code stringlengths 13 6.09M | order_type stringclasses 2
values | original_example dict | step_ids listlengths 1 5 |
|---|---|---|---|
<|reserved_special_token_0|>
class Station(Named):
<|reserved_special_token_0|>
def __init__(self, name, long_name, time_zone_name, latitude=None,
longitude=None, elevation=None):
super().__init__(name)
self._long_name = long_name
self._time_zone = ZoneInfo(time_zone_name)
... | flexible | {
"blob_id": "ad09880b9e06a129b9623be2a086ebcc8dc55c2c",
"index": 9079,
"step-1": "<mask token>\n\n\nclass Station(Named):\n <mask token>\n\n def __init__(self, name, long_name, time_zone_name, latitude=None,\n longitude=None, elevation=None):\n super().__init__(name)\n self._long_name ... | [
6,
7,
9,
10,
11
] |
<|reserved_special_token_0|>
def autorotate(angle):
v.set_rotation_angle([0.0, -angle, 0.0])
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def autorotate(angle):
v.set_rotation_angle([0.0, -angle, 0.0])
<|reserved_special_token_0|>
v.animate(0, 360, autorotate... | flexible | {
"blob_id": "00be3d813ce4335ff9ea02ed9f1884d3210f3d5a",
"index": 3101,
"step-1": "<mask token>\n\n\ndef autorotate(angle):\n v.set_rotation_angle([0.0, -angle, 0.0])\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef autorotate(angle):\n v.set_rotation_angle([0.0, -angle, 0.0])\n\n\n<mask token>\nv.a... | [
1,
2,
3,
4,
5
] |
# zip(),可以压缩 N 个列表成为一个zip对象（可迭代对象）。
a =['a', 'b', 'c']
b =[1, 2, 3]
[x for x in zip(a, b)] # [('a', 1), ('b', 2), ('c', 3)]
# 列表长度不等时，以短的为准
c =['x','y']
[x for x in zip(a, c)] # [('a', 'x'), ('b', 'y')]
# 例子
books =['简爱','小王子','瓦尔登湖']
prices =[56, 78, 66]
for book, price in zip(books, prices):
print("%s的价格是：%3.... | normal | {
"blob_id": "0eab23f4271f724da587707599eb0cbf2144efa1",
"index": 8178,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n[x for x in zip(a, b)]\n<mask token>\n[x for x in zip(a, c)]\n<mask token>\nfor book, price in zip(books, prices):\n print('%s的价格是：%3.1f' % (book, price))\n[y for y in reversed(b)]\nfo... | [
0,
1,
2,
3
] |
# -*- coding: utf-8 -*-
from ..general.utils import log_errors
from googleapiclient import discovery
from oauth2client.client import SignedJwtAssertionCredentials
from django.conf import settings
from celery import shared_task
from logging import getLogger
import httplib2
_logger = getLogger(__name__)
def create_ev... | normal | {
"blob_id": "36fb0d936be5c5d305c4076fd1c497664c9b770a",
"index": 8374,
"step-1": "<mask token>\n\n\ndef create_events_calendar():\n \"\"\" Create an events calendar if none already exists. This function mostly exists for\n creating calendars for dev environments, not used in prod.\n \"\"\"\n service ... | [
4,
6,
7,
8,
9
] |
ghj=input("enter your first name:")
print("Welcome to my Quiz:\nIf you go wrong once you lose but if you give all the answers correct then you win but no CHEATING.")
print("Q1:-Who is the president of India?")
winlist=("ramnath govind","multiple choice question","multiple choice questions","mumbai")
enter=input("en... | normal | {
"blob_id": "351421ef6a40e3a4bd4549a1851fbf4bed9ddf30",
"index": 5024,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(\n \"\"\"Welcome to my Quiz:\nIf you go wrong once you lose but if you give all the answers correct then you win but no CHEATING.\"\"\"\n )\nprint('Q1:-Who is the president of... | [
0,
1,
2,
3
] |
# -*- coding: utf-8 -*-
"""
Created on Sat Nov 2 08:04:11 2019
@author: yocoy
"""
import serial, time
arduino = serial.Serial('COM7', 9600)
time.sleep(4)
lectura = []
for i in range(100):
lectura.append(arduino.readline())
arduino.close()
print(lectura) | normal | {
"blob_id": "d514413c303dd174d8f56685158780a1681e1aba",
"index": 7925,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ntime.sleep(4)\n<mask token>\nfor i in range(100):\n lectura.append(arduino.readline())\narduino.close()\nprint(lectura)\n",
"step-3": "<mask token>\narduino = serial.Serial('COM7', 9... | [
0,
1,
2,
3,
4
] |
from enum import unique
from django.db import models
import secrets
import string
CARD_PACK_CHOICES = (
('1', 'Traditional Cards'),
('2', 'Special Cards'),
('3', 'Other Themed Cards')
)
MARKER_CHOICES = (
('1', 'Plastic Dots'),
('2', 'Quarters'),
('3', 'Beans')
)
def generate_game_code() -> ... | normal | {
"blob_id": "2fd33439d4403ec72f890a1d1b4f35f2b38d033b",
"index": 9268,
"step-1": "<mask token>\n\n\nclass Game(models.Model):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n\nclass Player(models.Model):\n \"\"\" Model that descr... | [
4,
7,
9,
10,
11
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def cnt():
s1 = input('enter a string :').strip()
count = 0
countu = 0
for i in s1:
if i.islower():
count += 1
elif i.isupper():
countu += 1
else:
pass
print('THE NUMBER OF UPPER ... | flexible | {
"blob_id": "6cfda09f360aaa560011b91db8316e5e3889eea1",
"index": 2017,
"step-1": "<mask token>\n",
"step-2": "def cnt():\n s1 = input('enter a string :').strip()\n count = 0\n countu = 0\n for i in s1:\n if i.islower():\n count += 1\n elif i.isupper():\n countu +... | [
0,
1,
2
] |
import unittest
import A1
import part_manager
import security
class test_A1(unittest.TestCase):
# ----------------------------------- set up the mock data for test cases -----------------------------------
def setUp(self):
self.security1 = security.Security("XXX-1234-ABCD-1234", None)
self... | normal | {
"blob_id": "2ba5cb1265090b42b9a4838b792a3e81b209ba1a",
"index": 3822,
"step-1": "<mask token>\n\n\nclass test_A1(unittest.TestCase):\n\n def setUp(self):\n self.security1 = security.Security('XXX-1234-ABCD-1234', None)\n self.security2 = security.Security(None, 'kkklas8882kk23nllfjj88290')\n ... | [
6,
7,
8,
9,
11
] |
"""Exercise 9c"""
import time
import numpy as np
import matplotlib.pyplot as plt
from plot_results import plot_2d
from run_simulation import run_simulation
from simulation_parameters import SimulationParameters
def exercise_9c(world, timestep, reset):
"""Exercise 9c"""
n_joints = 10
Rhead = 0.44
... | normal | {
"blob_id": "a0284eba1a0e6c498f240068c586e7f8b79cd86c",
"index": 5782,
