semvec/pypi-packages huggingface dataset:
llmformat 0.0.2.1
Format LLM language by using LALR(1) grammar. Supports JSON, XML, etc.
Requires Python: >=3.8
Keywords: python,llm,format,json,regex

#  LLM Format

![](./assets/llmlogo.jpg)

## Introduction

LLM Format is a Large-language model formatter that constrain the outputs of language model to follow certain rules. Our design is to make this tool flexible and efficient to adapt to different use cases.

We currently supports **vllm** and any **LALR(1) grammar** including **JSON** format.

Different from other packages including lm-format-enforcer, jsonformer and guidance. This package ensures the generated text from LLM to be sound and complete. 


## Tutorial 

### Installation

`pip install llmformat`

### Usage

To enforce a new type of grammar, a grammar file written in EBNF is needed. We provide the JSON example as [here](https://github.com/qiulingxu/llmformat/blob/main/llmformat/grammar_files/json_min.bnf).

Once it is written, we can only need one-line code change to enforce the generation.

In vllm, add this option to sampling_param.

sampling_param.logits_processors=[llmformat.llminterface.build_vllm_logits_processor(model, "/root/llmformat/llmformat/json_min.bnf")]

### Example
The example of working on Llama2 and vllm can be found [here](https://github.com/qiulingxu/llmformat/blob/main/examples/vllm_llama2.ipynb). Note that you may want to change the location of grammar file.


- Add support for customized JSON
- Add more integrations

## Known Issues

We use cache to accelerate grammar parsing. The speed will becomes faster as it runns longer.
rustimport-jupyter 0.1.3
Jupyter and IPython magic for compiling Python extensions written in Rust
Requires Python: >=3.8

# rustimport_jupyter

[![PyPI - Version](https://img.shields.io/pypi/v/rustimport_jupyter.svg)](https://pypi.org/project/rustimport_jupyter)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/rustimport_jupyter.svg)](https://pypi.org/project/rustimport_jupyter)

Jupyter and IPython magic for compiling Python extensions written in Rust. This project builds on top of [rustimport](https://github.com/mityax/rustimport) by adding a IPython magic.

## Installation

0. Install Rust by following Rust's [Getting started Guide](https://www.rust-lang.org/learn/get-started).
1. Install package: `pip install rustimport_jupyter`

## How To Use

`rustimport_jupyter` compiles Python extensions written in Rust in Jupyter notebooks.
We enable the feature by loading the `rustimport_jupyter` extension from within a Jupyter notebook:

```python
%load_ext rustimport_jupyter
```

Next, we prefix a cell with the `%%rustimport` marker to compile it:

```rust
%%rustimport
use pyo3::prelude::*;

#[pyfunction]
fn square(x: i32) -> i32 {
    x * x
}
```

## Notebook Examples

- [NumPy in Rust](https://github.com/thomasjpfan/rustimport_jupyter/blob/main/examples/numpy.ipynb) - Using `rustimport_jupyter` to write NumPy code in Rust! [Run in Google Colab 💻](http://colab.research.google.com/github/thomasjpfan/rustimport_jupyter/blob/main/examples/numpy.ipynb)
- [Running in Google Colab](https://github.com/thomasjpfan/rustimport_jupyter/blob/main/examples/rust_import_colab.ipynb) - For Google Colab, `rustimport_jupyter` and the `rust` toolchain needs to be installed first. [Run in Google Colab 💻](http://colab.research.google.com/github/thomasjpfan/rustimport_jupyter/blob/main/examples/rust_import_colab.ipynb).
- [Polars Expression Plugins](https://github.com/thomasjpfan/rustimport_jupyter/blob/main/examples/rust_import_colab.ipynb) - Use `rustimport_jupyter` to quickly iterate on [Polars expression plugins](https://pola-rs.github.io/polars/user-guide/expressions/plugins/). [Run in Google Colab 💻](http://colab.research.google.com/github/thomasjpfan/rustimport_jupyter/blob/main/examples/polars_expression_plugins.ipynb).

## License

`rustimport_jupyter` is distributed under the terms of the MIT license.
ms-outlook-calendar-utils 0.2.0
Collection of Python utility scripts for manipulating MS Outlook Calendar events.
Requires Python: >=3.6
Keywords: ms_outlook_calendar_utils

=========================
MS Outlook Calendar Utils
=========================






Collection of Python utility scripts for manipulating MS Outlook Calendar events.



Features
--------

* TODO

Credits
-------

This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.

