code stringlengths 13 6.09M | order_type stringclasses 2
values | original_example dict | step_ids listlengths 1 5 |
|---|---|---|---|
<|reserved_special_token_0|>
@app.route('/')
def interactive_input():
return render_template('main.html')
@app.route('/food_1_star')
def food_1_star():
return render_template('food_1.html')
<|reserved_special_token_0|>
@app.route('/general_5_star')
def general_5_star():
return render_template('gener... | flexible | {
"blob_id": "1e41cc5d2661f1fb4f3a356318fabcb2b742cbdf",
"index": 1826,
"step-1": "<mask token>\n\n\n@app.route('/')\ndef interactive_input():\n return render_template('main.html')\n\n\n@app.route('/food_1_star')\ndef food_1_star():\n return render_template('food_1.html')\n\n\n<mask token>\n\n\n@app.route('... | [
6,
7,
9,
11,
13
] |
from flask import Flask, app
from flask_sqlalchemy import SQLAlchemy
db = SQLAlchemy()
DBNAME = 'database.db'
def create_app():
app = Flask(__name__)
app.config['SECRET_KEY'] = 'KARNISINGHSHEKHAWAT'
app.config['SQLALCHEMY_DATABASE_URL'] = f'sqlite:///{DBNAME}'
db.init_app(app)
from .views import v... | normal | {
"blob_id": "c6fdb9c405427a3583a59065f77c75c4aa781405",
"index": 5417,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef create_app():\n app = Flask(__name__)\n app.config['SECRET_KEY'] = 'KARNISINGHSHEKHAWAT'\n app.config['SQLALCHEMY_DATABASE_URL'] = f'sqlite:///{DBNAME}'\n db.init_app(... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def glInitYuvTargetEXT():
"""Return boolean indicating whether this extension is available"""
from OpenGL import extensions
return extensions.hasGLExtension(_EXTENSION_NAME)
<|reserved_special_token_1|>
<|reserved... | flexible | {
"blob_id": "08420d31713859946b2f19cebf68c333331cb80e",
"index": 1494,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef glInitYuvTargetEXT():\n \"\"\"Return boolean indicating whether this extension is available\"\"\"\n from OpenGL import extensions\n return extensions.hasGLExtension(_EXTE... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for i in range(N):
c, t = map(int, input().split())
if nm > c and T >= t:
nm = c
if nm == 1000000:
print('TLE')
else:
print(nm)
<|reserved_special_token_1|>
N, T = map(int, input().split())
nm = 1000000
... | flexible | {
"blob_id": "8a0e781f29c426161240e33b9d2adc7537b3d352",
"index": 2513,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(N):\n c, t = map(int, input().split())\n if nm > c and T >= t:\n nm = c\nif nm == 1000000:\n print('TLE')\nelse:\n print(nm)\n",
"step-3": "N, T = map(... | [
0,
1,
2,
3
] |
def test_{{ project_name }}():
assert True
| normal | {
"blob_id": "1c1f1dab1ae2e8f18536784a5dec9de37c8a8582",
"index": 3995,
"step-1": "def test_{{ project_name }}():\n assert True\n",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
} | [
0
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
@pytest.mark.parametrize('path', glob.glob(join(data_dir('structure'),
'*.cif')))
def test_array_conversion(path):
pdbx_file = pdbx.PDBxFile.read(path)
ref_structure = pdbx.get_structure(pdbx_file, model=1, extra_fie... | flexible | {
"blob_id": "cc637d14ce2106fcc3b8bbb54e497691e72a3f65",
"index": 2858,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@pytest.mark.parametrize('path', glob.glob(join(data_dir('structure'),\n '*.cif')))\ndef test_array_conversion(path):\n pdbx_file = pdbx.PDBxFile.read(path)\n ref_structure =... | [
0,
1,
2,
3
] |
#!/usr/bin/python
try:
from Queue import Queue
except ImportError: # Python 3
from queue import Queue
class BFSWithQueue:
"""Breadth-First Search.
Attributes
----------
graph : input graph
color : dict with nodes, private
distance : dict with nodes (distances to source node)
... | normal | {
"blob_id": "0bce5d590b96e434cd8aee7531a321bc648c1981",
"index": 8722,
"step-1": "<mask token>\n\n\nclass BFSWithQueue:\n <mask token>\n <mask token>\n\n def run(self, source=None, pre_action=None, post_action=None):\n \"\"\"Executable pseudocode.\"\"\"\n if source is not None:\n ... | [
8,
10,
11,
12,
14
] |
<|reserved_special_token_0|>
@app.route('/predict', methods=['POST'])
def predict():
arr = [int(x) for x in request.form.values()]
arr2 = [np.array(arr)]
output = model.predict(arr2)
return render_template('index.html', prediction_text=output)
<|reserved_special_token_0|>
<|reserved_special_token_... | flexible | {
"blob_id": "02b760b16cdcd42f8d8d7222b439da87fb8076a3",
"index": 4959,
"step-1": "<mask token>\n\n\n@app.route('/predict', methods=['POST'])\ndef predict():\n arr = [int(x) for x in request.form.values()]\n arr2 = [np.array(arr)]\n output = model.predict(arr2)\n return render_template('index.html', p... | [
1,
3,
4,
5,
6
] |
import glob
pyfiles = glob.glob('*.py')
modulenames = [f.split('.')[0] for f in pyfiles]
# print(modulenames)
for f in pyfiles:
contents = open(f).read()
for m in modulenames:
v1 = "import " + m
v2 = "from " + m
if v1 or v2 in contents:
contents = contents.replace(v1, "im... | normal | {
"blob_id": "d6a73365aa32c74798b6887ff46c0ed2323ed1a6",
"index": 2324,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor f in pyfiles:\n contents = open(f).read()\n for m in modulenames:\n v1 = 'import ' + m\n v2 = 'from ' + m\n if v1 or v2 in contents:\n contents =... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class LoginForm(forms.Form):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class LoginForm(forms.Form):
usuario = forms.CharField(label='Usua... | flexible | {
"blob_id": "7da5a7476c807619bed805cb892774c23c04c6f7",
"index": 4917,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass LoginForm(forms.Form):\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass LoginForm(forms.Form):\n usuario = forms.CharField(label='Usuario', max_le... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class DLModeler(object):
def __init__(self, model_path, hf_path, num_examples, class_percentages,
predictors, model_args, model_type):
self.model_path = model_path
self.hf_path = hf_path
self.num_examples = num_examples
self.class_percentages =... | flexible | {
"blob_id": "a0a6bd5de39a7599f7872639cdf3a59b8cda5498",
"index": 5230,
"step-1": "<mask token>\n\n\nclass DLModeler(object):\n\n def __init__(self, model_path, hf_path, num_examples, class_percentages,\n predictors, model_args, model_type):\n self.model_path = model_path\n self.hf_path = ... | [
8,
9,
10,
12,
13
] |
from datetime import date
def diff_in_date(first, second):
value = str(second - first)
if value.__contains__(','):
generated_sum = value.split(',')
return generated_sum[0]
else:
return value
first_date = date(2014, 7, 2)
second_date = date(2014, 7, 11)
current_date = date.today()... | normal | {
"blob_id": "9b6d30a40bafa0e9e4760843d6a2f750f0f88a57",
"index": 6106,
"step-1": "<mask token>\n\n\ndef diff_in_date(first, second):\n value = str(second - first)\n if value.__contains__(','):\n generated_sum = value.split(',')\n return generated_sum[0]\n else:\n return value\n\n\n<... | [
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class NormalizeImageDict(object):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def __call__(self, sample):
for key in self.image_keys:
if self.normalizeRange:
sample[key] /= 255.0
sample[key] = self.normalize(sample... | flexible | {
"blob_id": "4293ad0b2a4a352d6bdc4b860448c4a3b14ca629",
"index": 8648,
"step-1": "<mask token>\n\n\nclass NormalizeImageDict(object):\n <mask token>\n <mask token>\n\n def __call__(self, sample):\n for key in self.image_keys:\n if self.normalizeRange:\n sample[key] /= 25... | [
2,
3,
4,
5
] |
from torch.utils.data.sampler import Sampler
import torch
import random
class SwitchingBatchSampler(Sampler):
def __init__(self, data_source, batch_size, drop_last=False):
self.data_source = data_source
self.batch_size = batch_size
self.drop_last = drop_last
# Divide the indices into two indices groups
se... | normal | {
"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
] |
# -*- coding: utf-8 -*-
'''
=======================================================================
AutoTest Team Source File.