"step-1": "<mask token>\n\n\ndef main():\n n_joints = 10\n parameter_set = [SimulationParameters(simulation_duration=15, drive=4.0,\n amplitudes=None, phase_lag=None, turn=None, amplitude_gradient=[\n Rhead, Rtail], backwa... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def train(token2id, train_data, lr, batch_size, epochs, model):
dataset = DataGenerator(token2id, train_data)
dataloader = DataLoader(dataset, batch_size=batch_size, collate_fn=
my_collate)
model = to_device(... | flexible | {
"blob_id": "d0364b7cad29c639af9df5c78e810144ffd6ce2e",
"index": 2415,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef train(token2id, train_data, lr, batch_size, epochs, model):\n dataset = DataGenerator(token2id, train_data)\n dataloader = DataLoader(dataset, batch_size=batch_size, collate... | [
0,
1,
2,
3,
4
] |
# coding: utf-8
# In[5]:
import os
import numpy as np
import pandas as pd
from PIL import Image
import argparse
import time
import shutil
from sklearn.metrics import accuracy_score, mean_squared_error
import torch
import torch.optim
from torch.utils.data import Dataset, DataLoader
from torch.autograd import Variab... | normal | {
"blob_id": "f3a3746c48617754aad5ae8d0d7a0b8908c34562",
"index": 7852,
"step-1": "<mask token>\n\n\nclass ProtestDataset(Dataset):\n <mask token>\n <mask token>\n\n def __len__(self):\n return len(self.label_frame)\n <mask token>\n\n\nclass ProtestDatasetEval(Dataset):\n \"\"\"\n dataset... | [
20,
21,
22,
25,
35
] |
import sys
from Decks.Virtual_World.vw_sets import *
from tools import *
hand_3playable_hts = ["Nibiru, the Primal Being", "Effect Veiler", "Fantastical Dragon Phantazmay", "Dragon Buster Destruction Sword", "Dragon Buster Destruction Sword"]
hand_2playable_hts = ["Nibiru, the Primal Being", "Nibiru, the Primal Being"... | normal | {
"blob_id": "43179b8b096836758271a791b4aacb7bbe398ea9",
"index": 1807,
"step-1": "<mask token>\n\n\ndef test_playable_hts_in_hand():\n assert playable_hts_in_hand(hand_3playable_hts) == 3\n assert playable_hts_in_hand(hand_2playable_hts) == 2\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef test_pl... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
def generate_colour_data(width, height, imagiry_data, pixel2coord):
"""Extract color data from the .tiff file """
for i in range(1, height):
for j in range(1, width):
colour_data.append([pixel2coord(j, i)[0], pixel2coord(j, i)[1],
imagiry_data.r... | flexible | {
"blob_id": "7e8b192e77e857f1907d5272d03c1138a10c61f4",
"index": 4803,
"step-1": "<mask token>\n\n\ndef generate_colour_data(width, height, imagiry_data, pixel2coord):\n \"\"\"Extract color data from the .tiff file \"\"\"\n for i in range(1, height):\n for j in range(1, width):\n colour_d... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
class TestTTT(unittest.TestCase):
def test_mcts(self):
if 0 in skip:
print('Skipping ai self-play')
return
ttt = TTT()
for i in range(1000):
mcts = MCTS(ttt)
state = mcts.root.state
while not mcts.boa... | flexible | {
"blob_id": "d0a3f332e04627eb275168972bd92cd1ea9b9447",
"index": 227,
"step-1": "<mask token>\n\n\nclass TestTTT(unittest.TestCase):\n\n def test_mcts(self):\n if 0 in skip:\n print('Skipping ai self-play')\n return\n ttt = TTT()\n for i in range(1000):\n ... | [
4,
6,
7,
8,
10
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for i in NICList:
os.system('sudo ifconfig ' + i + ' promisc')
os.system('sudo python ./src/top.py')
<|reserved_special_token_1|>
<|reserved_special_token_0|>
NICList = [i for i in netifaces.interfaces() if i != 'lo']
for i... | flexible | {
"blob_id": "b38d23a7de3c805ddde4ed2d236e3c6e7bb5e2d0",
"index": 118,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in NICList:\n os.system('sudo ifconfig ' + i + ' promisc')\nos.system('sudo python ./src/top.py')\n",
"step-3": "<mask token>\nNICList = [i for i in netifaces.interfaces() if i ... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for instance in instances['instances']:
inst_names.append(instance['name'])
inst_dict[instance['name']] = []
print(inst_names)
<|reserved_special_token_0|>
for snapshot in snapshots['instanceSnapshots']:
inst_dict[snap... | flexible | {
"blob_id": "2023e0b749338488e63cbbb475b7a915bccccce0",
"index": 7531,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor instance in instances['instances']:\n inst_names.append(instance['name'])\n inst_dict[instance['name']] = []\nprint(inst_names)\n<mask token>\nfor snapshot in snapshots['instanc... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class TestStudent(unittest.TestCase):
def setUp(self):
desired_caps = {}
desired_caps['platformName'] = 'Android'
desired_caps['platformVersion'] = '7.0'
desired_caps['automationName'] = 'UIAutomator2'
desired_caps['deviceName'] = 'PRA-AL00'
... | flexible | {
"blob_id": "8d7697a0e49dc9e966b9657171c66ccda57279d6",
"index": 1930,
"step-1": "<mask token>\n\n\nclass TestStudent(unittest.TestCase):\n\n def setUp(self):\n desired_caps = {}\n desired_caps['platformName'] = 'Android'\n desired_caps['platformVersion'] = '7.0'\n desired_caps['au... | [
5,
6,
7,
8,
9
] |
class BruteForceSolution:
<|reserved_special_token_0|>
class Solution:
def smallerNumbersThanCurrent(self, nums):
answer = []
sortedNums = sorted(nums)
for num in nums:
answer.append(sortedNums.index(num))
return answer
<|reserved_special_token_0|>
<|reserved_s... | flexible | {
"blob_id": "58e023c3c453d1e190fdb5bc457358f42d1bd93f",
"index": 397,
"step-1": "class BruteForceSolution:\n <mask token>\n\n\nclass Solution:\n\n def smallerNumbersThanCurrent(self, nums):\n answer = []\n sortedNums = sorted(nums)\n for num in nums:\n answer.append(sortedNu... | [
3,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print('loading data...')
with open('movienumbers.pickle', 'rb') as input_file:
movienumbers = pickle.load(input_file)
with open('ratings.pickle', 'rb') as input_file:
ratings = pickle.load(input_file)
with open('userrating... | flexible | {
"blob_id": "1f69cf5f6d15048e6ead37b5da836c9e2f783f74",
"index": 803,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('loading data...')\nwith open('movienumbers.pickle', 'rb') as input_file:\n movienumbers = pickle.load(input_file)\nwith open('ratings.pickle', 'rb') as input_file:\n ratings =... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
sys.path.append('../..')
