code stringlengths 13 6.09M | order_type stringclasses 2
values | original_example dict | step_ids listlengths 1 5 |
|---|---|---|---|
#library
import pandas as pd
import numpy as np
import sys
from tqdm import tqdm # appear the precess of running situation.
import time
from scipy.spatial.distance import pdist, squareform
#0. Data Load
data = pd.read_csv(sys.argv[1], delimiter='\t') # Load train (input text file)
#1. Data Preprocessing
all_element... | normal | {
"blob_id": "267695555e876dc2fe5820dc194490aad9e5e344",
"index": 1361,
"step-1": "<mask token>\n\n\ndef avg_dissim_within_group_element(node, element_list):\n max_diameter = -np.inf\n sum_dissm = 0\n for i in element_list:\n sum_dissm += dissimilarity_matrix[node][i]\n if dissimilarity_mat... | [
4,
5,
6,
8,
9
] |
from typing import Callable, List, Optional
import numpy as np
import lab1.src.grad.grad_step_strategy as st
import lab1.src.grad.stop_criteria as sc
DEFAULT_EPSILON = 1e-9
DEFAULT_MAX_ITERATIONS = 1e5
def gradient_descent(f: Callable[[np.ndarray], float],
f_grad: Callable[[np.ndarray], np.nda... | normal | {
"blob_id": "919e1f8a4b021d75496f3bcff369261a09362a65",
"index": 3645,
"step-1": "<mask token>\n\n\ndef gradient_descent(f: Callable[[np.ndarray], float], f_grad: Callable[[np\n .ndarray], np.ndarray], start: np.ndarray, step_strategy: st.\n StepStrategy, stop_criteria: sc.StopCriteria, eps_strategy: float... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
f.close()
<|reserved_special_token_0|>
for c, cl in jsonObject.items():
for d in cl:
d.update({'player': c})
l.append(d)
<|reserved_special_token_0|>
ax.set_xticks(labels)
ax.set_xticklabels(labels, rotation=45... | flexible | {
"blob_id": "33e9e45fbe0e3143d75d34c1db283c01e2693f68",
"index": 4967,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nf.close()\n<mask token>\nfor c, cl in jsonObject.items():\n for d in cl:\n d.update({'player': c})\n l.append(d)\n<mask token>\nax.set_xticks(labels)\nax.set_xticklabels(... | [
0,
1,
2,
3,
4
] |
# Generated by Django 2.2.13 on 2021-08-11 15:38
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
("notifications", "0011_auto_20171229_1747"),
]
operations = [
migrations.AlterField(
model_name="notification",
name... | normal | {
"blob_id": "fa045ccd4e54332f6c05bf64e3318e05b8123a10",
"index": 3317,
"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 = [('notificatio... | [
0,
1,
2,
3,
4
] |
# encoding: utf-8
"""
File: demo.py
Author: Rock Johnson
Description: 此文件为案例文件
"""
import sys
sys.path.append('../')
try:
from panicbuying.panic import Panic
except:
from panicbuying.panicbuying.panic import Panic
def main():
'''
公共参数:
store: 商城或书店名称(小米|文泉), browser: 浏览器(目前只支持Chrome),
versio... | normal | {
"blob_id": "2f8dff78f5bc5ed18df97e2574b47f0a7711d372",
"index": 547,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef main():\n \"\"\"\n 公共参数:\n store: 商城或书店名称(小米|文泉), browser: 浏览器(目前只支持Chrome),\n version: 浏览器版本号, quit: 运行完后是否退出浏览器(默认不退出),\n hidden: 是否启用界面(默认启用),\n\n 商城抢购:\n u... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print(parsed['var1'])
<|reserved_special_token_1|>
<|reserved_special_token_0|>
data = '{"var1": "harry", "var2":56}'
parsed = json.loads(data)
print(parsed['var1'])
<|reserved_special_token_1|>
import json
data = '{"var1": ... | flexible | {
"blob_id": "f0f9541eba29b4488c429c889f3b346d53d0239d",
"index": 7193,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(parsed['var1'])\n",
"step-3": "<mask token>\ndata = '{\"var1\": \"harry\", \"var2\":56}'\nparsed = json.loads(data)\nprint(parsed['var1'])\n",
"step-4": "import json\ndata = '{\... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def test_point3d_wkt():
p = GeometryPoint3D(10, 20, 30)
assert p.wkt == 'POINT Z (10 20 30)'
def test_point2d_to_shapely():
p = GeometryPoint2D(10, 20)
sp = p.to_shapely()
assert sp.x == 10
assert sp.y == 20
assert sp.wkt == p.wkt
<|reserved_special_token_0... | flexible | {
"blob_id": "ae45a4967a8ee63c27124d345ad4dc0c01033c0e",
"index": 6749,
"step-1": "<mask token>\n\n\ndef test_point3d_wkt():\n p = GeometryPoint3D(10, 20, 30)\n assert p.wkt == 'POINT Z (10 20 30)'\n\n\ndef test_point2d_to_shapely():\n p = GeometryPoint2D(10, 20)\n sp = p.to_shapely()\n assert sp.x... | [
2,
3,
4,
5,
6
] |
from django.contrib import admin
# Register your models here.
from .models import HuyenQuan
admin.site.register(HuyenQuan)
| normal | {
"blob_id": "16e5a44cb4fbe71eaa9c1f5b00505578de0d2cea",
"index": 6403,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nadmin.site.register(HuyenQuan)\n",
"step-3": "from django.contrib import admin\nfrom .models import HuyenQuan\nadmin.site.register(HuyenQuan)\n",
"step-4": "from django.contrib import... | [
0,
1,
2,
3
] |
from convert_data2 import array_rule
from convert_data2 import array_packet
import tensorflow as tf
import numpy as np
train_x, train_y = array_packet()
x_input, input_ip = array_rule()
n_nodes_hl1 = 210
n_nodes_hl2 = 210
n_nodes_hl3 = 210
n_classes = 2
batch_size = 500
hm_epochs = 20
x = tf.placeholder('float')
y ... | normal | {
"blob_id": "1446268583bf9fa3375319eae3c21cf47f47faca",
"index": 7279,
"step-1": "<mask token>\n\n\ndef neural_network_model(data):\n l1 = tf.add(tf.matmul(data, hidden_1_layer['weight']), hidden_1_layer[\n 'bias'])\n l1 = tf.nn.relu(l1)\n l2 = tf.add(tf.matmul(l1, hidden_2_layer['weight']), hidd... | [
2,
3,
4,
5,
6
] |
import sys
import sucessor
import expande
from collections import deque
def busca_caminho(nodo_final, nodo_inicial):
pilha_acoes = deque() # iremos empilhar as acoes já que a estaremos com a ordem reversa a priori
v = nodo_final
while v != nodo_inicial:
pilha_acoes.append(v.acao)
v = v.pai
return pilha_acoes
... | normal | {
"blob_id": "a85a7ad6ffb2b9aa5f5326d11c75ddbee680fac4",
"index": 673,
"step-1": "<mask token>\n\n\ndef busca_dfs(nodo_inicial, custo_maximo_atual):\n objetivo = '12345678_'\n custo_maximo_absoluto = 100\n explorados = set()\n fronteira = deque()\n fronteira.append(nodo_inicial)\n if custo_maxim... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def visualize_data(positive_images, negative_images):
figure = plt.figure()
count = 0
for i in range(positive_images.shape[0]):
count += 1
figure.add_subplot(2, positive_images.shape[0], count)
... | flexible | {
"blob_id": "ebe79cf1b54870055ce8502430f5fae833f3d96d",
"index": 3121,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef visualize_data(positive_images, negative_images):\n figure = plt.figure()\n count = 0\n for i in range(positive_images.shape[0]):\n count += 1\n figure.add_... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def linear_combination_plus_error(X, num_dependent_cols=5, parameter_mean=0,
parameter_std=1, error_mean=0, error_std=1):
"""
Generate a column that is a random linear combination of
X1, X2 and X3 plus some random error
"""
length = X.shape[0]
param = np.random... | flexible | {
"blob_id": "48f2cc5b6d53c7317ad882947cabbc367cda0fb7",
"index": 905,
"step-1": "<mask token>\n\n\ndef linear_combination_plus_error(X, num_dependent_cols=5, parameter_mean=0,\n parameter_std=1, error_mean=0, error_std=1):\n \"\"\"\n Generate a column that is a random linear combination of\n X1, X2 a... | [
1,
2,
3,
4,
5
] |
def descending_order(num):
return int(''.join(sorted(str(num), reverse=True)))
import unittest
class TestIsBalanced(unittest.TestCase):
def test_is_balanced(self):
self.assertEquals(descending_order(0), 0)
self.assertEquals(descending_order(15), 51)
self.assertEquals(descending_orde... | normal | {
"blob_id": "fc5d0dd16b87ab073bf4b054bd2641bdec88e019",
"index": 6594,
"step-1": "<mask token>\n\n\nclass TestIsBalanced(unittest.TestCase):\n\n def test_is_balanced(self):\n self.assertEquals(descending_order(0), 0)\n self.assertEquals(descending_order(15), 51)\n self.assertEquals(descen... | [
2,
3,
4,
5
] |
<|reserved_special_token_0|>
def switch_y_z(inter, liq_cutoff, vap_cutoff, liq_in, vap_in, int_in):
triangles = inter.triangulated_surface[0][inter.triangulated_surface[1]]
interface1 = np.zeros_like(triangles)
interface2 = np.zeros_like(triangles)
xlim, zlim, ylim = inter.universe.dimensions[0], inte... | flexible | {
"blob_id": "086c74669b6762a6b35e8a46f816db2f4f172caa",
"index": 1437,
"step-1": "<mask token>\n\n\ndef switch_y_z(inter, liq_cutoff, vap_cutoff, liq_in, vap_in, int_in):\n triangles = inter.triangulated_surface[0][inter.triangulated_surface[1]]\n interface1 = np.zeros_like(triangles)\n interface2 = np.... | [
2,
3,
4,
5,
6
] |
from enum import IntEnum
class DaqListType(IntEnum):
"""
This class describes a daq list type.
