semvec/pypi-packages huggingface dataset:
UnlimitedChatGPTWebAPI 0.1.2
Unlimited ChatGPT WebAPI
License: MIT
Requires Python: >=3.8

## UnlimitedChatGPTWebAPI

Resolve Cloudflare Challenge To use ChatGPT Web API

### Installation

```shell
pip3 install UnlimitedChatGPTWebAPI
```

then you should run `playwright install chromium`

> For windows,  maybe you should use it -> `playwright install-deps chromium`

### Configuration

1. Make sure your network or agent can access `https://chat.openai.com`
2. Please make sure that the VPS RAM is not too small.

### Usage

#### Basic example

```python
import asyncio

from UnlimitedChatGPTWebAPI import ChatSession


async def main():
    async with ChatSession(proxies="socks5://localhost:7890") as session:
        # or use this if you want to use the same session for multiple requests
        # example:
        # session = ChatSession(proxies="socks5://localhost:7890")
        # await session.init_page()
        async with session.fetch(
            method="GET",
            url="/backend-api/models",
            headers={"Authorization": "Bearer xxx"}
        ) as resp:
            print(await resp.json())

if __name__ == "__main__":
    asyncio.run(main())

```

#### Streaming example

```python
import asyncio

from UnlimitedChatGPTWebAPI import ChatSession


async def main():
    async with ChatSession(proxies="socks5://localhost:7890") as session:
        # or use this if you want to use the same session for multiple requests
        # example:
        # session = ChatSession(proxies="socks5://localhost:7890")
        # await session.init_page()
        async with session.fetch(
            method="GET",
            url="/backend-api/models",
            headers={"Authorization": "Bearer xxx"}
        ) as resp:
            data = b""
            async for chunk in resp.iter_chunked():
                data += chunk
            print(data.decode())

if __name__ == "__main__":
    asyncio.run(main())

```
playsoundsimple.py 0.7.0
A simple library for playing sound files
License: MIT
Keywords: playsoundsimple

# playsoundsimple.py
## Description
Library for working with sound files of the format: `MP3`, `WAV`, `OGG`, `MIDI`.

## Installation
```
pip install playsoundsimple.py
```

### About MIDI support
On Windows, the `MIDI` processing package is the default.
This means if you have a MacOS or Linux system you will need to manually install the [FluidSynth](https://github.com/FluidSynth/fluidsynth/wiki/Download) package.

## More
```python
import playsoundsimple as pss

s = pss.Sound("main.wav")

s.play(1)

s.wait()
```

## Author
- Roman Slabicky
    - [Vkontakte](https://vk.com/romanin2)
    - [GitHub](https://github.com/romanin-rf)
panti 0.0.1
Panti
License: MIT
Requires Python: >=3.7,<4.0

# Panti(แผนที่)

[![PyPI](https://img.shields.io/pypi/v/panti.svg)][pypi_]
[![Status](https://img.shields.io/pypi/status/panti.svg)][status]
[![Python Version](https://img.shields.io/pypi/pyversions/panti)][python version]
[![License](https://img.shields.io/pypi/l/panti)][license]

[![Read the documentation at https://panti.readthedocs.io/](https://img.shields.io/readthedocs/panti/latest.svg?label=Read%20the%20Docs)][read the docs]
[![Tests](https://github.com/wasdee/panti/workflows/Tests/badge.svg)][tests]
[![Codecov](https://codecov.io/gh/wasdee/panti/branch/main/graph/badge.svg)][codecov]

[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)][pre-commit]
[![Black](https://img.shields.io/badge/code%20style-black-000000.svg)][black]

[pypi_]: https://pypi.org/project/panti/
[status]: https://pypi.org/project/panti/
[python version]: https://pypi.org/project/panti
[read the docs]: https://panti.readthedocs.io/
[tests]: https://github.com/wasdee/panti/actions?workflow=Tests
[codecov]: https://app.codecov.io/gh/wasdee/panti
[pre-commit]: https://github.com/pre-commit/pre-commit
[black]: https://github.com/psf/black

provide thailand geodata starter and API access to Vallaris Maps

## Features

- TODO

## Requirements

- TODO

## Installation

You can install _Panti_ via [pip] from [PyPI]:

```console
$ pip install panti
```

## Usage

Please see the [Command-line Reference] for details.

