code stringlengths 13 6.09M | order_type stringclasses 2
values | original_example dict | step_ids listlengths 1 5 |
|---|---|---|---|
import os
import random
readpath = './DBLP/'
writepath = './DBLP/'
dataname = 'dblp.txt'
labelname = 'node2label.txt'
testsetname = writepath + 'dblp_testset.txt'
def run(save_rate):
rdataname = readpath + dataname
rlabelname = readpath + labelname
wdataname = writepath + dataname
wlabelname = writepath + labelna... | normal | {
"blob_id": "4bd6a7c7fc6a788b2cb010f6513872bd3e0d396c",
"index": 5011,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef run(save_rate):\n rdataname = readpath + dataname\n rlabelname = readpath + labelname\n wdataname = writepath + dataname\n wlabelname = writepath + labelname\n orda... | [
0,
2,
3,
4,
5
] |
# obtain the dataset
import pandas as pd
titanic = pd.read_csv('http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic.txt')
#titanic.info()
print(titanic.head())
# preprocessing
x = titanic.drop(['row.names', 'name', 'survived'], axis=1)
y = titanic['survived']
x['age'].fillna(x['age'].mean(),... | normal | {
"blob_id": "f1475d651c3b52611657a9767ad62796b55d8711",
"index": 3676,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(titanic.head())\n<mask token>\nx['age'].fillna(x['age'].mean(), inplace=True)\nx.fillna('UNKNOWN', inplace=True)\n<mask token>\ndtc.fit(x_train, y_train)\nprint(dtc.score(x_test, y_... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class RefrigeratorRaider:
"""Raid a refrigerator"""
def open(self):
print('Open fridge door.')
def take(self, food):
print('Finding {}...'.format(food))
if food == 'deep fried pizza':
raise RuntimeError('Health warning!')
print('Ta... | flexible | {
"blob_id": "7455eb670c2c019b8d066fcc6f2878a2136b7fd0",
"index": 5051,
"step-1": "<mask token>\n\n\nclass RefrigeratorRaider:\n \"\"\"Raid a refrigerator\"\"\"\n\n def open(self):\n print('Open fridge door.')\n\n def take(self, food):\n print('Finding {}...'.format(food))\n if food ... | [
6,
7,
8,
9,
10
] |
#!/usr/bin/env python
#
# Copyright 2017-2021 University Of Southern California
#
# 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.org/licenses/LICENSE-2.0
#
# Unless... | normal | {
"blob_id": "12fd4e3bfb6821205a9b65b4d236b4158ec4ef1e",
"index": 7345,
"step-1": "<mask token>\n\n\nclass Version(BaseVersion):\n\n def __init__(self, connection):\n super().__init__(connection)\n\n def update(self, force=False):\n \"\"\"\n\n :param force:\n :return:\n \"... | [
3,
4,
5,
6,
7
] |
from torchvision import datasets, transforms
import torch
def load_data(data_folder, batch_size, train, num_workers=0, **kwargs):
transform = {
'train': transforms.Compose(
[transforms.Resize([256, 256]),
transforms.RandomCrop(224),
transforms.RandomHorizontalFli... | normal | {
"blob_id": "d99fd3dc63f6a40dde5a6230111b9f3598d3c5fd",
"index": 7830,
"step-1": "<mask token>\n\n\nclass _InfiniteSampler(torch.utils.data.Sampler):\n \"\"\"Wraps another Sampler to yield an infinite stream.\"\"\"\n\n def __init__(self, sampler):\n self.sampler = sampler\n\n def __iter__(self):\... | [
8,
9,
10,
11,
12
] |
import SimpleITK as sitk
import numpy as np
from sklearn.ensemble import RandomForestClassifier
# # Estimation function # #
# --------------------------- #
# Linear registration function
# --------------------------- #
# --- Input --- #
# im_ref : The common image [numpy.ndarray]
# im_mov : The group ima... | normal | {
"blob_id": "2b7d9ded82fa980eeae06beb2d84d89612d53df1",
"index": 821,
"step-1": "<mask token>\n\n\ndef est_lin_transf(im_ref, im_mov, mov_mask=None, show_parameters=False):\n initial_transform = sitk.CenteredTransformInitializer(im_ref, im_mov,\n sitk.ScaleSkewVersor3DTransform(), sitk.\n Center... | [
5,
6,
7,
8,
9
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Migration(migrations.Migration):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Migration(migrations.Migration):
dependencies = [(... | flexible | {
"blob_id": "61484d9a08f2e3fcd15573ce89be4118a442dc2e",
"index": 6062,
"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 = [('bcs', '0002... | [
0,
1,
2,
3,
4
] |
import cv2
import numpy as np
import show_imgs as si
IMG_PATH = "../sample_imgs"
def blur():
image = cv2.imread(IMG_PATH + "/jjang.jpg")
kernel_sizes = [(1, 1), (3, 3), (5, 5), (7, 7), (7, 1), (1, 7)]
filter_imgs = {}
blur_imgs = {}
for ksize in kernel_sizes:
title = f"ksize: {ksize}"
... | normal | {
"blob_id": "8e5d05d925d47a85ad7c211f26af7951be048d32",
"index": 9351,
"step-1": "<mask token>\n\n\ndef blur():\n image = cv2.imread(IMG_PATH + '/jjang.jpg')\n kernel_sizes = [(1, 1), (3, 3), (5, 5), (7, 7), (7, 1), (1, 7)]\n filter_imgs = {}\n blur_imgs = {}\n for ksize in kernel_sizes:\n ... | [
2,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
help_txt = """
:help, show this help menu. :help [command] for detail
:dict [word], only find translation on dict.cn
:google [sentence], only find translation on google api
:lan2lan [sentence], translate from one language to another language
:add [word], add ... | flexible | {
"blob_id": "3fadb91bd2367819a540f687530f4b48ed878423",
"index": 9149,
"step-1": "<mask token>\n",
"step-2": "help_txt = \"\"\"\n:help, show this help menu. :help [command] for detail\n:dict [word], only find translation on dict.cn\n:google [sentence], only find translation on google api\n:lan2lan [sentence], ... | [
0,
1,
2
] |
<|reserved_special_token_0|>
class colour:
purple = '\x1b[95m'
cyan = '\x1b[96m'
darkcyan = '\x1b[36m'
blue = '\x1b[94m'
green = '\x1b[92m'
yellow = '\x1b[93m'
red = '\x1b[91m'
bold = '\x1b[1m'
underline = '\x1b[4m'
end = '\x1b[0m'
<|reserved_special_token_1|>
<|reserved_spe... | flexible | {
"blob_id": "4e9fd3ee2a78fae164d9f38704443ac5b2f4c11c",
"index": 1189,
"step-1": "<mask token>\n\n\nclass colour:\n purple = '\\x1b[95m'\n cyan = '\\x1b[96m'\n darkcyan = '\\x1b[36m'\n blue = '\\x1b[94m'\n green = '\\x1b[92m'\n yellow = '\\x1b[93m'\n red = '\\x1b[91m'\n bold = '\\x1b[1m'\... | [
2,
3,
4,
5,
6
] |
#!/usr/bin/env python
# -*- coding: utf-8 -*
#Perso
from signalManipulation import *
from manipulateData import *
#Module
import pickle
from sklearn import svm, grid_search
from sklearn.linear_model import ElasticNetCV, ElasticNet, RidgeClassifier
from sklearn.metrics import confusion_matrix, f1_score, accuracy_score... | normal | {
"blob_id": "d8e8ecbf77828e875082abf8dcbfbc2c29564e20",
"index": 4892,
"step-1": "#!/usr/bin/env python\n# -*- coding: utf-8 -*\n#Perso\nfrom signalManipulation import *\nfrom manipulateData import *\n\n#Module\nimport pickle\n\nfrom sklearn import svm, grid_search\nfrom sklearn.linear_model import ElasticNetCV,... | [
0
] |
import pygame, states, events
from settings import all as settings
import gui
def handleInput(world, event):
if event == events.btnSelectOn or event == events.btnEscapeOn:
bwd(world)
if event%10 == 0:
world.sounds['uiaction'].play(0)
# world.shouldRedraw = True
def bwd(world):
if wor... | normal | {
"blob_id": "8650e0f1e7f2ac42c3c78191f79810f5befc9f41",
"index": 3298,
"step-1": "<mask token>\n\n\ndef bwd(world):\n if world.state >= states.Config:\n return left(world)\n world.shouldRedraw = True\n world.state = states.Intro\n\n\ndef draw(world):\n if not world.shouldRedraw:\n retur... | [
2,
3,
4,
5,
6
] |
import json
from constants import *
from coattention_layer import *
from prepare_generator import *
from tensorflow.keras.layers import Input
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import LearningRateScheduler, ModelCheckpoint, Early... | normal | {
"blob_id": "a8d52d81ef6538e9cb8a0a9cab7cd0a778454c8e",
