# 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_7/inductor_cache/2u/c2uscogbzv5w5toj2f5doql67p5kdfjl24e5exykcpjjl2uv3zyj.py
# Topologically Sorted Source Nodes: [h, h_1, lt, zeros_like, where, gt, where_1, mul, add_1], Original ATen: [aten.add, aten.sigmoid, aten.lt, aten.zeros_like, aten.where, aten.gt, aten.mul]
# Source node to ATen node mapping:
#   add_1 => add_1
#   gt => gt
#   h => add
#   h_1 => sigmoid
#   lt => lt
#   mul => mul
#   where => where
#   where_1 => where_1
#   zeros_like => full_default
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mm, %primals_3), kwargs = {})
#   %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%add,), kwargs = {})
#   %lt : [num_users=1] = call_function[target=torch.ops.aten.lt.Scalar](args = (%primals_2, 0), kwargs = {})
#   %full_default : [num_users=2] = call_function[target=torch.ops.aten.full.default](args = ([4, 4], 0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%lt, %full_default, %primals_2), kwargs = {})
#   %gt : [num_users=1] = call_function[target=torch.ops.aten.gt.Scalar](args = (%primals_2, 0), kwargs = {})
#   %where_1 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%gt, %full_default, %primals_2), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %where_1), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%where, %mul), kwargs = {})
triton_poi_fused_add_gt_lt_mul_sigmoid_where_zeros_like_0 = async_compile.triton('triton_poi_fused_add_gt_lt_mul_sigmoid_where_zeros_like_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: '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_poi_fused_add_gt_lt_mul_sigmoid_where_zeros_like_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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_gt_lt_mul_sigmoid_where_zeros_like_0(in_ptr0, in_ptr1, in_ptr2, 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
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp4 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + (0))
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp1 = 0.0
    tmp2 = tmp0 < tmp1
    tmp3 = tl.where(tmp2, tmp1, tmp0)
    tmp7 = tmp4 + tmp6
    tmp8 = tl.sigmoid(tmp7)
    tmp9 = tmp0 > tmp1
    tmp10 = tl.where(tmp9, tmp1, tmp0)
    tmp11 = tmp8 * tmp10
    tmp12 = tmp3 + tmp11
    tl.store(out_ptr0 + (x2), tmp12, 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, 1), (1, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (1, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mm], Original ATen: [aten.mm]
        extern_kernels.mm(primals_2, primals_1, out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [h, h_1, lt, zeros_like, where, gt, where_1, mul, add_1], Original ATen: [aten.add, aten.sigmoid, aten.lt, aten.zeros_like, aten.where, aten.gt, aten.mul]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_gt_lt_mul_sigmoid_where_zeros_like_0.run(primals_2, buf0, primals_3, buf1, 16, grid=grid(16), stream=stream0)
    return (buf1, primals_2, primals_3, buf0, )


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, 1), (1, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((1, ), (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.utils.data
import torch.nn as nn
import torch.nn.functional as F


class NodeAdaptiveEncoder(nn.Module):

    def __init__(self, num_features, dropout=0.5):
        super(NodeAdaptiveEncoder, self).__init__()
        self.fc = nn.Parameter(torch.zeros(size=(num_features, 1)))
        nn.init.xavier_normal_(self.fc.data, gain=1.414)
        self.bf = nn.Parameter(torch.zeros(size=(1,)))
        self.dropout = torch.nn.Dropout(dropout)

    def forward(self, x):
        h = torch.mm(x, self.fc) + self.bf
        h = F.sigmoid(h)
        h = self.dropout(h)
        return torch.where(x < 0, torch.zeros_like(x), x) + h * torch.where(
            x > 0, torch.zeros_like(x), x)


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


def get_init_inputs():
    return [[], {'num_features': 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.utils.data
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_gt_lt_mul_sigmoid_where_zeros_like_0(in_ptr0,
    in_ptr1, in_ptr2, 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
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp4 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr2 + 0)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp1 = 0.0
    tmp2 = tmp0 < tmp1
    tmp3 = tl.where(tmp2, tmp1, tmp0)
    tmp7 = tmp4 + tmp6
    tmp8 = tl.sigmoid(tmp7)
    tmp9 = tmp0 > tmp1
    tmp10 = tl.where(tmp9, tmp1, tmp0)
    tmp11 = tmp8 * tmp10
    tmp12 = tmp3 + tmp11
    tl.store(out_ptr0 + x2, tmp12, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 1), (1, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.mm(primals_2, primals_1, out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_gt_lt_mul_sigmoid_where_zeros_like_0[grid(16)](
            primals_2, buf0, primals_3, buf1, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
    return buf1, primals_2, primals_3, buf0


class NodeAdaptiveEncoderNew(nn.Module):

    def __init__(self, num_features, dropout=0.5):
        super(NodeAdaptiveEncoderNew, self).__init__()
        self.fc = nn.Parameter(torch.zeros(size=(num_features, 1)))
        nn.init.xavier_normal_(self.fc.data, gain=1.414)
        self.bf = nn.Parameter(torch.zeros(size=(1,)))
        self.dropout = torch.nn.Dropout(dropout)

    def forward(self, input_0):
        primals_1 = self.fc
        primals_3 = self.bf
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
