id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
22,219 |
The provided code snippet includes necessary dependencies for implementing the `run_async` function. Write a Python function `def run_async(cor)` to solve the following problem:
在同步环境中运行异步代码.
Here is the function:
def run_async(cor):
'''
在同步环境中运行异步代码.
'''
try:
loop = asyncio.get_event_loop()... | 在同步环境中运行异步代码. |
22,220 |
The provided code snippet includes necessary dependencies for implementing the `iter_over_async` function. Write a Python function `def iter_over_async(ait, loop=None)` to solve the following problem:
将异步生成器封装成同步生成器.
Here is the function:
def iter_over_async(ait, loop=None):
'''
将异步生成器封装成同步生成器.
'''
... | 将异步生成器封装成同步生成器. |
22,221 |
def get_all_model_worker_configs() -> dict:
result = {}
model_names = set(FSCHAT_MODEL_WORKERS.keys())
for name in model_names:
if name != "default":
result[name] = get_model_worker_config(name)
return result | null |
22,222 | import os
from configs import (
KB_ROOT_PATH,
CHUNK_SIZE,
OVERLAP_SIZE,
ZH_TITLE_ENHANCE,
logger,
log_verbose,
text_splitter_dict,
LLM_MODELS,
TEXT_SPLITTER_NAME,
)
import importlib
from text_splitter import zh_title_enhance as func_zh_title_enhance
import langchain.document_loaders
... | null |
22,223 | import os
from configs import (
KB_ROOT_PATH,
CHUNK_SIZE,
OVERLAP_SIZE,
ZH_TITLE_ENHANCE,
logger,
log_verbose,
text_splitter_dict,
LLM_MODELS,
TEXT_SPLITTER_NAME,
)
import importlib
from text_splitter import zh_title_enhance as func_zh_title_enhance
import langchain.document_loaders
... | null |
22,224 | import os
from configs import (
KB_ROOT_PATH,
CHUNK_SIZE,
OVERLAP_SIZE,
ZH_TITLE_ENHANCE,
logger,
log_verbose,
text_splitter_dict,
LLM_MODELS,
TEXT_SPLITTER_NAME,
)
import importlib
from text_splitter import zh_title_enhance as func_zh_title_enhance
import langchain.document_loaders
... | null |
22,225 | import os
from configs import (
KB_ROOT_PATH,
CHUNK_SIZE,
OVERLAP_SIZE,
ZH_TITLE_ENHANCE,
logger,
log_verbose,
text_splitter_dict,
LLM_MODELS,
TEXT_SPLITTER_NAME,
)
import importlib
from text_splitter import zh_title_enhance as func_zh_title_enhance
import langchain.document_loaders
... | 根据loader_name和文件路径或内容返回文档加载器。 |
22,226 | import os
from configs import (
KB_ROOT_PATH,
CHUNK_SIZE,
OVERLAP_SIZE,
ZH_TITLE_ENHANCE,
logger,
log_verbose,
text_splitter_dict,
LLM_MODELS,
TEXT_SPLITTER_NAME,
)
import importlib
from text_splitter import zh_title_enhance as func_zh_title_enhance
import langchain.document_loaders
... | 根据参数获取特定的分词器 |
22,227 | from configs import (
EMBEDDING_MODEL, DEFAULT_VS_TYPE, ZH_TITLE_ENHANCE,
CHUNK_SIZE, OVERLAP_SIZE,
logger, log_verbose
)
from server.knowledge_base.utils import (
get_file_path, list_kbs_from_folder,
list_files_from_folder, files2docs_in_thread,
KnowledgeFile
)
from server.knowledge_base.kb_ser... | null |
22,228 | from configs import (
EMBEDDING_MODEL, DEFAULT_VS_TYPE, ZH_TITLE_ENHANCE,
CHUNK_SIZE, OVERLAP_SIZE,
logger, log_verbose
)
from server.knowledge_base.utils import (
get_file_path, list_kbs_from_folder,
list_files_from_folder, files2docs_in_thread,
KnowledgeFile
)
from server.knowledge_base.kb_ser... | 在知识库与向量库无变化的情况下，从备份数据库中导入数据到 info.db。 适用于版本升级时，info.db 结构变化，但无需重新向量化的情况。 请确保两边数据库表名一致，需要导入的字段名一致 当前仅支持 sqlite |
22,229 | from configs import (
EMBEDDING_MODEL, DEFAULT_VS_TYPE, ZH_TITLE_ENHANCE,
CHUNK_SIZE, OVERLAP_SIZE,
logger, log_verbose
)
from server.knowledge_base.utils import (
get_file_path, list_kbs_from_folder,
list_files_from_folder, files2docs_in_thread,
KnowledgeFile
)
from server.knowledge_base.kb_ser... | use existed files in local folder to populate database and/or vector store. set parameter `mode` to: recreate_vs: recreate all vector store and fill info to database using existed files in local folder fill_info_only(disabled): do not create vector store, fill info to db using existed files only update_in_db: update ve... |
22,230 | from configs import (
EMBEDDING_MODEL, DEFAULT_VS_TYPE, ZH_TITLE_ENHANCE,
CHUNK_SIZE, OVERLAP_SIZE,
logger, log_verbose
)
from server.knowledge_base.utils import (
get_file_path, list_kbs_from_folder,
list_files_from_folder, files2docs_in_thread,
KnowledgeFile
)
from server.knowledge_base.kb_ser... | delete docs in database that not existed in local folder. it is used to delete database docs after user deleted some doc files in file browser |
22,231 | from configs import (
EMBEDDING_MODEL, DEFAULT_VS_TYPE, ZH_TITLE_ENHANCE,