"index": 6424,
"step-1": "<mask token>\n\n\ndef coattention(num_embeddings):\n image_input = Input(shape=(196, 512))\n question_input = Input(shape=(SEQ_LENGTH,))\n output = CoattentionModel(num_embeddings)(question_input, image_input)\n model =... | [
3,
4,
5,
6,
7
] |
from flask import Flask, jsonify, request
import requests, json, random
from bs4 import BeautifulSoup
import gspread
import pandas as pd
import dataservices as dss
from oauth2client.service_account import ServiceAccountCredentials
# page = requests.get("https://www.worldometers.info/coronavirus/")
# soup = BeautifulSou... | normal | {
"blob_id": "267cb37f2ccad5b02a809d9b85327eacd9a49515",
"index": 1061,
"step-1": "<mask token>\n\n\n@app.route('/')\ndef hello():\n return 'Flask setup'\n\n\ndef sheets_row_writer(data_list):\n print('sheets method invoked')\n credentials = ServiceAccountCredentials.from_json_keyfile_name(\n 'mec... | [
5,
6,
9,
10,
11
] |
import numpy as np
import mysql.connector
from mysql.connector import Error
import matplotlib.pyplot as plt
def readData():
connection = mysql.connector.connect(host='localhost',database='cad_ultrasound',user='root',password='')
sql_select_Query = "SELECT id_pasien,nama,pathdata FROM datasets"
cu... | normal | {
"blob_id": "4d7696c832f9255fbc68040b61fde12e057c06fa",
"index": 3899,
"step-1": "<mask token>\n\n\ndef getFiturEkstraksi():\n connection = mysql.connector.connect(host='localhost', database=\n 'cad_ultrasound', user='root', password='')\n cursor = connection.cursor()\n sql_select_Query = 'SELECT... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
class GeneralizedQSamplingModel(OdfModel, Cache):
def __init__(self, gtab, method='gqi2', sampling_length=1.2,
normalize_peaks=False):
""" Generalized Q-Sampling Imaging [1]_
This model has the same assumptions as the DSI method i.e. Cartesian
grid sa... | flexible | {
"blob_id": "2f193cb1eaf7b5e99d20025716a248144af90b92",
"index": 1925,
"step-1": "<mask token>\n\n\nclass GeneralizedQSamplingModel(OdfModel, Cache):\n\n def __init__(self, gtab, method='gqi2', sampling_length=1.2,\n normalize_peaks=False):\n \"\"\" Generalized Q-Sampling Imaging [1]_\n\n ... | [
14,
16,
17,
19,
20
] |
import docker
import logging
import sys
if __name__ == '__main__':
# setting up logger
logging.basicConfig(stream=sys.stdout,
format='[%(asctime)s] {%(filename)s:%(lineno)d} %(levelname)s - %(message)s',
level=logging.DEBUG)
# get the docker client
clie... | normal | {
"blob_id": "a5c9ff1fe250310216e2eaa7a6ff5cc76fc10f94",
"index": 4324,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n logging.basicConfig(stream=sys.stdout, format=\n '[%(asctime)s] {%(filename)s:%(lineno)d} %(levelname)s - %(message)s',\n level=logging.DEBUG... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class Images(models.Model):
wordroot_text = models.CharField(max_length=255, verbose_name='词根')
wordroot_id = models.IntegerField(default=0, null=True, blank=True,
verbose_name='词根id, 可空')
url = models.CharField(max_length=255, null=True, blank=True,
verbose_na... | flexible | {
"blob_id": "512a13084a860e2784020664a3d5824d9dace6db",
"index": 7764,
"step-1": "<mask token>\n\n\nclass Images(models.Model):\n wordroot_text = models.CharField(max_length=255, verbose_name='词根')\n wordroot_id = models.IntegerField(default=0, null=True, blank=True,\n verbose_name='词根id, 可空')\n ... | [
14,
20,
22,
26,
27
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
if __name__ == '__main__':
app.run(host='0.0.0.0', port=5000, debug=True)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
app = create_app(Config)
if __name__ == '__main__':
app.run(host='0.0.0.0', port=5000, ... | flexible | {
"blob_id": "bea90bbcd4d34b64c21f022b6f3af2bee2d978e4",
"index": 1123,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n app.run(host='0.0.0.0', port=5000, debug=True)\n",
"step-3": "<mask token>\napp = create_app(Config)\nif __name__ == '__main__':\n app.run(host='0.0.0... | [
0,
1,
2,
3,
4
] |
# Generated by Django 2.2.6 on 2020-06-18 14:16
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('gestionadmin', '0133_auto_20200618_1339'),
]
operations = [
migrations.RemoveField(
model_name='comprasenc',
name='empleado'... | normal | {
"blob_id": "f96a7bef48e7df2899343029a2fae9697125a5b2",
"index": 5203,
"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 = [('gestionadmi... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def save2txt(songname, lyric, path):
print('歌词下载完成：' + songname)
lyric_path = path + '\\' + songname + '.txt'
with open(lyric_path, 'a', encoding='utf-8') as f:
f.write(lyric)
<|reserved_special_token_0|>
def get_lyrics(songids):
url = 'http://music.163.com/api... | flexible | {
"blob_id": "3b11d514b15775e4c818a7a2adf9a80e89dca968",
"index": 5801,
"step-1": "<mask token>\n\n\ndef save2txt(songname, lyric, path):\n print('歌词下载完成：' + songname)\n lyric_path = path + '\\\\' + songname + '.txt'\n with open(lyric_path, 'a', encoding='utf-8') as f:\n f.write(lyric)\n\n\n<mask ... | [
17,
20,
21,
24,
33
] |
class Solution:
def subsets(self, nums: List[int]) -> List[List[int]]:
'''
ans = set()
n = len(nums)
for x, val in enumerate(nums):
for y in range(x + 1, n + 1):
ans.add(frozenset(nums[x:y]))
for u in range(0, x + 1):
fo... | normal | {
"blob_id": "7d873ed216355d1688ec79ff337304d8ebfd2754",
"index": 7625,
"step-1": "<mask token>\n",
"step-2": "class Solution:\n <mask token>\n",
"step-3": "class Solution:\n\n def subsets(self, nums: List[int]) ->List[List[int]]:\n \"\"\"\n ans = set()\n n = len(nums)\n for ... | [
0,
1,
2,
3
] |
import requests
from bs4 import BeautifulSoup
import json
headers = {'User-Agent':'Mozilla/5.0 (Windows NT 6.3; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.132 Safari/537.36'}
url = 'http://api.tvmaze.com/singlesearch/shows?q=game+of+throne&embed=episodes'
response = requests.get(url, headers = hea... | normal | {
"blob_id": "d625e6724a3fe077a6f80b6de6b1f5bb0b95d47d",
"index": 4612,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open('got_info.json', 'w') as f:\n f.write(x)\n",
"step-3": "<mask token>\nheaders = {'User-Agent':\n 'Mozilla/5.0 (Windows NT 6.3; WOW64) AppleWebKit/537.36 (KHTML, like Gec... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def solve(n, seq):
flag = True
freq = defaultdict()
i = 1
p = len(seq)
j = 0
while j < p:
c = seq[j]
if i > n:
flag = False
break
if c in freq.keys():
... | flexible | {
"blob_id": "89b03bb5ca86e426459e23866f86f8770e4a1613",
"index": 3420,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef solve(n, seq):\n flag = True\n freq = defaultdict()\n i = 1\n p = len(seq)\n j = 0\n while j < p:\n c = seq[j]\n if i > n:\n flag = Fals... | [
0,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def Preprocessing(instancia):
instancia = re.sub('#\\S+', '', instancia)
instancia = re.sub('@\\S+', '', instancia).lower().replace('.', ''
).replace(';', '').replace('-', '').replace(':', '').replace(')', ''
... | flexible | {
"blob_id": "bffd211a2d2dc3dd9b596f69909be7f0437ab0c8",
"index": 9322,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef Preprocessing(instancia):\n instancia = re.sub('#\\\\S+', '', instancia)\n instancia = re.sub('@\\\\S+', '', instancia).lower().replace('.', ''\n ).replace(';', '').r... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class PriorityQueue:
pq = []
elements = {}
task = 0
def insert(self, priority, x_val, y_val):
entry = [priority, self.task, x_val, y_val]
self.elements[self.task] = entry
heapq.heappush(self.pq, entry)
self.task += 1
def delete(self, t... | flexible | {
"blob_id": "6192099bdecffd9ce3576f4034567478145115a0",
