semvec/pypi-packages huggingface dataset:
ezpermissions 1.0.0
A simple permission manager for Discord bots.
Requires Python: >=3.8

# ezpermissions

A simple and efficient Discord permission checker that helps you validate user permissions effortlessly! 🚀

## ✨ Features

- 🔧 **Quick Setup** – Easily integrate permission checks into your bot.
- 🎛 **Full Permission Coverage** – Supports all Discord permissions.
- ✅ **True_ & False_ Checks** – Validate if a user has or lacks a permission.
- 📦 **Lightweight & Easy to Use** – No complex setup required.

---

## 📌 Installation

### Prerequisites
Make sure you have **Python 3.8+** installed.

```bash
python --version
```

### Install ezpermissions

```bash
pip install ezpermissions
```

---

## 🚀 Usage

To use `ezpermissions` in your bot, import and apply permission checks:

```python
import discord
from discord.ext import commands
from ezpermissions import permissions

bot = commands.Bot(command_prefix="!")

@bot.command()
@permissions.Administrator.True_  # ✅ Requires Administrator permission
async def admin_only(ctx):
    await ctx.send("✅ You have Administrator permission!")

@bot.command()
@permissions.ManageMessages.False_  # ❌ Requires NOT having Manage Messages
async def non_moderators(ctx):
    await ctx.send("✅ You do NOT have Manage Messages permission!")

bot.run("YOUR_BOT_TOKEN")
```

---

## 🔧 Available Permission Checks

`ezpermissions` provides decorators for all Discord permissions:

| Permission | True_ (Has) | False_ (Lacks) |
|------------|------------|----------------|
| Administrator | `@permissions.Administrator.True_` | `@permissions.Administrator.False_` |
| Manage Guild | `@permissions.ManageGuild.True_` | `@permissions.ManageGuild.False_` |
| Manage Roles | `@permissions.ManageRoles.True_` | `@permissions.ManageRoles.False_` |
| Ban Members | `@permissions.BanMembers.True_` | `@permissions.BanMembers.False_` |
| Kick Members | `@permissions.KickMembers.True_` | `@permissions.KickMembers.False_` |
| Send Messages | `@permissions.SendMessages.True_` | `@permissions.SendMessages.False_` |
| Read Message History | `@permissions.ReadMessageHistory.True_` | `@permissions.ReadMessageHistory.False_` |
| And More! | Supports all permissions | 🎉 |

---

## 📁 Project Structure

```
📦 YourBotProject
├── 📂 cogs/             # Stores command files (if using cogs)
│   ├── __init__.py
│   ├── example.py       # Example cog
├── 📜 main.py           # Main bot file
├── 📜 permissions.py    # ezpermissions integration
├── 📜 requirements.txt  # Installed dependencies
```

---

## 🎯 Contributing

We welcome contributions! Feel free to submit a pull request or open an issue.

---

## 📜 License

ezpermissions is open-source and licensed under the **MIT License**.

---

## 📬 Need Help?

- 📖 [Official Discord.py Documentation](https://discordpy.readthedocs.io/en/stable/)
- 🛠️ Open an issue if you run into problems!

Happy coding! 🎩✨
nnue-trainer 0.1.1
A PyTorch Lightning NNUE model for chess engines – train and quantize with ease!

# NNUE Trainer

## Installation

To install the library, use the following command:

```bash
pip install nnue-trainer
```

Additionally, the `nnue-parser` library is required for parsing training data:

```bash
pip install nnue-parser
```


## Usage

A simple example for training an NNUE model:

```python
from nnue_trainer import train_nnue

if __name__ == "__main__":
    train_nnue(
        train_file="train.bin",
        val_file="val.bin",
        epochs=10,
        batch_size=4096,
        device="gpu",
        num_workers=5,
        quantize=True
    )
```

### Parameter Description:

| Parameter      | Description |
|---------------|-------------|
| `train_file`  | Path to the training file in `.bin` format |
| `val_file`    | Path to the validation file in `.bin` format |
| `epochs`      | Number of training iterations |
| `batch_size`  | Batch size for processing |
| `device`      | Choose `"cpu"` or `"gpu"` to specify the device |
| `num_workers` | Number of parallel processes for data loading |
| `quantize`    | If `True`, the model will be quantized |

## Creating `.bin` Training Files

The `.bin` training files can be generated using the following tool:  
[FireFather/sf-nnue-aio](https://github.com/FireFather/sf-nnue-aio/releases/tag/08-01-2022-AIO)

Use the following command:

```bash
gensfen depth 8 loop 100000
```

This command generates SFEN data with a search depth of 8 over 100,000 iterations.

