semvec/pypi-packages huggingface dataset:
subprocess-pipe 0.11
Pipe commands with subprocess
License: MIT
Keywords: subprocess,pipe


# Pipe commands with subprocess
### Update 15.11.

added: pipe_commands_shelltrue / commands can be passed as strings
```python
print(pipe_commands_shelltrue('ls',"grep .*py$","grep .*_.*").stdout.decode('utf-8', 'ignore'))
```

```python
$pip install subprocess-pipe
from subprocess_pipe import pipe_commands
pa = pipe_commands(["ls"], ["grep", r".*py$"], ["grep", r"^w"])
print(pa.stdout.decode('utf-8'))

#output:
win_debugger.py
windowcapture.py

```
sphinx-tsegsearch 1.2
Sphinx extension to split searchword with TinySegmenter
License: MIT
Keywords: sphinx,japanese,word,segmentation,search

sphinx-tsegsearch
===================

A Sphinx extension for tokenize japanese query word with TinySegmenter.js

This extension tweaks searchtools.js of sphinx-generated html document
to tokenize Japanese composite words.

Since Japanese is an agglutinative language, query word for document search
usually becomes composite form like 'システム標準' (system standard).
This makes difficult to search pages containing phrase such as
'システムの標準', '標準システム', because TinySegmenter.py (Sphinx's default
Japanese index tokenizer) tokenizes 'システム' and '標準' as indexes.

sphinx-tsegsearch patches searchtools.js to override query tokinization
step so that query input is re-tokenized by TinySegmenter.js (original
JavaScript implementation of TinySegmenter).
As a result, roughly say, this tiny hack improves recall of Japanese
document search in exchange of precision.

Usage:

#. Add 'sphinx_tsegsearch' in conf.extensions
#. Rebuild document.
vqvae 1.0.2
PyTorch implementation of VQ-VAE
License: MIT
Requires Python: <3.10,>=3.7
Keywords: pytorch,vq-vae,vqvae,vector-quantized-vae,vector-quantized-autoencoder

# Pytorch VQVAE implementation

## Example

```python
from vqvae import VQVAE, sequential_encoder, sequential_decoder
from torch.optim import  Adam
from functools import partial

input_channels = 3
output_channels = 3
embedding_length = 256
hidden_channels = 64
beta = 0.25
embedding_size = 512
opt = partial(Adam, lr=2e-4)

encoder = sequential_encoder(input_channels, embedding_size, hidden_channels)  # Encoder from the paper
decoder = sequential_decoder(embedding_size, output_channels, hidden_channels)  # Decoder from the paper
vqvae = VQVAE(encoder, decoder, opt, beta, embedding_length, embedding_size)  # Pytorch-Lightning module, 
                                                                              # hence usable to train the model

```