id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
189,213 | import argparse
import megengine as mge
import numpy as np
from megengine import jit
from build import build_and_load
def make_parser():
parser = argparse.ArgumentParser("YOLOX Demo Dump")
parser.add_argument("-n", "--name", type=str, default="yolox-s", help="model name")
parser.add_argument("-c", "--ckpt"... | null |
189,214 | import argparse
import megengine as mge
import numpy as np
from megengine import jit
from build import build_and_load
def dump_static_graph(model, graph_name="model.mge"):
model.eval()
model.head.decode_in_inference = False
data = mge.Tensor(np.random.random((1, 3, 640, 640)))
@jit.trace(capture_as_c... | null |
189,215 | import argparse
import os
import time
import cv2
import megengine as mge
import megengine.functional as F
from loguru import logger
from yolox.data.datasets import COCO_CLASSES
from yolox.utils import vis
from yolox.data.data_augment import preproc as preprocess
from build import build_and_load
def make_parser():
... | null |
189,216 | import argparse
import os
import time
import cv2
import megengine as mge
import megengine.functional as F
from loguru import logger
from yolox.data.datasets import COCO_CLASSES
from yolox.utils import vis
from yolox.data.data_augment import preproc as preprocess
from build import build_and_load
def postprocess(predict... | null |
189,217 | import argparse
import os
import time
import cv2
import megengine as mge
import megengine.functional as F
from loguru import logger
from yolox.data.datasets import COCO_CLASSES
from yolox.utils import vis
from yolox.data.data_augment import preproc as preprocess
from build import build_and_load
def get_image_list(path)... | null |
189,218 | import argparse
import os
import time
import cv2
import megengine as mge
import megengine.functional as F
from loguru import logger
from yolox.data.datasets import COCO_CLASSES
from yolox.utils import vis
from yolox.data.data_augment import preproc as preprocess
from build import build_and_load
def imageflow_demo(pred... | null |
189,219 | import argparse
from collections import OrderedDict
import megengine as mge
import torch
def make_parser():
parser = argparse.ArgumentParser()
parser.add_argument("-w", "--weights", type=str, help="path of weight file")
parser.add_argument(
"-o",
"--output",
default="weight_mge.pkl"... | null |
189,220 | import argparse
from collections import OrderedDict
import megengine as mge
import torch
def numpy_weights(weight_file):
torch_weights = torch.load(weight_file, map_location="cpu")
if "model" in torch_weights:
torch_weights = torch_weights["model"]
new_dict = OrderedDict()
for k, v in torch_weig... | null |
189,221 | import megengine.functional as F
import megengine.module as M
class SiLU(M.Module):
def forward(x):
def get_activation(name="silu"):
if name == "silu":
module = SiLU()
elif name == "relu":
module = M.ReLU()
elif name == "lrelu":
module = M.LeakyReLU(0.1)
else:
raise... | null |
189,222 | import megengine.functional as F
import megengine.module as M
from .network_blocks import BaseConv, DWConv
The provided code snippet includes necessary dependencies for implementing the `meshgrid` function. Write a Python function `def meshgrid(x, y)` to solve the following problem:
meshgrid wrapper for megengine
Her... | meshgrid wrapper for megengine |
189,223 | import megengine as mge
import megengine.module as M
from models.yolo_fpn import YOLOFPN
from models.yolo_head import YOLOXHead
from models.yolo_pafpn import YOLOPAFPN
from models.yolox import YOLOX
def build_yolox(name="yolox-s"):
num_classes = 80
# value meaning: depth, width
param_dict = {
"yolox... | null |
189,224 | import argparse
import os
import cv2
import numpy as np
import onnxruntime
from yolox.data.data_augment import preproc as preprocess
from yolox.data.datasets import COCO_CLASSES
from yolox.utils import mkdir, multiclass_nms, demo_postprocess, vis
def make_parser():
parser = argparse.ArgumentParser("onnxruntime inf... | null |
189,225 | import argparse
import random
import warnings
from loguru import logger
import torch
import torch.backends.cudnn as cudnn
from yolox.core import launch
from yolox.exp import Exp, check_exp_value, get_exp
from yolox.utils import configure_module, configure_nccl, configure_omp, get_num_devices
def make_parser():
par... | null |
189,226 | import argparse
import os
from loguru import logger
import torch
from torch import nn
from yolox.exp import get_exp
from yolox.models.network_blocks import SiLU
from yolox.utils import replace_module
def make_parser():
parser = argparse.ArgumentParser("YOLOX onnx deploy")
parser.add_argument(
"--output... | null |
189,227 | import argparse
import os
from loguru import logger
import torch
from yolox.exp import get_exp
