code stringlengths 13 1.2M | order_type stringclasses 1
value | original_example dict | step_ids listlengths 1 5 |
|---|---|---|---|
import os
import unittest
from mock import Mock
from tfsnippet.utils import *
class HumanizeDurationTestCase(unittest.TestCase):
cases = [
(0.0, '0 sec'),
(1e-8, '1e-08 sec'),
(0.1, '0.1 sec'),
(1.0, '1 sec'),
(1, '1 sec'),
(1.1, '1.1 secs'),
(59, '59 secs... | normal | {
"blob_id": "9189c1dd21b0858df3138bcf4fc7568b378e6271",
"index": 885,
"step-1": "<mask token>\n\n\nclass NotSetTestCase(unittest.TestCase):\n <mask token>\n\n\nclass _CachedPropertyHelper(object):\n\n def __init__(self, value):\n self.value = value\n\n @cached_property('_cached_value')\n def c... | [
11,
12,
13,
18,
22
] |
# uncompyle6 version 3.2.3
# Python bytecode 3.6 (3379)
# Decompiled from: Python 2.7.5 (default, Jul 13 2018, 13:06:57)
# [GCC 4.8.5 20150623 (Red Hat 4.8.5-28)]
# Embedded file name: ./authx/migrations/0001_initial.py
# Compiled at: 2018-08-23 19:33:14
# Size of source mod 2**32: 2715 bytes
from __future__ import un... | normal | {
"blob_id": "1073845131afb2446ca68ee10092eeb00feef800",
"index": 3585,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Migration(migrations.Migration):\n <mask token>\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Migration(migrations.Migration):\n initial = T... | [
0,
1,
2,
3,
4
] |
# -*- coding: utf-8 -*-
"""Chatbot learning
학습시 생성된 vocab 딕셔너리 파일을 Cindy ui 실행시 경로를 동일시 해주어야 연결성 있는 문장을 생성해줍니다.
"""
from tensorflow.keras import models
from tensorflow.keras import layers
from tensorflow.keras import optimizers, losses, metrics
from tensorflow.keras import preprocessing
import numpy as np
import pa... | normal | {
"blob_id": "bd06030ace665a0686c894a863e5c779b6d0931c",
"index": 6447,
"step-1": "<mask token>\n\n\ndef convert_text_to_index(sentences, vocabulary, type):\n sentences_index = []\n for sentence in sentences:\n sentence_index = []\n if type == DECODER_INPUT:\n sentence_index.extend(... | [
3,
6,
7,
8,
9
] |
import argparse
import datetime
import importlib
import pprint
import time
import random
import numpy as np
import torch
from torch.utils.tensorboard import SummaryWriter
from utils import get_git_state, time_print, AverageMeter, ProgressMeter, save_checkpoint
def train(cfg, epoch, data_loader, model):
data_tim... | normal | {
"blob_id": "81688d51696156905736b5de7a4929387fd385ab",
"index": 91,
"step-1": "<mask token>\n\n\ndef train(cfg, epoch, data_loader, model):\n data_time = AverageMeter('Data', ':6.3f')\n batch_time = AverageMeter('Time', ':6.3f')\n losses = AverageMeter('Loss', ':.4e')\n progress = ProgressMeter(len(... | [
2,
3,
4,
5,
6
] |
from django import forms
class TeacherForm(forms.Form):
name = forms.CharField(label='Your Name', max_length=100, widget=forms.
TextInput(attrs={'class': 'form-control text-center w-75 mx-auto'}))
email = forms.EmailField(widget=forms.TextInput(attrs={'class':
'form-control text-center w-75 mx... | normal | {
"blob_id": "7c5877eea78c3fa8b7928219edd52e2502c16c09",
"index": 6392,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass TeacherForm(forms.Form):\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass TeacherForm(forms.Form):\n name = forms.CharField(label='Your Name', max... | [
0,
1,
2,
3
] |
zi=["L","Ma","Mi","J","Vi","S","D"]
V=[]
for i in range(0,len(zi)):
x=input("dati salariul de: {} ".format(zi[i]))
V.append(int(x))
print("Salariul in fiecare zi: {}".format(V))
print(sum(V))
print(round(sum(V)/7,2))
print(max(V))
vMax=[]
vMin=[]
for i in range(0,len(zi)):
if V[i]==max(V):
... | normal | {
"blob_id": "6c91114e0c32628b64734000c82354105032b2fd",
"index": 7954,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(0, len(zi)):\n x = input('dati salariul de: {} '.format(zi[i]))\n V.append(int(x))\nprint('Salariul in fiecare zi: {}'.format(V))\nprint(sum(V))\nprint(round(sum(V) /... | [
0,
1,
2,
3
] |
#https://codecombat.com/play/level/village-champion
# Incoming munchkins! Defend the town!
# Define your own function to fight the enemy!
# In the function, find an enemy, then cleave or attack it.
def attttaaaaacccckkkk():
enemy = hero.findNearest(hero.findEnemies())
#enemy = hero.findNearestEnemy()
if e... | normal | {
"blob_id": "ce365e011d8cc88d9aa6b4df18ea3f4e70d48f5c",
"index": 4887,
"step-1": "<mask token>\n",
"step-2": "def attttaaaaacccckkkk():\n enemy = hero.findNearest(hero.findEnemies())\n if enemy:\n if enemy and hero.isReady('cleave'):\n hero.cleave(enemy)\n else:\n hero... | [
0,
1,
2,
3
] |
import numpy as np
catogory = np.array([50, 30, 40, 20])
data = np.array([[20, 50, 10, 15, 20], [30, 40, 20, 65, 35], [75, 30, 42,
70, 45], [40, 25, 35, 22, 55]])
print(catogory)
print(data)
print(catogory.dot(data))
print(data.T.dot(catogory))
| normal | {
"blob_id": "e4b49faaad648c6e85274abb18f994083a74013d",
"index": 7160,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(catogory)\nprint(data)\nprint(catogory.dot(data))\nprint(data.T.dot(catogory))\n",
"step-3": "<mask token>\ncatogory = np.array([50, 30, 40, 20])\ndata = np.array([[20, 50, 10, 15... | [
0,
1,
2,
3
] |
import xlsxwriter
workbook = xlsxwriter.Workbook('商品编码.xlsx')
worksheet = workbook.add_worksheet()
with open('商品编码.txt', 'rt') as f:
data = f.read()
data = data.splitlines(True)
count = 1
row = 0
for x in data:
if count < 3:
count += 1
continue
x = x.split(',')
column = 0
for e in x:... | normal | {
"blob_id": "59a8a4cf4b04a191bfb70fd07668141dbfeda790",
"index": 6822,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open('商品编码.txt', 'rt') as f:\n data = f.read()\n<mask token>\nfor x in data:\n if count < 3:\n count += 1\n continue\n x = x.split(',')\n column = 0\n fo... | [
0,
1,
2,
3
] |
x = 1
while x <= 24:
if x % 5 == 0:
x = x + 1
continue
print(x)
x = x + 1
| normal | {
"blob_id": "61cfc583cd87ac0528cb07f4e051392167414920",
"index": 1960,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile x <= 24:\n if x % 5 == 0:\n x = x + 1\n continue\n print(x)\n x = x + 1\n",
"step-3": "x = 1\nwhile x <= 24:\n if x % 5 == 0:\n x = x + 1\n ... | [
0,
1,
2
] |
#os for file system
import os
from sys import platform as _platform
import fnmatch
import inspect
files = 0
lines = 0
extension0 = '.c'
extension1 = '.cpp'
extension2 = '.h'
extension3 = '.hpp'
filename = inspect.getframeinfo(inspect.currentframe()).filename
startPath = os.path.dirname(os.path.abspath(... | normal | {
"blob_id": "d287123acdbabdd5a223e774c89945ab888fcbcc",
"index": 5439,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open('files_with_extensions.txt', 'w', encoding='utf-8') as filewrite:\n for r, d, f in os.walk(startPath):\n for file in f:\n if file.endswith(extension0) or fi... | [
