code stringlengths 13 6.09M | order_type stringclasses 2
values | original_example dict | step_ids listlengths 1 5 |
|---|---|---|---|
#5.8-5.9
users = ['user1', 'user2', 'user3', 'user4', 'admin']
#users = []
if users:
for user in users:
if user == 'admin':
print(f"Hello, {user}, would you like to see a status report?")
else:
print(f"Hello, {user}, thank you for logging in again")
else:
print("We need t... | normal | {
"blob_id": "c355be4e05d1df7f5d6f2e32bbb5a8086babe95b",
"index": 7946,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif users:\n for user in users:\n if user == 'admin':\n print(f'Hello, {user}, would you like to see a status report?')\n else:\n print(f'Hello, {use... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def construct_param_dict(params, K_RC, K_CP, m_P):
"""
Construct all the parameters from its relationships with body size and temperature, using the normalizing constants and scaling exponent w
"""
w = params['w']
pd = params['pd']
pv = params['pv']
Er = params... | flexible | {
"blob_id": "99c12e925850fe7603831df5b159db30508f4515",
"index": 3832,
"step-1": "<mask token>\n\n\ndef construct_param_dict(params, K_RC, K_CP, m_P):\n \"\"\"\n Construct all the parameters from its relationships with body size and temperature, using the normalizing constants and scaling exponent w\n \... | [
5,
8,
9,
10,
11
] |
import sys
import os
PROJ_DIR = os.path.dirname(os.path.dirname(__file__))
sys.path.append(PROJ_DIR)
| normal | {
"blob_id": "54276074d84e63e6418f8738bb7f910424f1c94d",
"index": 9469,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsys.path.append(PROJ_DIR)\n",
"step-3": "<mask token>\nPROJ_DIR = os.path.dirname(os.path.dirname(__file__))\nsys.path.append(PROJ_DIR)\n",
"step-4": "import sys\nimport os\nPROJ_DIR ... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def _create_event_statement(event_name):
"""Return a SQL statement to create a Event vertex."""
field_name_to_value = {'name': event_name, 'event_date':
get_random_date(), 'uuid': get_uuid()}
return create_vertex_statement('Event', field_name_to_value)
<|reserved_spe... | flexible | {
"blob_id": "a521befba58aa85c2fcfe6006db4b161123585f1",
"index": 5341,
"step-1": "<mask token>\n\n\ndef _create_event_statement(event_name):\n \"\"\"Return a SQL statement to create a Event vertex.\"\"\"\n field_name_to_value = {'name': event_name, 'event_date':\n get_random_date(), 'uuid': get_uuid... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
def create_app():
app = Flask(__name__)
Bootstrap(app)
return app
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def create_app():
app = Flask(__name__)
Bootstrap(app)
return app
logging.basicConfig(level=logging.DEB... | flexible | {
"blob_id": "bd726c86bdecd0b63eb48d056932706d3ecf147d",
"index": 7665,
"step-1": "<mask token>\n\n\ndef create_app():\n app = Flask(__name__)\n Bootstrap(app)\n return app\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef create_app():\n app = Flask(__name__)\n Bootstrap(app)\n return ap... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
helpMessage = (
"""
**Vocal / Musique**
`{0}join`
Va rejoindre le salon vocale dans laquelle vous êtes.
`{0}leave`
Va partir du salon vocale dans laquelle vous êtes.
`{0}play [YouTube Url]` *ou* `{0}play [musique ou video à... | flexible | {
"blob_id": "f7283750923e1e430ff1f648878bbb9a0c73d2c4",
"index": 7880,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nhelpMessage = (\n \"\"\"\n**Vocal / Musique**\n\n`{0}join`\nVa rejoindre le salon vocale dans laquelle vous êtes.\n\n`{0}leave`\nVa partir du salon vocale dans laquelle vous êtes.\n\n`... | [
0,
1,
2,
3
] |
from pwn import *
hostname = "pwnable.kr"
portnum = 2222
username = "input2"
passwd = "guest"
def main():
args = ["./input"]
print("./input", end="")
for x in range(99):
print(" AA", end="")
args.append("AA")
print(args)
'''
s = ssh(host=hostname,
port=portnum,
... | normal | {
"blob_id": "9184779731d6102498934d77b6d3c0283fc594d9",
"index": 7498,
"step-1": "<mask token>\n\n\ndef main():\n args = ['./input']\n print('./input', end='')\n for x in range(99):\n print(' AA', end='')\n args.append('AA')\n print(args)\n\n\n<mask token>\n",
"step-2": "<mask token>\... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
class SpecificationsEventHandler(FileSystemEventHandler):
<|reserved_special_token_0|>
def __init__(self):
self.paused = False
self.banner = (
'============================================================')
def on_modified(self, event):
su... | flexible | {
"blob_id": "95ea8a21d3ac44c7760179bc4ebf67f0c16e6a19",
"index": 2421,
"step-1": "<mask token>\n\n\nclass SpecificationsEventHandler(FileSystemEventHandler):\n <mask token>\n\n def __init__(self):\n self.paused = False\n self.banner = (\n '==========================================... | [
3,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def main():
batch_size = 64
valid_batch_size = 8
dataset_size = 500
learning_rate = 0.001
weight_decay = 0.0001
epochs = 30
show_frq = 20
negative_size = 10
negative_expand = 1
negative_si... | flexible | {
"blob_id": "41f2a5ba0d7a726389936c1ff66a5724209ee99c",
"index": 4099,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef main():\n batch_size = 64\n valid_batch_size = 8\n dataset_size = 500\n learning_rate = 0.001\n weight_decay = 0.0001\n epochs = 30\n show_frq = 20\n negat... | [
0,
1,
2,
3,
4
] |
def raizCubica(numero):
r = pow(numero,(1/3))
return r
numeros = []
raices = []
for x in range(5):
numeros.insert(x, float(input("Ingrese Numero: ")))
raices.insert(x, round(raizCubica(numeros[x]),3))
print("Numeros: ", numeros)
print("Raices: ", raices) | normal | {
"blob_id": "180f7f0ade9770c6669680bd13ac8f2fd55cc8c7",
"index": 357,
"step-1": "<mask token>\n",
"step-2": "def raizCubica(numero):\n r = pow(numero, 1 / 3)\n return r\n\n\n<mask token>\n",
"step-3": "def raizCubica(numero):\n r = pow(numero, 1 / 3)\n return r\n\n\n<mask token>\nfor x in range(5... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
@click.group()
def main():
"""
An empty click group, required in order to bundle the other commands.