def make_parser():
parser = argparse.ArgumentParser("YOLOX torchscript deploy")
parser.add_argument(
"--output-name", type=str, default="yolox.torchscript.pt", help="output name of models"
)
parser.ad... | null |
189,228 | import argparse
import os
import shutil
from loguru import logger
import tensorrt as trt
import torch
from torch2trt import torch2trt
from yolox.exp import get_exp
def make_parser():
parser = argparse.ArgumentParser("YOLOX ncnn deploy")
parser.add_argument("-expn", "--experiment-name", type=str, default=None)
... | null |
189,229 | import argparse
import os
import time
from loguru import logger
import cv2
import torch
from yolox.data.data_augment import ValTransform
from yolox.data.datasets import COCO_CLASSES
from yolox.exp import get_exp
from yolox.utils import fuse_model, get_model_info, postprocess, vis
def make_parser():
parser = argpar... | null |
189,230 | import argparse
import os
import time
from loguru import logger
import cv2
import torch
from yolox.data.data_augment import ValTransform
from yolox.data.datasets import COCO_CLASSES
from yolox.exp import get_exp
from yolox.utils import fuse_model, get_model_info, postprocess, vis
def get_image_list(path):
def image_de... | null |
189,231 | import argparse
import os
import time
from loguru import logger
import cv2
import torch
from yolox.data.data_augment import ValTransform
from yolox.data.datasets import COCO_CLASSES
from yolox.exp import get_exp
from yolox.utils import fuse_model, get_model_info, postprocess, vis
def imageflow_demo(predictor, vis_fold... | null |
189,232 | import argparse
import os
import random
import warnings
from loguru import logger
import torch
import torch.backends.cudnn as cudnn
from torch.nn.parallel import DistributedDataParallel as DDP
from yolox.core import launch
from yolox.exp import get_exp
from yolox.utils import (
configure_module,
configure_nccl,... | null |
189,233 | import os
import sys
import random
import time
import warnings
from loguru import logger
import torch
import torch.backends.cudnn as cudnn
from yolox.exp import Exp, get_exp
from yolox.core import Trainer
from yolox.utils import configure_module, configure_omp
from yolox.tools.train import make_parser
def assign_vis_p... | null |
189,234 | from collections import namedtuple
from functools import wraps
from packaging import version
import torch
from torch import nn, einsum
import torch.nn.functional as F
from einops import rearrange
def once(fn):
called = False
@wraps(fn)
def inner(x):
nonlocal called
if called:
re... | null |
189,235 | from typing import Tuple
import numpy as np
import torch
from torch import nn, Tensor
from torch.nn import Module
import torch.nn.functional as F
from einops import rearrange, repeat
from beartype import beartype
from beartype.typing import Optional
def exists(val):
return val is not None
def pad_tensor(input, pad,... | null |
189,236 | import math
import copy
from multiprocessing import cpu_count
from pathlib import Path
from random import random
from functools import partial
from collections import namedtuple
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn, einsum, Tensor
from torch.optim import Adam
from torch.u... | null |
189,237 | import math
import copy
from multiprocessing import cpu_count
from pathlib import Path
from random import random
from functools import partial
from collections import namedtuple
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn, einsum, Tensor
from torch.optim import Adam
from torch.u... | null |
189,238 | import math
import copy
from multiprocessing import cpu_count
from pathlib import Path
from random import random
from functools import partial
from collections import namedtuple
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn, einsum, Tensor
from torch.optim import Adam
from torch.u... | null |
189,239 | import math
import copy
from multiprocessing import cpu_count
from pathlib import Path
from random import random
from functools import partial
from collections import namedtuple
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn, einsum, Tensor
from torch.optim import Adam
from torch.u... | null |
189,240 | import math
import copy
from multiprocessing import cpu_count
from pathlib import Path
from random import random
from functools import partial
from collections import namedtuple
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn, einsum, Tensor
from torch.optim import Adam
from torch.u... | null |
189,241 | import math
import copy
from multiprocessing import cpu_count
from pathlib import Path
from random import random
from functools import partial
from collections import namedtuple
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn, einsum, Tensor
from torch.optim import Adam
from torch.u... | null |
