semvec/pypi-packages huggingface dataset:
uthana 0.1.6
Python wrapper around Uthana's HTTP API
Requires Python: >=3.10

uthana-api
=================

Python wrapper around uthana's http api.

## Install

```bash
pip install uthana
```

## Usage

### Auto-Rig a Character

Upload a GLB or FBX mesh and get back a rigged character:

```python
from uthana import Client

client = Client("your-api-key")

# Upload and auto-rig a mesh file
output = client.create_character("path/to/character.glb")

print(output.character_id)        # character ID for use with other endpoints
print(output.auto_rig_confidence) # confidence score of the auto-rig

# Download the rigged character
data = client.download_character(output.character_id, output_format="glb")
with open("character_rigged.glb", "wb") as f:
    f.write(data)
```

### Text to Motion (v1)

Generate a motion clip from a text prompt:

```python
from uthana import Client

client = Client("your-api-key")

# Generate motion from a text prompt
output = client.create_text_to_motion("vqvae-v1", "a person walking forward")

print(output.character_id)
print(output.motion_id)

# Download as GLB at 30 fps
data = client.download_motion(
    output.character_id,
    output.motion_id,
    output_format="glb",
    fps=30,
)
with open("walking_forward.glb", "wb") as f:
    f.write(data)
```
cmeel-mimalloc 3.2.8
cmeel distribution for mimalloc, a compact general purpose allocator with excellent performance
Requires Python: >=3.9

# CMeel mimalloc

[![PyPI version](https://badge.fury.io/py/cmeel-mimalloc.svg)](https://pypi.org/project/cmeel-mimalloc)
[![Release](https://github.com/cmake-wheel/cmeel-mimalloc/actions/workflows/release.yml/badge.svg)](https://github.com/cmake-wheel/cmeel-mimalloc/actions/workflows/release.yml)
[![Build Status](https://api.cirrus-ci.com/github/cmake-wheel/cmeel-mimalloc.svg)](https://cirrus-ci.com/github/cmake-wheel/cmeel-mimalloc)

[mimalloc](https://github.com/microsoft/mimalloc) distribution for cmeel packages.
aioplatega 0.1.0
Modern and fully asynchronous SDK for the Platega payment API
Requires Python: <3.15,>=3.10
Keywords: aiohttp, api, asyncio, payment, platega, sdk

##########
aioplatega
##########

.. image:: https://img.shields.io/pypi/l/aioplatega.svg?style=flat-square
    :target: https://opensource.org/licenses/MIT
    :alt: MIT License

.. image:: https://img.shields.io/pypi/v/aioplatega.svg?style=flat-square
    :target: https://pypi.python.org/pypi/aioplatega
    :alt: PyPi Package Version

.. image:: https://img.shields.io/pypi/dm/aioplatega.svg?style=flat-square
    :target: https://pypi.python.org/pypi/aioplatega
    :alt: Downloads

.. image:: https://img.shields.io/pypi/pyversions/aioplatega.svg?style=flat-square
    :target: https://pypi.python.org/pypi/aioplatega
    :alt: Supported Python Versions

.. image:: https://img.shields.io/github/actions/workflow/status/DOFER998/aioplatega/tests.yml?branch=main&style=flat-square
    :target: https://github.com/DOFER998/aioplatega/actions
    :alt: Tests

**aioplatega** is a modern and fully asynchronous SDK for the
`Platega payment API <https://platega.io>`_ written in Python 3.10+ using
`asyncio <https://docs.python.org/3/library/asyncio.html>`_ and
`aiohttp <https://github.com/aio-libs/aiohttp>`_.

Documentation: `aioplatega docs <https://DOFER998.github.io/aioplatega/>`_


Features
========

- Fully asynchronous (`asyncio <https://docs.python.org/3/library/asyncio.html>`_)
- Type-safe with `Pydantic v2 <https://docs.pydantic.dev/latest/>`_ models and full type annotations
- Supports `mypy <http://mypy-lang.org/>`_ strict mode
- aiogram-style command pattern for API methods
- Typed exception hierarchy for every HTTP status
- Lazy connection pool with ``aiohttp``


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

.. code-block:: bash

    pip install aioplatega

.. code-block:: bash

    uv add aioplatega


Quick Start
===========

.. code-block:: python

    import asyncio
    from aioplatega import Platega, PaymentMethodInt, PaymentDetails

    async def main():
        async with Platega(merchant_id="your-id", secret="your-secret") as client:
            result = await client.create_transaction(
                payment_method=PaymentMethodInt.SBP_QR,
                payment_details=PaymentDetails(amount=100.0, currency="RUB"),
            )
            print(result.transaction_id, result.status)

    asyncio.run(main())