"""
pass
<|reserved_special_token_0|>
@main.command(help=
"""Reads the route list between a source airport and a destination airport and
write... | flexible | {
"blob_id": "234aad868ea71bbe476b303bcff37221820f1d90",
"index": 4310,
"step-1": "<mask token>\n\n\n@click.group()\ndef main():\n \"\"\"\n An empty click group, required in order to bundle the other commands.\n \"\"\"\n pass\n\n\n<mask token>\n\n\n@main.command(help=\n \"\"\"Reads the route list b... | [
4,
6,
7,
8,
10
] |
import os, pygame
import sys
from os import path
from random import choice
WIDTH = 1000
HEIGHT = 800
FPS = 60
BLACK = (0, 0, 0)
WHITE = (255, 255, 255)
RED = (255, 0, 0)
GREEN = (0, 255, 0)
BLUE = (0, 0, 255)
YELLOW = (255, 255, 0)
GRAY80 = (204, 204, 204)
GRAY = (26, 26, 26)
screen = pygame.disp... | normal | {
"blob_id": "7301a521586049ebb5e8e49b604cc96e3acc1fe9",
"index": 3512,
"step-1": "<mask token>\n\n\ndef draw_text(surf, text, size, x, y):\n font_name = pygame.font.match_font('OCR A Extended')\n font = pygame.font.Font(font_name, size)\n text_surface = font.render(text, True, WHITE)\n text_rect = te... | [
3,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print('haha')
<|reserved_special_token_1|>
import cv2
import torch
print('haha')
| flexible | {
"blob_id": "00f8992173321dfa5ac5b125a2e663b159fafb23",
"index": 4267,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('haha')\n",
"step-3": "import cv2\nimport torch\nprint('haha')\n",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
2
]
} | [
0,
1,
2
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def largestVar(s: str):
freq = {i: (0) for i in range(26)}
for i in range(len(s)):
freq[int(chr(i) - 'a')] += 1
max_var = 0
for a in range(26):
for b in range(26):
left_a = freq[a]
left_b = freq[b]
<|r... | flexible | {
"blob_id": "4bd2923381cd3ead9a5605363a86f41b3743bf27",
"index": 7223,
"step-1": "<mask token>\n",
"step-2": "def largestVar(s: str):\n freq = {i: (0) for i in range(26)}\n for i in range(len(s)):\n freq[int(chr(i) - 'a')] += 1\n max_var = 0\n for a in range(26):\n for b in range(26):... | [
0,
1,
2
] |
from wtforms import Form, StringField
class SearchForm(Form):
criteria = StringField("Texto a buscar")
| normal | {
"blob_id": "1896f4d5b304915d5cbbb30b0a83854c4a8cc60c",
"index": 7566,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass SearchForm(Form):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass SearchForm(Form):\n criteria = StringField('Texto a buscar')\n",
"step-4": "from wtforms import... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class TestDefaultApi(unittest.TestCase):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def tearDown(self):
pass
<|reserved_special_token_0|>
def test_meme_meme_id_delete(self):
"""Test case for meme_meme_id_delete
Delete meme by I... | flexible | {
"blob_id": "fca46c095972e8190ee9c93f3bddbb2a49363a7f",
"index": 6903,
"step-1": "<mask token>\n\n\nclass TestDefaultApi(unittest.TestCase):\n <mask token>\n <mask token>\n\n def tearDown(self):\n pass\n <mask token>\n\n def test_meme_meme_id_delete(self):\n \"\"\"Test case for meme_... | [
5,
8,
9,
10,
11
] |
import torch
import torch.multiprocessing as mp
import random
class QManeger(object):
def __init__(self, opt, q_trace, q_batch):
self.traces_s = []
self.traces_a = []
self.traces_r = []
self.lock = mp.Lock()
self.q_trace = q_trace
self.q_batch = q_batch
sel... | normal | {
"blob_id": "b693cc63e2ee4c994ef7b5e44faea99f15a021f6",
"index": 68,
"step-1": "<mask token>\n\n\nclass QManeger(object):\n <mask token>\n <mask token>\n\n def listening(self):\n while True:\n traces = self.q_trace.get(block=True)\n for s, a, r in zip(traces[0], traces[1], t... | [
2,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
class BruteForceAttackState(State):
def run(self):
os_val = np.random.choice(['Windows7', 'Windows10', 'Ubuntu16',
'MacOS10'])
addr_val = np.random.choice(['127.0.0.6', '127.0.0.7', '127.0.0.13',
'127.0.0.42'])
for i in range(self.itera... | flexible | {
"blob_id": "cf3b4e2c76091f95d24e8a987a63ece46503d6e8",
"index": 3459,
"step-1": "<mask token>\n\n\nclass BruteForceAttackState(State):\n\n def run(self):\n os_val = np.random.choice(['Windows7', 'Windows10', 'Ubuntu16',\n 'MacOS10'])\n addr_val = np.random.choice(['127.0.0.6', '127.0... | [
4,
7,
8,
9,
10
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class MarketingemailsConfig(AppConfig):
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class MarketingemailsConfig(AppConfig):
name = 'marketingemails'
<|reserved_special_to... | flexible | {
"blob_id": "19bb58ab440ca00bf6410a70a8b6bbc24eec96c1",
"index": 492,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass MarketingemailsConfig(AppConfig):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass MarketingemailsConfig(AppConfig):\n name = 'marketingemails'\n",
"step-4": "from... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def layer_forward(x, w):
"""
input:
- inputs (x): (N, d_1, ..., d_k),
- weights (w): (D, M)
"""
z = None
output = []
cache = x, w, z, output
return output, cache
<|reserved_special_token_0|>
def affine_backward(d_output, cache):
... | flexible | {
"blob_id": "c1fd6e940b3b15ae01a102b3c0aba9bd327c77b2",
"index": 8403,
"step-1": "<mask token>\n\n\ndef layer_forward(x, w):\n \"\"\"\n input:\n - inputs (x): (N, d_1, ..., d_k),\n - weights (w): (D, M)\n \"\"\"\n z = None\n output = []\n cache = x, w, z, output\n r... | [
4,
5,
6,
7,
8
] |
import numpy as np
#1
def longest_substring(string1,string2):
mat=np.zeros(shape=(len(string1),len(string2)))
for x in range(len(string1)):
for y in range(len(string2)):
if x==0 or y==0:
if string1[x]==string2[y]:
mat[x,y]=1
else:
... | normal | {
"blob_id": "6bb7dafea73aff7aca9b0ddc1393e4db6fcf0151",
"index": 4828,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef longest_substring(string1, string2):\n mat = np.zeros(shape=(len(string1), len(string2)))\n for x in range(len(string1)):\n for y in range(len(string2)):\n ... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class SwarmModel:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
class NeighbourhoodModel:
_particles = []
_bestPosition = None
_bestPositionFitness = -1
... | flexible | {
"blob_id": "5c06229f8e80a7225620f25941cc5276a9021e53",
"index": 5353,
"step-1": "<mask token>\n\n\nclass SwarmModel:\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n\nclass NeighbourhoodModel:\n _particles = []\n _bestPosition = None\n _bestPositionFitness =... | [
10,
11,
12,
15,
16
] |
"""
module : watcher.py
description : Script to automatically watch a directory (via watchdog) for tests and run them via py.test
"""
import sys
import os.path
import subprocess
import time
from watchdog.observers import Observer
from watchdog.events import FileSystemEventHandler
class SpecificationsEventHandler(Fil... | normal | {
"blob_id": "95ea8a21d3ac44c7760179bc4ebf67f0c16e6a19",
"index": 2421,
"step-1": "<mask token>\n\n\nclass SpecificationsEventHandler(FileSystemEventHandler):\n <mask token>\n\n def __init__(self):\n self.paused = False\n self.banner = (\n '==========================================... | [
3,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
def trans_screen():
pyautogui.doubleClick(492, 974)
pyautogui.typewrite(['enter'], 0.01)
def trans_chinese():
for c_rubbish in chinese_rubbish:
pin = p.get_pinyin(c_rubbish, '')
pin_list = list(pin)
pin_list.append('1')
rubbish_set.append(pin_... | flexible | {
"blob_id": "23e673909b2f1eb9a265ce84ad63464e20e99c6a",
