code stringlengths 13 6.09M | order_type stringclasses 2
values | original_example dict | step_ids listlengths 1 5 |
|---|---|---|---|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys
# Add gumpy path
sys.path.append('../shared')
from gumpy import signal
import numpy as np
def preprocess_data(data, sample_rate=160, ac_freq=60, hp_freq=0.5, bp_low=2, bp_high=60, notch=False,
hp_filter=False, bp_filter=False, artifact_rem... | normal | {
"blob_id": "5f1cbe1019f218d2aad616ea8bbe760ea760534c",
"index": 9359,
"step-1": "<mask token>\n\n\ndef preprocess_data(data, sample_rate=160, ac_freq=60, hp_freq=0.5, bp_low=\n 2, bp_high=60, notch=False, hp_filter=False, bp_filter=False,\n artifact_removal=False, normalize=False):\n if notch:\n ... | [
4,
6,
7,
8,
9
] |
<|reserved_special_token_0|>
class MaskShadowGANModel(BaseModel):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def generate_dataset(self):
"""
Add ops for dataset loaders to graph
"""
if self.training:
dataset = UnpairedDataset(self.opt, self.train... | flexible | {
"blob_id": "cbbe273a19a4e60b760e35aeb8d43972a46760f5",
"index": 3436,
"step-1": "<mask token>\n\n\nclass MaskShadowGANModel(BaseModel):\n <mask token>\n <mask token>\n\n def generate_dataset(self):\n \"\"\"\n Add ops for dataset loaders to graph\n \"\"\"\n if self.training:\... | [
8,
9,
11,
12,
13
] |
<|reserved_special_token_0|>
class Card(namedtuple('Card', 'face, suit')):
def __repr__(self):
return ''.join(self)
def royal_flush(hand):
royalface = 'TJQKA'
ordered = sorted(hand, key=lambda card: (faces.index(card.face), card.suit)
)
first_card = ordered[0]
other_cards = orde... | flexible | {
"blob_id": "561763d4d7b613446f2890ef629b631542f2f472",
"index": 2776,
"step-1": "<mask token>\n\n\nclass Card(namedtuple('Card', 'face, suit')):\n\n def __repr__(self):\n return ''.join(self)\n\n\ndef royal_flush(hand):\n royalface = 'TJQKA'\n ordered = sorted(hand, key=lambda card: (faces.index... | [
7,
8,
10,
17,
19
] |
from django.db import models
from django.utils import timezone
from pprint import pprint
class Cast(models.Model):
name = models.CharField(max_length=50, blank=True, null=True)
image = models.ImageField(upload_to='cast', blank=True, null=True)
description = models.CharField(max_length=400, blank=True, null... | normal | {
"blob_id": "45dc9d362a2ddfd408f93452bda0b7338057ca81",
"index": 8322,
"step-1": "<mask token>\n\n\nclass Comic(models.Model):\n MAX_PAGES_PER_ISSUE = 1000\n sort_number = models.IntegerField(blank=True, null=True)\n page_number = models.IntegerField(blank=True, null=True)\n last_page = models.Intege... | [
12,
13,
14,
19,
20
] |
"""
Copyright (C) 2019-2020 Zilliz. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | normal | {
"blob_id": "65a9f732fc8c7b9c63f6ef0d7b2172bb4138a895",
"index": 2761,
"step-1": "<mask token>\n\n\nclass TestScope:\n\n @pytest.mark.run(order=1)\n def test_create_scope(self, host, port):\n url = 'http://' + host + ':' + port + '/scope'\n r = requests.post(url=url)\n print(r.text)\n ... | [
10,
14,
17,
18,
20
] |
# nomer7
import no2_modul2 # Atau apapun file-nya yang kamu buat tadi
class MhsTIF(no2_modul2.Mahasiswa): # perhatikan class induknya : Mahasiswa
"""Class MhsTIF yang dibangun dari class Mahasiswa"""
def kataKanPy(self):
print('Python is cool.')
"Apakah metode / state itu berasal ... | normal | {
"blob_id": "b54f47de85fe95d47a1b1be921997ad86d7b450d",
"index": 8777,
"step-1": "# nomer7\r\n\r\nimport no2_modul2 # Atau apapun file-nya yang kamu buat tadi\r\n\r\nclass MhsTIF(no2_modul2.Mahasiswa): # perhatikan class induknya : Mahasiswa\r\n \"\"\"Class MhsTIF yang dibangun dari class Mahasiswa... | [
0
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def get_read_stats(isize=400):
stats = readstatistics.ReadStatistics(None)
stats.insertSizes = numpy.random.normal(400, 20, 2000).astype(int)
stats.orientations = ['+-']
return stats
<|reserved_special_token_0|... | flexible | {
"blob_id": "97a362fc65731bb8fc3743c49a669b4cd3f0e155",
"index": 9426,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef get_read_stats(isize=400):\n stats = readstatistics.ReadStatistics(None)\n stats.insertSizes = numpy.random.normal(400, 20, 2000).astype(int)\n stats.orientations = ['+-'... | [
0,
1,
2,
3,
4
] |
# __ __ __ ______ __
# / | / | / | / \ / |
# $$ | $$ |_$$ |_ ______ ______ _______ /$$$$$$ | ______ $$/ _______ _______
# $$ \/$$// $$ | / \ / \ / \ ... | normal | {
"blob_id": "ae72d832039f36149988da02d8a4174d80a4ecfb",
"index": 2350,
"step-1": "\n # __ __ __ ______ __\n# / | / | / | / \\ / |\n# $$ | $$ |_$$ |_ ______ ______ _______ /$$$$$... | [
0
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def is_power(n):
if n == 0:
return 'not power of two'
if n & n - 1 == 0:
return 'power of 2'
return 'not power of 2'
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def is_power(n):
if n == 0:
return 'not... | flexible | {
"blob_id": "676aec735dd7441b0c481956ad18b012b8d98ea4",
"index": 8459,
"step-1": "<mask token>\n",
"step-2": "def is_power(n):\n if n == 0:\n return 'not power of two'\n if n & n - 1 == 0:\n return 'power of 2'\n return 'not power of 2'\n\n\n<mask token>\n",
"step-3": "def is_power(n):... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class PidorWeekly:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
@classmethod
def get_top_pidor(cls, cid, date=None):
monday = cls.__get_current_monday(
) if date is None else cls.__get_date_monday(date)
... | flexible | {
"blob_id": "109ca06685eece74034f77a98b1d7172a17aca21",
"index": 7469,
"step-1": "<mask token>\n\n\nclass PidorWeekly:\n <mask token>\n <mask token>\n <mask token>\n\n @classmethod\n def get_top_pidor(cls, cid, date=None):\n monday = cls.__get_current_monday(\n ) if date is None ... | [
9,
12,
13,
14,
15
] |
from django.urls import path
from . import views
app_name = 'restuarant'
urlpatterns = [path('orderplaced/', views.orderplaced), path('restaurant/',
views.restuarent, name='restuarant'), path('login/restaurant/', views.
restLogin, name='rlogin'), path('register/restaurant/', views.