0,
1,
2,
3,
4
] |
# Definition for a Node.
class Node:
def __init__(self, val, children):
self.val = val
self.children = children
class Solution(object):
def postorder(self, root):
"""
:type root: Node
:rtype: List[int]
"""
if not root:
return([])
if no... | normal | {
"blob_id": "93ec15a37bd5f022e8f6e226e3bf0e91cc0457c6",
"index": 2178,
"step-1": "class Node:\n <mask token>\n\n\nclass Solution(object):\n\n def postorder(self, root):\n \"\"\"\n :type root: Node\n :rtype: List[int]\n \"\"\"\n if not root:\n return []\n ... | [
3,
4,
5,
6,
7
] |
# coding: utf-8
import os, sys
import numpy as np
from math import exp, sqrt, pi
def factorial(n):
value = 1
for i in range(n,1,-1):
value *= i
return value
def double_factorial(n):
k = 1
for i in range(n, 1, -2):
k *= i
#print("n:", n, "double factorial:", k)
return k
... | normal | {
"blob_id": "005650e2747c61b730960a29891b6ba6c8bd381b",
"index": 1334,
"step-1": "<mask token>\n\n\ndef double_factorial(n):\n k = 1\n for i in range(n, 1, -2):\n k *= i\n return k\n\n\n<mask token>\n\n\ndef gaussian_integral(alpha, m):\n if int(m / 2) * 2 == m:\n n = int(m / 2)\n ... | [
2,
3,
4,
5,
6
] |
"""
默认查询所有
> db.test1000.find()
{ "_id" : ObjectId("5c3559ab648171cce9135dd6"), "name" : "zhangdapeng" }
{ "_id" : ObjectId("5c3559af648171cce9135dd7"), "name" : "zhangdapeng1" }
{ "_id" : ObjectId("5c3559b2648171cce9135dd8"), "name" : "zhangdapeng2" }
{ "_id" : ObjectId("5c3559b4648171cce9135dd9"),... | normal | {
"blob_id": "d8e0198244c3df77fa0258cc97a55042e36d056f",
"index": 7756,
"step-1": "<mask token>\n",
"step-2": "\"\"\"\n默认查询所有\n > db.test1000.find()\n { \"_id\" : ObjectId(\"5c3559ab648171cce9135dd6\"), \"name\" : \"zhangdapeng\" }\n { \"_id\" : ObjectId(\"5c3559af648171cce9135dd7\"), \"name\" : \"zhan... | [
0,
1
] |
from pathlib import Path
file = Path(__file__).parent / 'input.txt'
Y = 2000000
MAX_X = 4000000
MIN_X = 0
MAX_Y = 4000000
MIN_Y = 0
# file = Path(__file__).parent / 'test_input.txt'
# Y = 10
# MAX_X = 20
# MIN_X = 0
# MAX_Y = 20
# MIN_Y = 0
text = file.read_text().splitlines()
class Beacon():
def __init__(self, ... | normal | {
"blob_id": "f3a1a926feabcabc870f0a41ae239939c331d09d",
"index": 4106,
"step-1": "<mask token>\n\n\nclass Beacon:\n\n def __init__(self, pos, sensor) ->None:\n self.pos = pos\n self.sensor = sensor\n <mask token>\n\n def __repr__(self) ->str:\n return f'{self}'\n <mask token>\n ... | [
24,
28,
30,
34,
35
] |
import requests
from lxml import etree
from pymongo import MongoClient
from lib.rabbitmq import Rabbit
from lib.log import LogHandler
from lib.proxy_iterator import Proxies
import yaml
import json
import datetime
import re
import time
setting = yaml.load(open('config_local.yaml'))
log = LogHandler('article_consumer')... | normal | {
"blob_id": "cd1d8a73b6958775a212d80b50de74f4b4de18bf",
"index": 6319,
"step-1": "<mask token>\n\n\nclass CrawlerDetail:\n\n def __init__(self):\n self.proxy = Proxies()\n\n def start_consume(self):\n channel = connection.channel()\n channel.queue_declare(queue='usual_article')\n ... | [
4,
5,
6,
8,
9
] |
import scraperwiki, lxml.html, urllib2, re
from datetime import datetime
#html = scraperwiki.scrape("http://www.public.health.wa.gov.au/2/1035/2/publication_of_names_of_offenders_list.pm")
doc = lxml.html.parse(urllib2.urlopen("http://www.public.health.wa.gov.au/2/1035/2/publication_of_names_of_offenders_list.pm"))
ro... | normal | {
"blob_id": "e870900249b121f2416d7be543752ebf6392b6be",
"index": 6868,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor tr in root.xpath(\"//div[@id='verdiSection10']/div/div/table/tbody/tr\")[1:]:\n data = {'conviction_date': datetime.strptime(re.match(\n '(\\\\d+/\\\\d+/\\\\d+)', tr[0].text... | [
0,
1,
2,
3,
4
] |
# -*- coding: UTF-8 -*-
import lava
from lava.api.constants.vk import QueueType
from lava.api.device import Device
from lava.api.util import Destroyable
__all__ = ["Session"]
sessions = set()
class Session(Destroyable):
def __init__(self, physical_device, queue_index=None):
super(Session, self).__init... | normal | {
"blob_id": "193dcf7bd658f88afe0a1f2fa28605f262e45bc2",
"index": 1554,
"step-1": "<mask token>\n\n\nclass Session(Destroyable):\n\n def __init__(self, physical_device, queue_index=None):\n super(Session, self).__init__()\n self.instance = lava.instance()\n if physical_device not in lava.d... | [
5,
6,
7,
8,
9
] |
#roblem: Have the function PrimeTime(num)
# take the num parameter being passed and return
# the string true if the parameter is a prime number, \
# otherwise return the string false.
# The range will be between 1 and 2^16.
def PrimeTime(num):
prime1 = (num-1)%6
prime2 = (num+1)%6
if prime1 * prime2 =... | normal | {
"blob_id": "5068a78a1aa31a277b3b5854ddd1d8990d07b104",
"index": 3627,
"step-1": "<mask token>\n",
"step-2": "def PrimeTime(num):\n prime1 = (num - 1) % 6\n prime2 = (num + 1) % 6\n if prime1 * prime2 == 0:\n return 'True'\n else:\n return 'False'\n\n\n<mask token>\n",
"step-3": "de... | [
0,
1,
2,
3
] |
import os
import json
import librosa
# Constants
# Dataset used for training
DATASET_PATH = "dataset"
# Where the data is stored
JSON_PATH = "data.json"
# Number of samples considered to preprocess data
SAMPLES_TO_CONSIDER = 22050 # 1 sec worth of sound
# Main function to preprocess the data
def prepare_dataset(dat... | normal | {
"blob_id": "ba808d23f6a8226f40e1c214012a1535ee1e9e98",
"index": 2947,
"step-1": "<mask token>\n\n\ndef prepare_dataset(dataset_path, json_path, n_mfcc=13, hop_length=512,\n n_fft=2048):\n data = {'mappings': [], 'labels': [], 'MFCCs': [], 'files': []}\n for i, (dir_path, dir_names, filenames) in enumer... | [
1,
2,
3,
4,
5
] |
# Generated by Django 2.0.4 on 2018-04-30 14:01
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
('base... | normal | {
"blob_id": "d13589979ba7b6facd8339111323270c9920a9bf",
"index": 8127,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Migration(migrations.Migration):\n <mask token>\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Migration(migrations.Migration):\n initial = T... | [
0,
1,
2,
3,
4
] |
# -*- coding: utf-8 -*-
class Solution:
"""
@param head: The first node of the linked list.
@return: The node where the cycle begins.
if there is no cycle, return null
"""
def detectCycle(self, head):
# write your code here
# 先确定是否有环，然后确定环的大小，再遍历确定位置。
cycle_... | normal | {
"blob_id": "3319614d154b16190f3cd8f4f65c3b0e0da277e9",
"index": 9751,
"step-1": "<mask token>\n",
"step-2": "class Solution:\n <mask token>\n <mask token>\n",
"step-3": "class Solution:\n <mask token>\n\n def detectCycle(self, head):\n cycle_len = -1\n one_node, two_node = head, he... | [
0,
1,
2,
3,
4
] |
import requests
from bs4 import BeautifulSoup
'''
OCWから学院一覧を取得するスクリプト(6個くらいだから必要ない気もする)
gakuinListの各要素は次のような辞書に鳴っている
{
'name' : 学院名,
'url' : その学院の授業の一覧のurl,
}
'''
def getGakuinList():
url = "http://www.ocw.titech.ac.jp/"
response = requests.get(url)
soup = BeautifulSoup(response.content,"lxml")
topMainNav = sou... | normal | {
"blob_id": "24274dddbeb1be743cfcac331ee688d48c9a46dd",
"index": 8647,
"step-1": "<mask token>\n\n\ndef getLectures(name, url):\n urlprefix = 'http://www.ocw.titech.ac.jp'\n response = requests.get(url)\n soup = BeautifulSoup(response.content, 'lxml')\n table = soup.find('table', class_='ranking-list... | [
1,
2,
3,
4,
5
] |
# -*- coding: utf-8 -*-
"""
Created on Thu Nov 8 17:14:14 2018
@author: Winry
"""
import pandas as pd
# 显示所有的列
pd.set_option('display.max_columns', None)
# 读取数据
file_name = "data_11_8.csv"
file_open = open(file_name)
df = pd.read_csv(file_open)
file_open.close()
Newtaxiout_time = df['Newtaxiout_time']
time = df['t... | normal | {
"blob_id": "f5a474cdc8aa22322b252b980c0334a9db21bd5c",
"index": 9300,
"step-1": "<mask token>\n",
"step-2": "<mask token>\npd.set_option('display.max_columns', None)\n<mask token>\nfile_open.close()\n<mask token>\nfor i in range(len(df)):\n count = []\n count = df2['Newappend1'][(df2['Newappend1'] > New... | [
0,
1,
2,
3,
4
] |
def login():
usernameInput = input("Username : ")
passwordInput = input("Password : ")
if usernameInput == "admin" and passwordInput == "1234":
return (showMenu())
else:
print("User or Password Wrong.")