"""
DAQ = 0x01
STIM = 0x02
DAQ_STIM = 0x03 | normal | {
"blob_id": "71e0137fc02b4f56bdf87cc15c275f5cca1588c4",
"index": 8925,
"step-1": "<mask token>\n\n\nclass DaqListType(IntEnum):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass DaqListType(IntEnum):\n <mask token>\n DAQ = 1\n STIM = 2\n ... | [
1,
2,
3,
4,
5
] |
# Generated by Django 2.2.2 on 2019-07-17 10:02
from django.db import migrations, models
import django.db.models.deletion
import modelcluster.fields
class Migration(migrations.Migration):
dependencies = [
('users', '0003_delete_userprofile'),
]
operations = [
migrations.CreateModel(
... | normal | {
"blob_id": "cf2c57dbb2c1160321bcd6de98691db48634d5d6",
"index": 5388,
"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 = [('users', '00... | [
0,
1,
2,
3,
4
] |
import os
import numpy as np
from argparse import ArgumentParser
from collections import Counter
from typing import Iterable, Dict, Any, Tuple
from utils.constants import TRAIN, VALID, TEST, SAMPLE_ID, INPUTS, OUTPUT
from utils.file_utils import make_dir
from utils.data_writer import DataWriter
WINDOW = 50
STRIDE = ... | normal | {
"blob_id": "e82dd2792ecbb8ed5a33012239102d2c6a02202b",
"index": 1749,
"step-1": "<mask token>\n\n\ndef get_partition(subject_id: int) ->str:\n if subject_id <= 10:\n return TEST\n elif subject_id <= 15:\n return VALID\n else:\n return TRAIN\n\n\ndef data_generator(input_folder: str... | [
3,
4,
5,
6,
7
] |
import sqlite3
class DatabaseHands(object):
def __init__(self, database):
self.conn = sqlite3.connect(database)
self.cur = self.conn.cursor()
self.cur.execute("CREATE TABLE IF NOT EXISTS hands"
+ "(id INTEGER PRIMARY KEY, first INTEGER,"
+ ... | normal | {
"blob_id": "f8c85f34fb55ee1c3b3020bcec87b60ae80e4ed2",
"index": 3126,
"step-1": "<mask token>\n\n\nclass DatabaseHands(object):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n\nclass DatabaseProbability(object):\n\n def __init__(self, database):\n self.con... | [
13,
16,
17,
19,
20
] |
"""tables
Revision ID: 35f6815c3112
Revises: None
Create Date: 2013-07-28 21:15:38.385006
"""
# revision identifiers, used by Alembic.
revision = '35f6815c3112'
down_revision = None
from alembic import op
import sqlalchemy as sa
def upgrade():
### commands auto generated by Alembic - please adjust! ###
op... | normal | {
"blob_id": "9989d31dfe13809d67f629cc283cd02ce354a74e",
"index": 115,
"step-1": "<mask token>\n\n\ndef upgrade():\n op.create_table('users', sa.Column('id', sa.Integer(), nullable=False),\n sa.Column('firstname', sa.String(length=64), nullable=True), sa.\n Column('lastname', sa.String(length=64)... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def main():
lemonpie.debug = True
lemonpie.config['DEBUG_TB_INTERCEPT_REDIRECTS'] = False
toolbar = DebugToolbarExtension(lemonpie)
lemonpie.run('0.0.0.0')
<|reserved_special_token_0|>
<|reserved_special_toke... | flexible | {
"blob_id": "328c483bf59c6b84090e6bef8814e829398c5a56",
"index": 6954,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef main():\n lemonpie.debug = True\n lemonpie.config['DEBUG_TB_INTERCEPT_REDIRECTS'] = False\n toolbar = DebugToolbarExtension(lemonpie)\n lemonpie.run('0.0.0.0')\n\n\n<m... | [
0,
1,
2,
3,
4
] |
<|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": "d6e06a78c9a5d8184e5adf9b99cc6030c3434558",
"index": 8464,
"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', '001... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def missing_philippine_hokkien_words_generator(synonyms: ZhTopolectSynonyms,
hokprons: ZhTopolectPronunciations):
all_hokkien = set()
for word, syn_data in synonyms.all_words():
minnan = set(syn_data['Philippine-MN'])
minnan.update(syn_data['Quanzhou'])
... | flexible | {
"blob_id": "18366633489d905c96b0c30d65442bc2e2b188ea",
"index": 4703,
"step-1": "<mask token>\n\n\ndef missing_philippine_hokkien_words_generator(synonyms: ZhTopolectSynonyms,\n hokprons: ZhTopolectPronunciations):\n all_hokkien = set()\n for word, syn_data in synonyms.all_words():\n minnan = se... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
class Modell(Resource):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def put(self, name):
item = StoreModel.find_by_name(name)
item.save_to_db()
return item.json()
<|reserved_special_token_0|>
class Storelist(Resource):
def get(... | flexible | {
"blob_id": "5616ec135a2233e742ff3b2b1f378ec12298b935",
"index": 9578,
"step-1": "<mask token>\n\n\nclass Modell(Resource):\n <mask token>\n <mask token>\n\n def put(self, name):\n item = StoreModel.find_by_name(name)\n item.save_to_db()\n return item.json()\n <mask token>\n\n\nc... | [
4,
5,
6,
8,
9
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
e.showWindow()
<|reserved_special_token_1|>
<|reserved_special_token_0|>
e = Editor()
e.showWindow()
<|reserved_special_token_1|>
from editor.editor import Editor
e = Editor()
e.showWindow()
| flexible | {
"blob_id": "46d6771fd9f589e2498cd019ba72232cbda06e5a",
"index": 3108,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ne.showWindow()\n",
"step-3": "<mask token>\ne = Editor()\ne.showWindow()\n",
"step-4": "from editor.editor import Editor\ne = Editor()\ne.showWindow()\n",
"step-5": null,
"step-id... | [
0,
1,
2,
3
] |
def login():
usernameInput = input("Username : ")
passwordInput = input("Password : ")
if usernameInput == "admin" and passwordInput == "1234":
return (showMenu())
else:
print("User or Password Wrong.")
