code stringlengths 13 6.09M | order_type stringclasses 2
values | original_example dict | step_ids listlengths 1 5 |
|---|---|---|---|
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
out.write('SEQUENCE_ID,TYPE,DOMAINS,TP,FP,FN,Sens,PPV,Jaccard\n')
<|reserved_special_token_0|>
for file in os.listdir(docpath + 'isoSegmenter100'):
if file.endswith('.csv') and 'E' in file:
predict_data = csv.DictReade... | flexible | {
"blob_id": "af2aa236f6bfc582093faf868a374be1ebdfabf2",
"index": 1235,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nout.write('SEQUENCE_ID,TYPE,DOMAINS,TP,FP,FN,Sens,PPV,Jaccard\\n')\n<mask token>\nfor file in os.listdir(docpath + 'isoSegmenter100'):\n if file.endswith('.csv') and 'E' in file:\n ... | [
0,
1,
2,
3,
4
] |
import cv2
import numpy as np
result=cv2.VideoCapture(0)
while True:
ret,square=result.read()
area=square[100:200,100:200]
cv2.imshow("video",square)
cv2.imshow("video2",area)
print(square)
if cv2.waitKey(25) & 0xff == ord('q'):
break
result.release()
cv2.destroyAllWindows()
| normal | {
"blob_id": "934921b22d036bd611134ce74f6eba3a2710018e",
"index": 529,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile True:\n ret, square = result.read()\n area = square[100:200, 100:200]\n cv2.imshow('video', square)\n cv2.imshow('video2', area)\n print(square)\n if cv2.waitKey(25... | [
0,
1,
2,
3,
4
] |
class Wspak:
"""Iterator zwracający wartości w odwróconym porządku"""
def __init__(self, data):
self.data = data
self.index = -2
self.i=len(data)-1
def __iter__(self):
return self
def __next__(self):
if self.index >= self.i:
raise StopIteration
... | normal | {
"blob_id": "ea1d62c4a8c406dde9bb138ee045be5e682fdbfe",
"index": 566,
"step-1": "class Wspak:\n <mask token>\n\n def __init__(self, data):\n self.data = data\n self.index = -2\n self.i = len(data) - 1\n <mask token>\n <mask token>\n\n\n<mask token>\n",
"step-2": "class Wspak:\n... | [
2,
5,
6,
7,
8
] |
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from sklearn.cluster import KMeans
from kneed import KneeLocator
#Create a panda data frame from the csv file
df = pd.read_csv('ClusterPlot.csv', usecols=['V1','V2'])
#Convert the panda data frame to a NumPy array
arr = df.to_numpy()
#Code used t... | normal | {
"blob_id": "09417014963172fc71b4268aafdec1405c04f34d",
"index": 3472,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(1, 11):\n km = KMeans(n_clusters=i, init='random', n_init=10, max_iter=300, tol=\n 0.0001, random_state=0)\n km.fit(arr)\n distortions.append(km.inertia_)\n... | [
0,
1,
2,
3,
4
] |
#!/usr/bin/env python
import argparse
import csv
import glob
import os
import sys
def run_main():
"""
Main function to process user input and then generate the description files for each run
:return: exit code -- 0 on success, 1 otherwise
"""
parser = argparse.ArgumentParser(description="Scan a... | normal | {
"blob_id": "6e6c6c5795e8723a86ae5dfc8f40df57d3dd10f7",
"index": 3336,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef run_main():\n \"\"\"\n Main function to process user input and then generate the description files for each run\n\n :return: exit code -- 0 on success, 1 otherwise\n \... | [
0,
1,
2,
3,
4
] |
# Generated by Django 4.0.5 on 2023-02-14 18:57
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('core', '0020_festival_boxoffice_close_festival_boxoffice_open'),
]
operations = [
migrations.AlterModelOptions(
name='user',
... | normal | {
"blob_id": "e9bf5a40360d35f32bd2ad5aa404225f49895a14",
"index": 4221,
"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 = [('core',\n ... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
os.system('clear')
<|reserved_special_token_0|>
print(banner)
<|reserved_special_token_0|>
os.system('clear')
print('\n' + '\x1b[94m - Loading Data ...')
time.sleep(3)
for u in usrf:
userlist.append(u.replace('\n', ''))
f... | flexible | {
"blob_id": "6ab5ac0caa44366268bb8b70ac044376d9c062f0",
"index": 6976,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nos.system('clear')\n<mask token>\nprint(banner)\n<mask token>\nos.system('clear')\nprint('\\n' + '\\x1b[94m - Loading Data ...')\ntime.sleep(3)\nfor u in usrf:\n userlist.append(u... | [
0,
1,
2,
3,
4
] |
N=int(input("N="))
K=int()
K=0
while N>=2:
N=N/2
K=K+1
print("K=",K) | normal | {
"blob_id": "7f4c6e4a5627b44b9a700d2de4f9caca0ae8b17c",
"index": 2808,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile N >= 2:\n N = N / 2\n K = K + 1\nprint('K=', K)\n",
"step-3": "N = int(input('N='))\nK = int()\nK = 0\nwhile N >= 2:\n N = N / 2\n K = K + 1\nprint('K=', K)\n",
"ste... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
while True:
if suppressRoomPrint:
suppressRoomPrint = False
else:
print(player.location)
print(
f"""
{player.location.name}
{player.location.description}
{player.location.getItems()}
"""
... | flexible | {
"blob_id": "07a172c28057dc803efdbdc10a9e2e11df4e527b",
"index": 3134,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile True:\n if suppressRoomPrint:\n suppressRoomPrint = False\n else:\n print(player.location)\n print(\n f\"\"\"\n{player.location.name}\n {player.locatio... | [
0,
1,
2,
3,
4
] |
import re
from django import forms
from django.contrib.auth import password_validation
from django.contrib.auth.forms import PasswordChangeForm
from django.contrib.auth.password_validation import validate_password
from .models import Account
class EditProfileModelForm(forms.ModelForm):
class Meta:
model... | normal | {
"blob_id": "af442d4a78930a0ebcd85a1cdfe4aa86461be5c1",
"index": 1274,
"step-1": "<mask token>\n\n\nclass PasswordChangeFormExt(PasswordChangeForm):\n \"\"\"Form for changing user's password.\"\"\"\n\n def clean(self):\n user = self.user\n new_password = self.cleaned_data.get('new_password1')... | [
3,
4,
5,
6,
7
] |
"""Handles loading and tokenising of datasets"""
import enum
import numpy as np
import os.path
import pickle
from tqdm import tqdm
import nltk
from nltk import WordPunctTokenizer
nltk.download('punkt')
from nltk.tokenize import word_tokenize
from lib.utils import DATASETS_BASE_PATH, SAVED_POS_BASE_PATH
from lib.pos im... | normal | {
"blob_id": "0150e1db3ef2f6c07280f21971b43ac71fc4cada",
"index": 8984,
"step-1": "<mask token>\n\n\nclass DatasetType(enum.Enum):\n \"\"\"\n Represents the type of dataset\n \"\"\"\n TRAIN = 0\n VAL = 1\n TEST = 2\n\n\nclass Language(enum.Enum):\n \"\"\"\n Represents the dataset language\... | [
7,
8,
11,
12,
13
] |
from setuptools import setup
setup(name='google-drive-helpers',
version='0.1',
description='Helper functions for google drive',
url='https://github.com/jdoepfert/google-drive-helpers',
license='MIT',
packages=['gdrive_helpers'],
install_requires=[
'google-api-python-client... | normal | {
"blob_id": "c0218acadb9e03359ac898cf3bb4898f516400e5",
"index": 5361,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsetup(name='google-drive-helpers', version='0.1', description=\n 'Helper functions for google drive', url=\n 'https://github.com/jdoepfert/google-drive-helpers', license='MIT',\n ... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def train(cls, data, target, model_path):
cls = cls.fit(data, target)
with open(model_path, 'wb') as f:
pickle.dump(cls, f)
<|reserved_special_token_0|>
def load_models(matrix_path, model_path):
tfidf, cls = None, None
if os.path.isfile(model_path):
wit... | flexible | {
"blob_id": "199872ea459a9dba9975c6531034bdbc1e77f1db",
"index": 5875,
"step-1": "<mask token>\n\n\ndef train(cls, data, target, model_path):\n cls = cls.fit(data, target)\n with open(model_path, 'wb') as f:\n pickle.dump(cls, f)\n\n\n<mask token>\n\n\ndef load_models(matrix_path, model_path):\n ... | [
