# 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/7g/c7g7othpnsbmovs44m7nfet7tt65cm2vlyavdn5zikazjtnmjrqw.py
# Topologically Sorted Source Nodes: [sigmoid, mul, s, zeros_like, max_1, ones_like, mask, mul_1], Original ATen: [aten.sigmoid, aten.mul, aten.add, aten.zeros_like, aten.maximum, aten.ones_like, aten.minimum]
# Source node to ATen node mapping:
#   mask => minimum
#   max_1 => maximum
#   mul => mul
#   mul_1 => mul_1
#   ones_like => full_default_1
#   s => add
#   sigmoid => sigmoid
#   zeros_like => full_default
# Graph fragment:
#   %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%primals_1,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, 1.2000000000000002), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, -0.1), kwargs = {})
#   %full_default : [num_users=1] = 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})
#   %maximum : [num_users=1] = call_function[target=torch.ops.aten.maximum.default](args = (%add, %full_default), kwargs = {})
#   %full_default_1 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([4, 4], 1), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %minimum : [num_users=1] = call_function[target=torch.ops.aten.minimum.default](args = (%maximum, %full_default_1), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, %minimum), kwargs = {})
triton_poi_fused_add_maximum_minimum_mul_ones_like_sigmoid_zeros_like_0 = async_compile.triton('triton_poi_fused_add_maximum_minimum_mul_ones_like_sigmoid_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: '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_add_maximum_minimum_mul_ones_like_sigmoid_zeros_like_0', '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_maximum_minimum_mul_ones_like_sigmoid_zeros_like_0(in_ptr0, in_ptr1, 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 + (x0), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask)
    tmp2 = tl.sigmoid(tmp1)
    tmp3 = 1.2000000000000002
    tmp4 = tmp2 * tmp3
    tmp5 = -0.1
    tmp6 = tmp4 + tmp5
    tmp7 = 0.0
    tmp8 = triton_helpers.maximum(tmp6, tmp7)
    tmp9 = 1.0
    tmp10 = triton_helpers.minimum(tmp8, tmp9)
    tmp11 = tmp0 * tmp10
    tl.store(out_ptr0 + (x0), tmp11, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sigmoid, mul, s, zeros_like, max_1, ones_like, mask, mul_1], Original ATen: [aten.sigmoid, aten.mul, aten.add, aten.zeros_like, aten.maximum, aten.ones_like, aten.minimum]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_maximum_minimum_mul_ones_like_sigmoid_zeros_like_0.run(primals_2, primals_1, buf0, 16, grid=grid(16), stream=stream0)
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_3, reinterpret_tensor(primals_4, (64, 4), (4, 1), 0), reinterpret_tensor(buf0, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf1)
        del buf0
        del primals_3
    return (reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0), primals_1, primals_2, reinterpret_tensor(primals_4, (64, 4), (4, 1), 0), )


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, 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((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = 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, primals_4])
    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 numpy as np
import torch.nn as nn
from torch.nn import functional as F
from torch.autograd import Variable
import logging as lg


def hard_sigmoid(x):
    """Hard Sigmoid function."""
    return torch.min(torch.max(x, torch.zeros_like(x)), torch.ones_like(x))


class _L0Norm(nn.Module):
    """L0 norm."""

    def __init__(self, origin, loc_mean: 'float'=0.0, loc_sdev: 'float'=
        0.01, beta: 'float'=2 / 3, gamma: 'float'=-0.1, zeta: 'float'=1.1,
        fix_temp: 'bool'=True, l02: 'bool'=False, l02_alpha=0.01):
        """Class of layers using L0 Norm.

        :param origin: original layer such as nn.Linear(..), nn.Conv2d(..)
        :param loc_mean: mean of the normal of initial location parameters
        :param loc_sdev: standard deviation of initial location parameters
        :param beta: initial temperature parameter
        :param gamma: lower bound of "stretched" s
        :param zeta: upper bound of "stretched" s
        :param fix_temp: True if temperature is fixed
        """
        super(_L0Norm, self).__init__()
        self._origin = origin
        self._size = self._origin.weight.size()
        self.loc = nn.Parameter(torch.zeros(self._size).normal_(loc_mean,
            loc_sdev))
        self.temp = beta if fix_temp else nn.Parameter(torch.zeros(1).fill_
            (beta))
        self.register_buffer('uniform', torch.zeros(self._size))
        self.gamma = gamma
        self.zeta = zeta
        self.gamma_zeta_ratio = np.log(-gamma / zeta)
        self.l02_alpha = l02_alpha
        self.l02 = l02
        if self.l02:
            assert self.l02_alpha > 0
            lg.info('Using L_0,2 norm')

    def _get_mask(self):
        if self.training:
            self.uniform.uniform_()
            u = Variable(self.uniform)
            s = F.sigmoid((torch.log(u) - torch.log(1 - u) + self.loc) /
                self.temp)
            s = s * (self.zeta - self.gamma) + self.gamma
            penalty = F.sigmoid(self.loc - self.temp * self.gamma_zeta_ratio
                ).sum()
            if self.l02:
                l02Norm = (F.sigmoid(self.loc - self.temp * self.
                    gamma_zeta_ratio) * self._origin.weight ** 2).sum()
                penalty = penalty + self.l02_alpha * l02Norm
        else:
            s = F.sigmoid(self.loc) * (self.zeta - self.gamma) + self.gamma
            penalty = 0
        return hard_sigmoid(s), penalty


class L0Linear(_L0Norm):
    """Linear model with L0 norm."""