189,242 | import math
import copy
from multiprocessing import cpu_count
from pathlib import Path
from random import random
from functools import partial
from collections import namedtuple
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn, einsum, Tensor
from torch.optim import Adam
from torch.u... | null |
189,243 | import math
import copy
from multiprocessing import cpu_count
from pathlib import Path
from random import random
from functools import partial
from collections import namedtuple
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn, einsum, Tensor
from torch.optim import Adam
from torch.u... | null |
189,244 | import math
import copy
from multiprocessing import cpu_count
from pathlib import Path
from random import random
from functools import partial
from collections import namedtuple
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn, einsum, Tensor
from torch.optim import Adam
from torch.u... | null |
189,245 | import math
import copy
from multiprocessing import cpu_count
from pathlib import Path
from random import random
from functools import partial
from collections import namedtuple
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn, einsum, Tensor
from torch.optim import Adam
from torch.u... | null |
189,246 | import math
import copy
from multiprocessing import cpu_count
from pathlib import Path
from random import random
from functools import partial
from collections import namedtuple
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn, einsum, Tensor
from torch.optim import Adam
from torch.u... | null |
189,247 | import math
import copy
from multiprocessing import cpu_count
from pathlib import Path
from random import random
from functools import partial
from collections import namedtuple
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn, einsum, Tensor
from torch.optim import Adam
from torch.u... | null |
189,248 | import math
import copy
from multiprocessing import cpu_count
from pathlib import Path
from random import random
from functools import partial
from collections import namedtuple
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn, einsum, Tensor
from torch.optim import Adam
from torch.u... | null |
189,249 | import math
import copy
from multiprocessing import cpu_count
from pathlib import Path
from random import random
from functools import partial
from collections import namedtuple
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn, einsum, Tensor
from torch.optim import Adam
from torch.u... | null |
189,250 | import math
import copy
from multiprocessing import cpu_count
from pathlib import Path
from random import random
from functools import partial
from collections import namedtuple
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn, einsum, Tensor
from torch.optim import Adam
from torch.u... | null |
189,251 | import math
import copy
from multiprocessing import cpu_count
from pathlib import Path
from random import random
from functools import partial
from collections import namedtuple
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn, einsum, Tensor
from torch.optim import Adam
from torch.u... | null |
189,252 | import math
import copy
from multiprocessing import cpu_count
from pathlib import Path
from random import random
from functools import partial
from collections import namedtuple
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn, einsum, Tensor
from torch.optim import Adam
from torch.u... | null |
189,253 | import math
import copy
from multiprocessing import cpu_count
from pathlib import Path
from random import random
from functools import partial
from collections import namedtuple
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn, einsum, Tensor
from torch.optim import Adam
from torch.u... | null |
189,254 | import math
import copy
from multiprocessing import cpu_count
from pathlib import Path
from random import random
from functools import partial
from collections import namedtuple
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn, einsum, Tensor
from torch.optim import Adam
from torch.u... | null |
189,255 | import logging
import re
import subprocess
from typing import Dict, List
from packaging.version import Version
from naturalspeech2_pytorch.utils.phonemizers.base import BasePhonemizer
from naturalspeech2_pytorch.utils.phonemizers.punctuation import Punctuation
def is_tool(name):
from shutil import which
retur... | null |
189,256 | import logging
import re
import subprocess
from typing import Dict, List
from packaging.version import Version
from naturalspeech2_pytorch.utils.phonemizers.base import BasePhonemizer
from naturalspeech2_pytorch.utils.phonemizers.punctuation import Punctuation
espeak_version_pattern = re.compile(r"text-to-speech:\s(?P<... | null |
189,257 | import logging
import re