3,
4,
5,
6,
8
] |
class Port(object):
def __init__(self, mac):
self.mac = mac
| normal | {
"blob_id": "cd89c9eaea9d331288fd07f1968ef9dce89b4a4b",
"index": 7228,
"step-1": "<mask token>\n",
"step-2": "class Port(object):\n <mask token>\n",
"step-3": "class Port(object):\n\n def __init__(self, mac):\n self.mac = mac\n",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
... | [
0,
1,
2
] |
<|reserved_special_token_0|>
class Google_Cloud:
<|reserved_special_token_0|>
def sentiment(self):
google_sentiment = self.client.analyze_sentiment(self.document
).document_sentiment
sent = {}
sent['sentiment'] = google_sentiment.score
sent['magnitude'] = google_se... | flexible | {
"blob_id": "6868a8b5d36403f1417301acdca5f5dc9e45c682",
"index": 9849,
"step-1": "<mask token>\n\n\nclass Google_Cloud:\n <mask token>\n\n def sentiment(self):\n google_sentiment = self.client.analyze_sentiment(self.document\n ).document_sentiment\n sent = {}\n sent['sentime... | [
7,
8,
10,
11,
14
] |
import string
import pandas as pd
import nltk
from nltk import word_tokenize
from nltk.stem import SnowballStemmer
from nltk.tokenize import WordPunctTokenizer
import json
from sklearn.model_selection import train_test_split
from keras.preprocessing.text import Tokenizer
import pickle
import re
import nlpaug.augmenter.... | normal | {
"blob_id": "326b2dcbef339aeb196bef23debad75fa079b121",
"index": 6435,
"step-1": "<mask token>\n\n\nclass Processing:\n <mask token>\n\n @property\n def vocab_size(self):\n return self.__vocab_size\n\n def normalize(self, s):\n s = s.lower()\n replacements = ('á', 'a'), ('é', 'e'... | [
12,
16,
17,
18,
19
] |
<|reserved_special_token_0|>
def set_gcs_credentials():
if os.path.exists(GLOBALS.google_application_credentials):
return
secrets_client = boto3.client('secretsmanager', region_name=GLOBALS.
aws_region, endpoint_url=GLOBALS.aws_endpoint_uri)
response = secrets_client.get_secret_value(Secre... | flexible | {
"blob_id": "a5eeafef694db04770833a4063358e8f32f467b0",
"index": 8310,
"step-1": "<mask token>\n\n\ndef set_gcs_credentials():\n if os.path.exists(GLOBALS.google_application_credentials):\n return\n secrets_client = boto3.client('secretsmanager', region_name=GLOBALS.\n aws_region, endpoint_ur... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
def test_evm_contracts_data(globaldb):
"""Test that all evm contract entries in the packaged global DB have legal data"""
serialized_chain_ids = [x.serialize_for_db() for x in ChainID]
with globaldb.conn.read_ctx() as cursor:
cursor.execute(
'SELECT address... | flexible | {
"blob_id": "52dc8a4f9165a88dddc1da16e0adb045c4d851ed",
"index": 5017,
"step-1": "<mask token>\n\n\ndef test_evm_contracts_data(globaldb):\n \"\"\"Test that all evm contract entries in the packaged global DB have legal data\"\"\"\n serialized_chain_ids = [x.serialize_for_db() for x in ChainID]\n with gl... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
class RNNClassifier(nn.Module):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class RNNClassifier(nn.Module):
def __init__(self, batch_size, num_classes, hidden_size, vocab_size,
embed_size, we... | flexible | {
"blob_id": "41417e3ce52edf6aee432886bbab6d16ec5bc88d",
"index": 164,
"step-1": "<mask token>\n\n\nclass RNNClassifier(nn.Module):\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass RNNClassifier(nn.Module):\n\n def __init__(self, batch_size, num_classes, hidden_size, vocab_size,\n ... | [
1,
2,
3,
4,
5
] |
<|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": "848374ea7d706bbd2ef5a76489cabeff998acb82",
"index": 6040,
"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 = [('fuser', '00... | [
0,
1,
2,
3,
4
] |
'''
O(n) time complexity
O(n) space complexity
'''
class Solution:
def twoSum(self, nums, target):
"""
:type nums: List[int]
:type target: int
:rtype: List[int]
"""
seenum = dict()
for idx, val in enumerate(nums):
if target - val in seenum:
... | normal | {
"blob_id": "b3f62c331ff4ae9f909fc90cc7303997b32daceb",
"index": 1876,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Solution:\n <mask token>\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\nclass Solution:\n\n def twoSum(self, nums, target):\n \"\"\"\n :type nums: List... | [
0,
1,
2,
3,
4
] |
#!/usr/bin/env python3
"""(Optional) Test for GameDealer class."""
import unittest
import os, sys
from functools import reduce
sys.path.insert(0, os.path.join(os.path.split(__file__)[0], ".."))
import Lab19_Extending_Builtins.lab19_3 as game_dealer
WHOLE_DECK = sorted(game_dealer.Deck())
class ReportingDealer(... | normal | {
"blob_id": "06a721c12e3140d4d1cf544a598f512595c4ab66",
"index": 3013,
"step-1": "<mask token>\n\n\nclass ReportingDealer(game_dealer.GameDealer):\n <mask token>\n\n def Report(self):\n \"\"\"For testing.\"\"\"\n return [p.hand for p in self.players]\n\n\nclass TestPlayCards(unittest.TestCase... | [
8,
10,
11,
12,
13
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def iteration_spider():
max_errors = 5
num_errors = 0
for page in itertools.count(1):
url = 'http://example.webscraping.com/view/-{}'.format(page)
html = download(url)
if html is None:
... | flexible | {
"blob_id": "0eaba8f570772de864f52168a597b47a4150d015",
"index": 5924,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef iteration_spider():\n max_errors = 5\n num_errors = 0\n for page in itertools.count(1):\n url = 'http://example.webscraping.com/view/-{}'.format(page)\n htm... | [
0,
1,
2,
3,
4
] |
a=raw_input("Enter the column\n")
b=raw_input("Enter the row\n")
i=0
k=0
m=0
c=""
d=""
while (m<int(b)):
while(i<int(a)):
c=c+" "
for j in xrange(1,4):
c=c+"-"
i=i+1
while(k<int(a)):
d=d+"|"
for l in xrange(1,4):
d=d+" "
k=k+1
m=m+1
... | normal | {
"blob_id": "c28d7fc45be9a6efa7b7ef00520898c3d238ac63",
"index": 5518,
"step-1": "a=raw_input(\"Enter the column\\n\")\nb=raw_input(\"Enter the row\\n\")\ni=0\nk=0\nm=0\nc=\"\"\nd=\"\"\nwhile (m<int(b)):\n while(i<int(a)):\n c=c+\" \"\n for j in xrange(1,4):\n c=c+\"-\"\n i=i+1... | [
0
] |
## Author: Aleem Juma
import os
from app import app
import pandas as pd
# read in the quotes database
q = pd.read_csv(os.path.join('app','data','quotes_all.csv'), sep=';', skiprows=1, header=0)
# there are a few quote genres that don't occur in the model vocab
# replace them with appropriate words so the similarity ... | normal | {
"blob_id": "8f854f4f2c807f988945af4dc53dba93cfb31168",
"index": 9441,
"step-1": "<mask token>\n\n\ndef get_similarity(word1, word2):\n \"\"\"\n Returns a similarity score between two words\n \"\"\"\n tok1 = cache.get(word1, nlp(word1))\n tok2 = cache.get(word2, nlp(word2))\n return tok1.simila... | [
3,
5,
6,
7,
8
] |
from azureml.core.compute import AksCompute
from azureml.core.model import Model, InferenceConfig
from azureml.core.webservice import AksWebservice
workspace_name = ""
subscription_id = "XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX"
resource_group = "XXXXXXXXXXXXXXXXX"
workspace_region = "eastus2"
https_cert = "XXXXX"
aks_nam... | normal | {
"blob_id": "2941ecde72325d46b5c3899d4b1a213daff67147",
"index": 2613,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprov_config.enable_ssl(leaf_domain_label=https_cert)\n<mask token>\naks_service.wait_for_deployment(show_output=True)\nprint(aks_service.state)\n",
"step-3": "<mask token>\nworkspace_na... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
try:
in_str = input()
except Exception as e:
print('WRONG FORMAT!')
