semvec/pypi-packages huggingface dataset:
osl-cluster-agent 1.6.0
Cluster API data aggregator
License: MIT
Keywords: armada,hpc

# cluster-agent

# Table of contents

- [Project setup](#project-setup)
  - [Dependencies](#dependencies)
- [Setup parameters](#setup-parameters)
- [Local usage example](#local-usage-exemple)

## Project Setup

### Dependencies

* python3-venv

## Setup parameters

1. Setup dependencies
  You can use whenever dependency manager you want to. Just run the command below (and the ones following) on behalf of the manager you prefer.

  ```bash
  make dependencies
  ```

2. Setup `.env` parameters
  ```bash
  CLUSTER_AGENT_BASE_API_URL="<base-api-url>"
  CLUSTER_AGENT_BASE_SLURMRESTD_URL="<slurmrestd-endpoint>"
  CLUSTER_AGENT_X_SLURM_USER_NAME="<slurmrestd-user-name>"
  CLUSTER_AGENT_SENTRY_DSN="<sentry-dsn-key>"
  CLUSTER_AGENT_AUTH0_DOMAIN="<auth0-domain>"
  CLUSTER_AGENT_AUTH0_AUDIENCE="<auth0-audience>"
  CLUSTER_AGENT_AUTH0_CLIENT_ID="<auth0-app-client-id>"
  CLUSTER_AGENT_AUTH0_CLIENT_SECRET="<auth0-app-client-secret>"
  CLUSTER_AGENT_LDAP_HOST="<hostname-for-ldap>"
  CLUSTER_AGENT_LDAP_DOMAIN="<LDAP-domain>" # Defaults to match LDAP_HOST
  CLUSTER_AGENT_LDAP_USERNAME="<admin-user>"
  CLUSTER_AGENT_LDAP_PASSWORD="<admin-password>"
  ```

  NOTE: `CLUSTER_AGENT_SENTRY_DSN` is optional. If you do not pass it the agent understands Sentry will not be used.

## Local usage example

1. Run app
  ```bash
  agentrun
  ```

**Note**: this command assumes you're inside a virtual environment in which the package is installed.

**NOTE**: beware you should care about having the same user name you're using to run the code in the slurmctld node. For example, if `cluster_agent` will run the `make run` command then the slurmctld node also must have a user called `cluster_agent`.
sliced-prediction 0.0.7
Provides a way to perform sliced inferencing on a directory of images
License: MIT
Requires Python: >=3.8,<4.0

# sliced-prediction
## Installation & Use

```shell
# Install sliced-prediction
> pip install sliced-prediction

> sliced-prediction --help
```

## Example
```shell
sliced-prediction \
--overlap_width_ratio 0 \
--overlap_height_ratio 0 \
--slice_width 1024 \
--slice_height 1024 \
--input_directory ./tests/data \
--output_manifest_file ./tmp/manifest.jsonl \
--model_path ./tmp/best.pt \
--confidence_threshold .2 \
--device cpu
```
mautrix-wechat 0.1.0
A Matrix-Wechat puppeting bridge, based on https://github.com/ChisBread/wechat-box
License: MIT
Requires Python: >=3.8

# mautrix-wechat
A Matrix-Wechat puppeting bridge, based on https://github.com/ChisBread/wechat-box

# Usage

## Run wechat-service

Firstly, you need to have a wechat-service (dockerized wine running Wechat Desktop with injected dll) running, to do that, simply run:
```shell
docker run -d --name wechat-service --rm  \
    -e HOOK_PROC_NAME=WeChat \
    -e HOOK_DLL=auto.dll \
    -e TARGET_AUTO_RESTART="yes" \
    -e INJ_CONDITION="[ \"\`sudo netstat -tunlp | grep 5555\`\" != '' ] && exit 0 ; sleep 5 ; curl 'http://127.0.0.1:8680/hi' 2>/dev/null | grep -P 'code.:0'" \
    -e TARGET_CMD=wechat-start \
    -v "$(pwd)/WeChat Files:/home/app/WeChat Files" \
    -p 8080:8080 -p 5555:5555 -p 5900:5900 \
    --add-host=dldir1.qq.com:127.0.0.1 \
    chisbread/wechat-service:latest
```

And then, go to `localhost:8080/vnc.html` to finish the login step.