"index": 3449,
"step-1": "<mask token>\n\n\ndef trans_screen():\n pyautogui.doubleClick(492, 974)\n pyautogui.typewrite(['enter'], 0.01)\n\n\ndef trans_chinese():\n for c_rubbish in chinese_rubbish:\n pin = p.get_pinyin(c_rubbish, '')\n ... | [
4,
5,
6,
7,
8
] |
def selectionSort(arr, low, high):
for i in range(len(arr)):
mini = i
for j in range(i + 1, len(arr)):
if arr[mini] > arr[j]:
mini = j
arr[i], arr[mini] = arr[mini], arr[i]
return arr
| normal | {
"blob_id": "c91be6cc332139c5b1e7ee5a3512482d0f8620b1",
"index": 7322,
"step-1": "<mask token>\n",
"step-2": "def selectionSort(arr, low, high):\n for i in range(len(arr)):\n mini = i\n for j in range(i + 1, len(arr)):\n if arr[mini] > arr[j]:\n mini = j\n arr[... | [
0,
1
] |
#!/usr/bin/env python3
#
# main.py - By Steven Chen Hao Nyeo
# Graphical interface for Socionics Engine
# Created: August 8, 2019
import wx
from cognitive_function import *
from entity import Entity
from function_to_type import Translator
from function_analysis import *
class TypeFrame(wx.Frame):
def __init__(s... | normal | {
"blob_id": "519dbe97ce9de30e616d660ef168e686c52b01b5",
"index": 5452,
"step-1": "<mask token>\n\n\nclass TypeFrame(wx.Frame):\n <mask token>\n\n def createCogButtons(self, row):\n cogButtons = self.domButtons if row == 0 else self.auxButtons\n labels = ['N', 'S', 'T', 'F']\n for i in ... | [
4,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
parser.add_argument('-p', type=str, default='default', help=
'name of a a policy file')
parser.add_argument('-n', type=int, default=100000, help='number of patients')
<|reserved_special_token_0|>
if len(matchingPolicies) == 0:... | flexible | {
"blob_id": "894ce07c6443208483be2d3ef1409f12f24d99f3",
"index": 2852,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nparser.add_argument('-p', type=str, default='default', help=\n 'name of a a policy file')\nparser.add_argument('-n', type=int, default=100000, help='number of patients')\n<mask token>\... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def calcula_elongacao(A, φ, ω, t):
x = A * m.cos(φ + φ * t)
return x
<|reserved_special_token_1|>
import math as m
def calcula_elongacao(A, φ, ω, t):
x = A * m.cos(φ + φ * t)
return x
<|reserved_special_to... | flexible | {
"blob_id": "225687729b64f455bcc841e83105c7444efdfad3",
"index": 5545,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef calcula_elongacao(A, φ, ω, t):\n x = A * m.cos(φ + φ * t)\n return x\n",
"step-3": "import math as m\n\n\ndef calcula_elongacao(A, φ, ω, t):\n x = A * m.cos(φ + φ * t)\... | [
0,
1,
2,
3
] |
import renderdoc as rd
from typing import List
import rdtest
class D3D12_Resource_Mapping_Zoo(rdtest.TestCase):
demos_test_name = 'D3D12_Resource_Mapping_Zoo'
def test_debug_pixel(self, x, y, test_name):
pipe: rd.PipeState = self.controller.GetPipelineState()
if not pipe.GetShaderReflection(... | normal | {
"blob_id": "565888d771f53934805555390e48d4886a43bdb6",
"index": 189,
"step-1": "<mask token>\n\n\nclass D3D12_Resource_Mapping_Zoo(rdtest.TestCase):\n <mask token>\n <mask token>\n\n def check_capture(self):\n if not self.controller.GetAPIProperties().shaderDebugging:\n rdtest.log.suc... | [
2,
3,
4,
5,
6
] |
__author__ = 'christopher'
import fabio
import pyFAI
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
from pims.tiff_stack import TiffStack_tifffile as TiffStack
from skxray.io.save_powder_output import save_output
from xpd_workflow.mask_tools import *
geo = pyFAI.load(
'/mnt/bulk-data/researc... | normal | {
"blob_id": "50f6bcb4d2223d864cca92778ab3483a2d2c3214",
"index": 5283,
"step-1": "__author__ = 'christopher'\nimport fabio\nimport pyFAI\nimport matplotlib.pyplot as plt\nfrom matplotlib.colors import LogNorm\nfrom pims.tiff_stack import TiffStack_tifffile as TiffStack\nfrom skxray.io.save_powder_output import s... | [
0
] |
import logging
import os
import callbacks
import commands
import dice
import echo
import inline
import keyboards
import mybot
import myenigma
import poll
import rocketgram
import send
import unknown
# avoid to remove "unused" imports by optimizers
def fix_imports():
_ = callbacks
_ = commands
_ = echo
... | normal | {
"blob_id": "fd904c70b350c650362c55ccb3b915371f24e267",
"index": 9623,
"step-1": "import logging\nimport os\n\nimport callbacks\nimport commands\nimport dice\nimport echo\nimport inline\nimport keyboards\nimport mybot\nimport myenigma\nimport poll\nimport rocketgram\nimport send\nimport unknown\n\n\n# avoid to ... | [
0
] |
<|reserved_special_token_0|>
def calculate_data_list():
counter = 0
btc = 'BTC'
symbols = []
all_positions = []
positions_final = []
volume = []
c = []
price_change = []
data = client.get_ticker()
for x in range(len(data)):
if btc in data[x]['symbol'] and data[x]['symbo... | flexible | {
"blob_id": "dcc85b143f2394b7839f2fb9c2079a7dd9fa8e88",
"index": 4733,
"step-1": "<mask token>\n\n\ndef calculate_data_list():\n counter = 0\n btc = 'BTC'\n symbols = []\n all_positions = []\n positions_final = []\n volume = []\n c = []\n price_change = []\n data = client.get_ticker()\... | [
5,
8,
9,
11,
12
] |
<|reserved_special_token_0|>
class TestFunctionalHumannEndtoEndBiom(unittest.TestCase):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def test_humann_gene_families_biom_input(self):
"""
Test the standard humann flow on a gene families output fi... | flexible | {
"blob_id": "27702f72ae147c435617acaab7dd7e5a5a737b13",
"index": 8152,
"step-1": "<mask token>\n\n\nclass TestFunctionalHumannEndtoEndBiom(unittest.TestCase):\n <mask token>\n <mask token>\n <mask token>\n\n def test_humann_gene_families_biom_input(self):\n \"\"\"\n Test the standard hu... | [
2,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Migration(migrations.Migration):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Migration(migrations.Migration):
dependencies = [(... | flexible | {
"blob_id": "21c8078a18ee4579fa9b4b1b667d6ea0c1ce99b3",
"index": 6005,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Migration(migrations.Migration):\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Migration(migrations.Migration):\n dependencies = [('blog', '001... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for line in lines:
strpline = line.rstrip()
arr = strpline.split(',')
newline = []
for i in range(len(arr)):
if arr[i] == 'y':
newline.append(i)
if arr[0] == 'republican':
newline.ap... | flexible | {
"blob_id": "07b05093b630fc0167532884ec69a00420ed70b4",
"index": 4021,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor line in lines:\n strpline = line.rstrip()\n arr = strpline.split(',')\n newline = []\n for i in range(len(arr)):\n if arr[i] == 'y':\n newline.append(i)\... | [
0,
1,
2,
3,
4
] |
#-*- coding: utf-8 -*-
import argparse
import pickle
def str2bool(v):
return v.lower() in ('true', '1')
arg_lists = []
parser = argparse.ArgumentParser()
def add_argument_group(name):
arg = parser.add_argument_group(name)
arg_lists.append(arg)
return arg
# Network
net_arg = add_argument_group('Network')
n... | normal | {
"blob_id": "dfaea1687238d3d09fee072689cfdea392bc78f9",
"index": 8967,
"step-1": "<mask token>\n\n\ndef str2bool(v):\n return v.lower() in ('true', '1')\n\n\n<mask token>\n\n\ndef add_argument_group(name):\n arg = parser.add_argument_group(name)\n arg_lists.append(arg)\n return arg\n\n\n<mask token>\... | [
2,
3,
5,
6,
7
] |
#!ipython3
pi_f = 0.1415926
pi = []
for i in range(10):
pi.append(str(pi_f * i*16)[0])
print(pi)
def convertBase(digits, baseA, baseB, precisionB):
return output