sys.exit(0)
<|reserved_special_token_0|>
try:
in_exp = eval(in_str)
except Exception as e:
print('WRONG FORMAT!')
sys.exit(0)
<|reserved_s... | flexible | {
"blob_id": "1634ae0e329b4f277fa96a870fbd19626c0ece81",
"index": 6516,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ntry:\n in_str = input()\nexcept Exception as e:\n print('WRONG FORMAT!')\n sys.exit(0)\n<mask token>\ntry:\n in_exp = eval(in_str)\nexcept Exception as e:\n print('WRONG FO... | [
0,
1,
2,
3,
4
] |
import sys
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sea
import sklearn
import glob
import pydub
from pydub import AudioSegment
import time
import librosa
import noisereduce as nr
from scipy.io import wavfile
import IPython
import sounddevice as sd
from pysndfx ... | normal | {
"blob_id": "14bf4befdce4270b4514b4e643964182f9c49ff4",
"index": 8434,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(IPython.display.Audio(data=my, rate=sr))\nsd.play(my, sr)\n<mask token>\n",
"step-3": "<mask token>\nmy, sr = librosa.load(\n 'C:\\\\Users\\\\pranj\\\\Downloads\\\\IEMOCAP_full... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
@app.route('/api/v1/users/<int:user_id>', methods=['GET'])
def get_user(user_id):
try:
user = User.query.filter_by(id=user_id).first()
return jsonify({'user': user.serialize})
except:
abort(404)
@app.route('/api/v1/users', methods=['POST'])
def create_use... | flexible | {
"blob_id": "f4519fa82ffc6bf945c7bb36d3761a708a06f641",
"index": 5933,
"step-1": "<mask token>\n\n\n@app.route('/api/v1/users/<int:user_id>', methods=['GET'])\ndef get_user(user_id):\n try:\n user = User.query.filter_by(id=user_id).first()\n return jsonify({'user': user.serialize})\n except:\... | [
4,
6,
7,
8
] |
from mathmodule import *
import sys
print("Welcome to my basic \'Calculator\'")
print("Please choose your best option (+, -, *, /) ")
# user input part
while True:
try:
A = int(input("Now Enter your first Value="))
break
except:
print("Oops!", sys.exc_info()[0], "occurred.")
while True:
mathoparet... | normal | {
"blob_id": "1cca94040cdd8db9d98f587c62eff7c58eae7535",
"index": 6974,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(\"Welcome to my basic 'Calculator'\")\nprint('Please choose your best option (+, -, *, /) ')\nwhile True:\n try:\n A = int(input('Now Enter your first Value='))\n b... | [
0,
1,
2,
3
] |
target = []
with open('IntegerArray.txt', 'r') as f:
target = f.readlines()
for x in range(len(target)):
target[x] = int(target[x])
def f(A):
if len(A) == 1:
return 0
else:
rightStart = len(A) // 2
leftArray = A[0:rightStart]
righArray = A[rightStart:]
B, b = co... | normal | {
"blob_id": "b5611c668a40e1735c92d6d00867885023ad713f",
"index": 248,
"step-1": "<mask token>\n\n\ndef f(A):\n if len(A) == 1:\n return 0\n else:\n rightStart = len(A) // 2\n leftArray = A[0:rightStart]\n righArray = A[rightStart:]\n B, b = count_and_sort(leftArray)\n ... | [
2,
3,
4,
5
] |
<|reserved_special_token_0|>
class Widget2:
def setup(self, MainWindow, res):
self.widget = QWidget()
self.grid = QGridLayout()
self.results = QLineEdit()
self.results.setText(res)
row = 3
col = 0
self.cb = QComboBox()
self.cb.addItems(['Advance Mod... | flexible | {
"blob_id": "b08cface601ee07125090f3ae03a3120974688f2",
"index": 8765,
"step-1": "<mask token>\n\n\nclass Widget2:\n\n def setup(self, MainWindow, res):\n self.widget = QWidget()\n self.grid = QGridLayout()\n self.results = QLineEdit()\n self.results.setText(res)\n row = 3\n... | [
8,
9,
11,
13,
17
] |
<|reserved_special_token_0|>
def fibonaci(n):
if n <= 1:
return n
F = np.empty(shape=n + 1)
F[0] = 0
F[1] = 1
for i in range(2, len(F)):
F[i] = F[i - 1] + F[i - 2]
return F[n]
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def fibo... | flexible | {
"blob_id": "67516551b595c02e70a0ba4005df8a97ba71b17e",
"index": 1419,
"step-1": "<mask token>\n\n\ndef fibonaci(n):\n if n <= 1:\n return n\n F = np.empty(shape=n + 1)\n F[0] = 0\n F[1] = 1\n for i in range(2, len(F)):\n F[i] = F[i - 1] + F[i - 2]\n return F[n]\n\n\n<mask token>\... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def main():
ap = argparse.ArgumentParser()
ap.add_argument('-i', '--image', required=True, help='Path to the image')
args = vars(ap.parse_args())
image = cv2.imread(args['image'])
image = cv2.cvtColor(image, ... | flexible | {
"blob_id": "0547751af7bbac42351476dde591d13d40fb37eb",
"index": 7811,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef main():\n ap = argparse.ArgumentParser()\n ap.add_argument('-i', '--image', required=True, help='Path to the image')\n args = vars(ap.parse_args())\n image = cv2.imrea... | [
0,
1,
2,
3,
4
] |
from __future__ import absolute_import, division, print_function, unicode_literals
import os
from collections import defaultdict
from past.builtins import basestring
from pycolocstats.core.config import REF_COLL_GSUITES_PATH
__metaclass__ = type
class RefTrackCollectionRegistry(object):
PREBUILT = '__prebuilt_... | normal | {
"blob_id": "9c2cc5b993f020b8a1c96ea4cd5c2fb2da44a251",
"index": 1534,
"step-1": "<mask token>\n\n\nclass RefTrackCollectionRegistry(object):\n <mask token>\n\n def __init__(self):\n self._genome2TrackIndexReg = defaultdict(set)\n self._trackIndex2CollectionReg = defaultdict(set)\n sel... | [
6,
7,
8,
10,
11
] |
from pyplasm import *
import random as r
def gen_windows(plan_grid, n, m, window_model):
return STRUCT([
T([1,2])([j,i])(
gen_cube_windows(plan_grid, window_model)(i, j, n, m))
for i in range(n)
for j in range(m)
if plan_grid[i][j]])
def gen_cube_windows(plan_grid, wi... | normal | {
"blob_id": "cb48a1601798f72f9cf3759d3c13969bc824a0f6",
"index": 707,
"step-1": "<mask token>\n\n\ndef gen_windows(plan_grid, n, m, window_model):\n return STRUCT([T([1, 2])([j, i])(gen_cube_windows(plan_grid,\n window_model)(i, j, n, m)) for i in range(n) for j in range(m) if\n plan_grid[i][j]]... | [
5,
7,
8,
9,
11
] |
'''
Given an expression with numbers, brackets and operators. But in this task only brackets are important. Brackets can be one of three types -- "{}" "()" "[]". Brackets are determine the scope or restricted some expression. So each if was opened, then must be closed with the same type. The scopes of brackets must not... | normal | {
"blob_id": "f69b4d022ebed5a0b660f55704bbe762d5d765d5",
"index": 1332,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef checkio(data):\n return True or False\n",
"step-3": "'''\nGiven an expression with numbers, brackets and operators. But in this task only brackets are important. Brackets can... | [
0,
1,
2
] |
<|reserved_special_token_0|>
class Game:
<|reserved_special_token_0|>
def __init__(self, grid_size):
self.grid_size = grid_size
self.start_game(grid_size)
plt.title("Nate's Lame Game")
def start_game(self, grid_size):
self.score = 0
self.goal_pos = 0, 0
se... | flexible | {
"blob_id": "a74f2050a057f579a8a8b77ac04ef09073cdb6cf",
"index": 6057,