## Contributing

Contributions are very welcome.
To learn more, see the [Contributor Guide].

## License

Distributed under the terms of the [MIT license][license],
_Panti_ is free and open source software.

## Issues

If you encounter any problems,
please [file an issue] along with a detailed description.

## Credits

This project was generated from [@cjolowicz]'s [Hypermodern Python Cookiecutter] template.

[@cjolowicz]: https://github.com/cjolowicz
[pypi]: https://pypi.org/
[hypermodern python cookiecutter]: https://github.com/cjolowicz/cookiecutter-hypermodern-python
[file an issue]: https://github.com/wasdee/panti/issues
[pip]: https://pip.pypa.io/

<!-- github-only -->

[license]: https://github.com/wasdee/panti/blob/main/LICENSE
[contributor guide]: https://github.com/wasdee/panti/blob/main/CONTRIBUTING.md
[command-line reference]: https://panti.readthedocs.io/en/latest/usage.html
pac-bayes-toolbox 0.1.0
A toolbox algorithm for PAC-Bayesian theory
License: MIT
Requires Python: >=3.9,<3.12

# pac_bayes_toolbox

A toolbox algorithm for PAC-Bayesian theory

## Installation

```bash
$ pip install pac_bayes_toolbox
```

## Usage

- TODO

## Contributing

Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

## License

`pac_bayes_toolbox` was created by Mehdi Hennequin. It is licensed under the terms of the MIT license.

## Credits
binvox 0.1.6
Library for loading & saving the Binvox files
License: MIT
Keywords: Binvox

binvox
=======

Binvox is a file-format developed by Patrick Min that helps to store
the 3D binary-volume (e.g. occupancy grid) in compact manner. 
This repository contains a library for loading &amp; saving the Binvox files.

A popular script to load/save binvox files has been published
at `here <https://github.com/dimatura/binvox-rw-py>`_. However, the code is
almost 10 years old now and every project duplicated the script file
when using it.

This project is based on the available code-base. The main aim is to
re-implement binvox file I/O as a library while taking OOP structure and
easy distribution (packaging) into account.

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

Pip can be used to install the library:

``pip install binvox``

Understanding the binvox file-format
====================================

Information regarding the format can be found here: `patrickmin.com/binvox <https://www.patrickmin.com/binvox/>`_
einsumt 0.9.4
Multithreaded version of numpy.einsum function
License: MIT
Requires Python: >=2.7
Keywords: numpy,einsum,hpc

# einsumt
Multithreaded version of numpy.einsum function.

# Reasoning
Numpy's einsum is a fantastic function which allows for sophisticated array operations with a single, clear line of code. However, this function in general does not benefit from the underlaying multicore architecture and all operations are performed on a single CPU.

The idea is then to split the einsum input operands along the chosen subscript, perform computation in threads and then compose the final result by summation (if subscript is not present in output) or concatenation of partial results.

# Usage
This function can be used as a replacement for numpy's einsum:

    from einsumt import einsumt as einsum
    result = einsum(*operands, **kwargs)

In current implementation first operand *must* be a subscripts string. Other differences will be treated as unintended bugs.

# Benchmarking
In order to test, if `einsumt` would be beneficial in your particular case please run the benchmark, e.g.:

    import numpy as np
    from einsumt import bench_einsumt

    bench_einsumt('aijk,bkl->ail',
                  np.random.rand(100, 100, 10, 10),
                  np.random.rand(50, 10, 50))
and the result is:

    Platform:           Linux
    CPU type:           Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz
    Subscripts:         aijk,bkl->ail
    Shapes of operands: (100, 100, 10, 10), (50, 10, 50)
    Leading index:      automatic
    Pool type:          default
    Number of threads:  12
    Execution time:
        np.einsum:      2755 ms  (average from 1 runs)
        einsumt:        507.9 ms  (average from 5 runs)
    Speed up:           5.424x
More exemplary benchmark calls are contained in bench_einsum.py file.

# Disclaimer
Before you start to blame me because of little or no speedups please keep in mind that threading costs additional time (because of splitting and joining data for example), so `einsumt` function would become beneficial for larger arrays only. Note also that in many cases numpy's einsum can be efficiently replaced with combination of optimized dots, tensordots, matmuls, transpositions and so on, instead of `einsumt` (at cost of code clarity of course).