restRegister, name='rregi... | normal | {
"blob_id": "63830a3c09a2d0a267b030a336062d5e95b9a71a",
"index": 3308,
"step-1": "<mask token>\n",
"step-2": "<mask token>\napp_name = 'restuarant'\nurlpatterns = [path('orderplaced/', views.orderplaced), path('restaurant/',\n views.restuarent, name='restuarant'), path('login/restaurant/', views.\n restL... | [
0,
1,
2
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
df_grade.head()
<|reserved_special_token_0|>
df_sinfo.head()
<|reserved_special_token_0|>
df_sinfo.head()
<|reserved_special_token_0|>
df_merge.head()
<|reserved_special_token_0|>
for name in ['姓名', '性别'][::-1]:
new_columns.re... | flexible | {
"blob_id": "f6c48731b2a4e0a6f1f93034ee9d11121c2d0427",
"index": 6810,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ndf_grade.head()\n<mask token>\ndf_sinfo.head()\n<mask token>\ndf_sinfo.head()\n<mask token>\ndf_merge.head()\n<mask token>\nfor name in ['姓名', '性别'][::-1]:\n new_columns.remove(name)\n... | [
0,
1,
2,
3,
4
] |
'''
Utility functions to do get frequencies of n-grams
Author: Jesus I. Ramirez Franco
December 2018
'''
import nltk
import pandas as pd
from nltk.stem.snowball import SnowballStemmer
from pycorenlp import StanfordCoreNLP
import math
from nltk.tokenize import RegexpTokenizer
from nltk.corpus import stopwords
import st... | normal | {
"blob_id": "367c3b4da38623e78f2853f9d3464a414ad049c2",
"index": 9596,
"step-1": "<mask token>\n\n\ndef clean_doc(text, language='english'):\n \"\"\"\n\tRemoves unknown characters and punctuation, change capital to lower letters and remove\n\tstop words. If stem=False\n\tInputs:\n\tsentence (string): a sting ... | [
3,
8,
10,
11,
12
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
db.create_all()
<|reserved_special_token_0|>
db.session.add(admin)
db.session.add(guest)
db.session.commit()
<|reserved_special_token_0|>
print(users)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
db.create_all()
ad... | flexible | {
"blob_id": "99c2bd56deccc327faf659e91fc1fd0f6ff7a219",
"index": 3932,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ndb.create_all()\n<mask token>\ndb.session.add(admin)\ndb.session.add(guest)\ndb.session.commit()\n<mask token>\nprint(users)\n",
"step-3": "<mask token>\ndb.create_all()\nadmin = User('... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
helper.greeting('Hey, dummy')
<|reserved_special_token_1|>
<|reserved_special_token_0|>
__author__ = 'AdrianLeo'
helper.greeting('Hey, dummy')
<|reserved_special_token_1|>
import helper
__author__ = 'AdrianLeo'
helper.greeti... | flexible | {
"blob_id": "03156992355a756b2ae38735a98251eb611d4245",
"index": 2611,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nhelper.greeting('Hey, dummy')\n",
"step-3": "<mask token>\n__author__ = 'AdrianLeo'\nhelper.greeting('Hey, dummy')\n",
"step-4": "import helper\n__author__ = 'AdrianLeo'\nhelper.greet... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
with open('ratings.csv') as in_file:
csvreader = csv.reader(in_file)
with open('ratings_train.csv', 'w') as train_out:
with open('ratings_test.csv', 'w') as test_out:
for row in csvreader:
... | flexible | {
"blob_id": "e48a6a84268a0fe64e90714bd32712665934fc39",
"index": 2223,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open('ratings.csv') as in_file:\n csvreader = csv.reader(in_file)\n with open('ratings_train.csv', 'w') as train_out:\n with open('ratings_test.csv', 'w') as test_out:\n... | [
0,
1,
2,
3,
4
] |
import tkinter
import csv
import datetime
import time
root = tkinter.Tk()
root.title("Attendance")
root.geometry("+450+250")
ts = time.time()
date = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d')
fileName = "Attendance/Attendance_"+date+".csv"
# open file
with open(fileName, newline="") as file:
reader ... | normal | {
"blob_id": "2343a9d3e253b5a0347b5890a5d7b9c3be777669",
"index": 5958,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nroot.title('Attendance')\nroot.geometry('+450+250')\n<mask token>\nwith open(fileName, newline='') as file:\n reader = csv.reader(file)\n r = 0\n for col in reader:\n c = ... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for i in range(len(x)):
ans += abs(x[i] - y[i])
for i in range(1, len(y)):
ans += abs(x[i - 1] - y[i])
if n % 2 == 1:
ans += max(abs(a[n // 2] - x[-1]), abs(a[n // 2] - y[0]))
print(ans)
<|reserved_special_token_1|>
... | flexible | {
"blob_id": "0e9d0927e8d69b0c0fad98479d47f2409c95a751",
"index": 794,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(len(x)):\n ans += abs(x[i] - y[i])\nfor i in range(1, len(y)):\n ans += abs(x[i - 1] - y[i])\nif n % 2 == 1:\n ans += max(abs(a[n // 2] - x[-1]), abs(a[n // 2] - y[... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
setup(name='ckanext-MYEXTENSION', version=version, description=
'description', long_description='\t', classifiers=[], keywords='',
author='ldhspace', author_email='ldhspace@yahoo.co.kr', url=
'www.naver.com', license='... | flexible | {
"blob_id": "9d2c0d59b0b2b4e4fca942e648059738053c53d0",
"index": 9376,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsetup(name='ckanext-MYEXTENSION', version=version, description=\n 'description', long_description='\\t', classifiers=[], keywords='',\n author='ldhspace', author_email='ldhspace@yah... | [
0,
1,
2,
3,
4
] |
import csv
from matplotlib import pyplot as plt
from datetime import datetime
file_one = 'data/dwifh_all_sales.csv'
file_two = 'data/dwifh_bc_sales.csv'
# create code to automatically build a dictionary for each album?
with open(file_one) as fo:
reader = csv.reader(fo)
header = next(reader)
album = {}
... | normal | {
"blob_id": "53380810a3d9787fe7c373cf1829f2d849a91c3c",
"index": 8456,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open(file_one) as fo:\n reader = csv.reader(fo)\n header = next(reader)\n album = {}\n dates, cd_income, dd_income, total_profit, artist_payout = [], [], [], [\n ]... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
while a == 1:
b = source()
<|reserved_special_token_0|>
<|reserved_special_token_1|>
a = 2
while a == 1:
b = source()
c = function(b)
| flexible | {
"blob_id": "56cae7b7a0338bd4a405cdc3cdcd9945a9df8823",
"index": 5839,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile a == 1:\n b = source()\n<mask token>\n",
"step-3": "a = 2\nwhile a == 1:\n b = source()\nc = function(b)\n",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1... | [
0,
1,
2
] |
import os
import time
from datetime import datetime, timedelta
from git import Repo
class CommitAnalyzer():
"""
Takes path of the repo
"""
def __init__(self, repo_path):
self.repo_path = repo_path
self.repo = Repo(self.repo_path)
assert not self.repo.bare
def get_conflict_commits(self):
conflict_commits... | normal | {
"blob_id": "8479c70fed36dc6f1e6094c832fb22d8c2e53e3a",
"index": 920,
"step-1": "<mask token>\n\n\nclass CommitAnalyzer:\n <mask token>\n\n def __init__(self, repo_path):\n self.repo_path = repo_path\n self.repo = Repo(self.repo_path)\n assert not self.repo.bare\n <mask token>\n\n\n... | [
2,
5,
6,
7,
8
] |
def non_dupulicates_lette(word):
text = list(word);
print(text)
i=0
for i in range(len(text)):
for k in text:
print(c)
def has_dupulicates(word):
d= dict()
for c in word:
if c not in d:
d[c]=1
else:
d[c]+=1
... | normal | {
"blob_id": "8cd234c2ec1b36abd992cc1a46147376cc241ede",
"index": 3276,
"step-1": "<mask token>\n\n\ndef has_dupulicates(word):\n d = dict()\n for c in word:\n if c not in d:\n d[c] = 1\n else:\n d[c] += 1\n for k in d:\n if d[k] == 1:\n print(k)\n ... | [
1,
2,
3,
4,
5
] |
"""
"""
#####################################################################
#This software was developed by the University of Tennessee as part of the
#Distributed Data Analysis of Neutron Scattering Experiments (DANSE)
#project funded by the US National Science Foundation.
#See the license text in license.txt
#copyr... | normal | {
"blob_id": "3cdb39e201983e672f6c22c25492a120be3d0d48",
"index": 9937,
"step-1": "\"\"\"\n\"\"\"\n#####################################################################\n#This software was developed by the University of Tennessee as part of the\n#Distributed Data Analysis of Neutron Scattering Experiments (DANSE)... | [
0
] |
# -*- coding: utf-8 -*-
"""Application configuration.
See https://github.com/sloria/cookiecutter-flask for configuration options with other flask-extensions
"""
import os
class Config(object):
"""Base configuration."""