"index": 1291,
"step-1": "<mask token>\n\n\nclass PriorityQueue:\n pq = []\n elements = {}\n task = 0\n\n def insert(self, priority, x_val, y_val):\n entry = [priority, self.task, x_val, y_val]\n self.elements[self.task] = entry\n ... | [
12,
17,
18,
23,
25
] |
import json
# numbers=[2,3,5,7,11,13]
filename='numbers.json'
with open(filename) as f:
numbers=json.load(f)
print(numbers) | normal | {
"blob_id": "8da775bd87bfeab5e30956e62bcdba6c04e26b27",
"index": 6720,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open(filename) as f:\n numbers = json.load(f)\nprint(numbers)\n",
"step-3": "<mask token>\nfilename = 'numbers.json'\nwith open(filename) as f:\n numbers = json.load(f)\nprin... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
@udf(returnType=BooleanType())
def filter_host(item):
for i in filter_hosts:
if item.find(i) != -1:
return False
return True
<|reserved_special_token_0|>
@udf(returnType=BooleanType())
def contains_host(item):
for i in contains_hosts:
if item.fi... | flexible | {
"blob_id": "e7d63c3b56459297eb67c56e93a3c640d93e5f6d",
"index": 8683,
"step-1": "<mask token>\n\n\n@udf(returnType=BooleanType())\ndef filter_host(item):\n for i in filter_hosts:\n if item.find(i) != -1:\n return False\n return True\n\n\n<mask token>\n\n\n@udf(returnType=BooleanType())\n... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
def reverse(text):
"""将字符串翻转"""
return text[::-1]
def is_palindrome(text):
print(e for e in text if e.isalnum())
m = ''.join(e for e in text if e.isalnum())
print(m)
"""是否是回文数"""
return m == reverse(m)
<|reserved_special_token_0|>
<|reserved_special_token... | flexible | {
"blob_id": "03a1f9f533f7550db32fa25578ef2f7f4c741510",
"index": 8583,
"step-1": "<mask token>\n\n\ndef reverse(text):\n \"\"\"将字符串翻转\"\"\"\n return text[::-1]\n\n\ndef is_palindrome(text):\n print(e for e in text if e.isalnum())\n m = ''.join(e for e in text if e.isalnum())\n print(m)\n \"\"\"... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
def action_scaling(env, action_scaler):
"""
This is actually going to just be "action scaling". Because,
it's all about the ratio, and the ratio doesn't change!
"""
try:
state_dim = len(env.observation_space.low)
except AttributeError:
print('Using ... | flexible | {
"blob_id": "5529813e10e4a30a60c28242be9d1a8822fb58af",
"index": 9685,
"step-1": "<mask token>\n\n\ndef action_scaling(env, action_scaler):\n \"\"\"\n This is actually going to just be \"action scaling\". Because,\n it's all about the ratio, and the ratio doesn't change!\n \"\"\"\n try:\n s... | [
3,
4,
6,
7,
8
] |
from selenium import webdriver
import time
with webdriver.Chrome() as browser:
browser.get("http://suninjuly.github.io/selects1.html")
time.sleep(1)
x = int(browser.find_element_by_id("num1").text)
y = int(browser.find_element_by_id("num2").text)
sum_xy = str(int(x)+int(y))
browser.find_element... | normal | {
"blob_id": "42be9077ec51a9be1d4923011a38cd64d829f876",
"index": 1529,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith webdriver.Chrome() as browser:\n browser.get('http://suninjuly.github.io/selects1.html')\n time.sleep(1)\n x = int(browser.find_element_by_id('num1').text)\n y = int(brow... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def maths(num):
int(num)
if num % 5 == 0 and num % 3 == 0:
print('bizzfizz')
elif num % 3 == 0:
print('fizz')
elif num % 5 == 0:
print('bizz')
else:
print(num)
<|reserved_special_token_0|>
<|reserved_spe... | flexible | {
"blob_id": "91f83adbe01e2d8070f9286031b77eae71beb83e",
"index": 1107,
"step-1": "<mask token>\n",
"step-2": "def maths(num):\n int(num)\n if num % 5 == 0 and num % 3 == 0:\n print('bizzfizz')\n elif num % 3 == 0:\n print('fizz')\n elif num % 5 == 0:\n print('bizz')\n else:\... | [
0,
1,
2,
3,
4
] |
# encoding: utf-8
import paramiko
import select
import os
import sys
ssh = paramiko.SSHClient()
ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())
host = "47.107.229.100"
user = "root"
pwd = "aliyun1996874353...A"
class SSH:
def __init__(self, host, user, pwd, port=22):
self.host = host
sel... | normal | {
"blob_id": "2342a651ec45623b887c4bc1168adb0731ba5ff6",
"index": 8443,
"step-1": "<mask token>\n\n\nclass SSH:\n <mask token>\n\n def exec_cmd(self, cmd):\n stdin, stdout, stderr = self.client.exec_command(cmd)\n res, err = stdout.read(), stderr.read()\n result = res if res else err\n ... | [
5,
6,
8,
11,
12
] |
import math
import numpy as np
import basis.robot_math as rm
import grasping.annotation.utils as gu
from scipy.spatial import cKDTree
def plan_contact_pairs(objcm,
max_samples=100,
min_dist_between_sampled_contact_points=.005,
angle_between_contact_... | normal | {
"blob_id": "738e6d4d608aa977094420a432cbd8a05ea8a1b5",
"index": 4384,
"step-1": "<mask token>\n\n\ndef plan_grasps(hnd_s, objcm, angle_between_contact_normals=math.radians(\n 160), openning_direction='loc_x', rotation_interval=math.radians(22.5),\n max_samples=100, min_dist_between_sampled_contact_points=... | [
3,
4,
5,
6,
7
] |
import mxnet as mx
import numpy as np
import cv2
import random
class Even_iterator(mx.io.DataIter):
'''
data iterator, shuffle data but always make pairs as neighbors
for verification and triplet loss
'''
def __init__(self, lst_name, batch_size, aug_params=dict(), shuffle=False):
super(Eve... | normal | {
"blob_id": "a824bd7577134227f5c136f2a4382c056f1175be",
"index": 9663,
"step-1": "import mxnet as mx\nimport numpy as np\nimport cv2\nimport random\n\n\nclass Even_iterator(mx.io.DataIter):\n '''\n data iterator, shuffle data but always make pairs as neighbors\n for verification and triplet loss\n ''... | [
0
] |
Ylist = ['yes', 'Yes', 'Y', 'y']
Nlist = ['no', 'No', 'N', 'n']
America = ['America', 'america', 'amer', 'rica']
TRW = ['1775', 'The Revolutionary war', 'the Revolutionary war', 'the revolutionary war', 'The Revolutionary War',
'trw', 'Trw', 'TRW']
TCW = ['1861', 'The civil war', 'The civil War', 'The Civil... | normal | {
"blob_id": "6e07dcc3f3b8c7fbf8ce8d481b9612e7496967bd",
"index": 8316,
"step-1": "<mask token>\n",
"step-2": "Ylist = ['yes', 'Yes', 'Y', 'y']\nNlist = ['no', 'No', 'N', 'n']\nAmerica = ['America', 'america', 'amer', 'rica']\nTRW = ['1775', 'The Revolutionary war', 'the Revolutionary war',\n 'the revolution... | [
0,
1,
2
] |
def patternCount(dnaText, pattern):
count = 0
for i in range(0, len(dnaText) - len(pattern)):
word = dnaText[i:i+len(pattern)]
if (word == pattern):
count = count + 1
return count
def freqWordProblem(text, k):
countWords = []
for i in range(0, len(text) - k):
pa... | normal | {
"blob_id": "29c1a989365408bf5c3d6196f7afc969be63df85",
"index": 5942,
"step-1": "<mask token>\n\n\ndef complimentDNA(text):\n result = ''\n for letter in text:\n result = result + mapDNA[letter]\n return result[::-1]\n\n\ndef patternFind(text, pattern):\n index = []\n for i in range(0, len... | [
2,
4,
5,
6,
7
] |
# https://www.acmicpc.net/problem/3584
import sys, collections
input = sys.stdin.readline
N = int(input())
for _ in range(N):
n = int(input())
arr = collections.defaultdict(list)
parent = [i for i in range(n + 1)]
for i in range(n - 1):
a, b = map(int, input().split())
arr[a].append(b)
... | normal | {
"blob_id": "d60a2d4c819f701e8e439b8839415aa2838df185",
"index": 6415,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor _ in range(N):\n n = int(input())\n arr = collections.defaultdict(list)\n parent = [i for i in range(n + 1)]\n for i in range(n - 1):\n a, b = map(int, input().spli... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def walk2(dirname):
"""Prints the names of all files in
dirname and its subdirectories.
