semvec/pypi-packages huggingface dataset:
pyromark 0.9.8
Blazingly fast Markdown parser
Requires Python: >=3.10
Keywords: converter, html

# pyromark

[![CI](https://github.com/monosans/pyromark/actions/workflows/ci.yml/badge.svg)](https://github.com/monosans/pyromark/actions/workflows/ci.yml)
[![Downloads](https://static.pepy.tech/badge/pyromark)](https://pepy.tech/project/pyromark)

pyromark (stands for Python Rust Optimized Markdown) is a blazingly fast CommonMark-compliant Markdown parser for Python.

Uses [pulldown-cmark](https://github.com/raphlinus/pulldown-cmark) Rust crate under the hood.

## Installation

```bash
pip install -U pyromark
```

## Documentation

<https://pyromark.readthedocs.io>

## Basic examples

See documentation for more comprehensive examples.

### Convert Markdown to HTML

```python
import pyromark

html = pyromark.html("# Hello world")
assert html == "<h1>Hello world</h1>\n"
```

### Iterating over Markdown elements

```python
import pyromark

for event in pyromark.events("# Hello world"):
    # All event types are fully type annotated
    # so you will get static type checking
    # and Tab completions in your IDE!
    match event:
        case {"Start": {"Heading": {"level": heading_level}}}:
            print(f"Heading with {heading_level} level started")
        case {"Text": text}:
            print(f"Got {text!r} text")
        case {"End": {"Heading": heading_level}}:
            print(f"Heading with {heading_level} level ended")
        case other_event:
            print(f"Got {other_event!r}")
```

## Performance

160x faster than [markdown2](https://pypi.org/project/markdown2/),
130x faster than [Markdown](https://pypi.org/project/Markdown/),
119x faster than [mistletoe](https://pypi.org/project/markdown-it-py/),
103x faster than [markdown-it-py](https://pypi.org/project/markdown-it-py/),
75x faster than [mistune](https://pypi.org/project/mistune/).

If you use threading, the difference with other libraries will be even more enormous, since pyromark releases the [GIL](https://docs.python.org/3/glossary.html#term-global-interpreter-lock).

See [benchmark](https://pyromark.readthedocs.io/en/latest/performance/).

## License

[MIT](https://github.com/monosans/pyromark/blob/main/LICENSE)
insein 1.0.2
The Shapeshifting Network Scanner with Kernel Injection
Requires Python: >=3.6

INSEIN - The Shapeshifting Network Scanner"Logic Beyond Sanity"WARNING: This tool contains advanced stealth mechanics and low-level kernel injection techniques. It is designed for educational purposes and authorized security auditing only.ðŸ‘‘ AUTHOR & CREATORR H A Ashan Imalka (scxr-dev)ðŸ§  ARCHITECTYumi (AI) & Asagi (Visionary)ðŸ“œ LICENSEAll rights reserved to R H A Ashan Imalka.Any usage of this tool's source code for malicious purposes is strictly prohibited.ðŸ©¸ CORE PHILOSOPHY"Why use a door when you can become the wall?"-- scxr-dev
pyspamsum 1.0.8
A Python wrapper for Andrew Tridgell's spamsum algorithm
Requires Python: >=3.10
Keywords: spamsum

# PySpamSum

[![Python Versions](https://img.shields.io/pypi/pyversions/pyspamsum.svg)](https://pypi.python.org/pypi/pyspamsum)
[![PyPI Version](https://img.shields.io/pypi/v/pyspamsum.svg)](https://pypi.python.org/pypi/pyspamsum)
[![Maturity](https://img.shields.io/pypi/status/pyspamsum.svg)](https://pypi.python.org/pypi/pyspamsum)
[![BSD License](https://img.shields.io/pypi/l/pyspamsum.svg)](https://github.com/freakboy3742/pyspamsum/blob/main/LICENSE)
[![Build Status](https://github.com/freakboy3742/pyspamsum/workflows/CI/badge.svg?branch=main)](https://github.com/freakboy3742/pyspamsum/actions)

spamsum is a fuzzy hash specifically designed for hashing email messages
to detect if they are SPAM. The spamsum utility includes the ability to
generate the spamsum hash and check a new message against a existing set
of hashes to find a match.

pyspamsum is a Python wrapper for the core API of spamsum.

The original spamsum code has been licensed under the terms of the
the Perl Artistic License. It has been slightly modified to support
being packaged as a Python package, and to address some C code style
issues.

The original code is Copyright Andrew Tridgell
([tridge@samba.org](mailto:tridge@samba.org)) 2002. It forms part of Andrew's
junkcode, and is available:

* [on Github](https://github.com/tridge/junkcode/tree/master/spamsum)
* [On Andrew Tridgell's website](https://www.samba.org/ftp/unpacked/junkcode/spamsum/)

For details on spamsum itself, please see the README in the spamsum repo.

