# 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_6/inductor_cache/4k/c4kdwkpn25ze2sy4jo664gaosro3ydp6dglkpzbrwrf3i4lxuy5w.py
# Topologically Sorted Source Nodes: [activ, sum_1, prob, setitem, sum_2, truediv], Original ATen: [aten.relu, aten.sum, aten.div, aten.lift_fresh, aten.index_put]
# Source node to ATen node mapping:
#   activ => relu
#   prob => div
#   setitem => full_default, index_put
#   sum_1 => sum_1
#   sum_2 => sum_2
#   truediv => div_1
# Graph fragment:
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%relu, [-1]), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%relu, %unsqueeze), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 1.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cpu, pin_memory: False})
#   %index_put : [num_users=2] = call_function[target=torch.ops.aten.index_put_.default](args = (%div, [%ne], %full_default), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%index_put, [-1], True), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%index_put, %sum_2), kwargs = {})
triton_per_fused_div_index_put_lift_fresh_relu_sum_0 = async_compile.triton('triton_per_fused_div_index_put_lift_fresh_relu_sum_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=[64, 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, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_div_index_put_lift_fresh_relu_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 2, '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_div_index_put_lift_fresh_relu_sum_0(in_ptr0, out_ptr3, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    rnumel = 9
    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 = rindex < rnumel
    r3 = rindex
    x0 = xindex % 2
    x1 = (xindex // 2) % 2
    x2 = (xindex // 4)
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (4*x1) + (4*(r3 // 3)) + (16*x2) + (r3 % 3)), rmask & xmask, other=0.0)
    tmp1 = tl.full([1, 1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.where(rmask & xmask, tmp3, 0)
    tmp6 = tl.sum(tmp5, 1)[:, None]
    tmp7 = tmp2 / tmp6
    tmp8 = tmp7 != tmp7
    tmp9 = 1.0
    tmp10 = tl.where(tmp8, tmp9, tmp7)
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK, RBLOCK])
    tmp13 = tl.where(rmask & xmask, tmp11, 0)
    tmp14 = tl.sum(tmp13, 1)[:, None]
    tmp15 = tmp10 / tmp14
    tl.store(out_ptr3 + (r3 + (9*x4)), tmp15, rmask & xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/4n/c4nzadr7rtbzpynqv2cuars3hvej42ujtc4rgccqcernmsyu53is.py
# Topologically Sorted Source Nodes: [out, out_1], Original ATen: [aten.gather, aten.sum]
# Source node to ATen node mapping:
#   out => gather
#   out_1 => sum_3
# Graph fragment:
#   %gather : [num_users=1] = call_function[target=torch.ops.aten.gather.default](args = (%view, -1, %unsqueeze_1), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%gather, [-1]), kwargs = {})
triton_poi_fused_gather_sum_1 = async_compile.triton('triton_poi_fused_gather_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=[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_gather_sum_1', '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_gather_sum_1(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
    x0 = xindex % 2
    x1 = (xindex // 2) % 2
    x2 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.full([XBLOCK], 9, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert(((0 <= tmp4) & (tmp4 < 9)) | ~(xmask), "index out of bounds: 0 <= tmp4 < 9")
    tmp6 = tl.load(in_ptr1 + (x0 + (4*x1) + (4*((tmp4 // 3) % 3)) + (16*x2) + (tmp4 % 3)), xmask)
    tl.store(out_ptr0 + (x3), tmp6, 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)
        buf3 = empty_strided_cuda((4, 4, 2, 2, 9), (144, 36, 18, 9, 1), torch.float32)
        # Topologically Sorted Source Nodes: [activ, sum_1, prob, setitem, sum_2, truediv], Original ATen: [aten.relu, aten.sum, aten.div, aten.lift_fresh, aten.index_put]
        stream0 = get_raw_stream(0)
        triton_per_fused_div_index_put_lift_fresh_relu_sum_0.run(arg0_1, buf3, 64, 9, grid=grid(64), stream=stream0)
        # Topologically Sorted Source Nodes: [multinomial], Original ATen: [aten.multinomial]
        buf4 = torch.ops.aten.multinomial.default(reinterpret_tensor(buf3, (64, 9), (9, 1), 0), 1, True)
        del buf3
        buf5 = buf4
        del buf4
        buf6 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out, out_1], Original ATen: [aten.gather, aten.sum]
        triton_poi_fused_gather_sum_1.run(buf5, arg0_1, buf6, 64, grid=grid(64), stream=stream0)
        del arg0_1
        del buf5
    return (buf6, )