CHUNK_SIZE, OVERLAP_SIZE,
logger, log_verbose
)
from server.knowledge_base.utils import (
get_file_path, list_kbs_from_folder,
list_files_from_folder, files2docs_in_thread,
KnowledgeFile
)
from server.knowledge_base.kb_ser... | delete doc files in local folder that not existed in database. it is used to free local disk space by delete unused doc files. |
22,232 | from configs import CACHED_VS_NUM, CACHED_MEMO_VS_NUM
from server.knowledge_base.kb_cache.base import *
from server.knowledge_base.kb_service.base import EmbeddingsFunAdapter
from server.utils import load_local_embeddings
from server.knowledge_base.utils import get_vs_path
from langchain.vectorstores.faiss import FAISS... | null |
22,233 | from configs import CACHED_VS_NUM, CACHED_MEMO_VS_NUM
from server.knowledge_base.kb_cache.base import *
from server.knowledge_base.kb_service.base import EmbeddingsFunAdapter
from server.utils import load_local_embeddings
from server.knowledge_base.utils import get_vs_path
from langchain.vectorstores.faiss import FAISS... | null |
22,234 | import uuid
from typing import Any, Dict, List, Tuple
import chromadb
from chromadb.api.types import (GetResult, QueryResult)
from langchain.docstore.document import Document
from configs import SCORE_THRESHOLD
from server.knowledge_base.kb_service.base import (EmbeddingsFunAdapter,
... | null |
22,235 | import uuid
from typing import Any, Dict, List, Tuple
import chromadb
from chromadb.api.types import (GetResult, QueryResult)
from langchain.docstore.document import Document
from configs import SCORE_THRESHOLD
from server.knowledge_base.kb_service.base import (EmbeddingsFunAdapter,
... | from langchain_community.vectorstores.chroma import Chroma |
22,236 | import operator
from abc import ABC, abstractmethod
import os
from pathlib import Path
import numpy as np
from langchain.embeddings.base import Embeddings
from langchain.docstore.document import Document
from server.db.repository.knowledge_base_repository import (
add_kb_to_db, delete_kb_from_db, list_kbs_from_db, ... | sklearn.preprocessing.normalize 的替代（使用 L2），避免安装 scipy, scikit-learn |
22,237 | import operator
from abc import ABC, abstractmethod
import os
from pathlib import Path
import numpy as np
from langchain.embeddings.base import Embeddings
from langchain.docstore.document import Document
from server.db.repository.knowledge_base_repository import (
add_kb_to_db, delete_kb_from_db, list_kbs_from_db, ... | null |
22,238 | from fastchat.conversation import Conversation
from server.model_workers.base import *
from fastchat import conversation as conv
import sys
import json
from server.model_workers import SparkApi
import websockets
from server.utils import iter_over_async, asyncio
from typing import List, Dict
async def request(appid, ap... | null |
22,239 | from contextlib import contextmanager
import httpx
from fastchat.conversation import Conversation
from httpx_sse import EventSource
from server.model_workers.base import *
from fastchat import conversation as conv
import sys
from typing import List, Dict, Iterator, Literal, Any
import jwt
import time
def connect_sse(c... | null |
22,240 | from contextlib import contextmanager
import httpx
from fastchat.conversation import Conversation
from httpx_sse import EventSource
from server.model_workers.base import *
from fastchat import conversation as conv
import sys
from typing import List, Dict, Iterator, Literal, Any
import jwt
import time
def generate_toke... | null |
22,241 | import sys
from fastchat.conversation import Conversation
from server.model_workers.base import *
from server.utils import get_httpx_client
from cachetools import cached, TTLCache
import json
from fastchat import conversation as conv
import sys
from server.model_workers.base import ApiEmbeddingsParams
from typing impor... | 使用 AK，SK 生成鉴权签名（Access Token） :return: access_token，或是None(如果错误) |
22,242 | import json
import time
import hashlib
from fastchat.conversation import Conversation
from server.model_workers.base import *
from server.utils import get_httpx_client
from fastchat import conversation as conv
import sys
import json
from typing import List, Literal, Dict
from configs import logger, log_verbose
def cal... | null |
22,243 | import sys
import os
from llm_api_stale import launch_all, parser, controller_args, worker_args, server_args
from api import create_app
import uvicorn
def create_app(run_mode: str = None):
