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|>
for i in range(n):
if cl[i] == a + b:
print(i + 1)
<|reserved_special_token_1|>
n, a, b = map(int, input().split())
cl = list(map(int, input().split()))
for i in range(n):
if cl[i] == a + b:
print(i + 1)... | flexible | {
"blob_id": "ff081a5ff46ab37dc5a144fb4616c06ef3bca490",
"index": 7286,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(n):\n if cl[i] == a + b:\n print(i + 1)\n",
"step-3": "n, a, b = map(int, input().split())\ncl = list(map(int, input().split()))\nfor i in range(n):\n if cl[... | [
0,
1,
2
] |
#!/bin/python
import numpy as np
import os
from sklearn.svm.classes import SVC
import pickle
import sys
# Apply the SVM model to the testing videos; Output the score for each video
if __name__ == '__main__':
if len(sys.argv) != 5:
print("Usage: {0} model_file feat_dir feat_dim output_file".format(sys.ar... | normal | {
"blob_id": "385dccfab4d7c37d10d968658b51e231691a7b49",
"index": 1556,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n if len(sys.argv) != 5:\n print('Usage: {0} model_file feat_dir feat_dim output_file'.format(\n sys.argv[0]))\n print('model_file -... | [
0,
1,
2,
3
] |
# import sys
# sys.stdin = open("농작물input.txt")
T = int(input())
for n in range(1, T+1):
N = int(input())
arr = [list(map(int, list(input()))) for _ in range(N)]
# print(arr)
a = N//2
b = N//2
result = 0
for i in range(N):
for j in range(a, b+1):
result += arr[i][j]
... | normal | {
"blob_id": "2236591b3a30f51442beb20c6c43cc9e6cd921d2",
"index": 7530,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor n in range(1, T + 1):\n N = int(input())\n arr = [list(map(int, list(input()))) for _ in range(N)]\n a = N // 2\n b = N // 2\n result = 0\n for i in range(N):\n ... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def precip_stats_to_climatology(fili, start_year=1981, end_year=2015):
"""
Calculates average climatology for annual data - either Jan to Dec or accummulation period
"""
nyear = end_year - start_year + 1
ds =... | flexible | {
"blob_id": "eb403fbb307332c18ffdcdf52589c714f0719960",
"index": 3052,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef precip_stats_to_climatology(fili, start_year=1981, end_year=2015):\n \"\"\"\n Calculates average climatology for annual data - either Jan to Dec or accummulation period\n ... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def make_moves_from_path(path):
moves = []
p = path[:]
for i in range(len(p) - 1):
moves.append((p[i + 1], p[i], 1, [p[i + 1], p[i]]))
return moves
def find_nearest_hole(o, r, graph, start):
visited, queue = [], [(start, [start])]
results = []
while q... | flexible | {
"blob_id": "800edfc61635564abf8297c4f33c59d48cc99960",
"index": 4058,
"step-1": "<mask token>\n\n\ndef make_moves_from_path(path):\n moves = []\n p = path[:]\n for i in range(len(p) - 1):\n moves.append((p[i + 1], p[i], 1, [p[i + 1], p[i]]))\n return moves\n\n\ndef find_nearest_hole(o, r, gra... | [
7,
11,
12,
16,
19
] |
<|reserved_special_token_0|>
def open_lid():
motor_lid.throttle = 1
time.sleep(0.25)
motor_lid.throttle = 0
def close_lid():
motor_lid.throttle = -1
time.sleep(0.25)
motor_lid.throttle = 0
def blink(times):
for _ in range(times):
ss.digital_write(LED, True)
time.sleep(0... | flexible | {
"blob_id": "608c116cd42132bd63be5056f0aaf5c78933886e",
"index": 7536,
"step-1": "<mask token>\n\n\ndef open_lid():\n motor_lid.throttle = 1\n time.sleep(0.25)\n motor_lid.throttle = 0\n\n\ndef close_lid():\n motor_lid.throttle = -1\n time.sleep(0.25)\n motor_lid.throttle = 0\n\n\ndef blink(tim... | [
3,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
class FactorGraphTrueSkillCalculator(SkillCalculator):
def __init__(self):
super(FactorGraphTrueSkillCalculator, self).__init__(
SupportedOptions.PARTIAL_PLAY | SupportedOptions.PARTIAL_UPDATE,
atLeast(2), atLeast(1))
<|reserved_special_token_0|>
... | flexible | {
"blob_id": "009be282e45d191eb8f4d7d2986a2f182d64c1dd",
"index": 2935,
"step-1": "<mask token>\n\n\nclass FactorGraphTrueSkillCalculator(SkillCalculator):\n\n def __init__(self):\n super(FactorGraphTrueSkillCalculator, self).__init__(\n SupportedOptions.PARTIAL_PLAY | SupportedOptions.PARTIA... | [
6,
7,
8,
9,
10
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Migration(migrations.Migration):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Migration(migrations.Migration):
dependencies = [(... | flexible | {
"blob_id": "f85a703b47d981397ed6048e941030a3fbee7b6d",
"index": 229,
"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 = [('talk', '0023... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
if __name__ == '__main__':
fieldnames = None
field_max_width = dict()
result = {'headers': [], 'details': []}
is_header = True
tidpid = dict()
for line in subprocess.run(['/usr/bin/procstat', '-ath'],
... | flexible | {
"blob_id": "f4ae34be2be2b47b3394e6da751c53c51a1c3174",
"index": 6678,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n fieldnames = None\n field_max_width = dict()\n result = {'headers': [], 'details': []}\n is_header = True\n tidpid = dict()\n for line in su... | [
0,
1,
2,
3
] |
from django.db import models
class Location(models.Model):
id_location = models.AutoField(primary_key=True)
city = models.CharField(max_length=100, null=True)
street_name = models.CharField(max_length=100, null=True)
street_number = models.IntegerField(null=True)
zip = models.IntegerField(null=Tru... | normal | {
"blob_id": "914f477518918619e0e42184bd03c2a7ed16bb01",
"index": 86,
"step-1": "<mask token>\n\n\nclass Relation_type(models.Model):\n <mask token>\n <mask token>\n\n def __str__(self):\n return str(self.name)\n\n\nclass Relation(models.Model):\n id_relation = models.AutoField(primary_key=True... | [
9,
18,
20,
21,
24
] |
"""
Copyright (C) 2005 - 2016 Splunk Inc. All Rights Reserved.
"""
import logging
import sys
if sys.platform == "win32":
import os, msvcrt
msvcrt.setmode(sys.stdin.fileno(), os.O_BINARY)
msvcrt.setmode(sys.stdout.fileno(), os.O_BINARY)
msvcrt.setmode(sys.stderr.fileno(), os.O_BINARY)
import splunk.adm... | normal | {
"blob_id": "675dc9467dd6db9c2a429941af56d78d6c0e1c08",
"index": 4135,
"step-1": "<mask token>\n\n\nclass MissingTransitionException(InvalidConfigException):\n \"\"\"\n Describes a capability that is missing.\n \"\"\"\n\n def __init__(self, transitions):\n self.transitions = transitions\n ... | [
14,
16,
18,
23,
26
] |
# create item based on name using post method, get specific item or list of items using get method, update item using put and delete item using del method.
import os
from flask import Flask
from flask_restful import Api
from flask_jwt import JWT, timedelta
from security import authenticate, identity
from resources.us... | normal | {
"blob_id": "7525691ece4fe66bb175e470db3ac78f701e3730",
"index": 199,
"step-1": "<mask token>\n",
"step-2": "<mask token>\napi.add_resource(Store, '/store/<string:name>')\napi.add_resource(Item, '/item/<string:name>')\napi.add_resource(ItemList, '/items')\napi.add_resource(StoreList, '/stores')\napi.add_resour... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class ImageProcessor(object):
<|reserved_special_token_0|>
def __init__(self, config):
self.config = config
self.is_first_img = True
self.next_feature_id = 0
self.detector = cv2.FastFeatureDetector_create(self.config.