Copyright(C), Changyou.com
-----------------------------------------------------------------------
Created: 2017/3/2 by ChengLongLong
----------------------------------------------------... | normal | {
"blob_id": "38f7c529cd0a8d85de266c6a932e6c8342aee273",
"index": 4969,
"step-1": "<mask token>\n",
"step-2": "# -*- coding: utf-8 -*-\n'''\n=======================================================================\nAutoTest Team Source File.\nCopyright(C), Changyou.com\n------------------------------------------... | [
0,
1
] |
import time
from helpers.handler import port_handler
from helpers.functions import fetch_all
class ascii_handler(port_handler):
"""
Serve ASCII server list
"""
def handle_data(self):
"""
Show a nicely formatted server list and immediately close connection
"""
self.ls.... | normal | {
"blob_id": "cbf93eb96f40ff0aedc4b8d9238669da72934b27",
"index": 2400,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass ascii_handler(port_handler):\n <mask token>\n\n def handle_data(self):\n \"\"\"\n Show a nicely formatted server list and immediately close connection\n ... | [
0,
2,
3,
4,
5
] |
class NlpUtility:
<|reserved_special_token_0|>
def get_nouns(self, tokens):
nouns = []
for word, pos in tokens:
if pos == 'NN':
nouns.push(word)
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def get_nouns(self, tokens):
nouns = []
... | flexible | {
"blob_id": "c6502ea2b32ad90c76b6dfaf3ee3218d029eba15",
"index": 56,
"step-1": "class NlpUtility:\n <mask token>\n\n def get_nouns(self, tokens):\n nouns = []\n for word, pos in tokens:\n if pos == 'NN':\n nouns.push(word)\n <mask token>\n <mask token>\n\n d... | [
4,
5,
6,
7,
8
] |
'''
Implement GreedyMotifSearch
http://rosalind.info/problems/ba2d/
Given: Integers k and t, followed by a collection of strings Dna.
Return: A collection of strings BestMotifs resulting from running GreedyMotifSearch(Dna, k, t). If at any step you find more than one Profile-most probable k-mer in a given string, use... | normal | {
"blob_id": "ed7fa6e6f30eb06400cb38128617967a597f6c04",
"index": 2450,
"step-1": "<mask token>\n\n\ndef greedy_motif_search(dnas, k, t):\n best_motifs = [dna[:k] for dna in dnas]\n best_score = score_motifs(best_motifs)\n for i in range(len(dnas[0]) - k + 1):\n print(i)\n motifs = [dnas[0]... | [
3,
5,
6,
7,
8
] |
# joiner = '+'
# seq = ["Sushil","Bahadur","KC"]
# txt = joiner.join(seq)
# txt
# txt = " Sam "
# ljus = txt.ljust(7,"*")
# ljus
# txtstrip = txt.strip().strip('S')
# txtstrip
# txt = "This is my world."
# txtSplit = txt.split(maxsplit=1)
# txtSplit
# name = input("Enter your full name")
# name = name.strip()
# txt = ... | normal | {
"blob_id": "32b22cccac75c87b8638c76c0c6d27db0de4d750",
"index": 8480,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(type(list1))\nprint(list1[0])\nprint(list1[len(list1) - 1])\n<mask token>\nprint(list1)\n<mask token>\nlist4\n<mask token>\nlist4\n<mask token>\nlist4\n<mask token>\nlist4\n<mask to... | [
0,
1,
2,
3
] |
import pygame
from .Coin import Coin
from .Snake import Snake, Block
from .Bomb import Bomb
from .Rocket import Rocket
from pygame.math import Vector2
cell_size = 16
cell_number = 30
sprite_cell = pygame.image.load("Assets/Cell.png")
bg = pygame.image.load("Assets/BG.png")
bg2 = pygame.image.load("Assets/BG2.png")
c... | normal | {
"blob_id": "2b14607aa2527f5da57284917d06ea60e89f784c",
"index": 1659,
"step-1": "<mask token>\n\n\nclass GAME:\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def check_timer(self):\n if self.count >= self.crowd:\n self.game_timer += 1\n if self.game_t... | [
5,
7,
9,
10,
13
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
ba0563.pngMap = [
'00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000'
,
'00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000... | flexible | {
"blob_id": "dab1adcd185092fc425b5d87150f27e7b67bff6c",
"index": 151,
"step-1": "<mask token>\n",
"step-2": "ba0563.pngMap = [\n '00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000'\n ,\n '00000000000000000000000000000000000... | [
0,
1,
2
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def test_template():
assert True
<|reserved_special_token_1|>
import pytest
def test_template():
assert True
| flexible | {
"blob_id": "e7fa84dbc037253c7f852aa618e6ea88d1fda909",
"index": 1939,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef test_template():\n assert True\n",
"step-3": "import pytest\n\n\ndef test_template():\n assert True\n",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
... | [
0,
1,
2
] |
import sys
byte = int(sys.argv[1])
qlty = float(sys.argv[2])
n = 0
while True:
o = sys.stdin.read(byte)
if qlty>(qlty*n)%1:
oo = o
sys.stdout.write(o)
else:
sys.stdout.write(oo)
if not o:
break
n=n+1 | normal | {
"blob_id": "70845ab4aab80d988a5c01d0b4fb76e63b800527",
"index": 6484,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile True:\n o = sys.stdin.read(byte)\n if qlty > qlty * n % 1:\n oo = o\n sys.stdout.write(o)\n else:\n sys.stdout.write(oo)\n if not o:\n break\... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class cGAN:
def __init__(self, input_dim1, input_dim2, input_dim3, latent_size):
self.input_dim1 = input_dim1
self.input_dim2 = input_dim2
self.input_dim3 = input_dim3
self.latent_size = latent_size
def discriminator(self):
input_shape = s... | flexible | {
"blob_id": "fc6c220f8a3a0e9dd1d6e6e1ca131136db8f8a58",
"index": 9155,
"step-1": "<mask token>\n\n\nclass cGAN:\n\n def __init__(self, input_dim1, input_dim2, input_dim3, latent_size):\n self.input_dim1 = input_dim1\n self.input_dim2 = input_dim2\n self.input_dim3 = input_dim3\n se... | [
10,
11,
12,
13,
14
] |
<|reserved_special_token_0|>
def version_info():
return 'dansfunctions version %s (%s)' % (fg.__version__, fg.__date__)
<|reserved_special_token_0|>
def check_general_functions():
print('dansfunctions/functions_general.py')
print('Version: %s (%s)' % (fg.__version__, fg.__date__))
print('Methods:'... | flexible | {
"blob_id": "0f266db39988cfce475380036f4f4f5b1a1fee1a",
"index": 3647,
"step-1": "<mask token>\n\n\ndef version_info():\n return 'dansfunctions version %s (%s)' % (fg.__version__, fg.__date__)\n\n\n<mask token>\n\n\ndef check_general_functions():\n print('dansfunctions/functions_general.py')\n print('Ve... | [
4,
5,
6,
7,
8
] |
import os
from flask import Flask,render_template,request,redirect,url_for
from sqlalchemy import create_engine
from sqlalchemy.orm import scoped_session,sessionmaker
app = Flask(__name__)
engine = create_engine("postgres://lkghylsqhggivp:d827f6dc5637928e95e060761de590b7d9514e9463c5241ed3d652d777a4a3a9@ec2-5... | normal | {
"blob_id": "af9430caff843242381d7c99d76ff3c964915700",
"index": 6753,
"step-1": "<mask token>\n\n\n@app.route('/')\ndef index():\n return render_template('a.html')\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\n@app.route('/')\ndef index():\n return render_template('a.html')\n\n\n@app.route('/insert... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