"step-1": "<mask token>\n\n\nclass Game:\n <mask token>\n\n def __init__(self, grid_size):\n self.grid_size = grid_size\n self.start_game(grid_size)\n plt.title(\"Nate's Lame Game\")\n\n def start_game(self, grid... | [
8,
9,
10,
12,
13
] |
<|reserved_special_token_0|>
class ApiException(Exception):
def __init__(self, message, code=400, data=None):
Exception.__init__(self, message)
self.code = code
self.msg = message
self.data = data
def __str__(self):
return self.msg
<|reserved_special_token_0|>
<... | flexible | {
"blob_id": "0ac14b023c51bfd1cf99bd2d991baa30a671e066",
"index": 9994,
"step-1": "<mask token>\n\n\nclass ApiException(Exception):\n\n def __init__(self, message, code=400, data=None):\n Exception.__init__(self, message)\n self.code = code\n self.msg = message\n self.data = data\n\... | [
3,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
class EncyclopediaDao:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
@staticmethod
def get_faq_content(query: str, page: str) ->list:
"""
获取指定query的faq检索内容
:param query:
:param page:
:return:
"""
url = 'https://zhidao.baidu.com/search... | flexible | {
"blob_id": "a7f348b258e1d6b02a79c60e4fe54b6d53801f70",
"index": 3877,
"step-1": "<mask token>\n\n\nclass EncyclopediaDao:\n <mask token>\n <mask token>\n\n @staticmethod\n def get_faq_content(query: str, page: str) ->list:\n \"\"\"\n\t\t获取指定query的faq检索内容\n\t\t:param query:\n\t\t:param page:\n... | [
2,
3,
4,
5,
6
] |
from flask import render_template, flash, redirect, url_for, request
from flask_login import current_user, login_user, logout_user, login_required
from werkzeug.urls import url_parse
from app import db
# from app.main.forms import [list forms here]
from app.models import User
from app.main import bp
@bp.route('/')
@bp... | normal | {
"blob_id": "495d606304e07a097033366d1a7e1d856a4cf61f",
"index": 1935,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@bp.route('/')\n@bp.route('/index')\n@login_required\ndef index():\n return render_template('index.html')\n",
"step-3": "from flask import render_template, flash, redirect, url_f... | [
0,
1,
2,
3
] |
from django.db import models
class Category(models.Model):
name = models.CharField(max_length=50, unique=True)
created_at = models.DateTimeField(auto_now_add=True)
def __str__(self):
return self.name
class Meta:
verbose_name = 'Categoria'
class Books(models.Model):
name = model... | normal | {
"blob_id": "0584ff5cb252fba0fe1fc350a5fb023ab5cbb02b",
"index": 6750,
"step-1": "<mask token>\n\n\nclass Student(models.Model):\n name = models.CharField(max_length=70)\n cpf = models.CharField(max_length=14)\n birth_date = models.DateField()\n city = models.CharField(max_length=50)\n registratio... | [
3,
7,
8,
9,
11
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
myLabel1.grid(row=0, column=0)
myLabel2.grid(row=1, column=0)
root.mainloop()
<|reserved_special_token_1|>
<|reserved_special_token_0|>
root = Tk()
myLabel1 = Label(root, text='Hello User!')
myLabel2 = Label(root, text='Welcome... | flexible | {
"blob_id": "93fe16e5a97ec2652c4f6b8be844244d9776ea2e",
"index": 4921,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nmyLabel1.grid(row=0, column=0)\nmyLabel2.grid(row=1, column=0)\nroot.mainloop()\n",
"step-3": "<mask token>\nroot = Tk()\nmyLabel1 = Label(root, text='Hello User!')\nmyLabel2 = Label(ro... | [
0,
1,
2,
3,
4
] |
# coding=utf-8
import datetime
from django.http import JsonResponse
from django.shortcuts import render, redirect
from models import *
from hashlib import sha1
from user_decorators import user_login
from df_goods.models import GoodsInfo
# Create your views here.
def register(request):
context={'title':'注册','top':'0... | normal | {
"blob_id": "1ef40d4162ca1b1bd6a5a5010485c78eb9d8d736",
"index": 9621,
"step-1": "<mask token>\n\n\ndef register(request):\n context = {'title': '注册', 'top': '0'}\n return render(request, 'df_user/register.html', context)\n\n\ndef login(request):\n context = {'title': '登录', 'top': '0'}\n return rende... | [
6,
7,
9,
11,
12
] |
import json
import logging
import os
import sys
from io import StringIO
import pytest
from allure.constants import AttachmentType
from utils.tools import close_popups
_beautiful_json = dict(indent=2, ensure_ascii=False, sort_keys=True)
# LOGGING console ##############################################################... | normal | {
"blob_id": "37fdfddb471e2eec9e5867d685c7c56fc38c5ae7",
"index": 8363,
"step-1": "<mask token>\n\n\nclass CustomLogger(logging.Logger):\n <mask token>\n\n @staticmethod\n def format_message(message):\n return json.dumps(message, **_beautiful_json) if isinstance(message,\n (dict, list, ... | [
10,
13,
14,
15,
16
] |
# cook your dish here
t=int(input())
while t:
n=int(input())
a=list(map(int,input().split()))
a.sort(reverse=True)
s=0
for i in range(n):
k=a[i]-i
if k>=0:
s+=k
print(s%1000000007)
t-=1
| normal | {
"blob_id": "44bf409d627a6029ab4c4f1fff99f102b8d57279",
"index": 3954,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile t:\n n = int(input())\n a = list(map(int, input().split()))\n a.sort(reverse=True)\n s = 0\n for i in range(n):\n k = a[i] - i\n if k >= 0:\n ... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def full_adder(a: bool, b: bool, c: bool) ->(bool, bool):
"""Returns a + b + c in the form of a tuple of two bools representing the two
bits.
Carried value is ignored.
"""
nand_a_b = nand(a, b)
nand_... | flexible | {
"blob_id": "66f6639ae62fe8c0b42171cf3e3fb450d8eee2b2",
"index": 7671,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef full_adder(a: bool, b: bool, c: bool) ->(bool, bool):\n \"\"\"Returns a + b + c in the form of a tuple of two bools representing the two\n bits.\n \n Carried value is ... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class priority_customer(models.Model):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_... | flexible | {
"blob_id": "f2bb00d06023ef7b3ea3dc33f7ec00d1f48d46ae",
"index": 8477,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass priority_customer(models.Model):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass priority_customer(models.Model)... | [
0,
1,
2,
3,
4
] |
def entete():
entete = """
<!DOCTYPE HTML>
<html lang=“fr”>
<head>
<title>AMAP'PATATE</title>
<meta charset="UTF-8" />
<link rel="stylesheet" type="text/css" href="/IENAC15/amapatate/css/font-awesome.min.css" />
<link rel=... | flexible | {
"blob_id": "933758002c5851a2655ed4c51b2bed0102165116",
"index": 4742,
"step-1": "def entete():\n entete = \"\"\"\n <!DOCTYPE HTML>\n<html lang=“fr”>\n <head>\n <title>AMAP'PATATE</title>\n <meta charset=\"UTF-8\" />\n <link rel=\"stylesheet... | [
1,
2,
3,
4,
5
] |
import re
import itertools
import setpath
import functions
import lib.jopts as jopts
from operator import itemgetter
import random
__docformat__ = 'reStructuredText en'
re_params=re.compile('(\w*):(.*)')
def consumer(func):
"""A decorator, advances func to its first yield point when called.