fast_threshold)
... | flexible | {
"blob_id": "02f196623907703255bf149db0435104d086da97",
"index": 8292,
"step-1": "<mask token>\n\n\nclass ImageProcessor(object):\n <mask token>\n\n def __init__(self, config):\n self.config = config\n self.is_first_img = True\n self.next_feature_id = 0\n self.detector = cv2.Fas... | [
18,
20,
23,
25,
31
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for i in range(2, N + 1):
prime -= set(range(i ** 2, N + 1, i))
for number in prime:
print(number)
<|reserved_special_token_1|>
M, N = 3, 16
prime = set(range(M, N + 1))
for i in range(2, N + 1):
prime -= set(range(... | flexible | {
"blob_id": "d190eb27ea146cf99ac7f8d29fb5f769121af60e",
"index": 9437,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(2, N + 1):\n prime -= set(range(i ** 2, N + 1, i))\nfor number in prime:\n print(number)\n",
"step-3": "M, N = 3, 16\nprime = set(range(M, N + 1))\nfor i in range(2... | [
0,
1,
2
] |
<|reserved_special_token_0|>
class Dataset:
def __init__(self, config, path, batch_size, shuffle, is_training,
is_testing):
self.config = config
self.is_training = is_training
self.is_testing = is_testing
self.path = path
"""
each archive contains:
fa... | flexible | {
"blob_id": "f94fcf6ed54f247093050216c0c331ce188da919",
"index": 9228,
"step-1": "<mask token>\n\n\nclass Dataset:\n\n def __init__(self, config, path, batch_size, shuffle, is_training,\n is_testing):\n self.config = config\n self.is_training = is_training\n self.is_testing = is_te... | [
2,
4,
5,
6,
7
] |
import openpyxl
from openpyxl import Workbook
import openpyxl as openpyxl
from openpyxl.chart import BarChart
wb = openpyxl.load_workbook('/Users/mac/Desktop/stu_scores _Grade 2.xlsx')
sheet = wb['stu_scores_01']
data = openpyxl.chart.Reference(sheet, min_col=3, min_row=34, max_row=34,max_col=7)
cat = openpyxl.chart.... | normal | {
"blob_id": "bb9ff561ff94bbe4d20f14287ba313386ea78609",
"index": 9121,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ncharObj.append(seriesObj)\ncharObj.set_categories(cat)\nsheet.add_chart(charObj, 'I2')\n<mask token>\ncharObj.append(seriesObj)\ncharObj.set_categories(cat)\nsheet.add_chart(charObj, 'I18... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def add(size, cont):
sh.sendlineafter('Your choice :', '1')
sh.sendlineafter('Size of Heap(0x10 or 0x20 only) : ', str(size))
sh.sendlineafter('Content:', str(cont))
def edit(index, cont):
sh.sendlineafter('Your choice :', '2')
sh.sendlineafter('Index :', str(index))... | flexible | {
"blob_id": "eeedf4930a7fa58fd406a569db6281476c2e3e35",
"index": 4870,
"step-1": "<mask token>\n\n\ndef add(size, cont):\n sh.sendlineafter('Your choice :', '1')\n sh.sendlineafter('Size of Heap(0x10 or 0x20 only) : ', str(size))\n sh.sendlineafter('Content:', str(cont))\n\n\ndef edit(index, cont):\n ... | [
4,
6,
7,
8,
9
] |
<|reserved_special_token_0|>
def f_1(x, a):
return 1 / (x + 5) * np.sin(a * x)
def f_2(x, a):
return np.sin(a * x) + 1
def f_3(x, a):
return np.sin(a * x ** 2)
<|reserved_special_token_0|>
def f_5(x):
return x * np.tan(x)
def f_6(x, a, b):
return (1 + a * x + b * x ** 2) / (2 / 3 * (b + ... | flexible | {
"blob_id": "27edc753ebb9d60715a2ffa25d77e69ef363d010",
"index": 3568,
"step-1": "<mask token>\n\n\ndef f_1(x, a):\n return 1 / (x + 5) * np.sin(a * x)\n\n\ndef f_2(x, a):\n return np.sin(a * x) + 1\n\n\ndef f_3(x, a):\n return np.sin(a * x ** 2)\n\n\n<mask token>\n\n\ndef f_5(x):\n return x * np.tan... | [
6,
9,
12,
13,
14
] |
from pyspark import SparkContext, SparkConf
conf = SparkConf().setAppName("same_host").setMaster("local")
sc = SparkContext(conf=conf)
julyFirstLogs = sc.textFile("/Users/iamsuman/src/iamsuman/myspark/mypyspark/data/nasa_19950701.tsv")
augFirstLogs = sc.textFile("/Users/iamsuman/src/iamsuman/myspark/mypyspark/data/na... | normal | {
"blob_id": "36fce3837e0341d94ff6099a06be8cf757a1cfa9",
"index": 3596,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ncleanedHostIntersection.saveAsTextFile('out/nasa_logs_same_hosts.csv')\n",
"step-3": "<mask token>\nconf = SparkConf().setAppName('same_host').setMaster('local')\nsc = SparkContext(conf... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class TensorflowV1ModelStep(BaseTensorflowModelStep):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def __init__(self, create_graph, create_loss, create_optimizer,
create_feed_dict=None, data_inputs_dtype=None,
expe... | flexible | {
"blob_id": "76a22408bb423d9a5bc5bc007decdbc7c6cc98f7",
"index": 8397,
"step-1": "<mask token>\n\n\nclass TensorflowV1ModelStep(BaseTensorflowModelStep):\n <mask token>\n <mask token>\n <mask token>\n\n def __init__(self, create_graph, create_loss, create_optimizer,\n create_feed_dict=None, da... | [
13,
15,
16,
19,
21
] |
<|reserved_special_token_0|>
class Obj:
def __init__(self, name):
self.name = name
self.down = []
def add_child(self, obj):
self.down.append(obj)
def prnt(self, prev):
if not self.down:
print(prev + '=' + self.name)
else:
for d in self.dow... | flexible | {
"blob_id": "7d3f4e0a5031f9ce618c568b440c7425489060a1",
"index": 4122,
"step-1": "<mask token>\n\n\nclass Obj:\n\n def __init__(self, name):\n self.name = name\n self.down = []\n\n def add_child(self, obj):\n self.down.append(obj)\n\n def prnt(self, prev):\n if not self.down:... | [
5,
6,
7,
8
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
@register.filter
def jsonify(object):
return mark_safe(json.dumps(object, cls=DjangoJSONEncoder))
@register.simple_tag
def get_crop_url(crop, width=None, scale=1):
if width:
return crop.url_at_width(width * sca... | flexible | {
"blob_id": "987579da6b7ae208a66e375e0c9eca32b97199c5",
"index": 4704,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@register.filter\ndef jsonify(object):\n return mark_safe(json.dumps(object, cls=DjangoJSONEncoder))\n\n\n@register.simple_tag\ndef get_crop_url(crop, width=None, scale=1):\n if... | [
0,
3,
4,
5
] |
def phi(n):
r = n
d = 2
p = n
while r > 1:
if r % d == 0:
p -= int(r/d)
while r % d == 0:
r = int(r/d)
d += 1
return p
m = (0, 1)
for n in range(2, 1000000):
p = phi(n)
m = max(m, (n/p, n))
if n % 10000 == 0:
print(n)
prin... | normal | {
"blob_id": "e4f97018567559fc2714b75654974fb7c51f770f",
"index": 5266,
"step-1": "<mask token>\n",
"step-2": "def phi(n):\n r = n\n d = 2\n p = n\n while r > 1:\n if r % d == 0:\n p -= int(r / d)\n while r % d == 0:\n r = int(r / d)\n d += 1\n r... | [
0,
1,
2,
3,
4
] |
# Generated by Django 3.1.2 on 2020-10-25 01:19
import datetime
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('jobs', '0001_initial'),
]
operations = [
migrations.AddField(
model_name='job',
name='link',
... | normal | {
"blob_id": "562888201719456ed2f3c32e81ffd7d2c39dabc3",
"index": 7303,
"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 = [('jobs', '000... | [
0,
1,
2,
3,
4
] |
import boring.dialog
import boring.form
FORMSTRING = '''
Project name@string
Width@int|Height@int
Background color@color
Fullscreen@check
'''
class NewProjectWindow(boring.dialog.DefaultDialog):
def __init__(self, master, _dict=None):
self._dict = _dict
self.output = None
boring.dialog.Def... | normal | {
"blob_id": "76420ec1b37d4b9b85f35764a7f8a0e1f19a15dd",
"index": 5745,
"step-1": "<mask token>\n\n\nclass NewProjectWindow(boring.dialog.DefaultDialog):\n\n def __init__(self, master, _dict=None):\n self._dict = _dict\n self.output = None\n boring.dialog.DefaultDialog.__init__(self, maste... | [
4,
5,
6,
7,
8
] |
import mxnet as mx
import numpy as np
import logging
# Example performance:
# INFO:root:Epoch[34] Train-accuracy=0.601388
# INFO:root:Epoch[34] Validation-accuracy=0.620949
logger = logging.getLogger()
logger.setLevel(logging.DEBUG)
# running device
dev = mx.gpu()
# batch size and input shape
batch_size = 64
data_sh... | normal | {
"blob_id": "e82b9aa0f7dc669b3d5622c093b766c7e168221c",
"index": 5757,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nlogger.setLevel(logging.DEBUG)\n<mask token>\nmodel.fit(X=train, eval_data=val, batch_end_callback=mx.callback.\n Speedometer(batch_size, 50), epoch_end_callback=mx.callback.\n do_c... | [
0,
1,
2,
3,
4
] |
_base_ = [
'../models/cascade_rcnn_r50_fpn.py',
#'coco_instance.py',
'../datasets/dataset.py',
'../runtime/valid_search_wandb_runtime.py',
'../schedules/schedule_1x.py'
]
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth' # noqa
model... | normal | {
"blob_id": "2874e05d6d5e0f13924e5920db22ea3343707dfa",
"index": 3898,
"step-1": "<mask token>\n",
"step-2": "_base_ = ['../models/cascade_rcnn_r50_fpn.py', '../datasets/dataset.py',\n '../runtime/valid_search_wandb_runtime.py', '../schedules/schedule_1x.py']\npretrained = (\n 'https://github.com/SwinTra... | [
0,
1,
2
] |
import sqlite3
import os
#Search for a patient name
#Every doctor enter a name, it will find the patinet name that is similar to the patient name
#Once a match is found, the system will output a list of matched patient names.