import subprocess
from typing import Dict, List
from packaging.version import Version
from naturalspeech2_pytorch.utils.phonemizers.base import BasePhonemizer
from naturalspeech2_pytorch.utils.phonemizers.punctuation import Punctuation
def get_espeakng_version():
output = subprocess.getout... | null |
189,258 | import logging
import re
import subprocess
from typing import Dict, List
from packaging.version import Version
from naturalspeech2_pytorch.utils.phonemizers.base import BasePhonemizer
from naturalspeech2_pytorch.utils.phonemizers.punctuation import Punctuation
The provided code snippet includes necessary dependencies ... | Run espeak with the given arguments. |
189,259 | import torch
from torch import Tensor
from typing import Callable, List, Optional, Tuple
from torch.nn.utils.rnn import pad_sequence
from naturalspeech2_pytorch.utils.cleaner import TextProcessor
from naturalspeech2_pytorch.utils.phonemizers.espeak_wrapper import ESpeak
def exists(val):
def default(val, d):
return... | null |
189,260 | import torch
from einops import repeat, rearrange
The provided code snippet includes necessary dependencies for implementing the `average_over_durations` function. Write a Python function `def average_over_durations(values, durs)` to solve the following problem:
- in: - values: B, 1, T_de - durs: B, T_en - out: - avg:... | - in: - values: B, 1, T_de - durs: B, T_en - out: - avg: B, 1, T_en |
189,261 | import torch
from einops import repeat, rearrange
def create_mask(sequence_length, max_len):
dtype, device = sequence_length.dtype, sequence_length.device
seq_range = torch.arange(max_len, dtype=dtype, device=device)
sequence_length = rearrange(sequence_length, 'b -> b 1')
seq_range = rearrange(seq_ran... | null |
189,262 | import sys
import os
import urllib3
import openpyxl
from uuid import uuid4
import dns.resolver
import re
from threading import Thread
from IPy import IP
from collections import Counter
from queue import Queue
from urllib.parse import urlparse
from termcolor import cprint
from optparse import OptionParser
import os
impo... | null |
189,263 | import sys
import os
import urllib3
import openpyxl
from uuid import uuid4
import dns.resolver
import re
from threading import Thread
from IPy import IP
from collections import Counter
from queue import Queue
from urllib.parse import urlparse
from termcolor import cprint
from optparse import OptionParser
import os
impo... | null |
189,264 | import sys
import os
import urllib3
import openpyxl
from uuid import uuid4
import dns.resolver
import re
from threading import Thread
from IPy import IP
from collections import Counter
from queue import Queue
from urllib.parse import urlparse
from termcolor import cprint
from optparse import OptionParser
import os
impo... | null |
189,265 | import sys
import os
import urllib3
import openpyxl
from uuid import uuid4
import dns.resolver
import re
from threading import Thread
from IPy import IP
from collections import Counter
from queue import Queue
from urllib.parse import urlparse
from termcolor import cprint
from optparse import OptionParser
import os
impo... | null |
189,266 | import sys
import os
import urllib3
import openpyxl
from uuid import uuid4
import dns.resolver
import re
from threading import Thread
from IPy import IP
from collections import Counter
from queue import Queue
from urllib.parse import urlparse
from termcolor import cprint
from optparse import OptionParser
import os
impo... | null |
189,267 | import sys
import os
import urllib3
import openpyxl
from uuid import uuid4
import dns.resolver
import re
from threading import Thread
from IPy import IP
from collections import Counter
from queue import Queue
from urllib.parse import urlparse
from termcolor import cprint
from optparse import OptionParser
import os
impo... | null |
189,268 | import re
import sys
from aiodns import DNSResolver
from ipaddress import IPv4Network
from typing import Callable, List, Optional
from Plugins.infoGather.subdomain.theHarvester.runTheHarvester.lib import hostchecker
NETWORK_REGEX = r'\b({})(?:\:({}))?(?:\/({}))?\b'.format(
IP_REGEX,
PORT_REGEX,
NETMASK_REGE... | Serialize a network range in a constant format, 'x.x.x.x/y'. Parameters ---------- ip: str. A serialized ip in the format 'x.x.x.x'. Extra information like port (':z') or subnet ('/n') will be ignored. netmask: str. The subnet subdivision, represented by a 2 digit netmask. Returns ------- out: str. The network OSI addr... |
189,269 | import re
import sys
from aiodns import DNSResolver
from ipaddress import IPv4Network
from typing import Callable, List, Optional
from Plugins.infoGather.subdomain.theHarvester.runTheHarvester.lib import hostchecker
def list_ips_in_network_range(iprange: str) -> List[str]:
"""
List all the IPs in the range.
