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from datetime import datetime
str_datetime = "2022-04-11"
_until_time_created_unaware = datetime.strptime(str_datetime, "%Y-%M-%d") if str_datetime!= "" else datetime.now()
until_datetime_created_unaware = _until_time_created_unaware.replace(hour=23, minute=59, second=59)
until_datetime_created_aware = pytz.utc.localize(until_datetime_created_unaware)

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import torch
from torch import nn

from labml import experiment
from labml.configs import option
from labml_helpers.module import Module
from labml_nn.experiments.nlp_classification import NLPClassificationConfigs
from labml_nn.transformers import Encoder
from labml_nn.transformers import TransformerConfigs

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import chardet
import codecs
infile = 'unreadable.txt'
outfile = 'readable.txt'
with open(infile,'rb') as raw_file:
	result = chardet.detect(raw_file)
	char_enc = result['encoding']
with open(outfile, 'r', charenc):
	read_as_utf = f.read()
	out = codecs.open(outfile, 'w', 'utf-8')

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import matplotlib.pyplot as plt
def label_prop_test(model, kernel, params_list, X_train, X_test, y_train, y_test):
    plt.figure(figsize=(20,10))
    n, g = 0, 0
    roc_scores = []
    if kernel == 'rbf':
        for g in params_list:
            lp = model(kernel=kernel, n_neighbors=n, gamma=g, max_iter=100000, tol=0.0001)
            lp.fit(X_train, y_train)
            roc_scores.append(roc_auc_score(y_test, lp.predict_proba(X_test), multi_class='ovo'))

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# This is an application transferring csv data into database
# We choose a NoSQL database MongoDB since we have to store data and the logs of data GET requests# We prefer to choose PostgreSQL rather than MySQL(purely relation) or SQLite (no concurrency support)
import json
import csv
import pandas as pd
import os
from pymongo import MongoClient

cwd = os.path.dirname(__file__)
csvpath=os.path.join(cwd,'task_data.csv')

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import itertools
def countPairs(arr):
    # Write your code here
    count = 0
    for i, j in itertools.combinations(arr, 2):
        v = i & j
        if v != 0 :
            if (v % 2 == 0) | (v == 1):
                count+=1
    return count

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import numpy as np
from sklearn import linear_model

def desired_marketing_expenditure(marketing_expenditure, units_sold, desired_units_sold):
    """
    :param marketing_expenditure: (list) A list of integers with the expenditure for each previous campaign.
    :param units_sold: (list) A list of integers with the number of units sold for each previous campaign.
    :param desired_units_sold: (integer) Target number of units to sell in the new campaign.
    :returns: (float) Required amount of money to be invested.
    """

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package main

import (
	"bufio"
	"fmt"
	"io/ioutil"
	"log"
	"net/http"
	"os"
	"os/signal"

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import os
import sqlite3
import operator
from collections import OrderedDict

#path to user's history database (Chrome)
data_path = os.path.expanduser('~')+"/Library/Application Support/Google/Chrome/Default"
files = os.listdir(data_path)
history_db = os.path.join(data_path, 'history')
#querying the db

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CREATE TABLE accounts(
JE_ID serial PRIMARY KEY,
ACCOUNT_DR integer NOT NULL,
ACCOUNT_CR integer NOT NULL,
SUMM integer NOT NULL,
REVERSED integer NOT NULL,
USER_ID integer NOT NULL,
DATA integer NOT NULL,
DATE_DOC integer NOT NULL,
APPROVER_ID varchar(256) NOT NULL,