return login()
def showMenu():
print("---Please Choose Menu---")
prin... | normal | {
"blob_id": "34dd6966a971e3d32e82a17cd08c3b66bb88163b",
"index": 1277,
"step-1": "<mask token>\n\n\ndef showMenu():\n print('---Please Choose Menu---')\n print('1. Vat7')\n print('2. Calculation')\n print('3. Vat Calulation')\n return menuSelect()\n\n\n<mask token>\n\n\ndef priceResult():\n pri... | [
2,
4,
5,
6,
7
] |
# operatorTest02.py
x = 5
x += 3 #복함 대입 연산자
print("x : ", x)
print("-"*30)
total = 0
total += 1
total | normal | {
"blob_id": "4f8bc19bb113c9eac7c2ac774ac7b16f569d9704",
"index": 3083,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nx += 3\nprint('x : ', x)\nprint('-' * 30)\n<mask token>\ntotal += 1\ntotal\n",
"step-3": "x = 5\nx += 3\nprint('x : ', x)\nprint('-' * 30)\ntotal = 0\ntotal += 1\ntotal\n",
"step-4": ... | [
0,
1,
2,
3
] |
from tkinter import ttk
import tkinter as tk
import pyodbc
#ConnectingDatabase#
from tkinter import messagebox
conn = pyodbc.connect('Driver={SQL Server};'
'Server=MUTHUCOMPUTER;'
'Database=Class4c v1;'
'Trusted_Connection=yes;')
cursor = ... | normal | {
"blob_id": "8058ff209af03b7365ffad2a9ce2e2805b548f53",
"index": 9927,
"step-1": "<mask token>\n\n\ndef save():\n Names = Name.get()\n Ages = Age.get()\n Genders = Gender.get()\n Heights = height.get()\n weights = weight.get()\n rollnos = StudentId.get()\n Sports = Sport.get()\n cursor.ex... | [
4,
5,
6,
7,
8
] |
#!/usr/local/bin/python
# -*- coding: utf-8 -*-
# importing regular stuff
import os
import sys
import thread
import threading
import time
import datetime
from datetime import datetime
import random
import filecmp
import ConfigParser
import socket
#my stuff will go here
import include.action as action
import include.l... | normal | {
"blob_id": "a3301180e53da4a6970c082e72d8721b29dcae2e",
"index": 1403,
"step-1": "#!/usr/local/bin/python\n# -*- coding: utf-8 -*-\n\n# importing regular stuff\nimport os\nimport sys\nimport thread\nimport threading\nimport time\nimport datetime\nfrom datetime import datetime\nimport random\nimport filecmp\nimpo... | [
0
] |
# -*- coding: utf-8 -*-
# Generated by Django 1.10.5 on 2017-01-30 14:50
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('books', '0007_auto_20170127_2254'),
]
operations = [
migrations.AlterField(... | normal | {
"blob_id": "65ea27851d9db0f0a06d42bd37eff633d22a1548",
"index": 9528,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Migration(migrations.Migration):\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Migration(migrations.Migration):\n dependencies = [('books', '00... | [
0,
1,
2,
3,
4
] |
''' 단어 수학
시간 : 68ms (~2초), 메모리 : 29200KB (~256MB)
분류 : greedy
'''
import sys
input = sys.stdin.readline
# 입력
N = int(input()) # 단어의 개수
arr = [list(input().strip()) for _ in range(N)]
# 풀이
alphabet = []
for word in arr:
for a in word:
if a not in alphabet:
alphabet.append(a)
value_list = []
... | normal | {
"blob_id": "6efc7ff304a05dfc5a7bed7d646e5d6ac034ce85",
"index": 4706,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor word in arr:\n for a in word:\n if a not in alphabet:\n alphabet.append(a)\n<mask token>\nfor a in alphabet:\n value = 0\n for word in arr:\n if a no... | [
0,
1,
2,
3,
4
] |
api_id = "2168275"
api_hash = "e011a9cb95b7e7e153aa5840985fc883"
| normal | {
"blob_id": "c6d6fcc242e1b63104a3f3eb788880635257ff4c",
"index": 7503,
"step-1": "<mask token>\n",
"step-2": "api_id = '2168275'\napi_hash = 'e011a9cb95b7e7e153aa5840985fc883'\n",
"step-3": "api_id = \"2168275\"\napi_hash = \"e011a9cb95b7e7e153aa5840985fc883\"\n",
"step-4": null,
"step-5": null,
"step-... | [
0,
1,
2
] |
# -*- coding: utf-8 -*-
"""pytest People functions, fixtures and tests."""
import pytest
import ciscosparkapi
from tests.utils import create_string
# Helper Functions
# pytest Fixtures
@pytest.fixture(scope="session")
def me(api):
return api.people.me()
| normal | {
"blob_id": "9b7ffa2bb62a8decbec51c6bdea38b4338726816",
"index": 1891,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@pytest.fixture(scope='session')\ndef me(api):\n return api.people.me()\n",
"step-3": "<mask token>\nimport pytest\nimport ciscosparkapi\nfrom tests.utils import create_string\n\... | [
0,
1,
2,
3
] |
from kivy.uix.boxlayout import BoxLayout
from kivy.graphics import *
from kivy.clock import Clock
from kivy.properties import StringProperty, BooleanProperty
from kivy.uix.popup import Popup
import time
from math import sin, pi
from kivy.lang import Builder
from ui.custom_widgets import I18NPopup, I18NLabel
Builder.... | normal | {
"blob_id": "96086885e5353f3b4b3277c1daf4ee74831c3b73",
"index": 8841,
"step-1": "<mask token>\n\n\nclass Dripper(BoxLayout):\n\n def __init__(self, **kwargs):\n super(Dripper, self).__init__(**kwargs)\n self.index = 0.0\n self.sections = 20\n self.section_height = 1\n self.... | [
10,
12,
14,
15,
19
] |
# 효율적인 해킹
# https://www.acmicpc.net/problem/1325
from collections import deque
import sys
input = sys.stdin.readline
n, m = map(int, input().split())
graph = [[] for _ in range(n + 1)]
for _ in range(m):
a, b = map(int, input().split())
graph[b].append(a) # B를 해킹하면 A도 해킹할 수 있다
def bfs(start):
visited =... | normal | {
"blob_id": "8a631adc8d919fb1dded27177818c4cb30148e94",
"index": 610,
"step-1": "<mask token>\n\n\ndef bfs(start):\n visited = [False] * (n + 1)\n visited[start] = True\n q = deque()\n q.append(start)\n cnt = 1\n while q:\n now = q.popleft()\n for i in graph[now]:\n if ... | [
1,
2,
3,
4,
5
] |
"""Implementation of the Brainpool standard, see
https://tools.ietf.org/pdf/rfc5639.pdf#15
"""
from sage.all import ZZ, GF, EllipticCurve
from utils import increment_seed, embedding_degree, find_integer, SimulatedCurves, VerifiableCurve, \
class_number_check
CHECK_CLASS_NUMBER = False
def gen_brainpool_prime... | normal | {
"blob_id": "b717abaeecea2e97c6ec78d3e0e4c97a8de5eec3",
"index": 9169,
"step-1": "<mask token>\n\n\nclass Brainpool(VerifiableCurve):\n <mask token>\n\n def security(self):\n self._secure = False\n try:\n curve = EllipticCurve(GF(self._p), [self._a, self._b])\n except Arithm... | [
8,
9,
10,
12,
17
] |
##############################################
# Binary Tree #
# by Vishal Nirmal #
# #
# A Binary Tree ADT implementation. #
##############################################
class BinaryTree:
def __init_... | normal | {
"blob_id": "3eaced9609c7adfa5457d7dcad8b2dfaeb697b16",
"index": 3220,
"step-1": "class BinaryTree:\n\n def __init__(self, data=None):\n self.data = data\n self.left = None\n self.right = None\n\n def insert(self, data):\n if self.data != None:\n arr = [self]\n ... | [
12,
15,
18,
19,
22
] |
import sys
def ler (t):
i =0
for s in sys.stdin:
l=s.split(" ")
t.append(l)
def melhor (t):
i=1
x=int(t[0][0].strip("\n"))
n=len(t)
while(i<n):
u=int((t[i][2]).strip())
if(u<x)
i+=1
def vendedor():
t=[]
ler(t)
melhor(t)
vendedor() | normal | {
"blob_id": "76664114382bdeb0bffb996e4dd4448b6c87520d",
"index": 9719,
"step-1": "import sys \n\ndef ler (t):\n\ti =0\n\tfor s in sys.stdin:\n\t\tl=s.split(\" \")\n\t\tt.append(l)\n\ndef melhor (t):\n\ti=1\n\tx=int(t[0][0].strip(\"\\n\"))\n\tn=len(t)\n\twhile(i<n):\n\t\tu=int((t[i][2]).strip())\n\t\tif(u<x)\n\t\... | [
0
] |
"""This is the body of the low-level worker tool.