.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage


=======
History
=======

0.1.0 (2023-12-27)
------------------

* First release on PyPI.
GSpreadManager 0.1.5
Un módulo de Python para gestionar y automatizar tareas en Google Sheets.
Requires Python: >=3.7

# GSpreadManager

## Descripción
GSpreadManager es un módulo de Python diseñado para facilitar la interacción con Google Sheets. Permite a los usuarios realizar operaciones comunes como leer, escribir y actualizar hojas de cálculo de Google Sheets de manera eficiente y automatizada.

## Características
- Conexión segura a Google Sheets usando la API de Google.
- Funciones para leer, escribir, y actualizar hojas de cálculo.
- Métodos para buscar y manipular datos dentro de las hojas de cálculo.

## Pre-requisitos

Para utilizar el conector `GSpreadManager`, es necesario realizar una configuración inicial en Google Cloud y en tus hojas de cálculo de Google. Sigue estos pasos:

### 1. Crear una Cuenta de Servicio en Google Cloud
- Accede a [Google Cloud Console](https://console.cloud.google.com/).
- Crea un nuevo proyecto o selecciona uno existente.
- En el menú lateral, ve a "IAM y administración" > "Cuentas de servicio" y crea una nueva cuenta de servicio.
- Descarga la clave de la cuenta de servicio en formato JSON. Este archivo será necesario para autenticar tu aplicación.

### 2. Habilitar las APIs de Google Drive y Google Sheets
- Dentro del mismo proyecto en Google Cloud Console, ve a la sección "Biblioteca" en el menú "APIs y servicios".
- Busca y habilita las APIs de "Google Sheets" y "Google Drive".

### 3. Dar Permiso a la Cuenta de Servicio en las Hojas de Cálculo
- Abre la hoja de cálculo de Google que deseas manipular.
- Comparte la hoja de cálculo con el email de la cuenta de servicio creada, asignándole permisos de edición.
- Este paso es necesario para que la cuenta de servicio pueda acceder y modificar tus hojas de cálculo.

Una vez completados estos pasos, tu entorno estará listo para utilizar `GSpreadManager`.


## Requisitos
- Python 3.7 o superior.
- Paquetes: `gspread`, `oauth2client`.

## Instalación
Para instalar GSpreadManager, sigue estos pasos:

Localmente (desde el código fuente):
```bash
pip install GSpreadManager
```

## Uso Básico
Aquí hay un ejemplo básico de cómo usar GSpreadManager para conectarse a una hoja de cálculo de Google y leer datos:

```python
from gspreadmanager import GoogleSheetConector

# Crear una instancia del conector
conector = GoogleSheetConector(doc_name='NombreDelDocumento', json_google_file='ruta/credenciales.json', sheet_name='NombreDeLaHoja')

# Leer datos
datos = conector.read_sheet_data()
```
## Contribuir
Las contribuciones son bienvenidas. 

## Licencia
MIT License
gmaps-locationshare-api 1.0.2
Fully asynchronous (unofficial) API client for Google Map's location share functionality
Requires Python: >=3.7
Keywords: google,maps,async,client,location,share,location sharing

# Google Maps location share API

A fully asynchronous API client for Google Map's location share functionality.

## Install

You must install the following libraries:
- `aiohttp` (asynchronous HTTP)
- `playwright` (asynchronous webdriver)
- `undetected-playwright-patch` ([Bypass Google's bot detection](https://github.com/kaliiiiiiiiii/undetected-playwright-python))
- `pickle` (session persistence)

It is recommended to install the following libraries:
- `asyncio` (Running asynchronous code)

Run `pip install gmaps-locationshare-api` to install.

## How it works

Playwright obtains a session via a manual log-in attempt by the user.
The client also has the ability to persist a session between different instances by
locally caching session cookies, and session refresh capabilities.

The client then uses said session to call Google's internal location share API to obtain relevant data.

## Documentation

Sample usages are in `main.py`

`class Client(persist: bool = False, persist_directory: str = None, timeout: int = 30000)`
- `persist`: Whether or not the session is locally cached to be used by subsequent invocation of `Client` in the same or a different program instance.
- `persist_directory`: Cache location. Note that the cache location must be consistent for all clients that wishes to use the cached session.
- `timeout`: Max allowed time to login before browser timeouts (in milliseconds)

`Client.refresh_session()`: Refresh session cookies to prevent stale sessions.

`Client.get_data() -> [Person]`: Get current location share data. Each call represent one reading at the current time, it doesn't not contain historical readings.

Relevant type definitions are in `gmaps_locationshare_api/types.py`