#Then, the doctor select the patient to continue
def patientSelect(CONN, staff):
c = CONN... | normal | {
"blob_id": "b3b4d27b60c71cbd979ad4887fa80408665ea1ac",
"index": 2853,
"step-1": "<mask token>\n\n\ndef patientSelect(CONN, staff):\n c = CONN.cursor()\n print('Search for Patient')\n select = input(\"Enter patient name(type 'exit' to leave): \")\n if select == 'exit':\n os.system('clear')\n ... | [
7,
9,
11,
12,
13
] |
from random import randint
#funções
def leialetra():
'''
=>Função para validar letras.
parm=msg: Recebe dados to tipo string sendo Ss ou Nn.
return: String de valor S.
'''
while True:
try:
msg = str(input('Deseja fazer uma pergunta? [s/n] ')).upper()[0]
e... | normal | {
"blob_id": "1fff681363c4c91c47c2818681a3f2f125dd8c83",
"index": 2022,
"step-1": "<mask token>\n\n\ndef leialetra():\n \"\"\"\n =>Função para validar letras.\n parm=msg: Recebe dados to tipo string sendo Ss ou Nn.\n return: String de valor S.\n \"\"\"\n while True:\n try:\n ... | [
2,
3,
4,
5,
6
] |
# Generated by Django 2.0.3 on 2018-04-30 16:25
from django.db import migrations, models
import django.utils.timezone
class Migration(migrations.Migration):
dependencies = [
('threads', '0007_auto_20180430_1617'),
]
operations = [
migrations.AlterField(
model_name='thread',
... | normal | {
"blob_id": "6cd250b3bffd87657ec7cc28eaffe817c6d9f73f",
"index": 9794,
"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 = [('threads', '... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class DataSource:
def __init__(self, name, usableRows, errorRows, indices):
self.name = name
self.usableRows = usableRows
self.errorRows = errorRows
self.indices = indices
def getHeaderIndexes(indices, headers):
counter = -1
a, b, c, d, e, f,... | flexible | {
"blob_id": "38c1b82a29a5ad0b4581e63fb083ca2487a79817",
"index": 9544,
"step-1": "<mask token>\n\n\nclass DataSource:\n\n def __init__(self, name, usableRows, errorRows, indices):\n self.name = name\n self.usableRows = usableRows\n self.errorRows = errorRows\n self.indices = indice... | [
5,
6,
8,
9,
10
] |
# Generated by Django 2.1.2 on 2018-10-19 22:13
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('core', '0001_initial'),
]
operations = [
migrations.AlterField(
model_name='mascota',
name='descripcion',
... | normal | {
"blob_id": "fcfec60a2302ee0c1385add053d4371040a2aff4",
"index": 3667,
"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', '000... | [
0,
1,
2,
3,
4
] |
import items
import grupo
class Conexion:
def __init__(self, direccion, destino):
self.set_direccion(direccion)
self.set_destino(destino)
def __repr__(self):
return str(self.direccion()) + ' => ' + str(self.destino())
def direccion(self):
return self._direccion
def se... | normal | {
"blob_id": "f59e61977f7c72ab191aadccbd72d23f831b3a1c",
"index": 7050,
"step-1": "<mask token>\n\n\nclass Conexion:\n\n def __init__(self, direccion, destino):\n self.set_direccion(direccion)\n self.set_destino(destino)\n <mask token>\n <mask token>\n\n def set_direccion(self, direccion... | [
20,
22,
25,
26,
27
] |
<|reserved_special_token_0|>
class KibbleESWrapper(object):
<|reserved_special_token_0|>
def __init__(self, ES):
self.ES = ES
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def s... | flexible | {
"blob_id": "f4b704a1416bfd6524340a68a20981957abf4340",
"index": 9850,
"step-1": "<mask token>\n\n\nclass KibbleESWrapper(object):\n <mask token>\n\n def __init__(self, ES):\n self.ES = ES\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def scroll(sel... | [
17,
19,
23,
25,
28
] |
#!/usr/bin/python3
import os
import sys
import subprocess
path = sys.argv[1]
name, ext = os.path.splitext(path)
options = ['g++',
'-O3',
'src/' + path,
'-o', f'./bin/{name}',
'-std=c++11',
'-lgmp']
subprocess.call(options)
subprocess.call([f'./bin/{name}'])
| normal | {
"blob_id": "5dd79f8ebd74099871d4367cafd83359c4f24e26",
"index": 5385,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsubprocess.call(options)\nsubprocess.call([f'./bin/{name}'])\n",
"step-3": "<mask token>\npath = sys.argv[1]\nname, ext = os.path.splitext(path)\noptions = ['g++', '-O3', 'src/' + path,... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Office(Room):
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Office(Room):
def __init__(self):
pass
<|reserved_special_token_1|>
from room import Room
... | flexible | {
"blob_id": "d3af5ac87474a99f1ade222995884bc8e035ce35",
"index": 6142,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Office(Room):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Office(Room):\n\n def __init__(self):\n pass\n",
"step-4": "from room import Room\n\n\nclass... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class TestingPenaltyTracker(unittest.TestCase):
<|reserved_special_token_0|>
@classmethod
def tearDownClass(cls):
cls.testPenaltyTracker = None
cls.controlDatabase = None
os.remove(os.path.join(os.getcwd(), 'Tests', 'test_penalty.db'))
<|reserved_s... | flexible | {
"blob_id": "607d8bc79caa9d767bdb7e77a5db52295d90236f",
"index": 1759,
"step-1": "<mask token>\n\n\nclass TestingPenaltyTracker(unittest.TestCase):\n <mask token>\n\n @classmethod\n def tearDownClass(cls):\n cls.testPenaltyTracker = None\n cls.controlDatabase = None\n os.remove(os.p... | [
3,
5,
6,
7,
8
] |
import sys
from PyQt5.QtWidgets import (QMainWindow, QWidget, QHBoxLayout, QVBoxLayout, QFrame,
QSplitter, QStyleFactory, QApplication, QPushButton, QTextEdit, QLabel, QFileDialog, QMessageBox)
from PyQt5.QtCore import Qt
from PyQt5.QtGui import QFont, QColor
import myLoadData
from UIPack import setLossParameterDia... | normal | {
"blob_id": "302605d8bb45b1529742bf9441d476f0276085b9",
"index": 9,
"step-1": "<mask token>\n\n\nclass MyMainWindow(QMainWindow):\n <mask token>\n <mask token>\n\n def initConnect(self):\n self.dataFileChooseButton.clicked.connect(self.chooseData)\n self.dataFileChooseButtonT.clicked.conne... | [
9,
11,
12,
15,
18
] |
<|reserved_special_token_0|>
class WiiGestureClassifier:
<|reserved_special_token_0|>
def __init__(self):
super(self.__class__, self).__init__()
<|reserved_special_token_0|>
def parseArrays(self, data):
parsedData = []
for gesture in data:
parsedGesture = WiiGestu... | flexible | {
"blob_id": "0b7bba826b82c3751c072395431e17bc1dc9bb90",
"index": 6037,
"step-1": "<mask token>\n\n\nclass WiiGestureClassifier:\n <mask token>\n\n def __init__(self):\n super(self.__class__, self).__init__()\n <mask token>\n\n def parseArrays(self, data):\n parsedData = []\n for ... | [
7,
8,
10,
13,
14
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print(mydoc)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
myclient = pymongo.MongoClient('mongodb://localhost:27017/')
mydb = myclient['mydatabase']
mycol = mydb['customers']
mydict = [{'name': 'Eric', 'address': '... | flexible | {
"blob_id": "6c6026a7ff0345c37e62de7c0aac0ee3bcde2c82",
"index": 5879,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(mydoc)\n",
"step-3": "<mask token>\nmyclient = pymongo.MongoClient('mongodb://localhost:27017/')\nmydb = myclient['mydatabase']\nmycol = mydb['customers']\nmydict = [{'name': 'Eri... | [