Community
=========

.. image:: https://img.shields.io/badge/Telegram-Chat-blue.svg?style=flat-square&logo=telegram
    :target: https://t.me/platega_sdk
    :alt: Telegram Chat

- 🇷🇺 `@platega_sdk <https://t.me/platega_sdk>`_
fal 1.69.13
fal is an easy-to-use Serverless Python Framework
Requires Python: >=3.8

[![PyPI](https://img.shields.io/pypi/v/fal.svg?logo=PyPI)](https://pypi.org/project/fal)
[![Tests](https://img.shields.io/github/actions/workflow/status/fal-ai/fal/fal-unit-tests.yml?label=Tests)](https://github.com/fal-ai/fal/actions)

# fal

fal is a serverless Python runtime that lets you run and scale code in the cloud with no infra management.

With fal, you can build pipelines, serve ML models and scale them up to many users. You scale down to 0 when you don't use any resources.

## Quickstart

First, you need to install the `fal` package. You can do so using pip:

```shell
pip install fal
```

Then you need to authenticate:

```shell
fal auth login
```

You can also use fal keys that you can get from [our dashboard](https://fal.ai/dashboard/keys).

Now can use the fal package in your Python scripts as follows:

```py
import fal

@fal.function(
    "virtualenv",
    requirements=["pyjokes"],
)
def tell_joke() -> str:
    import pyjokes

    joke = pyjokes.get_joke()
    return joke

print("Joke from the clouds: ", tell_joke())
```

A new virtual environment will be created by fal in the cloud and the set of requirements that we passed will be installed as soon as this function is called. From that point on, our code will be executed as if it were running locally, and the joke prepared by the pyjokes library will be returned.

## Next steps

If you would like to find out more about the capabilities of fal, check out to the [docs](https://fal.ai/docs). You can learn more about persistent storage, function caches and deploying your functions as API endpoints.

## Contributing

### Installing in editable mode with dev dependencies

```py
pip install -e 'projects/fal[dev]'
pip install -e 'projects/fal_client[dev]'
pip install -e 'projects/isolate_proto[dev]'
```

### Running tests

```py
pytest
```

### Pre-commit

```bash
cd projects/fal
pre-commit install
```

### Commit format

Please follow [conventional commits specification](https://www.conventionalcommits.org/) for descriptions/messages.
phonlab 0.0.48
Python tools for phonetics
Requires Python: >=3.10

=======
phonlab
=======

A collection of python functions for doing phonetics.

==============================
Installation and Documentation
==============================

* The package includes a yaml file that can be used to create a conda environment with the command:

.. code-block:: 

  conda env create --file phonlab_env.yaml

* To install phonlab into an existing environment use pip:

.. code-block:: 

  pip install phonlab

* The phonlab documentation is here:  https://phonlab.readthedocs.io

========
Examples
========

See the `Phonlab Workshop slide deck <https://docs.google.com/presentation/d/1gfwlxLWZaZY7Zth8zP1LvmHAi21qQs6uBAm1Ep7DpYw/edit?usp=sharing>`_ for some background on this package.  There are example jupyter notebooks in the google drive linked in those slides.

See also the `examples` folder in this repository!

============
Contributing
============

We solicit contributions/corrections from the research community.  Here are some steps you can follow to contribute to this project.

#. Open a new issue in the phonetics-projects/phonlab repository on github
#. Use git to Clone the repository
#. Make your changes on your local clone of the repository

    * if you are adding a function, add it to the repository in a separate file, and write a good doc string for it
    * if you are correcting or extending an existing function, modify the existing file

#. If you are adding a function, also do these things:

    * edit the __init__.pyi file to include your file in the package, following the examples in that file.
    * edit the appropriate .rst file in the docs/source folder to include your function in the package documentation.

#. If your change requires a new python package, update the pyproject.toml and phonlab_env.yaml files to include the dependency.
#. Add/Commit your changes (preferably one commit per change rather than a set of unrelated changes in one big commit).
#. Push to your clone of the repository on github
#. Issue a pull request to add your change to the phonetics-projects/phonlab repository

    * the changes will be reviewed before they become part of the published repository
pyrefly 0.53.0
A fast type checker and language server for Python with powerful IDE features
Requires Python: >=3.8
Keywords: typechecker, typechecking

# Pyrefly

Pyrefly is a type checker and language server for Python, which provides
lightning-fast type checking along with IDE features such as code navigation,
semantic highlighting, and code completion. It is available as a
[command-line tool](https://pyrefly.org/en/docs/installation/) and a
[VSCode extension](https://marketplace.visualstudio.com/items?itemName=meta.pyrefly).