vals -= vals[:, np.newaxis].mean(-1)
vals /= vals[:, np.newaxis].std(-1)
<|reserved_special_token_0|>
km.fit(vals)
<|reserved_special_token_0|>
for ii in range(len(zips)):
tzip = int(zips.ZIPCODE[ii])
if tzip in dzips:
... | flexible | {
"blob_id": "2c181a33c84ce262404c192abdc515924a1916a9",
"index": 6165,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nvals -= vals[:, np.newaxis].mean(-1)\nvals /= vals[:, np.newaxis].std(-1)\n<mask token>\nkm.fit(vals)\n<mask token>\nfor ii in range(len(zips)):\n tzip = int(zips.ZIPCODE[ii])\n if ... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def info(msg):
if config['log_level'] not in ('ERROR', 'WARNING', 'WARN'):
print(config['prefix'] + 'INFO> ' + msg)
log_count['INFO'] += 1
<|reserved_special_token_0|>
def warning(msg):
if config.get('log_level') != 'ERROR':
print(config['prefix'] + 'WA... | flexible | {
"blob_id": "e15ea7d167aad470d0a2d95a8a328b35181e4dc3",
"index": 7832,
"step-1": "<mask token>\n\n\ndef info(msg):\n if config['log_level'] not in ('ERROR', 'WARNING', 'WARN'):\n print(config['prefix'] + 'INFO> ' + msg)\n log_count['INFO'] += 1\n\n\n<mask token>\n\n\ndef warning(msg):\n if co... | [
2,
4,
8,
9,
10
] |
<|reserved_special_token_0|>
def prepare_output_directory(config: ConfigSchema) ->None:
formatted = datetime.now().strftime(config.output_path_format)
output_path = Path(formatted)
output_path.mkdir(parents=True, exist_ok=False)
config.output_path = output_path.as_posix()
<|reserved_special_token_1|... | flexible | {
"blob_id": "d8fb5aeb5453b986cc698165749992e4a7677257",
"index": 1506,
"step-1": "<mask token>\n\n\ndef prepare_output_directory(config: ConfigSchema) ->None:\n formatted = datetime.now().strftime(config.output_path_format)\n output_path = Path(formatted)\n output_path.mkdir(parents=True, exist_ok=False... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
def load_image(filename):
return mpimg.imread(filename)
def calibrate_camera(rows=6, cols=9):
mtx = None
dist = None
save_file = 'calibration.npz'
try:
data = np.load(save_file)
mtx = data['mtx']
dist = data['dist']
print('using saved ... | flexible | {
"blob_id": "3ac30240577eda08343796abbd051d5d3b45beaf",
"index": 3416,
"step-1": "<mask token>\n\n\ndef load_image(filename):\n return mpimg.imread(filename)\n\n\ndef calibrate_camera(rows=6, cols=9):\n mtx = None\n dist = None\n save_file = 'calibration.npz'\n try:\n data = np.load(save_fi... | [
14,
17,
18,
19,
20
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print(int(h7 * i))
<|reserved_special_token_1|>
g7 = int(input())
h7 = g7 / 2
i = g7 - 1
print(int(h7 * i))
| flexible | {
"blob_id": "abb08956f55fd1e8af27ce12fa94a4137d7d908e",
"index": 7251,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(int(h7 * i))\n",
"step-3": "g7 = int(input())\nh7 = g7 / 2\ni = g7 - 1\nprint(int(h7 * i))\n",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
2
]
} | [
0,
1,
2
] |
'''import pyttsx3
#engine = pyttsx3.init()
#Conficuração das vozes
#voices = engine.getProperty('voices')
#engine.setProperty('voice', voices[2].id)
engine=pyttsx3.init()
voices=engine.getProperty('voices')
engine.setProperty('voice',voices[3].id)
#Falar texto
engine.say('Olá meu nome é Jarvis. Sou uma inteligênci... | normal | {
"blob_id": "d9bf58dc76d4e8d7146fac3bb2bdfb538ebf78a5",
"index": 7102,
"step-1": "<mask token>\n",
"step-2": "'''import pyttsx3\n\n#engine = pyttsx3.init()\n\n#Conficuração das vozes\n#voices = engine.getProperty('voices')\n#engine.setProperty('voice', voices[2].id)\n\nengine=pyttsx3.init()\n\nvoices=engine.ge... | [
0,
1
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print('#1 map')
<|reserved_special_token_0|>
print(new_list)
print('\n#2 reduce')
<|reserved_special_token_0|>
print(reduce_data)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print('#1 map')
a_list = [2, 18, 9, 22,... | flexible | {
"blob_id": "8e3b26826752b6b3482e8a29b9b58f5025c7ef58",
"index": 4758,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('#1 map')\n<mask token>\nprint(new_list)\nprint('\\n#2 reduce')\n<mask token>\nprint(reduce_data)\n",
"step-3": "<mask token>\nprint('#1 map')\na_list = [2, 18, 9, 22, 17, 24, 8, ... | [
0,
1,
2,
3,
4
] |
import tkinter as tk
import tkinter.messagebox as tkmb
import psutil
import os
import re
import subprocess
from subprocess import Popen, PIPE, STDOUT, DEVNULL
import filecmp
import re
import time
import threading
import datetime
import re
debian = '/etc/debian_version'
redhat = '/etc/redhat-release'
def PrintaLog(tex... | normal | {
"blob_id": "fde62dd3f5ee3cc0a1568b037ada14835c327046",
"index": 6298,
"step-1": "<mask token>\n\n\ndef PrintaLog(texto):\n t = time.time()\n logtime = time.ctime(t)\n stringprint = '%s %s\\n' % (logtime, texto)\n f = open('/var/log/patriot', 'a')\n f.write(stringprint)\n f.flush()\n f.close... | [
4,
6,
9,
10,
11
] |
def merge(items, temp, low, mid, high):
i = low
j = mid + 1
for k in range(low, high+1):
if i > mid:
# 왼쪽 리스트의 순회를 마쳤음
# 남은 오른쪽 리스트의 원소들은 모두 왼쪽 리스트 원소보다 작음
temp[k] = items[j]
# 뒤에 나머지는 정렬되어있으니 그대로 넣기
j += 1
elif j > high:
... | normal | {
"blob_id": "9ab119b32ceac370b744658e5fa679292609373a",
"index": 2517,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef merge_sort(items, temp, low, high):\n if high <= low:\n return None\n mid = low + (high - low) // 2\n merge_sort(items, temp, low, mid)\n merge_sort(items, temp... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class Movie:
def __init__(self, id: int):
self.actors = set()
self.name = ''
self.id = id
self.year = 0
def getName(self):
return self.name
def getActors(self):
return self.actors
def getId(self):
return self.id
... | flexible | {
"blob_id": "0934163fc6461e30a73c06e74b3a5e983ed2fa02",
"index": 4211,
"step-1": "<mask token>\n\n\nclass Movie:\n\n def __init__(self, id: int):\n self.actors = set()\n self.name = ''\n self.id = id\n self.year = 0\n\n def getName(self):\n return self.name\n\n def get... | [
7,
11,
17,
22,
26
] |
<|reserved_special_token_0|>
def scrape(event_id, event_cost):
page = get(event_id, resource='events').json()
venue = get(page['venue_id'], resource='venues').json()
start = datetime.strptime(page['start']['local'], '%Y-%m-%dT%H:%M:%S')
end = datetime.strptime(page['end']['local'], '%Y-%m-%dT%H:%M:%S'... | flexible | {
"blob_id": "edfc8794fab2c95e01ae254f9f13d446faafe6fd",
"index": 9213,
"step-1": "<mask token>\n\n\ndef scrape(event_id, event_cost):\n page = get(event_id, resource='events').json()\n venue = get(page['venue_id'], resource='venues').json()\n start = datetime.strptime(page['start']['local'], '%Y-%m-%dT%... | [
5,
7,
8,
9,
10
] |
import thumt.utils.bleu as bleu
import argparse
parser = argparse.ArgumentParser("Compute sentence bleu.")
