# 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/qf/cqfhskey3zun6jxe3jyn6ntmnpvxnpmqob4lhxlqwvyiiesmz5gl.py
# Topologically Sorted Source Nodes: [sort, gather], Original ATen: [aten.sort, aten.gather]
# Source node to ATen node mapping:
#   gather => gather
#   sort => sort
# Graph fragment:
#   %sort : [num_users=1] = call_function[target=torch.ops.aten.sort.default](args = (%getitem_1, 2), kwargs = {})
#   %gather : [num_users=1] = call_function[target=torch.ops.aten.gather.default](args = (%arg0_1, 2, %getitem_2), kwargs = {})
triton_per_fused_gather_sort_0 = async_compile.triton('triton_per_fused_gather_sort_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=[16, 4],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*i64', 1: '*fp32', 2: '*fp32', 3: 'i32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_gather_sort_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}
)
@triton.jit
def triton_per_fused_gather_sort_0(in_ptr0, in_ptr1, out_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    rnumel = 4
    RBLOCK: tl.constexpr = 4
    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 + (r1 + (4*x0)), xmask, other=0.0)
    tmp1 = r1
    tmp2 = tmp1.to(tl.int16)
    tmp3 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp4 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5, tmp6, = triton_helpers.sort_with_index(tmp3, tmp4, None, 1, stable=False, descending=False)
    tmp7 = tl.full([XBLOCK, RBLOCK], 4, tl.int32)
    tmp8 = tmp5 + tmp7
    tmp9 = tmp5 < 0
    tmp10 = tl.where(tmp9, tmp8, tmp5)
    tl.device_assert(((0 <= tmp10) & (tmp10 < 4)) | ~(xmask), "index out of bounds: 0 <= tmp10 < 4")
    tmp12 = tl.load(in_ptr1 + (tmp10 + (4*x0)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr1 + (r1 + (4*x0)), tmp12, 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), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [topk], Original ATen: [aten.topk]
        buf0 = torch.ops.aten.topk.default(arg0_1, 4, 2)
        buf2 = buf0[1]
        del buf0
        buf5 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sort, gather], Original ATen: [aten.sort, aten.gather]
        stream0 = get_raw_stream(0)
        triton_per_fused_gather_sort_0.run(buf2, arg0_1, buf5, 16, 4, grid=grid(16), stream=stream0)
        del arg0_1
        del buf2
    return (buf5, )


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), (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
import torch.nn as nn


class KMaxPool1d(nn.Module):

    def __init__(self, top_k: 'int'):
        super(KMaxPool1d, self).__init__()
        self.top_k = top_k

    def forward(self, inputs):
        assert inputs.dim() == 3
        top_idxs = torch.topk(inputs, k=self.top_k, dim=2)[1]
        sorted_top_idxs = top_idxs.sort(dim=2)[0]
        return inputs.gather(dim=2, index=sorted_top_idxs)


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


def get_init_inputs():
    return [[], {'top_k': 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_per_fused_gather_sort_0(in_ptr0, in_ptr1, out_ptr1, xnumel,
    rnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 4
    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 + (r1 + 4 * x0), xmask, other=0.0)
    tmp1 = r1
    tmp2 = tmp1.to(tl.int16)
    tmp3 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp4 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5, _tmp6 = triton_helpers.sort_with_index(tmp3, tmp4, None, 1,
        stable=False, descending=False)
    tmp7 = tl.full([XBLOCK, RBLOCK], 4, tl.int32)
    tmp8 = tmp5 + tmp7
    tmp9 = tmp5 < 0
    tmp10 = tl.where(tmp9, tmp8, tmp5)
    tl.device_assert((0 <= tmp10) & (tmp10 < 4) | ~xmask,
        'index out of bounds: 0 <= tmp10 < 4')
    tmp12 = tl.load(in_ptr1 + (tmp10 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tl.store(out_ptr1 + (r1 + 4 * x0), tmp12, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = torch.ops.aten.topk.default(arg0_1, 4, 2)
        buf2 = buf0[1]
        del buf0
        buf5 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused_gather_sort_0[grid(16)](buf2, arg0_1, buf5, 16, 4,
            XBLOCK=8, num_warps=2, num_stages=1)
        del arg0_1
        del buf2
    return buf5,


class KMaxPool1dNew(nn.Module):

    def __init__(self, top_k: 'int'):
        super(KMaxPool1dNew, self).__init__()
        self.top_k = top_k

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