#0.56 b8 to b10
#(1/base) ^ (i+1) *x
to10('56')
test = list(str(56))
test
27 9 3
33
0.3212 * 3
4*1.5
0.3212* 4/6
3*3**-1
2*3**-2
1*3**... | normal | {
"blob_id": "cffc64970cb82072e5fb949f62e9778942b2be96",
"index": 8269,
"step-1": "#!ipython3\n\npi_f = 0.1415926\npi = []\nfor i in range(10):\n pi.append(str(pi_f * i*16)[0])\n\nprint(pi)\n\n\ndef convertBase(digits, baseA, baseB, precisionB):\n return output\n\n#0.56 b8 to b10\n#(1/base) ^ (i+1) *x\n\n\n... | [
0
] |
<|reserved_special_token_0|>
class ToolBusiness(object):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class ToolBusiness(object):
@classmethod
def get_tool_ip(cls):
ip = request.args.get('ip')
url = 'http://ap... | flexible | {
"blob_id": "bf45349a9fdfcef7392c477e089c5e3916cb4c8e",
"index": 8502,
"step-1": "<mask token>\n\n\nclass ToolBusiness(object):\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass ToolBusiness(object):\n\n @classmethod\n def get_tool_ip(cls):\n ip = request.args.get('ip')\n ... | [
1,
2,
3,
4,
5
] |
vocales = "aeiou"
resultado = []
frase = input("Por favor ingrese la frase que desea verificar").lower()
print(frase)
for vocal in vocales:
conteo_vocales = frase.count(vocal)
mensaje = (f"En la frase hay {conteo_vocales} veces, la vocal{vocal}")
resultado.append(mensaje)
for elemento in resultado:
p... | normal | {
"blob_id": "f0a03f9a6dc78d01455913f7db3ab1948b19ea63",
"index": 6250,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(frase)\nfor vocal in vocales:\n conteo_vocales = frase.count(vocal)\n mensaje = f'En la frase hay {conteo_vocales} veces, la vocal{vocal}'\n resultado.append(mensaje)\nfor ... | [
0,
1,
2,
3
] |
from __future__ import annotations
from functools import cache
class Solution:
def countArrangement(self, n: int) -> int:
cache = {}
def helper(perm):
digits = len(perm)
if digits == 1:
return 1
if perm in cache:
return cache[per... | normal | {
"blob_id": "e6acc7b022001d8419095ad6364a6ae9504ec7aa",
"index": 508,
"step-1": "<mask token>\n\n\nclass Solution:\n <mask token>\n\n\nclass Solution:\n\n def countArrangement(self, n: int) ->int:\n\n @cache\n def dfs(bm, i):\n if i == 0:\n return 1\n cnt ... | [
5,
6,
7,
8,
10
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def surfaceAreaCone(maxRadius=20, maxHeight=50, unit='m'):
a = random.randint(1, maxHeight)
b = random.randint(1, maxRadius)
slopingHeight = math.sqrt(a ** 2 + b ** 2)
problem = (
f'Surface area of cone w... | flexible | {
"blob_id": "3e19ede2112a109a776b607e927e2f0a095ba5cc",
"index": 7677,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef surfaceAreaCone(maxRadius=20, maxHeight=50, unit='m'):\n a = random.randint(1, maxHeight)\n b = random.randint(1, maxRadius)\n slopingHeight = math.sqrt(a ** 2 + b ** 2)\... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class UploadFileForm(forms.ModelForm):
class Meta:
model = Submit
fields = ['email', 'student_no', 'file']
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class... | flexible | {
"blob_id": "dabc38db6a5c4d97e18be2edc9d4c6203e264741",
"index": 3849,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass UploadFileForm(forms.ModelForm):\n\n\n class Meta:\n model = Submit\n fields = ['email', 'student_no', 'file']\n\n\n<mask token>\n",
"step-3": "<mask token>\n... | [
0,
1,
2,
3,
4
] |
import unittest
from traceback import print_tb
from ml_base.utilities.model_manager import ModelManager
from tests.mocks import MLModelMock
class ModelManagerTests(unittest.TestCase):
def test_model_manager_will_return_same_instance_when_instantiated_many_times(self):
"""Testing that the ModelManager wi... | normal | {
"blob_id": "8355faf7c0d3742be34a56ddc982cb389c80d0a9",
"index": 1063,
"step-1": "<mask token>\n\n\nclass ModelManagerTests(unittest.TestCase):\n\n def test_model_manager_will_return_same_instance_when_instantiated_many_times(\n self):\n \"\"\"Testing that the ModelManager will return the same i... | [
9,
13,
14,
15,
16
] |
n=int(input("please enter the number : "))
for i in range(11):
print(n," X ",i," = ",n*i) | normal | {
"blob_id": "ea4a55ed17c5cc2c6f127112af636ca885159c86",
"index": 5768,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(11):\n print(n, ' X ', i, ' = ', n * i)\n",
"step-3": "n = int(input('please enter the number : '))\nfor i in range(11):\n print(n, ' X ', i, ' = ', n * i)\n",
"... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def busca_dfs(nodo_inicial, custo_maximo_atual):
objetivo = '12345678_'
custo_maximo_absoluto = 100
explorados = set()
fronteira = deque()
fronteira.append(nodo_inicial)
if custo_maximo_atual > custo_maximo_absoluto:
return -1
while True:
if not... | flexible | {
"blob_id": "a85a7ad6ffb2b9aa5f5326d11c75ddbee680fac4",
"index": 673,
"step-1": "<mask token>\n\n\ndef busca_dfs(nodo_inicial, custo_maximo_atual):\n objetivo = '12345678_'\n custo_maximo_absoluto = 100\n explorados = set()\n fronteira = deque()\n fronteira.append(nodo_inicial)\n if custo_maxim... | [
2,
3,
4,
5,
6
] |
#Program to create and store Employee Salary Records in a file
import os
def appendEmployee(eno,name,basic):
fh=open("Employee.txt","a")
hra=basic*0.10
da=basic*0.73
gross=basic+hra+da
tax=gross*0.3
net=gross-tax
line=str(eno)+","+name+","+str(basic)+","+str(hra)+","+str(da)+","+str(gross)+","+str(tax)+","+str... | normal | {
"blob_id": "5b6241907cc97f82d6c6e0a461f4f71a9a567204",
"index": 5395,
"step-1": "<mask token>\n\n\ndef displayEmployees():\n fh = open('Employee.txt', 'r')\n for line in fh:\n emp = line.split(',')\n print('\\nEmployee No:', emp[0], '\\nEmployee Name:', emp[1],\n '\\nBasic:', emp[... | [
3,
5,
6,
7,
8
] |
from nltk.tokenize import sent_tokenize, word_tokenize
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from nltk.classify import NaiveBayesClassifier
from nltk.probability import FreqDist
import csv
f = open('trolls.csv', 'r')
file = csv.reader(f)
sentences=[]
remarks=[]
psObject = PorterStemmer(... | normal | {
"blob_id": "0dbdd7f7adffed850f126a2054c764b421c6ab84",
"index": 6799,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor kk in file:\n paragraph += kk[0]\nf.close()\n<mask token>\nprint('most commons below...')\nprint(most_common_words)\n<mask token>\nfor i, j in most_common_words:\n most_cm_1.app... | [
0,
1,
2,
3,
4
] |
# -*- coding: utf-8 -*-
"""
Created on Thu Apr 4 12:47:30 2019
Title: MP4-Medical Image Processing
@author: MP4 Team
"""
# Validate window controller
class ValidateWindowCtr(object):
# Initialization
def __init__(self, fig, im_trans, im_truth, im_segmen, vol_trans, vol_truth, vol_segmen, ax_trans,... | normal | {
"blob_id": "e0b28fdcbc3160bcccbb032949317a91a32eeb1b",
"index": 5394,
"step-1": "<mask token>\n\n\nclass ValidateWindowCtr(object):\n\n def __init__(self, fig, im_trans, im_truth, im_segmen, vol_trans,\n vol_truth, vol_segmen, ax_trans, ax_truth, ax_segmen, index_trans,\n index_truth, index_seg... | [
4,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
class Config(object):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class ... | flexible | {
"blob_id": "118380f58cd173d2de5572a1591766e38ca4a7f8",
"index": 8846,
"step-1": "<mask token>\n\n\nclass Config(object):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass Config(object):\n SQLALCHEMY_DATABASE_U... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def song_inference():
sp_total_model_path = 'sp_total'
train = pd.read_json('./dataset/train.json', typ='frame', encoding='utf-8')
song = pd.read_json('./dataset/song_meta.json', typ='frame', encoding=