return login()
def showMenu():
print("---Please Choose Menu---")
prin... | normal | {
"blob_id": "34dd6966a971e3d32e82a17cd08c3b66bb88163b",
"index": 1277,
"step-1": "<mask token>\n\n\ndef showMenu():\n print('---Please Choose Menu---')\n print('1. Vat7')\n print('2. Calculation')\n print('3. Vat Calulation')\n return menuSelect()\n\n\n<mask token>\n\n\ndef priceResult():\n pri... | [
2,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
print('Welcome to the Guessing Game 2.0\n')
print('1 = Easy\t(1 - 10)')
print('2 = Medium\t(1 - 50)')
print('3 = Hard\t(1 - 100)')
<|reserved_special_token_1|>
print ("Welcome to the Guessing Game 2.0\n")
print ("1 = Easy\t(1 - 10)")
print ("2 = Medium\t(... | flexible | {
"blob_id": "7f2489aa440441568af153b231420aa2736716ca",
"index": 4052,
"step-1": "<mask token>\n",
"step-2": "print('Welcome to the Guessing Game 2.0\\n')\nprint('1 = Easy\\t(1 - 10)')\nprint('2 = Medium\\t(1 - 50)')\nprint('3 = Hard\\t(1 - 100)')\n",
"step-3": "print (\"Welcome to the Guessing Game 2.0\\n\"... | [
0,
1,
2
] |
import json
import requests
from pyyoutube import Api
def get_data(YOUTUBE_API_KEY, videoId, maxResults, nextPageToken):
"""
Получение информации со страницы с видео по video id
"""
YOUTUBE_URI = 'https://www.googleapis.com/youtube/v3/commentThreads?key={KEY}&textFormat=plainText&' + \
... | normal | {
"blob_id": "4ed5ceb784fb1e3046ab9f10c4b556f2e94274db",
"index": 7054,
"step-1": "<mask token>\n\n\ndef get_data(YOUTUBE_API_KEY, videoId, maxResults, nextPageToken):\n \"\"\"\n Получение информации со страницы с видео по video id\n \"\"\"\n YOUTUBE_URI = (\n 'https://www.googleapis.com/youtub... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
def line(start, end):
"""Draw line from start to end."""
up()
goto(start.x, start.y)
down()
goto(end.x, end.y)
def square(start, end):
"""Draw square from start to end."""
up()
goto(start.x, start.y)
down()
begin_fill()
for count in range(4):
... | flexible | {
"blob_id": "803283c9dac78c821373fa1025008b04919df72c",
"index": 5404,
"step-1": "<mask token>\n\n\ndef line(start, end):\n \"\"\"Draw line from start to end.\"\"\"\n up()\n goto(start.x, start.y)\n down()\n goto(end.x, end.y)\n\n\ndef square(start, end):\n \"\"\"Draw square from start to end.\... | [
7,
8,
9,
10,
11
] |
#!/usr/bin/env python
"""
maskAOI.py
Dan Fitch 20150618
"""
from __future__ import print_function
import sys, os, glob, shutil, fnmatch, math, re, numpy, csv
from PIL import Image, ImageFile, ImageDraw, ImageColor, ImageOps, ImageStat
ImageFile.MAXBLOCK = 1048576
DEBUG = False
AOI_DIR='/study/reference/public/IAP... | normal | {
"blob_id": "833053a5a75636267feaad5ddaa21dce1de34038",
"index": 5319,
"step-1": "<mask token>\n\n\ndef RepresentsInt(s):\n try:\n int(s)\n return True\n except ValueError:\n return False\n\n\n<mask token>\n\n\ndef drawOneEllipse(aoi, img, draw):\n if DEBUG:\n print('Ellipse ... | [
6,
8,
12,
13,
17
] |
'''This class contains a custom made format for printing complex numbers'''
class ComplexCustom(complex):
'''
This class contains function for
a custom made printing format for complex numbers
'''
def __format__(self, fmt):
'''This function creates a custom made format for printing complex n... | normal | {
"blob_id": "c62647b0b226d97926d1f53975a7aac7c39949d8",
"index": 7959,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass ComplexCustom(complex):\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass ComplexCustom(complex):\n <mask token>\n\n def __format__(self, fmt):\... | [
0,
1,
2,
3,
4
] |
from math import *
def heron(a, b, c):
tmp = [a, b, c]
tmp.sort()
if tmp[0] + tmp[1] <= tmp[-1]:
raise ValueError ("Warunek trojkata jest nie spelniony")
halfPerimeter = (a + b + c)/2
return sqrt(halfPerimeter * (halfPerimeter - a)*(halfPerimeter-b)*(halfPerimeter-c))
print heron... | normal | {
"blob_id": "bbd421d39894af163b56e7104c3b29a45635d5a3",
"index": 5425,
"step-1": "from math import *\r\n\r\ndef heron(a, b, c):\r\n tmp = [a, b, c]\r\n tmp.sort()\r\n if tmp[0] + tmp[1] <= tmp[-1]:\r\n raise ValueError (\"Warunek trojkata jest nie spelniony\")\r\n halfPerimeter = (a + b + c)/2... | [
0
] |
import warnings
warnings.filterwarnings('ignore', category=FutureWarning)
from cv2 import cv2
from tqdm import tqdm
import os
import pickle
import numpy as np
import csv
import sys
from collections import defaultdict
from dataset_utils import *
sys.path.append("../training")
from dataset_tools import enclosing_square... | normal | {
"blob_id": "0b7d1564ecbd78086d59629a2058716f41b4b8c8",
"index": 9686,
"step-1": "<mask token>\n\n\ndef get_emotion_label(emotion):\n return LABELS['emotion'][emotion]\n\n\ndef _load_meta_from_csv(csv_meta, output_dict):\n data = readcsv(csv_meta)\n for row in data:\n output_dict[row[0]]['gender'... | [
9,
12,
13,
18,
19
] |
from torch import nn
class MNIST3dModel(nn.Module):
def __init__(self, input_c=3, num_filters=8, num_classes=10):
super().__init__()
self.conv1 = nn.Conv3d(in_channels=input_c, out_channels=
num_filters, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv3d(in_channels=nu... | normal | {
"blob_id": "f6838906c961a9ca7d91d2ab02fd2af72797b880",
"index": 4628,
"step-1": "<mask token>\n\n\nclass MNIST3dModel(nn.Module):\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass MNIST3dModel(nn.Module):\n <mask token>\n\n def forward(self, x):\n x = self.conv1(x)\n ... | [
1,
2,
3,
4
] |
# -*- coding: utf-8 -*-
from django import forms
from django.utils.translation import ugettext_lazy as _
# import models
from apps.qa.models.coupon import Coupon
from apps.qa.models.coupon_type import CouponType
COUPONTYPE_CHOICES = (
('text', _("text")),
('url', _("url")),
('questionnaire', ... | normal | {
"blob_id": "a0f83f0a2c6ddaa2fc641bd4fa48a6f50fd1d978",
"index": 1755,
"step-1": "<mask token>\n\n\nclass CouponForm(forms.ModelForm):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def clean(self):\n cleaned_type = self.cleaned_data.get(... | [
2,
3,
4,
5,
6
] |
#!/usr/bin/python3
import os
import netifaces
# nicList = netifaces.interfaces()
NICList = [i for i in netifaces.interfaces() if i != "lo"]
for i in NICList:
os.system("sudo ifconfig " + i + " promisc")
os.system("sudo python ./src/top.py")
| normal | {
"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|>
class spotinst(terrascript.Provider):
pass
<|reserved_special_token_1|>
import terrascript
class spotinst(terrascript.Provider):
pass
<|reserved_special_token_1|>
# terrascript/spotinst/__init__.py
import terras... | flexible | {
"blob_id": "0ae626df5a471af77f7361bb765b46b861ee8a2c",
"index": 7142,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass spotinst(terrascript.Provider):\n pass\n",
"step-3": "import terrascript\n\n\nclass spotinst(terrascript.Provider):\n pass\n",
"step-4": "# terrascript/spotinst/__init... | [
0,
1,
2,
3
] |
import pandas as pd
import matplotlib.pyplot as plt
from netCDF4 import Dataset
from cftime import num2date
import os
import numpy as np
from datetime import datetime, timedelta, date
def plot_temperatures_by_country(values, country, start, end):
"""
Returns a plot for temperature values for a coun... | normal | {
"blob_id": "2b579c3def4c2d02d365f019518e8e0b25664460",
"index": 7436,
"step-1": "<mask token>\n\n\ndef plot_temperatures_by_country(values, country, start, end):\n \"\"\"\n Returns a plot for temperature values for a country\n from a start point to an end point\n \"\"\"\n filtered = values.loc[(v... | [
7,
8,
9,
10,
11
] |
from os import wait
import cv2
import numpy as np
import math
import sys
import types
import operator
## orb 및 bf matcher 선언
orb = cv2.cv2.ORB_create(
nfeatures=5000,
scaleFactor=1.2,
nlevels=8,
edgeThreshold=31,
... | normal | {
"blob_id": "73e7e43e9cfb3c0884480809bc03ade687d641d6",
"index": 733,
"step-1": "<mask token>\n\n\ndef getScale(NumFrame, t_gt, seq_num):\n txt_file = open('/media/cordin/새 볼륨/rosbag/dataset/poses/{0:02d}.txt'.\n format(seq_num))\n x_prev = float(t_gt[0])\n y_prev = float(t_gt[1])\n z_prev = f... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
class GpsDataBlockIndex(object):
def __init__(self, position: int, size: int):
if position <= 0:
raise ValueError(f"An invalid position: `{position}'.")
