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#!/usr/bin/env python

"""
    ablr.py
"""

import numpy as np

import torch
from torch import nn

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"""
    openml_rerf_test.py
"""

import sys
import openml
import argparse
import numpy as np
import pandas as pd

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from multiprocessing import Process
from time import time
import numpy as np
from exline.helpers import with_timeout
from sklearn.svm import SVC


def __SVC_fit(model, X, y, out):
    out['result'] = model.fit(X, y)

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#!/usr/bin/env python

"""
    timed_parmap.py
"""

import sys
from joblib import delayed
from concurrent.futures import ProcessPoolExecutor, as_completed

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#!/usr/bin/env python

"""
    faiss_svc.py
"""

import faiss
import numpy as np
from time import time
from sklearn.svm import LinearSVC

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#!/usr/bin/env python

"""
    bohb.py
"""

import numpy as np
from uuid import uuid4

import ConfigSpace as CS

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import pandas as pd
import torch.nn.functional as F

def sparse_bce_with_logits(x, i, j):
    t1 = x.clamp(min=0).mean()
    t2 = - x[(i, j)].sum() / x.numel()
    t3 = torch.log(1 + torch.exp(-torch.abs(x))).mean()
    
    return t1 + t2 + t3

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#!/bin/bash

# download_google_drive.sh

function download_google_drive {
   SRC=$1
   DST=$2
   echo "$SRC -> $DST"
   
   curl -c /tmp/cookies "https://drive.google.com/uc?export=download&id=$SRC" > /tmp/intermezzo.html

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#!/usr/bin/env python

"""
    mtx2edgelist.py
"""

from scipy.io import mmread
import numpy as np
import pandas as pd
import argparse

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#!/usr/bin/env python

"""
    edgelist2mtx.py
"""


from scipy.io import mmwrite
import numpy as np
import pandas as pd