"""
from fun... | normal | {
"blob_id": "60411e922bfec8f98028f959a370f954eef5437e",
"index": 1329,
"step-1": "import re\nimport itertools\nimport setpath\nimport functions\nimport lib.jopts as jopts\nfrom operator import itemgetter\nimport random\n\n__docformat__ = 'reStructuredText en'\n\nre_params=re.compile('(\\w*):(.*)')\n\ndef consume... | [
0
] |
import mysql.connector
import json
mysql_user = 'root'
mysql_pass = 'funwfats'
mysql_host = 'localhost'
mysql_base = 'sys'
wn8_file = "wn8exp.json"
def fill_wn8_table():
with open(wn8_file, encoding="utf-8") as file:
wn8_dict = json.loads(file.read())
cnx_wn8 = mysql.connector.connect(us... | normal | {
"blob_id": "291052c22059b32f3f300c323a10b260fbd0c20f",
"index": 9210,
"step-1": "import mysql.connector\r\nimport json\r\n\r\nmysql_user = 'root'\r\nmysql_pass = 'funwfats'\r\nmysql_host = 'localhost'\r\nmysql_base = 'sys'\r\nwn8_file = \"wn8exp.json\"\r\n\r\n\r\ndef fill_wn8_table():\r\n with open(wn8_file,... | [
0
] |
<|reserved_special_token_0|>
class ChamferCylinder(pynewton.ChamferCylinder):
pass
class ConvexHull(pynewton.ConvexHull):
pass
class ConvexHullModifier(pynewton.ConvexHullModifier):
pass
class NullCollider(pynewton.NullCollider):
pass
class TreeCollision(pynewton.TreeCollision):
pass
cla... | flexible | {
"blob_id": "90d792fe18e589a0d74d36797b46c6ac1d7946be",
"index": 4303,
"step-1": "<mask token>\n\n\nclass ChamferCylinder(pynewton.ChamferCylinder):\n pass\n\n\nclass ConvexHull(pynewton.ConvexHull):\n pass\n\n\nclass ConvexHullModifier(pynewton.ConvexHullModifier):\n pass\n\n\nclass NullCollider(pynewt... | [
33,
52,
66,
68,
76
] |
class UF(object):
def __init__(self, n):
self.parents = [i for i in range(n)]
self.weights = [1 for i in range(n)]
self.n = n
def find(self, i):
while i != self.parents[i]:
self.parents[i] = self.parents[self.parents[i]]
i = self.parents[i]
return... | normal | {
"blob_id": "c8d5b8515a468190d14311118e12a7d414908be6",
"index": 8109,
"step-1": "class UF(object):\n <mask token>\n\n def find(self, i):\n while i != self.parents[i]:\n self.parents[i] = self.parents[self.parents[i]]\n i = self.parents[i]\n return i\n\n def union(sel... | [
4,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def module_exists(module_name):
try:
__import__(module_name)
except ImportError:
return False
else:
return True
def quote(items):
return [("'" + item + "'") for item in items]
if modul... | flexible | {
"blob_id": "68371acc58da6d986d94d746abb4fea541d65fdd",
"index": 3384,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef module_exists(module_name):\n try:\n __import__(module_name)\n except ImportError:\n return False\n else:\n return True\n\n\ndef quote(items):\n r... | [
0,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
class Ui_FindResultWindow(object):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Ui_FindResultWindow(object):
<|reserved_special_token_0|>
def retranslateUi(self, FindResultWindow):
_... | flexible | {
"blob_id": "2fdbf418b5cec50ee6568897e0e749681efeef6b",
"index": 6584,
"step-1": "<mask token>\n\n\nclass Ui_FindResultWindow(object):\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass Ui_FindResultWindow(object):\n <mask token>\n\n def retranslateUi(self, FindResultWindow):\n ... | [
1,
2,
3,
4,
5
] |
#!/usr/bin/env python
# encoding: utf-8
import os
import argparse
import coaddBatchCutout as cbc
def run(args):
min = -0.0
max = 0.5
Q = 10
if os.path.isfile(args.incat):
cbc.coaddBatchCutFull(args.root, args.incat,
filter=args.filter,
... | normal | {
"blob_id": "c0503536672aa824eaf0d19b9d4b5431ef910432",
"index": 1028,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef run(args):\n min = -0.0\n max = 0.5\n Q = 10\n if os.path.isfile(args.incat):\n cbc.coaddBatchCutFull(args.root, args.incat, filter=args.filter,\n id... | [
0,
1,
2,
3,
4
] |
__author__ = "Yong Peng"
__version__ = "1.0"
import time
import re
import getpass
from netmiko import (
ConnectHandler,
NetmikoTimeoutException,
NetmikoAuthenticationException,
)
with open('./device_list.txt','r') as f:
device_list = [i.strip() for i in f.readlines() if len(i.strip()) != 0] # rea... | normal | {
"blob_id": "31a0c9a143a06ac86c8e8616fb273a0af844a352",
"index": 6895,
"step-1": "<mask token>\n\n\ndef send_show_command(device, commands):\n OutputPath = 'c:/script/output/' + str(device['host']) + '.txt'\n result = open(OutputPath, 'w')\n flag = True\n try:\n with ConnectHandler(**device) a... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
db.news.drop()
db.news.insert_many(scrape(info, url))
<|reserved_special_token_0|>
db.images.drop()
db.images.insert_many(scrape(info, url))
<|reserved_special_token_0|>
db.weather.drop()
db.weather.insert_many(scrape(info, url))
... | flexible | {
"blob_id": "e3ac8039ffb6787b0e3e80b234c2689c66a184bf",
"index": 1704,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ndb.news.drop()\ndb.news.insert_many(scrape(info, url))\n<mask token>\ndb.images.drop()\ndb.images.insert_many(scrape(info, url))\n<mask token>\ndb.weather.drop()\ndb.weather.insert_many(s... | [
0,
1,
2,
3,
4
] |
from SPARQLWrapper import SPARQLWrapper, JSON
sparql = SPARQLWrapper(
'http://localhost:3030/ds/query'
)
#Pizzas
def get_response_pizzas():
sparql.setQuery('''
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX saidi: <http://www.semanticweb.org/japor/ontologies/2021/5/Pizzas... | normal | {
"blob_id": "9690366a88a87951f5c51902118888cce8159ffc",
"index": 7219,
"step-1": "<mask token>\n\n\ndef get_response_carnes():\n sparql.setQuery(\n \"\"\"\n PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>\n PREFIX saidi: <http://www.semanticweb.org/japor/ontologies/2021/5/PizzasLojan... | [
6,
7,
8,
10,
12
] |
#encoding: utf-8
"""
Desc:
Author: Makoto OKITA
Date: 2016/09/03
"""
import numpy as np
import chainer
from chainer import cuda, Function, gradient_check, Variable, optimizers, serializers, utils
from chainer import Link, Chain, ChainList
import chainer.functions as F
import chainer.links as L
import itertools
... | normal | {
"blob_id": "13e89e13f88ac306a62be3390f5292665f128a4d",
"index": 9332,
"step-1": "#encoding: utf-8\n\"\"\"\nDesc: \nAuthor: Makoto OKITA\nDate: 2016/09/03 \n\"\"\"\nimport numpy as np\nimport chainer\nfrom chainer import cuda, Function, gradient_check, Variable, optimizers, serializers, utils\nfrom chainer... | [
0
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def solution(record):
answer = []
arr = dict()
history = []
for i in record:
tmp = i.split()
if tmp[0] == 'Enter':
arr[tmp[1]] = tmp[2]
history.append([tmp[1], '님이 들어왔습니다.'])