SECRET_KEY = os.environ.get('DELIVERY_ASSISTANT_SECRET', 'secret-key') # TODO: Change me... | normal | {
"blob_id": "4cc1c8668a84cc6faadf60053568d155b8852c5f",
"index": 5643,
"step-1": "<mask token>\n\n\nclass DevConfig(Config):\n <mask token>\n ENV = 'dev'\n DEBUG = True\n\n\nclass TestConfig(Config):\n \"\"\"Test configuration.\"\"\"\n TESTING = True\n DEBUG = True\n",
"step-2": "<mask token>... | [
5,
9,
11,
13,
14
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
if __name__ == '__main__':
url_arg = sys.argv[1]
email = sys.argv[2]
params = {'email': email}
response = requests.post(url_arg, data=params)
print(response.text)
<|reserved_special_token_1|>
<|reserved_spec... | flexible | {
"blob_id": "0d9c50e55df5aa5614bd5a9679729cf7fa69c5df",
"index": 1461,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n url_arg = sys.argv[1]\n email = sys.argv[2]\n params = {'email': email}\n response = requests.post(url_arg, data=params)\n print(response.text)... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
@app.task
def delete_kube_by_name(name):
try:
logging.info(kubectl['delete', name]())
return True
except ProcessExecutionError:
return False
<|reserved_special_token_1|>
<|reserved_special_token_0|>
@app.task
def create_kube_from_template(file_name, *a... | flexible | {
"blob_id": "137e80b3bfdc0dba33a3108b37d21d298a8f251d",
"index": 1544,
"step-1": "<mask token>\n\n\n@app.task\ndef delete_kube_by_name(name):\n try:\n logging.info(kubectl['delete', name]())\n return True\n except ProcessExecutionError:\n return False\n",
"step-2": "<mask token>\n\n\... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
class Node:
object_id = 0
weight = 0
value = 0
def __init__(self, object_id, weight, value):
self.object_id = object_id
self.weight = weight
self.value = value
<|reserved_special_token_0|>
def read_file(file):
f = open(file, 'r')
f.seek... | flexible | {
"blob_id": "be408b349e2795101b525ad8d948dbf52cab81bf",
"index": 4281,
"step-1": "<mask token>\n\n\nclass Node:\n object_id = 0\n weight = 0\n value = 0\n\n def __init__(self, object_id, weight, value):\n self.object_id = object_id\n self.weight = weight\n self.value = value\n\n\... | [
4,
5,
7,
8,
9
] |
<|reserved_special_token_0|>
def main():
root = tk.Tk()
root.title('DailyFudan')
set_win_center(root, 700, 350)
root.resizable(0, 0)
lblid = tk.Label(root, text='学号：')
lblid.grid(row=0, column=0)
entID = tk.Entry(root)
entID.grid(row=0, column=1, padx=25, pady=0)
lblPW = tk.Label(r... | flexible | {
"blob_id": "d133a07f69d2dadb5559d881b01050abb2a9602b",
"index": 3891,
"step-1": "<mask token>\n\n\ndef main():\n root = tk.Tk()\n root.title('DailyFudan')\n set_win_center(root, 700, 350)\n root.resizable(0, 0)\n lblid = tk.Label(root, text='学号：')\n lblid.grid(row=0, column=0)\n entID = tk.... | [
1,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
def main():
print('\n', sys.version_info)
try:
while True:
print('\n\nPress Ctrl+C to exit.')
usr = test()
out = binascii.hexlify(bytes(usr, encoding='utf8'))
print('\nHex:\t\t', out)
print('Base 10:\t', int(out, ... | flexible | {
"blob_id": "a52cbe6dbf4b4fc82d09e5f34e6e135933f3af38",
"index": 1418,
"step-1": "<mask token>\n\n\ndef main():\n print('\\n', sys.version_info)\n try:\n while True:\n print('\\n\\nPress Ctrl+C to exit.')\n usr = test()\n out = binascii.hexlify(bytes(usr, encoding='u... | [
1,
2,
3,
4,
5
] |
from checkio.home.long_repeat import long_repeat
def test_long_repeat():
assert long_repeat("sdsffffse") == 4, "First"
assert long_repeat("ddvvrwwwrggg") == 3, "Second"
def test_fails_1():
assert long_repeat("") == 0, "Empty String"
def test_fails_2():
assert long_repeat("aa") == 2
| normal | {
"blob_id": "b459919e779063247c176e127368c687c903cf0f",
"index": 7869,
"step-1": "<mask token>\n\n\ndef test_fails_1():\n assert long_repeat('') == 0, 'Empty String'\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef test_fails_1():\n assert long_repeat('') == 0, 'Empty String'\n\n\ndef test_fails_2(... | [
1,
2,
3,
4,
5
] |
class Solution:
def longestConsecutive(self, nums) -> int:
s = set(nums)
answer = 0
# n = len(s)
for value in s:
if value - 1 not in s:
j = value
while (j in s):
j = j + 1
answer = max(answer, j - val... | normal | {
"blob_id": "9cb5573fada7a1529507da1d031f836044c10066",
"index": 2474,
"step-1": "<mask token>\n",
"step-2": "class Solution:\n <mask token>\n",
"step-3": "class Solution:\n\n def longestConsecutive(self, nums) ->int:\n s = set(nums)\n answer = 0\n for value in s:\n if v... | [
0,
1,
2,
3
] |
from django.contrib import admin
# from django.contrib.admin import AdminSite
# class MyAdminSite(AdminSite):
# site_header = 'Finder Administration'
# admin_site = MyAdminSite(name='Finder Admin')
from finder.models import Database, Column, GpsData, Alarm, System
class ColumnInline(admin.TabularInline):
mo... | normal | {
"blob_id": "e1968e0d6146ce7656505eeed8e9f31daa4b558a",
"index": 5447,
"step-1": "<mask token>\n\n\nclass DatabaseAdmin(admin.ModelAdmin):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n\n<mask token>\n\n\nclass AlarmAdmin(admin.ModelAdmin):\n list_display = ['nam... | [
5,
7,
10,
11,
13
] |
import discord
from collections import Counter
from db import readDB, writeDB
INFO_DB_SUCCESS = 'Database updated successfully!'
ERROR_DB_ERROR = 'Error: Unable to open database for writing'
ERROR_DB_NOT_FOUND = 'Error: Database for specified game does not exist. Check your spelling or use !addgame first.'