parser.add_argument("-pred_path", type=str, required=True)
parser.add_argument("-n_list_path", type=str, required=True)
parser.add_argument("-refer_path", type=str, required=True)
args = parser.parse_args()
n_l... | normal | {
"blob_id": "4437075901751adeaf3df63345e270a9b0090c14",
"index": 1918,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nparser.add_argument('-pred_path', type=str, required=True)\nparser.add_argument('-n_list_path', type=str, required=True)\nparser.add_argument('-refer_path', type=str, required=True)\n<mas... | [
0,
1,
2,
3,
4
] |
import logging
import terrestrial.config as config
logger = logging.getLogger(f'{__name__}.common')
def health():
return 'OK', 200
def verify_token(token):
"""
Verifies Token from Authorization header
"""
if config.API_TOKEN is None:
logger.error('API token is not configured, auth will f... | normal | {
"blob_id": "167bd2c405171443c11fbd13575f8c7b20877289",
"index": 8470,
"step-1": "<mask token>\n\n\ndef health():\n return 'OK', 200\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef health():\n return 'OK', 200\n\n\ndef verify_token(token):\n \"\"\"\n Verifies Token from Authorization header\... | [
1,
2,
3,
4
] |
Easy = [["4 + 12 = ?", 16],
["45 -34 = ?", 11],
["27 + 12 -18 = ?", 21],
['25 - 5 * 4 = ?', 5],
["18 + 45 / 5 - 3 * 2 = ?", 21],
["5! = ?", 120],
["3! + 2! = ?", 8],
["7 + 5! / 4! - 6 / 3 = ?", 10],
["(25 + 5) / 6 * 4 = ?", 20],
["4(3+c)... | normal | {
"blob_id": "66edf0d2f7e25e166563bdb1063a1ed45ecda0e6",
"index": 541,
"step-1": "<mask token>\n",
"step-2": "Easy = [['4 + 12 = ?', 16], ['45 -34 = ?', 11], ['27 + 12 -18 = ?', 21], [\n '25 - 5 * 4 = ?', 5], ['18 + 45 / 5 - 3 * 2 = ?', 21], ['5! = ?', 120],\n ['3! + 2! = ?', 8], ['7 + 5! / 4! - 6 / 3 = ?... | [
0,
1,
2
] |
<|reserved_special_token_0|>
class NearestStudents(Task):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def output(self):
return luigi.LocalTarget('/Users/adcxdpf/Downloads/pset_03/sd.csv')
def requires(self):
return {'data': HashedStudent... | flexible | {
"blob_id": "15eed401728e07bfe9299edd12add43ad8b9cb71",
"index": 3802,
"step-1": "<mask token>\n\n\nclass NearestStudents(Task):\n <mask token>\n <mask token>\n <mask token>\n\n def output(self):\n return luigi.LocalTarget('/Users/adcxdpf/Downloads/pset_03/sd.csv')\n\n def requires(self):\n... | [
5,
6,
7,
8,
9
] |
species(
label = 'C=C([CH]C)C(=C)[CH]C(24182)',
structure = SMILES('[CH2]C(=CC)C([CH2])=CC'),
E0 = (249.687,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([325,375,415,465,420,450,1700,1750,2750,2770,2790,2810,2830,2850,1350,1400,1450,1500,700,800,1000,1100,1350,1400,900,1100,3000,3033.33,... | normal | {
"blob_id": "63093190ee20e10698bd99dcea94ccf5d076a006",
"index": 8921,
"step-1": "<mask token>\n",
"step-2": "species(label='C=C([CH]C)C(=C)[CH]C(24182)', structure=SMILES(\n '[CH2]C(=CC)C([CH2])=CC'), E0=(249.687, 'kJ/mol'), modes=[\n HarmonicOscillator(frequencies=([325, 375, 415, 465, 420, 450, 1700, ... | [
0,
1,
2
] |
import pygame
# import random
# import text_scroll
from os import path
img_dir = path.join(path.dirname(__file__), 'img')
# define screen and refresh rate
WIDTH = 720
HEIGHT = 720
FPS = 30
# define colors
RED = (255, 0, 0)
GREEN = (0, 255, 0)
BLUE = (0, 0, 255)
BLACK = (0, 0, 0)
YELLOW = (255, 255, 0)
BROWN = (165, ... | normal | {
"blob_id": "88dfb422b1c9f9a9a8f497e1dbba5598c2710e9b",
"index": 5718,
"step-1": "<mask token>\n",
"step-2": "<mask token>\npygame.display.set_caption('Space Force Prime')\n<mask token>\n",
"step-3": "<mask token>\nimg_dir = path.join(path.dirname(__file__), 'img')\nWIDTH = 720\nHEIGHT = 720\nFPS = 30\nRED =... | [
0,
1,
2,
3,
4
] |
from django.contrib import admin
from .models import Client, Adress
# Register your models here.
class ClientInline(admin.StackedInline):
model = Adress
can_delete = False
extra = 1
class ClientAdmin(admin.ModelAdmin):
inlines = [ClientInline]
admin.site.register(Client, ClientAdmin) | normal | {
"blob_id": "ffd7aef2e72e64ac5b9f85b9d12845479187d89b",
"index": 2010,
"step-1": "<mask token>\n\n\nclass ClientInline(admin.StackedInline):\n <mask token>\n <mask token>\n <mask token>\n\n\nclass ClientAdmin(admin.ModelAdmin):\n inlines = [ClientInline]\n\n\n<mask token>\n",
"step-2": "<mask token... | [
3,
4,
5,
6,
7
] |
# #1
# def bi_search(l, r, arr, x):
# # Code Here
# if(l == r):
# return arr[r] == x
# mid = (l + r)//2 + 1
# if(arr[mid] > x):
# return bi_search(l,mid-1,arr,x)
# else:
# return bi_search(mid,r,arr,x)
# inp = input('Enter Input : ').split('/')
# arr, k = list(map(int, ... | normal | {
"blob_id": "883b4de18dddede97f850e3a184a0e1072bda99e",
"index": 814,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef solve(dpArr, list, box, i):\n global boxes\n global ans\n if box == boxes:\n s = 0\n for j in list:\n s += len(j)\n if s == len(dpArr):\n ... | [
0,
1,
2,
3,
4
] |
import torch
import torch.nn as nn
import torch.optim as optim
import torchtext
import absl.flags
import absl.app
import pickle
import yaml
import numpy as np
from tqdm import tqdm
from core import model
import core.dnc.explanation
from core import functions
from core.config import ControllerConfig, MemoryConfig, Train... | normal | {
"blob_id": "00dbcae2d3941c9ef4c8b6753b8f6f7a46417400",
"index": 5110,
"step-1": "<mask token>\n\n\ndef run_explanations(network, explanation_module, data_iterator):\n network.eval()\n best_accuracy = 0\n worst_accuracy = 0\n best_correct = 0\n worst_correct = 0\n covered = 0\n total = 0\n ... | [
3,
5,
6,
7,
9
] |
<|reserved_special_token_0|>
def test_creating_objects():
teacher = Teacher('Daniil', 'Shadrin')
student = Student('Roman', 'Petrov')
homework = teacher.create_homework('Learn OOP', 1)
homework_result = student.do_homework(homework, 'I have done this hw')
assert isinstance(teacher, Teacher)
as... | flexible | {
"blob_id": "8f971ee3b98691a887ee0632afd613bbf4f19aa0",
"index": 3505,
"step-1": "<mask token>\n\n\ndef test_creating_objects():\n teacher = Teacher('Daniil', 'Shadrin')\n student = Student('Roman', 'Petrov')\n homework = teacher.create_homework('Learn OOP', 1)\n homework_result = student.do_homework... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
class bcolors:
HEADER = '\x1b[95m'
OKBLUE = '\x1b[94m'
OKGREEN = '\x1b[92m'
WARNING = '\x1b[93m'
FAIL = '\x1b[91m'
ENDC = '\x1b[0m'
BOLD = '\x1b[1m'
UNDERLINE = '\x1b[4m'
def get_image(f_sdss):
img = f_sdss[0].data
return img
<|reserved_special_... | flexible | {
"blob_id": "736fee6f9a46b8568b2dd217b81d54d689306630",
"index": 970,
"step-1": "<mask token>\n\n\nclass bcolors:\n HEADER = '\\x1b[95m'\n OKBLUE = '\\x1b[94m'\n OKGREEN = '\\x1b[92m'\n WARNING = '\\x1b[93m'\n FAIL = '\\x1b[91m'\n ENDC = '\\x1b[0m'\n BOLD = '\\x1b[1m'\n UNDERLINE = '\\x1b... | [
3,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
class VertexArrayObject:
def __init__(self, primitive):
self._primitive = primitive
self._buffers: List[pxng.BufferObject] = []
self._indices = pxng.BufferObject(data_type=self.index_data_type,
array_type=gl.GL_ELEMENT_ARRAY_BUFFER)
self._v... | flexible | {
"blob_id": "7530c2c85f83d1714840ba97c1ec702f063658c5",
"index": 379,
"step-1": "<mask token>\n\n\nclass VertexArrayObject:\n\n def __init__(self, primitive):\n self._primitive = primitive\n self._buffers: List[pxng.BufferObject] = []\n self._indices = pxng.BufferObject(data_type=self.ind... | [
9,
11,
12,
13,
17
] |
# ----------------------------------------------------------------------------
# Copyright (c) 2016-2018, q2-chemistree development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE, distributed with this software.