See the [Pyrefly website](https://pyrefly.org) for full documentation and how to
add Pyrefly to your editor of choice.

Currently under active development with known issues. Please
[open an issue](https://github.com/facebook/pyrefly/issues/new/choose) if you
find bugs.
statedict2pytree 2.0.1
Converts torch models into PyTrees for Equinox
Requires Python: >=3.11

# statedict2pytree


## Update:

For examples for `statedict2pytree`, check out my other repository [jaxonmodels](https://github.com/Artur-Galstyan/jaxonmodels).

## Docs

Docs can be found [here](https://artur-galstyan.github.io/statedict2pytree/).


## Info

`statedict2pytree` is a powerful tool for converting PyTorch state dictionaries to JAX pytrees, specifically for Equinox


## Installation

```bash
pip install statedict2pytree
```

The goal of this package is to simplify the conversion from PyTorch models into JAX PyTrees (which can be used e.g. in Equinox). The way this works is by putting both models side my side and aligning the weights in the right order. Then, all statedict2pytree is doing, is iterating over both lists and matching the weight matrices.

Usually, if you _declared the fields in the same order as in the PyTorch model_, you don't have to rearrange anything -- but the option is there if you need it.


## Shape Matching? What's that?

Currently, there is no sophisticated shape matching in place. Two matrices are considered "matching" if the product of their shape match. For example:

(8, 1, 1) and (8, ) match, because (8 _ 1 _ 1 = 8)


### Disclaimer

Some of the docstrings and the docs have been written with the help of
Claude.
edgefirst-validator 5.4.0
EdgeFirst Validator
Requires Python: >=3.8

# EdgeFirst Validator

This repository contains the Packer configuration, entrypoint script, and the validation implementation used by EdgeFirst Studio to perform validation of Vision (detection, segmentation, multitask) models with support for Keras, ONNX, TensorRT, and TFLite either on the cloud or on target validation. 

## Overview
- [Changelog](#changelog)
- [Installation](#installation)
- [Usage](#usage)
- [Documentation](#documentation)

## Changelog
- Nov 26, 2025 [v5.0.0]: Merge Ultralytics and Deployment (EdgeFirst) metrics. Computation of deployment metrics @ optimal thresholds. Defined validation stages.
- Nov 14, 2025 [v4.2.0]: Added HAL support (TBR). Added Kinara model support. Distinguish semantic and instance segmentation metrics. 
- Aug 24, 2025 [v4.1.0]: Added ModelPack external decoding. Added Keras support. Added TP vs FP scores chart. Removed +1 padding to the NumPy NMS. 
- June 16, 2025 [v4.0.0]: EdgeFirst Validator (formerly Deep View Validator) - Ultralytics metrics and EdgeFirst Studio integrations.

## Installation

For on target validation, the system may already contain the dependencies needed to run the model such as `tflite_runtime` for TensorFlow Lite. In such cases, it is recommended to first [create a python virtual environment](https://docs.python.org/3/library/venv.html#creating-virtual-environments) with the `--system-site-packages` option enabled.

EdgeFirst-Validator is available in PyPI and can be installed via pip.

```shell
pip install edgefirst-validator
```

Validation of specific models requires these installations which includes the packages needed to run the models, in case the system at present does not contain these packages.

* Keras `pip install edgefirst-validator[keras]`
* ONNX: `pip install edgefirst-validator[onnx]`
* TensorRT: `pip install edgefirst-validator[tensorrt]`
* TFLite: `pip install edgefirst-validator[tflite]`


## Usage

1. As a quickstart, deploy default validation of a YOLOv5 model with COCO128 dataset.

    ```shell
    edgefirst-validator
    ```

2. Deploy validation as a user-managed session in EdgeFirst Studio. First login with EdgeFirst Client.

    ```shell
    edgefirst-client login
    ```

    ```shell
    edgefirst-validator --session-id <validator session ID>
    ```

    Otherwise, you can specify the credentials directly.

    ```shell
    edgefirst-validator --token <token> --session-id <validator session ID>
    ```

    ```shell
    edgefirst-validator --username <username> --password <password> --session-id <Validator Session ID>
    ```

3. Deploy standalone validation.

    ```shell
    edgefirst-validator <model path> <dataset path>
    ```

## Documentation

Run the following commands to generate the documentation.

```shell
cd doc
pip install -r requirements.txt
```

```shell
make clean
make html
```