A worker is intended to run as a process that imports a module, mutates it in
one location with one operator, runs the tests, reports the results, and dies.
"""
import difflib
import importlib
import inspect
import json
import logging
import subprocess
import sys
impo... | normal | {
"blob_id": "73a778c6e4216c23ac8d82eef96ce7b73b18f661",
"index": 9100,
"step-1": "<mask token>\n\n\nclass WorkerOutcome:\n \"\"\"Possible outcomes for a worker.\n \"\"\"\n NORMAL = 'normal'\n EXCEPTION = 'exception'\n NO_TEST = 'no-test'\n TIMEOUT = 'timeout'\n SKIPPED = 'skipped'\n\n\n<mask... | [
3,
5,
6,
8,
9
] |
"""
Module for generic standard analysis plots.
"""
import numpy as np
import matplotlib.pyplot as plt
import cartopy as cart
import xarray as xr
import ecco_v4_py as ecco
def global_and_stereo_map(lat, lon, fld,
plot_type='pcolormesh',
cmap='YlOrRd',
... | normal | {
"blob_id": "b039ed74e62f3a74e8506d4e14a3422499046c06",
"index": 860,
"step-1": "<mask token>\n\n\ndef plot_depth_slice(x, depth, fld, stretch_depth=-500, plot_type=\n 'pcolormesh', cmap='YlOrRd', title=None, cmin=None, cmax=None, dpi=100,\n show_colorbar=True):\n \"\"\"2D plot of depth vs some other va... | [
1,
2,
3,
4,
5
] |
#
# struct_test.py
# Nazareno Bruschi <nazareno.bruschi@unibo.it>
#
# Copyright (C) 2019-2020 University of Bologna
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.o... | normal | {
"blob_id": "d8d0c181fcfc9e0692369cc7a65259c43a68e931",
"index": 5688,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nPULPNNInstallPath = cwd = os.getcwd() + '/../'\nPULPNNSrcDirs = {'script': PULPNNInstallPath + 'scripts/'}\nPULPNNInstallPath32bit = cwd = os.getcwd() + '/../32bit/'\nPULPNNInstallPath64b... | [
0,
1,
2,
3
] |
'''
Created on 27 Mar 2015
@author: Jon
'''
import matplotlib.pyplot as plt
from numerical_functions import Timer
import numerical_functions.numba_funcs.indexing as indexing
import numpy as np
import unittest
class Test(unittest.TestCase):
def test_take(self):
x = np.linspace( 0, 100 )
... | normal | {
"blob_id": "ee80169afd4741854eff8619822a857bbf757575",
"index": 291,
"step-1": "<mask token>\n\n\nclass Test(unittest.TestCase):\n <mask token>\n\n def test_take_comparison(self):\n x = np.arange(1000000.0)\n idx = np.random.random_integers(0, 100000.0, 1000000.0)\n indexing.take(x, i... | [
5,
7,
8,
11,
12
] |
#!/usr/bin/python
import gzip
import os
infiles = []
ids=[]
ages=[]
with open('all_C_metadata.txt') as f:
f.readline()
f.readline()
for line in f:
infiles.append(line.split('\t')[0])
ids.append(line.split('\t')[1])
ages.append(line.split('\t')[2])
with open('all_C_samples/diversi... | normal | {
"blob_id": "c02f46e8d89dd4b141c86df461ecbb8ed608b61b",
"index": 7826,
"step-1": " #!/usr/bin/python\n\nimport gzip\nimport os\n\ninfiles = []\nids=[]\nages=[]\nwith open('all_C_metadata.txt') as f:\n f.readline()\n f.readline()\n for line in f:\n infiles.append(line.split('\\t')[0])\n ids... | [
0
] |
from get_info import parse_matches as pm
def all_match_data(year):
"""
Searches through the parse_matches data for all games in a specific season prints them out with a game ID and
returns the data in a list to the main program
:param year: Specific format YYYY between 2008 - 2017
:return: year_ma... | normal | {
"blob_id": "bc53af24bb46d2be3122e290c4732b312f4ebdf5",
"index": 5313,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef all_match_data(year):\n \"\"\"\n Searches through the parse_matches data for all games in a specific season prints them out with a game ID and\n returns the data in a lis... | [
0,
1,
2,
3
] |
from datetime import datetime
import whois
def age_domain(url):
try:
w = whois.whois(url)
if(w):
for l in w.expiration_date:
d1 = datetime.date(l)
print(d1)
for l1 in w.creation_date:
d2 = datetime.date(l1)
print(d2)
diff = (d1 - ... | normal | {
"blob_id": "07d574060ded0d98734b4f184dcba7377b3a5480",
"index": 685,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef age_domain(url):\n try:\n w = whois.whois(url)\n if w:\n for l in w.expiration_date:\n d1 = datetime.date(l)\n print(d1)\n ... | [
0,
1,
2,
3
] |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"Widget for exporting the data"
import asyncio
from pathlib import Path
from typing import List
from bokeh.models import Div, CustomAction, CustomJS
from view.dialog import FileDialog
from utils.gui import startfile
class ... | normal | {
"blob_id": "d120172e65f329b1137df38b693e5fe7145bc80d",
"index": 2840,
"step-1": "<mask token>\n\n\nclass CSVExporter:\n <mask token>\n <mask token>\n\n def reset(self, *_):\n \"\"\"reset all\"\"\"\n\n @staticmethod\n async def _run(dlg: SaveFileDialog, mainview, ctrl, doc):\n paths ... | [
2,
7,
8,
9,
10
] |
# -*- coding: utf-8 -*-
#!/bin/python3
import websocket
import json
import time
from loraCrypto import LoRaCrypto
from binascii import hexlify
'''
没有加密的数据
{
cmd: 'tx';
EUI: string;
port: number;
data: string
}
加密的数据
{
cmd: 'tx';
EUI: string;
port: number;
encdata: string;
seqno: number;
}
'''
GATEWAY_ID = "... | normal | {
"blob_id": "3683b1f799fa315d736e4b62c9c093360afa893f",
"index": 2052,
"step-1": "# -*- coding: utf-8 -*-\n#!/bin/python3\nimport websocket\nimport json\nimport time\nfrom loraCrypto import LoRaCrypto\nfrom binascii import hexlify\n\n'''\n没有加密的数据\n{\n\tcmd: 'tx';\n\tEUI: string;\n\tport: number;\n\tdata: string\... | [
0
] |
import math
from historia.utils import unique_id, position_in_range
from historia.pops.models.inventory import Inventory
from historia.economy.enums.resource import Good, NaturalResource
from historia.economy.enums.order_type import OrderType
from historia.economy.models.price_range import PriceRange
from historia.econ... | normal | {
"blob_id": "887a39f1eeb81e6472938c2451e57866d3ac4a45",
"index": 661,
"step-1": "<mask token>\n\n\nclass Pop(object):\n <mask token>\n\n def __init__(self, province, pop_job, population):\n \"\"\"\n Creates a new Pop.\n manager (Historia)\n province (SecondaryDivision)\n ... | [
15,
26,
28,
32,
33
] |
import torch
from torchvision import datasets, transforms
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
from PIL import Image
import requests
from io import BytesIO
from net import Net
class predict_guitar():
def __init__(self):
"""Model is lo... | normal | {
"blob_id": "8743be809953f59bd14431e509042c4c51d9fab4",
"index": 4175,
"step-1": "<mask token>\n\n\nclass predict_guitar:\n <mask token>\n\n def softmax(self, vector):\n \"\"\"Softmax function for calculating probs\"\"\"\n e = np.exp(vector)\n return e / e.sum()\n <mask token>\n",
... | [
2,
3,
4,
5,
6
] |
#!/usr/bin/env python
import sys
def solve(n, k):
wrap = 2 ** n
snaps_that_matter = k % wrap
return snaps_that_matter == wrap - 1
def main():
lines = sys.stdin.readlines()
T = int(lines[0])
for i, line in enumerate(lines[1:]):
N, K = line.split(' ')
on = solve(int(N), int... | normal | {
"blob_id": "1803f634c8e833f4a92ae35bcfafb04dfd1d2305",
"index": 7661,
"step-1": "#!/usr/bin/env python\n\nimport sys\n\ndef solve(n, k):\n wrap = 2 ** n\n snaps_that_matter = k % wrap\n return snaps_that_matter == wrap - 1\n\ndef main():\n lines = sys.stdin.readlines()\n T = int(lines[0])\n \n... | [
0
] |
# -*- coding: utf-8 -*-
"""
Created on Fri Nov 2 16:07:25 2018
@author: Yigao
"""
import re
from nltk.tokenize import TweetTokenizer
from nltk.corpus import stopwords
from wordcloud import WordCloud
import matplotlib.pyplot as plt
## create a tokenizer
hfilename = "file.txt"
linecount=0
hashcount=0
wordcount=0
BagO... | normal | {
"blob_id": "fd04f6f4a03fdbe40e400d04e5759ef9ef30f974",
"index": 6634,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open(hfilename, 'r') as file:\n for line in file:\n tweetSplitter = TweetTokenizer(strip_handles=True, reduce_len=True)\n WordList = tweetSplitter.tokenize(line)\n ... | [
0,
1,
2,
3,
4
] |
from time import perf_counter_ns
from anthony.utility.distance import compare, compare_info
from icecream import ic
start = perf_counter_ns()
ic(compare("tranpsosed", "transposed"))
print(f"Example Time: {(perf_counter_ns() - start)/1e+9} Seconds")
ic(compare_info("momther", "mother"))
| normal | {
"blob_id": "98b0e42f3ed1a234f63c4d3aa76ceb9fce7c041d",
"index": 3631,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nic(compare('tranpsosed', 'transposed'))\nprint(f'Example Time: {(perf_counter_ns() - start) / 1000000000.0} Seconds')\nic(compare_info('momther', 'mother'))\n",
"step-3": "<mask token>... | [
0,
1,
2,
3,
4
] |
# This file is Copyright (c) 2020 LambdaConcept <contact@lambdaconcept.com>
# License: BSD
from math import log2
from nmigen import *
from nmigen.utils import log2_int
from nmigen_soc import wishbone
from nmigen_soc.memory import MemoryMap
from lambdasoc.periph import Peripheral
class gramWishbone(Peripheral, Elab... | normal | {
"blob_id": "3775ba538d6fab13e35e2f0761a1cacbe087f339",
"index": 4723,
"step-1": "<mask token>\n\n\nclass gramWishbone(Peripheral, Elaboratable):\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass gramWishbone(Peripheral, Elaboratable):\n\n def __init__(self, core, data_width=32, gra... | [
1,
2,
3,
4,
5
] |
"""
Test the OOD-detection capabilities of models by scaling a random feature for all sample in the data set.
