptr1 + (12 + x0 + 64 * x1), xmask)
    tmp15 = tl.load(in_ptr0 + (1 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp16 = tl.load(in_ptr1 + (16 + x0 + 64 * x1), xmask)
    tmp18 = tl.load(in_ptr0 + (5 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp19 = tl.load(in_ptr1 + (20 + x0 + 64 * x1), xmask)
    tmp22 = tl.load(in_ptr0 + (9 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp23 = tl.load(in_ptr1 + (24 + x0 + 64 * x1), xmask)
    tmp26 = tl.load(in_ptr0 + (13 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp27 = tl.load(in_ptr1 + (28 + x0 + 64 * x1), xmask)
    tmp45 = tl.load(in_ptr0 + (2 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp46 = tl.load(in_ptr1 + (32 + x0 + 64 * x1), xmask)
    tmp48 = tl.load(in_ptr0 + (6 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp49 = tl.load(in_ptr1 + (36 + x0 + 64 * x1), xmask)
    tmp52 = tl.load(in_ptr0 + (10 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp53 = tl.load(in_ptr1 + (40 + x0 + 64 * x1), xmask)
    tmp56 = tl.load(in_ptr0 + (14 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp57 = tl.load(in_ptr1 + (44 + x0 + 64 * x1), xmask)
    tmp74 = tl.load(in_ptr0 + (3 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp75 = tl.load(in_ptr1 + (48 + x0 + 64 * x1), xmask)
    tmp77 = tl.load(in_ptr0 + (7 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp78 = tl.load(in_ptr1 + (52 + x0 + 64 * x1), xmask)
    tmp81 = tl.load(in_ptr0 + (11 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp82 = tl.load(in_ptr1 + (56 + x0 + 64 * x1), xmask)
    tmp85 = tl.load(in_ptr0 + (15 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp86 = tl.load(in_ptr1 + (60 + x0 + 64 * x1), xmask)
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tmp17 = tmp15 * tmp16
    tmp20 = tmp18 * tmp19
    tmp21 = tmp17 + tmp20
    tmp24 = tmp22 * tmp23
    tmp25 = tmp21 + tmp24
    tmp28 = tmp26 * tmp27
    tmp29 = tmp25 + tmp28
    tmp30 = tmp14 > tmp29
    tmp31 = tmp14 == tmp29
    tmp32 = tmp14 != tmp14
    tmp33 = tmp29 != tmp29
    tmp34 = tmp32 > tmp33
    tmp35 = tmp30 | tmp34
    tmp36 = tmp32 & tmp33
    tmp37 = tmp31 | tmp36
    tmp38 = tl.full([1], 0, tl.int64)
    tmp39 = tl.full([1], 1, tl.int64)
    tmp40 = tmp38 < tmp39
    tmp41 = tmp37 & tmp40
    tmp42 = tmp35 | tmp41
    tmp43 = tl.where(tmp42, tmp14, tmp29)
    tmp44 = tl.where(tmp42, tmp38, tmp39)
    tmp47 = tmp45 * tmp46
    tmp50 = tmp48 * tmp49
    tmp51 = tmp47 + tmp50
    tmp54 = tmp52 * tmp53
    tmp55 = tmp51 + tmp54
    tmp58 = tmp56 * tmp57
    tmp59 = tmp55 + tmp58
    tmp60 = tmp43 > tmp59
    tmp61 = tmp43 == tmp59
    tmp62 = tmp43 != tmp43
    tmp63 = tmp59 != tmp59
    tmp64 = tmp62 > tmp63
    tmp65 = tmp60 | tmp64
    tmp66 = tmp62 & tmp63
    tmp67 = tmp61 | tmp66
    tmp68 = tl.full([1], 2, tl.int64)
    tmp69 = tmp44 < tmp68
    tmp70 = tmp67 & tmp69
    tmp71 = tmp65 | tmp70
    tmp72 = tl.where(tmp71, tmp43, tmp59)
    tmp73 = tl.where(tmp71, tmp44, tmp68)
    tmp76 = tmp74 * tmp75
    tmp79 = tmp77 * tmp78
    tmp80 = tmp76 + tmp79
    tmp83 = tmp81 * tmp82
    tmp84 = tmp80 + tmp83
    tmp87 = tmp85 * tmp86
    tmp88 = tmp84 + tmp87
    tmp89 = tmp72 > tmp88
    tmp90 = tmp72 == tmp88
    tmp91 = tmp72 != tmp72
    tmp92 = tmp88 != tmp88
    tmp93 = tmp91 > tmp92
    tmp94 = tmp89 | tmp93
    tmp95 = tmp91 & tmp92
    tmp96 = tmp90 | tmp95
    tmp97 = tl.full([1], 3, tl.int64)
    tmp98 = tmp73 < tmp97
    tmp99 = tmp96 & tmp98
    tmp100 = tmp94 | tmp99
    tl.where(tmp100, tmp72, tmp88)
    tmp102 = tl.where(tmp100, tmp73, tmp97)
    tl.store(out_ptr0 + x2, tmp102, xmask)


@triton.jit
def triton_poi_fused_index_2(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex // 4
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last')
    tmp1 = tl.full([XBLOCK], 4, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert((0 <= tmp4) & (tmp4 < 4) | ~xmask,
        'index out of bounds: 0 <= tmp4 < 4')
    tmp6 = tl.load(in_ptr1 + (tmp4 + 4 * x4), xmask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + x4, tmp6, xmask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(16, 16)](arg0_1, buf0, 16, 16, XBLOCK
            =16, YBLOCK=16, num_warps=4, num_stages=1)
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (64, 4), (4, 1), 0),
            reinterpret_tensor(arg1_1, (4, 4), (1, 4), 0), out=buf1)
        del arg1_1
        del buf0
        buf2 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.int64)
        triton_poi_fused_max_mul_sum_1[grid(16)](arg0_1, buf1, buf2, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del buf1
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_index_2[grid(64)](buf2, arg0_1, buf3, 64, XBLOCK=
            64, num_warps=1, num_stages=1)
        del arg0_1
        del buf2
    return buf3,


class VNMaxPoolNew(nn.Module):

    def __init__(self, in_channels):
        super(VNMaxPoolNew, self).__init__()
        self.map_to_dir = nn.Linear(in_channels, in_channels, bias=False)

    def forward(self, input_0):
        arg1_1 = self.map_to_dir.weight
        arg0_1 = input_0
        output = call([arg0_1, arg1_1])
        return output[0]