dirname: string name of directory
"""
for root, dirs, files in os.walk(dirname):
for filename in files:
print(os.path.join(root, filename))
<|reserved_specia... | flexible | {
"blob_id": "de1262da699a18266ad8673597391f625783a44d",
"index": 5721,
"step-1": "<mask token>\n\n\ndef walk2(dirname):\n \"\"\"Prints the names of all files in \n dirname and its subdirectories.\n\n dirname: string name of directory\n \"\"\"\n for root, dirs, files in os.walk(dirname):\n f... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
def merge_sort(merged_arr: list):
"""
функция делит поданный на вход массив,
и рекурсивно все сортирует слиянием
:param merged_arr: - список на входе
:return: - список отсортированный слиянием на выходе
"""
if len(merged_arr) <= 1:
return
middle = l... | flexible | {
"blob_id": "cd1987f09ca3e09ac251b1ebdec4168fd5dbdd0e",
"index": 7607,
"step-1": "<mask token>\n\n\ndef merge_sort(merged_arr: list):\n \"\"\"\n функция делит поданный на вход массив,\n и рекурсивно все сортирует слиянием\n :param merged_arr: - список на входе\n :return: - список отсортированный с... | [
2,
3,
4,
5,
6
] |
import cv2 as cv
import numpy as np
import sys
from meio_tom_lib import *
imgname = sys.argv[1]
imgpath = "img/" + imgname
try:
img = cv.imread(imgpath)
newimg1 = jarvis_judice_ninke_1(img)*255
newimg2 = jarvis_judice_ninke_2(img)*255
cv.imshow("Imagem original",img)
cv.imshow("Jarvis, Judice e... | normal | {
"blob_id": "bf764457e6af25d2d9406b18af51f63b36ab823a",
"index": 8564,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ntry:\n img = cv.imread(imgpath)\n newimg1 = jarvis_judice_ninke_1(img) * 255\n newimg2 = jarvis_judice_ninke_2(img) * 255\n cv.imshow('Imagem original', img)\n cv.imshow('J... | [
0,
1,
2,
3,
4
] |
# Given an unsorted integer array nums, find the smallest missing positive integer.
class Solution:
def firstMissingPositive(self, nums: List[int]) -> int:
# if nums is emtpy, first pos int is 1
if not nums:
return 1
maxnum = max(nums) # for speed we assign max of nums to var max... | normal | {
"blob_id": "09905d4b5ad2e59578d874db171aafb6c42db105",
"index": 8609,
"step-1": "<mask token>\n",
"step-2": "class Solution:\n <mask token>\n",
"step-3": "class Solution:\n\n def firstMissingPositive(self, nums: List[int]) ->int:\n if not nums:\n return 1\n maxnum = max(nums)\... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
from .chair_model import run_chair_simulation, init_omega_t, JumpingModel, H_to_L
from .utils import load_hcp_peaks, Condition, average_peak_counts
<|reserved_special_token_1|>
from .chair_model import run_chair_simulation, init_omega_t, \
JumpingM... | flexible | {
"blob_id": "9087a7bf42070fdb8639c616fdf7f09ad3903656",
"index": 6755,
"step-1": "<mask token>\n",
"step-2": "from .chair_model import run_chair_simulation, init_omega_t, JumpingModel, H_to_L\nfrom .utils import load_hcp_peaks, Condition, average_peak_counts\n",
"step-3": "from .chair_model import run_chair_... | [
0,
1,
2
] |
from source.ga.population import create_population, random_genome
def test_create_population(race_example):
population = create_population(race_example, 20)
assert population
def test_random_genome(race_basic):
genome = random_genome(race_basic)
assert genome
def test_random_genome_example(race_ex... | normal | {
"blob_id": "0802aac57cd28104cdb6ff45d993aa224f80b830",
"index": 2877,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef test_random_genome(race_basic):\n genome = random_genome(race_basic)\n assert genome\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef test_create_population(race_exa... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
from .signals import get_restaurant_coordinates, count_average_price, count_total_calories
from .dish import Dish
from .ingredients import Ingredient
from .restaurants import Restaurant
| flexible | {
"blob_id": "1935cab249bf559aeadf785ce7abcecb03344c04",
"index": 6058,
"step-1": "<mask token>\n",
"step-2": "from .signals import get_restaurant_coordinates, count_average_price, count_total_calories\nfrom .dish import Dish\nfrom .ingredients import Ingredient\nfrom .restaurants import Restaurant\n",
"step-... | [
0,
1
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
print('Convertidor de pies y pulgadas a centímetros')
<|reserved_special_token_0|>
print('{} pies y {} pulgadas son {} cm'.format(pies, pulgadas, cm))
<|reserved_special_token_1|>
print('Convertidor de pies y pulgadas a centímetros')
pies = float(input('Es... | flexible | {
"blob_id": "b0ab97f5c05cdeee4c01460109a76cef75ac72ce",
"index": 5342,
"step-1": "<mask token>\n",
"step-2": "print('Convertidor de pies y pulgadas a centímetros')\n<mask token>\nprint('{} pies y {} pulgadas son {} cm'.format(pies, pulgadas, cm))\n",
"step-3": "print('Convertidor de pies y pulgadas a centíme... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
while True:
url = raw_input('Enter URL: ')
if len(url) < 1:
break
print('Retrieving', url)
connection = urllib.urlopen(url)
data = connection.read()
print('Retrieved', len(data), 'characters')
t... | flexible | {
"blob_id": "4cdd5fc15096aac01ad6d97d38ef7397859de18b",
"index": 5470,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile True:\n url = raw_input('Enter URL: ')\n if len(url) < 1:\n break\n print('Retrieving', url)\n connection = urllib.urlopen(url)\n data = connection.read()\n ... | [
0,
1,
2,
3
] |
import pysftp
import time
import threading
def sftp_connection():
while True:
cnopts = pysftp.CnOpts()
cnopts.hostkeys = None
try:
with pysftp.Connection('sb-emea.avl.com', username='abhishek.hingwasia@avl.com', password='AvlAvl2931!!',
... | normal | {
"blob_id": "676ccbac9385a4b63d599c3f85f16e28d839e9b8",
"index": 3731,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef sftp_connection():\n while True:\n cnopts = pysftp.CnOpts()\n cnopts.hostkeys = None\n try:\n with pysftp.Connection('sb-emea.avl.com', username... | [
0,
1,
2,
3,
4
] |
#!/usr/bin/python2.7
'''USAGE: completeness.py BLAST_output (tab formatted)
Prints % completeness based on marker gene BLAST of caled genes from a genome
Markers from Lan et al. (2016)
'''
import sys
with open(sys.argv[1],'r') as blastOut:
geneHits = []
orgHits = []
hits = 0.0
for line in blastOut:
hits += 1.0
... | normal | {
"blob_id": "a8659ca7d7a5870fc6f62b3dfee1779e33373e7b",
"index": 8388,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open(sys.argv[1], 'r') as blastOut:\n geneHits = []\n orgHits = []\n hits = 0.0\n for line in blastOut:\n hits += 1.0\n currHit = line.split()[1]\n c... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for i in range(100, 1000):
for j in range(100, 1000):
s = str(i * j)
if s[::-1] == s:
pal.append(int(s))
print(max(pal))
<|reserved_special_token_1|>
pal = []
for i in range(100, 1000):
for j... | flexible | {
"blob_id": "179a9cf0713001e361f39aa30192618b392c78c7",
"index": 6972,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(100, 1000):\n for j in range(100, 1000):\n s = str(i * j)\n if s[::-1] == s:\n pal.append(int(s))\nprint(max(pal))\n",
"step-3": "pal = []\nfo... | [
0,
1,
2
] |
import os, sys, shutil
import fnmatch, logging, zipfile
logging.basicConfig(format='%(asctime)s [%(levelname)s] %(message)s', datefmt='%Y-%m-%d,%H:%M:%S', level=logging.DEBUG)
def scan_files(dir, pattern):
fileList = []
for root, subFolders, files in os.walk(dir):
for file in files:
... | normal | {
"blob_id": "187c2a56ba9360b89c8ded09861091e2deedf32e",
"index": 7783,
