# AOT ID: ['0_forward']
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_1/inductor_cache/fw/cfw3p53dnoaisg2fotxobujo3hcyrj245b6443ptshy5r65izoxh.py
# Topologically Sorted Source Nodes: [conv2d, alpha, max_pool2d, mul, sub, avg_pool2d, mul_1, add], Original ATen: [aten.convolution, aten.sigmoid, aten.max_pool2d_with_indices, aten.mul, aten.rsub, aten.avg_pool2d, aten.add]
# Source node to ATen node mapping:
#   add => add
#   alpha => sigmoid
#   avg_pool2d => avg_pool2d
#   conv2d => convolution
#   max_pool2d => getitem
#   mul => mul
#   mul_1 => mul_1
#   sub => sub
# Graph fragment:
#   %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_3, %primals_1, %primals_2, [4, 4], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %sigmoid : [num_users=2] = call_function[target=torch.ops.aten.sigmoid.default](args = (%convolution,), kwargs = {})
#   %getitem : [num_users=2] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 0), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %getitem), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %sigmoid), kwargs = {})
#   %avg_pool2d : [num_users=2] = call_function[target=torch.ops.aten.avg_pool2d.default](args = (%primals_3, [4, 4], [4, 4]), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %avg_pool2d), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {})
triton_poi_fused_add_avg_pool2d_convolution_max_pool2d_with_indices_mul_rsub_sigmoid_0 = async_compile.triton('triton_poi_fused_add_avg_pool2d_convolution_max_pool2d_with_indices_mul_rsub_sigmoid_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], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_avg_pool2d_convolution_max_pool2d_with_indices_mul_rsub_sigmoid_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 18, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_avg_pool2d_convolution_max_pool2d_with_indices_mul_rsub_sigmoid_0(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, out_ptr1, out_ptr2, 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
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (16*x2), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (1 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (2 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (3 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (4 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + (5 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr1 + (6 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr1 + (7 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp18 = tl.load(in_ptr1 + (8 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr1 + (9 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr1 + (10 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp24 = tl.load(in_ptr1 + (11 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp26 = tl.load(in_ptr1 + (12 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr1 + (13 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp30 = tl.load(in_ptr1 + (14 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp32 = tl.load(in_ptr1 + (15 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = triton_helpers.maximum(tmp4, tmp3)
    tmp7 = triton_helpers.maximum(tmp6, tmp5)
    tmp9 = triton_helpers.maximum(tmp8, tmp7)
    tmp11 = triton_helpers.maximum(tmp10, tmp9)
    tmp13 = triton_helpers.maximum(tmp12, tmp11)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tmp17 = triton_helpers.maximum(tmp16, tmp15)
    tmp19 = triton_helpers.maximum(tmp18, tmp17)
    tmp21 = triton_helpers.maximum(tmp20, tmp19)
    tmp23 = triton_helpers.maximum(tmp22, tmp21)
    tmp25 = triton_helpers.maximum(tmp24, tmp23)
    tmp27 = triton_helpers.maximum(tmp26, tmp25)
    tmp29 = triton_helpers.maximum(tmp28, tmp27)
    tmp31 = triton_helpers.maximum(tmp30, tmp29)
    tmp33 = triton_helpers.maximum(tmp32, tmp31)
    tmp34 = tmp4 + tmp3
    tmp35 = tmp6 + tmp34
    tmp36 = tmp8 + tmp35
    tmp37 = tmp10 + tmp36
    tmp38 = tmp12 + tmp37
    tmp39 = tmp14 + tmp38
    tmp40 = tmp16 + tmp39
    tmp41 = tmp18 + tmp40
    tmp42 = tmp20 + tmp41
    tmp43 = tmp22 + tmp42
    tmp44 = tmp24 + tmp43
    tmp45 = tmp26 + tmp44
    tmp46 = tmp28 + tmp45
    tmp47 = tmp30 + tmp46
    tmp48 = tmp32 + tmp47
    tmp49 = 0.0625
    tmp50 = tmp48 * tmp49
    tmp51 = tl.sigmoid(tmp2)
    tmp52 = tmp51 * tmp33
    tmp53 = 1.0
    tmp54 = tmp53 - tmp51
    tmp55 = tmp54 * tmp50
    tmp56 = tmp52 + tmp55
    tl.store(in_out_ptr0 + (x2), tmp2, xmask)
    tl.store(out_ptr0 + (x2), tmp33, xmask)
    tl.store(out_ptr1 + (x2), tmp50, xmask)
    tl.store(out_ptr2 + (x2), tmp56, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(4, 4), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 1, 1), (4, 1, 1, 1))
        buf1 = buf0; del buf0  # reuse
        buf2 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [conv2d, alpha, max_pool2d, mul, sub, avg_pool2d, mul_1, add], Original ATen: [aten.convolution, aten.sigmoid, aten.max_pool2d_with_indices, aten.mul, aten.rsub, aten.avg_pool2d, aten.add]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_avg_pool2d_convolution_max_pool2d_with_indices_mul_rsub_sigmoid_0.run(buf1, primals_2, primals_3, buf2, buf3, buf4, 16, grid=grid(16), stream=stream0)
        del primals_2
    return (buf4, primals_1, primals_3, buf1, buf2, buf3, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3])
    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 GatedPooling(nn.Module):
    """
        Gated pooling as defined in https://arxiv.org/abs/1509.08985
        This implementation is the LR variant
    """

    def __init__(self, kernel_size, filter):
        super(GatedPooling, self).__init__()
        self.avgpool = nn.AvgPool2d(kernel_size)
        self.maxpool = nn.MaxPool2d(kernel_size)
        self.transform = nn.Conv2d(filter, filter, kernel_size=kernel_size,
            stride=kernel_size)

    def forward(self, x):
        alpha = torch.sigmoid(self.transform(x))
        return alpha * self.maxpool(x) + (1 - alpha) * self.avgpool(x)