ERROR_PLA... | normal | {
"blob_id": "5869669f1e3f648c0ddc68683f0b1d2754b40169",
"index": 8714,
"step-1": "<mask token>\n\n\ndef roundMultiple(num, multiple):\n if num % multiple:\n return num + (multiple - num % multiple)\n return num\n\n\n<mask token>\n\n\ndef incrementStats(msgChannel, statsFile, winner, losers):\n da... | [
4,
5,
6,
7,
9
] |
"""
common tests
"""
from django.test import TestCase
from src.core.common import get_method_config
from src.predictive_model.classification.models import ClassificationMethods
from src.predictive_model.models import PredictiveModels
from src.utils.tests_utils import create_test_job, create_test_predictive_model
cl... | normal | {
"blob_id": "824038a56e8aaf4adf6ec813a5728ab318547582",
"index": 1638,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass TestCommon(TestCase):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass TestCommon(TestCase):\n\n def test_get_method_config(self):\n job = create_test_job(pr... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
log.setLevel(logging.DEBUG)
<|reserved_special_token_0|>
stream_hander.setFormatter(formatter)
log.addHandler(stream_hander)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
formatter = logging.Formatter('%(asctime)s ... | flexible | {
"blob_id": "675fbdfd519d00ab10bf613e8abb7338e484fe65",
"index": 57,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nlog.setLevel(logging.DEBUG)\n<mask token>\nstream_hander.setFormatter(formatter)\nlog.addHandler(stream_hander)\n",
"step-3": "<mask token>\nformatter = logging.Formatter('%(asctime)s [%... | [
0,
1,
2,
3,
4
] |
#str
owog="Delger"
# len()- urt
# lower()- jijigruuleh
# upper()- tomruulah
# capitalize()- ehnii useg tomruulah
# replace()- temdegt solih
print(owog.find("e"))
print(owog.count("e"))
print(owog[2:10])
a=21
b=21
if a>b:
print("a too ih")
elif a==b:
print("tentsuu")
else:
print("b to... | normal | {
"blob_id": "c4ca4b5c77c3c912b44a4853be30298ec845c4fd",
"index": 243,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(owog.find('e'))\nprint(owog.count('e'))\nprint(owog[2:10])\n<mask token>\nif a > b:\n print('a too ih')\nelif a == b:\n print('tentsuu')\nelse:\n print('b too ih')\n<mask to... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def get_offset_date(modifed_date, offset_in_days):
return date.isoformat(modifed_date + timedelta(days=int(offset_in_days)))
def get_trending_repositories(start_search_date, number_of_results=20):
github_api_uri = 'https://api.github.com'
query_search_url = '{}/search/reposi... | flexible | {
"blob_id": "8a7536b998a6d122e2e7529af1ebe2a0f025303f",
"index": 5620,
"step-1": "<mask token>\n\n\ndef get_offset_date(modifed_date, offset_in_days):\n return date.isoformat(modifed_date + timedelta(days=int(offset_in_days)))\n\n\ndef get_trending_repositories(start_search_date, number_of_results=20):\n g... | [
3,
4,
5,
6
] |
<|reserved_special_token_0|>
def skip_func(list):
cnt = 0
for i in list:
padd = [0] * 200
try:
got = api.friends_ids(i, count=200)
except:
print('========NG=============', cnt)
follower_list.append(padd)
else:
print('==========OK=... | flexible | {
"blob_id": "8fac4571a3a1559e297754e89375be06d6c45c2d",
"index": 4795,
"step-1": "<mask token>\n\n\ndef skip_func(list):\n cnt = 0\n for i in list:\n padd = [0] * 200\n try:\n got = api.friends_ids(i, count=200)\n except:\n print('========NG=============', cnt)\n ... | [
1,
2,
3,
4,
5
] |
import os
import imageio
import h5py
import numpy as np
def create_segmentation_test_data(data_path, raw_key, label_key, shape, chunks):
with h5py.File(data_path, 'a') as f:
f.create_dataset(raw_key, data=np.random.rand(*shape), chunks=chunks)
f.create_dataset(label_key, data=np.random.randint(0, ... | normal | {
"blob_id": "e3417980599448f1293b56cb95312088e7a8abe3",
"index": 9713,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef create_segmentation_test_data(data_path, raw_key, label_key, shape, chunks\n ):\n with h5py.File(data_path, 'a') as f:\n f.create_dataset(raw_key, data=np.random.rand... | [
0,
1,
2,
3,
4
] |
conf = {'PROJECT': 'WCCIA', 'NAS_FOLDER':
'Q:\\GROUPS\\CORP_JGS_DSE\\ATI\\quotations', 'DB_SERVER': '10.0.36.129',
'DB_PORT': '34000/'}
| normal | {
"blob_id": "fbce185671267bd70cf7b91696867b72dfcc8d5b",
"index": 1585,
"step-1": "<mask token>\n",
"step-2": "conf = {'PROJECT': 'WCCIA', 'NAS_FOLDER':\n 'Q:\\\\GROUPS\\\\CORP_JGS_DSE\\\\ATI\\\\quotations', 'DB_SERVER': '10.0.36.129',\n 'DB_PORT': '34000/'}\n",
"step-3": null,
"step-4": null,
"step... | [
0,
1
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
np.set_printoptions(formatter={'float_kind': float_formatter})
<|reserved_special_token_0|>
if __name__ == '__main__':
import numpy.random as random
import sys
if len(sys.argv) == 1:
sys.exit('{} [directory]'.f... | flexible | {
"blob_id": "f1c6340880b52ba86856913f74c7d589d9b49f49",
"index": 5179,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nnp.set_printoptions(formatter={'float_kind': float_formatter})\n<mask token>\nif __name__ == '__main__':\n import numpy.random as random\n import sys\n if len(sys.argv) == 1:\n ... | [
0,
1,
2,
3,
4
] |
# -*- coding: utf-8 -*-
"""
Created on Wed Feb 7 17:42:18 2018
@author: Tim
"""
import music21 as m21
import music21.features.jSymbolic as jsym
import scipy.stats
from collections import Counter
import numpy as np
import matplotlib.pyplot as plt
from timeit import default_timer as timer
# round all duration values t... | normal | {
"blob_id": "eb9135c6bcf89a62534cfc8480e5d44a089fe5a8",
"index": 1216,
"step-1": "<mask token>\n\n\ndef extractPatternOccurrence(songName, inStart, inEnd, useTies, songs):\n \"\"\"\n given song name, occurrence start, occurrence end, and the database of score files,\n return the notes of the associated ... | [
3,
8,
9,
10,
11
] |
# Mezzanine Django Framework createdb error on Max OSX 10.9.2
import django
django.version
| normal | {
"blob_id": "56afde2a31ad9dddee35e84609dff2eb0fc6fe1a",
"index": 9438,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ndjango.version\n",
"step-3": "import django\ndjango.version\n",
"step-4": "# Mezzanine Django Framework createdb error on Max OSX 10.9.2\nimport django\ndjango.version\n",
"step-5":... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class RLTrainer(object):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class RLTrainer(object):
<|reserved_special_token_0|>
def __init__(self, config_, grid_search... | flexible | {
"blob_id": "7c004cb0c9eefa5e88f5085fb3b2878db98d2b20",
"index": 3200,
"step-1": "<mask token>\n\n\nclass RLTrainer(object):\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass RLTrainer(object):\n <mask token>\n\n def __init__(self, config_, grid_search=False):\n... | [
1,
3,
4,
5,
6
] |
def quick_sort(arr):
q_sort(arr, 0, len(arr) - 1)
def q_sort(arr, left, right):
if left < right:
pivot_index = partition(arr, left, right)
q_sort(arr, left, pivot_index - 1)
q_sort(arr, pivot_index + 1, right)
def partition(arr, left, right):
pivot = arr[left]
while left < ... | normal | {
"blob_id": "09a5c96b7f496aca6b34d7f0a83d5b1e182ca409",
"index": 1627,
"step-1": "def quick_sort(arr):\n q_sort(arr, 0, len(arr) - 1)\n\n\ndef q_sort(arr, left, right):\n if left < right:\n pivot_index = partition(arr, left, right)\n q_sort(arr, left, pivot_index - 1)\n q_sort(arr, piv... | [
2,
3,
4,
5,
6
] |
#grabbed the following from moses marsh -- https://github.com/sidetrackedmind/gimme-bus/blob/master/gimmebus/utilities.py
from datetime import datetime as dt
from math import radians, cos, sin, acos, asin, sqrt
import networkx as nx
## These functions will go in model.py for matching historical GPS
## positions to th... | normal | {
"blob_id": "89ce3d3ec9691ab8f54cc0d9d008e06c65b5f2cc",
"index": 7847,
"step-1": "<mask token>\n\n\ndef haversine(pt1, pt2):\n \"\"\"\n INPUT: tuples (lon1, lat1), (lon2, lat2)\n\n OUTPUT: The great circle distance between two points\n on the earth (specified in decimal degrees)\n \"\"\"\n lon1... | [
2,
3,
4,
5,
6
] |
from scipy.optimize import newton
from math import sqrt
import time
def GetRadius(Ri,DV,mu):
def f(Rf):
return sqrt(mu/Ri)*(sqrt(2*Rf/(Rf+Ri))-1)+sqrt(mu/Rf)*(1-sqrt(2*Ri/(Rf+Ri)))-DV
return newton(f,Ri)
if __name__ == '__main__':
starttime = time.time()
print(GetRadius(10000.0,23546.2146710... | normal | {
"blob_id": "20722cf82371d176942e068e91b8fb38b4db61fd",
"index": 6951,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef GetRadius(Ri, DV, mu):\n\n def f(Rf):\n return sqrt(mu / Ri) * (sqrt(2 * Rf / (Rf + Ri)) - 1) + sqrt(mu / Rf\n ) * (1 - sqrt(2 * Ri / (Rf + Ri))) - DV\n re... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def path_check(hosts):
"""
Parses username, port, host and local and remote path,
finds all local and remote files, using find_local_files and find_remote_files functions,
and then opens ssh session using paramiko for each given host.