if size <= 0:
raise ValueError(f"An invalid size: `{size}'.")
self._position = posi... | flexible | {
"blob_id": "fbb1254c7166fa2aa9cd8a0b9c6525dbe5b652a0",
"index": 2625,
"step-1": "<mask token>\n\n\nclass GpsDataBlockIndex(object):\n\n def __init__(self, position: int, size: int):\n if position <= 0:\n raise ValueError(f\"An invalid position: `{position}'.\")\n if size <= 0:\n ... | [
47,
55,
64,
81,
82
] |
from __future__ import annotations
from typing import Generator, Optional
from collections import Counter
from itertools import zip_longest
from re import finditer
codon_table = """UUU F CUU L AUU I GUU V
UUC F CUC L AUC I GUC V
UUA L CUA L AUA I GUA V
UUG L CUG L ... | normal | {
"blob_id": "3d742505d480493fbc729e7a0febdcab3a7dc041",
"index": 9386,
"step-1": "<mask token>\n\n\nclass Seq:\n <mask token>\n\n def __init__(self, sequence: str, id: str=None, codons: dict=codons):\n self.sequence = sequence\n self.id = id\n self.codons = codons\n\n def __repr__(s... | [
20,
24,
31,
33,
35
] |
<|reserved_special_token_0|>
def enter():
global Start_menu
Start_menu = Menu()
menu_world.add_object(Start_menu, 0)
<|reserved_special_token_0|>
def handle_events():
global Start_menu, menu_time
events = get_events()
for event in events:
if event.type == SDL_QUIT:
game... | flexible | {
"blob_id": "fee2ddca5888c9db00d2d7a4fe11ba20c4e31685",
"index": 1909,
"step-1": "<mask token>\n\n\ndef enter():\n global Start_menu\n Start_menu = Menu()\n menu_world.add_object(Start_menu, 0)\n\n\n<mask token>\n\n\ndef handle_events():\n global Start_menu, menu_time\n events = get_events()\n ... | [
4,
6,
7,
8,
10
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
tkinter.filedialog.askopenfilename()
<|reserved_special_token_0|>
from_file.close()
<|reserved_special_token_0|>
to_file.write('Copy\n')
to_file.write(contents)
to_file.close()
<|reserved_special_token_1|>
<|reserved_special_to... | flexible | {
"blob_id": "0372cdbae8c5b0bbcbade86a5a7de28c1ee513b1",
"index": 2486,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ntkinter.filedialog.askopenfilename()\n<mask token>\nfrom_file.close()\n<mask token>\nto_file.write('Copy\\n')\nto_file.write(contents)\nto_file.close()\n",
"step-3": "<mask token>\ntkin... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class userSerializer(serializers.ModelSerializer):
class Meta:
model = User
fields = ['username', 'password', 'email']
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class dataSerializer(ser... | flexible | {
"blob_id": "972c479ea40232e14fbf678ca2ccf9716e473fe8",
"index": 9736,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass userSerializer(serializers.ModelSerializer):\n\n\n class Meta:\n model = User\n fields = ['username', 'password', 'email']\n",
"step-3": "<mask token>\n\n\ncl... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def shuffle():
l_digits = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
random.shuffle(l_digits)
return ''.join(l_digits)
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0... | flexible | {
"blob_id": "b0468e58c4d0387a92ba96e8fb8a876ece256c78",
"index": 6507,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef shuffle():\n l_digits = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']\n random.shuffle(l_digits)\n return ''.join(l_digits)\n\n\n<mask token>\n",
"step-3": "<mask ... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class RegisterView(View):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
class HomeView(View):
def get(self, request):
return HttpResponse(f'Home Page | Logged in as - {request.user}')
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class R... | flexible | {
"blob_id": "c9191df0fc04818b4df9c93a9479f75a60688aa9",
"index": 6372,
"step-1": "<mask token>\n\n\nclass RegisterView(View):\n <mask token>\n <mask token>\n\n\nclass HomeView(View):\n\n def get(self, request):\n return HttpResponse(f'Home Page | Logged in as - {request.user}')\n",
"step-2": "<... | [
3,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
class Point(object):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
class Rect(object):
"""A rectangle identified by its lower left
and upper right corners.
... | flexible | {
"blob_id": "7b9660bba6fcb8c725251971f3733a1cc915c0e7",
"index": 760,
"step-1": "<mask token>\n\n\nclass Point(object):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n\nclass Rect(object):\n \"\"\"A rectangle identified by its lower left\n and upper right corne... | [
9,
12,
16,
17,
18
] |
import pickle
import torch
data = pickle.load(open('dd0eb7901523d494d4aa324f474c782063e9e231.p', 'rb'))
torch.nn.functional.adaptive_avg_pool3d(**data)
| normal | {
"blob_id": "20d09a616133295a6162a7ab1d7970ccbaf6de95",
"index": 1331,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ntorch.nn.functional.adaptive_avg_pool3d(**data)\n",
"step-3": "<mask token>\ndata = pickle.load(open('dd0eb7901523d494d4aa324f474c782063e9e231.p', 'rb'))\ntorch.nn.functional.adaptive_a... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class Article:
<|reserved_special_token_0|>
title: str
target: str
g: float
f: float
parent: typing.Union[Article, Type(None)]
heuristic: Callable[[str, str], float]
def __init__(self, title: str, target: str, parent: typing.Union[
Article, Type(No... | flexible | {
"blob_id": "1fad591fde707c73bd52aa8518828c8b8be9cd32",
"index": 2283,
"step-1": "<mask token>\n\n\nclass Article:\n <mask token>\n title: str\n target: str\n g: float\n f: float\n parent: typing.Union[Article, Type(None)]\n heuristic: Callable[[str, str], float]\n\n def __init__(self, ti... | [
12,
15,
17,
18,
23
] |
import random
from datetime import datetime
from slackbot.bot import respond_to
from .term_model import Term, Response
from ..botmessage import botsend, botwebapi
# すでに存在するコマンドは無視する
RESERVED = (
'drive', 'manual', 'jira', 'wikipedia', 'plusplus',
'translate', '翻訳',
'weather', '天気',
'term',
'shuff... | normal | {
"blob_id": "86e97e7eaf0d23ccf4154b5ffc853c5aee966326",
"index": 5769,
"step-1": "<mask token>\n\n\n@respond_to('^term\\\\s+([\\\\w-]+)$')\n@respond_to('^term\\\\s+create\\\\s+([\\\\w-]+)$')\n@respond_to('^term\\\\s+add\\\\s+([\\\\w-]+)$')\ndef term_create(message, command):\n \"\"\"\n 指定されたコマンドを生成する\n ... | [
7,
9,
12,
15,
19
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print(art.guess)
print(art.the)
print(art.number)
print("I'm thinking of a number between 1 and 100")
<|reserved_special_token_0|>
if difficulty == 'easy':
turns += 10
else:
turns += 5
<|reserved_special_token_0|>
while no... | flexible | {
"blob_id": "f2bf4f5b057af1d2362ec8d1472aa76e774be1c7",
"index": 2736,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(art.guess)\nprint(art.the)\nprint(art.number)\nprint(\"I'm thinking of a number between 1 and 100\")\n<mask token>\nif difficulty == 'easy':\n turns += 10\nelse:\n turns += 5\... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def valid_anagram(filename):
f = open(filename, 'r')
lines = f.readlines()
f.close()
result = len(lines)
for line in lines:
split = line.rstrip().split(' ')
split = [sorted(s) for s in split]
... | flexible | {
"blob_id": "7dce240a891e807b1f5251a09a69368f4e513973",
"index": 4472,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef valid_anagram(filename):\n f = open(filename, 'r')\n lines = f.readlines()\n f.close()\n result = len(lines)\n for line in lines:\n split = line.rstrip().spl... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class LinkedList:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def atEnd(self, data):