"step-1": "<mask token>\n\n\ndef scan_files(dir, pattern):\n fileList = []\n for root, subFolders, files in os.walk(dir):\n for file in files:\n if fnmatch.fnmatch(file, pattern):\n fileList.append(os.pa... | [
1,
2,
3,
4,
5
] |
from .plutotv_html import PlutoTV_HTML
class Plugin_OBJ():
def __init__(self, fhdhr, plugin_utils):
self.fhdhr = fhdhr
self.plugin_utils = plugin_utils
self.plutotv_html = PlutoTV_HTML(fhdhr, plugin_utils)
| normal | {
"blob_id": "ee0cf2325c94821fa9f5115e8848c71143eabdbf",
"index": 4775,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Plugin_OBJ:\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Plugin_OBJ:\n\n def __init__(self, fhdhr, plugin_utils):\n self.fhdhr = fhdhr\n self.plug... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
nx.draw(g)
nx.draw(h)
plt.show()
nx.write_gexf(g, 'test.gexf')
<|reserved_special_token_1|>
<|reserved_special_token_0|>
g = nx.Graph()
g = nx.complete_graph(10)
h = nx.gnp_random_graph(10, 0.5)
nx.draw(g)
nx.draw(h)
plt.show()... | flexible | {
"blob_id": "3bfa9d42e3fd61cf6b7ffaac687f66c2f4bc073e",
"index": 3906,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nnx.draw(g)\nnx.draw(h)\nplt.show()\nnx.write_gexf(g, 'test.gexf')\n",
"step-3": "<mask token>\ng = nx.Graph()\ng = nx.complete_graph(10)\nh = nx.gnp_random_graph(10, 0.5)\nnx.draw(g)\nn... | [
0,
1,
2,
3,
4
] |
from django.db import models
class ScggjyList(models.Model):
title = models.CharField(max_length=255)
pubData = models.CharField(db_column='pubData', max_length=255)
detailLink = models.CharField(db_column='detailLink', max_length=255)
detailTitle = models.CharField(db_column='detailTitle', max_length... | normal | {
"blob_id": "951fafe9f1b9a3273f30d101831d1e59e26fe85d",
"index": 1535,
"step-1": "<mask token>\n\n\nclass ZakerNewsTab(models.Model):\n code = models.IntegerField(blank=True, null=True)\n tabName = models.CharField(db_column='tabName', max_length=20, blank=\n True, null=True)\n\n\n class Meta:\n ... | [
4,
7,
8,
9,
10
] |
"""
Декоратор parser_stop - парсер результата вывода комманды docker stop.
"""
from functools import wraps
def parser_stop(func):
@wraps(func)
def wrapper(*args, **kwargs):
result = func(*args, **kwargs)
stdout = result['stdout']
"""
stdout: строки разделены \n
"""
... | normal | {
"blob_id": "4af573fa17f86ee067b870dce1f6ee482d1b14ff",
"index": 8281,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef parser_stop(func):\n\n @wraps(func)\n def wrapper(*args, **kwargs):\n result = func(*args, **kwargs)\n stdout = result['stdout']\n \"\"\"\n stdou... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
urlpatterns = [url('^add/$', views.cart_add, name='add'), url('^count/$',
views.cart_count, name='count'), url('^del/$', views.cart_del, name=
'delete'), url('update/$', views.cart_update, name='update'), url('^&',
vie... | flexible | {
"blob_id": "5b3a6b44bd9ea80da1983d8254c73bba3e2338e1",
"index": 5166,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurlpatterns = [url('^add/$', views.cart_add, name='add'), url('^count/$',\n views.cart_count, name='count'), url('^del/$', views.cart_del, name=\n 'delete'), url('update/$', views.c... | [
0,
1,
2,
3
] |
with open('rosalind_ba3d.txt','r') as f:
kmer_length = int(f.readline().strip())
seq = f.readline().strip()
dict = {}
for offset in range(len(seq)-kmer_length+1):
prefix = seq[offset:offset+kmer_length-1]
suffix = seq[offset+1:offset+kmer_length]
if prefix in dict:
dict[prefix].append(suffix)
else:
... | normal | {
"blob_id": "050f060bb9d3d46f8b87c9802356bd0da8f926f8",
"index": 6244,
"step-1": "<mask token>\n",
"step-2": "with open('rosalind_ba3d.txt', 'r') as f:\n kmer_length = int(f.readline().strip())\n seq = f.readline().strip()\n<mask token>\nfor offset in range(len(seq) - kmer_length + 1):\n prefix = seq[... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for name in lst:
name = name.strip().upper()
rname = name[::-1]
if name == rname:
print(name)
<|reserved_special_token_0|>
print('-' * 20)
<|reserved_special_token_0|>
for name in names:
name = name.upper()... | flexible | {
"blob_id": "622b388beb56eba85bbb08510c2bcea55f23da9a",
"index": 721,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor name in lst:\n name = name.strip().upper()\n rname = name[::-1]\n if name == rname:\n print(name)\n<mask token>\nprint('-' * 20)\n<mask token>\nfor name in names:\n ... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def train_once():
os.system('xterm -e "pwd ; cd ~ ; torcs -r ~/quickrace.xml " &')
os.system('xterm -e "pwd ; ./start.sh " &')
return True
<|reserved_special_token_1|>
import os
import time
def train_once():
... | flexible | {
"blob_id": "c2cf74893c7f7515a95141bb10be6a446b45a0cc",
"index": 1447,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef train_once():\n os.system('xterm -e \"pwd ; cd ~ ; torcs -r ~/quickrace.xml \" &')\n os.system('xterm -e \"pwd ; ./start.sh \" &')\n return True\n",
"step-3": "import... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class Stack(object):
def __init__(self):
self.items = []
def is_empty(self):
return self.items == []
def clear(self):
self.items = []
def push(self, item):
self.items.append(item)
def pop(self):
return self.items.pop()
... | flexible | {
"blob_id": "6fa9dfadc60108e1718c6688f07de877b0ac0afd",
"index": 5885,
"step-1": "<mask token>\n\n\nclass Stack(object):\n\n def __init__(self):\n self.items = []\n\n def is_empty(self):\n return self.items == []\n\n def clear(self):\n self.items = []\n\n def push(self, item):\n ... | [
7,
8,
9,
10,
11
] |
<|reserved_special_token_0|>
def parseSexLabel(string):
if string.startswith('male'):
return 0
if string.startswith('female'):
return 1
print('ERROR parsing sex from ' + string)
<|reserved_special_token_0|>
def parseExpLabel(string):
if string.startswith('serious'):
return ... | flexible | {
"blob_id": "6822a0a194e8b401fecfed2b617ddd5489302389",
"index": 4718,
"step-1": "<mask token>\n\n\ndef parseSexLabel(string):\n if string.startswith('male'):\n return 0\n if string.startswith('female'):\n return 1\n print('ERROR parsing sex from ' + string)\n\n\n<mask token>\n\n\ndef pars... | [
2,
3,
4,
5,
7
] |
"""Step (with Warm up) learning rate scheduler module."""
from typing import Union
import torch
from torch.optim.lr_scheduler import _LRScheduler
from typeguard import check_argument_types
from espnet2.schedulers.abs_scheduler import AbsBatchStepScheduler
class WarmupStepLR(_LRScheduler, AbsBatchStepScheduler):
... | normal | {
"blob_id": "bce16762c0739087a8309872da4ac04298c50893",
"index": 7695,
"step-1": "<mask token>\n\n\nclass WarmupStepLR(_LRScheduler, AbsBatchStepScheduler):\n <mask token>\n <mask token>\n\n def __repr__(self):\n return (\n f'{self.__class__.__name__}(warmup_steps={self.warmup_steps}, ... | [
3,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for i in range(k):
l, r = map(int, input().split())
k_list.append([l, r])
<|reserved_special_token_0|>
for i in range(1, n):
dpsum[i] = dp[i] + dpsum[i - 1]
for j in range(k):
l, r = k_list[j]
li = ... | flexible | {
"blob_id": "97720baab961d50ceae832d52350b9871c552c84",
"index": 9071,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(k):\n l, r = map(int, input().split())\n k_list.append([l, r])\n<mask token>\nfor i in range(1, n):\n dpsum[i] = dp[i] + dpsum[i - 1]\n for j in range(k):\n ... | [
0,
1,
2
] |
"""TcEx Framework Key Value Redis Module"""
class KeyValueRedis:
"""TcEx Key Value Redis Module.