"""
# STD
import os
import pickle
from copy import deepcopy
from collections import defaultdict
import argparse
from typing import Tuple, Dict, List
# EXT
import numpy as np
from tqdm import tqdm
import torch... | normal | {
"blob_id": "bf3e7f1aa9fd20b69e751da9ac8970c88b1144eb",
"index": 9363,
"step-1": "<mask token>\n\n\ndef run_perturbation_experiment(nov_an: NoveltyAnalyzer, X_test: np.ndarray,\n scoring_func: str=None) ->Tuple[Dict[str, List[float]], Dict[str, List[\n float]]]:\n \"\"\"Runs the perturbation experiment ... | [
1,
2,
3,
4,
5
] |
#
# * Python 57, Correct Lineup
# * Easy
# * For the opening ceremony of the upcoming sports event an even number of
# * athletes were picked. They formed a correct lineup, i.e. such a lineup in
# * which no two boys or two girls stand together. The first person in the lineup
# * was a girl. As a part of the perfor... | normal | {
"blob_id": "6c5f60e7a122e3da5e6705bfacf73a361f6c1362",
"index": 1120,
"step-1": "def correctLineup1(athletes: list) ->list:\n return [(athletes[i + 1] if i % 2 == 0 else athletes[i - 1]) for i in\n range(len(athletes))]\n\n\n<mask token>\n",
"step-2": "def correctLineup1(athletes: list) ->list:\n ... | [
1,
2,
3,
4,
5
] |
import smtplib
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
from email.header import Header
class SENDMAIL(object):
def __init__(self):
self.smtpserver = 'smtp.qq.com'
self.username = 'wu_chang_hao@qq.com' # 比如QQ邮箱
self.password = 'xxxxxxxxx... | normal | {
"blob_id": "bcab83e0ae6ee4925393b50bdefdfeb85c42ad2c",
"index": 1914,
"step-1": "<mask token>\n\n\nclass SENDMAIL(object):\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass SENDMAIL(object):\n\n def __init__(self):\n self.smtpserver = 'smtp.qq.com'\n ... | [
1,
3,
4,
5,
6
] |
from MyFeistel import MyFeistel, LengthPreservingCipher
import pytest
import base64
import os
class TestMyFeistel:
def test_Functionality(self):
key = base64.urlsafe_b64encode(os.urandom(16))
feistel = MyFeistel(key, 10)
# decrypt(encrypt(msg)) == msg
for i in xrange(20):
... | normal | {
"blob_id": "2464da1c4d2ddab3a053f0a14e3cc9a8beabe031",
"index": 6031,
"step-1": "<mask token>\n\n\nclass TestLengthPreservingCipher:\n\n def test_Functionality(self):\n key = base64.urlsafe_b64encode(os.urandom(16))\n lpc = LengthPreservingCipher(key, 10)\n for i in xrange(20):\n ... | [
2,
4,
5,
6,
7
] |
# Copyright (c) 2011-2014 by California Institute of Technology
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# 1. Redistributions of source code must retain the above copyright
# notice,... | normal | {
"blob_id": "707c83bc83f606b570af973094574e6675cfc83f",
"index": 8793,
"step-1": "<mask token>\n\n\nclass Ridge(object):\n \"\"\"A ridge.\n\n Attributes:\n\n - `E_r`: Equality set of a facet\n\n - `ar, br`: Affine hull of the facet\n s.t. P_{E_0} = P intersection {x | ar x = br}.\n \"\"\"\n\n... | [
7,
9,
16,
18,
20
] |
#!/usr/bin/env pytest
# -*- coding: utf-8 -*-
###############################################################################
# $Id$
#
# Project: GDAL/OGR Test Suite
# Purpose: TopJSON driver test suite.
# Author: Even Rouault
#
###############################################################################
# Copyr... | normal | {
"blob_id": "270dba92af583e37c35ed5365f764adfdc2f947d",
"index": 2112,
"step-1": "<mask token>\n\n\ndef test_ogr_toposjon_objects_is_dict():\n ds = ogr.Open('data/topojson/topojson2.topojson')\n lyr = ds.GetLayer(0)\n assert lyr.GetName() == 'a_layer'\n assert lyr.GetLayerDefn().GetFieldCount() == 2\... | [
1,
2,
3,
4,
5
] |
import torch
import tarfile
import pickle
import pandas
import json
import argparse
from pathlib import Path
import numpy as np
import shutil
from shutil import copyfile
import os
import re
import pandas as pd
import sys
from numpy import asarray
from numpy import savetxt
sys.path.append("..")
def parse_arguments():
... | normal | {
"blob_id": "da55d9a6534525e58b6c1d2db997e90a1c9b0f36",
"index": 1427,
"step-1": "<mask token>\n\n\ndef parse_arguments():\n parser = argparse.ArgumentParser()\n parser.add_argument('--data_dir', type=str, required=True, help=\n 'dir holding sequences as separate files')\n parser.add_argument('--... | [
2,
3,
4,
5,
6
] |
# coding: utf-8
from django.test.client import Client
from django.contrib.contenttypes.models import ContentType
from main.models import Descriptor, ResourceThematic, ThematicArea
from utils.tests import BaseTestCase
from models import *
def minimal_form_data():
'''
Define a minimal fields for submit a medi... | normal | {
"blob_id": "a253ab5ef80a61c3784862625cde81de4c4ef984",
"index": 2094,
"step-1": "<mask token>\n\n\nclass MultimediaTest(BaseTestCase):\n <mask token>\n <mask token>\n <mask token>\n\n def test_add_media(self):\n \"\"\"\n Tests create media\n \"\"\"\n self.login_editor()\n... | [
8,
9,
12,
14,
16
] |
from flask import render_template, url_for, escape, redirect, abort
from app import core
from database import db
@core.route('/post')
@core.route('/categorie')
@core.route('/tag')
def returnToHome():
return redirect(url_for('home'))
| normal | {
"blob_id": "c27d6279d1ea84bab3c0abd4ca9a08de202219da",
"index": 1748,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@core.route('/post')\n@core.route('/categorie')\n@core.route('/tag')\ndef returnToHome():\n return redirect(url_for('home'))\n",
"step-3": "from flask import render_template, url... | [
0,
1,
2
] |
import logging
from sleekxmpp import ClientXMPP
from sleekxmpp.exceptions import IqError, IqTimeout
class EchoBot(ClientXMPP):
def __init__(self, jid, password):
ClientXMPP.__init__(self, jid, password)
self.add_event_handler("session_start", self.session_start)
self.register_plugin('xep_0... | normal | {
"blob_id": "3b531c5935f0be89536c95ff471f96b4249d951c",
"index": 2521,
"step-1": "<mask token>\n\n\nclass EchoBot(ClientXMPP):\n\n def __init__(self, jid, password):\n ClientXMPP.__init__(self, jid, password)\n self.add_event_handler('session_start', self.session_start)\n self.register_pl... | [
2,
3,
4,
5,
6
] |
# 홍준이는 요즘 주식에 빠져있다. 그는 미래를 내다보는 눈이 뛰어나, 날 별로 주가를 예상하고 언제나 그게 맞아떨어진다. 매일 그는 아래 세 가지 중 한 행동을 한다.
# 1. 주식 하나를 산다.
# 2. 원하는 만큼 가지고 있는 주식을 판다.
# 3. 아무것도 안한다.
# 홍준이는 미래를 예상하는 뛰어난 안목을 가졌지만, 어떻게 해야 자신이 최대 이익을 얻을 수 있는지 모른다. 따라서 당신에게 날 별로 주식의 가격을 알려주었을 때, 최대 이익이 얼마나 되는지 계산을 해달라고 부탁했다.