0,
1,
2,
3,
4
] |
from compas.geometry import Line
# This import is use to test __repr__.
from compas.geometry import Point # noqa: F401
def test_line():
p1 = [0, 0, 0]
p2 = [1, 0, 0]
line = Line(p1, p2)
assert line.start == p1
assert line.end == p2
def test_equality():
p1 = [0, 0, 0]
p2 = [1, 0, 0]
... | normal | {
"blob_id": "03629e62b11e66eeb0e111fee551c75c8463cbb8",
"index": 1059,
"step-1": "<mask token>\n\n\ndef test_line():\n p1 = [0, 0, 0]\n p2 = [1, 0, 0]\n line = Line(p1, p2)\n assert line.start == p1\n assert line.end == p2\n\n\n<mask token>\n\n\ndef test___getitem__():\n p1 = [0, 0, 0]\n p2 ... | [
2,
3,
4,
5,
6
] |
import os
bind = "0.0.0.0:" + str(os.environ.get("MAESTRO_PORT", 5005))
workers = os.environ.get("MAESTRO_GWORKERS", 2)
| normal | {
"blob_id": "818e6842d4a1f8978ec14bca06981ec933c00376",
"index": 6280,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nbind = '0.0.0.0:' + str(os.environ.get('MAESTRO_PORT', 5005))\nworkers = os.environ.get('MAESTRO_GWORKERS', 2)\n",
"step-3": "import os\nbind = '0.0.0.0:' + str(os.environ.get('MAESTRO_... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class Auction(models.Model):
title = models.CharField(max_length=20)
current_price = models.DecimalField(max_digits=10, decimal_places=2,
default=0, null=True, blank=True, verbose_name='current bid')
updated_time = models.DateTimeField(auto_now_add=False, auto_now=
... | flexible | {
"blob_id": "9bb15842b39c7fd3e6f6c0048a51c2b2112ddb94",
"index": 8082,
"step-1": "<mask token>\n\n\nclass Auction(models.Model):\n title = models.CharField(max_length=20)\n current_price = models.DecimalField(max_digits=10, decimal_places=2,\n default=0, null=True, blank=True, verbose_name='current ... | [
15,
20,
23,
25,
26
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def chat(request):
chat_list = Chat.objects.order_by('id_chat')
chat_dict = {'chat': chat_list}
return render(request, 'chats/Chat.html', context=chat_dict)
<|reserved_special_token_1|>
from django.shortcuts impor... | flexible | {
"blob_id": "4a14265a9a2338be66e31110bba696e224b6a70f",
"index": 8395,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef chat(request):\n chat_list = Chat.objects.order_by('id_chat')\n chat_dict = {'chat': chat_list}\n return render(request, 'chats/Chat.html', context=chat_dict)\n",
"step... | [
0,
1,
2,
3
] |
from sys import exit
from os import stat
file = open("fiunamfs.img","r")
nombre = file.read(8)
file.seek(10)
version = file.read(3)
file.seek(20)
etiqueta = file.read(15)
file.seek(40)
cluster = file.read(5)
file.seek(47)
numero = file.read(2)
file.seek(52)
numeroCompleto = file.read(8)
file.close()
archivos = []
tam... | normal | {
"blob_id": "da69fd937153fe2112b9f64411882527274247ef",
"index": 1878,
"step-1": "<mask token>\n\n\ndef clusterVacio():\n arreAux = []\n busca = 1\n bandera = True\n for i in range(len(clusters)):\n clu = clusters[i]\n arreAux.append(int(clu[0]))\n print(arreAux)\n while bandera:\... | [
8,
9,
10,
12,
13
] |
from collections import Counter
def main():
N = int(input())
A = tuple(map(int, input().split()))
c = Counter(A).most_common()
if c[0][0] == 0 and c[0][1] == N:
print("Yes")
elif len(c) == 2 and c[0][1] == 2*N//3 and c[1][0] == 0 and c[1][1] == N//3:
print("Yes")
elif ... | normal | {
"blob_id": "7c6ada250770e04b395dda774a78042da69e2854",
"index": 8681,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef main():\n N = int(input())\n A = tuple(map(int, input().split()))\n c = Counter(A).most_common()\n if c[0][0] == 0 and c[0][1] == N:\n print('Yes')\n elif le... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
app = Flask(__name__)
app.config['SECRET_KEY'] = 'SuperSecretKey'
app.config['SQLALCHEMY_DATABASE_URI'
] = 'postgresql://info2180-project1:password123@localhost/profilebook'
app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = True
... | flexible | {
"blob_id": "7b45c9e31bfb868b1abde6af0d8579b52f86d9c3",
"index": 5689,
"step-1": "<mask token>\n",
"step-2": "<mask token>\napp = Flask(__name__)\napp.config['SECRET_KEY'] = 'SuperSecretKey'\napp.config['SQLALCHEMY_DATABASE_URI'\n ] = 'postgresql://info2180-project1:password123@localhost/profilebook'\napp.c... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def create_database(cursor):
try:
cursor.execute("CREATE DATABASE {} DEFAULT CHARACTER SET 'utf8'".
format(DB_NAME))
except mysql.connector.Error as err:
print('Failed creating database: {}'.format(err))
exit(1)
<|reserved_special_token_0|>
... | flexible | {
"blob_id": "38abc4bc99f3b15b416c77481818464a6c7f11ef",
"index": 3844,
"step-1": "<mask token>\n\n\ndef create_database(cursor):\n try:\n cursor.execute(\"CREATE DATABASE {} DEFAULT CHARACTER SET 'utf8'\".\n format(DB_NAME))\n except mysql.connector.Error as err:\n print('Failed cr... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
class WordsSetAccumulatorParam(AccumulatorParam):
def zero(self, v):
return set()
def addInPlace(self, acc1, acc2):
return acc1.union(acc2)
class WordsDictAccumulatorParam(AccumulatorParam):
def zero(self, v):
return dict()
def addInPlace(self... | flexible | {
"blob_id": "ee58ed68d2f3c43f9611f6c6e4cd2b99adcb43d2",
"index": 2616,
"step-1": "<mask token>\n\n\nclass WordsSetAccumulatorParam(AccumulatorParam):\n\n def zero(self, v):\n return set()\n\n def addInPlace(self, acc1, acc2):\n return acc1.union(acc2)\n\n\nclass WordsDictAccumulatorParam(Accu... | [
10,
14,
15,
18,
20
] |
"""
Author: Alan Danque
Date: 20210323
Purpose:Final Data Wrangling, strips html and punctuation.
