semvec/pypi-packages huggingface dataset:
nonebot-plugin-bilibilicover 0.1.0
通过发送视频 AV 或 BV 号来获取视频封面图
License: MIT
Requires Python: >=3.8.0

# bilibili 封面图查看

nonebot-plugin-bilibilicover

## 功能

通过发送视频 AV 或 BV 号来获取视频封面图

## 安装

### 1.从 github 仓库下载

```url
url:https://github.com/kamuxiy/nonebot-plugin-bilibilicover
```

### 2.通过 pip

```shell script
python install nonebot-plugin-bilibilicover
```

## 开始使用

示例命令：

```
/封面图 av545905283
/封面图 BV1eq4y1j7BQ
```

## 特别感谢

- Mrs4s / go-cqhttp
- nonebot / nonebot2

## 提供建议

[issues](https://github.com/kamuxiy/nonebot-plugin-bilibilicover/issues)
neuralsampler 0.0.16
neural sampler
License: MIT
Keywords: artificial intelligence,generative models,transformers

[![PyPI version](https://badge.fury.io/py/neuralsampler.svg)](https://badge.fury.io/py/neuralsampler)
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1gEUjfsmoinemOb3P6p0UCI3zfYx-T8uI?usp=sharing)

## NeuralSampler - Pytorch

Implementation of Neural Sampler. 

## Install

``` bash
$ pip install neuralsampler
```

or install the latest version by 
``` bash 
pip install -U git+https://JiahaoYao:{password}@github.com/JiahaoYao/neuralsampler.git@main
```

Install the jax (follow the official instruction [here](https://github.com/google/jax#installation))
```bash 
pip install jax jaxlib==0.1.64+[YOUR_CUDA_VERSION] -f https://storage.googleapis.com/jax-releases/jax_releases.html
```
e.g. 
```bash
pip install --upgrade jax jaxlib==0.1.64+cuda101 -f https://storage.googleapis.com/jax-releases/jax_releases.html
```


## Usage

```python
import torch
from neuralsampler import neuralsampler
```

## Run the code 

```bash 
python main.py 
```

## Run the scripts 

```bash 
python scripts/test_job.py 
```


## Demonstrations and tutorials
| Link | Description|
|:----:|:-----|
|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/154kY7PE4dzXERUw_bkm-Vu-W10nhoKXd?usp=sharing)  | Train the neural sampler for the **double well potential** in PyTorch |
|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/17lTrPLTt_0EDXa4hkbHmbAFQEkpRDZnh?usp=sharing) | Train the neural sampler for the **Müller-Brown Model** in PyTorch |
|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1SeXMpILhkJPjXUaesvzEhc3Ke6Zl_zxJ?usp=sharing)  | Train the neural sampler for the **periodic potential** in PyTorch |
|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/120kYYBOVa1i0TD85RjlEkFjaWDxSFUx3?usp=sharing)| Train the neural sampler for the **Ginzburg-Landau potential** in PyTorch |


## Ignore me (random things)
```
this repo is to collect all the random results and reproduce the experiments here 

and then jax 

i will use jax and flax, like shown here: https://github.com/yang-song/score_sde

there are the templates of building the jax neural networks (quite interesting to try this functional programming )
```


## Todo list 
- [x] this library is on the pytorch
- [x] i am also going to prepare the colab notebook 
- [x] the dataset is through the gdown: you can download the dataset from the google drive  
- [ ] plz check the abf-mmd can reproduce the results! I have checked the codes are the same? I guessed the only issue might be just run enough runs! (Sun 09/05/2021 21:06)
- [x] at least lots of things are connected now! 
- [ ] update the colab module, going to download the dataset from the cloud!
xytestlib 1.0.3
不打印顶级测试代码
License: MIT

虽然说我的苦不是很牛逼，但是，我的描述文档很牛逼，会让人觉着我的库也很牛逼

Why Should I Use This?
=======================

**加粗**

*斜体*

``this is my code``

# python setup.py check -r -s 检查rst语法是否出错