elif tmp[0] == 'Leav... | flexible | {
"blob_id": "d9f66cc3ba40292c49da08d7573d4c605a2771ae",
"index": 3730,
"step-1": "<mask token>\n",
"step-2": "def solution(record):\n answer = []\n arr = dict()\n history = []\n for i in record:\n tmp = i.split()\n if tmp[0] == 'Enter':\n arr[tmp[1]] = tmp[2]\n h... | [
0,
1,
2
] |
#PortableKanban 4.3.6578.38136 - Encrypted Password Retrieval
#Python3 -m pip install des
#or
#pip install des
import json
import base64
from des import * #python3 -m pip install des, pip install des
import sys
def decode(hash):
hash = base64.b64decode(hash.encode('utf-8'))
key = DesKey(b"7ly6UznJ")
r... | normal | {
"blob_id": "136215a3ba99f74160373181c458db9bec4bb6b7",
"index": 977,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef decode(hash):\n hash = base64.b64decode(hash.encode('utf-8'))\n key = DesKey(b'7ly6UznJ')\n return key.decrypt(hash, initial=b'XuVUm5fR', padding=True).decode('utf-8')\n\n... | [
0,
1,
2,
3,
4
] |
test_case = int(input())
while test_case != 0:
test_case -= 1
(n, m) = map(int, input().split())
ans = n * m
A = []
for i in range(n):
t = list(map(int, input().split()))
A.append(t)
for i in range(1, n - 1):
for j in range(1, m - 1):
k = 1
while ... | normal | {
"blob_id": "dbc3e51fed63fe0fadea67d05c4b4efc693938a3",
"index": 1487,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile test_case != 0:\n test_case -= 1\n n, m = map(int, input().split())\n ans = n * m\n A = []\n for i in range(n):\n t = list(map(int, input().split()))\n ... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class OrgApacheJackrabbitOakSecurityAuthenticationTokenTokenConfiguraProperties(
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... | flexible | {
"blob_id": "0ddac0aac5bd001504ed37d31b74c6442304e350",
"index": 5729,
"step-1": "<mask token>\n\n\nclass OrgApacheJackrabbitOakSecurityAuthenticationTokenTokenConfiguraProperties(\n object):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask... | [
12,
18,
19,
22,
25
] |
from solution import find_days
import pudb
def test():
T = [1, 2, 3, 1, 0, 4]
# pudb.set_trace()
res = find_days(T)
assert res == [1, 1, 3, 2, 1, 0]
| normal | {
"blob_id": "db36c82717aa0bacffce7a3e2724ed2bb586c7fb",
"index": 7862,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef test():\n T = [1, 2, 3, 1, 0, 4]\n res = find_days(T)\n assert res == [1, 1, 3, 2, 1, 0]\n",
"step-3": "from solution import find_days\nimport pudb\n\n\ndef test():\n ... | [
0,
1,
2,
3
] |
#!/usr/bin/env python
# coding: utf-8
# In[ ]:
import numpy as np
import pickle
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_absolute_error
from pyspark.sql.functions import split, concat,col
from sklearn.svm import SVR
test = True
# In[ ]:
dbutils.widgets.removeAll()
d... | normal | {
"blob_id": "e48addecdde632607a9c782ff78a769122daab6f",
"index": 1738,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ndbutils.widgets.removeAll()\ndbutils.widgets.text('input_path', 'Not found', 'input_path')\n<mask token>\ndbutils.widgets.text('model_path', 'Not found', 'model_path')\n<mask token>\nif t... | [
0,
1,
2,
3,
4
] |
import hive
from ..bind import Instantiator as _Instantiator
from ..event import bind_info as event_bind_info
bind_infos = (event_bind_info,)
def build_scene_instantiator(i, ex, args, meta_args):
bind_bases = tuple((b_i.environment_hive for b_i in bind_infos if b_i.is_enabled(meta_args)))
# Update bind env... | normal | {
"blob_id": "23d4619527b5fce7fed0b0a66d834e26bb984129",
"index": 6443,
"step-1": "<mask token>\n\n\nclass SceneClass:\n\n def __init__(self):\n self._entities = {}\n self.scene = None\n\n def get_entity_id(self, identifier):\n return self._entities[identifier]\n\n def get_position_a... | [
9,
10,
11,
12,
14
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
#!/usr/bin/env python3
"""Test telegram_menu package."""
| flexible | {
"blob_id": "8d4ffed90e103e61a85a54d6163770966fb2e5c9",
"index": 5049,
"step-1": "<mask token>\n",
"step-2": "#!/usr/bin/env python3\n\n\"\"\"Test telegram_menu package.\"\"\"\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
} | [
0,
1
] |
# -*- coding: utf-8 -*-
# Generated by Django 1.11.6 on 2017-10-16 12:35
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('blog', '0033_auto_20171016_1334'),
]
operations = [
migrations.AlterField(
... | normal | {
"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
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print(s_stemmer.stem('writing'))
<|reserved_special_token_1|>
<|reserved_special_token_0|>
p_stemmer = PorterStemmer()
s_stemmer = SnowballStemmer(language='english')
print(s_stemmer.stem('writing'))
<|reserved_special_token_... | flexible | {
"blob_id": "67e6d39ef291e4bb30c0b6bab7b71d97c86b0ef1",
"index": 4108,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(s_stemmer.stem('writing'))\n",
"step-3": "<mask token>\np_stemmer = PorterStemmer()\ns_stemmer = SnowballStemmer(language='english')\nprint(s_stemmer.stem('writing'))\n",
"step-... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
cursor.execute(
'SELECT tweet_date, COUNT(*) FROM projekt_election.tweet as tweet , projekt_election.hashtag_use as use WHERE tweet.tweet_id = use.tweet_id GROUP BY tweet_date ORDER BY tweet_date ASC'
)
<|reserved_special_... | flexible | {
"blob_id": "076b852010ddcea69a294f9f2a653bb2fa2f2676",
"index": 3531,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ncursor.execute(\n 'SELECT tweet_date, COUNT(*) FROM projekt_election.tweet as tweet , projekt_election.hashtag_use as use WHERE tweet.tweet_id = use.tweet_id GROUP BY tweet_date ORDER ... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
@register(NewsModel)
class ProjectTranslationOptions(TranslationOptions):
fields = 'name', 'text'
<|reserved_special_token_1|>
<|reserved_special_token_0|>
@register(PageTitleModel)
class TitleTranslationOptions(TranslationOptions):
<|reserved_special_token_0|>
@register(Ne... | flexible | {
"blob_id": "9c29f04746de6847ad1bbdf08964d14e6c3766db",
"index": 8700,
"step-1": "<mask token>\n\n\n@register(NewsModel)\nclass ProjectTranslationOptions(TranslationOptions):\n fields = 'name', 'text'\n",
"step-2": "<mask token>\n\n\n@register(PageTitleModel)\nclass TitleTranslationOptions(TranslationOption... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class BackendSerializer(serializers.ModelSerializer):
class Meta:
model = Backend
fields = '__all__'
<|reserved_special_token_1|>
from rest_framework import serializers
from .models import Backend
clas... | flexible | {
"blob_id": "b4787d65fb8adf5dc6a99c1a13922c8f9acc2087",
"index": 1971,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass BackendSerializer(serializers.ModelSerializer):\n\n\n class Meta:\n model = Backend\n fields = '__all__'\n",
"step-3": "from rest_framework import serializers... | [
0,
1,
2
] |
# -*- coding: utf-8 -*-
# Generated by Django 1.11.6 on 2017-10-18 07:31
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
('auth', '0008_alter_user_username_max_length'),
]
operations = [... | normal | {
"blob_id": "ab343f88c84d45cf90bddd52623362f047c72d3c",
"index": 5754,
"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
] |
# Generated by Selenium IDE
import pytest
import time
import json
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.common.action_chains import ActionChains
from selenium.webdriver.support import expected_conditions
from selenium.webdriver.support.wait import WebDriverWa... | normal | {
"blob_id": "87f8cc65cf7d0ea932de79a6daf5b29ad387ec6f",
"index": 7103,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass TestSTCHANGE:\n <mask token>\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass TestSTCHANGE:\n\n def setup_method(self, method):\n self.d... | [
0,
1,
4,
5,
6
] |
<|reserved_special_token_0|>
@app.route('/hello/')
def hello():