# 예를 들어 날 수가 3일이고 날 별로 주가가 10, 7, 6일 때, 주... | normal | {
"blob_id": "d3f6fb612e314ee2b86f6218719ecac2cc642c59",
"index": 2992,
"step-1": "# 홍준이는 요즘 주식에 빠져있다. 그는 미래를 내다보는 눈이 뛰어나, 날 별로 주가를 예상하고 언제나 그게 맞아떨어진다. 매일 그는 아래 세 가지 중 한 행동을 한다.\n\n# 1. 주식 하나를 산다.\n# 2. 원하는 만큼 가지고 있는 주식을 판다.\n# 3. 아무것도 안한다.\n\n# 홍준이는 미래를 예상하는 뛰어난 안목을 가졌지만, 어떻게 해야 자신이 최대 이익을 얻을 수 있는지 모른다. 따라서 당신에게... | [
1
] |
import gdalnumeric
#Input File
src = "../dati/islands/islands.tif"
#Output
tgt = "../dati/islands/islands_classified.jpg"
srcArr = gdalnumeric.LoadFile(src)
classes = gdalnumeric.numpy.histogram(srcArr,bins=2)[1]
print classes
#Color look-up table (LUT) - must be len(classes)+1.
#Specified as R,G,B tuples
lut = [[... | normal | {
"blob_id": "f29d377e8a8fd6d2e156da665478d7a4c167f7d5",
"index": 3601,
"step-1": "import gdalnumeric\n\n#Input File\nsrc = \"../dati/islands/islands.tif\"\n\n#Output\ntgt = \"../dati/islands/islands_classified.jpg\"\n\nsrcArr = gdalnumeric.LoadFile(src)\n\nclasses = gdalnumeric.numpy.histogram(srcArr,bins=2)[1]\... | [
0
] |
# Copyright (c) 2008-2016 MetPy Developers.
# Distributed under the terms of the BSD 3-Clause License.
# SPDX-License-Identifier: BSD-3-Clause
"""Test the `interpolation` module."""
from __future__ import division
import logging
import numpy as np
from numpy.testing import assert_almost_equal, assert_array_almost_eq... | normal | {
"blob_id": "9e987e057ee5322765415b84e84ef3c4d2827742",
"index": 5466,
"step-1": "<mask token>\n\n\n@pytest.fixture()\ndef test_data():\n \"\"\"Return data used for tests in this file.\"\"\"\n x = np.array([8, 67, 79, 10, 52, 53, 98, 34, 15, 58], dtype=float)\n y = np.array([24, 87, 48, 94, 98, 66, 14, ... | [
7,
9,
10,
11,
13
] |
class cal4:
def setdata(self,n1):
self.n1 = n1
def display(self):
return n1*n1
n1 = int(input("Enter number: "))
c = cal4()
print(c.display()) | normal | {
"blob_id": "65b90fccd0ee74b369475aa9fe33f159881c8b82",
"index": 6645,
"step-1": "class cal4:\n\n def setdata(self, n1):\n self.n1 = n1\n <mask token>\n\n\n<mask token>\n",
"step-2": "class cal4:\n\n def setdata(self, n1):\n self.n1 = n1\n\n def display(self):\n return n1 * n1\... | [
2,
3,
4,
5,
6
] |
import pytest
from ansiblediscover.graph.node import Node
def test_build_identifier():
assert 'role:server_base' == Node.build_identifier('server_base', 'role')
def test_identifier():
node = Node('server_base', 'role', 'irrelevant')
assert 'role:server_base' == node.identifier()
def test_add_successo... | normal | {
"blob_id": "8e22db940124f92d3048055cf72dcaa79564cdc6",
"index": 1953,
"step-1": "<mask token>\n\n\ndef test_build_identifier():\n assert 'role:server_base' == Node.build_identifier('server_base', 'role')\n\n\ndef test_identifier():\n node = Node('server_base', 'role', 'irrelevant')\n assert 'role:serve... | [
5,
6,
7,
8,
9
] |
# Кицела Каролина ИВТ 3 курс
# Вариант 6
# Найти сумму всех чисел с плавающей точкой
b = ("name", " DeLorean DMC-12", "motor_pos", "rear", "n_of_wheels", 4,
"n_of_passengers", 2, "weight", 1.230, "height", 1.140, "length", 4.216,
"width", 1.857, "max_speed", 177)
print sum(b[9:16:2])
| normal | {
"blob_id": "b5160a2574dd2c4eec542d7aca8288da0feadaba",
"index": 5702,
"step-1": "# Кицела Каролина ИВТ 3 курс \n# Вариант 6 \n# Найти сумму всех чисел с плавающей точкой\n\nb = (\"name\",\t\"\tDeLorean\tDMC-12\",\t\"motor_pos\",\t\"rear\",\t\"n_of_wheels\",\t4,\n\"n_of_passengers\",\t2,\t\"weight\",\t1.230,\t\... | [
0
] |
import random
import json
import os
from pico2d import *
import game_framework
import game_world
import menu_world
import game_state
from Start_menu import Menu
name = "MenuState"
boy = None
Start_menu = None
menu_time =None
def enter():
global Start_menu
Start_menu = Menu()
menu_world.add_object(Start... | normal | {
"blob_id": "fee2ddca5888c9db00d2d7a4fe11ba20c4e31685",
"index": 1909,
"step-1": "<mask token>\n\n\ndef enter():\n global Start_menu\n Start_menu = Menu()\n menu_world.add_object(Start_menu, 0)\n\n\n<mask token>\n\n\ndef handle_events():\n global Start_menu, menu_time\n events = get_events()\n ... | [
4,
6,
7,
8,
10
] |
from unittest import TestCase
from attendance import Member
__author__ = 'colin'
class TestMember(TestCase):
def test_here(self):
member = Member("John", "Doe")
self.assertFalse(member.attended)
member.here()
self.assertTrue(member.attended) | normal | {
"blob_id": "a6713a4edece14a88bd9c8ddd483ff8e16acdbcc",
"index": 9695,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass TestMember(TestCase):\n\n def test_here(self):\n member = Member('John', 'Doe')\n self.assertFalse(member.attended)\n member.here()\n self.assertT... | [
0,
2,
3,
4,
5
] |
#!/usr/bin/env python3
import torch
import torch.nn as nn
import torch.nn.functional as F
import pytorch_lightning as pl
import torchmetrics
class BaselineModule(pl.LightningModule):
def __init__(self, input_size, num_classes=4, lr=3e-4):
super().__init__()
self.backbone = nn.Sequential( # CBR-Ti... | normal | {
"blob_id": "7d43b20ebee2f4cd509bbd896c9e6ae8b2c4b354",
"index": 7128,
"step-1": "<mask token>\n\n\nclass BaselineModule(pl.LightningModule):\n <mask token>\n\n def _get_hidden_size(self, input_size):\n self.backbone(torch.randn(1, 3, input_size, input_size))\n\n def forward(self, input_tensor):\... | [
3,
5,
6,
7,
9
] |
## Import modules
import matplotlib, sys, datetime, time
matplotlib.use('TkAgg')
from math import *
from numpy import *
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2TkAgg
from matplotlib.figure import Figure
from matplotlib import dates
import matplotlib.pyplot as plt
from Tkinter ... | normal | {
"blob_id": "2de12085ddc73fed85dda8ce3d6908b42fdc4bcc",
"index": 3046,
"step-1": "<mask token>\n\n\ndef show_humidity():\n a.clear()\n a.plot(fds, humidity, 'b.--')\n a.set_ylabel('Humidity %', color='b')\n a.xaxis.set_major_formatter(hfmt)\n a.grid(color='blue')\n for tick in a.xaxis.get_major... | [
2,
3,
5,
6,
7
] |
import numpy as np
import matplotlib.pyplot as plt
import sys
import os
from azavg_util import plot_azav
from binormalized_cbar import MidpointNormalize
from diagnostic_reading import ReferenceState
dirname = sys.argv[1]
datadir = dirname + '/data/'
plotdir = dirname + '/plots/'
if (not os.path.isdir(plotdir)):
... | normal | {
"blob_id": "e5c30488c8c1682171c57a11a8ecedc5ccd4d851",
"index": 5607,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif not os.path.isdir(plotdir):\n os.makedirs(plotdir)\n<mask token>\nplot_azav(fig, ax, ro_m, rr, cost, sint, contours=False, notfloat=False,\n units='')\nplt.title('$({\\\\rm{Ro}}_... | [
0,
1,
2,
3,
4
] |
# i change it for change1
# change 1.py in master
i = 1
# fix bug for boss
| normal | {
"blob_id": "92f4f1c8a4e04b07ed7c05d5bb733c0b9c28bd05",
"index": 5325,
"step-1": "<mask token>\n",
"step-2": "i = 1\n",
"step-3": "# i change it for change1\n# change 1.py in master\ni = 1\n# fix bug for boss\n",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
2
]
} | [
0,
1,
2
] |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import division, print_function, absolute_import, unicode_literals
import os
from qtpy.QtCore import *
# from qtpy.QtGui import *
from qtpy.QtWidgets import *
from six import string_types
from ..widgets import PathParamWidget, RelPathParamWidget, FilePath... | normal | {
"blob_id": "ee91e8c9dcb940882733b2d23b74a76d0392f4fe",
"index": 2126,