NewNode = Node(data)
NewNode.nextVal = None
if self.headVal is None:
self.headVal = NewNode
return NewNode
last = self.he... | flexible | {
"blob_id": "00260e23614a7b0a11ff3649e71392e4892de423",
"index": 4511,
"step-1": "<mask token>\n\n\nclass LinkedList:\n <mask token>\n <mask token>\n\n def atEnd(self, data):\n NewNode = Node(data)\n NewNode.nextVal = None\n if self.headVal is None:\n self.headVal = NewNo... | [
6,
9,
12,
15,
17
] |
import pandas
import os
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.support.ui import Select
from selenium.common.exceptions import NoSuchElementException
import json
CONFIG_FILE_NAME = os.path.join(os.path.dirname(__file__), 'input_info.json')
def create_ne... | normal | {
"blob_id": "14cb702054b8caaa8899a2a3d8b65aae9b063cb6",
"index": 5600,
"step-1": "<mask token>\n\n\ndef create_new_report(chrome_driver_inner, report_info_inner):\n add_new_report = chrome_driver_inner.find_element_by_id(\n 'MainContent_MainActionCreate')\n add_new_report.click()\n next_button = ... | [
2,
3,
4,
5,
6
] |
import json
def main():
with open('./src/test/predictions.json', 'r') as f:
data = json.load(f)
total = len(data['label'])
google = 0
sphinx = 0
for i in range(len(data['label'])):
label = data['label'][i]
google_entry = data['google'][i]
sphinx_entry = data['p... | normal | {
"blob_id": "9fc184fe3aa498138138403bef719c59b85b3a80",
"index": 4392,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef main():\n with open('./src/test/predictions.json', 'r') as f:\n data = json.load(f)\n total = len(data['label'])\n google = 0\n sphinx = 0\n for i in range(l... | [
0,
1,
2,
3,
4
] |
#coding=UTF-8
import random
import random
list=[]
s=0
for i in range(1,5):
for j in range(1,5):
for k in range(1,5):
if i!=j and j<>k:
list.append(str(i)+str(j)+str(k))
s=s+1
print len(list)
print s
if len(list)==s:
print "是相等的！"
else:
print "不相等！"
print l... | normal | {
"blob_id": "fa07553477e3bb2ecbeb87bd1383a2194282579c",
"index": 4081,
"step-1": "#coding=UTF-8\nimport random\nimport random\nlist=[]\ns=0\nfor i in range(1,5):\n for j in range(1,5):\n for k in range(1,5):\n if i!=j and j<>k:\n list.append(str(i)+str(j)+str(k))\n ... | [
0
] |
<|reserved_special_token_0|>
class ICrawlerLog:
<|reserved_special_token_0|>
def __init__(self, name, logger=None):
self.logger = logger
self.name = name
@property
def save(self, *args, **kwargs):
"""
指定保存日志的文件路径，日志级别，以及调用文件
将日志存入到指定的文件中
"""
... | flexible | {
"blob_id": "63001128d9cb934d6f9d57db668a43ba58f4ece3",
"index": 1679,
"step-1": "<mask token>\n\n\nclass ICrawlerLog:\n <mask token>\n\n def __init__(self, name, logger=None):\n self.logger = logger\n self.name = name\n\n @property\n def save(self, *args, **kwargs):\n \"\"\"\n ... | [
3,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
def all_subsets(ss, i):
return chain(*map(lambda x: combinations(ss, x), range(i, i + 1)))
<|reserved_special_token_0|>
def f1(i):
return wi[i[0] - 1] * max(0, pi[i[0] - 1] - di[i[0] - 1])
def f2(i):
ci = 0
for j in range(len(i)):
ci += pi[int(i[j]) - 1]
... | flexible | {
"blob_id": "ddab4d014c000dd96bad932adac75e4eec065483",
"index": 9644,
"step-1": "<mask token>\n\n\ndef all_subsets(ss, i):\n return chain(*map(lambda x: combinations(ss, x), range(i, i + 1)))\n\n\n<mask token>\n\n\ndef f1(i):\n return wi[i[0] - 1] * max(0, pi[i[0] - 1] - di[i[0] - 1])\n\n\ndef f2(i):\n ... | [
3,
5,
6,
7,
8
] |
# Get Facebook's bAbi dataset
from utils import maybe_download
from shutil import rmtree
import os
import tarfile
def get_babi_en(get_10k=False):
data_dir = "datasets/tasks_1-20_v1-2/en/"
if get_10k == True:
data_dir = "datasets/tasks_1-20_v1-2/en-10k/"
maybe_download('https://s3.amazonaws... | normal | {
"blob_id": "7a4d04bd60b5f5555982af372145f9f4bcd83ca2",
"index": 8194,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef get_babi_en(get_10k=False):\n data_dir = 'datasets/tasks_1-20_v1-2/en/'\n if get_10k == True:\n data_dir = 'datasets/tasks_1-20_v1-2/en-10k/'\n maybe_download(\n ... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def create_user(open_ldap, smtp, entries):
"""
If the 'ldap_insert' returns True, then
the email will be send with the account info.
"""
try:
if open_ldap.ldap_insert(entries):
smtp.send_email(entries)
return True
else:
... | flexible | {
"blob_id": "4f0a0089ad128edca3052da58a4c71f935592e25",
"index": 4499,
"step-1": "<mask token>\n\n\ndef create_user(open_ldap, smtp, entries):\n \"\"\"\n If the 'ldap_insert' returns True, then\n the email will be send with the account info.\n \"\"\"\n try:\n if open_ldap.ldap_insert(entrie... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
def process_all(folder):
exclude = ['OpenITI.github.io', 'Annotation', '_maintenance', 'i.mech']
for root, dirs, files in os.walk(folder):
dirs[:] = [d for d in dirs if d not in exclude]
for file in files:
if re.search('^\\d{4}\\w+\\.\\w+\\.\\w+-ara\\d$... | flexible | {
"blob_id": "5c001303962315afe2512eb307376f6f7a883cf9",
"index": 6831,
"step-1": "<mask token>\n\n\ndef process_all(folder):\n exclude = ['OpenITI.github.io', 'Annotation', '_maintenance', 'i.mech']\n for root, dirs, files in os.walk(folder):\n dirs[:] = [d for d in dirs if d not in exclude]\n ... | [
1,
3,
4,
5,
6
] |
from helper import *
async def main(URL, buy_time):
browser, page = await get_window()
# 30s登陆时间
await page.goto('https://account.xiaomi.com/pass/serviceLogin?callback=http%3A%2F%2Forder.mi.com%2Flogin%2Fcallback%3Ffollowup%3Dhttps%253A%252F%252Fwww.mi.com%252F%26sign%3DNzY3MDk1YzczNmUwMGM4ODAxOWE0NjRiNTU... | normal | {
"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
] |
<|reserved_special_token_0|>
def test_index_with_url():
with Client(app.app) as client:
response = client.http.get('/?url=https://google.com')
assert response.status_code == HTTPStatus.MOVED_PERMANENTLY
assert response.headers['Location'] is not None
<|reserved_special_token_0|>
<|rese... | flexible | {
"blob_id": "e7e9a53d4c41448521b324d51641a46827faa692",
"index": 2607,
"step-1": "<mask token>\n\n\ndef test_index_with_url():\n with Client(app.app) as client:\n response = client.http.get('/?url=https://google.com')\n assert response.status_code == HTTPStatus.MOVED_PERMANENTLY\n assert ... | [
1,
2,
3,
4,
5
] |
import re
def detectPeriod(data):
numWord = "[0-9,一二三四五六七八九十兩半]"
hourWord = "小時鐘頭"
minWord = "分鐘"
secWord = "秒鐘"
timePat = "["+numWord+"]+點?\.?["+numWord+"]*個?半?["+hourWord+"]*半?又?["+numWord+"]*["+minWord+"]*又?["+numWord+"]*["+secWord+"]*"
def main():
detectPeriod("我要去游泳一個小時")
if _... | normal | {
"blob_id": "397686964acbf640a5463a3a7095d85832545d9e",
"index": 6462,
"step-1": "<mask token>\n\n\ndef main():\n detectPeriod('我要去游泳一個小時')\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef detectPeriod(data):\n numWord = '[0-9,一二三四五六七八九十兩半]'\n hourWord = '小時鐘頭'\n minWord = '分鐘'\n secWord =... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for i in range(1, n + 1):
for j in range(1, n + 1):
if i == j:
graph[i][j] = 0
for _ in range(m):
a, b = map(int, Read().split())
graph[a][b] = 1
for k in range(1, n + 1):
for i in range(1, n + ... | flexible | {
"blob_id": "6ec39aa712c8abe610418e410883ff168d73126d",