Args:
context (str): The Redis context (hash) for hashed based operations.
redis_client (redis.Client): An instance of redis client.
"""
def __init__(self, context, redis_client):
... | normal | {
"blob_id": "a5b74c31aed103b55404afc538af60c3eb18cb1b",
"index": 9738,
"step-1": "<mask token>\n\n\nclass KeyValueRedis:\n <mask token>\n\n def __init__(self, context, redis_client):\n \"\"\"Initialize the Class properties.\"\"\"\n self._context = context\n self._redis_client = redis_c... | [
5,
6,
8,
9,
10
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print('C:\\some\name')
print('C:\\some\\name')
print('C:\\some\\name')
<|reserved_special_token_0|>
print(s)
<|reserved_special_token_0|>
print(s)
<|reserved_special_token_1|>
x = '我是一个字符串'
y = '我也是一个字符串'
z = '我还是一个字符串'
s = "Ye... | flexible | {
"blob_id": "8fe9d21bb65b795a6633ab390f7f5d24a90146d5",
"index": 6774,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('C:\\\\some\\name')\nprint('C:\\\\some\\\\name')\nprint('C:\\\\some\\\\name')\n<mask token>\nprint(s)\n<mask token>\nprint(s)\n",
"step-3": "x = '我是一个字符串'\ny = '我也是一个字符串'\nz = '我还... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def gen(task_id):
while True:
print('Thread runned ' + str(task_id))
img = Cameras[task_id].getImg()
ret, jpeg = cv2.imencode('.jpg', img)
frame = jpeg.tobytes()
yield b'--frame\r\nContent-Type: image/jpeg\r\n\r\n' + frame + b'\r\n\r\n'
@restA... | flexible | {
"blob_id": "5cdedce5f984f53b8e26d1580a9040b26023f247",
"index": 2910,
"step-1": "<mask token>\n\n\ndef gen(task_id):\n while True:\n print('Thread runned ' + str(task_id))\n img = Cameras[task_id].getImg()\n ret, jpeg = cv2.imencode('.jpg', img)\n frame = jpeg.tobytes()\n y... | [
6,
7,
8,
9,
10
] |
<|reserved_special_token_0|>
def validate_email(value, row_number):
error_message = _(u'Invalid e-mail address on "%d" line.')
return validators.EmailValidator(validators.email_re, unicode(
error_message % row_number), 'invalid')(value)
<|reserved_special_token_0|>
def get_externalsubscribers(file... | flexible | {
"blob_id": "2ec41e02c95a270455c096e85829b7220eeda0c7",
"index": 1317,
"step-1": "<mask token>\n\n\ndef validate_email(value, row_number):\n error_message = _(u'Invalid e-mail address on \"%d\" line.')\n return validators.EmailValidator(validators.email_re, unicode(\n error_message % row_number), 'i... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
@app.route('/')
def root():
return render_template('index.html')
@app.route('/api')
def index():
return render_template('index.html')
@app.route('/api/total/counties')
def total_counties():
return process_counties_total(read_macro('county'), get_args())
@app.route('/api/... | flexible | {
"blob_id": "af00c6f443426b1f61e1816d7d14ebc7e6871a82",
"index": 5562,
"step-1": "<mask token>\n\n\n@app.route('/')\ndef root():\n return render_template('index.html')\n\n\n@app.route('/api')\ndef index():\n return render_template('index.html')\n\n\n@app.route('/api/total/counties')\ndef total_counties():\... | [
34,
39,
40,
41,
42
] |
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy import stats
# prevent numpy exponential
# notation on print, default False
np.set_printoptions(suppress=True)
y_cord_df = pd.DataFrame(data=None, columns=['Time', 'Orien'])
list_no = np.arange(0.0, 108000.0, 1.0)
y_cord_df['... | normal | {
"blob_id": "ba5171d3de87ec01770a7174d9783d5058b0fced",
"index": 9896,
"step-1": "<mask token>\n\n\ndef vel_det(file, legend_label, line_color):\n fps = 60\n data_df = pd.read_hdf(path_or_buf=file)\n bodyparts = data_df.columns.get_level_values(1)\n coords = data_df.columns.get_level_values(2)\n b... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
urlpatterns = [path('admin/', admin.site.urls), path('post/', post_views.
post_list, nam... | flexible | {
"blob_id": "63c0786d277c5576822d6e521f65850762ab5eb0",
"index": 9198,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)\n",
"step-3": "<mask token>\nurlpatterns = [path('admin/', admin.site.urls), path('post/', post_views.\n ... | [
0,
1,
2,
3,
4
] |
import requests
from requests import Response
from auditlogging.Trail import Trail
from utils.Utils import is_empty
from auditlogging.agents.AuditAgent import AuditAgent
class APIAuditAgent(AuditAgent):
"""
Captures the audit trail using a REST endpoint URL (POST)
Add this agent to Auditor in order to cap... | normal | {
"blob_id": "45a57fac564f23253f9d9cd5d0fd820e559c15b9",
"index": 1212,
"step-1": "<mask token>\n\n\nclass APIAuditAgent(AuditAgent):\n <mask token>\n\n def __init__(self):\n self._url = 'http://localhost:3000/auditlogs/create'\n self._resp = None\n\n def change_endpoint(self, url: str):\n ... | [
8,
9,
10,
11
] |
<|reserved_special_token_0|>
def explore_cartpole():
for i_episode in range(2):
observation = env.reset()
for t in range(100):
env.render()
print(observation)
action = env.action_space.sample()
observation, reward, done, info = env.step(action)
... | flexible | {
"blob_id": "7789e54acc02fe0277ff80ce14efbcdc4ee6e7f1",
"index": 8009,
"step-1": "<mask token>\n\n\ndef explore_cartpole():\n for i_episode in range(2):\n observation = env.reset()\n for t in range(100):\n env.render()\n print(observation)\n action = env.action_s... | [
2,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
def load_img(path_to_img):
img = tf.io.read_file(path_to_img)
img = tf.io.decode_image(img, channels=3)
img = tf.image.convert_image_dtype(img, tf.float32)
img = img[tf.newaxis, :]
return img
def preprocess_image(image, target_dim):
shape = tf.cast(tf.shape(image... | flexible | {
"blob_id": "36ce0de4cb760632959392a9f982532436bd37b0",
"index": 7272,
"step-1": "<mask token>\n\n\ndef load_img(path_to_img):\n img = tf.io.read_file(path_to_img)\n img = tf.io.decode_image(img, channels=3)\n img = tf.image.convert_image_dtype(img, tf.float32)\n img = img[tf.newaxis, :]\n return ... | [
4,
5,
7,
8,
9
] |
from concurrent.futures import ThreadPoolExecutor
from concurrent.futures import ProcessPoolExecutor
import ATLAS1
import ATLAS_v2
from atlas.config import dbConfig
import pandas as pd
import ContentCategories
import NgramMapping
import SentimentAnalysis_2
import TrigDriv_2
import TopicModeling
import logging
import tr... | normal | {
"blob_id": "41698e9d8349ddf3f42aa3d4fc405c69077d1aa3",
"index": 3160,
"step-1": "from concurrent.futures import ThreadPoolExecutor\nfrom concurrent.futures import ProcessPoolExecutor\nimport ATLAS1\nimport ATLAS_v2\nfrom atlas.config import dbConfig\nimport pandas as pd\nimport ContentCategories\nimport NgramMa... | [
0
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def roles_required(roles):
def decorator(func):
@wraps(func)
def wrapper(*args, **kwargs):
print(roles, 'required')
print(args, kwargs, 'provided')
if kwargs['role']:
... | flexible | {
"blob_id": "1adaca88cf41d4e4d3a55996022278102887be07",
"index": 3707,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef roles_required(roles):\n\n def decorator(func):\n\n @wraps(func)\n def wrapper(*args, **kwargs):\n print(roles, 'required')\n print(args, kw... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def perm(n, inc):