"""
local_files = []
local... | flexible | {
"blob_id": "6e3aa677985d7bd91bfbbd2078665206839bac63",
"index": 3578,
"step-1": "<mask token>\n\n\ndef path_check(hosts):\n \"\"\"\n Parses username, port, host and local and remote path,\n finds all local and remote files, using find_local_files and find_remote_files functions,\n and then opens ssh... | [
4,
6,
7,
8,
9
] |
import argparse
import debug.debug as dbg
import helper.helper as hlp
import prep.preprocessor as pre
import sample.sample as s
def main(dir_train, C, gamma, number_partitions, do_subsampling, write_labels):
hlp.setup_logging()
# Files as folds?
if number_partitions is None or number_partitions == 0: #... | normal | {
"blob_id": "4a63431aa71ca3f4b75fcd89a50bf599e7717645",
"index": 2442,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef main(dir_train, C, gamma, number_partitions, do_subsampling, write_labels):\n hlp.setup_logging()\n if number_partitions is None or number_partitions == 0:\n do_conca... | [
0,
1,
2,
3,
4
] |
# Copyright Materialize, Inc. and contributors. All rights reserved.
#
# Use of this software is governed by the Business Source License
# included in the LICENSE file at the root of this repository.
#
# As of the Change Date specified in that file, in accordance with
# the Business Source License, use of this software... | normal | {
"blob_id": "97ca134ffce404f4b2bc7352d4aac73a7bb764bd",
"index": 5708,
"step-1": "<mask token>\n\n\nclass OptbenchRun(MeasurementSource):\n\n def __init__(self, optbench_scenario: str, query: int):\n self._executor: Optional[Executor] = None\n self._optbench_scenario = optbench_scenario\n ... | [
7,
9,
12,
13,
14
] |
import pygame
import sys
# класс для хранения настроек
class Settings():
"""docstring for Setting"""
def __init__(self):
# параметры экрана
self.colour = (230, 230, 230)
self.screen_width = 1200
self.screen_height = 800
# параметры коробля
self.ship_speed = 1.5
# параметры пули
self.bullet_speed = ... | normal | {
"blob_id": "2402188380bc0189b88e3cfcbaabf64a9919b3d5",
"index": 8810,
"step-1": "<mask token>\n\n\nclass Settings:\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass Settings:\n <mask token>\n\n def __init__(self):\n self.colour = 230, 230, 230\n self.screen_width =... | [
1,
2,
3,
4,
5
] |
import pygame
import time
import math
from pygame.locals import *
from pygux.widgets.widget import Widget, hlBox
from pygux.colours import Colours
class Sprite(Widget):
def __init__(self, x, y, w, h, image=None, callback=None, **kw):
"""Sprite widget
"""
Widget.__init__(self, x, y, w, h, ... | normal | {
"blob_id": "0003d104a4dcd5a5b2357016cbc0317738c2cd3c",
"index": 2007,
"step-1": "<mask token>\n\n\nclass Sprite(Widget):\n\n def __init__(self, x, y, w, h, image=None, callback=None, **kw):\n \"\"\"Sprite widget\n \"\"\"\n Widget.__init__(self, x, y, w, h, **kw)\n if image:\n ... | [
3,
4,
5,
6
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
db.create_all()
Patient.add_patient()
Appointment.add_appointment()
PhoneCalls.add_call()
<|reserved_special_token_1|>
from config import SQLALCHEMY_DATABASE_URI
from app.models import Patient, Appointment, PhoneCalls
from app ... | flexible | {
"blob_id": "173e6017884a1a4df64018b306ea71bcaa1c5f1d",
"index": 4528,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ndb.create_all()\nPatient.add_patient()\nAppointment.add_appointment()\nPhoneCalls.add_call()\n",
"step-3": "from config import SQLALCHEMY_DATABASE_URI\nfrom app.models import Patient, A... | [
0,
1,
2,
3
] |
import json
import sys
with open(sys.argv[1], 'r') as f:
x = json.load(f)
with open('my_wire_to_quartus_wire.json', 'r') as f:
wirenamemap = json.load(f)
print("----- There are {} muxes in the database".format(len(x)))
print("----- There are {} routing pairs in the database".format(sum((len(v) for k, v in x.i... | normal | {
"blob_id": "95163a28a35cc88240d9d6edc2e9b416e5493909",
"index": 6021,
"step-1": "<mask token>\n\n\ndef bits2str(bits):\n ret = ''\n for row in bits:\n rowstr = ''\n for bit in row:\n rowstr += '1' if bit else '0'\n ret += rowstr + '\\n'\n return ret\n\n\ndef parse_xyi(in... | [
6,
7,
9,
10,
11
] |
from urllib.error import URLError
from urllib.request import urlopen
from bs4 import BeautifulSoup
import re
import pymysql
import ssl
from pymysql import Error
def decode_page(page_bytes, charsets=('utf-8',)):
"""通过指定的字符集对页面进行解码(不是每个网站都将字符集设置为utf-8)"""
page_html = None
for charset in charsets:
try... | normal | {
"blob_id": "53fae0103168f4074ba0645c33e4640fcefdfc96",
"index": 731,
"step-1": "<mask token>\n\n\ndef decode_page(page_bytes, charsets=('utf-8',)):\n \"\"\"通过指定的字符集对页面进行解码(不是每个网站都将字符集设置为utf-8)\"\"\"\n page_html = None\n for charset in charsets:\n try:\n page_html = page_bytes.decode(c... | [
5,
6,
7,
8,
9
] |
def name_of_function():
"""
Docstring explains function.
"""
return 'Hello'
def dog_check(mystring):
if 'dog' in mystring.lower():
return True
else:
return False
<|reserved_special_token_0|>
def dog_check(mystring):
return 'dog' in mystring.lower()
<|reserved_special_toke... | flexible | {
"blob_id": "1deb070dd91c01190b70fa678add31ecb82f34fa",
"index": 3404,
"step-1": "def name_of_function():\n \"\"\"\n Docstring explains function.\n \"\"\"\n return 'Hello'\n\n\ndef dog_check(mystring):\n if 'dog' in mystring.lower():\n return True\n else:\n return False\n\n\n<mask tok... | [
5,
6,
7,
8,
9
] |
<|reserved_special_token_0|>
class JobTest(TestCase):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def test_field_order(self):
"""
Job test with field order.
"""
... | flexible | {
"blob_id": "d2298ad1e4737b983ba6d1f2fff59750137510b5",
"index": 904,
"step-1": "<mask token>\n\n\nclass JobTest(TestCase):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def test_field_order(self):\n \"\"\"\n Job test with field order.\n \"\... | [
10,
15,
16,
17,
20
] |
from jabberbot import JabberBot, botcmd
import datetime
import logging
import sys
import time;
from config import username, password, chatroom, adminuser
class SystemInfoJabberBot(JabberBot):
@botcmd
def serverinfo( self, mess, args):
"""Displays information about the server"""
version = open(... | normal | {
"blob_id": "c9872fb536fd6552e2a5353566305555808747f7",
"index": 1777,
"step-1": "<mask token>\n\n\nclass SystemInfoJabberBot(JabberBot):\n\n @botcmd\n def serverinfo(self, mess, args):\n \"\"\"Displays information about the server\"\"\"\n version = open('/proc/version').read().strip()\n ... | [
4,
5,
6,
7,
9
] |
"""
This module is used to extract features from the lines extracted from documents
using BERT encodings. This package leverages the bert-as-a-server package to create the
embeddings.