<|reserved_special_token_0|>
for epoch in range(60):
batch_count = 0
for i in range(len(X)):
feature = np.mat(X.values[i]).reshape(img_shape)
label = np.mat(one_hot_label[i]).T
... | flexible | {
"blob_id": "63f155f7da958e9b6865007c701f7cf986b0cbac",
"index": 7800,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsys.path.append('../..')\n<mask token>\nfor epoch in range(60):\n batch_count = 0\n for i in range(len(X)):\n feature = np.mat(X.values[i]).reshape(img_shape)\n label ... | [
0,
1,
2,
3,
4
] |
# coding: utf-8
from mrcnn import utils
import numpy as np
import os
import skimage
class SlicesDataset(utils.Dataset):
""" Extension of maskrcnn dataset class to be used with our provided data. """
def load_slices(self, dataset_dir, n_images, n_patches, channels = ["base"]):
"""Load a ... | normal | {
"blob_id": "8675deb69eae04a722073432eaf69ce3d24a11ad",
"index": 9041,
"step-1": "<mask token>\n\n\nclass SlicesDataset(utils.Dataset):\n <mask token>\n <mask token>\n\n def load_image(self, image_id):\n \"\"\"Returns an image with a given id.\"\"\"\n info = self.image_info[image_id]\n ... | [
2,
4,
5,
6,
7
] |
import sys
from PyQt5 import QtWidgets
from PyQt5.QtWidgets import QMainWindow, QApplication
#---Import that will load the UI file---#
from PyQt5.uic import loadUi
import detechRs_rc #---THIS IMPORT WILL DISPLAY THE IMAGES STORED IN THE QRC FILE AND _rc.py FILE--#
#--CLASS CREATED THAT WILL LOAD THE UI FILE... | normal | {
"blob_id": "a9b1cc9b928b8999450b6c95656b863c476b273b",
"index": 7355,
"step-1": "<mask token>\n\n\nclass Login(QMainWindow):\n\n def __init__(self):\n super(Login, self).__init__()\n loadUi('login_UI.ui', self)\n self.loginButton.clicked.connect(self.loginFunction)\n\n def loginFuncti... | [
3,
4,
5,
6,
7
] |
import asyncio
import multiprocessing
from concurrent.futures import ProcessPoolExecutor
from apscheduler.schedulers.asyncio import AsyncIOScheduler
from datetime import datetime
import time
from apscheduler.schedulers.blocking import BlockingScheduler
from apscheduler.triggers.combining import OrTrigger
from apschedu... | normal | {
"blob_id": "f5a953d91e95d82e84e3e6d18ee89d28ba1b1515",
"index": 6022,
"step-1": "import asyncio\nimport multiprocessing\nfrom concurrent.futures import ProcessPoolExecutor\nfrom apscheduler.schedulers.asyncio import AsyncIOScheduler\nfrom datetime import datetime\nimport time\n\nfrom apscheduler.schedulers.bloc... | [
0
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
x += 3
print('x : ', x)
print('-' * 30)
<|reserved_special_token_0|>
total += 1
total
<|reserved_special_token_1|>
x = 5
x += 3
print('x : ', x)
print('-' * 30)
total = 0
total += 1
total
<|reserved_special_token_1|>
# opera... | flexible | {
"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
] |
import numpy as np
import faiss
from util import vecs_io, vecs_util
from time import time
import os
'''
提取vecs, 输出numpy文件
'''
def vecs2numpy(fname, new_file_name, file_type, file_len=None):
if file_type == 'bvecs':
vectors, dim = vecs_io.bvecs_read_mmap(fname)
elif file_type == 'ivecs':... | normal | {
"blob_id": "5f84c8654c976bca2fa33e8f9ba5e28e3249253d",
"index": 7312,
"step-1": "<mask token>\n\n\ndef vecs2numpy(fname, new_file_name, file_type, file_len=None):\n if file_type == 'bvecs':\n vectors, dim = vecs_io.bvecs_read_mmap(fname)\n elif file_type == 'ivecs':\n vectors, dim = vecs_io.... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
class StockPagination(PageNumberPagination):
page_size = 20
page_size_query_param = 'page_size'
max_page_size = 500
class StockView(APIView):
def get(self, request, *args, **kwargs):
if request.GET.get('ticker'):
qs = Stock.objects.filter(ticker=requ... | flexible | {
"blob_id": "34536e3112c8791c8f8d48bb6ffd059c1af38e2f",
"index": 8978,
"step-1": "<mask token>\n\n\nclass StockPagination(PageNumberPagination):\n page_size = 20\n page_size_query_param = 'page_size'\n max_page_size = 500\n\n\nclass StockView(APIView):\n\n def get(self, request, *args, **kwargs):\n ... | [
5,
6,
7,
8,
9
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def _camel_to_snake(s):
""" Convert CamelCase to snake_case.
"""
return '_'.join([i.lower() for i in _camel_words.split(s)[1::2]])
<|reserved_special_token_1|>
<|reserved_special_token_0|>
_camel_words = re.compil... | flexible | {
"blob_id": "6c9f9363a95ea7dc97ccb45d0922f0531c5cfec9",
"index": 6572,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef _camel_to_snake(s):\n \"\"\" Convert CamelCase to snake_case.\n \"\"\"\n return '_'.join([i.lower() for i in _camel_words.split(s)[1::2]])\n",
"step-3": "<mask token>\n... | [
0,
1,
2,
3,
4
] |
class Solution:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
class Solution:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def backtracing(self, arr, start):
if start == len(arr):
self.resu... | flexible | {
"blob_id": "632c690261b31c7ac0e1d90c814e3b9a7a0dcb29",
"index": 7663,
"step-1": "class Solution:\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "class Solution:\n <mask token>\n <mask token>\n\n def backtracing(self, arr, start):\n if start == len(arr):\n self.r... | [
1,
2,
3,
4,
5
] |
water = 400
milk = 540
coffee = 120
cups = 9
money = 550
def buying():
global water
global coffee
global cups
global milk
global money
choice_coffee = input("What do you want to buy? 1 - espresso, 2 - latte, 3 - cappuccino, back - to main menu:")
if choice_coffee == "1":
... | normal | {
"blob_id": "4e98ebd040297cb9472368478452bc484e0aaa04",
"index": 3255,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef stats_print():\n print('The coffee machine has:')\n print(str(water) + ' of water')\n print(str(milk) + ' of milk')\n print(str(coffee) + ' of coffee beans')\n prin... | [
0,
2,
6,
7,
8
] |
<|reserved_special_token_0|>
def code_pre_block(func):
"""
formats a code block according to rst format
"""
@functools.wraps(func)
def wrapper(*args, **kwargs):
block = func(*args, **kwargs)
new_block = '\n.. code-block::\n\n'
for line in block.split('\n'):
new... | flexible | {
"blob_id": "d1b2420778e788d78be2a12a27c80f5fa1b15a0f",
"index": 465,
"step-1": "<mask token>\n\n\ndef code_pre_block(func):\n \"\"\"\n formats a code block according to rst format\n \"\"\"\n\n @functools.wraps(func)\n def wrapper(*args, **kwargs):\n block = func(*args, **kwargs)\n n... | [
7,
8,
9,
10,
11
] |
from python_logging.Demo_CustomLogger import CustomLogger
CustomLogger.init_log()
# CustomLogger.info()
log_str = '%s/%s/%s\n' % ("demo1", "demo2", "demo3")
CustomLogger.info('[main]', log_str)
| normal | {
"blob_id": "ed5653455062cb3468c232cf0fa3f1d18793626a",
"index": 591,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nCustomLogger.init_log()\n<mask token>\nCustomLogger.info('[main]', log_str)\n",
"step-3": "<mask token>\nCustomLogger.init_log()\nlog_str = '%s/%s/%s\\n' % ('demo1', 'demo2', 'demo3')\nC... | [
0,
1,
2,
3,
4
] |
"""
Массив размером 2m + 1, где m — натуральное число, заполнен случайным образом. Найдите в массиве медиану.
Медианой называется элемент ряда, делящий его на две равные части:
в одной находятся элементы, которые не меньше медианы, в другой — не больше медианы.
Примечание: задачу можно решить без сортировки исходного м... | normal | {
"blob_id": "fbcbad9f64c0f9b68e29afde01f3a4fdba012e10",
"index": 4868,
"step-1": "<mask token>\n\n\ndef heapify(array, size, ind):\n largest = ind\n left = 2 * ind + 1\n right = 2 * ind + 2\n if left < size and array[left] > array[largest]:\n largest = left\n if right < size and array[right... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
def china_lunar():
today = str(date.today())
today_list = today.split('-')
lunar_day = lunar.getDayBySolar(int(datetime.datetime.now().year), int(
datetime.datetime.now().month), int(datetime.datetime.now().day))
if lunar_day.Lleap:
china_day = '农历:{0}月{1}'... | flexible | {
"blob_id": "e1d0648825695584d3ea518db961a9178ea0c66a",
"index": 50,
"step-1": "<mask token>\n\n\ndef china_lunar():\n today = str(date.today())\n today_list = today.split('-')\n lunar_day = lunar.getDayBySolar(int(datetime.datetime.now().year), int(\n datetime.datetime.now().month), int(datetime... | [
4,
6,
7,
8,
9
] |
<|reserved_special_token_0|>
def gridsearchcv(X, y):
accuracy = []
stdlist = []
classifier = RandomForestClassifier(verbose=2, n_jobs=1, oob_score=1)
param_grid = {'n_estimators': np.arange(1, 100, 10)}
grid = GridSearchCV(classifier, param_grid=param_grid)
grid.fit(X, y)
fig = plt.figure(... | flexible | {
"blob_id": "08f0b261b5a9b0f5133c468b3f92dc00285eda6a",
"index": 4477,
"step-1": "<mask token>\n\n\ndef gridsearchcv(X, y):\n accuracy = []\n stdlist = []\n classifier = RandomForestClassifier(verbose=2, n_jobs=1, oob_score=1)\n param_grid = {'n_estimators': np.arange(1, 100, 10)}\n grid = GridSea... | [
1,
2,
3,
4,
5
] |
import numpy as np
import math
import activations
class FC_layer():
def __init__(self, input_size, output_size, weight_init_range, activation, debug):
self.type = "FC"
self.activation_name = activation
self.shape = (input_size, output_size)
self.activation = activations.get_activati... | normal | {
"blob_id": "ff99b5fd168d7987e488d7f6d0455619e988f15a",
"index": 3574,
"step-1": "<mask token>\n\n\nclass conv2D:\n <mask token>\n <mask token>\n\n def forward(self, input_feature_maps):\n output = np.zeros(self.output_shape)\n input_feature_maps = self.apply_zero_padding(input_feature_map... | [