"""
from sklearn.tree import export_graphviz
import pydot
import pickle
from pathlib import Path
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.ensemble import ... | normal | {
"blob_id": "b9678b447bc6e7c4e928ffa6b8cd58639e41a801",
"index": 2688,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nresults_dir.mkdir(parents=True, exist_ok=True)\n<mask token>\nprint(data.shape)\n<mask token>\nprint(\"\"\"\nDataFrame Shape :\"\"\", shape)\nprint(\"\"\"\nNumber of rows :\"\"\", shape[0... | [
0,
1,
2,
3,
4
] |
from typing import List, Tuple
import pytest
def fit_transform(*args: str) -> List[Tuple[str, List[int]]]:
if len(args) == 0:
raise TypeError('expected at least 1 arguments, got 0')
categories = args if isinstance(args[0], str) else list(args[0])
uniq_categories = set(categories)
bi... | normal | {
"blob_id": "b236abaa5e206a8244083ee7f9dcdb16741cb99d",
"index": 3072,
"step-1": "<mask token>\n\n\ndef test_str_fit_transformr():\n assert fit_transform(['Moscow', 'New York', 'Moscow', 'London']) == [(\n 'Moscow', [0, 0, 1]), ('New York', [0, 1, 0]), ('Moscow', [0, 0, 1]\n ), ('London', [1, 0,... | [
3,
4,
6,
7,
8
] |
#!/usr/bin/python
#===============================================================================
#
# Board Data File Analyzer
#
# Copyright (c) 2017 by QUALCOMM Atheros, Incorporated.
# All Rights Reserved
# QUALCOMM Atheros Confidential and Proprietary
#
# Notifications and licenses are retained for attribution purp... | normal | {
"blob_id": "5c12ff4f88af991fa275cd08adf3678ee4a678f3",
"index": 8532,
"step-1": "#!/usr/bin/python\n#===============================================================================\n#\n# Board Data File Analyzer\n#\n# Copyright (c) 2017 by QUALCOMM Atheros, Incorporated.\n# All Rights Reserved\n# QUALCOMM Ather... | [
0
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
if k2 >= k:
print(' Yes, the scene can be set.')
else:
print(" Sorry, but the scene can't be set.")
<|reserved_special_token_1|>
<|reserved_special_token_0|>
s = int(input('Input your area of square (S): '))
r = i... | flexible | {
"blob_id": "31a2fa5b2febc2ef80b57e45c2ebb662b886c4b7",
"index": 6043,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif k2 >= k:\n print(' Yes, the scene can be set.')\nelse:\n print(\" Sorry, but the scene can't be set.\")\n",
"step-3": "<mask token>\ns = int(input('Input your area of squ... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
driver.get(upload_page)
driver.find_element_by_id('inputfile').send_keys(file_path + '\\test.txt')
<|reserved_special_token_1|>
<|reserved_special_token_0|>
file_path = os.path.abspath('./files//')
driver = webdriver.Firefox()
... | flexible | {
"blob_id": "9e28fa1f221df13f9cc8e6b71586da961ebdc0e0",
"index": 4580,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ndriver.get(upload_page)\ndriver.find_element_by_id('inputfile').send_keys(file_path + '\\\\test.txt')\n",
"step-3": "<mask token>\nfile_path = os.path.abspath('./files//')\ndriver = web... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
with open(forbidpath, 'rb') as f:
for line in f:
word = line.strip()
forbidkword[word] = 0
<|reserved_special_token_0|>
with open(inputpath, 'rb') as f:
for line in f:
splits = line.strip().split('\... | flexible | {
"blob_id": "84a516e924252d897be7444e11acfecd66474090",
"index": 1177,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open(forbidpath, 'rb') as f:\n for line in f:\n word = line.strip()\n forbidkword[word] = 0\n<mask token>\nwith open(inputpath, 'rb') as f:\n for line in f:\n ... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def predict(model, row):
preds = []
for perc in range(-10, 11):
new_row = row.copy()
row_copy = row.copy()
new_row = new_row.drop(labels=['Area', 'Year', 'Crop',
'Previous crop', 'Yield'])
nitrogen = new_row['N'] * ((100 + perc) / 100)
... | flexible | {
"blob_id": "0ebd3ca5fd29b0f2f2149dd162b37f39668f1c58",
"index": 7397,
"step-1": "<mask token>\n\n\ndef predict(model, row):\n preds = []\n for perc in range(-10, 11):\n new_row = row.copy()\n row_copy = row.copy()\n new_row = new_row.drop(labels=['Area', 'Year', 'Crop',\n '... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
setup(name='sgcharts-pointer-generator', version=__version__,
python_requires='>=3.5.0', install_requires=['tensorflow==1.10.0',
'pyrouge==0.1.3', 'spacy==2.0.12', 'en_core_web_sm==2.0.0',
'sgcharts-stringx==1.1.1'], p... | flexible | {
"blob_id": "e52b01cc7363943f5f99b1fa74720c6447b1cfae",
"index": 6266,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsetup(name='sgcharts-pointer-generator', version=__version__,\n python_requires='>=3.5.0', install_requires=['tensorflow==1.10.0',\n 'pyrouge==0.1.3', 'spacy==2.0.12', 'en_core_web_... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for i in range(1, count + 1):
something = '='
num1, num2 = map(int, input().split())
if num1 > num2:
something = '>'
elif num1 < num2:
something = '<'
print(f'#{i} {something}')
<|reserved_spe... | flexible | {
"blob_id": "abcefa0a3312e158517ec8a15421d1d07220da6a",
"index": 5271,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(1, count + 1):\n something = '='\n num1, num2 = map(int, input().split())\n if num1 > num2:\n something = '>'\n elif num1 < num2:\n something = '<... | [
0,
1,
2
] |
#!/usr/bin/python3
# -*- coding: utf-8 -*-
import random
a = random.sample(range(100), 10)
print("All items: {}".format(a))
it = iter(a) # call a.__iter__()
print("Num01: {}".format(next(it))) # call it.__next__()
print("Num02: {}".format(next(it)))
print("Num03: {}".format(it.__next__()))
it = iter(a)
i = 1
while... | normal | {
"blob_id": "f5513bea4ca5f4c2ac80c4bf537a264a4052d1e9",
"index": 8866,
"step-1": "<mask token>\n\n\nclass Node2:\n\n def __init__(self, value):\n self._value = value\n self._children = []\n self._idx = 0\n\n def __repr__(self):\n return 'Node2({!r})'.format(self._value)\n <ma... | [
12,
13,
15,
16,
24
] |
<|reserved_special_token_0|>
class EngageScraper(ABC):
def __init__(self, tz_string):
super().__init__()
self._agenda_locations = []
self._tz = timezone(tz_string)
@property
def agenda_locations(self):
return self._agenda_locations
@agenda_locations.setter
def ag... | flexible | {
"blob_id": "ec224924206c41cf8203c6aa8002ddf6b0e70e9b",
"index": 1116,
"step-1": "<mask token>\n\n\nclass EngageScraper(ABC):\n\n def __init__(self, tz_string):\n super().__init__()\n self._agenda_locations = []\n self._tz = timezone(tz_string)\n\n @property\n def agenda_locations(s... | [
8,
10,
11,
12
] |
# coding: utf-8
from flask import Blueprint, make_response, render_template, request
from flask_restful import Resource
from flask_security import login_required
from ..clients.service import list_clients
from ..roles.service import list_roles
from ...models import Client, Role
admin = Blueprint('admin', __name__, u... | normal | {
"blob_id": "f5f1a4db33cea8421cb4236606dfb288efee7621",
"index": 2142,
"step-1": "<mask token>\n\n\n@admin.route('/', methods=['GET'])\n@login_required\ndef index():\n headers = {'Content-Type': 'text/html'}\n return make_response(render_template('index.html'), headers)\n\n\n<mask token>\n\n\n@admin.route(... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
class DataTello:
def __init__(self):
self.tello = Tello()
self.__data = []
self.__array = []
self.tempoVoo = 420000
"""
___Padrão para nome dos arquivos das tabelas___
Onde x é o nº da tabela e y a quantidade de tempo em segundo... | flexible | {
"blob_id": "9e751bbddabbec7c5e997578d99ef1b8c35efe06",
"index": 8108,
"step-1": "<mask token>\n\n\nclass DataTello:\n\n def __init__(self):\n self.tello = Tello()\n self.__data = []\n self.__array = []\n self.tempoVoo = 420000\n \"\"\"\n ___Padrão para nome dos arqui... | [
6,
7,
8,
9,
10
] |
# coding: utf-8
"""Supporting model logic for predicting emotional content of user input.