Example:
feature_extractor = FeatureExtractor(document) # document is of class Document
encoded_doc = feature_extracto... | normal | {
"blob_id": "882d265f14c04b2f2f626504d18e2cd07dcc8637",
"index": 3042,
"step-1": "<mask token>\n\n\nclass FeatureExtractor:\n <mask token>\n <mask token>\n\n def encode(self):\n \"\"\" encodes the text in the Document object, and then adds it to the encoding attribute \"\"\"\n text_lines =... | [
3,
4,
5,
6,
7
] |
from Socket import Socket
import threading
class Server(Socket):
def __init__(self):
super(Server, self).__init__()
print("server listening")
self.users = []
def set_up(self):
self.bind(("192.168.0.109", 1337))
self.listen(0)
self.accept_sockets()
def sen... | normal | {
"blob_id": "2027904401e5be7b1c95eebec3a1e6a88c25660c",
"index": 9338,
"step-1": "<mask token>\n\n\nclass Server(Socket):\n\n def __init__(self):\n super(Server, self).__init__()\n print('server listening')\n self.users = []\n\n def set_up(self):\n self.bind(('192.168.0.109', 13... | [
5,
6,
7,
8,
9
] |
from deuces.card import Card
from deuces.deck import Deck
from fast_utils.hand_strength.original_HS import *
from fast_utils.hand_strength.nn_HS import encode_hs
from fast_utils.expected_hand_strength.nn_EHS import *
from keras.models import load_model
def read_lookup_table(hole_cards, lookup_table):
"""
Read... | normal | {
"blob_id": "8503998fc881f47dc695d3ea4c7f56fa65a96e8a",
"index": 2874,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef read_lookup_table(hole_cards, lookup_table):\n \"\"\"\n Reads the preflop lookup table preflop_EHSs.txt.\n Args: \n hole_cards: list of int (deuces cards)\n ... | [
0,
1,
2
] |
from random import randint
cantidad = int(input("Numero de preguntas: "))
contador_bien = 0
contador_mal = 0
while cantidad <= 0:
print ("El numero de preguntas debe ser al menos 1")
cantidad = int(input("Numero de preguntas: "))
for i in range(cantidad):
numero = randint(2,10)
numero2 = randint(2,10)
aleatorio ... | normal | {
"blob_id": "48bc5d4b191fa631650b60240560dbece6396312",
"index": 655,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile cantidad <= 0:\n print('El numero de preguntas debe ser al menos 1')\n cantidad = int(input('Numero de preguntas: '))\nfor i in range(cantidad):\n numero = randint(2, 10)\n ... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print(mydict)
print(mylist0)
print(mylist1)
for c in ('0', '1'):
if c in mydict:
mydict[c] += mylist0
else:
mydict[c] = mylist0
print(mydict)
for c in ('0', '1'):
if c in mydict:
mydict[c] += my... | flexible | {
"blob_id": "6e5b8be6182f39f185f4547f0abd84a4e404bf34",
"index": 1861,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(mydict)\nprint(mylist0)\nprint(mylist1)\nfor c in ('0', '1'):\n if c in mydict:\n mydict[c] += mylist0\n else:\n mydict[c] = mylist0\nprint(mydict)\nfor c in ('0... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
while True:
sleep(0.5)
r = random.choice(mineral)
x, y, z = mc.entity.getTilePos(myID)
mc.setBlocks(x + 1, y + 3, z + 1, x - 1, y - 3, z - 1, r)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
mc = Min... | flexible | {
"blob_id": "b28ae19f31ae746f901dea645dfeaa211a15cd31",
"index": 1879,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile True:\n sleep(0.5)\n r = random.choice(mineral)\n x, y, z = mc.entity.getTilePos(myID)\n mc.setBlocks(x + 1, y + 3, z + 1, x - 1, y - 3, z - 1, r)\n",
"step-3": "<mask... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def get_results_df(fname, problem):
"""Process csv into dataframe.
"""
t = '\t'
val_cols = ['Actions', 'Expansions', 'GoalTests', 'NewNodes',
'PlanLength', 'ElapsedSeconds']
err = ''
df = pd.read_csv(fname, sep=t)
if df.shape[0] < len(val_cols):
... | flexible | {
"blob_id": "cd49230be3c418853aa2986ed727204e51a6b6ae",
"index": 3794,
"step-1": "<mask token>\n\n\ndef get_results_df(fname, problem):\n \"\"\"Process csv into dataframe.\n \"\"\"\n t = '\\t'\n val_cols = ['Actions', 'Expansions', 'GoalTests', 'NewNodes',\n 'PlanLength', 'ElapsedSeconds']\n ... | [
6,
12,
14,
16,
17
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
if __name__ == '__main__':
sac_gym_test()
<|reserved_special_token_1|>
from neodroidagent.entry_points.agent_tests import sac_gym_test
if __name__ == '__main__':
sac_gym_test()
<|reserved_special_token_1|>
from neodr... | flexible | {
"blob_id": "e9890fcf9ad2a78b3400f6e4eeb75deac8edcd6a",
"index": 1609,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n sac_gym_test()\n",
"step-3": "from neodroidagent.entry_points.agent_tests import sac_gym_test\nif __name__ == '__main__':\n sac_gym_test()\n",
"step... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class TRSInterface:
def toolsGet(self):
raise NotImplementedError
def metadataGet(self):
raise NotImplementedError
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_s... | flexible | {
"blob_id": "d122267e1da2d9cf68d245148bb496dfba3e7d19",
"index": 4467,
"step-1": "<mask token>\n\n\nclass TRSInterface:\n\n def toolsGet(self):\n raise NotImplementedError\n\n def metadataGet(self):\n raise NotImplementedError\n <mask token>\n <mask token>\n <mask token>\n <mask t... | [
18,
21,
24,
27,
30
] |
# Generated by Django 3.1.2 on 2021-07-02 05:38
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('asset', '0001_initial'),
]
operations = [
migrations.RemoveField(
model_name='balance',
name='title',
),
]
| normal | {
"blob_id": "257f18db95e069c037341d2af372269e988b0a80",
"index": 536,
"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 = [('asset', '000... | [
0,
1,
2,
3,
4
] |
from django.apps import AppConfig
from django.utils.translation import gettext_lazy as _
class StravaAuthConfig(AppConfig):
name = "strava.contrib.strava_django"
verbose_name = _("Strava Auth")
def ready(self):
pass
| normal | {
"blob_id": "9e43eb3c3ab3be4e695dbc80aa005332b8d8a4ec",
"index": 9515,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass StravaAuthConfig(AppConfig):\n <mask token>\n <mask token>\n\n def ready(self):\n pass\n",
"step-3": "<mask token>\n\n\nclass StravaAuthConfig(AppConfig):\n ... | [
0,
2,
3,
4,
5
] |
#!/usr/bin/python
from sqlalchemy import create_engine
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import Column, Integer, String, ForeignKey
from sqlalchemy.orm import sessionmaker, relationship
engine = create_engine("sqlite:///banco.db")
Base = declarative_base()
Session = sessionmaker(... | normal | {
"blob_id": "6d5257158a7d2eef63faf2fea27f36721d4349ae",
"index": 4273,
"step-1": "#!/usr/bin/python\n\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.ext.declarative import declarative_base\nfrom sqlalchemy import Column, Integer, String, ForeignKey\nfrom sqlalchemy.orm import sessionmaker, relationship\n... | [
0
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def split_the_bill(x):
owed_dict = {}
sum = 0
people = 0
for key in x:
sum = sum + x[key]
people = people + 1
price_pp = sum / people
for key in x:
owed_value = x[key] - price_pp
... | flexible | {
"blob_id": "69d7e7eb644a67ee921086005f0a55f39507f361",
"index": 2864,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef split_the_bill(x):\n owed_dict = {}\n sum = 0\n people = 0\n for key in x:\n sum = sum + x[key]\n people = people + 1\n price_pp = sum / people\n f... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class SoftmaxWithLossLayer:
<|reserved_special_token_0|>
def __init__(self):
self.y = None
self.t = None
def forward(self, x, t):
"""
x: input to softmax
t: teacher data
"""
self.t = t
self.y = softmax(x)
... | flexible | {
"blob_id": "8ae64c65d6d5dc9f2a99aeceff31657deff06c15",
"index": 5236,
"step-1": "<mask token>\n\n\nclass SoftmaxWithLossLayer:\n <mask token>\n\n def __init__(self):\n self.y = None\n self.t = None\n\n def forward(self, x, t):\n \"\"\"\n x: input to softmax\n t: teach... | [
4,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print(New_list)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
lst = [1, -3, 4, -56, 7, 3, -8, -5, 2, 4, 9]
New_list = list(filter(lambda x: x > 0, lst))
print(New_list)
<|reserved_special_token_1|>
'''4. Write a ... | flexible | {
"blob_id": "d61151859390ab1c907ac3753143312da434981e",
"index": 2624,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(New_list)\n",
"step-3": "<mask token>\nlst = [1, -3, 4, -56, 7, 3, -8, -5, 2, 4, 9]\nNew_list = list(filter(lambda x: x > 0, lst))\nprint(New_list)\n",
"step-4": "'''4. Write a ... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
class Solution(object):
<|reserved_special_token_0|>
<|reserved_special_token_1|>
class Solution(object):
def eventualSafeNodes(self, graph):
"""
:type graph: List[List[int]]
:rtype: List[int]
"""
WHITE, GRA... | flexible | {
"blob_id": "5c5cfcd240c8b05970dc8dff57bfbbdc98f1d100",
"index": 9838,
"step-1": "<mask token>\n",
"step-2": "class Solution(object):\n <mask token>\n",
"step-3": "class Solution(object):\n\n def eventualSafeNodes(self, graph):\n \"\"\"\n :type graph: List[List[int]]\n :rtype: List... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class MissingTransitionException(InvalidConfigException):
"""
Describes a capability that is missing.