## Parsing the `.jnn` File

After training, the final `.jnn` model file can be parsed using the `nnue-parser` library:

```python
from nnue_parser import parse_nnue

parsed_data = parse_nnue("model.jnn")
```

This allows you to inspect and analyze the trained NNUE model.

## Troubleshooting / Common Errors

### `TypeError: devices selected with CPUAccelerator should be an int > 0.`
**Solution:** Ensure that `devices` in `train_nnue()` is not `None` or `0`. If training on CPU, explicitly set:

```python
train_nnue(..., device="cpu", num_workers=1)
```

### `RuntimeError: CUDA out of memory`
**Solution:** If your GPU memory is insufficient, reduce the `batch_size`:

```python
train_nnue(..., batch_size=1024)
```

## Acknowledgments

A big thank you to the repository [TensorFlowNNUE](https://github.com/DanielUranga/TensorFlowNNUE) for the model.

## License

This project is released under an open-source license. See the `LICENSE` file for more details.

## Release Information

This is the **first and final release** of NNUE Trainer.
appium-guanfang-wendang-fix1 2025.3.25.0
Appium 官方文档
Requires Python: >=3.6

# Appium 官方文档

## 下载

### Docker

```
docker pull apachecn0/appium-guanfang-wendang
docker run -tid -p <port>:80 apachecn0/appium-guanfang-wendang
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install appium-guanfang-wendang
appium-guanfang-wendang <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g appium-guanfang-wendang
appium-guanfang-wendang <port>
# 访问 http://localhost:{port} 查看文档
```
apache-flink-lilun-yu-shizhan-jingjie-2021-ban-fix1 2025.3.25.0
Apache Flink 理论与实战精解 2021 版
Requires Python: >=3.6

# Apache Flink 理论与实战精解 2021 版

## 下载

### Docker

```
docker pull apachecn0/apache-flink-lilun-yu-shizhan-jingjie-2021-ban
docker run -tid -p <port>:80 apachecn0/apache-flink-lilun-yu-shizhan-jingjie-2021-ban
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install apache-flink-lilun-yu-shizhan-jingjie-2021-ban
apache-flink-lilun-yu-shizhan-jingjie-2021-ban <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g apache-flink-lilun-yu-shizhan-jingjie-erlingeryi-ban
apache-flink-lilun-yu-shizhan-jingjie-erlingeryi-ban <port>
# 访问 http://localhost:{port} 查看文档
```
anquan-ke-2020q4-fix1 2025.3.25.0
安全客 2020Q4
Requires Python: >=3.6

# 安全客 2020Q4

## 下载

### Docker

```
docker pull apachecn0/anquan-ke-2020q4
docker run -tid -p <port>:80 apachecn0/anquan-ke-2020q4
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install anquan-ke-2020q4
anquan-ke-2020q4 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g anquan-ke-erlingerlingq4
anquan-ke-erlingerlingq4 <port>
# 访问 http://localhost:{port} 查看文档
```
pc-agent 0.5.10
Pah Construir Agents
Requires Python: >=3.8

# Agent Test Project

This project contains an implementation of an Agent class and associated tests.

## Running Tests

To run the tests:

```bash
pytest
```

To run the tests with coverage:

```bash
pytest --cov=.
```

## Project Structure

- `agent.py`: Contains the Agent class implementation
- `tests/`: Directory containing test files
  - `test_agent.py`: Tests for the Agent class
- `tools/`: Directory for tool implementations 

## Publishing to GitHub Packages

This project is configured to automatically build and publish to GitHub Packages when a new tag is pushed.

To publish a new version:

1. Create a new tag following semantic versioning:
   ```bash
   git tag v0.1.0
   ```

2. Push the tag to GitHub:
   ```bash
   git push origin v0.1.0
   ```

3. The GitHub Actions workflow will automatically build and publish the package.

## Installing from GitHub Packages

To install this package from GitHub Packages:

```bash
pip install --index-url https://maven.pkg.github.com/USERNAME/pc-agent pc-agent
```

Replace `USERNAME` with your GitHub username or organization name.
anquan-ke-2020q2-fix1 2025.3.25.0
安全客 2020Q2
Requires Python: >=3.6

# 安全客 2020Q2

## 下载

### Docker

```
docker pull apachecn0/anquan-ke-2020q2
docker run -tid -p <port>:80 apachecn0/anquan-ke-2020q2
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install anquan-ke-2020q2
anquan-ke-2020q2 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g anquan-ke-erlingerlingq2
anquan-ke-erlingerlingq2 <port>
# 访问 http://localhost:{port} 查看文档
```