## Installation

At a prompt, run:

    $ python setup.py install

## Usage

Once installed, here are some examples of usage:

    # Import spamsum and set up some strings
    >>> import spamsum
    >>> s1 = "I am the very model of a modern Major-General, I've information animal and vegetable and mineral"
    >>> s2 = "I am the very model of a modern Brigadier, I've information animal and vegetable and something else"
    >>> s3 = "Huh? Gilbert and Who?"

    # Evaluate the edit distance between two strings
    >>> spamsum.edit_distance(s1, s2)
    27

    # Evaluate the spamsum of some strings
    >>> sum1 = spamsum.spamsum(s1)
    >>> sum2 = spamsum.spamsum(s2)
    >>> sum3 = spamsum.spamsum(s3)
    >>> print(sum1)
    3:kEvyc/sFIKwYclQY4MKLFE4Igu0uLzIKygn:kE6Ai3KQ/MKOgDKZn

    # Compare two spamsums. 0 = no match, 100 = perfect match.
    >>> spamsum.match(sum1, sum1)
    100
    >>> spamsum.match(sum1, sum2)
    72
    >>> spamsum.match(sum1, sum3)
    0
dify-user-client 0.3.0
A Python utility library for interacting with Dify as if you were using the UI. Perform any action available through the user interface.
Requires Python: >=3.11

# dify-user-client

A Python utility library for interacting with Dify as if you were using the UI. Perform any action available through the user interface.

## Installation

```bash
pip install dify-user-client
```

## Quick Start

```python
from dify_user_client import DifyClient, Credentials

# Initialize credentials with username and password
credentials = Credentials(username="your_username", password="your_password")

# Create a client with base URL
client = DifyClient(base_url="https://api.dify.ai", credentials=credentials)

# Use the client to interact with Dify API
# Example: Get all applications
applications = client.apps
```

## Features

- **DifyClient**: A client for interacting with the Dify API
- **Credentials**: A class for managing API credentials
- **Comprehensive API Coverage**: Support for all major Dify API endpoints

## Documentation

For more detailed documentation, please visit [the documentation page](https://github.com/ivan-pikulin/dify_user_client).

## Development

### Setup

1. Clone the repository:
   ```bash
   git clone https://github.com/ivan-pikulin/dify_user_client.git
   cd dify_user_client
   ```

2. Create a virtual environment and install dependencies:
   ```bash
   # Using uv (recommended)
   uv venv
   uv pip install -e .
   
   # Using pip
   python -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
   pip install -e .
   ```

### Running Tests

```bash
# Install test dependencies
uv pip install -e ".[dev]"

# Run tests
pytest
```

## License

This project is licensed under the MIT License - see the LICENSE file for details.

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.
refedez_t 0.0.6
Tool for automatizing the deployment of easy federated learning examples
Requires Python: >=3.12

<h1 align="center">ReFedEz</h1>

<p align="center">
  <img src="docs/docs/assets/refedez.png" alt="ReFedEz Logo">
</p>

**ReFedEz** 🚀 is a Python application and library designed to simplify the implementation and deployment of federated learning architectures. It provides a command-line interface (CLI) for deploying servers and clients directly in their target environments, ensuring consistency and reproducibility, and a Python library that seamlessly integrates into your machine learning code, enabling federated learning to work "like magic" with minimal modifications.

Federated learning is a powerful technique for training machine learning models across distributed data sources while maintaining privacy. ReFedEz serves as the "fast.ai of federated learning" – a beginner-friendly framework that prioritizes simplicity and rapid prototyping. It abstracts the underlying complexities, allowing researchers and developers to focus on their ML innovations rather than infrastructure challenges.

## Demo 🎥

Experience ReFedEz in action:

<script src="https://asciinema.org/a/S3k6bw7777EUIV1hscmVSvV7d.js" id="asciicast-S3k6bw7777EUIV1hscmVSvV7d" async="true"></script>

## Features ✨

- **Simplicity**: Deploy federated learning setups with ease, and adapt it with minimal code changes.
- **Multi-Backend Support**: Works with NumPy, PyTorch and TensorFlow.
- **Reproducible**: Bit by bit reproducible, thanks to nix and uv2nix.
- **Multi-Node encrypted by default**: Self-signed TLS certificates for the communication between nodes.

## Documentation 📚

For detailed guides, API reference, and more, visit the [Documentation](https://refedez.udl.cat).

## Contributing 🤝

Contributions are welcome! Please see the documentation for guidelines.

## License 📄

This project is licensed under the [MIT license](LICENSE)