... | Reverse all the IPs stored in a network range. All the queries are made concurrently. Parameters ---------- iprange: str. An IPv4 range formated as 'x.x.x.x/y'. The last 2 digits of the ip can be set to anything, they will be ignored. callback: Callable. Arbitrary postprocessing function. nameservers: List[str]. Option... |
189,270 | import re
import sys
from aiodns import DNSResolver
from ipaddress import IPv4Network
from typing import Callable, List, Optional
from Plugins.infoGather.subdomain.theHarvester.runTheHarvester.lib import hostchecker
The provided code snippet includes necessary dependencies for implementing the `generate_postprocessing... | Postprocess the query results asynchronously too, instead of waiting for the querying stage to be completely finished. Parameters ---------- target: str. The domain wanted as TLD. allhosts: List. A collection of all the subdomains -of target- found so far. Returns ------- out: Callable. A function that will update the ... |
189,271 | from Plugins.infoGather.subdomain.theHarvester.runTheHarvester.lib.core import *
from typing import Union
import random
The provided code snippet includes necessary dependencies for implementing the `splitter` function. Write a Python function `async def splitter(links)` to solve the following problem:
Method that tri... | Method that tries to remove duplicates LinkedinLists pulls a lot of profiles with the same name. This method tries to remove duplicates from the list. :param links: list of links to remove duplicates from :return: unique-ish list |
189,272 | from Plugins.infoGather.subdomain.theHarvester.runTheHarvester.lib.core import *
from typing import Union
import random
The provided code snippet includes necessary dependencies for implementing the `filter` function. Write a Python function `def filter(lst)` to solve the following problem:
Method that filters list :p... | Method that filters list :param lst: list to be filtered :return: new filtered list |
189,273 | from Plugins.infoGather.subdomain.theHarvester.runTheHarvester.lib.core import *
from typing import Union
import random
The provided code snippet includes necessary dependencies for implementing the `get_delay` function. Write a Python function `def get_delay() -> float` to solve the following problem:
Method that is ... | Method that is used to generate a random delay |
189,274 | from Plugins.infoGather.subdomain.theHarvester.runTheHarvester.lib.core import *
from typing import Union
import random
async def search(text: str) -> bool:
"""Helper function to check if Google has blocked traffic.