app = FastAPI(
title="Langchain-Chatchat API Server",
version=VERSION
)
MakeFastAPIOffline(app)
# ... | null |
22,244 | from langchain.utilities.bing_search import BingSearchAPIWrapper
from langchain.utilities.duckduckgo_search import DuckDuckGoSearchAPIWrapper
from configs import (BING_SEARCH_URL, BING_SUBSCRIPTION_KEY, METAPHOR_API_KEY,
LLM_MODELS, SEARCH_ENGINE_TOP_K, TEMPERATURE, OVERLAP_SIZE)
from langchain.cha... | null |
22,245 | from langchain.utilities.bing_search import BingSearchAPIWrapper
from langchain.utilities.duckduckgo_search import DuckDuckGoSearchAPIWrapper
from configs import (BING_SEARCH_URL, BING_SUBSCRIPTION_KEY, METAPHOR_API_KEY,
LLM_MODELS, SEARCH_ENGINE_TOP_K, TEMPERATURE, OVERLAP_SIZE)
from langchain.cha... | null |
22,246 | from langchain.utilities.bing_search import BingSearchAPIWrapper
from langchain.utilities.duckduckgo_search import DuckDuckGoSearchAPIWrapper
from configs import (BING_SEARCH_URL, BING_SUBSCRIPTION_KEY, METAPHOR_API_KEY,
LLM_MODELS, SEARCH_ENGINE_TOP_K, TEMPERATURE, OVERLAP_SIZE)
from langchain.cha... | null |
22,247 | import streamlit as st
from webui_pages.utils import *
from streamlit_option_menu import option_menu
from webui_pages import *
import os
from server.llm_api_stale import string_args,launch_all,controller_args,worker_args,server_args,LOG_PATH
from server.api_allinone_stale import parser, api_args
import subprocess
LOG_... | null |
22,248 | import streamlit as st
from webui_pages.utils import *
from streamlit_option_menu import option_menu
from webui_pages import *
import os
from server.llm_api_stale import string_args,launch_all,controller_args,worker_args,server_args,LOG_PATH
from server.api_allinone_stale import parser, api_args
import subprocess
web_a... | null |
22,249 | import json
from server.chat.search_engine_chat import search_engine_chat
from configs import VECTOR_SEARCH_TOP_K, MAX_TOKENS
import asyncio
from server.agent import model_container
from pydantic import BaseModel, Field
async def search_engine_iter(query: str):
response = await search_engine_chat(query=query,
... | null |
22,250 | import WolframAlphaAPIWrapper
from pydantic import BaseModel, Field
wolfram_alpha_appid = "your key"
def wolfram(query: str):
wolfram = WolframAlphaAPIWrapper(wolfram_alpha_appid=wolfram_alpha_appid)
ans = wolfram.run(query)
return ans | null |
22,251 | import BaseModel, Field
from langchain.tools import ShellTool
def shell(query: str):
tool = ShellTool()
return tool.run(tool_input=query) | null |
22,252 | import YouTubeSearchTool
from pydantic import BaseModel, Field
def search_youtube(query: str):
tool = YouTubeSearchTool()
return tool.run(tool_input=query) | null |
22,253 | from langchain.prompts import PromptTemplate
from langchain.chains import LLMMathChain
from server.agent import model_container
from pydantic import BaseModel, Field
PROMPT = PromptTemplate(
input_variables=["question"],
template=_PROMPT_TEMPLATE,
)
def calculate(query: str):
model = model_container.MODEL
... | null |
22,254 | from server.chat.knowledge_base_chat import knowledge_base_chat
from configs import VECTOR_SEARCH_TOP_K, SCORE_THRESHOLD, MAX_TOKENS
import json
import asyncio
from server.agent import model_container
async def search_knowledge_base_iter(database: str, query: str) -> str:
response = await knowledge_base_chat(query=... | null |
22,255 | from pydantic import BaseModel, Field
import requests
from configs.kb_config import SENIVERSE_API_KEY
def weather(location: str, api_key: str):
url = f"https://api.seniverse.com/v3/weather/now.json?key={api_key}&location={location}&language=zh-Hans&unit=c"
response = requests.get(url)
if response.status_cod... | null |
22,256 | import BaseModel, Field
from langchain.tools.arxiv.tool import ArxivQueryRun
def arxiv(query: str):
tool = ArxivQueryRun()
return tool.run(tool_input=query) | null |
22,257 | from __future__ import annotations
import json
import re
import warnings
from typing import Dict
from langchain.callbacks.manager import AsyncCallbackManagerForChainRun, CallbackManagerForChainRun
from langchain.chains.llm import LLMChain
from langchain.pydantic_v1 import Extra, root_validator
from langchain.schema imp... | null |
22,258 | from __future__ import annotations
import json
import re
import warnings
from typing import Dict
from langchain.callbacks.manager import AsyncCallbackManagerForChainRun, CallbackManagerForChainRun