24,
25,
28,
33,
39
] |
# Python 3.6. Written by Alex Clarke
# Breakup a large fits image into smaller ones, with overlap, and save to disk.
# Sourecfinding is run on each cutout, and catalogues are sifted to remove duplicates from the overlap.
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import multiprocessing... | normal | {
"blob_id": "a22aa66bd65033750f23f47481ee84449fa80dbc",
"index": 8995,
"step-1": "# Python 3.6. Written by Alex Clarke\n# Breakup a large fits image into smaller ones, with overlap, and save to disk.\n# Sourecfinding is run on each cutout, and catalogues are sifted to remove duplicates from the overlap.\n\nimpor... | [
0
] |
import matplotlib.pyplot as plt
plt.plot([1, 2, 3, 4, 5], [1, 2, 3, 4, 5],
'go-', label='line 1', linewidth=2)
plt.plot([1, 2, 3, 4, 5], [1, 4, 9, 16, 25],
'rs--', label='line 2', linewidth=4)
plt.axis([0, 6, 0, 26])
plt.legend(loc="upper right")
plt.show()
| normal | {
"blob_id": "7eeba06e78bd1e7139b1706574c4d040465d4566",
"index": 4178,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nplt.plot([1, 2, 3, 4, 5], [1, 2, 3, 4, 5], 'go-', label='line 1', linewidth=2)\nplt.plot([1, 2, 3, 4, 5], [1, 4, 9, 16, 25], 'rs--', label='line 2',\n linewidth=4)\nplt.axis([0, 6, 0, ... | [
0,
1,
2,
3
] |
import sys
def digit_sum(x):
sum = 0
while x != 0:
sum = sum + x % 10
x = x // 10
return sum
for i in sys.stdin:
test_num = int(i)
if test_num == 0:
break
count = 11
while digit_sum(test_num) != digit_sum(count * test_num):
count = count + 1
print('{}'... | normal | {
"blob_id": "0d37b6f0ea8854f9d4d4cd2ff235fa39bab7cc12",
"index": 6549,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef digit_sum(x):\n sum = 0\n while x != 0:\n sum = sum + x % 10\n x = x // 10\n return sum\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef digit_sum(x... | [
0,
1,
2,
3
] |
import sqlite3
connection = sqlite3.connect('database.db')
cursor = connection.cursor()
# cursor.execute('CREATE TABLE users (id int, username text, password text)')
cursor.execute('INSERT INTO users VALUES(?,?,?)',(1,'ilia','qwerty'))
users = [(2,'nika','asdf'),(3,'nino','sdfg')]
cursor.executemany('INSERT INTO ... | normal | {
"blob_id": "d6b49533573dfefba6286ac2bffc2bd7a4075063",
"index": 1731,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ncursor.execute('INSERT INTO users VALUES(?,?,?)', (1, 'ilia', 'qwerty'))\n<mask token>\ncursor.executemany('INSERT INTO users VALUES(?,?,?)', users)\nfor row in cursor.execute('SELECT * F... | [
0,
1,
2,
3,
4
] |
import queue
from enum import IntEnum
from time import sleep
import keyboard
# I know, I copy pasted this horrobly written class
# again...
# and again.. I should really write a proper intcode computer
class IntCodeComputer:
def __init__(self, code):
self.defaultCode = code
self.runningCode = self... | normal | {
"blob_id": "6eac04bc10ef712ab4e2cde4730950ddcbe42585",
"index": 8983,
"step-1": "<mask token>\n\n\nclass IntCodeComputer:\n\n def __init__(self, code):\n self.defaultCode = code\n self.runningCode = self.defaultCode.copy()\n self.instructionPointer = 0\n self.outputQueue = queue.Q... | [
5,
8,
9,
10,
12
] |
class Handlers():
change_store = "/change_store"
change_status = "/change_status"
mail = "/mail"
get_status = "/get_status"
create_order = "/create_order"
ask_store = "/ask_store"
check = "/check"
test = "/test"
| normal | {
"blob_id": "32e3eed2e279706bca2925d3d9d897a928243b4c",
"index": 4518,
"step-1": "<mask token>\n",
"step-2": "class Handlers:\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n",
"step-3": "class Handlers:\n cha... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
@pytest.mark.parametrize('n, result', ASSERTIONS)
def test_flatten_me(n, result):
"""Test flatten_me() for proper output in test cases."""
from flatten_me import flatten_me
assert flatten_me(n) == result
<|reserved... | flexible | {
"blob_id": "c233ce4e14e9a59a9fb0f29589ced947efeb73a9",
"index": 3120,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@pytest.mark.parametrize('n, result', ASSERTIONS)\ndef test_flatten_me(n, result):\n \"\"\"Test flatten_me() for proper output in test cases.\"\"\"\n from flatten_me import flat... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def rotate_pdf_pages(filename, rotation, output_name):
pdf_reader = PdfFileReader('{}.pdf'.format(filename))
pdf_writer = PdfFileWriter()
for page in range(pdf_reader.getNumPages()):
if rotation == '1':
... | flexible | {
"blob_id": "624027373f53f62ededc40bfc859f28b5a83ca04",
"index": 3266,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef rotate_pdf_pages(filename, rotation, output_name):\n pdf_reader = PdfFileReader('{}.pdf'.format(filename))\n pdf_writer = PdfFileWriter()\n for page in range(pdf_reader.g... | [
0,
1,
2,
3,
4
] |
from django.conf import settings
from django.db import migrations, models
import django_otp.plugins.otp_totp.models
class Migration(migrations.Migration):
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
migrations.CreateModel(
nam... | normal | {
"blob_id": "2e448176a755828e5c7c90e4224102a285098460",
"index": 4852,
"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 = [migrations.sw... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print(wikipedia.summary(input_))
<|reserved_special_token_1|>
<|reserved_special_token_0|>
input_ = input('Type in your question ')
print(wikipedia.summary(input_))
<|reserved_special_token_1|>
import wikipedia
input_ = inpu... | flexible | {
"blob_id": "5eb5388ffe7a7c880d8fcfaa137c2c9a133a0636",
"index": 713,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(wikipedia.summary(input_))\n",
"step-3": "<mask token>\ninput_ = input('Type in your question ')\nprint(wikipedia.summary(input_))\n",
"step-4": "import wikipedia\ninput_ = input... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
while True:
n = input('Right or left? ')
if n == 'right':
right(60)
forward(100)
elif n == 'left':
left(60)
forward(100)
<|reserved_special_token_1|>
from turtle import *
while True:
... | flexible | {
"blob_id": "6f698196e9391d73bd99cda0a098a5bf7a3832ff",
"index": 963,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile True:\n n = input('Right or left? ')\n if n == 'right':\n right(60)\n forward(100)\n elif n == 'left':\n left(60)\n forward(100)\n",
"step-3": ... | [
0,
1,
2,
3
] |
# Fuck you Disyer. Stealing my fucking paypal. GET FUCKED: toontown.shtiker.CogPageGlobals
COG_QUOTAS = ((30, 25, 20, 15, 10, 5, 2, 1), (45, 40, 35, 30, 25, 20, 15, 10))
COG_UNSEEN = 1
COG_BATTLED = 2
COG_DEFEATED = 3
COG_COMPLETE1 = 4
COG_COMPLETE2 = 5 | normal | {
"blob_id": "fdb680f12dfb4b29f25cfe4f7af80469dc4294cf",
"index": 2437,
"step-1": "<mask token>\n",
"step-2": "COG_QUOTAS = (30, 25, 20, 15, 10, 5, 2, 1), (45, 40, 35, 30, 25, 20, 15, 10)\nCOG_UNSEEN = 1\nCOG_BATTLED = 2\nCOG_DEFEATED = 3\nCOG_COMPLETE1 = 4\nCOG_COMPLETE2 = 5\n",
"step-3": "# Fuck you Disyer.... | [
0,
1,
2
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class First(BaseGame):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
de... | flexible | {
"blob_id": "81fa3129d971fe8296a89a7b772d61ff50a8b9f7",
"index": 9284,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass First(BaseGame):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def has_won(self, draws):\n return dra... | [
0,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
class Piece(Source, PageNumbersMixin):
"""A piece (e.g., essay)."""