:param text: See if certain text is returned which means Google is blocking us
:return bool:... | Function that makes a request on our behalf, if Google starts to block us :param visit_url: Url to scrape :return: Correct html that can be parsed by BS4 |
189,275 | import os
import sys
import re
import time
import requests
import random
import argparse
from functools import partial
from colored import fg, bg, attr
from multiprocessing.dummy import Pool
t_tokens = []
import tldextract
def githubApiSearchCode( search, page ):
headers = {"Authorization":"token "+random.choice(t... | null |
189,276 | import os
import sys
import re
import time
import requests
import random
import argparse
from functools import partial
from colored import fg, bg, attr
from multiprocessing.dummy import Pool
def getRawUrl( result ):
raw_url = result['html_url'];
raw_url = raw_url.replace( 'https://github.com/', 'https://raw.git... | null |
189,277 | import re
import sys
import os
import argparse
import time
import hashlib
import random
import multiprocessing
import threading
import socket
import json
from collections import Counter
from Plugins.infoGather.subdomain.Sublist3r.subbrute import subbrute
import dns.resolver
import requests
def write_file(filename, sub... | null |
189,278 | import re
import sys
import os
import argparse
import time
import hashlib
import random
import multiprocessing
import threading
import socket
import json
from collections import Counter
from Plugins.infoGather.subdomain.Sublist3r.subbrute import subbrute
import dns.resolver
import requests
The provided code snippet in... | Sorting key for subdomains This sorting key orders subdomains from the top-level domain at the right reading left, then moving '^' and 'www' to the top of their group. For example, the following list is sorted correctly: [ 'example.com', 'www.example.com', 'a.example.com', 'www.a.example.com', 'b.a.example.com', 'b.exa... |
189,279 | import re
import optparse
import os
import signal
import sys
import uuid
import random
import ctypes
import dns.resolver
import dns.rdatatype
import json
import multiprocessing
host_match = re.compile(r"((?<=[\s])[a-zA-Z0-9_-]+\.(?:[a-zA-Z0-9_-]+\.?)+(?=[\s]))")
def extract_hosts(data, hostname):
#made a global to... | null |
189,280 | import re
import optparse
import os
import signal
import sys
import uuid
import random
import ctypes
import dns.resolver
import dns.rdatatype
import json
import multiprocessing
domain_match = re.compile("([a-zA-Z0-9_-]*\.[a-zA-Z0-9_-]*\.[a-zA-Z0-9_-]*)+")
def trace(*args, **kwargs):
if verbose:
for a in arg... | null |
189,281 | import re
import optparse
import os
import signal
import sys
import uuid
import random
import ctypes
import dns.resolver
import dns.rdatatype
import json
import multiprocessing
def run(target, record_type = None, subdomains = "names.txt", resolve_list = "resolvers.txt", process_count = 16):
def print_target(target, re... | null |
189,282 | import re
import optparse
import os
import signal
import sys
import uuid
import random
import ctypes
import dns.resolver
import dns.rdatatype
import json
import multiprocessing
def killproc(signum = 0, frame = 0, pid = False):
if not pid:
pid = os.getpid()
if sys.platform.startswith('win'):
try:... | null |
189,283 | import multiprocessing
import gevent
from gevent import monkey
from gevent.queue import PriorityQueue
import re
import dns.resolver
import time
import signal
import os
import glob
from Plugins.infoGather.subdomain.lijiejie.lib.common import is_intranet, load_dns_servers, load_next_sub, print_msg, get_out_file_name, use... | null |
189,284 | import optparse
import sys
def parse_args():
parser = optparse.OptionParser('usage: %prog [options] target.com',
version="%prog 1.1")
parser.add_option('-f', dest='file', default='subnames.txt',
help='File contains new line delimited subs, default is sub... | null |
189,285 | import sys
import os
from gevent.pool import Pool
import dns.resolver
from Plugins.infoGather.subdomain.lijiejie.lib.consle_width import getTerminalSize
def is_intranet(ip):
ret = ip.split('.')
if len(ret) != 4:
return True
if ret[0] == '10':
return True
if ret[0] == '172' and 16 <= int... | null |
189,286 | def _getTerminalSize_windows():
res = None
try:
from ctypes import windll, create_string_buffer
# stdin handle is -10
# stdout handle is -11
# stderr handle is -12
h = windll.kernel32.GetStdHandle(-12)
csbi = create_string_buffer(22)
res = windll.kernel32.... | null |
189,287 | import requests
from bs4 import BeautifulSoup
import re
from urllib.parse import quote
import json
import math
from termcolor import cprint
headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64; rv:46.0) Gecko/20100101 Firefox/46.0'}
beianbeianApi(domain):
cprint('Load beianbeianApi: ', 'green')
# 获取备案... | null |
189,288 |
def filter_internal_ip(ip_subnet):
ip_subnet_list = ip_subnet.split('.')