return render_template('index.html', greeting='here we are')
<|reserved_special_token_0|>
@app.route('/api/1.0/create_playlists', methods=['POST'])
def do_create_playlists():
create_playlists(ALL_DBS)
retval = get_all_playlists(ALL_DBS)
... | flexible | {
"blob_id": "5193de15052f81460a23d993cfa039fa90c9de5e",
"index": 897,
"step-1": "<mask token>\n\n\n@app.route('/hello/')\ndef hello():\n return render_template('index.html', greeting='here we are')\n\n\n<mask token>\n\n\n@app.route('/api/1.0/create_playlists', methods=['POST'])\ndef do_create_playlists():\n ... | [
6,
9,
11,
13,
14
] |
<|reserved_special_token_0|>
def send_show_command(device, commands):
OutputPath = 'c:/script/output/' + str(device['host']) + '.txt'
result = open(OutputPath, 'w')
flag = True
try:
with ConnectHandler(**device) as ssh:
ssh.enable()
for command in commands:
... | flexible | {
"blob_id": "31a0c9a143a06ac86c8e8616fb273a0af844a352",
"index": 6895,
"step-1": "<mask token>\n\n\ndef send_show_command(device, commands):\n OutputPath = 'c:/script/output/' + str(device['host']) + '.txt'\n result = open(OutputPath, 'w')\n flag = True\n try:\n with ConnectHandler(**device) a... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def main():
"""
Trains an autoencoder on (generated) data and checks adversarial robustness
"""
architecture = [10, 5, 10]
print('----------Training autoencoder----------')
aut = autoencoder(architecture=archit... | flexible | {
"blob_id": "44e1208a2165fe68f71d0aa49baa29b26c961e02",
"index": 5681,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef main():\n \"\"\"\n\tTrains an autoencoder on (generated) data and checks adversarial robustness\n\t\"\"\"\n architecture = [10, 5, 10]\n print('----------Training autoenc... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class UserInfo(models.Model):
<|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... | flexible | {
"blob_id": "dbec74ecf488ca98f3f441e252f79bc2bc0959c1",
"index": 4068,
"step-1": "<mask token>\n\n\nclass UserInfo(models.Model):\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\n class Meta:\n verbose_n... | [
4,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
async def main(URL, buy_time):
browser, page = await get_window()
await page.goto(
'https://account.xiaomi.com/pass/serviceLogin?callback=http%3A%2F%2Forder.mi.com%2Flogin%2Fcallback%3Ffollowup%3Dhttps%253A%252F%... | flexible | {
"blob_id": "1e87f625fb7bd9f9bf4233229332c909702954a5",
"index": 4334,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nasync def main(URL, buy_time):\n browser, page = await get_window()\n await page.goto(\n 'https://account.xiaomi.com/pass/serviceLogin?callback=http%3A%2F%2Forder.mi.com%... | [
0,
1,
2,
3
] |
# Python 3 program - Currency Sum Validator
# def bill_count
def bill_count(amount_user, list_of_money_bills):
n = len(list_of_money_bills)
# Initialize Result
ans = []
# Traverse through all the list
i = n - 1
while (i >= 0):
# Find list
while (amount_user >= list_of_mo... | normal | {
"blob_id": "53c5f298dbfb21d7688fef8f0312858e2fd73d79",
"index": 4423,
"step-1": "<mask token>\n",
"step-2": "def bill_count(amount_user, list_of_money_bills):\n n = len(list_of_money_bills)\n ans = []\n i = n - 1\n while i >= 0:\n while amount_user >= list_of_money_bills[i]:\n am... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def get_sweeps(ref_params_d, n_writers):
params_d = copy.deepcopy(ref_params_d)
params_d['writer']['nprocs'].values = [n_writers]
params_d['writer']['decomposition'].values = [n_writers]
all_dicts = []
all_sweeps = []
for r in [8]:
par_r = copy.deepcopy(par... | flexible | {
"blob_id": "475cc5130e847b1a74a33bfa5cbc202a6bf31621",
"index": 6932,
"step-1": "<mask token>\n\n\ndef get_sweeps(ref_params_d, n_writers):\n params_d = copy.deepcopy(ref_params_d)\n params_d['writer']['nprocs'].values = [n_writers]\n params_d['writer']['decomposition'].values = [n_writers]\n all_di... | [
3,
4,
5,
6,
7
] |
from keras.preprocessing.image import img_to_array
from keras.models import load_model
import tensorflow as tf
import numpy as np
import argparse
import imutils
import pickle
import cv2
# USAGE
# python classify.py --model output/fashion.model --categorybin output/category_lb.pickle
# --colorbin output/color_lb.pickle... | normal | {
"blob_id": "8ff9961c1415c04899bbc15ba64811a1b3ade262",
"index": 3082,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nap.add_argument('-m', '--model', required=True, help=\n 'path to trained model model')\nap.add_argument('-l', '--categorybin', required=True, help=\n 'path to output category label ... | [
0,
1,
2,
3,
4
] |
#!/usr/bin/env python
import sys
def solve():
numEngines = int(sys.stdin.readline())
engines = []
for _ in range(numEngines):
engine = sys.stdin.readline()
engines.append(engine)
numQueries = int(sys.stdin.readline())
queries = []
for _ in range(numQueries):
query = sys.stdin.readline()
queries.append(... | normal | {
"blob_id": "174f5b04f02ec0c9651d5e34c8b04df8bfd4dff4",
"index": 1943,
"step-1": "#!/usr/bin/env python\n\nimport sys\n\ndef solve():\n\tnumEngines = int(sys.stdin.readline())\n\tengines = []\n\tfor _ in range(numEngines):\n\t\tengine = sys.stdin.readline()\n\t\tengines.append(engine)\n\n\tnumQueries = int(sys.s... | [
0
] |
"""Activate coverage at python startup if appropriate.
The python site initialisation will ensure that anything we import
will be removed and not visible at the end of python startup. However
we minimise all work by putting these init actions in this separate
module and only importing what is needed when needed.
For... | normal | {
"blob_id": "243794d36a1c6861c2c3308fe6a52ec19b73df72",
"index": 7820,
"step-1": "<mask token>\n\n\ndef multiprocessing_start(obj):\n cov = init()\n if cov:\n multiprocessing.util.Finalize(None, multiprocessing_finish, args=(\n cov,), exitpriority=1000)\n\n\n<mask token>\n\n\ndef init():\... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
class TestActor(Actor):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class TestActor(Actor):
<|reserved_special_token_0|>
def act(self):
self.key_commands(... | flexible | {
"blob_id": "9cb11c2bf032aa16abd3463ecdb8997addedc912",
"index": 1570,
"step-1": "<mask token>\n\n\nclass TestActor(Actor):\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass TestActor(Actor):\n <mask token>\n\n def act(self):\n self.key_commands()\n <m... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
def main():
N, K, D = map(int, input().split())
rules = [tuple(map(int, input().split())) for _ in range(K)]
minv, maxv = min([r[0] for r in rules]), max([r[1] for r in rules])
while minv + 1 < maxv:
midv = (minv + maxv) // 2
cnt, max_in = 0, 0
for ... | flexible | {
"blob_id": "f0b98a3d6015d57a49e315ac984cac1cccf0b382",
"index": 6084,
"step-1": "<mask token>\n\n\ndef main():\n N, K, D = map(int, input().split())\n rules = [tuple(map(int, input().split())) for _ in range(K)]\n minv, maxv = min([r[0] for r in rules]), max([r[1] for r in rules])\n while minv + 1 <... | [
1,
2,
3,
4,
5
] |
import turtle
def distance(x1, y1, x2, y2):
return ((x1 - x2) * (x1 - x2) + (y1 - y2) * (y1 - y2)) ** 0.5
x1, y1 = eval(input("Enter x1 and y1 for point 1: "))
x2, y2 = eval(input("Enter x2 and y2 for point 2: "))
distanceBetweenPoints = distance(x1, y1, x2, y2)
turtle.penup()
turtle.goto(x1, y1)
turtle.pendown... | normal | {
"blob_id": "9f8065dfdfe07985244e18d92b59e1c045388a72",
"index": 2557,
"step-1": "<mask token>\n\n\ndef distance(x1, y1, x2, y2):\n return ((x1 - x2) * (x1 - x2) + (y1 - y2) * (y1 - y2)) ** 0.5\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef distance(x1, y1, x2, y2):\n return ((x1 - x2) * (x1 - x2... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
def play_emergency_sound():
print('Playing emergency sound. There are ' + str(threading.
