semvec/pypi-packages huggingface dataset:
boto3-pydantic-rbin 1.0.2
Pydantic models for AWS Rbin
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, rbin

# boto3-pydantic-rbin

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Rbin.
boto3-pydantic-ram 1.0.2
Pydantic models for AWS Ram
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, ram

# boto3-pydantic-ram

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Ram.
boto3-pydantic-quicksight 1.0.2
Pydantic models for AWS Quicksight
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, quicksight

# boto3-pydantic-quicksight

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Quicksight.
boto3-pydantic-qldb-session 1.0.2
Pydantic models for AWS Qldb_session
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, qldb_session

# boto3-pydantic-qldb_session

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Qldb_session.
boto3-pydantic-qldb 1.0.2
Pydantic models for AWS Qldb
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, qldb

# boto3-pydantic-qldb

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Qldb.
boto3-pydantic-qconnect 1.0.2
Pydantic models for AWS Qconnect
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, qconnect

# boto3-pydantic-qconnect

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Qconnect.
boto3-pydantic-qbusiness 1.0.2
Pydantic models for AWS Qbusiness
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, qbusiness

# boto3-pydantic-qbusiness

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Qbusiness.
boto3-pydantic-qapps 1.0.2
Pydantic models for AWS Qapps
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, qapps

# boto3-pydantic-qapps

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Qapps.
boto3-pydantic-proton 1.0.2
Pydantic models for AWS Proton
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, proton

# boto3-pydantic-proton

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Proton.
boto3-pydantic-privatenetworks 1.0.2
Pydantic models for AWS Privatenetworks
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, privatenetworks

# boto3-pydantic-privatenetworks

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Privatenetworks.
boto3-pydantic-pricing 1.0.2
Pydantic models for AWS Pricing
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, pricing

# boto3-pydantic-pricing

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Pricing.
boto3-pydantic-polly 1.0.2
Pydantic models for AWS Polly
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, polly

# boto3-pydantic-polly

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Polly.
boto3-pydantic-pipes 1.0.2
Pydantic models for AWS Pipes
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, pipes

# boto3-pydantic-pipes

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Pipes.
boto3-pydantic-pinpoint-sms-voice-v2 1.0.2
Pydantic models for AWS Pinpoint_sms_voice_v2
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, pinpoint_sms_voice_v2

# boto3-pydantic-pinpoint_sms_voice_v2

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Pinpoint_sms_voice_v2.
qbittorrent-mcp 0.1.0
QBittorrent MCP Server
Requires Python: >=3.11

# QBittorrent MCP

## 概述

本项目提供了一个通过MCP协议与QBittorrent WebUI进行交互的Python客户端，可以方便地管理QBittorrent中的种子。

## 安装

1. 确保Python版本 >= 3.7
2. 克隆本仓库
3. 安装依赖：
```bash
pip install -r requirements.txt
```

## 配置

在`qbittorrent-mcp.py`中配置QBittorrent WebUI连接信息：
- 主机地址
- 端口号
- 用户名
- 密码

## 功能

- 连接QBittorrent WebUI
- 获取种子列表
- 暂停/恢复种子
- 删除种子
- 添加磁力链接

## 使用

```python
# 连接QBittorrent
await connect(host, port, username, password)

# 获取种子列表
torrents = await list_torrents()

# 暂停种子
await pause_torrent(torrent_hash)

# 恢复种子
await resume_torrent(torrent_hash)

# 删除种子
await delete_torrent(torrent_hash)

# 添加磁力链接
await add_magnet(magnet_url)
```

## 依赖

- httpx
- mcp

## 许可证

MIT License
libdiva 0.1.3
libdiva - a library for manitpulating files specific to the Project Diva series
Requires Python: >=3.8
Keywords: DLT, library, python, Project Diva

# libdiva

a Project Diva format library and command-line tool

## installation

open a terminal session and install using pip:

`pip install libdiva`

alternatively, clone the repo and install locally:

```
git clone https://github.com/Flipleerr/libdiva
cd libdiva
pip install .
```

libdiva should now be installed. test by running `libdiva --help`.

## supported formats

libdiva supports the following formats:

- `dlt`: format used on the PS3 and Vita onwards, has information about files inside of DLC
- 'DIVAFILE': encryption format used in F 2nd and X
- 'FARC': compression format used in F 2nd and X

## usage examples

`libdiva dlt --write file.dlt --entries "data"`

`libdiva divafile --encrypt file.txt`

`libdiva extract file.farc extracted`

> [!TIP]
> you can also use libdiva like a proper library by entering `import libdiva` in your script.

## to-do

- [ ] FARC support (extract and repack)
- [ ] DLT boilerplate
- [ ] support for other Project Diva formats (`.cpk`, Dreamy Theater and F files, etc.)
- [ ] proper documentation or wiki
- [ ] refactor codebase
- [ ] a verbose option (-v or --verbose)

> [!NOTE]
> feel free to try your hand at any of these issues - any improvements help!

## contributing

all contributions to libdiva are welcome! to start:

1. fork the repo
2. clone it using `git clone https://github.com/USERNAME/libdiva (REPLACE 'USERNAME' WITH YOUR OWN!)`
3. create a new branch in the repo
4. make your changes

then submit a pull request!