if ip_subnet_list[0] == '10' or '127':
return None
elif ip_subnet_list[0] == '172' and 15 < int(ip_subnet_list[1]) < 32:
return None
elif ip_subnet_list[0] == '192' and ip_subnet_list[1] == '168':
return N... | null |
189,289 | import struct
from tqdm import *
from colorama import Fore
import socket
from urllib.parse import urlparse
def pack_string(s):
if s is None:
return struct.pack(">h", -1)
l = len(s)
return struct.pack(">H%dsb" % l, l, s.encode('utf8'), 0) | null |
189,290 | import struct
from tqdm import *
from colorama import Fore
def unpack(stream, fmt):
import socket
from urllib.parse import urlparse
def unpack_string(stream):
size, = unpack(stream, ">h")
if size == -1: # null string
return None
res, = unpack(stream, "%ds" % size)
stream.read(1) # \0
return res | null |
189,291 | import struct
from tqdm import *
from colorama import Fore
class AjpForwardRequest(object):
def __init__(self, data_direction=None):
def pack_headers(self):
def pack_attributes(self):
def serialize(self):
def parse(self, raw_packet):
def send_and_receive(self, socket, stream, save_cookies=... | null |
189,292 | import struct
from tqdm import *
from colorama import Fore
import socket
class Tomcat(object):
def __init__(self, target_host, target_port):
def perform_request(self, req_uri, headers={}, method='GET', user=None, password=None, attributes=[]):
from urllib.parse import urlparse
def detect_AJP_LFI(url):
ip = ... | null |
189,293 | from termcolor import cprint
import requests
import threading
import re
import hashlib
from urllib.parse import urlparse
from collections import OrderedDict
from xml.dom import minidom
from queue import Queue
import time
import traceback
from tqdm import *
from colorama import Fore
import urllib3
payloads_dict = Or... | null |
189,294 | import RPi.GPIO as GPIO
import time
import os
import logging
import sys
from ctypes import *
def digital_read(pin):
return GPIO.input(pin) | null |
189,295 | import RPi.GPIO as GPIO
import time
import os
import logging
import sys
from ctypes import *
spi = None
if spi is None:
RuntimeError('Cannot find DEV_Config.so')
def spi_writebyte(value):
spi.DEV_SPI_WriteByte(value) | null |
189,296 | import RPi.GPIO as GPIO
import time
import os
import logging
import sys
from ctypes import *
EPD_SCK_PIN =11
EPD_MOSI_PIN =10
EPD_M1_CS_PIN =8
EPD_S1_CS_PIN =7
EPD_M2_CS_PIN =17
EPD_S2_CS_PIN =18
EPD_M1S1_DC_PIN =13
EPD_M2S2_DC_PIN =22
EPD_M1S1_RST_PIN =6
EPD_M2S2_RST_PIN =23
EPD_M1_BUSY_PIN =5
EPD_S1_BUSY_PI... | null |
189,297 | import RPi.GPIO as GPIO
import time
import os
import logging
import sys
from ctypes import *
EPD_M1_CS_PIN =8
EPD_S1_CS_PIN =7
EPD_M2_CS_PIN =17
EPD_S2_CS_PIN =18
EPD_M1S1_DC_PIN =13
EPD_M2S2_DC_PIN =22
EPD_M1S1_RST_PIN =6
EPD_M2S2_RST_PIN =23
def digital_write(pin, value):
GPIO.output(pin, value)
def module... | null |
189,298 | import RPi.GPIO as GPIO
import time
import os
import logging
import sys
from ctypes import *
EPD_SCK_PIN =11
EPD_MOSI_PIN =10
def spi_readbyte(Reg):
GPIO.setup(EPD_MOSI_PIN, GPIO.IN)
j=0
# time.sleep(0.01)
for i in range(0, 8):
GPIO.output(EPD_SCK_PIN, GPIO.LOW)
# time.sleep(0.01)
... | null |
189,299 | import itertools, os, token, tokenize
TOKEN_WHITELIST = [token.OP, token.NAME, token.NUMBER, token.STRING]
_ignored = ['_version.py']
_dir_package = {'openllm-python': 'openllm', 'openllm-core': 'openllm_core', 'openllm-client': 'openllm_client'}
def run_cz(args):
from tabulate import tabulate
headers = ['Name', ... | null |
189,300 | import openllm_core
def __dir__():
coreutils = set(dir(openllm_core.utils)) | set([it for it in openllm_core.utils._extras if not it.startswith('_')])
return sorted(list(coreutils)) | null |
189,301 | import openllm_core
def __getattr__(name):
if hasattr(openllm_core.utils, name):
return getattr(openllm_core.utils, name)