"step-1": "<mask token>\n\n\nclass TypeDirPath(TypeBase):\n\n @classmethod\n def control(cls, delegate, property_item, parent):\n return PathParamWidget(delegate, parent=parent)\n <mask token>\n\n @classmethod\n def valu... | [
21,
41,
43,
45,
61
] |
import time
import optparse
from IPy import IP as IPTEST
ttlValues = {}
THRESH = 5
def checkTTL(ipsrc,ttl):
if IPTEST(ipsrc).iptype() == 'PRIVATE':
return
if not ttlValues.has_key(ipsrc):
pkt = srl(IP(dst=ipsrc) / TCMP(),retry=0,timeout=0,verbose=0)
ttlValues[ipsrc] = pkt.ttl
... | normal | {
"blob_id": "7081211336793bfde60b5c922f6ab9461a475949",
"index": 1616,
"step-1": "import time\r\nimport optparse\r\nfrom IPy import IP as IPTEST\r\nttlValues = {}\r\nTHRESH = 5\r\ndef checkTTL(ipsrc,ttl):\r\n if IPTEST(ipsrc).iptype() == 'PRIVATE':\r\n return\r\n if not ttlValues.has_key(ipsrc):\r\n... | [
0
] |
import pygame
from Actor import Actor
import PlayerInput
class TestActor(Actor):
def __init__(self):
super(TestActor, self).__init__()
def act(self):
self.key_commands()
def key_commands(self):
if PlayerInput.is_key_down(pygame.K_LEFT):
self.set_location(self.x - 1, ... | normal | {
"blob_id": "9cb11c2bf032aa16abd3463ecdb8997addedc912",
"index": 1570,
"step-1": "<mask token>\n\n\nclass TestActor(Actor):\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass TestActor(Actor):\n <mask token>\n\n def act(self):\n self.key_commands()\n <m... | [
1,
2,
3,
4,
5
] |
'''
Created on Jul 10, 2018
@author: daniel
'''
#from multiprocessing import Process, Manager
#from keras.utils import np_utils
import sys
import os
from keras.utils import np_utils
from _codecs import decode
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
from DataHandlers.SegNetDataHandler import Seg... | normal | {
"blob_id": "cb03fcf9c9cb61b3546865fe40cc411745e1fc94",
"index": 6872,
"step-1": "<mask token>\n\n\ndef computeDice(im1, im2):\n im1 = np.asarray(im1).astype(np.bool)\n im2 = np.asarray(im2).astype(np.bool)\n if im1.shape != im2.shape:\n raise ValueError(\n 'Shape mismatch: im1 and im2... | [
2,
3,
4,
5,
6
] |
"""script for subpixel experiment (not tested)
"""
import numpy as np
from tqdm import tqdm
import logging
from pathlib import Path
import paddle
import paddle.optimizer
import paddle.io
from utils.loader import dataLoader
from utils.loader import modelLoader
from utils.loader import pretrainedLoader
from utils.tools... | normal | {
"blob_id": "fc89fdf17f887ea398be5b36d4d6f0444d64b3e0",
"index": 8026,
"step-1": "<mask token>\n\n\n@paddle.no_grad()\nclass Val_model_subpixel(object):\n <mask token>\n\n def loadModel(self):\n from utils.loader import modelLoader\n self.net = modelLoader(model=self.model, **self.params)\n ... | [
3,
5,
6,
7,
8
] |
from faker import Faker
from generators.uniform_distribution_gen import UniformDistributionGen
from generators.random_relation_gen import RandomRelationGen
from base.field_base import FieldBase
from generators.normal_distribution_gen import NormalDistributionGen
from generators.first_name_generator import FirstNameGene... | normal | {
"blob_id": "0926606a222e1277935a48ba7f0ea886fb4e298a",
"index": 5234,
"step-1": "<mask token>\n\n\nclass A:\n <mask token>\n\n\nclass B:\n\n def __init__(self) ->None:\n self.alpha: str = ''\n self.C: C = None\n\n\nclass C:\n\n def __init__(self) ->None:\n self.alpha: str = ''\n ... | [
5,
6,
7,
8,
9
] |
import utils
from problems_2019 import intcode
def run(commands=None):
memory = utils.get_input()[0]
initial_inputs = intcode.commands_to_input(commands or [])
program = intcode.Program(memory, initial_inputs=initial_inputs, output_mode=intcode.OutputMode.BUFFER)
while True:
_, return_signal... | normal | {
"blob_id": "e3aa38b5d01823ed27bca65331e9c7315238750a",
"index": 8974,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@utils.part\ndef part_1():\n commands = ['south', 'take food ration', 'west', 'north', 'north',\n 'east', 'take astrolabe', 'west', 'south', 'south', 'east', 'north',\n ... | [
0,
1,
2,
3,
4
] |
#!/usr/bin/env python3
import argparse
import boutvecma
import easyvvuq as uq
import chaospy
import os
import numpy as np
import time
from dask.distributed import Client
from dask_jobqueue import SLURMCluster
import matplotlib.pyplot as plt
if __name__ == "__main__":
parser = argparse.ArgumentParser(description... | normal | {
"blob_id": "416f4c6bbd2f2b9562ab2d1477df4ebc45070d8d",
"index": 5060,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='EasyVVUQ applied to BOUT++')\n parser.add_argument('--batch', '-b', help=\n 'Run on a batch (SLURM)... | [
0,
1,
2,
3
] |
import math
import numpy as np
import h5py
import matplotlib.pyplot as plt
import scipy
from PIL import Image
from scipy import ndimage
import tensorflow as tf
from tensorflow.python.framework import ops
from pathlib import Path
from scipy.io import loadmat
from skimage.transform import resize
from sklearn.... | normal | {
"blob_id": "5c315a49ead80e8d8ce057bd774f97bce098de59",
"index": 5443,
"step-1": "<mask token>\n\n\ndef model(input_shape):\n X_input = Input(input_shape)\n X = Conv2D(8, (4, 4), strides=(1, 1), name='conv0', kernel_regularizer=\n regularizers.l2(0.001), padding='same')(X_input)\n X = BatchNormal... | [
1,
2,
3,
4,
5
] |
import sys
import os.path
root_dir = os.path.dirname(os.path.dirname(__file__))
jsondb_dir = os.path.join(root_dir, 'jsondb')
sys.path.append(jsondb_dir)
| normal | {
"blob_id": "eeb588a162fa222c0f70eb832a0026d0d8adbe9b",
"index": 6769,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsys.path.append(jsondb_dir)\n",
"step-3": "<mask token>\nroot_dir = os.path.dirname(os.path.dirname(__file__))\njsondb_dir = os.path.join(root_dir, 'jsondb')\nsys.path.append(jsondb_dir... | [
0,
1,
2,
3
] |
from .ros_publisher import *
| normal | {
"blob_id": "6e7cca4f766ca89d2e2f82a73f22742b0e8f92a8",
"index": 5870,
"step-1": "<mask token>\n",
"step-2": "from .ros_publisher import *\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
} | [
0,
1
] |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
One cycle policy based on Leslie Smith's paper(https://arxiv.org/pdf/1803.09820.pdf)
Created on Wed Mar 31 13:53:39 2021
"""
import logging
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
logging.getLogger('tensorflow').setLevel(logging.ERR... | normal | {
"blob_id": "056235f8f65a3d6a310ee8a8742c1369b5398f28",
"index": 7749,
"step-1": "<mask token>\n\n\nclass OneCycleScheduler(Callback):\n <mask token>\n\n def __init__(self, lr_max, steps, mom_min=0.85, mom_max=0.95,\n phase_1_pct=0.3, div_factor=25.0):\n super(OneCycleScheduler, self).__init_... | [
7,
11,
15,
16,
19
] |
from django.conf.urls import url
from . import views
from .views import ShopView, ShopListView
urlpatterns = [
url(r'^coffeeshops/(\d+)$', ShopView.as_view()),
url(r'^coffeeshops$', ShopListView.as_view()),
]
| normal | {
"blob_id": "54a705de2597140a72e47f5afe86614b619461b7",
"index": 1109,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurlpatterns = [url('^coffeeshops/(\\\\d+)$', ShopView.as_view()), url(\n '^coffeeshops$', ShopListView.as_view())]\n",
"step-3": "from django.conf.urls import url\nfrom . import view... | [
0,
1,
2,
3
] |
import numpy as np
import torch
def pad_sequences_1d(sequences, dtype=torch.long, device=torch.device("cpu"), fixed_length=None):
""" Pad a single-nested list or a sequence of n-d array (torch.tensor or np.ndarray)
into a (n+1)-d array, only allow the first dim has variable lengths.