from langchain.chains.llm import LLMChain
from langchain.pydantic_v1 import Extra, root_validator
from langchain.schema imp... | null |
22,259 | from __future__ import annotations
import re
import warnings
from typing import Dict
from langchain.callbacks.manager import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
)
from langchain.chains.llm import LLMChain
from langchain.pydantic_v1 import Extra, root_validator
from langchain.schema im... | null |
22,260 | from __future__ import annotations
import re
import warnings
from typing import Dict
from langchain.callbacks.manager import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
)
from langchain.chains.llm import LLMChain
from langchain.pydantic_v1 import Extra, root_validator
from langchain.schema im... | null |
22,261 | from server.db.models.knowledge_metadata_model import SummaryChunkModel
from server.db.session import with_session
from typing import List, Dict
def list_summary_from_db(session,
kb_name: str,
metadata: Dict = {},
) -> List[Dict]:
'''
列出... | 删除知识库chunk summary，并返回被删除的Dchunk summary。 返回形式：[{"id": str, "summary_context": str, "doc_ids": str}, ...] |
22,262 | from server.db.models.knowledge_metadata_model import SummaryChunkModel
from server.db.session import with_session
from typing import List, Dict
class SummaryChunkModel(Base):
"""
chunk summary模型，用于存储file_doc中每个doc_id的chunk 片段，
数据来源:
用户输入: 用户上传文件，可填写文件的描述，生成的file_doc中的doc_id，存入summary_chunk中
... | 将总结信息添加到数据库。 summary_infos形式：[{"summary_context": str, "doc_ids": str}, ...] |
22,263 | from server.db.models.knowledge_metadata_model import SummaryChunkModel
from server.db.session import with_session
from typing import List, Dict
class SummaryChunkModel(Base):
"""
chunk summary模型，用于存储file_doc中每个doc_id的chunk 片段，
数据来源:
用户输入: 用户上传文件，可填写文件的描述，生成的file_doc中的doc_id，存入summary_chunk中
... | null |
22,264 | from server.db.models.knowledge_base_model import KnowledgeBaseModel
from server.db.session import with_session
class KnowledgeBaseModel(Base):
"""
知识库模型
"""
__tablename__ = 'knowledge_base'
id = Column(Integer, primary_key=True, autoincrement=True, comment='知识库ID')
kb_name = Column(String(50),... | null |
22,265 | from server.db.models.knowledge_base_model import KnowledgeBaseModel
from server.db.session import with_session
class KnowledgeBaseModel(Base):
"""
知识库模型
"""
__tablename__ = 'knowledge_base'
id = Column(Integer, primary_key=True, autoincrement=True, comment='知识库ID')
kb_name = Column(String(50),... | null |
22,266 | from server.db.models.knowledge_base_model import KnowledgeBaseModel
from server.db.session import with_session
class KnowledgeBaseModel(Base):
"""
知识库模型
"""
__tablename__ = 'knowledge_base'
id = Column(Integer, primary_key=True, autoincrement=True, comment='知识库ID')
kb_name = Column(String(50),... | null |
22,267 | from server.db.models.knowledge_base_model import KnowledgeBaseModel
from server.db.session import with_session
class KnowledgeBaseModel(Base):
"""
知识库模型
"""
__tablename__ = 'knowledge_base'
id = Column(Integer, primary_key=True, autoincrement=True, comment='知识库ID')
kb_name = Column(String(50),... | null |
22,268 | from server.db.session import with_session
from typing import Dict, List
import uuid
from server.db.models.message_model import MessageModel
class MessageModel(Base):
"""
聊天记录模型
"""
__tablename__ = 'message'
id = Column(String(32), primary_key=True, comment='聊天记录ID')
conversation_id = Column(St... | 新增聊天记录 |
22,269 | from server.db.session import with_session
from typing import Dict, List
import uuid
from server.db.models.message_model import MessageModel
def get_message_by_id(session, message_id) -> MessageModel:
"""
查询聊天记录
"""
m = session.query(MessageModel).filter_by(id=message_id).first()
return m
The provi... | 更新已有的聊天记录 |
22,270 | from server.db.session import with_session
from typing import Dict, List
import uuid
from server.db.models.message_model import MessageModel
class MessageModel(Base):
"""
聊天记录模型
"""
__tablename__ = 'message'
id = Column(String(32), primary_key=True, comment='聊天记录ID')
conversation_id = Column(St... | 反馈聊天记录 |
22,271 | from server.db.session import with_session
from typing import Dict, List
import uuid
from server.db.models.message_model import MessageModel
class MessageModel(Base):
"""
聊天记录模型
"""
__tablename__ = 'message'
id = Column(String(32), primary_key=True, comment='聊天记录ID')
conversation_id = Column(St... | null |