perm_set = set(map(lambda x: int(''.join(x)), permutations(str(n))))
perms = n, n + inc, n + inc * 2
if any(map(lambda x: x not in prime_set or x not in perm_set, perms)):
return None
else:
return perms
<|reserved_special_token_0|>
<|r... | flexible | {
"blob_id": "e03290746d6520fde63836e917f6af0c76596704",
"index": 3816,
"step-1": "<mask token>\n\n\ndef perm(n, inc):\n perm_set = set(map(lambda x: int(''.join(x)), permutations(str(n))))\n perms = n, n + inc, n + inc * 2\n if any(map(lambda x: x not in prime_set or x not in perm_set, perms)):\n ... | [
1,
2,
3,
4,
5
] |
from cryptography.hazmat.primitives import serialization
from cryptography.hazmat.primitives import hashes
from cryptography.hazmat.primitives.asymmetric import padding
import cryptography.hazmat.primitives.ciphers as ciphers
import struct
import secrets
import random
from typing import List
LOCO_PUBLICKEY = serializ... | normal | {
"blob_id": "db9919ab15988828d24b4430a124841f225860cc",
"index": 5764,
"step-1": "<mask token>\n\n\nclass V2SLClient:\n <mask token>\n <mask token>\n\n def handshake(self):\n encrypted_key = LOCO_PUBLICKEY.encrypt(self._aeskey, padding.OAEP(\n padding.MGF1(hashes.SHA1()), hashes.SHA1()... | [
4,
5,
6,
8,
11
] |
import sys
sys.path.append('preprocess')
import matplotlib
matplotlib.use("TkAgg")
import matplotlib.pyplot as plt
from matplotlib.pyplot import savefig
import numpy as np
import refit_cfg
import os
import random
from sklearn.model_selection import train_test_split
name = ['WashingMachine', 'Kettle', 'Microwave', 'Fr... | normal | {
"blob_id": "30405a6f20a44b2252b6894ef6d0e818861702f8",
"index": 9857,
"step-1": "<mask token>\n\n\ndef align_process(house_id):\n data = np.load('data\\\\REFIT\\\\original_data\\\\%d.npy' % house_id)\n new_data = []\n current_index = 0\n current_time = int(data[0][0])\n end_time = int(data[-1][0]... | [
14,
15,
22,
23,
26
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
admin.site.register(Leave)
admin.site.register(EmployeeProfile)
<|reserved_special_token_1|>
from django.contrib import admin
from employees.models import Leave, EmployeeProfile
admin.site.register(Leave)
admin.site.register(Em... | flexible | {
"blob_id": "77ea670b537e9ff7082aeb9ed54b011fa8e3a035",
"index": 6328,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nadmin.site.register(Leave)\nadmin.site.register(EmployeeProfile)\n",
"step-3": "from django.contrib import admin\nfrom employees.models import Leave, EmployeeProfile\nadmin.site.registe... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class testsolution(TestCase):
def setUp(self):
self.solution = Solution()
self.inout = [([1, 2, 3, 4], [24, 12, 8, 6]), ([4, 5, 1, 8, 2], [80,
64, 320, 40, 160])]
def test_productExceptSelf(self):
for p1, p2 in self.inout:
with sel... | flexible | {
"blob_id": "9e34fcec3af746af37cb68fd8617c706cc1066f6",
"index": 1743,
"step-1": "<mask token>\n\n\nclass testsolution(TestCase):\n\n def setUp(self):\n self.solution = Solution()\n self.inout = [([1, 2, 3, 4], [24, 12, 8, 6]), ([4, 5, 1, 8, 2], [80,\n 64, 320, 40, 160])]\n\n def t... | [
3,
4,
5,
6,
8
] |
<|reserved_special_token_0|>
def plan_grasps(hnd_s, objcm, angle_between_contact_normals=math.radians(
160), openning_direction='loc_x', rotation_interval=math.radians(22.5),
max_samples=100, min_dist_between_sampled_contact_points=0.005,
contact_offset=0.002):
"""
:param objcm:
:param hnd_s:... | flexible | {
"blob_id": "738e6d4d608aa977094420a432cbd8a05ea8a1b5",
"index": 4384,
"step-1": "<mask token>\n\n\ndef plan_grasps(hnd_s, objcm, angle_between_contact_normals=math.radians(\n 160), openning_direction='loc_x', rotation_interval=math.radians(22.5),\n max_samples=100, min_dist_between_sampled_contact_points=... | [
3,
4,
5,
6,
7
] |
# Import the SDK
import json
import boto3
from botocore.exceptions import ClientError
import uuid
#dbclient = boto3.client('dynamodb')
dbresource = boto3.resource('dynamodb', region_name='eu-west-1')
rekclient = boto3.client('rekognition','eu-west-1')
collection_name = 'swiftarycelebrity'
ScannedFacestable = dbresour... | normal | {
"blob_id": "6369c692e358c0dfd1193c6e961ecf9b521ea9ba",
"index": 4649,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor Images in Faces:\n lv_FaceId = Images['FaceId']\n lv_ImageId = Images['ImageId']\n lv_ExternalImageId = Images['ExternalImageId'],\n lv_Names = ExternalImageId.split('_')\... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def publish_data_on_redis(data, channel):
redis_client.publish(channel, json.dumps(data))
<|reserved_special_token_1|>
<|reserved_special_token_0|>
redis_client = redis.StrictRedis(host='redis', port=6379, db=1, password=... | flexible | {
"blob_id": "d61024ecbd092852fc3396e6919d6d3c8aa554db",
"index": 6178,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef publish_data_on_redis(data, channel):\n redis_client.publish(channel, json.dumps(data))\n",
"step-3": "<mask token>\nredis_client = redis.StrictRedis(host='redis', port=6379,... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for _ in range(tot):
id, pw = map(str, input().split())
ID_dict[id] = pw
for _ in range(inp):
print(ID_dict[input()])
<|reserved_special_token_1|>
tot, inp = map(int, input().split())
ID_dict = {}
for _ in range(tot... | flexible | {
"blob_id": "cf7556034020d88ddb6b71b9f908c905e2f03cdb",
"index": 4076,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor _ in range(tot):\n id, pw = map(str, input().split())\n ID_dict[id] = pw\nfor _ in range(inp):\n print(ID_dict[input()])\n",
"step-3": "tot, inp = map(int, input().split())... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
@api.route('/depthinthewild/transform')
@api.expect(upload_parser)
class DepthInTheWildDepthTransform(Resource):
<|reserved_special_token_0|>
@api.route('/depthinthewild/transform_raw')
@api.expect(upload_parser)
class DepthInTheWildDepthTransformRaw(Resource):
def post(self):
... | flexible | {
"blob_id": "acf409f2e56cd16b7dc07476b49b9c18675f7775",
"index": 5540,
"step-1": "<mask token>\n\n\n@api.route('/depthinthewild/transform')\n@api.expect(upload_parser)\nclass DepthInTheWildDepthTransform(Resource):\n <mask token>\n\n\n@api.route('/depthinthewild/transform_raw')\n@api.expect(upload_parser)\ncl... | [
3,
5,
6,
7
] |
<|reserved_special_token_0|>
def process_corpus(lcount, text, language, corpus, child, utts, owus, pdict,
bdict):
owu = owus / utts
lineout1 = [language, corpus, child, utts, owu]
ordered = sorted(pdict.items(), key=lambda pair: pair[1], reverse=True)
tokencount = sum(pdict.values())
lineout1.... | flexible | {
"blob_id": "4ba0affd3cbdc2652274213a8d410b541fb3edb4",
"index": 4584,
"step-1": "<mask token>\n\n\ndef process_corpus(lcount, text, language, corpus, child, utts, owus, pdict,\n bdict):\n owu = owus / utts\n lineout1 = [language, corpus, child, utts, owu]\n ordered = sorted(pdict.items(), key=lambda... | [
1,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def random():
"""Return a random parameter set for the model."""