"index": 3292,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(1, n + 1):\n for j in range(1, n + 1):\n if i == j:\n graph[i][j] = 0\nfor _ in range(m):\n a, b = map(int, Read().split())\n graph[a][b] = 1\nfo... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class LoginPageTests(RegistrationBaseTestCase):
def test_can_open_whatsapp_login_page(self):
self.assertTrue(self.login_page.is_title_matches())
self.assertTrue(self.login_page.is_instruction_title_matches())
def test_checkbox_remember_me_is_checked_by_default(se... | flexible | {
"blob_id": "380a28958fc6d1b403b29ede229860bf5f709572",
"index": 2550,
"step-1": "<mask token>\n\n\nclass LoginPageTests(RegistrationBaseTestCase):\n\n def test_can_open_whatsapp_login_page(self):\n self.assertTrue(self.login_page.is_title_matches())\n self.assertTrue(self.login_page.is_instruct... | [
8,
10,
12,
13,
14
] |
import src.integralimage as II
import src.adaboost as AB
import src.utils as UT
import numpy as np
if __name__ == "__main__":
pos_training_path = 'dataset-1/trainset/faces'
neg_training_path = 'dataset-1/trainset/non-faces'
pos_testing_path = 'dataset-1/testset/faces'
neg_testing_path = 'dataset-1/tes... | normal | {
"blob_id": "3f4f60ff315c8e7e4637a84629894012ed13280e",
"index": 3163,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n pos_training_path = 'dataset-1/trainset/faces'\n neg_training_path = 'dataset-1/trainset/non-faces'\n pos_testing_path = 'dataset-1/testset/faces'\n ... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def training(dict):
model = {}
model['µ'] = {}
model['sigma'] = {}
for x in dict:
model['µ'][x] = {}
model['sigma'][x] = {}
for y in dict[x]:
model['µ'][x][y] = {}
... | flexible | {
"blob_id": "6726c8f1b3ef9a0df74c25c1921203af3aaacb12",
"index": 8758,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef training(dict):\n model = {}\n model['µ'] = {}\n model['sigma'] = {}\n for x in dict:\n model['µ'][x] = {}\n model['sigma'][x] = {}\n for y in dic... | [
0,
1,
2,
3,
4
] |
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
dataset = pd.read_csv('Position_Salaries.csv')
X = dataset.iloc[:, 1:-1].values
y = dataset.iloc[:, dataset.shape[1]-1].values
#Fitting the Decision Tree Regression
from sklearn.tree import DecisionTreeRegressor
regressor = DecisionTreeRegressor(r... | normal | {
"blob_id": "c8565e1b5659dd0908aabf91e07738a798dc3232",
"index": 1366,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nregressor.fit(X, y)\n<mask token>\nplt.scatter(X, y, color='red')\nplt.plot(X_grid, regressor.predict(X_grid), color='blue')\nplt.scatter(6.5, y_pred, color='green')\nplt.title('Salary vs... | [
0,
1,
2,
3,
4
] |
from setuptools import setup, find_packages
setup(
name="champ",
version="0.0.1",
description='Channel modeling in Python',
url='https://github.com/sgherbst/champ',
author='Steven Herbst',
author_email='sherbst@stanford.edu',
packages=['champ'],
include_package_data=True,
zip_safe=F... | normal | {
"blob_id": "885fd32c9520dfdc2becd6b1a3d0c0f5f5397112",
"index": 7449,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsetup(name='champ', version='0.0.1', description=\n 'Channel modeling in Python', url='https://github.com/sgherbst/champ',\n author='Steven Herbst', author_email='sherbst@stanford.e... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
from .lasot import Lasot
from .got10k import Got10k
from .tracking_net import TrackingNet
from .imagenetvid import ImagenetVID
from .imagenetdet import ImagenetDET
from .coco_seq import MSCOCOSeq
from .vot import VOT
from .youtube_vos import YoutubeVOS
from .... | flexible | {
"blob_id": "e12ca2c4592a629ce78cae7211fedaf02352a603",
"index": 4700,
"step-1": "<mask token>\n",
"step-2": "from .lasot import Lasot\nfrom .got10k import Got10k\nfrom .tracking_net import TrackingNet\nfrom .imagenetvid import ImagenetVID\nfrom .imagenetdet import ImagenetDET\nfrom .coco_seq import MSCOCOSeq\... | [
0,
1
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def loudness_zwst(signal, fs=None, field_type='free', is_sdt_output=False):
"""Zwicker-loudness calculation for stationary signals
Calculates the acoustic loudness according to Zwicker method for
stationary signals.... | flexible | {
"blob_id": "75716aaaca63f8ca6d32c885021c1dc0f9a12dac",
"index": 793,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef loudness_zwst(signal, fs=None, field_type='free', is_sdt_output=False):\n \"\"\"Zwicker-loudness calculation for stationary signals\n\n Calculates the acoustic loudness accor... | [
0,
1,
2,
3,
4
] |
import time
import json
import pygame
from pygame.locals import *
import urllib.request
from pygame.color import THECOLORS
pygame.init()
Brack=[0,0,0]
White=[255,255,255]
Green=[0,255,0]
Red=[255,0,0]
Gray=[169,169,169]
button_text=["开 始","开 始","开 始","开 始","开 始"]
line=['http://localhost:5050/mixer/000','http://localhos... | normal | {
"blob_id": "609071fc3af1b526fbd4555ced2376f56ae0f3c3",
"index": 2174,
"step-1": "<mask token>\n\n\ndef Process(num, x, y, button_text, color):\n text_fmt1 = text_1.render(text[num], 1, Brack)\n screen.blit(text_fmt1, (x - 127, y))\n pygame.draw.rect(screen, Brack, [x, y, 60, 25], 2)\n pygame.draw.re... | [
2,
3,
4,
5,
6
] |
import iris
import numpy as np
import matplotlib.pyplot as plt
import glob
import iris.analysis.cartography
import iris.coord_categorisation
import iris.analysis
import time
def my_callback(cube, field, filename):
cube.remove_coord('forecast_reference_time')
cube.remove_coord('forecast_period')
... | normal | {
"blob_id": "6ea651e27620d0f26f7364e6d9d57e733b158d77",
"index": 6466,
"step-1": "import iris\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport glob\nimport iris.analysis.cartography\nimport iris.coord_categorisation\nimport iris.analysis\nimport time\n\ndef my_callback(cube, field, filename):\n ... | [
0
] |
#!/usr/bin/env python
# -*- coding:utf-8 _*-
"""
:Author :weijinlong
:Time: :2020/1/10 17:22
:File :graph.py
:content:
"""
import tensorflow as tf
from .base import TFLayer
class TFModel(TFLayer):
def build_model(self):
raise NotImplementedError
def add_outputs(self, *a... | normal | {
"blob_id": "cdabb4a118cb0ef55c271a446fa190a457ebe142",
"index": 7383,
"step-1": "<mask token>\n\n\nclass TFCompile(TFLayer):\n <mask token>\n <mask token>\n <mask token>\n\n\nclass TFComModel(TFModel, TFCompile):\n \"\"\"\n 基于TensorFlow的复合模型，即使用一个算子构建模型的和模型的编译\n \"\"\"\n\n def build_model(s... | [
5,
8,
10,
11,
13
] |
import pulumi
import pulumi_aws as aws
bar = aws.elasticache.get_replication_group(replication_group_id="example")
| normal | {
"blob_id": "4bf140ae01f2eaa0c67f667766c3ec921d552066",
"index": 6073,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nbar = aws.elasticache.get_replication_group(replication_group_id='example')\n",
"step-3": "import pulumi\nimport pulumi_aws as aws\nbar = aws.elasticache.get_replication_group(replicati... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class SlidingDoorIllustration(Scene):
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class SlidingDoorIllustration(Scene):
def construct(self):
waiting_room = Rectang... | flexible | {
"blob_id": "e93d5461a2604d3b8015489397c68e16d1cb222e",
"index": 3695,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass SlidingDoorIllustration(Scene):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass SlidingDoorIllustration(Scene):\n\n def construct(self):\n waiting_room = Re... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
assert ec.scale(4, ec.order) == 0
<|reserved_special_token_0|>
print('Factoring...')