22,272 | from server.db.models.knowledge_base_model import KnowledgeBaseModel
from server.db.models.knowledge_file_model import KnowledgeFileModel, FileDocModel
from server.db.session import with_session
from server.knowledge_base.utils import KnowledgeFile
from typing import List, Dict
class FileDocModel(Base):
"""
文件... | 列出某知识库某文件对应的所有Document的id。 返回形式：[str, ...] |
22,273 | from server.db.models.knowledge_base_model import KnowledgeBaseModel
from server.db.models.knowledge_file_model import KnowledgeFileModel, FileDocModel
from server.db.session import with_session
from server.knowledge_base.utils import KnowledgeFile
from typing import List, Dict
class KnowledgeFileModel(Base):
"""
... | null |
22,274 | from server.db.models.knowledge_base_model import KnowledgeBaseModel
from server.db.models.knowledge_file_model import KnowledgeFileModel, FileDocModel
from server.db.session import with_session
from server.knowledge_base.utils import KnowledgeFile
from typing import List, Dict
class KnowledgeFileModel(Base):
def... | null |
22,275 | from server.db.models.knowledge_base_model import KnowledgeBaseModel
from server.db.models.knowledge_file_model import KnowledgeFileModel, FileDocModel
from server.db.session import with_session
from server.knowledge_base.utils import KnowledgeFile
from typing import List, Dict
def add_docs_to_db(session,
... | null |
22,276 | from server.db.models.knowledge_base_model import KnowledgeBaseModel
from server.db.models.knowledge_file_model import KnowledgeFileModel, FileDocModel
from server.db.session import with_session
from server.knowledge_base.utils import KnowledgeFile
from typing import List, Dict
def delete_docs_from_db(session,
... | null |
22,277 | from server.db.models.knowledge_base_model import KnowledgeBaseModel
from server.db.models.knowledge_file_model import KnowledgeFileModel, FileDocModel
from server.db.session import with_session
from server.knowledge_base.utils import KnowledgeFile
from typing import List, Dict
class KnowledgeBaseModel(Base):
"""
... | null |
22,278 | from server.db.models.knowledge_base_model import KnowledgeBaseModel
from server.db.models.knowledge_file_model import KnowledgeFileModel, FileDocModel
from server.db.session import with_session
from server.knowledge_base.utils import KnowledgeFile
from typing import List, Dict
class KnowledgeFileModel(Base):
"""
... | null |
22,279 | from server.db.session import with_session
import uuid
from server.db.models.conversation_model import ConversationModel
class ConversationModel(Base):
"""
聊天记录模型
"""
__tablename__ = 'conversation'
id = Column(String(32), primary_key=True, comment='对话框ID')
name = Column(String(50), comment='对话框... | 新增聊天记录 |
22,280 | from functools import wraps
from contextlib import contextmanager
from server.db.base import SessionLocal
from sqlalchemy.orm import Session
def session_scope() -> Session:
"""上下文管理器用于自动获取 Session, 避免错误"""
session = SessionLocal()
try:
yield session
session.commit()
except:
sessi... | null |
22,281 | from functools import wraps
from contextlib import contextmanager
from server.db.base import SessionLocal
from sqlalchemy.orm import Session
SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
def get_db() -> SessionLocal:
db = SessionLocal()
try:
yield db
finally:
... | null |
22,282 | from functools import wraps
from contextlib import contextmanager
from server.db.base import SessionLocal
from sqlalchemy.orm import Session
SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
def get_db0() -> SessionLocal:
db = SessionLocal()
return db | null |
22,283 | from langchain.docstore.document import Document
from configs import EMBEDDING_MODEL, logger
from server.model_workers.base import ApiEmbeddingsParams
from server.utils import BaseResponse, get_model_worker_config, list_embed_models, list_online_embed_models
from fastapi import Body
from fastapi.concurrency import run_... | 对文本进行向量化。返回数据格式：BaseResponse(data=List[List[float]]) |
22,284 | import plistlib
import requests
import logging
logger = logging.getLogger("bags")
OLD_APNS_BAG = None
def apns_init_bag_old():
global OLD_APNS_BAG
if OLD_APNS_BAG is not None:
return OLD_APNS_BAG
r = requests.get("https://init.push.apple.com/bag", verify=False)
if r.status_code != 200:
... | null |
22,285 | import plistlib
import requests
import logging
logger = logging.getLogger("bags")
APNS_BAG = None
def apns_init_bag():
global APNS_BAG
if APNS_BAG is not None:
return APNS_BAG