# ------------------------------------------... | normal | {
"blob_id": "4296dc5b79fd1d2c872eb1115beab52a0f067423",
"index": 4816,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass PluginSetupTests(unittest.TestCase):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass PluginSetupTests(unittest.TestCase):\n\n def test_plugin_setup(self):\n ... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class UserClusters(JsonView):
logger = logging.getLogger('mliyweb.views.UserClusters')
cluster_service = ClusterService()
@log_enter_exit(logger)
def get_data(self, context):
username = self.request.user.username
try:
if session_is_okay(self.re... | flexible | {
"blob_id": "f882b73645c6a280a17f40b27c01ecad7e4d85ae",
"index": 5860,
"step-1": "<mask token>\n\n\nclass UserClusters(JsonView):\n logger = logging.getLogger('mliyweb.views.UserClusters')\n cluster_service = ClusterService()\n\n @log_enter_exit(logger)\n def get_data(self, context):\n usernam... | [
9,
11,
12,
13,
15
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print('...starting export')
<|reserved_special_token_0|>
logging.basicConfig(filename=timestr + '-export.log')
<|reserved_special_token_0|>
matCursor.execute(select_all_mat)
<|reserved_special_token_0|>
for m in materialTypes:
... | flexible | {
"blob_id": "d81e8478d60c9ee778e1aeb0dd7b05f675e4ecad",
"index": 2306,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('...starting export')\n<mask token>\nlogging.basicConfig(filename=timestr + '-export.log')\n<mask token>\nmatCursor.execute(select_all_mat)\n<mask token>\nfor m in materialTypes:\n ... | [
0,
1,
2,
3,
4
] |
from django.urls import reverse
from rest_framework import status
from rest_framework.test import APITestCase
from django.contrib.auth.models import User, Group
class UserTests(APITestCase):
def test_user_list(self):
# must be rejected without validation
response = self.client.get('/api/us... | normal | {
"blob_id": "ca7b0553e55e1c5e6cd23139a158101e72456a50",
"index": 8844,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass UserTests(APITestCase):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass UserTests(APITestCase):\n\n def test_user_list(self):\n response = self.client.get('... | [
0,
1,
2,
3,
4
] |
from xai.brain.wordbase.verbs._essay import _ESSAY
#calss header
class _ESSAYED(_ESSAY, ):
def __init__(self,):
_ESSAY.__init__(self)
self.name = "ESSAYED"
self.specie = 'verbs'
self.basic = "essay"
self.jsondata = {}
| normal | {
"blob_id": "dc2cbbaca3c35f76ac09c93a2e8ad13eb0bdfce6",
"index": 4086,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass _ESSAYED(_ESSAY):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass _ESSAYED(_ESSAY):\n\n def __init__(self):\n _ESSAY.__init__(self)\n self.name = 'ES... | [
0,
1,
2,
3,
4
] |
import os
import sys
try:
from setuptools import setup, find_packages
except ImportError:
from distutils.core import setup, find_packages
setup(
name='stripe-requests',
version='1.9.1-dev',
description='Stripe python bindings using requests',
author='Allan Lei',
author_email='allanlei@hel... | normal | {
"blob_id": "a6ee2be7bed59b419fa66fd6cfe4b5fff3fac260",
"index": 2596,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ntry:\n from setuptools import setup, find_packages\nexcept ImportError:\n from distutils.core import setup, find_packages\nsetup(name='stripe-requests', version='1.9.1-dev', descrip... | [
0,
1,
2,
3
] |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Sep 18 13:36:13 2019
@author: gennachiaro
"""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
import pyrolite.plot
from pyrolite.plot.spider import spider
#read in data
df = pd.read_csv('/users/ge... | normal | {
"blob_id": "f6fee18898636ad6b0dc6d96d28dead4e09b8035",
"index": 1650,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsns.set()\n<mask token>\nMG.pyroplot.spider(color='green', alpha=0.5, mode='fill')\nVCCR.pyroplot.spider(color='red', alpha=0.5, mode='fill')\nFG.pyroplot.spider(color='purple', alpha=0.5... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
default_app_config = 'reman.apps.RemanConfig'
| flexible | {
"blob_id": "0b0b928aef9a4e9953b02639bf5e7769cc4389d7",
"index": 2488,
"step-1": "<mask token>\n",
"step-2": "default_app_config = 'reman.apps.RemanConfig'\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
} | [
0,
1
] |
# -*- coding: utf-8 -*-
class Solution:
"""
@param head: The first node of the linked list.
@return: The node where the cycle begins.
if there is no cycle, return null
"""
def detectCycle(self, head):
# write your code here
# 先确定是否有环，然后确定环的大小，再遍历确定位置。
cycle_... | normal | {
"blob_id": "3319614d154b16190f3cd8f4f65c3b0e0da277e9",
"index": 9751,
"step-1": "<mask token>\n",
"step-2": "class Solution:\n <mask token>\n <mask token>\n",
"step-3": "class Solution:\n <mask token>\n\n def detectCycle(self, head):\n cycle_len = -1\n one_node, two_node = head, he... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
urlpatterns = [path('country', Country_Data, name='country_data'), path(
'tours', Scrape_Data, name='scrape_data'), path('draws', Draw_Data,
name='Draw_data')]
<|reserved_special_token_1|>
from django.urls import path
f... | flexible | {
"blob_id": "b39c783cbaff2915c8864ce0b081b5bf052baee5",
"index": 6731,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurlpatterns = [path('country', Country_Data, name='country_data'), path(\n 'tours', Scrape_Data, name='scrape_data'), path('draws', Draw_Data,\n name='Draw_data')]\n",
"step-3": "... | [
0,
1,
2
] |
import cv2
img = cv2.imread('imgs/1.png')
pixel = img[100, 100]
img[100, 100] = [57, 63, 99] # 设置像素值
b = img[100, 100, 0] # 57, 获取(100, 100)处, blue通道像素值
g = img[100, 100, 1] # 63
r = img[100, 100, 2] # 68
r = img[100, 100, 2] = 99 # 设置red通道
# 获取和设置
piexl = img.item(100, 100, 2)
img.itemset((100, 100, 2), 99)
| normal | {
"blob_id": "d13f06afeac938fc2cf4d3506b3f68c6de9de210",
"index": 6596,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nimg.itemset((100, 100, 2), 99)\n",
"step-3": "<mask token>\nimg = cv2.imread('imgs/1.png')\npixel = img[100, 100]\nimg[100, 100] = [57, 63, 99]\nb = img[100, 100, 0]\ng = img[100, 100, ... | [
0,
1,
2,
3,
4
] |
"""
Estructuras que extraen valores de una función y se almacenan en objetos iterables (que se pueden recorrer
Son mas eficientes que las funciones tradicionales
muy útiles con listas de valores infinitos
Bajos determinados escenarios, será muy útil que un generador devuelva los valores de uno en uno
Un generador ... | normal | {
"blob_id": "29abcfc010453e3a67346ea2df238e07b85502a8",
"index": 3107,
"step-1": "<mask token>\n\n\ndef generarPares(limite):\n num = 1\n milista = []\n while num < limite:\n milista.append(num * 2)\n num += 1\n return milista\n\n\n<mask token>\n\n\ndef devuelveCiudades2(*ciudades):\n ... | [
2,
5,
6,
7,
8
] |
"""
- Define a new class Student which is derived from Human and has:
grade field.