'utf-8... | flexible | {
"blob_id": "05573b4ff68ca8638f8e13946b410df2a012840a",
"index": 1829,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef song_inference():\n sp_total_model_path = 'sp_total'\n train = pd.read_json('./dataset/train.json', typ='frame', encoding='utf-8')\n song = pd.read_json('./dataset/song_m... | [
0,
1,
2,
3,
4
] |
import requests as r
import re
class web_scrap:
seed=""
result=""
tag_attr=[]
def __init__(self,seed):
self.seed=seed
self.set_tag()
self.set_attr()
self.fetch_web(self.seed)
self.crawl()
def fetch_web(self,link):
... | normal | {
"blob_id": "f26dc3139413c4ed4b04484c095a433e53039cdb",
"index": 3028,
"step-1": "<mask token>\n\n\nclass web_scrap:\n <mask token>\n <mask token>\n <mask token>\n\n def __init__(self, seed):\n self.seed = seed\n self.set_tag()\n self.set_attr()\n self.fetch_web(self.seed)... | [
8,
10,
11,
12,
13
] |
n = int(input("Please input the number of 1's and 0's you want to print:"))
for i in range (1, n+1):
if i%2 == 1:
print ("1 ", end = "")
else:
print ("0 ", end = "") | normal | {
"blob_id": "bd96b31c5de2f0ad4bbc28c876b86ec238db3184",
"index": 9108,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(1, n + 1):\n if i % 2 == 1:\n print('1 ', end='')\n else:\n print('0 ', end='')\n",
"step-3": "n = int(input(\"Please input the number of 1's and 0's ... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def get_logger():
return current_app.logger
<|reserved_special_token_0|>
def info(msg, *args, **kwargs):
get_logger().info(msg, *args, **kwargs)
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserv... | flexible | {
"blob_id": "355e2799e89dfea4f775480ea7d829a075f92473",
"index": 4241,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef get_logger():\n return current_app.logger\n\n\n<mask token>\n\n\ndef info(msg, *args, **kwargs):\n get_logger().info(msg, *args, **kwargs)\n\n\n<mask token>\n",
"step-3": ... | [
0,
2,
3,
4,
6
] |
# -*- coding: utf-8 -*-
"""
Created on Mon Jan 25 12:07:32 2021
@author: yashv
"""
import numpy as np
X= [0.7, 1.5]
Y= [3.9,0.2]
def f(w,b,x): #sigmoid logistic function
return 1.0/(1.0 + np.exp(-(w*x +b)))
def error(w,b): #loss function
err=0.0
for x,y in zip(X,Y):
fx= f(w,b,... | normal | {
"blob_id": "2387856757ad1c3ff911cf2a7537ca6df7786997",
"index": 9244,
"step-1": "<mask token>\n\n\ndef f(w, b, x):\n return 1.0 / (1.0 + np.exp(-(w * x + b)))\n\n\ndef error(w, b):\n err = 0.0\n for x, y in zip(X, Y):\n fx = f(w, b, x)\n err += 0.5 * (fx - y) ** 2\n return err\n\n\ndef... | [
5,
6,
7,
8,
9
] |
<|reserved_special_token_0|>
def standard_env() ->Env:
"""An environment with some scheme standard procedures"""
env = Env()
env.update(vars(math))
env.update({'+': op.add, '-': op.sub, '*': op.mul, '/': op.truediv, '>':
op.gt, '>': op.lt, '>=': op.ge, '<=': op.le, '=': op.eq, 'abs': abs,
... | flexible | {
"blob_id": "88862d6bee5d83dd5f1c656a06a9dc46a5254b10",
"index": 3608,
"step-1": "<mask token>\n\n\ndef standard_env() ->Env:\n \"\"\"An environment with some scheme standard procedures\"\"\"\n env = Env()\n env.update(vars(math))\n env.update({'+': op.add, '-': op.sub, '*': op.mul, '/': op.truediv, ... | [
5,
7,
8,
9,
10
] |
<|reserved_special_token_0|>
class Coin(object):
def __init__(self):
self.sideup = 'Heads'
def toss(self):
if random.randint(0, 1) == 0:
self.sideup = 'Heads'
else:
self.sideup = 'Tails'
def get_sideup(self):
return self.sideup
<|reserved_specia... | flexible | {
"blob_id": "eb246beb05249f5dfde019b773698ba3bb1b1118",
"index": 544,
"step-1": "<mask token>\n\n\nclass Coin(object):\n\n def __init__(self):\n self.sideup = 'Heads'\n\n def toss(self):\n if random.randint(0, 1) == 0:\n self.sideup = 'Heads'\n else:\n self.sideup... | [
4,
5,
6,
7,
8
] |
import datetime
if __name__ == "__main__" :
keys = {'a','e','i', 'o', 'u', 'y'}
values = [1]
dictionnaire = {cle : list(values) for cle in keys}
print("dictionnaire : ", dictionnaire)
values.append(2)
#for cle in keys : dictionnaire.update({cle:values})
#dictionnaire.update({cle2 ... | normal | {
"blob_id": "468c070aebff3124927c5595d68bb94321dd75e5",
"index": 4406,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n keys = {'a', 'e', 'i', 'o', 'u', 'y'}\n values = [1]\n dictionnaire = {cle: list(values) for cle in keys}\n print('dictionnaire : ', dictionnaire... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class CabbageController:
<|reserved_special_token_0|>
def service(self):
X = tf.placeholder(tf.float32, shape=[None, 4])
W = tf.Variable(tf.random_normal([4, 1]), name='weight')
b = tf.Variable(tf.random_normal([1]), name='bias')
saver = tf.train.S... | flexible | {
"blob_id": "90a220775efcc8ff9e83f1a1f011f424ddc3476d",
"index": 4487,
"step-1": "<mask token>\n\n\nclass CabbageController:\n <mask token>\n\n def service(self):\n X = tf.placeholder(tf.float32, shape=[None, 4])\n W = tf.Variable(tf.random_normal([4, 1]), name='weight')\n b = tf.Varia... | [
2,
3,
4,
5,
6
] |
from glob import glob
from PIL import Image
import numpy as np
from tqdm import tqdm
import cv2
import os
import matplotlib.pyplot as plt
np.set_printoptions(precision=3, suppress=True)
def get_index(path):
"""
get the length of index for voc2012 dataset.
path: the index of train,val or test path
"""... | normal | {
"blob_id": "b1b478965ad939a98478b19b4a94f3250167e25a",
"index": 2189,
"step-1": "<mask token>\n\n\ndef show_examples(images_base, labels_base, index_list, output_path):\n results = []\n for index in tqdm(index_list):\n img = cv2.imread(os.path.join(images_base, index + '.jpg'))\n lab = np.ar... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
def blackbox(name, backend, targets, params, target='target', path='/probe',
labels=None):
labels = {} if labels is None else labels
banned_oses = ['debian']
filtered_targets = [x for x in targets if lib.get_os(x) not in banned_oses]
return {'job_name': name, 'metrics_... | flexible | {
"blob_id": "f489058c922d405754ad32a737f67bc03c08772b",
"index": 701,
"step-1": "<mask token>\n\n\ndef blackbox(name, backend, targets, params, target='target', path='/probe',\n labels=None):\n labels = {} if labels is None else labels\n banned_oses = ['debian']\n filtered_targets = [x for x in targe... | [
3,
4,
5,
6,
7
] |
# -*- coding: utf-8 -*-
"""
VorRun
Runs Vorlax and plots wireframe output from Vorlax
(https://github.com/GalaxyHobo/VORLAX)
NOTE! Type: "%matplotlib auto" in iPython console to
switch to interactive plots, or "%matplotlib inline"
to switch to inline, in the console.
NOTE! Reads path to Vorlax .exe in "... | normal | {
"blob_id": "9aee715e976db632f0829a06cb9e0101c90512be",
"index": 2150,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfout.close()\n<mask token>\nif not drive:\n drive = 'C:'\n<mask token>\nos.system(runString)\n<mask token>\nfout.close()\n<mask token>\nfor index, line in enumerate(lines):\n panelD... | [
0,
1,
2,
3,
4
] |
'''
Created on Sep 23, 2016
@author: Andrew
'''
from pymongo import MongoClient
import re
client = MongoClient()
atMentions = re.compile(ur"@\w+", flags=re.I|re.U)
atMidnight = re.compile(u"@midnight", flags=re.I|re.U)
hashtag = re.compile(ur"#\w+", flags=re.I|re.U)
features = [("usf fwa forward most", ... | normal | {
"blob_id": "eb2bb06afb9aeb46ad02cbac145ccd817131074d",
"index": 1753,
"step-1": "'''\r\nCreated on Sep 23, 2016\r\n\r\n@author: Andrew\r\n'''\r\nfrom pymongo import MongoClient\r\nimport re\r\n\r\nclient = MongoClient()\r\n\r\natMentions = re.compile(ur\"@\\w+\", flags=re.I|re.U)\r\natMidnight = re.compile(u\"@... | [
