semvec/pypi-packages huggingface dataset:
watch-xfce-xfconf 1.0.5
A command-line tool that can help you to configure XFCE 4 programmatically
Requires Python: >=3.6, <4

# watch-xfce-xfconf - Automate XFCE Configuration!

## Introduction

The command-line tool `watch-xfce-xfconf`, written by
[James Cherti](https://www.jamescherti.com/), allows displaying and watching the
xfconf-query commands of all the XFCE 4 Xfconf settings that are being changed by XFCE
programs like xfce4-settings-manager, thunar, catfish, ristretto, among others.

The `watch-xfce-xfconf` command-line tool is especially useful for users who want to
replicate XFCE 4 settings on several computers.

### What is xfconf-query?

The xfconf-query command-line tool allows retrieving, modifying, and creating XFCE 4
Xfconf settings, such as the desktop background, panel preferences, window decorations,
window manager settings, and more.

### How can xfconf-query commands help automate the configuration of XFCE 4?

By displaying the xfconf-query commands, `watch-xfce-xfconf` allows to easily create a
Shell script that can be used to automate the configuration of XFCE 4, which provides
several benefits:
- It saves time and effort by eliminating the need to manually adjust settings on each
  individual machine,
- It reduces the risk of errors and inconsistencies that may arise from manually
  configuring settings on different machines,
- Finally, it allows focusing on other important tasks rather than spending time
  configuring XFCE 4 manually.

## Installation

The watch-xfce-xfconf command-line tool can be installed locally,
in `~/.local/bin/watch-xfce-xfconf`, using pip:
```console
pip install --user watch-xfce-xfconf
```

## Usage

Run xfce4-settings-manager in the background:
```console
xfce4-settings-manager &
```

After that, execute watch-xfce-xfconf:
```console
~/.local/bin/watch-xfce-xfconf
```

Once you begin modifying XFCE 4 settings using xfce4-settings-manager, `watch-xfce-xfconf`
will automatically display the corresponding xfconf-query commands in the terminal. These
xfconf-query commands can be easily copied and pasted into a Shell script, allowing for
quick and efficient automation of XFCE 4 configuration across multiple machines.

## Features
- Parses XML files that are located in the directory:
  `~/.config/xfce4/xfconf/xfce-perchannel-xml/`,
- Monitors changes in XFCE 4 settings / Xfconf,
- Displays xfconf-query commands,
- Supports the following Xfconf types: uint, int, string, bool, array, double.

## Links
- [watch-xfce-xfconf @PyPI](https://pypi.org/project/watch-xfce-xfconf/)
- [watch-xfce-xfconf @GitHub](https://github.com/jamescherti/watch-xfce-xfconf/)
- [General information about Xfconf](https://docs.xfce.org/xfce/xfconf/start)
hfloader 1.0.2
A package that loads a tokenizer and model from the HuggingFace model hub.
Requires Python: >=3.6

# HFLoader - Hugging Face Model Loader

### This package provides the user with one method that returns both a `tokenizer` and a `model` when loading `HuggingFace Models`, without the need for knowing which `AutoModel` to use.

This package utilizes the `transformers` library to load a tokenizer and model, without having to know the [AutoModel](https://huggingface.co/docs/transformers/model_doc/auto#auto-classes). This means that with one command, you can easily load the tokenizer and model of a given model on [HuggingFace Model Hub](https://huggingface.co/models). This can then be easily fed into the `pipeline` function of `transformers`.

# Installation
The package can be installed with the following command:

    

    pip install hfloader

# How to Use
Here is a bit of code you can reference to see how to use the package.

    import hfloader as hfl
    
    huggingface_model = "cardiffnlp/twitter-roberta-base-sentiment"
    tokenizer, model = hfl.load_model(huggingface_model)
    
    pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, torch_dtype=torch.bfloat16, device_map="auto")

The return of the command `load_model()` is going to be a tokenizer and a model, which can then be used in the pipeline method provided by transformers. It does so without the need to know which AutoModel to use, which can prove to be a hassle when trying out different models.

## Requirements
> pip install transformers
## Notes
I don't have plans to upkeep this project unless it necessitates it. I was able to achieve the goal I had set out when developing the package.
hyperer 0.3.0
Injects hyperlinks into terminal commands
Requires Python: >=3.9,<4.0

`hyperer` adds [terminal hyperlinks] to the output of other commands.
For example, `hyperer-rg` runs [ripgrep] and links to the files it finds.

[terminal hyperlinks]: https://gist.github.com/egmontkob/eb114294efbcd5adb1944c9f3cb5feda
[ripgrep]: https://github.com/BurntSushi/ripgrep


Installation
============
Install Python 3.9 or later and run `pip install hyperer` to install the hyperer commands into your system Python.
Alternatively, if you're using the [Nix package manager], depend on the flake.nix in this repo.

[Nix package manager]: https://nixos.org/manual/nix/stable/introduction.html

Commands
========
* hyperer-cargo - wraps [cargo] and links to compilation failures, test failures, and backtraces
* hyperer-rg - wraps [ripgrep] and links to files it finds
* hyperer-ruff - wraps [ruff] and links to failed checks

[cargo]: https://doc.rust-lang.org/cargo/
[ruff]: https://docs.astral.sh/ruff/

Credit
======
The basic idea and `hyperer-rg` comes from [kitty].
[hyperlinked_grep] is kitty's version of `hyperer-rg`.
If you only want ripgrep links and you already have kitty installed, you can run `kitty +kitten hyperlinked_grep` and you don't need to install `hyperer`.
I created this project to have a home for `hyperer-cargo` and to be able to hyperlink ripgrep without installing all of kitty.

[kitty]: https://sw.kovidgoyal.net/kitty/
[hyperlinked_grep]: https://sw.kovidgoyal.net/kitty/kittens/hyperlinked_grep/

Many thanks to the kitty project!
Consider [sponsoring its creator][sponsor Kovid] to help move the terminal forward.

[sponsor Kovid]: https://github.com/sponsors/kovidgoyal