r = requests.get("http://init-p01st.push.apple.com/bag", verify=False)
if r.status_code != 200:
raise Exc... | null |
22,286 | import plistlib
import requests
import logging
logger = logging.getLogger("bags")
IDS_BAG = None
def ids_bag():
global IDS_BAG
if IDS_BAG is not None:
return IDS_BAG
r = requests.get(
"https://init.ess.apple.com/WebObjects/VCInit.woa/wa/getBag?ix=3", verify=False
)
if r.status... | null |
22,287 | from __future__ import annotations
import random
import socket
import threading
import time
from hashlib import sha1
from base64 import b64encode, b64decode
import logging
logger = logging.getLogger("apns")
import tlslite
if tlslite.__version__ != "0.8.0-alpha43":
logger.warning("tlslite-ng is not the correct versi... | null |
22,288 | from __future__ import annotations
import random
import socket
import threading
import time
from hashlib import sha1
from base64 import b64encode, b64decode
import logging
import tlslite
import albert
import bags
def _serialize_field(id: int, value: bytes) -> bytes:
def _serialize_payload(id: int, fields: list[(int, b... | null |
22,289 | from __future__ import annotations
import random
import socket
import threading
import time
from hashlib import sha1
from base64 import b64encode, b64decode
import logging
import tlslite
import albert
import bags
def _deserialize_field(stream: bytes) -> tuple[int, bytes]:
id = int.from_bytes(stream[:1], "big")
... | null |
22,290 | from __future__ import annotations
import random
import socket
import threading
import time
from hashlib import sha1
from base64 import b64encode, b64decode
import logging
import tlslite
import albert
import bags
def _deserialize_field(stream: bytes) -> tuple[int, bytes]:
id = int.from_bytes(stream[:1], "big")
... | null |
22,291 | from __future__ import annotations
import random
import socket
import threading
import time
from hashlib import sha1
from base64 import b64encode, b64decode
import logging
import tlslite
import albert
import bags
def _get_field(fields: list[tuple[int, bytes]], id: int) -> bytes:
for field_id, value in fields:
... | null |
22,292 | import plistlib
from base64 import b64decode
from typing import Union
import requests
from ._helpers import PROTOCOL_VERSION, USER_AGENT, KeyPair, parse_key, serialize_key
from .signing import add_auth_signature, armour_cert
from io import BytesIO
from cryptography.hazmat.primitives.asymmetric import ec, rsa
import log... | null |
22,293 | import plistlib
import random
import uuid
from base64 import b64decode
import requests
from cryptography import x509
from cryptography.hazmat.backends import default_backend
from cryptography.hazmat.primitives import hashes, serialization
from cryptography.hazmat.primitives.asymmetric import padding, rsa
from cryptogra... | null |
22,294 | import plistlib
import random
import uuid
from base64 import b64decode
import requests
from cryptography import x509
from cryptography.hazmat.backends import default_backend
from cryptography.hazmat.primitives import hashes, serialization
from cryptography.hazmat.primitives.asymmetric import padding, rsa
from cryptogra... | null |
22,295 | import plistlib
import random
import uuid
from base64 import b64decode
import requests
from cryptography import x509
from cryptography.hazmat.backends import default_backend
from cryptography.hazmat.primitives import hashes, serialization
from cryptography.hazmat.primitives.asymmetric import padding, rsa
from cryptogra... | null |
22,296 | from collections import namedtuple
from cryptography.hazmat.primitives import serialization
from cryptography.hazmat.primitives.asymmetric import ec, rsa
def parse_key(key: str):
# Check if it is a public or private key
if "PUBLIC" in key:
return serialization.load_pem_public_key(key.encode())
else... | null |
22,297 | from collections import namedtuple
from cryptography.hazmat.primitives import serialization
from cryptography.hazmat.primitives.asymmetric import ec, rsa
def serialize_key(key):
if isinstance(key, ec.EllipticCurvePrivateKey) or isinstance(key, rsa.RSAPrivateKey):
return key.private_bytes(
encod... | null |
22,298 | import gzip
import plistlib
import random
from base64 import b64encode
import apns
import bags
from ._helpers import KeyPair, PROTOCOL_VERSION
from . import signing
PROTOCOL_VERSION = "1640"
KeyPair = namedtuple("KeyPair", ["key", "cert"])
def lookup(
conn: apns.APNSConnection,
self_uri: str,