<|reserved_special_token_0|>
for i in range(2, 2 ** 24):
if x % i == 0:
if x % (i * i) != 0:
factors.append(i)
x ... | flexible | {
"blob_id": "b5275fc068526063fd8baf13210052971b05503f",
"index": 585,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nassert ec.scale(4, ec.order) == 0\n<mask token>\nprint('Factoring...')\n<mask token>\nfor i in range(2, 2 ** 24):\n if x % i == 0:\n if x % (i * i) != 0:\n factors.app... | [
0,
1,
2,
3,
4
] |
from utils import *
import math
class State:
"This class represents the search state that will be used for ARA* search"
def __init__(self, x, y, theta, parent=None, parent_action=None, g=float('inf'), h=float('inf')):
self.x = x
self.y = y
self.theta = theta % (2*math.pi)
self.g... | normal | {
"blob_id": "c8f899958ce19e7e2bf1307a685e65873695f140",
"index": 9028,
"step-1": "<mask token>\n\n\nclass State:\n <mask token>\n\n def __init__(self, x, y, theta, parent=None, parent_action=None, g=\n float('inf'), h=float('inf')):\n self.x = x\n self.y = y\n self.theta = theta... | [
8,
9,
10,
11,
12
] |
class MyClass:
<|reserved_special_token_0|>
def set_name(self, name):
self.name = name
def get_name(self):
return self.name
def say_hello(self):
self.greet = 'Hello'
def say_hi(self):
print('HI~~~~~')
<|reserved_special_token_0|>
<|reserved_special_token_1|>
... | flexible | {
"blob_id": "babb5ac680c74e19db5c86c2c3323e8285d169ff",
"index": 9939,
"step-1": "class MyClass:\n <mask token>\n\n def set_name(self, name):\n self.name = name\n\n def get_name(self):\n return self.name\n\n def say_hello(self):\n self.greet = 'Hello'\n\n def say_hi(self):\n ... | [
5,
6,
7,
8,
9
] |
#!/usr/bin/env python3
"""Initiates connection to AWSIoT and provides helper functions
deviceshadowhandler.py
by Darren Dunford
"""
import json
import logging
import queue
from AWSIoTPythonSDK.MQTTLib import AWSIoTMQTTShadowClient
LOGGER = logging.getLogger(__name__)
class DeviceShadowHandler:
def status_pos... | normal | {
"blob_id": "a6d409b806dbd1e174cac65a26c5e8106a8b93ea",
"index": 3760,
"step-1": "<mask token>\n\n\nclass DeviceShadowHandler:\n\n def status_post(self, status, state=None):\n \"\"\"Post status message and device state to AWSIoT and LOGGER\n\n :param status: status string\n :param state: ... | [
6,
8,
9,
10,
12
] |
<|reserved_special_token_0|>
def write_fasta_file(pdb_names, pdb_sequences, filename, dump_dir=''):
"""
Use a list of <pdb_names> and their corresponding <pdb_sequences> to write out a FASTA formatted file
Need a <filename> to work with. Include a path to a dump directory, if desired
:param pdb_names:... | flexible | {
"blob_id": "876e9f03c908338a247b6bf1f23011e609bbc2a5",
"index": 8739,
"step-1": "<mask token>\n\n\ndef write_fasta_file(pdb_names, pdb_sequences, filename, dump_dir=''):\n \"\"\"\n Use a list of <pdb_names> and their corresponding <pdb_sequences> to write out a FASTA formatted file\n Need a <filename> ... | [
1,
2,
3,
4,
5
] |
import re
import z3
digit_search = re.compile('\-?\d+')
def get_sensor_beacon(data_in):
sensors = {}
beacons = set()
for line in data_in:
s_x, s_y, b_x, b_y = list(map(int, digit_search.findall(line)))
sensors[(s_x, s_y)] = abs(s_x - b_x) + abs(s_y - b_y)
beacons.add((b_x, b_y))
... | normal | {
"blob_id": "c4bd55be86c1f55d89dfcbba2ccde4f3b132edcb",
"index": 9981,
"step-1": "<mask token>\n\n\ndef manhat(point_one, point_two):\n return abs(point_one[0] - point_two[0]) + abs(point_one[1] - point_two[1])\n\n\ndef find_edge(sensors, pos, dir):\n x, row = pos\n closer = []\n for sensor in sensor... | [
3,
4,
5,
7,
9
] |
import sys, os
sys.path.append(os.pardir) # 親ディレクトリのファイルをインポートするための設定
import numpy as np
from dataset.mnist import load_mnist
from controller import Controller
# データの読み込み
(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True, one_hot_label=True)
# instance
controller = Controller()
# accuracy
trycount = ... | normal | {
"blob_id": "c2d8e34ab0b449a971c920fc86f259f093f16cc5",
"index": 7156,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsys.path.append(os.pardir)\n<mask token>\nfor i in range(len(x_test)):\n p = controller.accuracy(x_test[i])\n a = np.argmax(t_test[i])\n result[p][a] += 1\n if p == a:\n ... | [
0,
1,
2,
3,
4
] |
# ---------------------------------------------------------------------
# Iskratel.ESCOM.get_version
# ---------------------------------------------------------------------
# Copyright (C) 2007-2018 The NOC Project
# See LICENSE for details
# ---------------------------------------------------------------------
# Pyth... | normal | {
"blob_id": "40b3c403f99044eb61740d62eda15ddd08b0f739",
"index": 1980,
"step-1": "<mask token>\n\n\nclass Script(BaseScript):\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 <m... | [
1,
2,
3,
4,
5
] |
...
...
model = Sequential()
model.add(Conv2D(32, kernel_size=3, input_shape=(256, 256, 3))
...
...