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
import torch.nn.functional as F
from torch.nn.modules.utils import _pair


class StochasticPool2D(nn.Module):
    """
    Args: 
        kernel_size : size of the pooling kernel
        stride      : pool stride
    
    Note: valid padding is implemented
    """

    def __init__(self, kernel_size=3, stride=1, training=True):
        super(StochasticPool2D, self).__init__()
        self.k = _pair(kernel_size)
        self.stride = _pair(stride)
        self.training = training

    def forward(self, x, training=True):
        x = x.unfold(2, self.k[0], self.stride[0]).unfold(3, self.k[1],
            self.stride[1])
        x = x.contiguous().view(x.size()[:4] + (-1,))
        activ = F.relu(x)
        prob = torch.div(activ, activ.sum(dim=-1).unsqueeze_(-1))
        prob[prob != prob] = 1
        idx = torch.distributions.categorical.Categorical(prob).sample()
        out = x.gather(-1, idx.unsqueeze(-1))
        out = out.sum(dim=-1)
        return out


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
from torch.nn.modules.utils import _pair
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_div_index_put_lift_fresh_relu_sum_0(in_ptr0, out_ptr3,
    xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    rnumel = 9
    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, :]
    rmask = rindex < rnumel
    r3 = rindex
    x0 = xindex % 2
    x1 = xindex // 2 % 2
    x2 = xindex // 4
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x1 + 4 * (r3 // 3) + 16 * x2 + r3 % 
        3), rmask & xmask, other=0.0)
    tmp1 = tl.full([1, 1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.where(rmask & xmask, tmp3, 0)
    tmp6 = tl.sum(tmp5, 1)[:, None]
    tmp7 = tmp2 / tmp6
    tmp8 = tmp7 != tmp7
    tmp9 = 1.0
    tmp10 = tl.where(tmp8, tmp9, tmp7)
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK, RBLOCK])
    tmp13 = tl.where(rmask & xmask, tmp11, 0)
    tmp14 = tl.sum(tmp13, 1)[:, None]
    tmp15 = tmp10 / tmp14
    tl.store(out_ptr3 + (r3 + 9 * x4), tmp15, rmask & xmask)


@triton.jit
def triton_poi_fused_gather_sum_1(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
    x0 = xindex % 2
    x1 = xindex // 2 % 2
    x2 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.full([XBLOCK], 9, tl.int32)
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 < 0
    tmp4 = tl.where(tmp3, tmp2, tmp0)
    tl.device_assert((0 <= tmp4) & (tmp4 < 9) | ~xmask,
        'index out of bounds: 0 <= tmp4 < 9')
    tmp6 = tl.load(in_ptr1 + (x0 + 4 * x1 + 4 * (tmp4 // 3 % 3) + 16 * x2 +
        tmp4 % 3), xmask)
    tl.store(out_ptr0 + x3, tmp6, 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)
        buf3 = empty_strided_cuda((4, 4, 2, 2, 9), (144, 36, 18, 9, 1),
            torch.float32)
        get_raw_stream(0)
        triton_per_fused_div_index_put_lift_fresh_relu_sum_0[grid(64)](arg0_1,
            buf3, 64, 9, XBLOCK=1, num_warps=2, num_stages=1)
        buf4 = torch.ops.aten.multinomial.default(reinterpret_tensor(buf3,
            (64, 9), (9, 1), 0), 1, True)
        del buf3
        buf5 = buf4
        del buf4
        buf6 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        triton_poi_fused_gather_sum_1[grid(64)](buf5, arg0_1, buf6, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del arg0_1
        del buf5
    return buf6,


class StochasticPool2DNew(nn.Module):
    """
    Args: 
        kernel_size : size of the pooling kernel
        stride      : pool stride
    
    Note: valid padding is implemented
    """

    def __init__(self, kernel_size=3, stride=1, training=True):
        super(StochasticPool2DNew, self).__init__()
        self.k = _pair(kernel_size)
        self.stride = _pair(stride)
        self.training = training

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