type = models.CharField(verbose_name=_('piece type'), max_length=
TYPE_MAX_LENGTH, choices=PIECE_TYPES, default=PIECE_TYPES[0][0])
def __html__(self) ->str:
"""Return the piece's citation HTML... | flexible | {
"blob_id": "30c24b9a4738c1952fc5d36a4bc36d8d3576ed3b",
"index": 7201,
"step-1": "<mask token>\n\n\nclass Piece(Source, PageNumbersMixin):\n \"\"\"A piece (e.g., essay).\"\"\"\n type = models.CharField(verbose_name=_('piece type'), max_length=\n TYPE_MAX_LENGTH, choices=PIECE_TYPES, default=PIECE_TY... | [
4,
5,
6,
7,
8
] |
import os
import random
import cv2
import numpy as np
from keras.preprocessing.image import img_to_array
import numpy as np
import keras
from scipy import ndimage, misc
def preprocess_image(img):
img = img.astype(np.uint8)
(channel_b, channel_g, channel_r) = cv2.split(img)
result = ndimage.maximum_filter(... | normal | {
"blob_id": "586d39556d2922a288a2bef3bcffbc6f9e3dc39d",
"index": 6707,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef preprocess_image(img):\n img = img.astype(np.uint8)\n channel_b, channel_g, channel_r = cv2.split(img)\n result = ndimage.maximum_filter(channel_g, size=5)\n ret, resu... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
warnings.filterwarnings('ignore',
'Your application has authenticated using end user credentials')
<|reserved_special_token_0|>
for exam in exams:
print('checking', exam)
exam_json = json.dumps(get_exam(exam=exam))
... | flexible | {
"blob_id": "b74c759b51fb6591477757e2ff54b545f225991c",
"index": 7470,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwarnings.filterwarnings('ignore',\n 'Your application has authenticated using end user credentials')\n<mask token>\nfor exam in exams:\n print('checking', exam)\n exam_json = jso... | [
0,
1,
2,
3,
4
] |
import cv2
import numpy as np
import time
from dronekit import connect, VehicleMode
connection_string = "/dev/ttyACM0"
baud_rate = 115200
print(">>>> Connecting with the UAV <<<<")
vehicle = connect(connection_string, baud=baud_rate, wait_ready=True)
vehicle.wait_ready('autopilot_version')
print('ready')
cap = cv2.V... | normal | {
"blob_id": "8c11463e35fb32949abbb163a89f874040a33ad0",
"index": 5415,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('>>>> Connecting with the UAV <<<<')\n<mask token>\nvehicle.wait_ready('autopilot_version')\nprint('ready')\n<mask token>\nif cap.isOpened() == False:\n print('Unable to read cam... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def build_statements_features(df, vectorizer, train=True, tokenizer=
tokenizer_nltk):
filtered_statements_dic = {}
for index, row in df.iterrows():
filtered_statement = []
tokenized_statement = tokenizer(row['statement'].lower().decode(
'utf-8'))
... | flexible | {
"blob_id": "0356b408624988100c10b20facecef14f1552203",
"index": 4537,
"step-1": "<mask token>\n\n\ndef build_statements_features(df, vectorizer, train=True, tokenizer=\n tokenizer_nltk):\n filtered_statements_dic = {}\n for index, row in df.iterrows():\n filtered_statement = []\n tokenize... | [
3,
5,
7,
9,
10
] |
<|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": "d0dfea27128ca6966c85da6529ead5c95c86c4cf",
"index": 1183,
"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 = [('blog', '003... | [
0,
1,
2,
3,
4
] |
#!/usr/bin/env python3
import pandas
from matplotlib import pyplot as plt
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import AdaBoostRegressor
import numpy as np
from sklearn.metrics import mean_absolute_error, mean_squared_error
from math import sqrt
def main():
df = pandas.read_csv("201... | normal | {
"blob_id": "e35dbcdef8779ffabc34b5e5c543e35b29523971",
"index": 7989,
"step-1": "<mask token>\n\n\ndef make_scatter(df):\n plt.figure(figsize=(8, 6))\n plt.plot(df['Start station number'], df['Counts'], 'o')\n plt.xlabel('Station')\n plt.ylabel('Counts')\n plt.show()\n return\n\n\ndef train_pr... | [
3,
4,
5,
6,
7
] |
class User():
def __init__(self, first, last, gender, age):
self.first_name = first
self.last_name = last
self.gender = gender
self.age = age
self.full_name = self.first_name + " " + self.last_name
def describe_user(self):
print("The name of the user is " + self.... | normal | {
"blob_id": "93b712c60ba4bfa81d967ec59035b6fb7793ce87",
"index": 1974,
"step-1": "class User:\n <mask token>\n <mask token>\n\n def greet_user(self):\n if self.gender.lower() == 'male':\n print('Greetings, Mr. ' + self.last_name.title() + '!')\n elif self.gender.lower() == 'fema... | [
2,
3,
4,
6,
7
] |
import torch
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
# add DenseNet structure
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
# self.x = x
self.block0 = nn.Sequential(
# input image 96x96
nn.ReLU(),
... | normal | {
"blob_id": "49cdeb59e75ed93122b3a62fbdc508b7d66166d6",
"index": 2337,
"step-1": "<mask token>\n\n\nclass Net(nn.Module):\n <mask token>\n <mask token>\n\n def _initialize_weights(self):\n pass\n",
"step-2": "<mask token>\n\n\nclass Net(nn.Module):\n\n def __init__(self):\n super(Net,... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
if len(sys.argv) < 2:
raise NameError(
'Please add subject number (ex:1) as 1st argument in the command line!'
)
elif len(sys.argv) < 3:
raise NameError(
'Please select server being used (ex: aeneas... | flexible | {
"blob_id": "d9156e240d49e0a6570a5bc2315f95a7a670fd4f",
"index": 6327,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif len(sys.argv) < 2:\n raise NameError(\n 'Please add subject number (ex:1) as 1st argument in the command line!'\n )\nelif len(sys.argv) < 3:\n raise NameError(\n ... | [
0,
1,
2,
3,
4
] |
'''
selection review
very similar to quicksort in terms of set up.
no need to sort to find kth element in a list
but instead can be done in o(n)
quick sort can be o(nlogn) if we choose median
instead of pivot
tips:
raise value error for bad index not in between 0 <= k < n
basecase of n <=1 --> return arr[0]
use L, E, ... | normal | {
"blob_id": "69d3a39dc024929eaf6fb77e38a7a818d2886cf7",
"index": 8512,
"step-1": "<mask token>\n\n\ndef select(arr, k):\n n = len(arr)\n if not 0 <= k < n:\n raise ValueError('not valid index in array')\n if n <= 1:\n return arr[0]\n pivot = random.choice(arr)\n L, E, G = [], [], []\... | [
1,
2,
3,
4,
5
] |
"""Command generator for running a script against a BigQuery cluster.
Contains the method to compile the BigQuery specific script execution command
based on generic arguments (sql script, output destination) and BigQuery
specific arguments (flag values).