0
] |
# !usr/bin/env python
# -*- coding: utf-8 -*-
#
# Licensed under a 3-clause BSD license.
#
# @Author: Brian Cherinka
# @Date: 2018-08-16 11:43:42
# @Last modified by: Brian Cherinka
# @Last Modified time: 2018-08-16 11:58:06
from __future__ import print_function, division, absolute_import
import pytest
import os
f... | normal | {
"blob_id": "bd00644b9cf019fe8c86d52494389b7f0f03d3c3",
"index": 1276,
"step-1": "<mask token>\n\n\n@contextmanager\ndef captured_templates(app):\n \"\"\" Records which templates are used \"\"\"\n recorded = []\n\n def record(app, template, context, **extra):\n recorded.append((template, context)... | [
5,
6,
7,
8,
9
] |
class Solution:
def isPalindrome(self, x: int) ->bool:
num_str = str(x)
i, j = 0, len(num_str) - 1
while i < j:
if num_str[i] == num_str[j]:
i += 1
j -= 1
continue
return False
return True
def isPalindrome1... | flexible | {
"blob_id": "40f57ccb1e36d307b11e367a2fb2f6c97051c65b",
"index": 6759,
"step-1": "class Solution:\n\n def isPalindrome(self, x: int) ->bool:\n num_str = str(x)\n i, j = 0, len(num_str) - 1\n while i < j:\n if num_str[i] == num_str[j]:\n i += 1\n j ... | [
3,
4,
5,
6,
7
] |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
'''
Frame filtering
'''
import numpy as np
import cv2
def filter_frames(frames, method=cv2.HISTCMP_CORREL, target_size=(64, 64), threshold=0.65):
"""Filter noisy frames out
Args:
frames (list<numpy.ndarray[H, W, 3]>): video frames
method (int, o... | normal | {
"blob_id": "1da93e9113089f1a2881d4094180ba524d0d4a86",
"index": 8531,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef filter_frames(frames, method=cv2.HISTCMP_CORREL, target_size=(64, 64),\n threshold=0.65):\n \"\"\"Filter noisy frames out\n\n Args:\n frames (list<numpy.ndarray[H,... | [
0,
1,
2,
3
] |
import numpy as np
class LinearRegressor():
def __init__(self, alpha=0.1, epochs=1):
self.alpha = alpha
self.epochs = epochs
self.costs = []
self.theta = None
def _cost_function(self, y_pred, y, m):
"""
Gets the cost for the predicted values when contrasted wit... | normal | {
"blob_id": "d805a1290c107a8d768417a432e338b182b7cd6b",
"index": 5524,
"step-1": "<mask token>\n\n\nclass LinearRegressor:\n <mask token>\n\n def _cost_function(self, y_pred, y, m):\n \"\"\"\n Gets the cost for the predicted values when contrasted with the correct ones.\n y_pred: An (1... | [
4,
5,
6,
8,
9
] |
# file /home/hep/ss4314/cmtuser/Gauss_v45r10p1/Gen/DecFiles/options/16303437.py generated: Wed, 25 Jan 2017 15:25:22
#
# Event Type: 16303437
#
# ASCII decay Descriptor: [Xi_b- -> (rho- -> pi- pi0) K- p+]cc
#
from Configurables import Generation
Generation().EventType = 16303437
Generation().SampleGenerationTool = "Si... | normal | {
"blob_id": "7cc9d445d712d485eaebd090d2485dac0c38b3fb",
"index": 5918,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nGeneration().addTool(SignalRepeatedHadronization)\n<mask token>\nToolSvc().addTool(EvtGenDecay)\n<mask token>\n",
"step-3": "<mask token>\nGeneration().EventType = 16303437\nGeneration(... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def word_count(s):
cache = {}
ignore = '":;,.-+=/\\|[]{}()*^&'
lower = s.lower()
for i in lower:
if i in ignore:
lower = lower.replace(i, '')
words = lower.split()
for j in words:
if j not in cache:
... | flexible | {
"blob_id": "97d84f99264afa5e7df4b5d22cf4c49b2d14ff7a",
"index": 8291,
"step-1": "<mask token>\n",
"step-2": "def word_count(s):\n cache = {}\n ignore = '\":;,.-+=/\\\\|[]{}()*^&'\n lower = s.lower()\n for i in lower:\n if i in ignore:\n lower = lower.replace(i, '')\n words = l... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print(a)
print(b)
<|reserved_special_token_0|>
print(a)
print(b)
<|reserved_special_token_0|>
print(USER_NAME)
print(USER_NAME)
<|reserved_special_token_1|>
a = 1
b = a
print(a)
print(b)
a = 2
print(a)
print(b)
USER_NAME = '常量'... | flexible | {
"blob_id": "1cc9a7bbe1bda06ce76fa8ec1cdc17c7b2fde73b",
"index": 4051,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(a)\nprint(b)\n<mask token>\nprint(a)\nprint(b)\n<mask token>\nprint(USER_NAME)\nprint(USER_NAME)\n",
"step-3": "a = 1\nb = a\nprint(a)\nprint(b)\na = 2\nprint(a)\nprint(b)\nUSER_N... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def colorPrint(color, str):
print(color + str + '\x1b[0m')
def main():
if sys.argv.__len__() < 2:
print('Wrong usage, exit')
return
colorPrint(YELLOW, sys.argv[1])
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
d... | flexible | {
"blob_id": "a49c00dab8d445ce0b08fd31a4a41d6c8976d662",
"index": 2263,
"step-1": "<mask token>\n\n\ndef colorPrint(color, str):\n print(color + str + '\\x1b[0m')\n\n\ndef main():\n if sys.argv.__len__() < 2:\n print('Wrong usage, exit')\n return\n colorPrint(YELLOW, sys.argv[1])\n\n\n<mask... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def check_exsit(process_name):
WMI = win32com.client.GetObject('winmgmts:')
processCodeCov = WMI.ExecQuery(
'select * from Win32_Process where Name="%s"' % process_name)
if len(processCodeCov) > 0:
re... | flexible | {
"blob_id": "bb6d6061365fad809448d09a1c031b984423b5e0",
"index": 8658,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef check_exsit(process_name):\n WMI = win32com.client.GetObject('winmgmts:')\n processCodeCov = WMI.ExecQuery(\n 'select * from Win32_Process where Name=\"%s\"' % proces... | [
0,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
class LGBModel(ModelFT, LightGBMFInt):
<|reserved_special_token_0|>
def __init__(self, loss='mse', early_stopping_rounds=50,
num_boost_round=1000, **kwargs):
if loss not in {'mse', 'binary'}:
raise NotImplementedError
self.params = {'objective'... | flexible | {
"blob_id": "d37187f067ddff94015e639a1759dddced817945",
"index": 6205,
"step-1": "<mask token>\n\n\nclass LGBModel(ModelFT, LightGBMFInt):\n <mask token>\n\n def __init__(self, loss='mse', early_stopping_rounds=50,\n num_boost_round=1000, **kwargs):\n if loss not in {'mse', 'binary'}:\n ... | [
5,
6,
7,
8,
9
] |
""" view.py: Contains the View class. """
import random
import config
from graphics import *
class View:
""" The view class which handles the visual component of the application.
"""
def __init__(self, pygame, master):
""" Set up and initialise the view. Does not start the display. "... | normal | {
"blob_id": "2168d10a1b4796576cc7ebb6893e0dc8b58085ca",
"index": 4435,
"step-1": "<mask token>\n\n\nclass View:\n <mask token>\n\n def __init__(self, pygame, master):\n \"\"\" Set up and initialise the view. Does not start the display. \"\"\"\n self._pygame = pygame\n self._master = ma... | [
2,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
def car(env):
while True:
print('The car will start parking at: ', env.now)
parking_timeout = 5
yield env.timeout(parking_timeout)
print('The car will start driving at: ', env.now)
driving_timeout = 2
yield env.timeout(driving_timeout)
... | flexible | {
"blob_id": "892eb8d1802b01c035993232cc80c710211ab102",
"index": 802,
"step-1": "<mask token>\n\n\ndef car(env):\n while True:\n print('The car will start parking at: ', env.now)\n parking_timeout = 5\n yield env.timeout(parking_timeout)\n print('The car will start driving at: ', e... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print(decoded_predictions)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
model = ResNet50(weights='imagenet', include_top=True)
img_input = image.load_img('my_picture.jpg', target_size=(224, 224))
img_input = image.... | flexible | {
"blob_id": "1af6e66c19078a9ee971f608daa93247911d8406",
"index": 5881,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(decoded_predictions)\n",
"step-3": "<mask token>\nmodel = ResNet50(weights='imagenet', include_top=True)\nimg_input = image.load_img('my_picture.jpg', target_size=(224, 224))\nimg... | [
0,
1,
2,
3,
4
] |
#!/usr/bin/python
# -*- coding: UTF-8 -*-
# 可写函数说明
def sum(arg1, arg2):