| normal | {
"blob_id": "ad054febac3a04c625653a2f3864506eeb672d9e",
"index": 6273,
"step-1": "...\n...\nmodel = Sequential()\nmodel.add(Conv2D(32, kernel_size=3, input_shape=(256, 256, 3))\n...\n...\n",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
} | [
0
] |
<|reserved_special_token_0|>
class Cellule:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def basculer(self):
"""mutateur qui change l'état actuel de la ... | flexible | {
"blob_id": "cef904b70eb9a997c3c48884ee34665a77e18897",
"index": 8465,
"step-1": "<mask token>\n\n\nclass Cellule:\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def basculer(self):\n \"\"\"mutateur qui change l'état actuel de la cellule ... | [
20,
23,
24,
28,
32
] |
#coding=utf-8
from selenium import webdriver
from selenium.webdriver import ActionChains
# 常用鼠标操作
driver = webdriver.Chrome()
driver.get('https://www.baidu.com')
driver.maximize_window()
element = driver.find_element_by_link_text(u"新闻")
#˫ 双击 ‘新闻’ 这个超链接
ActionChains(driver).double_click(element).perform()
import time
... | normal | {
"blob_id": "e3f180d4309ade39ac42a895f7f73469fd20724f",
"index": 4538,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ndriver.get('https://www.baidu.com')\ndriver.maximize_window()\n<mask token>\nActionChains(driver).double_click(element).perform()\n<mask token>\ntime.sleep(2)\ndriver.quit()\n<mask token>... | [
0,
1,
2,
3,
4
] |
import vk_loader.vk_api as vk
from config import config
import uuid
import requests
from models import session, Meme
import os
PHOTO_URL_FIELDS = [
'photo_75',
'photo_130',
'photo_604',
'photo_807',
'photo_1280',
'photo_2560'
]
conf = config('loader', default={
'access_token': 'Enter VK a... | normal | {
"blob_id": "cb742701094a8060e524ba22a0af2f969bdbf3d9",
"index": 2365,
"step-1": "<mask token>\n\n\ndef get_random_id():\n return uuid.uuid4().hex\n\n\n<mask token>\n\n\ndef get_last_loaded_ids(source_id):\n try:\n with open('vk_loader/loaded_ids/' + str(source_id), 'r') as file:\n return... | [
5,
7,
10,
11,
12
] |
from flask import Flask, request, g
from flask_restful import Resource, Api
from sqlalchemy import create_engine
from flask import jsonify
import json
import eth_account
import algosdk
from sqlalchemy.orm import sessionmaker
from sqlalchemy.orm import scoped_session
from sqlalchemy.orm import load_only
from datetime im... | normal | {
"blob_id": "d9bdf466abecb50c399556b99b41896eead0cb4b",
"index": 2959,
"step-1": "<mask token>\n\n\n@app.before_request\ndef create_session():\n g.session = scoped_session(DBSession)\n\n\n<mask token>\n\n\ndef check_sig(payload, sig):\n pk = payload['sender_pk']\n platform = payload['platform']\n pay... | [
7,
8,
11,
12,
13
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
from . import *
from module import *
from transfer import *
from dataset import *
| flexible | {
"blob_id": "94d992ef4b9015aa8f42071bb1409703d509c313",
"index": 9810,
"step-1": "<mask token>\n",
"step-2": "from . import *\nfrom module import *\nfrom transfer import *\nfrom dataset import *\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
} | [
0,
1
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def main():
"""Restaurant"""
moeny = int(input())
service = moeny * 0.1
vat = moeny * 0.07
print('Service Charge : %.2f Baht' % service)
print('VAT : %.2f Baht' % vat)
print('Total : %.2f Baht' % (moe... | flexible | {
"blob_id": "ae6cbb181e024b8c0b222d14120b910919f8cc81",
"index": 3811,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef main():\n \"\"\"Restaurant\"\"\"\n moeny = int(input())\n service = moeny * 0.1\n vat = moeny * 0.07\n print('Service Charge : %.2f Baht' % service)\n print('VAT... | [
0,
1,
2,
3
] |
# https://daphne-dev.github.io/2020/09/24/algo-022/
def solution(n):
arr = [[0 for _ in range(i+1)] for i in range(n)]
# 경우의수 는 3가지
# 1. y축이 증가하면서 수가 증가
# 2. x축이 증가하면서 수가 증가
# 3. y,x축이 감소하면서 수가 증가
size = n
num = 0
x = 0
y = -1
while True:
# 1번
for _ in range(size)... | normal | {
"blob_id": "3c029adb59cd6db1e3d4a22e6561f5e2ae827d60",
"index": 2465,
"step-1": "<mask token>\n",
"step-2": "def solution(n):\n arr = [[(0) for _ in range(i + 1)] for i in range(n)]\n size = n\n num = 0\n x = 0\n y = -1\n while True:\n for _ in range(size):\n num += 1\n ... | [
0,
1,
2
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print(effort)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
size, k = map(int, input().split())
parcel = list(map(int, input().split()))
effort = 2 * parcel[k - 1] * min(parcel) + max(parcel) * min(parcel)
print(eff... | flexible | {
"blob_id": "92dea316889192824c353002670cdcf03dfbcd4c",
"index": 1457,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(effort)\n",
"step-3": "<mask token>\nsize, k = map(int, input().split())\nparcel = list(map(int, input().split()))\neffort = 2 * parcel[k - 1] * min(parcel) + max(parcel) * min(pa... | [
0,
1,
2,
3
] |
def max_product(n):
lst, lstnums, res, num = [], [], [], 1
for i in range(0, n+1):
lstnums.append(i)
for j in str(i):
num *= int(j)
lst.append(num)
num = 1
maxlst = max(lst)
for i in range(len(lst)):
if lst[i] == maxlst:
res.append(lstnu... | normal | {
"blob_id": "c804391cc199a242d1b54ece8487ef74065a40ad",
"index": 840,
"step-1": "\ndef max_product(n):\n lst, lstnums, res, num = [], [], [], 1\n for i in range(0, n+1):\n lstnums.append(i)\n for j in str(i):\n num *= int(j)\n lst.append(num)\n num = 1\n\n maxlst ... | [
0
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
file_handle.write(contents)
file_handle.close()
print(contents)
<|reserved_special_token_1|>
fileName = str(input(
'Please write the name of the file you would like to open: '))
file_handle = open(fileName, 'w')
contents = ... | flexible | {
"blob_id": "aed09a3c04f284fa0b8844a47c5bc9d1621a9b5f",
"index": 2034,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfile_handle.write(contents)\nfile_handle.close()\nprint(contents)\n",
"step-3": "fileName = str(input(\n 'Please write the name of the file you would like to open: '))\nfile_handle =... | [
0,
1,
2,
3
] |
"""This module will serve the api request."""
import json
from bson.json_util import dumps
from flask import abort, request, Response, jsonify
from api import app, collection
@app.route("/api/v1/users", methods=['POST'])
def create_user():
"""
Function to create new users.
"""
try:
# Creat... | normal | {
"blob_id": "0f4bb65b93df997ca1a9b7945ebcec53a2f43822",
"index": 3636,
"step-1": "<mask token>\n\n\n@app.route('/api/v1/users', methods=['POST'])\ndef create_user():\n \"\"\"\n Function to create new users.\n \"\"\"\n try:\n try:\n body = request.get_json()\n except:\n ... | [
3,
4,
5,
6,
7
] |
import giraffe.configuration.common_testing_artifactrs as commons
from giraffe.business_logic.ingestion_manger import IngestionManager
from redis import Redis
def test_parse_redis_key(config_helper, ingestion_manager):
im = ingestion_manager
job_name = config_helper.nodes_ingestion_operation
operation = c... | normal | {
"blob_id": "13451352e8dcdfe64771f9fc188b13a31b8109f5",
"index": 4555,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef test_parse_redis_key(config_helper, ingestion_manager):\n im = ingestion_manager\n job_name = config_helper.nodes_ingestion_operation\n operation = config_helper.nodes_in... | [
0,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
class LoggingMiddleware(object):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
class ScriptNameEdit(object):
def __init__(self, app):
self.app = app
def __call__(self, environ, start_response):
url = environ['SCRIPT_NAME']
environ['w... | flexible | {
"blob_id": "a2aa615ac660f13727a97cdd2feaca8f6e457da4",
"index": 4830,
"step-1": "<mask token>\n\n\nclass LoggingMiddleware(object):\n <mask token>\n <mask token>\n\n\nclass ScriptNameEdit(object):\n\n def __init__(self, app):\n self.app = app\n\n def __call__(self, environ, start_response):\n... | [
4,
5,
6,
9,
10
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
from .interface import AudioInterface
from .config import AudioConfig
from .buffer import CustomBuffer
| flexible | {
"blob_id": "cc33d0cf1b922a6b48fb83be07acb35a62372f2e",
"index": 8260,
"step-1": "<mask token>\n",
"step-2": "from .interface import AudioInterface\nfrom .config import AudioConfig\nfrom .buffer import CustomBuffer\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
} | [
0,
1
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
with open(hfilename, 'r') as file:
for line in file:
tweetSplitter = TweetTokenizer(strip_handles=True, reduce_len=True)
WordList = tweetSplitter.tokenize(line)
regex1 = re.compile('^#.+')
regex... | flexible | {
"blob_id": "fd04f6f4a03fdbe40e400d04e5759ef9ef30f974",
"index": 6634,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open(hfilename, 'r') as file:\n for line in file:\n tweetSplitter = TweetTokenizer(strip_handles=True, reduce_len=True)\n WordList = tweetSplitter.tokenize(line)\n ... | [
0,
1,
2,
3,
4
] |
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