semvec/pypi-packages huggingface dataset:
pki-tools 2.0.0
PKI tools for e.g. checking certificate CRL/OCSP revocation
License: MIT
Requires Python: <4.0,>=3.10

[![Python Badge](https://img.shields.io/badge/python-3.10%2B-7393B3.svg?logo=python&logoColor=white)](https://devguide.python.org/versions/)
[![Discord](https://img.shields.io/badge/chat-gray?logo=discord&logoColor=white)](https://discord.gg/x7k6kJC426)
[![Coverage](https://coverage-badge.samuelcolvin.workers.dev/fulder/pki-tools.svg)](https://coverage-badge.samuelcolvin.workers.dev/redirect/fulder/pki-tools)

# PKI Tools
<img src="./docs/img/icon.png" alt="pki-tools logo" style="height: 200px; width:200px;"/>

PKI tools exposes a high level `cryptography` API and wrappers for e.g.:

* Loading certificates from PEM strings/files/cryptography object into
  a [pydantic][pydantic-docs] model including all
  [x509 v3 extensions][ext-draft]
* Checking revocation of certificates using [OCSP][ocsp-draft] with
  [CRL][crl-draft] fallback
* Creating Certs, CSR, CRL easy with pure pydantic objects to e.g. get a
  PEM file

## Install

`pip install pki-tools`

## Docs

Documentation is available
at: [https://pki-tools.fulder.dev](https://pki-tools.fulder.dev)

## Chat

[Discord Chat](https://discord.gg/x7k6kJC426)

## Main features

* [Checking Revocation][revoke-check] - checking revocation status of certificates using OCSP and/or CRL
* [Creating objects][create-objects]
* [Loading objects][load-objects]
* [Saving objects][save-objects]


[pydantic-docs]: https://docs.pydantic.dev/latest/

[ocsp-draft]: https://datatracker.ietf.org/doc/html/rfc5280.html#section-4.2.2.1

[crl-draft]: https://datatracker.ietf.org/doc/html/rfc5280.html#section-4.2.1.13

[ext-draft]: https://datatracker.ietf.org/doc/html/rfc5280.html#section-4.2

[revoke-check]: https://pki-tools.fulder.dev/funcs/check_revocation/#pki_tools.funcs.check_revocation.is_revoked--examples

[create-objects]: https://pki-tools.fulder.dev/#creating-x509-objects

[load-objects]: https://pki-tools.fulder.dev/#loading-x509-objects

[save-objects]: https://pki-tools.fulder.dev/#saving-x509-objects
pytest-mongo-docker 0.0.4
A tiny plugin for pytest which runs MongoDB in Docker
License: MIT
Requires Python: >=3.9

Data directory is mounted to a tmpfs for speed.

# How to use?

```
import pymongo


def test_pymongo(mongo):
    client = pymongo.MongoClient(host=mongo.host, port=mongo.port)

    db = client["test_db"]
    collection = db["test_collection"]

    collection.insert_one({"key": "value"})
    doc = collection.find_one({"key": "value"})

    assert doc["key"] == "value"

    client.close()
```

## v0.0.1 (2026-02-11)

* A first version
bewer 0.1.0a5
Evaluation and analysis framework for automatic speech recognition in Python.
License: MIT
Requires Python: <3.15,>=3.10
Keywords: ASR, NLP, WER, evaluation, speech-recognition


# BeWER

*Beyond Word Error Rate → BeWER (/ˈbiːvər/) 🦫*

<p align="left">
  <img src="https://img.shields.io/badge/python-%203.10%20|%203.11%20|%203.12%20|%203.13%20|%203.14-green" alt="Python Versions">
  <img src="https://codecov.io/gh/corticph/bewer/graph/badge.svg?token=4QBH8TD4T4" alt="Coverage" style="margin-left:5px;">
  <img src="https://img.shields.io/badge/License-MIT-blue.svg" alt="License" style="margin-left:5px;">
</p>

**⚠️ Important:** This project is not production ready and is still in early development. Breaking changes may occur, and backwards compatibility between alpha versions is not guaranteed.

**BeWER is an evaluation and analysis framework for automatic speech recognition in Python.** It defines a transparent YAML-based approach for configuring evaluation pipelines and makes it easy to inspect and analyze individual examples through a web-based interface. The built-in preprocessing pipeline and metrics collection are designed to cover all conventional use cases and then some, while still being fully extensible.




__Contents__ | [Installation](#installation) | [Quickstart](#quickstart) |


<a name="installation">

## Installation

```bash
pip install bewer
```

## Quickstart

**Create a Dataset**

```python
from bewer import Dataset

dataset = Dataset()
```

**Add data**

From a file:
```python
dataset.load_csv(
    "data.csv",
    ref_col="reference",
    hyp_col="hypothesis",
)
```

Or manually:
```python
for ref, hyp in iterator:
    dataset.add(ref=ref, hyp=hyp)
```

**List available metrics**

```python
dataset.metrics.list_metrics()
```

**Compute metrics lazily**

```python
print(f"WER: {dataset.metrics.wer.value:.2%}")
```
nuaa 0.1.0
Word 转 Markdown 本地转换工具
License: MIT
Requires Python: >=3.8
Keywords: word, markdown, converter, docx

# nuaa

Word 转 Markdown 本地转换工具

## 简介

nuaa 是一个基于 PySide6 的图形界面工具，用于将 Word 文档（.docx 格式）批量转换为 Markdown 格式。采用纯本地转换引擎，无需联网，保护您的文档隐私。

## 功能特点

- 🔒 **纯本地转换**：无需上传文档，保护隐私
- 📁 **批量处理**：一次性转换整个目录下的所有 .docx 文件
- 🎨 **Material Design 界面**：现代化的用户界面
- ⚡ **多线程处理**：后台转换，界面不卡顿
- 📊 **实时进度**：显示转换进度和详细日志

## 安装

```bash
pip install nuaa
```

## 使用方法

安装后，在命令行中运行：

```bash
doc2md
```

然后：
1. 点击"浏览"按钮选择包含 Word 文档的目录
2. 点击"开始转换"按钮
3. 等待转换完成，Markdown 文件将生成在原文件同目录下

## 依赖项

- PySide6 >= 6.0.0
- python-docx >= 0.8.11
- qt-material >= 2.14 (可选，用于 Material Design 主题)

## 系统要求

- Python >= 3.8
- 支持 Windows、macOS、Linux

## 许可证

MIT License

## 作者

Your Name

## 贡献

欢迎提交 Issue 和 Pull Request！