"""
__author__ = 'p3rf@google.com'
from absl import flags
fla... | normal | {
"blob_id": "5e14eeaa3c79bfdd564f3bfd1575c9bbf1a3773d",
"index": 7881,
"step-1": "<mask token>\n\n\ndef generate_provider_specific_cmd_list(script, driver, output, error):\n \"\"\"Method to compile the BigQuery specific script execution command.\n\n Arguments:\n script: SQL script which contains the query... | [
1,
2,
3,
4,
5
] |
import io
import os
import sys
import whwn
from setuptools import setup, find_packages
from setuptools.command.test import test as TestCommand
here = os.path.abspath(os.path.dirname(__file__))
with open('README.md') as readme:
long_description = readme.read()
with open('requirements.txt') as reqs:
install_re... | normal | {
"blob_id": "bd2a5c2dd3eef5979c87a488fb584dce740ccb05",
"index": 3870,
"step-1": "<mask token>\n\n\nclass PyTest(TestCommand):\n\n def finalize_options(self):\n TestCommand.finalize_options(self)\n self.test_args = []\n self.test_suite = True\n\n def run_tests(self):\n import py... | [
3,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def solution(clothes):
answer = 1
hash_map = defaultdict(lambda : 0)
for value, key in clothes:
hash_map[key] += 1
for v in hash_map.values():
answer *= v + 1
return answer - 1
<|reserved_sp... | flexible | {
"blob_id": "601089c2555e6fc75803087ee1d8af7f8180f651",
"index": 4199,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef solution(clothes):\n answer = 1\n hash_map = defaultdict(lambda : 0)\n for value, key in clothes:\n hash_map[key] += 1\n for v in hash_map.values():\n an... | [
0,
1,
2
] |
<|reserved_special_token_0|>
@app.route('/')
def index():
return "<h1>Congratulations, it's a web app!</h1>"
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
@app.route('/')
def index():
return "<h1>Congratulations, it's a web app!</h1>"
if __name__ == '__main__'... | flexible | {
"blob_id": "612535d95e655f2e2d2c58f41b2aa99afa7fbcbc",
"index": 874,
"step-1": "<mask token>\n\n\n@app.route('/')\ndef index():\n return \"<h1>Congratulations, it's a web app!</h1>\"\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\n@app.route('/')\ndef index():\n return \"<h1>Congratulations, it's a w... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
class Session:
<|reserved_special_token_0|>
class APIStatisticsCollection:
API_ACTION = 'x-stats-api-action'
DICT_PARAMS = 'x-stats-param-dict'
DICT_RESPONSE = 'x-stats-resp-dict'
SUCCESS = 'x-stats-success'
COLLECT = 'x-stats-collect'
c... | flexible | {
"blob_id": "d0e5a3a6db0e27ecf157294850a48a19750a5ac2",
"index": 1667,
"step-1": "<mask token>\n\n\nclass Session:\n <mask token>\n\n\n class APIStatisticsCollection:\n API_ACTION = 'x-stats-api-action'\n DICT_PARAMS = 'x-stats-param-dict'\n DICT_RESPONSE = 'x-stats-resp-dict'\n ... | [
3,
4,
5,
6,
7
] |
<|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_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Migration(migrations.... | flexible | {
"blob_id": "e72962b644fab148741eb1c528d48ada45a43e51",
"index": 3978,
"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
] |
<|reserved_special_token_0|>
class TestEosLacpInterfacesModule(TestEosModule):
<|reserved_special_token_0|>
def setUp(self):
super(TestEosLacpInterfacesModule, self).setUp()
self.mock_get_config = patch(
'ansible_collections.ansible.netcommon.plugins.module_utils.network.common.ne... | flexible | {
"blob_id": "6efe3975f4d5d9f431391b3560c37a3e89e27f3d",
"index": 9172,
"step-1": "<mask token>\n\n\nclass TestEosLacpInterfacesModule(TestEosModule):\n <mask token>\n\n def setUp(self):\n super(TestEosLacpInterfacesModule, self).setUp()\n self.mock_get_config = patch(\n 'ansible_co... | [
4,
8,
11,
15,
16
] |
#!/usr/bin/env python
import sys
import struct
import Queue
import logging
import redis
logging.getLogger("scapy.runtime").setLevel(logging.ERROR)
from threading import Thread
from scapy.all import sniff, sendp, hexdump, get_if_list, get_if_hwaddr
from scapy.all import Packet, IPOption
from scapy.all import PacketList... | normal | {
"blob_id": "e4ecc1746e907f11936683384e1edb34dd637de7",
"index": 8171,
"step-1": "#!/usr/bin/env python\nimport sys\nimport struct\nimport Queue\nimport logging\nimport redis\nlogging.getLogger(\"scapy.runtime\").setLevel(logging.ERROR)\n\nfrom threading import Thread\nfrom scapy.all import sniff, sendp, hexdump... | [
0
] |
# Copyright (c) "Neo4j"
# Neo4j Sweden AB [https://neo4j.com]
#
# This file is part of Neo4j.
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2... | normal | {
"blob_id": "5b33615e1890631bac68801310e4b606ac41cb13",
"index": 1340,
"step-1": "<mask token>\n\n\nclass TestTimeDehydration(_TestTemporalDehydrationV1):\n\n @pytest.fixture\n def hydration_handler(self):\n return HydrationHandler()\n <mask token>\n <mask token>\n\n def test_pandas_date_ti... | [
6,
7,
8,
11,
13
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class TextPageContentModelTest(TestCase):
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class TextPageContentModelTest(TestCase):
def test_instance(self):
file = Ima... | flexible | {
"blob_id": "5287bd1847848aa527df8ce57e896bc30c70b43c",
"index": 4432,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass TextPageContentModelTest(TestCase):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass TextPageContentModelTest(TestCase):\n\n def test_instance(self):\n file ... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def test():
webbrowser.open_new_tab('Test.html')
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
ventana.geometry('1920x1080')
def test():
webbrowser.open_new_tab('Test.html')
<|reserved_special_token_0|>
boton1.grid(row=3, column=0)... | flexible | {
"blob_id": "8bf330dc7bee65ac9478722233477ebe5d0286c2",
"index": 1102,
"step-1": "<mask token>\n\n\ndef test():\n webbrowser.open_new_tab('Test.html')\n\n\n<mask token>\n",
"step-2": "<mask token>\nventana.geometry('1920x1080')\n\n\ndef test():\n webbrowser.open_new_tab('Test.html')\n\n\n<mask token>\nbo... | [
1,
2,
3,
4,
5
] |
"""Utilities for AnalysisModules."""
import inspect
from mongoengine import QuerySet
from numpy import percentile
from .modules import AnalysisModule
def get_primary_module(package):
"""Extract AnalysisModule primary module from package."""