## Run mautrix-wechat
```shell
mkdir mautrix-wechat && cd mautrix-wechat
wget https://raw.githubusercontent.com/LeiShi1313/mautrix-wechat/main/mautrix_wechat/example-config.yaml -o config.yaml
docker run -d --name mautrix-wechat  -v $(pwd):/app leishi1313/mautrix-wechat 
docker exec -ti mautrix-wechat python -m mautrix_wechat -g
```

Now copy the generated `registration.yaml` to your homeserver config, 


# TODO:

### WeChat box
- [ ] Auto reconnect

### WeChat -> Matrix
- [x] 接收微信文本消息
- [x] 接收微信图片消息 
- [ ] 接收微信引用消息
  - [ ] 接收链接
  - [x] 就收Quote消息

### Matrix -> WeChat
- [x] 发送文本消息
- [ ] 发送图片信息（可能无法做到）
- [ ] 发送at消息
- [ ] 从matrix端发送DM/群消息（matrix room未创建）

### Docker
- [ ] python not receiving SIGINT
discordpy-antispam 1.0.3
An efficient and simple anti-spam system made for your discord bots.
License: MIT

# Spam Filter
A simple and efficient spam filter built to be used in discord bots made with any fork of [discord.py](https://github.com/Rapptz/discord.py)!

# Installation
<!-- You can use [pip](https://pip.pypa.io/en/stable/) to install this library. -->
```
pip install git+https://github.com/DorianAarno/SpamFilter.git
```

# Usage
### Quickstart Guide
```py
from SpamFilter import AntiSpam

@bot.event
async def on_message(msg):
  spam_check = AntiSpam().check(bot, msg.channel, msg.author)
```

### Detailed Guide
```py
from SpamFilter import AntiSpam

dictionary_check = True # Default is False, DO NOT USE THIS IF YOUR SERVER IS MULTI-LINGUAL, Checks if any word in the message is present in english dictionary.
timer_check = True # Default is True, Checks if a member has sent more than 5 messages within 15 seconds, if yes, 6th message is marked as spam.
content_check = True # Default is True, Checks the message's content and if a letter whose occurance in the content is highest covers more than 85% of the content, it marks the message as spam.
history_check = True # Default is True, Checks if the message's content is duplicate of the previous message.

@bot.event
async def on_message(msg):
  spam_check = AntiSpam(
    dictionary = dictionary_check,
    timer = timer_check,
    content = content_check,
    history = history_check
  ).check(bot, msg.channel, msg.author)
```

### Example
You may view implementation of this library in [PointsBot](https://github.com/DorianAarno/PointsBot).

# Notes
* This library has only been tested with [discord-disnake](https://pypi.org/project/discord-disnake/).
* Your bot needs to have access to message content.

# Contributing
* Fork the repository.
* Add your desired change or filter.
* Open pull request.
* Issues are welcome.
* Consider giving this repository a ⭐, It is highly appreciated!

# License
This repository has been made available via [MIT](https://github.com/DorianAarno/SpamFilter/blob/main/LICENSE) License.
bakery 1.0.0
Tools to help students write code.
License: MIT

Bakery Support Library
======================

A collection of tools to help students write code, meant for the Python Bakery CS1 curriculum.

For now, mostly improved assertions.

Installation
============

Install from PyPi::
    
    pip install bakery

Or install from: https://github.com/python-bakery/bakery-support-library

Examples
========

.. code-block:: python
    
    from bakery import assert_equal
    
    def halve(number):
        return number / 2
    
    # Correctly handles floating points
    assert_equal(halve(10), 5.0)
    
Output
======

This library will print a message to STDOUT if an assertion fails, and returns True/False. It does not raise an exception or print to STDERR.

Supported Types
===============

* Numbers: strictly compares numeric types, but allows floats to have imprecision, defaults to 4 places
* Strings: can strictly compare types with exact_strings=True, but defaults to ignore whitespace on newlines and capitalization
* Lists, Tuples: applies same rules to inner types as container types
* Sets, Frozensets, Dictionary: checks that all elements are contained in both, in any order
* Generators: functions like `enumerate` and `.items()` that produce generators are converted to lists and sets (as appropriate), then checked that their values match.
* Other types should work as well, but require that the result of `type` match, and that `x == y`