# 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/lb/clb7usy7qfmld2idfcmg2r22i3nfxc6io5mv3kdkm6sxqnukrctc.py
# Topologically Sorted Source Nodes: [sort], Original ATen: [aten.sort]
# Source node to ATen node mapping:
#   sort => sort
# Graph fragment:
#   %sort : [num_users=2] = call_function[target=torch.ops.aten.sort.default](args = (%view, 2, True), kwargs = {})
triton_per_fused_sort_0 = async_compile.triton('triton_per_fused_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, 16],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i64', 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, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_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_sort_0(in_ptr0, out_ptr0, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    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 + (r1 + (16*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=True)
    tmp7 = tmp6.to(tl.int64)
    tl.store(out_ptr0 + (r1 + (16*x0)), tmp5, xmask)
    tl.store(out_ptr2 + (r1 + (16*x0)), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/y7/cy7svbrvgizoup47rqhzlbrrwnvehommsnpvckyc5k6loax47jcb.py
# Topologically Sorted Source Nodes: [sum_1, div, sum_2, yMin, add], Original ATen: [aten.sum, aten.div, aten.add]
# Source node to ATen node mapping:
#   add => add
#   div => div
#   sum_1 => sum_1
#   sum_2 => sum_2
#   yMin => div_1
# Graph fragment:
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%slice_6, [2], True), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_1, 1), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%slice_12, [2], True), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_2, 1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%div, %div_1), kwargs = {})
triton_poi_fused_add_div_sum_1 = async_compile.triton('triton_poi_fused_add_div_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: '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_add_div_sum_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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_add_div_sum_1(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')
    tmp3 = tl.load(in_ptr0 + (15 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp4 = tmp3 * tmp1
    tmp5 = tmp2 + tmp4
    tl.store(out_ptr0 + (x0), tmp5, 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, 16), (64, 16, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.int64)
        # Topologically Sorted Source Nodes: [sort], Original ATen: [aten.sort]
        stream0 = get_raw_stream(0)
        triton_per_fused_sort_0.run(arg0_1, buf0, buf3, 16, 16, grid=grid(16), stream=stream0)
        del arg0_1
        buf2 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sum_1, div, sum_2, yMin, add], Original ATen: [aten.sum, aten.div, aten.add]
        triton_poi_fused_add_div_sum_1.run(buf0, buf2, 16, grid=grid(16), stream=stream0)
        del buf0
    return (reinterpret_tensor(buf2, (4, 4, 1, 1), (4, 1, 1, 1), 0), reinterpret_tensor(buf3, (4, 4, 1), (64, 16, 1), 15), reinterpret_tensor(buf3, (4, 4, 1), (64, 16, 1), 0), )


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
import torch.nn as nn


class WeldonPooling(nn.Module):

    def __init__(self, nMax=1, nMin=None):
        super(WeldonPooling, self).__init__()
        self.nMax = nMax
        if nMin is None:
            self.nMin = nMax
        else:
            self.nMin = nMin
        self.input = torch.Tensor()
        self.output = torch.Tensor()
        self.indicesMax = torch.Tensor()
        self.indicesMin = torch.Tensor()

    def forward(self, input):
        self.batchSize = 0
        self.numChannels = 0
        self.h = 0
        self.w = 0
        if input.dim() == 4:
            self.batchSize = input.size(0)
            self.numChannels = input.size(1)
            self.h = input.size(2)
            self.w = input.size(3)
        elif input.dim() == 3:
            self.batchSize = 1
            self.numChannels = input.size(0)
            self.h = input.size(1)
            self.w = input.size(2)
        else:
            None
        self.input = input
        nMax = self.nMax
        if nMax <= 0:
            nMax = 0
        elif nMax < 1:
            nMax = torch.clamp(torch.floor(nMax * self.h * self.w), min=1)
        nMin = self.nMin
        if nMin <= 0:
            nMin = 0
        elif nMin < 1:
            nMin = torch.clamp(torch.floor(nMin * self.h * self.w), min=1)
        x = input.view(self.batchSize, self.numChannels, self.h * self.w)
        scoreSorted, indices = torch.sort(x, x.dim() - 1, True)
        self.indicesMax = indices[:, :, 0:nMax]
        self.output = torch.sum(scoreSorted[:, :, 0:nMax], dim=2, keepdim=True)
        self.output = self.output.div(nMax)
        if nMin > 0:
            self.indicesMin = indices[:, :, self.h * self.w - nMin:self.h *
                self.w]
            yMin = torch.sum(scoreSorted[:, :, self.h * self.w - nMin:self.
                h * self.w], 2, keepdim=True).div(nMin)
            self.output = torch.add(self.output, yMin)
        if input.dim() == 4:
            self.output = self.output.view(self.batchSize, self.numChannels,
                1, 1)
        elif input.dim() == 3:
            self.output = self.output.view(self.numChannels, 1, 1)
        return self.output