"""
import pandas as pd
import gensim
from sklearn.model_selection import train_test_split
from sklearn.multiclass import OneVsRestClassifier
from sklearn.svm import LinearSVC
#load data for emo2vec
loc = 'https://s3-us-west-1.a... | normal | {
"blob_id": "f5f26819be4b98fab3d46e57e1a5431e54342aed",
"index": 414,
"step-1": "<mask token>\n\n\ndef dropper():\n for ex in affected['word']:\n if ex not in model.vocab:\n idx_to_drop.append(affected.loc[affected.word == ex].index[0])\n\n\n<mask token>\n",
"step-2": "<mask token>\nprint(... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
trade_bot.start(sleep=1)
print('Done!')
<|reserved_special_token_1|>
<|reserved_special_token_0|>
brokers = create_brokers('LIVE', config.CURRENCIES, config.EXCHANGES)
gp = brokers[2]
trade_bot = ArbitrageBot(config, brokers)
t... | flexible | {
"blob_id": "4436fa36ec21edb3be467f74d8b9705780535f22",
"index": 6786,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ntrade_bot.start(sleep=1)\nprint('Done!')\n",
"step-3": "<mask token>\nbrokers = create_brokers('LIVE', config.CURRENCIES, config.EXCHANGES)\ngp = brokers[2]\ntrade_bot = ArbitrageBot(co... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
if r.status_code == 200:
text = r.text
pattern = 'Přispěvatel'
m = re.search(pattern, text)
pattern2 = '<strong>([0-9]{1,})'
m2 = re.search(pattern2, text[m.start():])
pattern3 = 'currency " >([0-9]{1,})'
... | flexible | {
"blob_id": "f3329962004a4454c04327da56d8dd1d0f1d45e7",
"index": 763,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif r.status_code == 200:\n text = r.text\n pattern = 'Přispěvatel'\n m = re.search(pattern, text)\n pattern2 = '<strong>([0-9]{1,})'\n m2 = re.search(pattern2, text[m.start(... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def get_branches_dir(root_dir):
branches_dir = []
folds = os.listdir(root_dir)
while folds:
branch_dir = root_dir + '/' + folds.pop()
branches_dir.append(branch_dir)
return branches_dir
def tolist(xml, detname):
try:
data = minidom.parse(xml)
... | flexible | {
"blob_id": "2b7bb02a25504e7481d3bc637ea09bcf9addb990",
"index": 7699,
"step-1": "<mask token>\n\n\ndef get_branches_dir(root_dir):\n branches_dir = []\n folds = os.listdir(root_dir)\n while folds:\n branch_dir = root_dir + '/' + folds.pop()\n branches_dir.append(branch_dir)\n return br... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
log.uploadLogs(4)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
log = LogUpload()
log.uploadLogs(4)
<|reserved_special_token_1|>
from logupload import *
log = LogUpload()
log.uploadLogs(4)
| flexible | {
"blob_id": "421837698b7fc188c84a3221271f11a40d1625d9",
"index": 7280,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nlog.uploadLogs(4)\n",
"step-3": "<mask token>\nlog = LogUpload()\nlog.uploadLogs(4)\n",
"step-4": "from logupload import *\nlog = LogUpload()\nlog.uploadLogs(4)\n",
"step-5": null,
... | [
0,
1,
2,
3
] |
# maze = [0, 3, 0, 1, -3]
with open('./day_5/input.txt') as f:
maze = f.readlines()
f.close
maze = [int(line.strip()) for line in maze]
# I think I will just expand on the original functions
# from now on rather than separating part one from two
def escape_maze(maze):
end = len(maze) - 1
step_counter = 0
... | normal | {
"blob_id": "a4dfac7e15064d92c806a4e3f972f06e4dca6b11",
"index": 5181,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef escape_maze(maze):\n end = len(maze) - 1\n step_counter = 0\n offset = 0\n while True:\n cur_index = offset\n offset = offset + maze[cur_index]\n ... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for cnt in range(20, len(rows)):
row_previous2 = rows[cnt - 2]
row_previous1 = rows[cnt - 1]
row = rows[cnt]
open = row[2]
high = row[3]
low = row[4]
close = row[5]
vol = row[6]
vol_buy, vol_sel... | flexible | {
"blob_id": "9aaaa744780dbd32b14e09a34976a2a0a3ce34f7",
"index": 7864,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor cnt in range(20, len(rows)):\n row_previous2 = rows[cnt - 2]\n row_previous1 = rows[cnt - 1]\n row = rows[cnt]\n open = row[2]\n high = row[3]\n low = row[4]\n cl... | [
0,
1,
2,
3,
4
] |
from django.contrib import admin
from django.urls import path, include
from serverside.router import router
from rest_framework.authtoken import views as auth_views
from . import views
from .views import CustomObtainAuthToken
urlpatterns = [path('users/', views.UserCreateAPIView.as_view(), name=
'user-list'), path(... | normal | {
"blob_id": "49d76458b8adcf6eea9db2ef127609ff96e03ad1",
"index": 6270,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurlpatterns = [path('users/', views.UserCreateAPIView.as_view(), name=\n 'user-list'), path('users/login/', CustomObtainAuthToken.as_view()),\n path('users/<int:pk>/', views.ReadUse... | [
0,
1,
2
] |
# -*- coding: utf-8 -*-
# Define your item pipelines here
#
# Don't forget to add your pipeline to the ITEM_PIPELINES setting
# See: http://doc.scrapy.org/en/latest/topics/item-pipeline.html
import codecs
import time
import json
import os
class OitYitikuscrapyDataPipeline(object):
def open_spider(self, spider):
... | normal | {
"blob_id": "315996a783d7b95fd87374a8fe2602a572de071e",
"index": 3495,
"step-1": "<mask token>\n\n\nclass OitYitikuscrapyDataPipeline(object):\n\n def open_spider(self, spider):\n path = 'D:\\\\xiti10001\\\\data\\\\{}\\\\'.format(time.strftime('%Y%m%d',\n time.localtime()))\n isExists... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
def chk_tmp_file(f_tmp):
pass
def get_file_base(path):
fname = os.path.basename(path)
return fname.split('.ts')[0]
def _exec_transcode(path):
f_base = get_file_base(path)
work_base = get_work_base(f_base)
f_in = os.path.join(dir_ts_files, f_base + '.ts')
f_... | flexible | {
"blob_id": "502e2d2222863236a42512ffc98c2cc9deaf454f",
"index": 7058,
"step-1": "<mask token>\n\n\ndef chk_tmp_file(f_tmp):\n pass\n\n\ndef get_file_base(path):\n fname = os.path.basename(path)\n return fname.split('.ts')[0]\n\n\ndef _exec_transcode(path):\n f_base = get_file_base(path)\n work_ba... | [
6,
7,
8,
9,
11
] |
import numpy as np
import torch
import torch.nn as nn
from torch.nn.functional import interpolate
from torchvision.ops.boxes import batched_nms
class MTCNN():
def __init__(self, device=None, model=None):
if device is None:
device = 'cuda' if torch.cuda.is_available() else 'cpu'
self.device = device
url = '... | normal | {
"blob_id": "865121e7eb5f9c70adf44d33d21f30c22f13ec56",
"index": 7012,
"step-1": "<mask token>\n\n\nclass MTCNN:\n\n def __init__(self, device=None, model=None):\n if device is None:\n device = 'cuda' if torch.cuda.is_available() else 'cpu'\n self.device = device\n url = 'https... | [
17,
18,
19,
21,
23
] |
<|reserved_special_token_0|>
class OiRAFixture(PloneSandboxLayer):
<|reserved_special_token_0|>
def setUpZope(self, app, configurationContext):
z2.installProduct(app, 'Products.membrane')
z2.installProduct(app, 'Products.statusmessages')
import Products.statusmessages
xmlconfi... | flexible | {
"blob_id": "eec2b818ea9d50161bad60e8bf83dcb7ce9bf9fa",
"index": 7428,
"step-1": "<mask token>\n\n\nclass OiRAFixture(PloneSandboxLayer):\n <mask token>\n\n def setUpZope(self, app, configurationContext):\n z2.installProduct(app, 'Products.membrane')\n z2.installProduct(app, 'Products.statusm... | [
3,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
class House2:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
class Vehicle(ABC):
def __init__(self, speed, year):
self.speed = speed
self.year = year
def start(self):
print('Starting engine')
def stop(self):
print('Stoppi... | flexible | {
"blob_id": "0e4c82d6eb77d2b6357925c9aab516bcc3310a4c",
"index": 140,
"step-1": "<mask token>\n\n\nclass House2:\n <mask token>\n <mask token>\n\n\nclass Vehicle(ABC):\n\n def __init__(self, speed, year):\n self.speed = speed\n self.year = year\n\n def start(self):\n print('Start... | [
9,
12,
13,
14,
17
] |
<|reserved_special_token_0|>
def calc_returns(batch, gamma):
"""
Calculate the simple returns (full rollout) for advantage
i.e. sum discounted rewards up till termination
"""
rewards = batch['rewards']
assert not np.any(np.isnan(rewards))
not_dones = 1 - batch['dones']
T = len(rewards)... | flexible | {
"blob_id": "07095bc815f5342b66ef4ca74b769321f3ef2ec5",
"index": 7240,
"step-1": "<mask token>\n\n\ndef calc_returns(batch, gamma):\n \"\"\"\n Calculate the simple returns (full rollout) for advantage\n i.e. sum discounted rewards up till termination\n \"\"\"\n rewards = batch['rewards']\n asse... | [
3,
4,
5,
6,
7
] |
# the age of some survivors
survived_age = [48.0, 15.0, 40.0, 36.0, 47.0, \
32.0, 60.0, 31.0, 17.0, 36.0, 39.0, 36.0, 32.5, \
39.0, 38.0, 36.0, 52.0, 29.0, 35.0, 35.0, 49.0, \
16.0, 27.0, 22.0, 27.0, 35.0, 3.0, 11.0, 36.0, \
1.0, 19.0, 24.0, 33.0, 43.0, 24.0, 32.0, 49.0, \
30.0, 49.0, 60.0, 23.0, 26.0, 24.0, 40.0, 25.0... | normal | {
"blob_id": "85c51f155439ff0cb570faafc48ac8da094515bf",
"index": 3362,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('The ave_age of survivors is {}'.format(ave_survived_age))\nprint('The ave_age of victims is {}'.format(ave_non_survived_age))\n",
"step-3": "survived_age = [48.0, 15.0, 40.0, 36.... | [
0,
1,
2,
3
] |
#!/usr/bin/env python3
# encoding: utf-8
"""
@version: ??