id_keypair:... | null |
22,299 | import plistlib
import zlib
from base64 import b64decode, b64encode
from hashlib import sha1
class bcolors:
HEADER = "\033[95m"
OKBLUE = "\033[94m"
OKCYAN = "\033[96m"
OKGREEN = "\033[92m"
WARNING = "\033[93m"
FAIL = "\033[91m"
ENDC = "\033[0m"
BOLD = "\033[1m"
UNDERLINE = "\033[4m"
... | null |
22,300 | import socket
import sys
import threading
import tlslite
cert: str = None
key: str = None
import printer
import apns
def handle(conn: socket.socket):
# Wrap the socket in TLS
s_conn = tlslite.TLSConnection(conn)
global cert, key
chain = tlslite.X509CertChain()
chain.parsePemList(cert)
# print(ch... | null |
22,301 | import hashlib
from . import mparser as macholibre
from .jelly import Jelly
import plistlib
import logging
logger = logging.getLogger("nac")
import struct
import requests, plistlib
def hook_code(uc, address: int, size: int, user_data):
logger.debug(">>> Tracing instruction at 0x%x, instruction size = 0x%x" % (addr... | null |
22,302 | import hashlib
from . import mparser as macholibre
from .jelly import Jelly
import plistlib
import logging
import struct
import requests, plistlib
class Jelly:
# Constants
UC_ARCH = unicorn.UC_ARCH_X86
UC_MODE = unicorn.UC_MODE_64
BINARY_BASE = 0x0
HOOK_BASE = 0xD00000
HOOK_SIZE = 0x1000
... | null |
22,303 | import hashlib
from . import mparser as macholibre
from .jelly import Jelly
import plistlib
import logging
logger = logging.getLogger("nac")
def nac_init(j: Jelly, cert: bytes):
# Allocate memory for the cert
cert_addr = j.malloc(len(cert))
j.uc.mem_write(cert_addr, cert)
# Allocate memory for the outpu... | null |
22,304 | from io import BytesIO
import unicorn
from . import mparser as macholibre
import logging
def round_to_page_size(size: int, page_size: int) -> int:
return (size + page_size - 1) & ~(page_size - 1) | null |
22,305 | from io import BytesIO
import unicorn
from . import mparser as macholibre
import logging
def decodeULEB128(bytes: BytesIO) -> int:
result = 0
shift = 0
while True:
b = bytes.read(1)[0]
result |= (b & 0x7F) << shift
if (b & 0x80) == 0:
break
shift += 7
return ... | null |
22,306 | from io import BytesIO
import unicorn
from . import mparser as macholibre
import logging
def c_string(bytes, start: int = 0) -> str:
out = ''
i = start
while True:
if i > len(bytes) or bytes[i] == 0:
break
out += chr(bytes[i])
#print(start)
#print(chr(bytes[... | null |
22,307 | import json
import logging
import os
import threading
import time
from base64 import b64decode, b64encode
from getpass import getpass
from rich.logging import RichHandler
import apns
import ids
import imessage
def safe_b64decode(s):
try:
return b64decode(s)
except:
return None | null |
22,308 | import json
import logging
import os
import threading
import time
from base64 import b64decode, b64encode
from getpass import getpass
from rich.logging import RichHandler
import apns
import ids
import imessage
INPUT_QUEUE = apns.IncomingQueue()
while True:
msg = im.receive()
if msg is not None:
# print(... | null |
22,309 | import json
import logging
import os
import threading
import time
from base64 import b64decode, b64encode
from getpass import getpass
from rich.logging import RichHandler
import apns
import ids
import imessage
def fixup_handle(handle):
if handle.startswith('tel:+'):
return handle
elif handle.startswith... | null |
22,310 | import plistlib
import re
import uuid
from base64 import b64decode, b64encode
import requests
from cryptography import x509
from cryptography.hazmat.backends import default_backend
from cryptography.hazmat.primitives import hashes, serialization
from cryptography.hazmat.primitives.asymmetric import padding, rsa
from cr... | null |
22,311 | import atexit
import numpy as np
import queue
import torch
import torch.multiprocessing as mp
import slowfast.utils.logging as logging
from slowfast.datasets import cv2_transform
from slowfast.visualization.predictor import Predictor
The provided code snippet includes necessary dependencies for implementing the `draw_... | Draw prediction for the given task. Args: task (TaskInfo object): task object that contain the necessary information for visualization. (e.g. frames, preds) All attributes must lie on CPU devices. video_vis (VideoVisualizer object): the video visualizer object. |
22,312 | import itertools
import numpy as np
import matplotlib.pyplot as plt
import torch
from sklearn.metrics import confusion_matrix