def test_submodule(submodule):
"""Test a submodule to see ... | normal | {
"blob_id": "3472dc0c9d00c10ab0690c052e70fbf6a4bdb13d",
"index": 7889,
"step-1": "<mask token>\n\n\ndef boxplot(values):\n \"\"\"Calculate percentiles needed for a boxplot.\"\"\"\n percentiles = percentile(values, [0, 25, 50, 75, 100])\n result = {'min_val': percentiles[0], 'q1_val': percentiles[1],\n ... | [
4,
6,
7,
8,
9
] |
<|reserved_special_token_0|>
def main():
subs = subdir_maker(os.path.dirname(os.path.realpath(__file__)))
for i in range(len(subs)):
daily_github_upload(subs[i])
print('_' * 40 + '\n\n' + 'Uploaded {0} to Github. '.format(i) +
'\n' + '_' * 40)
time.sleep(86400)
<|reserved... | flexible | {
"blob_id": "bcc3d4e9be0de575c97bb3bf11eeb379ab5be458",
"index": 5380,
"step-1": "<mask token>\n\n\ndef main():\n subs = subdir_maker(os.path.dirname(os.path.realpath(__file__)))\n for i in range(len(subs)):\n daily_github_upload(subs[i])\n print('_' * 40 + '\\n\\n' + 'Uploaded {0} to Github.... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
def partial_correlation_loop(solver, x, y, ensemble=None):
e_hat = np.zeros(y.shape[1])
for i in range(y.shape[1]):
y_i = y[:, i].reshape(-1, 1)
y_not_i = np.delete(y, i, axis=1)
r = partial_correlation_bagging(solver, x, y_i, y_not_i, ensemble)
e_h... | flexible | {
"blob_id": "dfd2b515e08f285345c750bf00f6a55f43d60039",
"index": 8379,
"step-1": "<mask token>\n\n\ndef partial_correlation_loop(solver, x, y, ensemble=None):\n e_hat = np.zeros(y.shape[1])\n for i in range(y.shape[1]):\n y_i = y[:, i].reshape(-1, 1)\n y_not_i = np.delete(y, i, axis=1)\n ... | [
4,
5,
6,
7,
9
] |
<|reserved_special_token_0|>
def workingDate(start, end):
cal = UnitedKingdom()
res = []
delta = end - start
for i in range(delta.days + 1):
day = start + timedelta(days=i)
if cal.is_working_day(day) or day.weekday() < 5:
res.append(day)
else:
pass
r... | flexible | {
"blob_id": "feed412278d9e711e49ef209ece0876c1de4a873",
"index": 886,
"step-1": "<mask token>\n\n\ndef workingDate(start, end):\n cal = UnitedKingdom()\n res = []\n delta = end - start\n for i in range(delta.days + 1):\n day = start + timedelta(days=i)\n if cal.is_working_day(day) or da... | [
1,
2,
3,
4,
5
] |
# -*- coding: utf-8 -*-
# Copyright (c) 2018-2020 Christiaan Frans Rademan <chris@fwiw.co.za>.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the ... | normal | {
"blob_id": "cdbf9427d48f0a5c53b6efe0de7dfea65a8afd83",
"index": 87,
"step-1": "<mask token>\n\n\ndef request_id():\n global req_c, pid\n if req_c is None:\n req_c = random.randint(1000 * 1000, 1000 * 1000 * 1000)\n if pid is None:\n pid = str(os.getpid())\n req_id = req_c = req_c + 1\n... | [
1,
2,
3,
4,
5
] |
#!/usr/bin/env python3
#
# compare-sorts.py
# Copyright (c) 2017 Dylan Brown. All rights reserved.
#
# Use Python 3. Run from within the scripts/ directory.
import os
import sys
import re
import subprocess
# Ensure we don't silently fail by running Python 2.
assert sys.version_info[0] >= 3, "This script requires P... | normal | {
"blob_id": "501d50fa933f55c178b4b2eba6cfc5b85592beaa",
"index": 8473,
"step-1": "<mask token>\n\n\ndef main():\n sorts = ['selection-sort', 'insertion-sort', 'shell-sort']\n for sort in sorts:\n exe_path = './build/{}'.format(sort.rstrip())\n if not os.path.isfile(exe_path):\n rai... | [
1,
2,
3,
4,
5
] |
import numpy as np
from math import ceil, log2
def avg(list):
return np.mean(list)
def dispersion(list):
res = 0
for i in list:
res += (i - np.mean(list)) ** 2
return res / len(list)
def variation_coefficient(list):
return (dispersion(list) ** (1/2) / np.mean(list)) * 100
def chi_squ... | normal | {
"blob_id": "f2b978b9a4c00469cdd2f5e1e9275df73c7379b8",
"index": 3904,
"step-1": "<mask token>\n\n\ndef dispersion(list):\n res = 0\n for i in list:\n res += (i - np.mean(list)) ** 2\n return res / len(list)\n\n\n<mask token>\n\n\ndef chi_square(list):\n b = sorted(list)\n k = ceil(log2(len... | [
2,
3,
4,
5,
6
] |
# Generated by Django 3.1.6 on 2021-04-03 20:16
import django.contrib.postgres.fields
from django.db import migrations, models
import enrolments.validators
class Migration(migrations.Migration):
dependencies = [
("enrolments", "0007_merge_20210320_1853"),
]
operations = [
migrations.Add... | normal | {
"blob_id": "dbea2b1555368460b7d14369d2dfe4f0a01f9e4f",
"index": 8423,
"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 = [('enrolments'... | [
0,
1,
2,
3,
4
] |
g#https://www.acmicpc.net/problem/9461
'''
1. Divide 2 case △ and ▽
d[0] is △ sequence
d[1] is ▽ sequence
2. find a role between d[0] and d[1]
'''
import math
t = int(input())
n = []
for _ in range(t):
n.append(int(input()))
index = math.ceil(max(n)/2)
d = [[0 for _ in range(52)] for _ in range(2)]
d[0][1],d[0][2],... | normal | {
"blob_id": "524b6ebd0be4c2285fac540627bb48baca71452e",
"index": 2989,
"step-1": "<mask token>\n",
"step-2": "g\n<mask token>\nfor _ in range(t):\n n.append(int(input()))\n<mask token>\nfor i in range(3, index + 1):\n d[0][i] = d[1][i - 1] + d[1][i - 3]\n d[1][i] = d[0][i] + d[0][i - 2]\nfor k in n:\n... | [
0,
1,
2,
3,
4
] |
# name: Ali
# date: 7/12/2016
# description: uses openweathermap.org's api to get weather data about
# the city that is inputted
# unbreakable? = idk
import json
import urllib2
from collections import OrderedDict
from pprint import pprint
api_key = "&APPID=507e30d896f751513350c41899382d89"
city_name_url = "http://api.... | normal | {
"blob_id": "94540561ba29d2fc1766dac7b199e0cbbbeecdfc",
"index": 8046,
"step-1": "# name: Ali\n# date: 7/12/2016\n# description: uses openweathermap.org's api to get weather data about\n# the city that is inputted\n\n# unbreakable? = idk\nimport json\nimport urllib2\nfrom collections import OrderedDict\nfrom ppr... | [
0
] |
#from __future__ import absolute_import
#import os
from celery import Celery
#from django.conf import settings
#os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'learning.settings')
app = Celery('tasks', broker="redis://localhost")
#app.config_from_object('django.conf:settings')
#app.autodiscover_tasks(lambda: setti... | normal | {
"blob_id": "3ef114dd35ef3995ae73bf85bbe38db4fb7045d8",
"index": 7315,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@app.task\ndef add(x, y):\n return x + y\n",
"step-3": "<mask token>\napp = Celery('tasks', broker='redis://localhost')\n\n\n@app.task\ndef add(x, y):\n return x + y\n",
"st... | [
0,
1,
2,
3,
4
] |
import cgi
from google.appengine.api import users
from google.appengine.ext import webapp
from google.appengine.ext.webapp.util import run_wsgi_app
from google.appengine.ext import db
from models.nutrient import *
class SoilRecord(db.Model):
year=db.DateProperty(auto_now_add=True)
stats=NutrientProfile()
amendmen... | normal | {
"blob_id": "01a6283d2331590082cdf1d409ecdb6f93459882",
"index": 4861,
"step-1": "<mask token>\n\n\nclass CropRecord(db.Model):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass CropRecord(db.Model):\n year = db... | [
1,
5,
9,
10,
11
] |
#https://www.hackerrank.com/challenges/caesar-cipher-1/problem
n=int(input())
stringy=input()
k=int(input())
s=""
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)
else... | normal | {
"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
] |
#!/usr/bin/env python
import json
import requests
from requests.auth import HTTPBasicAuth
if __name__ == "__main__":
auth = HTTPBasicAuth('cisco', 'cisco')
headers = {
'Accept': 'application/json',
'Content-Type': 'application/json'
}
url = "https://asav/api/interfaces/physical/Gigab... | normal | {
"blob_id": "6801d68ebcc6ff52d9be92efeeb8727997a14bbd",
"index": 523,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n auth = HTTPBasicAuth('cisco', 'cisco')\n headers = {'Accept': 'application/json', 'Content-Type': 'application/json'\n }\n url = 'https://asav/... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
TABLE_NAME = 'active_module'
| flexible | {
"blob_id": "ff3962d875da8e3f9e6c3178b1a8191ebb8a7b60",
"index": 3639,
"step-1": "<mask token>\n",