do_hobby - print 'dancing' or some another hobby
"""
import andy.Lesson_7.exercise_1
class Student(andy.Lesson_7.exercise_1.Human):
def __init__(self, firstname, lastname, grade):
super().__init__(firstname, lastname)
... | normal | {
"blob_id": "497f56891670f635feff983058e86055e54be493",
"index": 2618,
"step-1": "<mask token>\n\n\nclass Student(andy.Lesson_7.exercise_1.Human):\n\n def __init__(self, firstname, lastname, grade):\n super().__init__(firstname, lastname)\n self.grade = grade\n\n def do_hobby(self):\n ... | [
3,
4,
5,
6,
7
] |
# -*- coding: utf-8 -*-
# Define here the models for your scraped items
#
# See documentation in:
# http://doc.scrapy.org/en/latest/topics/items.html
import scrapy
class CnnArticleItem(scrapy.Item):
title = scrapy.Field()
developments = scrapy.Field()
body = scrapy.Field()
date = scrapy.Field()
clas... | normal | {
"blob_id": "cf0eb9685cdfc412871d3b36270ddab3e520bb8f",
"index": 104,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass CnnArticleItem(scrapy.Item):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n\nclass GoogleArticleItem(scrapy.Item):\n title = scrapy.Field()\n d... | [
0,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
class Game(models.Model):
gameName = models.CharField(max_length=100)
genre = models.ForeignKey(GameGenre)
def __str__(self):
return '%s, %s' % (self.gameName, self.genre)
class Players(models.Model):
playerName = models.CharField(max_length=100)
games = mod... | flexible | {
"blob_id": "092242cdb231e09ccf3dd4dccfb6d786c3e4aad2",
"index": 8036,
"step-1": "<mask token>\n\n\nclass Game(models.Model):\n gameName = models.CharField(max_length=100)\n genre = models.ForeignKey(GameGenre)\n\n def __str__(self):\n return '%s, %s' % (self.gameName, self.genre)\n\n\nclass Play... | [
6,
8,
9,
10,
11
] |
from django import forms
from .models import Profile
class ImageForm(forms.ModelForm):
userimage = forms.ImageField(required=False, error_messages={'invalid':("Image file only")}, widget=forms.FileInput)
class Meta:
model = Profile
fields = ['userimage',]
| normal | {
"blob_id": "9081d0f75ac53ab8d0bafb39cd46a2fec8a5135f",
"index": 3813,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass ImageForm(forms.ModelForm):\n <mask token>\n\n\n class Meta:\n model = Profile\n fields = ['userimage']\n",
"step-3": "<mask token>\n\n\nclass ImageForm(fo... | [
0,
1,
2,
3,
4
] |
# program name: an2_colour.py
# no optional arguments: Uses Wine data to display information about the relationship of
# various attributes with colour and hue
print('========================================================================================')
print('===================================================... | normal | {
"blob_id": "594479c22cada665dcdc76737085ce342d7d5faf",
"index": 1480,
"step-1": "<mask token>\n\n\ndef convert_type(data_value):\n try:\n return int(data_value)\n except ValueError:\n try:\n return float(data_value)\n except ValueError:\n return data_value\n\n\n<... | [
5,
6,
7,
8,
9
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print('hello', end='!')
print('python')
print('010', '1234', '1111', sep='-')
<|reserved_special_token_0|>
print('입력한 숫자 :', num)
print('num type :', type(num))
<|reserved_special_token_0|>
print('result :', result)
print('result ... | flexible | {
"blob_id": "cc628270a973866025a5e2a5d07e39b4dbdcd324",
"index": 1718,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('hello', end='!')\nprint('python')\nprint('010', '1234', '1111', sep='-')\n<mask token>\nprint('입력한 숫자 :', num)\nprint('num type :', type(num))\n<mask token>\nprint('result :', resu... | [
0,
1,
2,
3
] |
<|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": "a917dd6171a78142fefa8c8bfad0110729fc1bb0",
"index": 3190,
"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 = [('aposta', '0... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class Migration(SchemaMigration):
def forwards(self, orm):
db.add_column(u'main_videoad', 'compress', self.gf(
'django.db.models.fields.BooleanField')(default=False),
keep_default=False)
<|reserved_special_token_0|>
<|reserved_special_token_0|>... | flexible | {
"blob_id": "b4bcf9903f4a34c8b256c65cada29e952a436f74",
"index": 2215,
"step-1": "<mask token>\n\n\nclass Migration(SchemaMigration):\n\n def forwards(self, orm):\n db.add_column(u'main_videoad', 'compress', self.gf(\n 'django.db.models.fields.BooleanField')(default=False),\n keep... | [
2,
3,
4,
5,
6
] |
#!/usr/bin/env python
from LCClass import LightCurve
import matplotlib.pyplot as plt
import niutils
def main():
lc1821 = LightCurve("PSR_B1821-24/PSR_B1821-24_combined.evt")
lc0218 = LightCurve("PSR_J0218+4232/PSR_J0218+4232_combined.evt")
fig, ax = plt.subplots(2, 1, figsize=(8, 8))
ax[0], _ = lc18... | normal | {
"blob_id": "48311ee17a3f2eca8db32d7672f540fa45a7a900",
"index": 3524,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef main():\n lc1821 = LightCurve('PSR_B1821-24/PSR_B1821-24_combined.evt')\n lc0218 = LightCurve('PSR_J0218+4232/PSR_J0218+4232_combined.evt')\n fig, ax = plt.subplots(2, 1,... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
urlpatterns = [path('', views.home, name='park-home'), path('login/', views
.login, name='park-login')]
<|reserved_special_token_1|>
from django.urls import path
from . import views
urlpatterns = [path('', views.home, name=... | flexible | {
"blob_id": "2fd490ca54f5d038997cec59a3e07c3f2c2d2538",
"index": 6757,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurlpatterns = [path('', views.home, name='park-home'), path('login/', views\n .login, name='park-login')]\n",
"step-3": "from django.urls import path\nfrom . import views\nurlpattern... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class AttentionAgent(object):
<|reserved_special_token_0|>
def __init__(self, num_in_pol, num_out_pol, hidden_dim=64, lr=0.01,
onehot_dim=0):
"""
Inputs:
num_in_pol (int): number of dimensions for policy input
num_out_pol (int): num... | flexible | {
"blob_id": "845d04312abc0e64a7810b52bbee333d2bdf3dfb",
"index": 7164,
"step-1": "<mask token>\n\n\nclass AttentionAgent(object):\n <mask token>\n\n def __init__(self, num_in_pol, num_out_pol, hidden_dim=64, lr=0.01,\n onehot_dim=0):\n \"\"\"\n Inputs:\n num_in_pol (int): nu... | [
4,
5,
6,
7
] |
/home/sbm367/anaconda3/lib/python3.5/types.py | normal | {
"blob_id": "720d37e35eb335cc68ff27763cfe5c52f76b98d2",
"index": 5781,
"step-1": "/home/sbm367/anaconda3/lib/python3.5/types.py",
"step-2": null,
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0
]
} | [
0
] |
try:
alp="ABCDEFGHIJKLMNOPQRSTUVWXYZ"
idx=eval(input("请输入一个整数"))
print(alp[idx])
except NameError:
print("输入错误，请输入一个整数")
except:
print("其他错误")
else:
print("没有发生错误")
finally:
print("程序执行完毕，不知道是否发生了异常")
| normal | {
"blob_id": "99a6b450792d434e18b8f9ff350c72abe5366d95",
"index": 153,
"step-1": "<mask token>\n",
"step-2": "try:\n alp = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'\n idx = eval(input('请输入一个整数'))\n print(alp[idx])\nexcept NameError:\n print('输入错误，请输入一个整数')\nexcept:\n print('其他错误')\nelse:\n print('没有发生错误')\... | [
0,
1,
2
] |
import errno
import os
import shutil
from calendar import monthrange
from datetime import datetime, timedelta
from pavilion import output
from pavilion import commands
from pavilion.status_file import STATES
from pavilion.test_run import TestRun, TestRunError, TestRunNotFoundError
class CleanCommand(commands.Command... | normal | {