active_count()) + ' threads active')
while getattr(emergency_sound_thread, 'do_run', True):
pygame.mixer.init()
pygame.mixer.Channel(0).play(pygame.mixer.Sound(
... | flexible | {
"blob_id": "44274446673225c769f63191d43e4747d8ddfbf7",
"index": 6934,
"step-1": "<mask token>\n\n\ndef play_emergency_sound():\n print('Playing emergency sound. There are ' + str(threading.\n active_count()) + ' threads active')\n while getattr(emergency_sound_thread, 'do_run', True):\n pyga... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
OK = 200
CREATED = 201
NOT_MODIFIED = 304
UNAUTHORIZED = 401
FORBIDDEN = 403
BAD_REQUEST = 400
NOT_FOUND = 404
CONFLICT = 409
UNPROCESSABLE = 422
INTERNAL_SERVER_ERROR = 500
NOT_IMPLEMENTED = 501
SERVICE_UNAVAILABLE = 503
ADMIN = 'admin'
ELITE = 'elite'
NOOB ... | flexible | {
"blob_id": "d90942f22cbbd9cfc3a431b7857cd909a7690966",
"index": 92,
"step-1": "<mask token>\n",
"step-2": "OK = 200\nCREATED = 201\nNOT_MODIFIED = 304\nUNAUTHORIZED = 401\nFORBIDDEN = 403\nBAD_REQUEST = 400\nNOT_FOUND = 404\nCONFLICT = 409\nUNPROCESSABLE = 422\nINTERNAL_SERVER_ERROR = 500\nNOT_IMPLEMENTED = 5... | [
0,
1
] |
# -*- coding: utf-8 -*-
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties
from sklearn import svm
data=np.loadtxt('yucedata1.txt')
X=data[:,0]
y=data[:,1]
plt.figure(1,figsize=(8,6))
myfont = FontProperties(fname=r"c:\windo... | normal | {
"blob_id": "73d7b1895282df5b744d8c03ec7e6f8530366b76",
"index": 865,
"step-1": "# -*- coding: utf-8 -*-\r\nimport numpy as np\r\nimport matplotlib as mpl\r\nimport matplotlib.pyplot as plt \r\nfrom matplotlib.font_manager import FontProperties \r\nfrom sklearn import svm\r\n\r\n\r\ndata=np.loadtxt('yucedata1.tx... | [
0
] |
from Monument import Monument, Dataset
import importer_utils as utils
import importer as importer
class RoRo(Monument):
def set_adm_location(self):
counties = self.data_files["counties"]
self.set_from_dict_match(counties, "iso_code",
"judetul_iso", "located_adm")
... | normal | {
"blob_id": "5f8a9d82a3245671b438475d1fac7be4db769fbe",
"index": 8493,
"step-1": "<mask token>\n\n\nclass RoRo(Monument):\n\n def set_adm_location(self):\n counties = self.data_files['counties']\n self.set_from_dict_match(counties, 'iso_code', 'judetul_iso',\n 'located_adm')\n <mas... | [
4,
5,
8,
9,
11
] |
<|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": "6907a1e08d728732eebf81fec7c0dab8729448e2",
"index": 9712,
"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|>
def weights_init(m):
if type(m) == nn.Linear:
m.weight.data.normal_(0.0, 0.001)
m.bias.data.fill_(0.0)
def update_lr(optimizer, lr):
for param_group in optimizer.param_groups:
param_group['lr'] = lr
<|reserved_special_token_0|>
class ConvNet(nn.Module... | flexible | {
"blob_id": "0553bd4c7261197a1a80c5551305a16e7bfdc761",
"index": 2398,
"step-1": "<mask token>\n\n\ndef weights_init(m):\n if type(m) == nn.Linear:\n m.weight.data.normal_(0.0, 0.001)\n m.bias.data.fill_(0.0)\n\n\ndef update_lr(optimizer, lr):\n for param_group in optimizer.param_groups:\n ... | [
5,
6,
8,
9,
11
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
hacker_legends.append('Anonymous')
print(hacker_legends)
<|reserved_special_token_0|>
networking.insert(3, 'SSH')
print(networking)
<|reserved_special_token_0|>
ip_addy.remove(5102018)
print(ip_addy)
<|reserved_special_token_0|>
c... | flexible | {
"blob_id": "53fd020946a2baddb1bb0463d2a56744de6e3822",
"index": 5506,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nhacker_legends.append('Anonymous')\nprint(hacker_legends)\n<mask token>\nnetworking.insert(3, 'SSH')\nprint(networking)\n<mask token>\nip_addy.remove(5102018)\nprint(ip_addy)\n<mask token... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
from distributions.zero_inflated_poisson import ZeroInflatedPoisson
from distributions.negative_binomial import NegativeBinomial
from distributions.zero_inflated_negative_binomial import ZeroInflatedNegativeBinomial
from distributions.zero_inflated import Zer... | flexible | {
"blob_id": "dfae1007adc557a15d03b78f2bf790fb5b06141a",
"index": 4442,
"step-1": "<mask token>\n",
"step-2": "from distributions.zero_inflated_poisson import ZeroInflatedPoisson\nfrom distributions.negative_binomial import NegativeBinomial\nfrom distributions.zero_inflated_negative_binomial import ZeroInflated... | [
0,
1
] |
# Getting familiar with OOP and using Functions and Classes :)
class Dog():
species = 'mammal'
def __init__(self,breed,name):
self.breed = breed
self.name = name
def bark(self,number):
print(f'Woof! My name is {self.name} and the number is {number}')
my_dog = Dog('Corgi'... | normal | {
"blob_id": "c8137aacfb0f35c9630515442d5bdda870e9908a",
"index": 4827,
"step-1": "<mask token>\n\n\nclass Circle:\n <mask token>\n\n def __init__(self, radius=1):\n self.radius = radius\n self.area = radius * radius * Circle.pi\n\n def get_circumference(self):\n return self.radius *... | [
10,
11,
13,
16,
18
] |
<|reserved_special_token_0|>
def old_bracket(taxable_income, joint=True):
rate = [0.1, 0.15, 0.25, 0.28, 0.33, 0.35, 0.396]
if not joint:
bracket = [0, 9325, 37950, 91900, 191650, 416700, 418400]
else:
bracket = [0, 18650, 75900, 153100, 233350, 416700, 470700]
return tax_calculator(ta... | flexible | {
"blob_id": "70cb5673a13967247b6da1fa5948000db39a92c8",
"index": 7253,
"step-1": "<mask token>\n\n\ndef old_bracket(taxable_income, joint=True):\n rate = [0.1, 0.15, 0.25, 0.28, 0.33, 0.35, 0.396]\n if not joint:\n bracket = [0, 9325, 37950, 91900, 191650, 416700, 418400]\n else:\n bracket... | [
7,
10,
13,
15,
16
] |
marks = {
"S":"subject",
"O":"object",
"A":"attribute",
"C":"clause",
}
marks_reverse = {
"subject":"S",
"object":"O",
"attribute":"A",
"clause":"C",
}
| normal | {
"blob_id": "c66b07c45f4a675a6c7fcec82048a3197910d0d8",
"index": 3435,
"step-1": "<mask token>\n",
"step-2": "marks = {'S': 'subject', 'O': 'object', 'A': 'attribute', 'C': 'clause'}\nmarks_reverse = {'subject': 'S', 'object': 'O', 'attribute': 'A', 'clause': 'C'\n }\n",
"step-3": "marks = {\n \"S\":\"... | [
0,
1,
2
] |
#!/usr/bin/env python
import sys
total = 0
for line in sys.stdin:
edges = [int(x) for x in line.split("x")]
edges.sort()
ribbon = sum(x * 2 for x in edges[:2])
l, w, h = edges
bow = l * w * h
total += bow + ribbon
print(total)
| normal | {
"blob_id": "ed85cb61f4bc8bf758dafb10ffbabf87fb4521d0",
"index": 9281,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor line in sys.stdin:\n edges = [int(x) for x in line.split('x')]\n edges.sort()\n ribbon = sum(x * 2 for x in edges[:2])\n l, w, h = edges\n bow = l * w * h\n total +=... | [
0,
1,
2,
3,
4
] |
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