import slowfast.utils.logging as logging
from slowfast.datasets.utils import pack_pathway_output, tensor_normalize
The provided code snippet includes necessary dependencies for implementing the... | Calculate confusion matrix on the provided preds and labels. Args: preds (tensor or lists of tensors): predictions. Each tensor is in in the shape of (n_batch, num_classes). Tensor(s) must be on CPU. labels (tensor or lists of tensors): corresponding labels. Each tensor is in the shape of either (n_batch,) or (n_batch,... |
22,313 | import itertools
import numpy as np
import matplotlib.pyplot as plt
import torch
from sklearn.metrics import confusion_matrix
import slowfast.utils.logging as logging
from slowfast.datasets.utils import pack_pathway_output, tensor_normalize
def pack_pathway_output(cfg, frames):
"""
Prepare output as a list of ... | Normalize and prepare inputs as a list of tensors. Each tensor correspond to a unique pathway. Args: frames (list of array): list of input images (correspond to one clip) in range [0, 255]. cfg (CfgNode): configs. Details can be found in slowfast/config/defaults.py |
22,314 | import itertools
import numpy as np
import matplotlib.pyplot as plt
import torch
from sklearn.metrics import confusion_matrix
import slowfast.utils.logging as logging
from slowfast.datasets.utils import pack_pathway_output, tensor_normalize
The provided code snippet includes necessary dependencies for implementing the... | Return the targeted layer (nn.Module Object) given a hierarchical layer name, separated by /. Args: model (model): model to get layers from. layer_name (str): name of the layer. Returns: prev_module (nn.Module): the layer from the model with `layer_name` name. |
22,315 | import logging as log
import math
import os
import matplotlib.pyplot as plt
import torch
from torch.utils.tensorboard import SummaryWriter
from torchvision.utils import make_grid
import slowfast.utils.logging as logging
import slowfast.visualization.utils as vis_utils
from slowfast.utils.misc import get_class_names
Th... | Calculate and plot confusion matrix to a SummaryWriter. Args: writer (SummaryWriter): the SummaryWriter to write the matrix to. cmtx (ndarray): confusion matrix. num_classes (int): total number of classes. global_step (Optional[int]): current step. subset_ids (list of ints): a list of label indices to keep. class_names... |
22,316 | import logging as log
import math
import os
import matplotlib.pyplot as plt
import torch
from torch.utils.tensorboard import SummaryWriter
from torchvision.utils import make_grid
import slowfast.utils.logging as logging
import slowfast.visualization.utils as vis_utils
from slowfast.utils.misc import get_class_names
Th... | Given all predictions and all true labels, plot histograms of top-k most frequently predicted classes for each true class. Args: writer (SummaryWriter object): a tensorboard SummaryWriter object. cmtx (ndarray): confusion matrix. num_classes (int): total number of classes. k (int): top k to plot histograms. global_step... |
22,317 | import logging as log
import math
import os
import matplotlib.pyplot as plt
import torch
from torch.utils.tensorboard import SummaryWriter
from torchvision.utils import make_grid
import slowfast.utils.logging as logging
import slowfast.visualization.utils as vis_utils
from slowfast.utils.misc import get_class_names
def... | Visualize and add tensors of n-dimentionals to a Tensorboard SummaryWriter. Tensors will be visualized as a 2D grid image. Args: writer (SummaryWriter): Tensorboard SummaryWriter. array (tensor): tensor to visualize. name (str): name of the tensor. nrow (Optional[int]): number of 2D filters in each row in the grid imag... |
22,318 | import numpy as np
import os
import cv2
import torch
import tqdm
import slowfast.utils.checkpoint as cu
import slowfast.utils.logging as logging
from slowfast.datasets.ava_helper import parse_bboxes_file
from slowfast.datasets.cv2_transform import scale, scale_boxes
from slowfast.datasets.utils import get_sequence
from... | Merge data from precomputed and ground-truth boxes dictionaries. Args: pred_dict (dict): a dict which maps from `frame_idx` to a list of `boxes` and `labels`. Each `box` is a list of 4 box coordinates. `labels[i]` is a list of labels for `boxes[i]`. gt_dict (Optional[dict]): a dict which maps from `frame_idx` to a list... |
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