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]


Measured latency_ms: 0.02454