# 返回2个参数的和."
total = arg1 + arg2
print "函数内 : ", total
return total;
# 调用sum函数
total = sum(10, 20);
def nop():
pass
a = nop(); | normal | {
"blob_id": "9761070a75b043f6cc9e6134e09810b215ccd0c0",
"index": 6430,
"step-1": "#!/usr/bin/python\n# -*- coding: UTF-8 -*-\n\n# 可写函数说明\ndef sum(arg1, arg2):\n # 返回2个参数的和.\"\n total = arg1 + arg2\n print \"函数内 : \", total\n return total;\n\n\n# 调用sum函数\ntotal = sum(10, 20);\n\ndef nop():\n pass\n... | [
0
] |
something1
x = session.query(x).filter(y).count()
something2
y = session.query(
models.User, models.X,
).filter(
models.User.time > start_time,
models.User.id == user_id,
).count()
def something3():
x = session.query(
models.Review,
).filter(
models.Review.time < end_time,
).coun... | normal | {
"blob_id": "5b91b7025b0e574d45f95a0585128018d83c17ea",
"index": 563,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef something3():\n x = session.query(models.Review).filter(models.Review.time < end_time\n ).count()\n\n\n<mask token>\n",
"step-3": "something1\n<mask token>\nsomething2\... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class Game:
def __init__(self):
pygame.init()
global CLOCK, SURFACE
CLOCK = pygame.time.Clock()
SURFACE = pygame.display.set_mode((WINDOW_WIDTH, WINDOW_HEIGHT))
self.mouse_x = 0
self.mouse_y = 0
pygame.display.set_caption('Mines... | flexible | {
"blob_id": "030bc0c7bdbbb09f722ffe4c82866726062f5317",
"index": 1962,
"step-1": "<mask token>\n\n\nclass Game:\n\n def __init__(self):\n pygame.init()\n global CLOCK, SURFACE\n CLOCK = pygame.time.Clock()\n SURFACE = pygame.display.set_mode((WINDOW_WIDTH, WINDOW_HEIGHT))\n ... | [
10,
16,
17,
21,
22
] |
<|reserved_special_token_0|>
class SwitchingBatchSampler(Sampler):
<|reserved_special_token_0|>
def __iter__(self):
second_size = self.data_len - self.first_size
self.first_iter = iter(torch.randperm(self.first_size))
self.second_iter = iter(torch.randperm(second_size) + self.first_si... | flexible | {
"blob_id": "6b7bc40ba842ff565e7141fb1d51def99d9ab96a",
"index": 1124,
"step-1": "<mask token>\n\n\nclass SwitchingBatchSampler(Sampler):\n <mask token>\n\n def __iter__(self):\n second_size = self.data_len - self.first_size\n self.first_iter = iter(torch.randperm(self.first_size))\n s... | [
2,
3,
4,
5,
6
] |
from django.urls import path
from .views import MainView
app_name = "bio"
# app_name will help us do a reverse look-up latter.
urlpatterns = [
path('get_mtx_data', MainView.as_view()),
]
| normal | {
"blob_id": "e3a984294cad5830358df50fa00111017cbe226d",
"index": 3678,
"step-1": "<mask token>\n",
"step-2": "<mask token>\napp_name = 'bio'\nurlpatterns = [path('get_mtx_data', MainView.as_view())]\n",
"step-3": "from django.urls import path\nfrom .views import MainView\napp_name = 'bio'\nurlpatterns = [pat... | [
0,
1,
2,
3
] |
import sys
class Bus:
def __init__(self):
self.seats=0
self.dict_seats={}
self.num_passenger = 0
def conctructor(self,seats):
self.seats=seats
for i in range(1,self.seats+1):
self.dict_seats.update({i:"Free"})
return self.dict_seats
def getOn(sel... | normal | {
"blob_id": "1396509f65d194eeaefa3841e152b7078abf0032",
"index": 5549,
"step-1": "<mask token>\n\n\nclass Bus:\n <mask token>\n <mask token>\n <mask token>\n\n def getOn_2(self, *names):\n str_names = str(names)\n str_names.strip('')\n list_names = str_names.split(' ')\n f... | [
3,
6,
7,
8,
9
] |
# Generated by Django 3.2.7 on 2021-10-01 08:36
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('app', '0005_alter_users_is_active'),
]
operations = [
migrations.AlterModelManagers(
name='users',
managers=[
],... | normal | {
"blob_id": "6670295241516664e30c7db5cd3b5e2fb6c4fb05",
"index": 1985,
"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 = [('app', '0005... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class MyBot(BaseAgent):
<|reserved_special_token_0|>
def initialize_agent(self):
self.boost_pad_tracker.initialize_boosts(self.get_field_info())
self.info = MyInfo(self.team, self.index)
self.strat = Strategy(self.info)
self.car = Car()
def ge... | flexible | {
"blob_id": "1a0d4e77f09b4ce752631ae36a83ff57f96b89b1",
"index": 600,
"step-1": "<mask token>\n\n\nclass MyBot(BaseAgent):\n <mask token>\n\n def initialize_agent(self):\n self.boost_pad_tracker.initialize_boosts(self.get_field_info())\n self.info = MyInfo(self.team, self.index)\n self... | [
5,
6,
7,
8,
9
] |
# Stanley H.I. Lio
# hlio@hawaii.edu
# All Rights Reserved. 2018
import logging, time, sys
from serial import Serial
from . import aanderaa_3835
from . import aanderaa_4330f
from . import aanderaa_4531d
from . import aanderaa_4319a
logger = logging.getLogger(__name__)
# works with 3835 (DO), 4330F (DO), 4531D (DO),... | normal | {
"blob_id": "c52ad4040c14471319939605c400ff4d4ad982a7",
"index": 5213,
"step-1": "<mask token>\n\n\ndef aanderaa_read_universal(port, max_retry=3, parsers=[aanderaa_4531d.\n parse_4531d, aanderaa_4330f.parse_4330f, aanderaa_3835.parse_3835,\n aanderaa_4319a.parse_4319a]):\n logger.debug('aanderaa_read_u... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
def get_stock_time_series(data_df, stock_id):
curr_ID_data = data_df.loc[stock_id]
output = np.array(curr_ID_data[0])
for i in range(1, len(curr_ID_data.index)):
output = np.vstack((output, curr_ID_data[i]))
return output
<|reserved_special_token_0|>
<|reserved... | flexible | {
"blob_id": "6a7e5a78f516cecf083ca3900bdaaf427bedd497",
"index": 756,
"step-1": "<mask token>\n\n\ndef get_stock_time_series(data_df, stock_id):\n curr_ID_data = data_df.loc[stock_id]\n output = np.array(curr_ID_data[0])\n for i in range(1, len(curr_ID_data.index)):\n output = np.vstack((output, ... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
hbar = 1.0
m_e = 1.0
h22m = hbar ** 2 / (2 * m_e)
pi = np.pi
eV = 1 / 27.21138505
eV_Ha = eV
nm = 18.89726124565
kB_eV = 8.6173324e-05
kB = kB_eV * eV_Ha
<|reserved_special_token_1|>
<|reserved_special_token_0|>
import numpy as... | flexible | {
"blob_id": "f9f835b24aa8fc77109db9e2d89a3f43bcb4b181",
"index": 7079,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nhbar = 1.0\nm_e = 1.0\nh22m = hbar ** 2 / (2 * m_e)\npi = np.pi\neV = 1 / 27.21138505\neV_Ha = eV\nnm = 18.89726124565\nkB_eV = 8.6173324e-05\nkB = kB_eV * eV_Ha\n",
"step-3": "<mask to... | [
0,
1,
2,
3
] |
import tkinter as tk
import Widgets as wg
import Logic as lgc
from tkinter.ttk import Separator
from tkinter.messagebox import showerror, showinfo
# Fonts that we can utilise
FONTS = {"large":("Helvetica", 20), "medium":("Helvetica", 16), "small":("Helvetica", 12)}
class Handler: # Handles the window and... | normal | {
"blob_id": "9b8f3962172d4a867a3a070b6139bb302fd7e2f5",
"index": 9934,
"step-1": "<mask token>\n\n\nclass Window(tk.Tk):\n <mask token>\n <mask token>\n <mask token>\n\n def Get_Current_Player(self) ->str:\n return self.Handler.Get_Current_Player()\n <mask token>\n\n\nclass Pregame(tk.Frame... | [
39,
40,
41,
52,
59
] |
import numpy as np
class Element(object):
def __init__(self):
self.ndof = 0
self.nn = 0
self.ng = 0
self.element_type = 0
self.coord_position = np.array([])
self.setup()
def setup(self):
pass
def shape_function_value(self):
pass
def s... | normal | {
"blob_id": "ed2ae166c4881289b27b7e74e212ba2d6164998b",
"index": 2981,