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


def get_init_inputs():
    return [[], {'kernel_size': 4, 'filter': 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
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_poi_fused_add_avg_pool2d_convolution_max_pool2d_with_indices_mul_rsub_sigmoid_0(
    in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, out_ptr1, out_ptr2, 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
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + 16 * x2, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (1 + 16 * x2), xmask, eviction_policy='evict_last'
        )
    tmp6 = tl.load(in_ptr1 + (2 + 16 * x2), xmask, eviction_policy='evict_last'
        )
    tmp8 = tl.load(in_ptr1 + (3 + 16 * x2), xmask, eviction_policy='evict_last'
        )
    tmp10 = tl.load(in_ptr1 + (4 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp12 = tl.load(in_ptr1 + (5 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp14 = tl.load(in_ptr1 + (6 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp16 = tl.load(in_ptr1 + (7 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp18 = tl.load(in_ptr1 + (8 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp20 = tl.load(in_ptr1 + (9 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp22 = tl.load(in_ptr1 + (10 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp24 = tl.load(in_ptr1 + (11 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp26 = tl.load(in_ptr1 + (12 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp28 = tl.load(in_ptr1 + (13 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp30 = tl.load(in_ptr1 + (14 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp32 = tl.load(in_ptr1 + (15 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = triton_helpers.maximum(tmp4, tmp3)
    tmp7 = triton_helpers.maximum(tmp6, tmp5)
    tmp9 = triton_helpers.maximum(tmp8, tmp7)
    tmp11 = triton_helpers.maximum(tmp10, tmp9)
    tmp13 = triton_helpers.maximum(tmp12, tmp11)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tmp17 = triton_helpers.maximum(tmp16, tmp15)
    tmp19 = triton_helpers.maximum(tmp18, tmp17)
    tmp21 = triton_helpers.maximum(tmp20, tmp19)
    tmp23 = triton_helpers.maximum(tmp22, tmp21)
    tmp25 = triton_helpers.maximum(tmp24, tmp23)
    tmp27 = triton_helpers.maximum(tmp26, tmp25)
    tmp29 = triton_helpers.maximum(tmp28, tmp27)
    tmp31 = triton_helpers.maximum(tmp30, tmp29)
    tmp33 = triton_helpers.maximum(tmp32, tmp31)
    tmp34 = tmp4 + tmp3
    tmp35 = tmp6 + tmp34
    tmp36 = tmp8 + tmp35
    tmp37 = tmp10 + tmp36
    tmp38 = tmp12 + tmp37
    tmp39 = tmp14 + tmp38
    tmp40 = tmp16 + tmp39
    tmp41 = tmp18 + tmp40
    tmp42 = tmp20 + tmp41
    tmp43 = tmp22 + tmp42
    tmp44 = tmp24 + tmp43
    tmp45 = tmp26 + tmp44
    tmp46 = tmp28 + tmp45
    tmp47 = tmp30 + tmp46
    tmp48 = tmp32 + tmp47
    tmp49 = 0.0625
    tmp50 = tmp48 * tmp49
    tmp51 = tl.sigmoid(tmp2)
    tmp52 = tmp51 * tmp33
    tmp53 = 1.0
    tmp54 = tmp53 - tmp51
    tmp55 = tmp54 * tmp50
    tmp56 = tmp52 + tmp55
    tl.store(in_out_ptr0 + x2, tmp2, xmask)
    tl.store(out_ptr0 + x2, tmp33, xmask)
    tl.store(out_ptr1 + x2, tmp50, xmask)
    tl.store(out_ptr2 + x2, tmp56, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(4, 
            4), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 1, 1), (4, 1, 1, 1))
        buf1 = buf0
        del buf0
        buf2 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        buf4 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_avg_pool2d_convolution_max_pool2d_with_indices_mul_rsub_sigmoid_0[
            grid(16)](buf1, primals_2, primals_3, buf2, buf3, buf4, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_2
    return buf4, primals_1, primals_3, buf1, buf2, buf3


class GatedPoolingNew(nn.Module):
    """
        Gated pooling as defined in https://arxiv.org/abs/1509.08985
        This implementation is the LR variant
    """

    def __init__(self, kernel_size, filter):
        super(GatedPoolingNew, self).__init__()
        self.avgpool = nn.AvgPool2d(kernel_size)
        self.maxpool = nn.MaxPool2d(kernel_size)
        self.transform = nn.Conv2d(filter, filter, kernel_size=kernel_size,
            stride=kernel_size)

    def forward(self, input_0):
        primals_1 = self.transform.weight
        primals_2 = self.transform.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