"blob_id": "18aafb71d7e6f5caa2f282126c31eb052c08ad3c",
"index": 4307,
"step-1": "<mask token>\n\n\nclass CleanCommand(commands.Command):\n <mask token>\n\n def __init__(self):\n super().__init__('clean', 'Clean up Pavilion working directory.',\n short_help='Clean up Pavilion working dire... | [
4,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
@app.task
def update_banner_list():
banner_query = Banner.objects.filter(is_delete=False, is_show=True
).order_by('-orders')[:BANNER_COUNT]
banner_data = BannerModelSerializer(banner_query, many=True).data
fo... | flexible | {
"blob_id": "8e85740123467889bdeb6b27d5eaa4b39df280ed",
"index": 438,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@app.task\ndef update_banner_list():\n banner_query = Banner.objects.filter(is_delete=False, is_show=True\n ).order_by('-orders')[:BANNER_COUNT]\n banner_data = BannerMode... | [
0,
1,
2,
3
] |
'''
Created on May 17, 2016
@author: Shauryadeep Chaudhuri
'''
import json
import tornado
from engine import Constants as c
from engine.ResultGenerator import ResultGenerator
from ..ServerLogger import ServerLogger
class GetFromURL(tornado.web.RequestHandler):
'''
This class fetches the d... | normal | {
"blob_id": "5a13c7e3be8a0b5f3baf7106a938fc97f078c5bc",
"index": 7335,
"step-1": "<mask token>\n\n\nclass GetFromURL(tornado.web.RequestHandler):\n <mask token>\n <mask token>\n\n def get(self, index=None, schema=None, entry=None, query=None):\n query = dict()\n resultGenerator = ResultGen... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
while cont == 'y':
print('--enter underlay color in r,g,b--')
c2[0] = int(input('red: '))
c2[1] = int(input('green: '))
c2[2] = int(input('blue: '))
print('')
print('--enter desired color in r,g,b--')
c... | flexible | {
"blob_id": "5fa8ae36c4b4a5bffa64f4c65b74b74b29ba246f",
"index": 4578,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile cont == 'y':\n print('--enter underlay color in r,g,b--')\n c2[0] = int(input('red: '))\n c2[1] = int(input('green: '))\n c2[2] = int(input('blue: '))\n print('')\n ... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for k in range(1, 100):
a = []
for i in range(1, 100):
a.append([])
for j in range(1, 100):
a[i - 1].append(partisan_symmetry([5 * i / 100, 0.2, 5 * j /
100], 1000, False))
... | flexible | {
"blob_id": "cfa0937f1c49b52283c562d9ab1cb0542e71b990",
"index": 5970,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor k in range(1, 100):\n a = []\n for i in range(1, 100):\n a.append([])\n for j in range(1, 100):\n a[i - 1].append(partisan_symmetry([5 * i / 100, 0.2, 5... | [
0,
1,
2,
3
] |
#!/usr/bin/env python
import numpy as np
import rospy
import tf
from geometry_msgs.msg import PoseStamped, Twist, TwistStamped, Point
from nav_msgs.msg import Odometry
from visualization_msgs.msg import Marker
from bebop_nmpc_solver import BebopNmpcFormulationParam, bebop_nmpc_casadi_solver
# The frame by default is... | normal | {
"blob_id": "76d0dd2d6b2d580900283f2623f05dd02a70fcd8",
"index": 6825,
"step-1": "<mask token>\n\n\nclass BebopNmpcControl:\n <mask token>\n\n def set_bebop_odom(self, odom_msg):\n if self.received_first_odom_ is False:\n self.received_first_odom_ = True\n rospy.loginfo('First ... | [
10,
12,
13,
15,
18
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print('{:>3}-й день: {:.3}'.format(day, distance))
while target > distance:
day += 1
distance += distance / 10
print('{:>3}-й день: {:.3}'.format(day, distance))
print('Ответ: на {}-й день спортсмен достиг результата —... | flexible | {
"blob_id": "9033ba0a19d765a83737d59289735a9ffd02abb1",
"index": 7519,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('{:>3}-й день: {:.3}'.format(day, distance))\nwhile target > distance:\n day += 1\n distance += distance / 10\n print('{:>3}-й день: {:.3}'.format(day, distance))\nprint('О... | [
0,
1,
2,
3
] |
import os
import h5py
import numpy as np
from keras import backend as K
from keras.layers import Activation, BatchNormalization, Conv2D, Dense, Dot, \
Dropout, Flatten, Input, MaxPooling2D, GlobalAveragePooling2D
from keras import regularizers
from keras.layers import Average as KerasAverage
from keras.models imp... | normal | {
"blob_id": "0eefae7e0d341d74154bbe480f5ed766829e3ce3",
"index": 3734,
"step-1": "<mask token>\n\n\nclass TotalReshape(Layer):\n\n def __init__(self, target_shape, **kwargs):\n self.target_shape = target_shape\n super(TotalReshape, self).__init__(**kwargs)\n\n def compute_output_shape(self, i... | [
17,
20,
24,
29,
31
] |
from mikeio.spatial import GeometryPoint2D, GeometryPoint3D
# https://www.ogc.org/standard/sfa/
def test_point2d_wkt():
p = GeometryPoint2D(10, 20)
assert p.wkt == "POINT (10 20)"
p = GeometryPoint2D(x=-5642.5, y=120.1)
assert p.wkt == "POINT (-5642.5 120.1)"
def test_point3d_wkt():
p = Geomet... | normal | {
"blob_id": "ae45a4967a8ee63c27124d345ad4dc0c01033c0e",
"index": 6749,
"step-1": "<mask token>\n\n\ndef test_point3d_wkt():\n p = GeometryPoint3D(10, 20, 30)\n assert p.wkt == 'POINT Z (10 20 30)'\n\n\ndef test_point2d_to_shapely():\n p = GeometryPoint2D(10, 20)\n sp = p.to_shapely()\n assert sp.x... | [
2,
3,
4,
5,
6
] |
# Q. In How many ways N stair can be climb if allowesd steps are 1, 2 or 3.
# triple Sort
def noOfSteps(n, k):
if n<0: return 0
if n == 0: return 1
t_steps = 0
for i in range(1, k+1):
t_steps += noOfSteps(n-i, k)
return t_steps
def noOfStepsDP(n,k):
dp = [0]*max((... | normal | {
"blob_id": "6c2699ff8e739595a2648d53745dc3c788536d7b",
"index": 1907,
"step-1": "<mask token>\n\n\ndef noOfStepsDP(n, k):\n dp = [0] * max(n + 1, 3)\n dp[0] = 1\n dp[1] = 1\n dp[2] = 2\n for i in range(3, n + 1):\n dp[i] = dp[i - 1] + dp[i - 2] + dp[i - 3]\n return dp[n]\n\n\n<mask toke... | [
1,
2,
3,
4,
5
] |
# Generated by Django 2.2.7 on 2019-11-23 18:40
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('ml', '0003_auto_20191123_1835'),
]
operations = [
migrations.AlterField(
model_name='ml',
name='file',
f... | normal | {
"blob_id": "2bf5ec4b4c0f0eed8364dcc9f1be599a804846f2",
"index": 4981,
"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 = [('ml', '0003_... | [
0,
1,
2,
3,
4
] |
from crispy_forms.bootstrap import FormActions
from crispy_forms.helper import FormHelper
from crispy_forms.layout import Layout, Div, Submit
from django import forms
from django.forms import RadioSelect
from django.urls import reverse
from core.models import Person, Datapackage
from core.utils import cancel_button
... | normal | {
"blob_id": "5a59108084d943f6faa07ffea1467dc19c3dd790",
"index": 1101,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass DatapackageModelForm(forms.ModelForm):\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.helper = FormHelper(self)\n se... | [
0,
2,
3,
5,
6
] |
import smtplib
import requests
import datetime
import json
import time
from datetime import date
from urllib.request import Request,urlopen
today = date.today().strftime("%d-%m-%y")
count = 0
pincodes = ["784164","781017","784161","787001"]
date = 0
temp = str(14) + "-05-21"
while True:
for... | normal | {
"blob_id": "7c60ae58b26ae63ba7c78a28b72192373cc05a86",
"index": 1211,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile True:\n for i in range(0, 8):\n temp = str(23 + i) + '-05-21'\n for pincode in pincodes:\n req = Request(\n 'https://cdn-api.co-vin.in/api... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class Net(nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(Net, self).__init__()