"""
def __init__(self, transitions):
self.transitions = transitions
super(InvalidConfigException, self).__init__(
'Missing transition detected... | flexible | {
"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
] |
import markovify
import argparse
import sqlite3
import time
modelFile = './data/model.json'
corpusFile = './data/corpus.txt'
dbFile = './data/tweets.sqlite3'
def generate():
generate_count = 168
model_json = open(modelFile, 'r').read()
model = markovify.Text.from_json(model_json)
conn = sqlite3.conne... | normal | {
"blob_id": "cc71c0cc1ec21dc465486fb5894c4d389c39bd62",
"index": 8164,
"step-1": "<mask token>\n\n\ndef make_model():\n corpus = open(corpusFile).read()\n text_model = markovify.Text(corpus, state_size=4)\n model_json = text_model.to_json()\n f = open(modelFile, mode='w')\n f.write(model_json)\n ... | [
1,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
NUM_CLASSES = 31
AUDIO_SR = 16000
AUDIO_LENGTH = 16000
LIBROSA_AUDIO_LENGTH = 22050
EPOCHS = 25
categories = {'stop': 0, 'nine': 1, 'off': 2, 'four': 3, 'right': 4,
'eight': 5, 'one': 6, 'bird': 7, 'dog': 8, 'no': 9, 'on': 10, 'seven':
11, 'cat': 12,... | flexible | {
"blob_id": "6a9e18cde94258b01a37f459eceaac58118b4976",
"index": 5813,
"step-1": "<mask token>\n",
"step-2": "NUM_CLASSES = 31\nAUDIO_SR = 16000\nAUDIO_LENGTH = 16000\nLIBROSA_AUDIO_LENGTH = 22050\nEPOCHS = 25\ncategories = {'stop': 0, 'nine': 1, 'off': 2, 'four': 3, 'right': 4,\n 'eight': 5, 'one': 6, 'bir... | [
0,
1,
2
] |
class Date:
def __init__(self, strDate):
strDate = strDate.split('.')
self.day = strDate[0]
self.month = strDate[1]
self.year = strDate[2]
| normal | {
"blob_id": "805fc9a26650f85227d14da972311ffbd9dbd555",
"index": 16,
"step-1": "<mask token>\n",
"step-2": "class Date:\n <mask token>\n",
"step-3": "class Date:\n\n def __init__(self, strDate):\n strDate = strDate.split('.')\n self.day = strDate[0]\n self.month = strDate[1]\n ... | [
0,
1,
2
] |
<|reserved_special_token_0|>
class imageAnalyzer:
<|reserved_special_token_0|>
def getImage(self, img_number):
temp = open(self.temp_img_path + str(img_number) + '.jpeg', 'wb')
img = requests.get(self.url + '/image')
temp.write(img.content)
temp.close()
def analyzeHSV(sel... | flexible | {
"blob_id": "7d3264e9a90ebd72439f77983cbf4f9755048a85",
"index": 4300,
"step-1": "<mask token>\n\n\nclass imageAnalyzer:\n <mask token>\n\n def getImage(self, img_number):\n temp = open(self.temp_img_path + str(img_number) + '.jpeg', 'wb')\n img = requests.get(self.url + '/image')\n te... | [
6,
8,
9,
10,
11
] |
import requests
import toml
from pathlib import Path
imgs:list
config:dict
def parseTex(lines:list):
new_lines = []
for i, line in enumerate(lines):
if line == "\n":
continue
inline = False
if (line[0] == "$" and line[1] != "$"):
inline = True
line = li... | normal | {
"blob_id": "dbd04f7b88fa43ae920a6744e3979dbf917d3fc6",
"index": 7649,
"step-1": "<mask token>\n\n\ndef parseTex(lines: list):\n new_lines = []\n for i, line in enumerate(lines):\n if line == '\\n':\n continue\n inline = False\n if line[0] == '$' and line[1] != '$':\n ... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
exec(open('docker_zabbix_script_sender/version.py').read())
setup(name=NAME, version=version, author='Cyril Moreau', author_email=
'cyril.moreauu@gmail.com', url=GITHUB_ORG_URL + '/' + NAME,
download_url='{0}/{1}/tarball/v... | flexible | {
"blob_id": "0769003c248c099da5bcd75541d35234b01af5de",
"index": 2723,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nexec(open('docker_zabbix_script_sender/version.py').read())\nsetup(name=NAME, version=version, author='Cyril Moreau', author_email=\n 'cyril.moreauu@gmail.com', url=GITHUB_ORG_URL + '/... | [
0,
1,
2,
3,
4
] |
from collections import defaultdict
def k_most_frequent(arr:list, k:int):
''' '''
counts = defaultdict(int)
for n in nums:
counts[n] += 1
counts = [(k,v) for k,v in counts.items()]
ordered = list(reversed(sorted(counts, key=lambda d: d[1])))
return [o[0] for o in ordered[:k]]
num... | normal | {
"blob_id": "1298c2abae519a5365cc0d9d406196db987eb219",
"index": 5923,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef k_most_frequent(arr: list, k: int):\n \"\"\" \"\"\"\n counts = defaultdict(int)\n for n in nums:\n counts[n] += 1\n counts = [(k, v) for k, v in counts.items()]... | [
0,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
ANCHO = 600
ALTO = 800
| flexible | {
"blob_id": "71ca67948100fb7ad388934740cead1ebe4a2b52",
"index": 8549,
"step-1": "<mask token>\n",
"step-2": "ANCHO = 600\nALTO = 800\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
} | [
0,
1
] |
# -*- coding: utf-8 -*-
"""
Created on Tue Apr 24 18:50:16 2018
@author: User
"""
# -*- coding: utf-8 -*-
"""
Created on Mon Apr 23 19:05:42 2018
@author: User
"""
from bs4 import BeautifulSoup
import pandas as pd
import numpy as np
import lxml
import html5lib
import csv
path = 'E:/Data Scienc... | normal | {
"blob_id": "c7768e44464703552f579a1ec68b58fd9746a381",
"index": 8743,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nlen(dfhtml)\ndfhtml\ntype(dfhtml)\n<mask token>\nprint(txtnew)\n<mask token>\nf.writelines(str(txtnew))\nf.close()\n<mask token>\n",
"step-3": "<mask token>\npath = (\n 'E:/Data Scie... | [
0,
1,
2,
3,
4
] |
from threading import Thread, Lock
from utils import reloj
import random
class Imprimidor(Thread):
def __init__(self, nombre, berlin, bolsa_dinero):
super().__init__()
pass
def run(self):
'''
Funcionalidad de iMPRIMIDOR que imprime dinero cada 5 minutos, cada
iteracio... | normal | {
"blob_id": "ab79e2f9584dbbb526c62bde882a1bc9874b56f9",
"index": 7903,
"step-1": "<mask token>\n\n\nclass Imprimidor(Thread):\n\n def __init__(self, nombre, berlin, bolsa_dinero):\n super().__init__()\n pass\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\... | [
2,
4,
5,
6,
7
] |
import requests
from urllib.parse import urlparse, urlencode
from json import JSONDecodeError
from requests.exceptions import HTTPError
def validate_response(response):
"""
raise exception if error response occurred
"""
r = response
try:
r.raise_for_status()
except HTTPError as e:
... | normal | {
"blob_id": "5bd2cf2ae68708d2b1dbbe0323a5f83837f7b564",
"index": 7842,
"step-1": "<mask token>\n\n\nclass CpmsConnector:\n <mask token>\n <mask token>\n\n def __init__(self, config):\n \"\"\"initialize with config\n config(dict): must supply username, api_key, api_url\n \"\"\"\n ... | [
13,
16,
17,
19,
20
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
from socket import socket
<|reserved_special_token_1|>
#!/usr/bin python3
# coding: utf-8
"""
AUTHOR: bovenson
EMAIL: szhkai@qq.com
FILE: 03.py
DATE: 17-9-25 下午7:59
DESC:
"""
from socket import socket
| flexible | {
"blob_id": "74d1491280eba1ceb06ccf6f45546cdb41149687",
"index": 5642,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfrom socket import socket\n",
"step-3": "#!/usr/bin python3\n# coding: utf-8\n\n\"\"\"\nAUTHOR: bovenson\nEMAIL: szhkai@qq.com\nFILE: 03.py\nDATE: 17-9-25 下午7:59\nDESC:\n\"\"\"\n\nfrom ... | [
0,
1,
2
] |
alunos = list()
while True:
nome = str(input('Nome: '))
nota1 = float(input('Nota 1: '))
nota2 = float(input('Nota 2: '))
media = (nota1+nota2)/2
alunos.append([nome, [nota1, nota2], media])
pergunta = str(input('Quer continuar [S/N]? ')).upper()[0]
if pergunta == 'N':
break
print('-... | normal | {
"blob_id": "8dcd4914c58a7ecafdfdd70b698ef3b7141386a6",
"index": 2632,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile True:\n nome = str(input('Nome: '))\n nota1 = float(input('Nota 1: '))\n nota2 = float(input('Nota 2: '))\n media = (nota1 + nota2) / 2\n alunos.append([nome, [nota1,... | [