radius = 10 ** np.random.uniform(1.3, 4)
d_factor = 10 ** np.random.uniform(-2, -0.7)
dnn_fraction = np.random.beta(a=10, b=1)
dnn = radius * 4... | flexible | {
"blob_id": "7ccaa15f025b2c1ba560d07c1a30b06c9ebf9ad1",
"index": 1927,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef random():\n \"\"\"Return a random parameter set for the model.\"\"\"\n radius = 10 ** np.random.uniform(1.3, 4)\n d_factor = 10 ** np.random.uniform(-2, -0.7)\n dnn_fr... | [
0,
1,
2,
3,
4
] |
def favorite_book(name):
print(f"One of my favorite books is {name}...")
favorite_book("Alice in Wonderland")
| normal | {
"blob_id": "08848e51d5564bad927607be3fa3c86f2c1212c5",
"index": 9668,
"step-1": "<mask token>\n",
"step-2": "def favorite_book(name):\n print(f'One of my favorite books is {name}...')\n\n\n<mask token>\n",
"step-3": "def favorite_book(name):\n print(f'One of my favorite books is {name}...')\n\n\nfavor... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def start():
username = browser.find_element_by_name('username')
username.send_keys('Username')
password = browser.find_element_by_name('password')
password.send_keys('Password')
nextButton = browser.find_element_by_xpath("//button[@type='submit']")
nextButton.clic... | flexible | {
"blob_id": "6d18aa585c656b244d1e4272caa8419c04b20b6c",
"index": 2363,
"step-1": "<mask token>\n\n\ndef start():\n username = browser.find_element_by_name('username')\n username.send_keys('Username')\n password = browser.find_element_by_name('password')\n password.send_keys('Password')\n nextButto... | [
1,
3,
4,
5,
6
] |
import argparse
import pandas as pd
import random
import time
class Deck:
def __init__(self, num_cols, front, back):
self.flashcards = []
self.num_cols = num_cols
self.front = front
self.back = back
class Flashcard:
def __init__(self, deck, front, back, column, row):
self.deck = deck
self.front = front
... | normal | {
"blob_id": "d5903698eb8ed6be531b0cc522d4feff6b79da4e",
"index": 954,
"step-1": "<mask token>\n\n\nclass Deck:\n\n def __init__(self, num_cols, front, back):\n self.flashcards = []\n self.num_cols = num_cols\n self.front = front\n self.back = back\n\n\nclass Flashcard:\n\n def _... | [
8,
17,
18,
19,
20
] |
<|reserved_special_token_0|>
@Interface.staticderived
class Plugin(PluginBase):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
@staticmethod
@Interface.override
def Generate(open_file_func, global_custom_structs, global_custom_enums,
data, output_dir, status_stream):
re... | flexible | {
"blob_id": "d8befc4a79176aefcccd3dceddf04ca965601e5c",
"index": 2856,
"step-1": "<mask token>\n\n\n@Interface.staticderived\nclass Plugin(PluginBase):\n <mask token>\n <mask token>\n\n @staticmethod\n @Interface.override\n def Generate(open_file_func, global_custom_structs, global_custom_enums,\n... | [
8,
9,
10,
11,
15
] |
import logging
from unittest.mock import patch, Mock
from intake.tests.base_testcases import ExternalNotificationsPatchTestCase
from intake.tests import mock, factories
from intake.tests.mock_org_answers import get_answers_for_orgs
from intake.management.commands import send_followups
from user_accounts.models import O... | normal | {
"blob_id": "5cb67e5fcedafca4ce124e4094cbd8e1e9d95bb4",
"index": 3740,
"step-1": "<mask token>\n\n\nclass TestCommand(ExternalNotificationsPatchTestCase):\n <mask token>\n\n @patch('intake.management.commands.send_followups.is_the_weekend')\n @patch('intake.management.commands.send_followups.FollowupsSe... | [
2,
3,
4,
5,
6
] |
# -*- coding: utf-8 -*-
"""
Created on Sun Apr 19 12:28:39 2020
@author: Ксения
"""
import serial
import time
import serial.tools.list_ports as lp
def get_comports_list():
ports=list(lp.comports(include_links=False))
for p in ports:
print(p.device)
return ports
def read_while_LF(com, timeout... | normal | {
"blob_id": "e08fddefabf1b92aa97b939e05bb31d888df4e6a",
"index": 2241,
"step-1": "<mask token>\n\n\ndef get_comports_list():\n ports = list(lp.comports(include_links=False))\n for p in ports:\n print(p.device)\n return ports\n\n\ndef read_while_LF(com, timeout_ms=500):\n read_data = ''\n de... | [
3,
4,
5,
6,
7
] |
"""
@author Lucas
@date 2019/3/29 21:46
"""
# 二分查找
def search(nums, target):
left = 0
right = len(nums) - 1
while left <= right:
mid = int((left + right)/2)
if target > nums[mid]:
left = mid + 1
elif target < nums[mid]:
right = mid - 1
else:
... | normal | {
"blob_id": "3eeed39bf775e2ac1900142b348f20d15907c6e6",
"index": 4972,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef search(nums, target):\n left = 0\n right = len(nums) - 1\n while left <= right:\n mid = int((left + right) / 2)\n if target > nums[mid]:\n left =... | [
0,
1,
2,
3
] |
# -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'src/ui_LibraryTab.ui'
#
# Created: Tue Jun 9 21:46:41 2015
# by: PyQt5 UI code generator 5.4
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore, QtGui, QtWidgets
class Ui_Tab(object):
def setupUi(se... | normal | {
"blob_id": "ef85f94282bfd7c9491c4e28bab61aaab5c792a5",
"index": 232,
"step-1": "<mask token>\n\n\nclass Ui_Tab(object):\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass Ui_Tab(object):\n <mask token>\n\n def retranslateUi(self, Tab):\n _translate = QtCore.QCoreApplicatio... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
class Test_is_palindrome(TestCase):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Test_is_palindrome(TestCase):
def test_is_palindrome(self):
from identif... | flexible | {
"blob_id": "785b54dce76d6906df513a8bde0110ab6fd63357",
"index": 7083,
"step-1": "<mask token>\n\n\nclass Test_is_palindrome(TestCase):\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass Test_is_palindrome(TestCase):\n\n def test_is_palindrome(self):\n from i... | [
1,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
sys.path.append(os.environ['raco'])
sys.path.append(os.environ['rapl'])
sys.path.append(os.environ['rapl'] + '/timetrace')
<|reserved_special_token_0|>
kwargs_default.update(plot_timey_kwargs_default)
find_bad_keys(kwargs_default,... | flexible | {
"blob_id": "97a059d6d34b924a0512ebe6ff5ab1d5ccc072d5",
"index": 8966,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsys.path.append(os.environ['raco'])\nsys.path.append(os.environ['rapl'])\nsys.path.append(os.environ['rapl'] + '/timetrace')\n<mask token>\nkwargs_default.update(plot_timey_kwargs_default... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
async def sound(cube):
sound = bytearray()
sound.append(2)
sound.append(9)
sound.append(255)
await cube.write_gatt_char(TOIO_SOUND_UUID, sound)
async def motor(cube):
motor = bytearray()
motor.appen... | flexible | {
"blob_id": "923a433a3a04a8538b43d162d17d379daab4698a",
"index": 7753,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nasync def sound(cube):\n sound = bytearray()\n sound.append(2)\n sound.append(9)\n sound.append(255)\n await cube.write_gatt_char(TOIO_SOUND_UUID, sound)\n\n\nasync def... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for i in stringy:
if ord(i) >= 65 and ord(i) <= 90:
temp = (ord(i) + k - 65) % 26
s += chr(temp + 65)
elif ord(i) >= 97 and ord(i) <= 122:
temp = (ord(i) + k - 97) % 26
s += chr(temp + 97)
... | flexible | {
"blob_id": "acf787885834961a71fb2655b9d8a1eb026942c7",
"index": 4089,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in stringy:\n if ord(i) >= 65 and ord(i) <= 90:\n temp = (ord(i) + k - 65) % 26\n s += chr(temp + 65)\n elif ord(i) >= 97 and ord(i) <= 122:\n temp = (ord... | [
0,
1,
2,
3
] |
from threading import Thread
import time
def sleeping():
time.sleep(5)
print('Ended')
Thread(target=sleeping, daemon=True).start()
print('Hello world')
time.sleep(5.5)
| normal | {
"blob_id": "628fdf848079d0ecf5bf4f5bd46e07ad6cd10358",
"index": 5070,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef sleeping():\n time.sleep(5)\n print('Ended')\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef sleeping():\n time.sleep(5)\n print('Ended')\n\n\nThread(target=s... | [
0,
1,
2,
3
] |
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