self.h1 = nn.Linear(input_size, hidden_size)
self.h2 = nn.Linear(hidden_size, hidden_size_1)
self.h3 = nn.Linear(hidden_size_1, hidden_size_2)
... | flexible | {
"blob_id": "a4deb67d277538e61c32381da0fe4886016dae33",
"index": 85,
"step-1": "<mask token>\n\n\nclass Net(nn.Module):\n\n def __init__(self, input_size, hidden_size, num_classes):\n super(Net, self).__init__()\n self.h1 = nn.Linear(input_size, hidden_size)\n self.h2 = nn.Linear(hidden_s... | [
3,
4,
5,
6,
7
] |
import boto3
ec2 = boto3.resource('ec2')
response = client.allocate_address(Domain='standard')
print(response)
| normal | {
"blob_id": "6424fccb7990b0a1722d5d787e7eb5acb4ff1a74",
"index": 1863,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(response)\n",
"step-3": "<mask token>\nec2 = boto3.resource('ec2')\nresponse = client.allocate_address(Domain='standard')\nprint(response)\n",
"step-4": "import boto3\nec2 = bot... | [
0,
1,
2,
3
] |
import asyncio
import logging
import random
from aiogram.dispatcher import FSMContext
from aiogram.types import ContentTypes, Message, CallbackQuery
from aiogram.utils.exceptions import BotBlocked
import keyboards
from data.config import ADMINS, ADMIN_CHAT_ID
from keyboards.inline.activate_menu import active_menu_cal... | normal | {
"blob_id": "302accfd5001a27c7bbe6081856d43dbec704168",
"index": 339,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@dp.message_handler(commands='upload', user_id=ADMINS, state='*')\nasync def upload_profile(command_msg: Message, state: FSMContext):\n profile_msg = command_msg.reply_to_message\n ... | [
0,
1,
2,
3
] |
from rest_framework import viewsets, mixins
from .models import Comment, Post
from .serializer import CommentSerializer, PostSerializer, AllCommentSerializer
class PostViewSet(viewsets.ModelViewSet):
serializer_class = PostSerializer
queryset = Post.objects.all()
class CommentViewSet(viewsets.GenericViewSet... | normal | {
"blob_id": "9bc13c608c079cbf23ed04f29edd1fd836214cde",
"index": 282,
"step-1": "<mask token>\n\n\nclass CommentViewSet(viewsets.GenericViewSet, mixins.ListModelMixin, mixins\n .RetrieveModelMixin):\n queryset = Comment.objects.all()\n\n def get_serializer_class(self):\n if self.action == 'retrie... | [
3,
4,
5,
6
] |
# Uses python3
import sys
from operator import attrgetter
from collections import namedtuple
Segment = namedtuple('Segment', 'start end')
def optimal_points(segments):
segments = sorted(segments, key=attrgetter('end'), reverse=True)
points = []
#write your code here
while len(segments) > 0:
... | normal | {
"blob_id": "c007dc2416d3f7c883c44dea5471927ea6f816d6",
"index": 3973,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef optimal_points(segments):\n segments = sorted(segments, key=attrgetter('end'), reverse=True)\n points = []\n while len(segments) > 0:\n segement = segments.pop()\n... | [
0,
2,
3,
4,
5
] |
from flask import Flask, send_file
import StringIO
app = Flask(__name__)
@app.route('/')
def index():
strIO = StringIO.StringIO()
strIO.write('Hello from Dan Jacob and Stephane Wirtel !')
strIO.seek(0)
return send_file(strIO,
attachment_filename="testing.txt",
... | normal | {
"blob_id": "45335fa5d4773bdd0ef3e6c340fe06e84169be5e",
"index": 8708,
"step-1": "<mask token>\n\n\n@app.route('/')\ndef index():\n strIO = StringIO.StringIO()\n strIO.write('Hello from Dan Jacob and Stephane Wirtel !')\n strIO.seek(0)\n return send_file(strIO, attachment_filename='testing.txt',\n ... | [
1,
2,
3,
4,
5
] |
from django.contrib.auth.models import User
from django.db import models
class Chat(models.Model):
category = models.CharField(unique=True, max_length=100)
def __str__(self):
return self.category
class ChatMessage(models.Model):
context = models.CharField(max_length=1000)
user = models.Fore... | normal | {
"blob_id": "61179dc734069017adaabd53804ed0102d9416e3",
"index": 8865,
"step-1": "<mask token>\n\n\nclass ChatMessage(models.Model):\n context = models.CharField(max_length=1000)\n user = models.ForeignKey(User, on_delete=models.CASCADE)\n chat = models.ForeignKey(Chat, on_delete=models.CASCADE)\n ti... | [
3,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
class ChatMembersFilterAdministrators(Object):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def __init__(self, **kwargs):
pass
@staticmethod
def read(q: dict, *args) ->'ChatMembersFilterAdministrators':
return ChatMembersFilterAdministrat... | flexible | {
"blob_id": "6dfd59bbab74a3a657d2200d62964578c296ee54",
"index": 5713,
"step-1": "<mask token>\n\n\nclass ChatMembersFilterAdministrators(Object):\n <mask token>\n <mask token>\n\n def __init__(self, **kwargs):\n pass\n\n @staticmethod\n def read(q: dict, *args) ->'ChatMembersFilterAdminist... | [
3,
4,
5,
6,
7
] |
'''
Aaditya Upadhyay
oooo$$$$$$$$$$$
oo$$$$$$$$$$$$$$$$$$$$$$$o
oo$$$$$$$$$$$$$$$$$$$$$$$$$$$$$o o$ $$ o$
o $ oo o$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$o $$ $$ $o$
oo $ $ "$ o$$$$$$$$$ $$$$$$$$$... | normal | {
"blob_id": "9cd1cb84c457db64019fa542efcf6500aa8d6d42",
"index": 9275,
"step-1": "<mask token>\n\n\ndef li():\n return list(map(int, stdin.readline().split()))\n\n\ndef mp():\n return map(int, stdin.readline().split())\n\n\n<mask token>\n\n\ndef pr(n):\n return stdout.write(str(n) + '\\n')\n\n\n<mask to... | [
4,
7,
8,
9,
10
] |
<|reserved_special_token_0|>
def tree(l):
return max([(i + j + 2) for i, j in enumerate(l)])
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def tree(l):
return max([(i + j + 2) for i, j in enumerate(l)])
<|reserved_special_token_0|>
print(tree(t))
<|reserved_... | flexible | {
"blob_id": "e79cdd32977eb357c3f6709887b671c50eb1fa45",
"index": 7071,
"step-1": "<mask token>\n\n\ndef tree(l):\n return max([(i + j + 2) for i, j in enumerate(l)])\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef tree(l):\n return max([(i + j + 2) for i, j in enumerate(l)])\n\n\n<mask token>\npri... | [
1,
2,
3,
4,
5
] |
#!/usr/bin/env pytest
# -*- coding: utf-8 -*-
###############################################################################
# $Id$
#
# Project: GDAL/OGR Test Suite
# Purpose: TopJSON driver test suite.
# Author: Even Rouault
#
###############################################################################
# Copyr... | normal | {
"blob_id": "270dba92af583e37c35ed5365f764adfdc2f947d",
"index": 2112,
"step-1": "<mask token>\n\n\ndef test_ogr_toposjon_objects_is_dict():\n ds = ogr.Open('data/topojson/topojson2.topojson')\n lyr = ds.GetLayer(0)\n assert lyr.GetName() == 'a_layer'\n assert lyr.GetLayerDefn().GetFieldCount() == 2\... | [
1,
2,
3,
4,
5
] |
from django.shortcuts import render, HttpResponse
from django.views.generic import TemplateView
from .models import Person, Stock_history
from django.http import Http404, HttpResponseRedirect
from .forms import NameForm, UploadFileForm
from .back import handle_uploaded_file, read_file
class IndexView(TemplateView):
... | normal | {
"blob_id": "2d65ffa3fc8a5360702337d749884903b2cb0423",
"index": 2353,
"step-1": "<mask token>\n\n\nclass PersonView(TemplateView):\n\n def get(self, request):\n persons = Person.objects.all()\n context = {'persons': persons}\n return render(request, 'budget/person.html', context)\n\n\ncl... | [
10,
11,
13,
14,
16
] |
<|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": "c6170678b523a105312d8ce316853859657d3c94",
"index": 2235,
"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 = [('user_detail... | [
0,
1,
2,
3,
4
] |
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