    def __init__(self, in_features: 'int', out_features: 'int', bias:
        'bool'=True, **kwargs):
        """Linear model with L0 norm."""
        super(L0Linear, self).__init__(nn.Linear(in_features, out_features,
            bias=bias), **kwargs)

    def forward(self, input):
        """Forward function with mask and penalty."""
        mask, penalty = self._get_mask()
        out = F.linear(input, self._origin.weight * mask, self._origin.bias)
        return out, penalty


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


def get_init_inputs():
    return [[], {'in_features': 4, 'out_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
from torch._inductor.runtime import triton_helpers
import numpy as np
import torch.nn as nn
from torch.nn import functional as F
from torch.autograd import Variable
import logging as lg
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_poi_fused_add_maximum_minimum_mul_ones_like_sigmoid_zeros_like_0(
    in_ptr0, in_ptr1, 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 + x0, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask)
    tmp2 = tl.sigmoid(tmp1)
    tmp3 = 1.2000000000000002
    tmp4 = tmp2 * tmp3
    tmp5 = -0.1
    tmp6 = tmp4 + tmp5
    tmp7 = 0.0
    tmp8 = triton_helpers.maximum(tmp6, tmp7)
    tmp9 = 1.0
    tmp10 = triton_helpers.minimum(tmp8, tmp9)
    tmp11 = tmp0 * tmp10
    tl.store(out_ptr0 + x0, tmp11, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_maximum_minimum_mul_ones_like_sigmoid_zeros_like_0[
            grid(16)](primals_2, primals_1, buf0, 16, XBLOCK=16, num_warps=
            1, num_stages=1)
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_3, reinterpret_tensor(primals_4, (64, 
            4), (4, 1), 0), reinterpret_tensor(buf0, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf1)
        del buf0
        del primals_3
    return reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), primals_1, primals_2, reinterpret_tensor(primals_4, (64, 4), (4,
        1), 0)


def hard_sigmoid(x):
    """Hard Sigmoid function."""
    return torch.min(torch.max(x, torch.zeros_like(x)), torch.ones_like(x))


class _L0Norm(nn.Module):
    """L0 norm."""

    def __init__(self, origin, loc_mean: 'float'=0.0, loc_sdev: 'float'=
        0.01, beta: 'float'=2 / 3, gamma: 'float'=-0.1, zeta: 'float'=1.1,
        fix_temp: 'bool'=True, l02: 'bool'=False, l02_alpha=0.01):
        """Class of layers using L0 Norm.

        :param origin: original layer such as nn.Linear(..), nn.Conv2d(..)
        :param loc_mean: mean of the normal of initial location parameters
        :param loc_sdev: standard deviation of initial location parameters
        :param beta: initial temperature parameter
        :param gamma: lower bound of "stretched" s
        :param zeta: upper bound of "stretched" s
        :param fix_temp: True if temperature is fixed
        """
        super(_L0Norm, self).__init__()
        self._origin = origin
        self._size = self._origin.weight.size()
        self.loc = nn.Parameter(torch.zeros(self._size).normal_(loc_mean,
            loc_sdev))
        self.temp = beta if fix_temp else nn.Parameter(torch.zeros(1).fill_
            (beta))
        self.register_buffer('uniform', torch.zeros(self._size))
        self.gamma = gamma
        self.zeta = zeta
        self.gamma_zeta_ratio = np.log(-gamma / zeta)
        self.l02_alpha = l02_alpha
        self.l02 = l02
        if self.l02:
            assert self.l02_alpha > 0
            lg.info('Using L_0,2 norm')

    def _get_mask(self):
        if self.training:
            self.uniform.uniform_()
            u = Variable(self.uniform)
            s = F.sigmoid((torch.log(u) - torch.log(1 - u) + self.loc) /
                self.temp)
            s = s * (self.zeta - self.gamma) + self.gamma
            penalty = F.sigmoid(self.loc - self.temp * self.gamma_zeta_ratio
                ).sum()
            if self.l02:
                l02Norm = (F.sigmoid(self.loc - self.temp * self.
                    gamma_zeta_ratio) * self._origin.weight ** 2).sum()
                penalty = penalty + self.l02_alpha * l02Norm
        else:
            s = F.sigmoid(self.loc) * (self.zeta - self.gamma) + self.gamma
            penalty = 0
        return hard_sigmoid(s), penalty


class L0LinearNew(_L0Norm):
    """Linear model with L0 norm."""

    def __init__(self, in_features: 'int', out_features: 'int', bias:
        'bool'=True, **kwargs):
        """Linear model with L0 norm."""
        super(L0LinearNew, self).__init__(nn.Linear(in_features,
            out_features, bias=bias), **kwargs)

    def forward(self, input_0):
        primals_1 = self.loc
        primals_2 = self._origin.weight
        primals_3 = self._origin.bias
        primals_4 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0], output[1]