@author: ami
@license: Apache Licence
@file: dictTest.py
@time: 2019/9/25 18:26
@tools: PyCharm
"""
def func():
pass
class Main():
def __init__(self):
pass
if __name__ == '__main__':
pass
d = {'name': 'Bob', ... | normal | {
"blob_id": "797cedc9dc2a47713b9554e4f5975a4505ecf6d3",
"index": 9568,
"step-1": "<mask token>\n\n\nclass Main:\n <mask token>\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef func():\n pass\n\n\nclass Main:\n\n def __init__(self):\n pass\n\n\n<mask token>\n",
"step-3": "<mask token>\n\... | [
1,
3,
4,
5,
6
] |
from django.conf.urls import url
#from .views import CommandReceiveView
from .views import index, send_message
urlpatterns = [
#url(r'^bot/(?P<bot_token>.+)/$', CommandReceiveView.as_view(), name='command'),
url(r'^send_message$', send_message, name='send_message'),
url(r'^$', index, name='index'),
]
| normal | {
"blob_id": "6cc56f73e58366a3906da537cc27fdd5a066ee34",
"index": 2647,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurlpatterns = [url('^send_message$', send_message, name='send_message'),\n url('^$', index, name='index')]\n",
"step-3": "from django.conf.urls import url\nfrom .views import index, ... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
from django.db import models
from django.db.models import F, Q, Sum, Avg
from django.db import transaction
from django.contrib.contenttypes.models import ContentType
from django.contrib.contenttypes import generic
from django.contrib.sites.models import Site
... | flexible | {
"blob_id": "d551cab1856fbdb91918f9171d5c02b8dab84aba",
"index": 8223,
"step-1": "<mask token>\n",
"step-2": "from django.db import models\nfrom django.db.models import F, Q, Sum, Avg\nfrom django.db import transaction\nfrom django.contrib.contenttypes.models import ContentType\nfrom django.contrib.contenttype... | [
0,
1,
2
] |
<|reserved_special_token_0|>
def wheeln(pos, sft):
if pos + sft > 255:
pos = pos + sft - 256
else:
pos = pos + sft
if pos < 0 or pos > 255:
return 0, 0, 0
if pos < 85:
return int(255 - pos * 3), int(pos * 3), 0
elif pos < 170:
pos -= 85
return 0, int... | flexible | {
"blob_id": "1dd223854c10e69a397098511eab50b9ebd347c8",
"index": 6027,
"step-1": "<mask token>\n\n\ndef wheeln(pos, sft):\n if pos + sft > 255:\n pos = pos + sft - 256\n else:\n pos = pos + sft\n if pos < 0 or pos > 255:\n return 0, 0, 0\n if pos < 85:\n return int(255 - p... | [
4,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
class Timer:
def __init__(self):
self._time = 0
self.is_stopped = False
self._start()
<|reserved_special_token_0|>
<|reserved_special_token_0|>
@property
def time(self):
self._stop()
return self._time
def to_string(self):
... | flexible | {
"blob_id": "458124aa0d6f04268ad052f74d546b12d3f3f5f7",
"index": 8989,
"step-1": "<mask token>\n\n\nclass Timer:\n\n def __init__(self):\n self._time = 0\n self.is_stopped = False\n self._start()\n <mask token>\n <mask token>\n\n @property\n def time(self):\n self._stop... | [
23,
29,
31,
33,
35
] |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Check mean system norm errors in regression tests
This script determines the pass/fail status of a regression test by comparing
the "Mean System Norm" values output at each timestep against "gold values"
from the reference file provided by the user.
