semvec/pypi-packages huggingface dataset:
yt-dlp-custom 0.0.1
A youtube-dl fork with additional features and patches
Requires Python: >=3.7

Official repository: <https://github.com/yt-dlp/yt-dlp>

**PS**: Some links in this document will not work since this is a copy of the README.md from Github

<!-- MANPAGE: BEGIN EXCLUDED SECTION -->
<div align="center">

[![YT-DLP](https://raw.githubusercontent.com/yt-dlp/yt-dlp/master/.github/banner.svg)](#readme)

[[original package]](https://github.com/yt-dlp/yt-dlp)
</div>

## Introduce
This package only modified to get hastags and unique filename as post-id.<br>

* First modified
```python
#yt_dlp/YoutubeDL.py
def _get_filename_as_uniqid(self, info_dict):
  post_id_reg = re.compile('(?<=video\/)[0-9]+')
  post_id = post_id_reg.search(info_dict['webpage_url'])[0]
  filename = f"/tmp/{post_id}.{info_dict['ext']}"
  return filename

def prepare_filename(self, info_dict, dir_type='', *, outtmpl=None, warn=False):
  """Generate the output filename"""
  ...
  ...
  
  filename = self._get_filename_as_uniqid(info_dict)

```

* Second modified
```python
# yt_dlp/extractor/tiktok.py
def _parse_aweme_video_app(self, aweme_detail):
        aweme_id = aweme_detail['aweme_id']
        video_info = aweme_detail['video']
        author_name_info = aweme_detail['author']

        hashtags_info = []
        for hashtag_info in aweme_detail['text_extra']:
            if hashtag_info.get('hashtag_name'):
                hashtags_info.append(hashtag_info['hashtag_name'])

        return {
            ...
            ...
            'author': author_name_info,
            'hashtags_info': hashtags_info
        }
```
pyqode.python 4.0.2
pyqode.python is an extension of pyqode.core that brings support
Requires Python: >=3.7
Keywords: CodeEdit,PyQt,editor,widget,qt,Python

.. image:: https://raw.githubusercontent.com/pyQode/pyQode/master/media/pyqode-banner.png


About
-----

This is a fork of PyQode, which is now developed as the editor component for Rapunzel_ and OpenSesame_. The original PyQode repository (<= v2) is no longer maintained.

*pyqode.python* adds **python** support to `pyQode` (code completion,
calltips, ...).

.. _OpenSesame: https://osdoc.cogsci.nl/
.. _Rapunzel: https://rapunzel.cogsci.nl/

Features:
---------

* calltips mode (using `Jedi`)
* code completion provider (using `Jedi`)
* code folding mode
* auto indent mode
* on the fly code checkers (frosted (fork of PyFlakes), PEP8)
* a customisable python specific syntax highlighter
* a pre-configured QPythonCodeEdit (with the corresponding Qt Designer plugin)
* automatic PEP8 formatting

License
-------

pyQode is licensed under the **MIT license**.

Requirements
------------

pyqode.python depends on the following libraries:

- python (>= 3.7)
- pyqode.core
- jedi
- pep8
- frosted
- docutils
- autopep8
pyqode.core 4.0.10
The core package contains the core components needed for writing a pyqode based
Requires Python: >=3.7
Keywords: CodeEdit,PyQt,editor,widget,qt

.. image:: https://raw.githubusercontent.com/pyQode/pyQode/master/media/pyqode-banner.png

About
-----


This is a fork of PyQode, which is now developed as the editor component for Rapunzel_ and OpenSesame_. The original PyQode repository (<= v2) is no longer maintained.

**pyqode.core** is the core framework of the `pyQode` project.

It contains the base classes and a set of extensions (modes/panels/managers)
needed to develop a specialised code editor.

It also provides a basic generic code editor that you can use as a fallback
when there is no specialised editor for a given language.

.. _OpenSesame: https://osdoc.cogsci.nl/
.. _Rapunzel: https://rapunzel.cogsci.nl/


License
-------

pyQode is licensed under the **MIT license**.


Requirements
------------

pyqode.core depends on the following libraries:

- Python 3 (>= 3.7)
- PyQt5 or PyQt6 or PySide
- pygments
- pyqode.qt
- future
- qtawesome (optional)
mulambda 0.0.1
A Serverless Platform for Automated Machine Learning
Requires Python: >=3.11

# ![MuLambda Logo](assets/logo.svg)  MuLambda - A platform for automated machine learning

MuLambda is a serverless platform for automated machine learning, aimed at simplifying the deployment and execution of machine learning models in a serverless environment.
The platform provides a meta representation of machine learning serverless functions that takes model parameters into account, enabling infrastructure transparency for function developers.

## Components

The MuLambda platform is built on top of existing computing middleware and provides the following components:

- *Meta representation of machine learning serverless functions:* MuLambda provides a meta representation of machine learning serverless functions that takes model parameters into account, such as the type of model, the accuracy, or the type of data it operates on.
This enables infrastructure transparency for function developers, keeping the simplicity of the serverless paradigm.
- *Model management infrastructure:* MuLambda includes an emulation layer for Amazon SageMaker, working with local execution tools provided by Amazon.
Multiple storage configurations are analyzed and taken into account, and the utility of interfacing existing storage solutions like Amazon S3 is evaluated.
- *Classification scheme for models:* To enable automatic model selection for functions, MuLambda provides a classification scheme for models, which is used by algorithms of varying complexity to select the most appropriate model for a given task.

## Contributing

Contributions to MuLambda are always welcome! If you find a bug or have a feature request, please open an issue on the GitHub repository.
If you would like to contribute code, please open a pull request.

## Acknowledgments

We would like to thank [netidee](https://www.netidee.at) for providing a stipend for this project.
Their support has made it possible for us to develop MuLambda and explore new directions in serverless machine learning.
We would also like to thank our thesis advisor Dr. Thomas Rausch, for their guidance and feedback throughout the development of this project.
Finally, we would like to thank the open-source community for providing the tools and resources that made this project possible.

## License

MuLambda is licensed under the MIT license. See the LICENSE file for details.