    def backward(self, grad_output, _indices_grad=None):
        nMax = self.nMax
        if nMax <= 0:
            nMax = 0
        elif nMax < 1:
            nMax = torch.clamp(torch.floor(nMax * self.h * self.w), min=1)
        nMin = self.nMin
        if nMin <= 0:
            nMin = 0
        elif nMin < 1:
            nMin = torch.clamp(torch.floor(nMin * self.h * self.w), min=1)
        yMax = grad_output.clone().view(self.batchSize, self.numChannels, 1
            ).expand(self.batchSize, self.numChannels, nMax)
        z = torch.zeros(self.batchSize, self.numChannels, self.h * self.w
            ).type_as(self.input)
        z = z.scatter_(2, self.indicesMax, yMax).div(nMax)
        if nMin > 0:
            yMin = grad_output.clone().view(self.batchSize, self.numChannels, 1
                ).div(nMin).expand(self.batchSize, self.numChannels, nMin)
            self.gradInput = z.scatter_(2, self.indicesMin, yMin).view(self
                .batchSize, self.numChannels, self.h, self.w)
        else:
            self.gradInput = z.view(self.batchSize, self.numChannels, self.
                h, self.w)
        if self.input.dim() == 3:
            self.gradInput = self.gradInput.view(self.numChannels, self.h,
                self.w)
        return self.gradInput


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


def get_init_inputs():
    return [[], {}]

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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_per_fused_sort_0(in_ptr0, out_ptr0, out_ptr2, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    xnumel = 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, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * 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=True)
    tmp7 = tmp6.to(tl.int64)
    tl.store(out_ptr0 + (r1 + 16 * x0), tmp5, xmask)
    tl.store(out_ptr2 + (r1 + 16 * x0), tmp7, xmask)


@triton.jit
def triton_poi_fused_add_div_sum_1(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')
    tmp3 = tl.load(in_ptr0 + (15 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp4 = tmp3 * tmp1
    tmp5 = tmp2 + tmp4
    tl.store(out_ptr0 + x0, tmp5, 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, 16), (64, 16, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.int64)
        get_raw_stream(0)
        triton_per_fused_sort_0[grid(16)](arg0_1, buf0, buf3, 16, 16,
            XBLOCK=8, num_warps=2, num_stages=1)
        del arg0_1
        buf2 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        triton_poi_fused_add_div_sum_1[grid(16)](buf0, buf2, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del buf0
    return reinterpret_tensor(buf2, (4, 4, 1, 1), (4, 1, 1, 1), 0
        ), reinterpret_tensor(buf3, (4, 4, 1), (64, 16, 1), 15
        ), reinterpret_tensor(buf3, (4, 4, 1), (64, 16, 1), 0)


class WeldonPoolingNew(nn.Module):

    def __init__(self, nMax=1, nMin=None):
        super(WeldonPoolingNew, self).__init__()
        self.nMax = nMax
        if nMin is None:
            self.nMin = nMax
        else:
            self.nMin = nMin
        self.input = torch.Tensor()
        self.output = torch.Tensor()
        self.indicesMax = torch.Tensor()
        self.indicesMin = torch.Tensor()

    def backward(self, grad_output, _indices_grad=None):
        nMax = self.nMax
        if nMax <= 0:
            nMax = 0
        elif nMax < 1:
            nMax = torch.clamp(torch.floor(nMax * self.h * self.w), min=1)
        nMin = self.nMin
        if nMin <= 0:
            nMin = 0
        elif nMin < 1:
            nMin = torch.clamp(torch.floor(nMin * self.h * self.w), min=1)
        yMax = grad_output.clone().view(self.batchSize, self.numChannels, 1
            ).expand(self.batchSize, self.numChannels, nMax)
        z = torch.zeros(self.batchSize, self.numChannels, self.h * self.w
            ).type_as(self.input)
        z = z.scatter_(2, self.indicesMax, yMax).div(nMax)
        if nMin > 0:
            yMin = grad_output.clone().view(self.batchSize, self.numChannels, 1
                ).div(nMin).expand(self.batchSize, self.numChannels, nMin)
            self.gradInput = z.scatter_(2, self.indicesMin, yMin).view(self
                .batchSize, self.numChannels, self.h, self.w)
        else:
            self.gradInput = z.view(self.batchSize, self.numChannels, self.
                h, self.w)
        if self.input.dim() == 3:
            self.gradInput = self.gradInput.view(self.numChannels, self.h,
                self.w)
        return self.gradInput

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