Success is deter... | normal | {
"blob_id": "d03669924233edf33fcb6645f5ed7ab118f54a95",
"index": 7610,
"step-1": "<mask token>\n\n\ndef load_norm_file(fname):\n \"\"\"Parse the norm file and return the mean system norms\"\"\"\n try:\n with open(fname, 'r') as fh:\n lines = fh.readlines()\n norms = [float(ll.s... | [
5,
6,
7,
8,
9
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Solution:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Solution:
def snakesAndLadders(self, board: List[List[int]]) ->int:
... | flexible | {
"blob_id": "da5a366d1cc4f192a220dc38c7a74aeb3fba7cdb",
"index": 9839,
"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 snakesAndLadders(self, board: List[List[int]]) ->int:\n N = le... | [
0,
1,
2,
3,
4
] |
import torch
import torch.nn as nn
from tqdm import tqdm
import torch.nn.functional as F
import torch.multiprocessing as mp
from policy_network import Policy_Network
from util import safe_log
from util import index2word, rearrange_vector_list, get_num_gpus, set_seed
class TestWorker(mp.Process):
def __init__(self,... | normal | {
"blob_id": "c7333d838b87d4c275d9dbb6d7e3047c313b4bc0",
"index": 9212,
"step-1": "<mask token>\n\n\nclass TestWorker(mp.Process):\n <mask token>\n <mask token>\n\n def rollout(self):\n batch_question, batch_question_len, batch_head, batch_answers = (self\n .env.return_batch_data())\n ... | [
6,
9,
10,
12,
14
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
cmdline.execute('scrapy crawl ariz'.split())
<|reserved_special_token_1|>
from scrapy import cmdline
cmdline.execute('scrapy crawl ariz'.split())
<|reserved_special_token_1|>
from scrapy import cmdline
cmdline.execute("scrap... | flexible | {
"blob_id": "abb2cfd2113e8de6c7bba42c357f0ec140b224a9",
"index": 3311,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ncmdline.execute('scrapy crawl ariz'.split())\n",
"step-3": "from scrapy import cmdline\ncmdline.execute('scrapy crawl ariz'.split())\n",
"step-4": "from scrapy import cmdline\ncmdline... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
cursor.execute(
"select name from sqlite_master where type = 'table' order by name")
print('Tables name:', cursor.fetchall())
cursor.execute('PRAGMA table_info(user)')
print('Table structure:', cursor.fetchall())
cursor.execut... | flexible | {
"blob_id": "dd8f4b08b88d487b68e916e9f92c08c9c0bc39da",
"index": 2681,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ncursor.execute(\n \"select name from sqlite_master where type = 'table' order by name\")\nprint('Tables name:', cursor.fetchall())\ncursor.execute('PRAGMA table_info(user)')\nprint('Ta... | [
0,
1,
2,
3,
4
] |
from pymt_heat import Heatmodel
heat = Heatmodel()
n = heat.get_component_name()
print(n)
| normal | {
"blob_id": "82801ce564f4f29e084e6f842d7868eb60f582cb",
"index": 6225,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(n)\n",
"step-3": "<mask token>\nheat = Heatmodel()\nn = heat.get_component_name()\nprint(n)\n",
"step-4": "from pymt_heat import Heatmodel\nheat = Heatmodel()\nn = heat.get_comp... | [
0,
1,
2,
3
] |
import socket
import threading
import os
import time
import psutil
import shutil
class server:
def __init__(self):
self.commandSock = socket.socket()
self.commandPort = 8080
self.transferSock = socket.socket()
self.transferPort = 8088
self.chatSock=socket.socket()
... | normal | {
"blob_id": "4736f4e06f166b3c3fd8379a2021eb84a34fcbd3",
"index": 6099,
"step-1": "<mask token>\n\n\nclass server:\n\n def __init__(self):\n self.commandSock = socket.socket()\n self.commandPort = 8080\n self.transferSock = socket.socket()\n self.transferPort = 8088\n self.ch... | [
7,
9,
11,
15,
16
] |
import math
import time
t1 = time.time()
# n(3n-1)/2
def isPentagon(item):
num = math.floor(math.sqrt(item*2//3))+1
if num*(3*num-1)//2 == item:
return True
return False
# n(2n-1)
def isHexagon(item):
num = math.floor(math.sqrt(item//2))+1
if num*(2*num-1) == item:
return True
... | normal | {
"blob_id": "0aec3fbc9f4b9f33aee021fa417c43f0feb0e3d1",
"index": 3296,
"step-1": "<mask token>\n\n\ndef isPentagon(item):\n num = math.floor(math.sqrt(item * 2 // 3)) + 1\n if num * (3 * num - 1) // 2 == item:\n return True\n return False\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef i... | [
1,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def mge_to_caffe(mge_fpath, prototxt='out.prototxt', caffemodel=
'out.caffemodel', outspec=None, use_empty_blobs=False):
assert isinstance(mge_fpath, str), 'mge_fpath must be string'
irgraph = MGE_FrontEnd(mge_fpath,... | flexible | {
"blob_id": "a83230e71cc1bcc843d00487746f16114d304eec",
"index": 4908,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef mge_to_caffe(mge_fpath, prototxt='out.prototxt', caffemodel=\n 'out.caffemodel', outspec=None, use_empty_blobs=False):\n assert isinstance(mge_fpath, str), 'mge_fpath must b... | [
0,
1,
2,
3
] |
from pymouse import PyMouse
m = PyMouse()
w,h = m.screen_size()
class base_controller:
def __init__(self):
pass
def move(self,xy:list):
'''
移动
'''
m.move(xy[0]*w,xy[1]*h)
def click(self, xy:list):
'''
点击
'''
m.click(xy[0]*w,xy... | normal | {
"blob_id": "b2f2f1e4b7070ac867b71e538f759e527eb1ffb9",
"index": 416,
"step-1": "<mask token>\n\n\nclass base_controller:\n <mask token>\n\n def move(self, xy: list):\n \"\"\"\n 移动\n \"\"\"\n m.move(xy[0] * w, xy[1] * h)\n\n def click(self, xy: list):\n \"\"\"\n ... | [
6,
8,
10,
11,
12
] |
<|reserved_special_token_0|>
class Preprocessor(object):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
class WithUrlPreprocessor(Preprocessor):
def __init__(self, max_workers=4):
super... | flexible | {
"blob_id": "8cd50e1f0e0feb4d753443220f9fa9065e80e0ef",
"index": 6358,
"step-1": "<mask token>\n\n\nclass Preprocessor(object):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n\nclass WithUrlPreprocessor(Preprocessor):\n\n def __init__(self, max_workers=4):\n ... | [
4,
6,
7,
10,
11
] |
# Generated by Django 3.2.4 on 2021-07-18 02:05
from django.db import migrations, models
import django.db.models.deletion
import uuid
class Migration(migrations.Migration):
initial = True
dependencies = [
('tracker', '0003_auto_20210626_0735'),
]
operations = [
migrations.CreateMod... | normal | {
"blob_id": "ead843f1edcfe798613effb049e3ca79dcd03b71",
"index": 7919,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Migration(migrations.Migration):\n <mask token>\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Migration(migrations.Migration):\n initial = T... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class Graph:
def draw(self, directory, filename, rules, start_state, accept_states):
g = graphviz.Digraph(format='svg', graph_attr={'rankdir': 'LR'})
self.add_start_edge(g, start_state)
edges = {}
for rule in rules:
from_state = self.state_... | flexible | {
"blob_id": "c0e94a0d20397ebbbdddf726307b19b6c5c85ae6",
"index": 9082,
"step-1": "<mask token>\n\n\nclass Graph:\n\n def draw(self, directory, filename, rules, start_state, accept_states):\n g = graphviz.Digraph(format='svg', graph_attr={'rankdir': 'LR'})\n self.add_start_edge(g, start_state)\n ... | [
6,
7,
9,
10,
12
] |
# -*- coding: utf-8 -*-
# Copyright 2013, Achim Köhler
# All rights reserved, see accompanied file license.txt for details.
# $REV$
import argparse
import traylauncher
if __name__ == "__main__":
args = argparse.Namespace()
args.notray = False
traylauncher.start(args) | normal | {
"blob_id": "8faaf9eb2e78b7921dd1cac4772e2415671201c7",
"index": 8481,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n args = argparse.Namespace()\n args.notray = False\n traylauncher.start(args)\n",
"step-3": "import argparse\nimport traylauncher\nif __name__ == '_... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def get_data_path(file_name):
return os.path.join(DATA_DIR, file_name)
def assert_strings(test_case, actual, expected):
message = (
"""
Expected: ""\"%s""\"
Actual: ""\"%s""\"
Expected: %s
A... | flexible | {
"blob_id": "83d35c413af0cefb71964671b43df1e815aa2115",
"index": 3945,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef get_data_path(file_name):\n return os.path.join(DATA_DIR, file_name)\n\n\ndef assert_strings(test_case, actual, expected):\n message = (\n \"\"\"\n\n Expected: \"\... | [
0,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def cardinal(ordinal):
return int(''.join([char for char in ordinal if char.isdigit()]))
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def cardinal(ordinal):
return int(''.... | flexible | {
"blob_id": "d4b1b6bdf125f2791c219b7db579c234eda0a73c",
"index": 9220,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef cardinal(ordinal):\n return int(''.join([char for char in ordinal if char.isdigit()]))\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef cardinal(ordinal):\n return i... | [
0,
1,
2,
3,
4
] |
#THIS IS PYTHON3
import tkinter as tk
from tkinter import *
from PIL import ImageTk
from PIL import Image #to handle non-gif image formats
import cv2
import numpy as np
from statistics import mode
import time
import random
import predict as ml
def calcSuccess(predictedCounter, randAssault):
vidLabel.pack_forge... | normal | {
"blob_id": "8cf6a9243182a4f6b68199a8967e06790396dc10",
"index": 5967,
"step-1": "<mask token>\n\n\ndef calcSuccess(predictedCounter, randAssault):\n vidLabel.pack_forget()\n if predictedCounter == 'parry_R':\n instructionLabel.config(text='RIGHT PARRY')\n if randAssault == 4 or randAssault =... | [
3,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
class Usuario(Configuration.db.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|>
def __repr__(self):
return '<Usuario %r... | flexible | {
"blob_id": "598a0771dd1447034f2db95c67dd0dcf968f43a7",
"index": 8229,
"step-1": "<mask token>\n\n\nclass Usuario(Configuration.db.Model):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def __repr__(self):\n return '<Usuario %r>' % self.i... | [
8,
10,
12,
14,
16
] |
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