raise AttributeError(f'module {__name__} has no attribute {name}') | null |
189,304 | from __future__ import annotations
import importlib.metadata
import logging
import os
import typing as t
import attr
from ._schemas import Helpers, Metadata, Response, StreamingResponse
from ._shim import MAX_RETRIES, AsyncClient, Client
def _address_converter(addr: str):
return addr if '://' in addr else 'http://' ... | null |
189,305 | from __future__ import annotations
import asyncio
import email.utils
import logging
import platform
import random
import time
import typing as t
import anyio
import attr
import distro
import httpx
from ._stream import AsyncStream, Response, Stream
from ._typing_compat import Architecture, LiteralString, Platform
from .... | null |
189,306 | from __future__ import annotations
import asyncio
import email.utils
import logging
import platform
import random
import time
import typing as t
import anyio
import attr
import distro
import httpx
from ._stream import AsyncStream, Response, Stream
from ._typing_compat import Architecture, LiteralString, Platform
from .... | null |
189,307 | from __future__ import annotations
import asyncio
import email.utils
import logging
import platform
import random
import time
import typing as t
import anyio
import attr
import distro
import httpx
from ._stream import AsyncStream, Response, Stream
from ._typing_compat import Architecture, LiteralString, Platform
from .... | null |
189,308 | from __future__ import annotations
import asyncio
import email.utils
import logging
import platform
import random
import time
import typing as t
import anyio
import attr
import distro
import httpx
from ._stream import AsyncStream, Response, Stream
from ._typing_compat import Architecture, LiteralString, Platform
from .... | null |
189,309 | from __future__ import annotations
import types
import typing as t
import attr
import orjson
from openllm_core._schemas import (
CompletionChunk as CompletionChunk,
GenerationOutput as Response, # backward compatibility
_SchemaMixin as _SchemaMixin,
)
from ._utils import converter
if t.TYPE_CHECKING:
from ._sh... | null |
189,310 | from __future__ import annotations
from langchain.agents import AgentType, initialize_agent, load_tools
from langchain.llms import OpenLLM
import bentoml
from bentoml.io import Text
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION)
def chat(input_text: str):
return agent.run(input_tex... | null |
189,311 | from __future__ import annotations
import uuid
from typing import Any, AsyncGenerator, Dict, TypedDict, Union
from bentoml import Service
from bentoml.io import JSON, Text
from openllm import LLM
llm = LLM[Any, Any]('HuggingFaceH4/zephyr-7b-alpha', backend='vllm')
class GenerateInput(TypedDict):
prompt: str
stream:... | null |
189,312 | from __future__ import annotations
import typing as t
from langchain.chains import LLMChain
from langchain.llms import OpenLLM
from langchain.prompts import PromptTemplate
from pydantic import BaseModel
import bentoml
from bentoml.io import JSON, Text
def gen_llm(model_name: str, model_id: str | None = None, **attrs: ... | null |
189,313 | from __future__ import annotations
import typing as t
from langchain.chains import LLMChain
from langchain.llms import OpenLLM
from langchain.prompts import PromptTemplate
from pydantic import BaseModel
import bentoml
from bentoml.io import JSON, Text
class Query(BaseModel):
industry: str
product_name: str
keywor... | null |
189,314 | from __future__ import annotations
import openllm_core, pydantic, typing as t
from openllm_core._configuration import ModelSettings
def get_special_token_id(tokenizer: transformers.PreTrainedTokenizer, key: str) -> int:
token_ids = tokenizer.encode(key)
if len(token_ids) > 1:
raise ValueError(f"Expected only a... | null |
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