"step-1": "<mask token>\n\n\nclass Element(object):\n\n def __init__(self):\n self.ndof = 0\n self.nn = 0\n self.ng = 0\n self.element_type = 0\n self.coord_position = np.array([])\n self.setup()\n... | [
3,
4,
5,
6
] |
<|reserved_special_token_0|>
def read_input_RS():
low = np.loadtxt('LowerArray.csv', delimiter=',', skiprows=1)
lower_bound = np.ravel(low)
upper_bound = np.ravel(np.transpose(np.loadtxt('UpperArray.csv',
delimiter=',', skiprows=1)))
return lower_bound, upper_bound, low[0, :].size
<|reserved... | flexible | {
"blob_id": "4b44f4343da1677b5436ec2b153e573fda3c0cee",
"index": 2280,
"step-1": "<mask token>\n\n\ndef read_input_RS():\n low = np.loadtxt('LowerArray.csv', delimiter=',', skiprows=1)\n lower_bound = np.ravel(low)\n upper_bound = np.ravel(np.transpose(np.loadtxt('UpperArray.csv',\n delimiter=','... | [
2,
3,
4,
5,
6
] |
import math
import turtle
wn = turtle.Screen()
wn.bgcolor('lightblue')
PI=3.14
R_outer=50
R_inner=200
fred = turtle.Turtle()
fred.speed(99999)
def cycloid(r, k, nos_cycle, direction):
n=36
angle=2*PI/n
x=1
y=0
for i in range(nos_cycle*n):
beta = i * angle
x = r*(beta-math.sin(beta))
... | normal | {
"blob_id": "a62dd287f9fc6f79ef95a3de83f52c794efe00a7",
"index": 7407,
"step-1": "\nimport math\nimport turtle\n\nwn = turtle.Screen()\nwn.bgcolor('lightblue')\nPI=3.14\nR_outer=50\nR_inner=200\n\nfred = turtle.Turtle()\nfred.speed(99999)\n\ndef cycloid(r, k, nos_cycle, direction):\n n=36\n angle=2*PI/n\n ... | [
0
] |
<|reserved_special_token_0|>
def send_value(value):
port = create_port()
status = get_mov_parameters()[0]
if port_status(port):
if status == '1' or status == 'True':
string = ''.join([str(value), ' \n'])
port.write(string.encode())
print('True')
else:
... | flexible | {
"blob_id": "72cda573bf9c744213a2957d51171f437f211353",
"index": 3467,
"step-1": "<mask token>\n\n\ndef send_value(value):\n port = create_port()\n status = get_mov_parameters()[0]\n if port_status(port):\n if status == '1' or status == 'True':\n string = ''.join([str(value), ' \\n'])\... | [
1,
3,
4,
5,
6
] |
import gdalnumeric
#Input File
src = "../dati/islands/islands.tif"
#Output
tgt = "../dati/islands/islands_classified.jpg"
srcArr = gdalnumeric.LoadFile(src)
classes = gdalnumeric.numpy.histogram(srcArr,bins=2)[1]
print classes
#Color look-up table (LUT) - must be len(classes)+1.
#Specified as R,G,B tuples
lut = [[... | normal | {
"blob_id": "f29d377e8a8fd6d2e156da665478d7a4c167f7d5",
"index": 3601,
"step-1": "import gdalnumeric\n\n#Input File\nsrc = \"../dati/islands/islands.tif\"\n\n#Output\ntgt = \"../dati/islands/islands_classified.jpg\"\n\nsrcArr = gdalnumeric.LoadFile(src)\n\nclasses = gdalnumeric.numpy.histogram(srcArr,bins=2)[1]\... | [
0
] |
from django.shortcuts import render
from django.shortcuts import redirect
# Create your views here.
from .forms import AddBookForm ,UpdateBookForm,BookCreateModelForm,SearchForm,RegistrationForm,SignInForm
from book.models import Books
from django.contrib.auth import authenticate,login,logout
def book_add(reques... | normal | {
"blob_id": "aba2a0a262c14f286c278f21ba42871410c174f0",
"index": 953,
"step-1": "<mask token>\n\n\ndef book_add(request):\n if request.user.is_authenticated:\n context = {}\n if request.method == 'GET':\n form = BookCreateModelForm()\n context['form'] = form\n re... | [
4,
6,
7,
8,
10
] |
import chainer
import chainer.functions as F
import numpy as np
import argparse
from model import Generator, Discriminator
from chainer import cuda, serializers
from pathlib import Path
from utils import set_optimizer
from dataset import DatasetLoader
xp = cuda.cupy
cuda.get_device(0).use()
class CycleGANVC2LossCal... | normal | {
"blob_id": "32105a245f6945dbe8749140d811b20d634289bc",
"index": 2481,
"step-1": "<mask token>\n\n\nclass CycleGANVC2LossCalculator:\n\n def __init__(self):\n pass\n <mask token>\n\n @staticmethod\n def gen_loss(discriminator, y):\n y_dis = discriminator(y)\n return F.mean(F.soft... | [
3,
7,
8,
9,
11
] |
# -- coding: utf-8 --
from django.conf.urls import url
from myapp.view import views
from myapp.view import story
from myapp.view import img # 添加
from myapp.view import login
from myapp.view import tuling
from myapp.view import utilView
from myapp.view.wechat import wechat_modules
from myapp.view import router
urlpatt... | normal | {
"blob_id": "373c102018fdcc5211263304c368c2e8beef3257",
"index": 720,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurlpatterns = [url('get_img_api$', router.get_img_api), url('add_book$',\n views.add_book), url('show_books$', views.show_books), url('add_story$',\n story.add_story), url('show_stor... | [
0,
1,
2,
3
] |
#!/usr/bin/env python
"""
Otsu method for automatic estimation of $T$ threshold value
- assumes two maxima of grayscale histogram & searches for optimal separation
Parameters
Usage
Example
$ python <scriptname>.py --image ../img/<filename>.png
## Explain
"""
import numpy as np
import argparse
import mahota... | normal | {
"blob_id": "0547751af7bbac42351476dde591d13d40fb37eb",
"index": 7811,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef main():\n ap = argparse.ArgumentParser()\n ap.add_argument('-i', '--image', required=True, help='Path to the image')\n args = vars(ap.parse_args())\n image = cv2.imrea... | [
0,
1,
2,
3,
4
] |
# -*- coding: utf-8 -*-
from flask import jsonify
from flask.views import MethodView
class Users(MethodView):
def get(self):
return jsonify(
{
'status': 'OK',
'users': [
{'name': 'Pepe', 'age': 35, 'ocupation': "Engineer"},
... | normal | {
"blob_id": "781ce153d5053078ee11cecc13d055a67999a651",
"index": 3800,
"step-1": "<mask token>\n\n\nclass Users(MethodView):\n <mask token>\n <mask token>\n\n def put(self):\n pass\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass Users(MethodView):\n\n def get(self):\n return ... | [
2,
4,
5,
6,
7
] |
from utils import *
EvinceRelation("different from")
| normal | {
"blob_id": "4f15e2743b33e2f672cd258172da852edb7e4118",
"index": 2103,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nEvinceRelation('different from')\n",
"step-3": "from utils import *\nEvinceRelation('different from')\n",
"step-4": "from utils import *\n\nEvinceRelation(\"different from\")\n\n",
... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
from simple_avk.AVK import SimpleAVK
from simple_avk.exceptions import MethodError, LongpollError
| flexible | {
"blob_id": "2bccfba2448059a41185b117b224813e344b50f8",
"index": 5673,
"step-1": "<mask token>\n",
"step-2": "from simple_avk.AVK import SimpleAVK\nfrom simple_avk.exceptions import MethodError, LongpollError\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
} | [
0,
1
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
cursor.execute('SET NAMES utf8;')
cursor.execute('SET CHARACTER SET utf8;')
cursor.execute('SET character_set_connection=utf8;')
<|reserved_special_token_0|>
cursor.execute(sql)
<|reserved_special_token_0|>
for row in results:
... | flexible | {
"blob_id": "1d29ce58ca626155d626216fbbd70d7b241efa25",
"index": 6363,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ncursor.execute('SET NAMES utf8;')\ncursor.execute('SET CHARACTER SET utf8;')\ncursor.execute('SET character_set_connection=utf8;')\n<mask token>\ncursor.execute(sql)\n<mask token>\nfor ro... | [
0,
1,
2,
3,
4
] |
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