0,
1,
2,
3
] |
# Write a class to hold player information, e.g. what room they are in
# currently.
class Player():
def __init__(self, name, location, items=[]):
self.name = name
self.location = location
self.items = items
# def try_direction(self, user_action):
# attribute = user_action + '_... | normal | {
"blob_id": "b355bd5a519d65ea35d4e8d5e6a384424d79130a",
"index": 3620,
"step-1": "<mask token>\n",
"step-2": "class Player:\n <mask token>\n\n def pick_up_item(self, item):\n if len(self.items) <= 3:\n self.items.append(item)\n print(\n f\"\"\"\n\nNOW YOU HAVE ... | [
0,
2,
3,
4,
5
] |
# The actual code begins here
# This file is intended to load everything downloaded from loaddata.py, preventing user getting banned from IMDB
# The code is written to see what are some key words of the reviews from critics and normal viewers
# And to see what are some of the differences
# The second task is to asses t... | normal | {
"blob_id": "1f69cf5f6d15048e6ead37b5da836c9e2f783f74",
"index": 803,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('loading data...')\nwith open('movienumbers.pickle', 'rb') as input_file:\n movienumbers = pickle.load(input_file)\nwith open('ratings.pickle', 'rb') as input_file:\n ratings =... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def foi_tab_v1():
path_foi = f"{config.get_value(['paths', 'temp'])}/foi/"
path_foi_func = foi_v1.path_foi_func
progress = Output()
def outlog(*text):
with progress:
print(*text)
foi_info... | flexible | {
"blob_id": "2f9a081845685a4748c8b028ae4ee3a056a10284",
"index": 9779,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef foi_tab_v1():\n path_foi = f\"{config.get_value(['paths', 'temp'])}/foi/\"\n path_foi_func = foi_v1.path_foi_func\n progress = Output()\n\n def outlog(*text):\n ... | [
0,
2,
3,
4,
5
] |
import numpy as np
import imutils
import cv2
image = cv2.imread("D:\\Github\\python-opencv\\images\\trex.png")
cv2.imshow("Original", image)
cv2.waitKey(0)
(h, w) = image.shape[:2] # get height and width of the image
center = (w/2, h/2) # which point to rotate around
M = cv2.getRotationMatrix2D(center, 45, 1.0) # ro... | normal | {
"blob_id": "4462fec6e0edc25530c93ffeeae2372c86fef2cc",
"index": 528,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ncv2.imshow('Original', image)\ncv2.waitKey(0)\n<mask token>\ncv2.imshow('Rotated by 45 degrees', rotated)\ncv2.waitKey(0)\n<mask token>\ncv2.imshow('Rotated by -90 degrees', rotated)\ncv2.... | [
0,
1,
2,
3,
4
] |
from flask import Flask, render_template, request, jsonify, make_response
app = Flask(__name__)
@app.route("/")
def hello():
# return render_template('chat.html')
return make_response(render_template('chat.html'),200)
if __name__ == "__main__":
app.run(debug=True) | normal | {
"blob_id": "98841630964dd9513e51c3f13bfdb0719600712d",
"index": 6941,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@app.route('/')\ndef hello():\n return make_response(render_template('chat.html'), 200)\n\n\nif __name__ == '__main__':\n app.run(debug=True)\n",
"step-3": "<mask token>\napp ... | [
0,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
def disemvowel(s):
return s.translate(None, 'aeiouAEIOU')
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def disemvowel(string):
returnString = ''
vowels = ['a', 'e', 'i', 'o', 'u']
upperVowels = ['A', 'E', 'I', 'O', 'U']
... | flexible | {
"blob_id": "4dea0967a0ee3e9eb3b46145739dfeb233f3a120",
"index": 5307,
"step-1": "<mask token>\n\n\ndef disemvowel(s):\n return s.translate(None, 'aeiouAEIOU')\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef disemvowel(string):\n returnString = ''\n vowels = ['a', 'e', 'i', 'o', 'u']\n upper... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
df.to_csv('Tweets.csv', index=None, header=None)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
df1 = pd.read_csv('Tweets1.csv', names=['tweet'])
df2 = pd.read_csv('Tweets2.csv', names=['tweet'])
df3 = pd.read_csv('T... | flexible | {
"blob_id": "7d6196268b85861e76efaa53e14976f2eae09405",
"index": 3226,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ndf.to_csv('Tweets.csv', index=None, header=None)\n",
"step-3": "<mask token>\ndf1 = pd.read_csv('Tweets1.csv', names=['tweet'])\ndf2 = pd.read_csv('Tweets2.csv', names=['tweet'])\ndf3 =... | [
0,
1,
2,
3
] |
# Generated by Django 3.2.3 on 2021-07-02 08:18
from django.db import migrations, models
import django.utils.timezone
class Migration(migrations.Migration):
dependencies = [
('khovan', '0003_nhapkho'),
]
operations = [
migrations.AddField(
model_name='phieunhaphang',
... | normal | {
"blob_id": "016255d74ccf4ac547e4b212d33bb9a39295c830",
"index": 2715,
"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 = [('khovan', '0... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
plt.bar(pos, df['Mersenne Twister'], width, alpha=0.5, color='#EE3224')
plt.bar([(p + width) for p in pos], df['Xorshift 128+'], width, alpha=0.5,
color='#F78F1E')
plt.bar([(p + width * 2) for p in pos], df['SPCG64'], width, c... | flexible | {
"blob_id": "467b919f6953737eedd3f99596df244bd1177575",
"index": 5411,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nplt.bar(pos, df['Mersenne Twister'], width, alpha=0.5, color='#EE3224')\nplt.bar([(p + width) for p in pos], df['Xorshift 128+'], width, alpha=0.5,\n color='#F78F1E')\nplt.bar([(p + wi... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class Line(MapBase):
<|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 ("<Line(id='{}', len='{}', p0=... | flexible | {
"blob_id": "6b3cb7a42c8bc665e35206b135f6aefea3439758",
"index": 7381,
"step-1": "<mask token>\n\n\nclass Line(MapBase):\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 (\"<Line(id='{}', len='{}', p0='{}', p1='... | [
14,
16,
17,
18,
21
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
parser.add_argument('--batch_size', help='batch_size', required=False,
default=32)
parser.add_argument('--data_size', help='data_size', required=False,
default=1700)
parser.add_argument('--num_intra_threads', help='num_int... | flexible | {
"blob_id": "2e8d39d6d72672de8e4eac8295b90d68b1dff938",
"index": 9007,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nparser.add_argument('--batch_size', help='batch_size', required=False,\n default=32)\nparser.add_argument('--data_size', help='data_size', required=False,\n default=1700)\nparser.ad... | [
0,
1,
2,
3,
4
] |
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