"step-2": "TABLE_NAME = 'active_module'\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
} | [
0,
1
] |
import json
import argparse
import sys
import os
if __name__ == '__main__':
ap = argparse.ArgumentParser()
ap.add_argument("-sd","--startdate", help="Date to start scheduling trials, format is MM/DD.", required=True)
ap.add_argument("-r", "--round",help="A number.", required=True)
ap.add_argument("-hs... | normal | {
"blob_id": "e4767d8a4991a1180cc185c4c2d77104d63f9c7a",
"index": 6858,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n ap = argparse.ArgumentParser()\n ap.add_argument('-sd', '--startdate', help=\n 'Date to start scheduling trials, format is MM/DD.', required=True... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Link(models.Model):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Link(models.Model):
<|reserved... | flexible | {
"blob_id": "61a58b934c6663e87824e4f9f9ffd92c3236947c",
"index": 7930,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Link(models.Model):\n <mask token>\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Link(models.Model):\n <mask token>\n <mask token>\n\n ... | [
0,
1,
2,
3,
4
] |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from setuptools import setup, find_packages
config = {
'name': 'beziers',
'author': 'Simon Cozens',
'author_email': 'simon@simon-cozens.org',
'url': 'https://github.com/simoncozens/beziers.py',
'description': 'Bezier curve manipulation library',
'lo... | normal | {
"blob_id": "98ddf0be2c38cd9b10dfa9cc09f53907b34c1287",
"index": 7728,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n setup(**config)\n",
"step-3": "<mask token>\nconfig = {'name': 'beziers', 'author': 'Simon Cozens', 'author_email':\n 'simon@simon-cozens.org', 'url':... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def emphasize(sentence):
words = sentence.split(' ')
for i, word in enumerate(words):
words[i] = word[0].upper() + word[1:].lower()
return ' '.join(words)
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def emphasize(sentenc... | flexible | {
"blob_id": "518dcdca8f5e6b42624083e4327143dfba59b2ba",
"index": 9785,
"step-1": "<mask token>\n",
"step-2": "def emphasize(sentence):\n words = sentence.split(' ')\n for i, word in enumerate(words):\n words[i] = word[0].upper() + word[1:].lower()\n return ' '.join(words)\n\n\n<mask token>\n",
... | [
0,
1,
2,
3,
4
] |
import torch
import argparse
from DialogGenerator import DialogGenerator
from DialogDataset import DialogDataset
from DialogDiscriminator import DialogDiscriminator
from transformers import GPT2Tokenizer
import os
def prep_folder(args):
""" Append to slash to filepath if needed, and generate folder if it doesn't e... | normal | {
"blob_id": "18be97061c65185fcebf10c628e0e51bb08522cf",
"index": 3609,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef prep_folder(args):\n \"\"\" Append to slash to filepath if needed, and generate folder if it doesn't exist\"\"\"\n if args.save_folder[-1] != '/':\n args.save_folder ... | [
0,
1,
2,
3,
4
] |
""" sed_thermal.py
Author: Joshua Lande <joshualande@gmail.com>
"""
import numpy as np
from scipy import integrate
from . sed_integrate import logsimps
from . sed_spectrum import Spectrum
from . import sed_config
from . import units as u
class ThermalSpectrum(Spectrum):
vectorized = True
def __init__(s... | normal | {
"blob_id": "8560c0068eff894e5aa1d0788bd9e5ad05c14997",
"index": 2262,
"step-1": "<mask token>\n\n\nclass ThermalSpectrum(Spectrum):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n @staticmethod\n def units_string():\n return '1/erg/cm^3'\n\n def integrate(self, units=... | [
9,
12,
13,
14,
16
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def get_word_score(word_1, n_1):
"""string"""
sum_1 = 0
dictionary_ = {'a': 1, 'b': 3, 'c': 3, 'd': 2, 'e': 1, 'f': 4, 'g': 2,
'h': 4, 'i': 1, 'j': 8, 'k': 5, 'l': 1, 'm': 3, 'n': 1, 'o': 1, 'p':
3, '... | flexible | {
"blob_id": "325708d5e8b71bad4806b59f3f86a737c1baef8d",
"index": 3976,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef get_word_score(word_1, n_1):\n \"\"\"string\"\"\"\n sum_1 = 0\n dictionary_ = {'a': 1, 'b': 3, 'c': 3, 'd': 2, 'e': 1, 'f': 4, 'g': 2,\n 'h': 4, 'i': 1, 'j': 8, 'k... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class Bookings(models.Model):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Airlines(models.Model):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
... | flexible | {
"blob_id": "e57b30a7a1cf987918abfb3cb7d612bdead2ddcd",
"index": 406,
"step-1": "<mask token>\n\n\nclass Bookings(models.Model):\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass Airlines(models.Model):\n <mask token>\n <mask token>\n <mask token>\n <mask ... | [
1,
6,
7,
9,
10
] |
Relevance
Thus, designing an automatic MWP solver, with semantic understanding and
inference capability, has been considered as a crucial step towards general AI.
Solving a math problem manually involves too many steps. So MWP will reduc
Attachment final.pdf added.Conversation opened. 1 read message.
Skip ... | normal | {
"blob_id": "eb6a4170e5427f10eda4d650996c2cbd8a34ca21",
"index": 2667,
"step-1": "Relevance\r\n\r\nThus, designing an automatic MWP solver, with semantic understanding and\r\n inference capability, has been considered as a crucial step towards general AI. \r\n Solving a math problem manually involves too many s... | [
0
] |
<|reserved_special_token_0|>
class Model:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
@property
def form(self):
"""Contains the data send from the client."""
return security.get_field_storage()
@property
def cookie(self):
"""The client cookie"""
... | flexible | {
"blob_id": "7f21ab8d332d169226ef17276abbdd373e3a62c2",
"index": 8544,
"step-1": "<mask token>\n\n\nclass Model:\n <mask token>\n <mask token>\n\n @property\n def form(self):\n \"\"\"Contains the data send from the client.\"\"\"\n return security.get_field_storage()\n\n @property\n ... | [
7,
8,
9,
10,
11
] |
<|reserved_special_token_0|>
class Datafunction(object):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0... | flexible | {
"blob_id": "7611a57705939ce456e34d5ae379d6ca748b13c3",
"index": 1884,
"step-1": "<mask token>\n\n\nclass Datafunction(object):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n ... | [
3,
11,
12,
16,
18
] |
from flask import Flask
from flask_bcrypt import Bcrypt
from flask_jwt_extended import JWTManager
from flask_migrate import Migrate
from flask_restful import Api
from flask_apispec.extension import FlaskApiSpec
from server.admin import add_admin
from server.config import Config
from server.db import db
from server.cli ... | normal | {
"blob_id": "f1d813ccaf49c8941bf594e22d8683c0ab422a22",
"index": 7632,
"step-1": "<mask token>\n\n\n@jwt.user_lookup_loader\ndef user_loader_callback(_jwt_header, jwt_data):\n return user_service.first(id=jwt_data['sub'])\n\n\n@jwt.user_identity_loader\ndef user_identity_lookup(email):\n return user_servic... | [
4,
5,
6,
7
] |
i = 0
while i < 10:
print("Hello", 2 * i + 5)
i = i + 1 | normal | {
"blob_id": "e22574b5c458c23c48915274656f95a375cdc0e6",
"index": 6181,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile i < 10:\n print('Hello', 2 * i + 5)\n<mask token>\n",
"step-3": "i = 0\nwhile i < 10:\n print('Hello', 2 * i + 5)\ni = i + 1\n",
"step-4": "\r\ni = 0\r\nwhile i < 10:\r\n ... | [
0,
1,
2,
3
] |
def coroutine(func):
def start_coroutine(*args, **kwargs):
cr = func(*args, **kwargs)
next(cr)
return cr
return start_coroutine
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def coroutine(func):
def start_coroutine(*args, **kwargs):
cr = func(*args, **kwarg... | flexible | {
"blob_id": "bebe098c5abb579eb155a1dc325347d100ddfa8f",
"index": 1805,
"step-1": "def coroutine(func):\n\n def start_coroutine(*args, **kwargs):\n cr = func(*args, **kwargs)\n next(cr)\n return cr\n return start_coroutine\n\n\n<mask token>\n",
"step-2": "def coroutine(func):\n\n d... | [
1,
2,
3,
4,
6
] |
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