Args:
sequence... | normal | {
"blob_id": "788d9fa03c4311a8077d492b1a2b06d1f88826a3",
"index": 5570,
"step-1": "<mask token>\n\n\ndef pad_sequences_1d(sequences, dtype=torch.long, device=torch.device('cpu'\n ), fixed_length=None):\n \"\"\" Pad a single-nested list or a sequence of n-d array (torch.tensor or np.ndarray)\n into a (n+1... | [
3,
4,
5,
6,
7
] |
"""
time: X * Y
space: worst case X * Y
"""
class Solution:
def numIslands(self, grid: List[List[str]]) -> int:
if not grid:
return 0
Y = len(grid)
X = len(grid[0])
def dfs(y, x):
if y < 0 or x < 0 or y > Y-1 or x > X-1:
... | normal | {
"blob_id": "58bd14d240242ed58dcff35fe91cebeae4899478",
"index": 9087,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Solution:\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Solution:\n\n def numIslands(self, grid: List[List[str]]) ->int:\n if not grid:\... | [
0,
1,
2,
3,
4
] |
from job_description import JobDescription
from resume import Resume
from resume_manager import ResumeManager
| normal | {
"blob_id": "a998433e45c1d5135749c5164e8ec1f2eb0e572a",
"index": 1693,
"step-1": "<mask token>\n",
"step-2": "from job_description import JobDescription\nfrom resume import Resume\nfrom resume_manager import ResumeManager\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
... | [
0,
1
] |
from ctypes import *
class GF_IPMPX_Data(Structure):
_fields_=[
("tag", c_char),
("Version", c_char),
("dataID", c_char)
] | normal | {
"blob_id": "b3f4815495c781fe6cc15f77b4ee601680117419",
"index": 8592,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass GF_IPMPX_Data(Structure):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass GF_IPMPX_Data(Structure):\n _fields_ = [('tag', c_char), ('Version', c_char), ('dataID', ... | [
0,
1,
2,
3,
4
] |
list = [3, 1, 2, 5, 4, 7, 6]
def sort(list):
for i in range(len(list) - 1):
if list[i] > list[i + 1]:
a = list[i]
list[i] = list[i + 1]
list[i + 1] = a
print(list)
sort(list)
| normal | {
"blob_id": "219929d52b5f1a0690590e83b41d2b4f0b2b3a51",
"index": 336,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef sort(list):\n for i in range(len(list) - 1):\n if list[i] > list[i + 1]:\n a = list[i]\n list[i] = list[i + 1]\n list[i + 1] = a\n ... | [
0,
1,
2,
3
] |
import subprocess
import datetime
def ping_address(host,n):
ping = subprocess.Popen(
["ping","-c",str(n),host],
stdout = subprocess.PIPE,
stderr = subprocess.PIPE)
out,error = ping.communicate()
return out, error
def ping_address_windows(host,n):
ping = subprocess.Popen(
["... | normal | {
"blob_id": "3f2221f5f3a699020dd5986acb793e3083976dff",
"index": 7176,
"step-1": "<mask token>\n\n\ndef parse_msg(msg):\n line_org = msg.split('\\n')\n N = len(line_org) - 2\n line = line_org[N]\n return line\n\n\ndef get_vals(msg):\n rhs = msg.split('=')\n try:\n nums = rhs[1].split('/'... | [
4,
5,
6,
7,
8
] |
from typing import List
from pydantic import BaseModel
class BinBase(BaseModel):
name: str = None
title: str = None
class BinCreate(BinBase):
owner_id: int
password: str
class Bin(BinBase):
id: int
# TODO: token?
class Config():
orm_mode = True
class UserBase(BaseModel):
... | normal | {
"blob_id": "1c0f194bbdc6f7e3e4feb114e521aa958f11e83e",
"index": 3263,
"step-1": "<mask token>\n\n\nclass UserCreate(UserBase):\n password: str\n\n\nclass User(UserBase):\n id: int\n\n\n class Config:\n orm_mode = True\n",
"step-2": "<mask token>\n\n\nclass BinCreate(BinBase):\n owner_id: in... | [
2,
5,
6,
7,
8
] |
from django.db import models
class Category(models.Model):
name = models.CharField(max_length=50, unique=True)
created_at = models.DateTimeField(auto_now_add=True)
def __str__(self):
return self.name
class Meta:
verbose_name = 'Categoria'
class Books(models.Model):
name = model... | normal | {
"blob_id": "0584ff5cb252fba0fe1fc350a5fb023ab5cbb02b",
"index": 6750,
"step-1": "<mask token>\n\n\nclass Student(models.Model):\n name = models.CharField(max_length=70)\n cpf = models.CharField(max_length=14)\n birth_date = models.DateField()\n city = models.CharField(max_length=50)\n registratio... | [
3,
7,
8,
9,
11
] |
import pandas as pd
import os
"""
This code relies heavily on the form of the data. Namely it will fail if
the authors of the same book are not comma separated. It will also be inaccurate
or even fail if the same author for different books is not spelt in exactly the
same way.
"""
loc = r'C:\Users\james\OneDrive\Do... | normal | {
"blob_id": "f57490c8f4a5ba76824c3b41eb18905eb2213c23",
"index": 5107,
"step-1": "<mask token>\n\n\ndef split(string):\n \"\"\"\n Function takes input of a string and returns an array of strings\n the original string should be comma separated with a space after\n the comma in order for this function ... | [
1,
2,
3,
4,
5
] |
import re
from mapa import graficar_lista, graficar_matriz
class nodo:
def __init__(self, x, y, n, c):
self.columna = x
self.fila = y
self.nombre = n
self.color = c
pattern_matriz = r"[M|m][A|a][T|t][R|r][I|i][Z|z]\s*\(.*,.*,.*,.*,.*\)\{"
pattern_fila = r"[F|f][I|i][L|l][A|a]\s*\(... | normal | {
"blob_id": "70373c74e459efb2a310d94ae906910423e8bfd4",
"index": 6631,
"step-1": "<mask token>\n\n\nclass nodo:\n\n def __init__(self, x, y, n, c):\n self.columna = x\n self.fila = y\n self.nombre = n\n self.color = c\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass nodo:... | [
2,
3,
4,
5,
6
] |
#! /usr/bin/env python3
import EchooFunctions, cgi, MySQLdb, hashlib, time, requests, os
print ('Content-type: text/html\n')
form = cgi.FieldStorage()
#database connection
user = "i494f18_team34"
db_pass = "my+sql=i494f18_team34"
db_con = MySQLdb.connect(host="db.soic.indiana.edu", port = 3306, user=user, passwd=db_... | normal | {
"blob_id": "dc88686d3cbb4223b4de6847bf4fc29b93054b00",
"index": 495,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('Content-type: text/html\\n')\n<mask token>\nif 'echooUser' in str(os.environ):\n userName = EchooFunctions.getUserName()\n userName = userName[0]\n userID = EchooFunctions.... | [
0,
1,
2,
3,
4
] |
from .models import RecommendedArtifact
from .serializers import RecommendedArtifactSerialize
from rest_framework.decorators import api_view
from rest_framework.response import Response
from datetime import datetime
import requests, bs4
# constant value
service_key = "{jo's museum key}"
@api_view(['GET'])
def artifa... | normal | {
"blob_id": "707e3e60d6d9a3db5b9bc733e912b34e2cec5974",
"index": 8585,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@api_view(['GET'])\ndef artifact_save_recommend(request, pageNo):\n artifact_url = (\n f'http://www.emuseum.go.kr/openapi/relic/list?serviceKey={service_key}&numOfRows=100&p... | [
0,
2,
3,
4,
5
] |
from pydis.datastruct.sds import SdsImp
class RPCStub(object):
def __init__(self):
pass
def SET(self, key, value):
self
print("{}: {}".format(key, value))
| normal | {
"blob_id": "74f85732b4e1f4ef2b82a48818cbaedb18a56083",
"index": 8122,
"step-1": "<mask token>\n\n\nclass RPCStub(object):\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass RPCStub(object):\n\n def __init__(self):\n pass\n <mask token>\n",
"step-3": "<mask token>\n\n\ncl... | [
1,
2,
3,
4,
5
] |
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