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def qwk(a1, a2):
    """
    Source: https://www.kaggle.com/c/data-science-bowl-2019/discussion/114133#latest-660168

    :param a1:
    :param a2:
    :param max_rat:
    :return:
    """
    max_rat = 3

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def re_ranking(model, probFea,galFea,k1,k2,lambda_value):
    query_num = probFea.shape[0]
    all_num = query_num + galFea.shape[0]    
    feat = np.append(probFea,galFea,axis = 0)
    feat = feat.astype(np.float16)
    feat = torch.from_numpy(feat).half().cuda()
    print('computing original distance')

    sz = feat.shape[0]
    d = []

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import os
from sqlalchemy import create_engine
from sqlalchemy.types import VARCHAR
from pandas.io.sql import SQLTable, pandasSQL_builder
import tempfile
import numpy as np
import boto3
import json
import pandas as pd
from sqlalchemy import text

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import logging
import logging.handlers
import os

def get_logger(log_name, logdir=None, loglevel=logging.INFO):
    _logger = logging.getLogger(log_name)
    fmt = '%(asctime)s [%(levelname)s] %(message)s'
    formatter = logging.Formatter(fmt)

    if not _logger.hasHandlers():

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# from https://www.kaggle.com/samratp/wordbatch-ridge-fm-frtl-target-encoding-lgbm/notebook

class TargetEncoder:
    # Adapted from https://www.kaggle.com/ogrellier/python-target-encoding-for-categorical-features
    def __repr__(self):
        return 'TargetEncoder'

    def __init__(self, smoothing=1, min_samples_leaf=1, noise_level=0, keep_original=False, suffix='enc'):
        self.smoothing = smoothing
        self.min_samples_leaf = min_samples_leaf

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local function keyCode(key, modifiers)
   modifiers = modifiers or {}
   return function()
      hs.eventtap.event.newKeyEvent(modifiers, string.lower(key), true):post()
      hs.timer.usleep(1000)
      hs.eventtap.event.newKeyEvent(modifiers, string.lower(key), false):post()      
   end
end

local function keyCodeSet(keys)

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class UnionFind:
    def __init__(self, N):
        self.rank = [0]*N
        self.par = list(range(N))
 
    def find(self, x):
        if x != self.par[x]:
            self.par[x] = self.find(self.par[x])
        return self.par[x]
    

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#include "samurai.hpp"
#include <thread>
#include <signal.h>

Role::Role(int id, CommentedIStream& cs): id(id) {
  int reachSize;
  cs >> reachSize;
  for (int k = 0; k != reachSize; k++) {
    int x, y;
    cs >> x >> y;

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#!/usr/bin/env python
# -*- coding: utf-8 -*-
from heapq import heappush, heappop

q = []
for i in range(10**7):
    heappush(q,[i, 10*i])

while q != []:
    x,y = heappop(q)

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#!/usr/bin/env python
# -*- coding: utf-8 -*-

import sys
from math import log
from collections import Counter,defaultdict
from functools import reduce
from itertools import chain
import Help