> [!IMPORTANT]
> keep in mind that **GitHub Actions will automatically lint your code** when creating a pull request. if pylint fails, you'll be asked to fix the issues before merging. you can run pylint locally to check before pushing.
boto3-pydantic-pinpoint-sms-voice 1.0.2
Pydantic models for AWS Pinpoint_sms_voice
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, pinpoint_sms_voice

# boto3-pydantic-pinpoint_sms_voice

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Pinpoint_sms_voice.
pypitester 0.1.2
A simple Python package for PyPI testing.

# pypitester

A simple Python package for testing PyPI publishing.

## Installation

```bash
pip install pypitester
```

## Usage

### Command Line

```bash
# Show version
pypitester --version

# Greet someone
pypitester --name "Your Name"
```

### Python API

```python
from pypitester import utils

# Greet someone
utils.greet("Your Name")

# Add two numbers
result = utils.add(1, 2)
print(result)  # 3
```

## License

MIT
boto3-pydantic-pinpoint-email 1.0.2
Pydantic models for AWS Pinpoint_email
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, pinpoint_email

# boto3-pydantic-pinpoint_email

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Pinpoint_email.
kevins-torch 0.2.2
A collection of PyTorch utilities for image AI training
Requires Python: >=3.8

# Kevin's Torch Utils

[![PyPI version](https://badge.fury.io/py/kevins_torch.svg)](https://badge.fury.io/py/kevins_torch)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

這是一個用於圖像 AI 訓練的 PyTorch 工具組 (`kevins_torch`)，旨在提供一系列實用的功能來簡化深度學習模型的開發、訓練和評估過程。

## 主要功能模組

*   **`models`**:
    *   `base.py`: 提供模型的基本結構。
    *   `cnn.py`: 包含通用的卷積神經網路實現。
    *   `lenet.py`: LeNet 模型的實現。
    *   `pretrained.py`: 處理和載入預訓練模型的工具。
    *   `utils/activation_function_parser.py`: 解析活化函數配置。
*   **`utils`**:
    *   `load_parameters.py`: 從設定檔載入模型和訓練參數。
    *   `lightning_models.py`: 與 PyTorch Lightning 整合的相關工具。
    *   `logger_config.py`: 設定日誌記錄器。
    *   `repeat_channels.py`: 用於調整輸入圖像通道數的工具。
    *   `dataset/coffee_bean_dataset.py`: 針對特定咖啡豆資料集的處理。
*   **`examples`**:
    *   `settings.yaml`: 範例設定檔。
    *   `train_configs_generator.py`: 產生訓練設定檔的腳本。
*   **`tests`**:
    *   包含對各個模組的單元測試。

## 安裝

您可以透過 pip 安裝此套件：

```bash
pip install kevins_torch
```

## 使用範例

```python
import torch
from kevins_torch import models
from kevins_torch.utils import load_parameters

# 載入設定檔中的參數
config_path = 'examples/settings.yaml' # 假設您的設定檔路徑
params = load_parameters(config_path)

# 根據設定檔建立模型
# (假設您的設定檔中有模型定義)
# model = models.build_model(params['model_config']) # 實際函數可能不同

# 或者直接使用預定義的模型
model = models.LeNet(num_classes=10)

# 準備輸入數據 (範例)
dummy_input = torch.randn(1, 1, 28, 28) # LeNet 通常用於 MNIST (1x28x28)

# 進行預測
output = model(dummy_input)
print("模型輸出:", output.shape)
```

## 開發設定

如果您想為此專案貢獻，請先設定開發環境：

1.  複製儲存庫：
    ```bash
    git clone https://github.com/yourusername/kevins_torch # 請替換成實際的儲存庫 URL
    cd kevins_torch
    ```
2.  安裝開發依賴：
    ```bash
    pip install -r dev-requirements.txt
    ```
3.  設定 pre-commit hooks (建議)：
    ```bash
    pre-commit install
    ```

## 執行測試

使用 pytest 執行測試：

```bash
pytest tests/
```

## 貢獻

歡迎透過 Pull Requests 或 Issues 提出問題和改進建議！

## 授權

本專案採用 MIT License 授權。
boto3-pydantic-pi 1.0.2
Pydantic models for AWS Pi
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, pi

# boto3-pydantic-pi

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Pi.
boto3-pydantic-personalize-runtime 1.0.2
Pydantic models for AWS Personalize_runtime
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, personalize_runtime

# boto3-pydantic-personalize_runtime

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Personalize_runtime.
palaestrai-mosaik 3.5.7
Mosaik Environment for palaestrAI.
Requires Python: >=3.8

# Adversarial Resilience Learning --- Mosaik Environment

This projects contains the interface between palaestrAI and mosaik, 
the mosaik environment.

## Introduction

This package allows to use worlds created with the co-simulation 
framework mosaik as environment in palaestrAI. The package was 
developed with MIDAS in mind but should work for arbitrary mosaik
worlds. See documenation for more details on how to import a world.

## Installation

palaestrAI-mosaik is written in Python. Use, preferable in a
virtual environment:

```bash
pip install .
```

or, for development including Midas:

```bash
pip install -e .[dev,midas]
```

Alternatively, you can install it from pypi:

```bash
pip install palaestrai-mosaik
```

## Usage

Under tests, you find the `example_experiment_midas.yml` that should be
passed to the palaestrai command line interface::

```bash
palaestrai experiment-start /path/to/tests/example_experiment_midas.yml
```
boto3-pydantic-personalize-events 1.0.2
Pydantic models for AWS Personalize_events
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, personalize_events

# boto3-pydantic-personalize_events

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Personalize_events.