semvec/pypi-packages huggingface dataset:
pylsp-inlay-hints 0.1.0
Experimental support for inlay hints provider for `python-lsp-server`
License: MIT
Requires Python: >=3.8

# pylsp-inlay-hints

Experimental inlay hints plugin for PyLSP.

This is a plugin for [Python LSP Server](https://github.com/python-lsp/python-lsp-server).

## Installation

To use this plugin, you need to install this plugin in the same virtualenv as python-lsp-server itself.

``` bash
pip install pylsp-inlay-hints
```

Then run `python-lsp-server` as usual, the plugin will be auto-discovered by
`python-lsp-server` if you've installed it to the right environment. Refer to
`python-lsp-server` and your IDE/text editor documentation on how to setup
`python-lsp-server`.

## Configuration

In order for inlay hints to work, your LSP implementation needs to respect `inlayHintProvider` capability in `experimental` section of server capabilties.

## Features

This plugin adds the following features to `pylsp`:

* "inlayHintProvider"

## Developing

Install development dependencies with (you might want to create a virtualenv first):

``` bash
git clone https://github.com/mpaliwoda/pylsp-inlay-hints pylsp-inlay-hints
cd pylsp-inlay-hints
pip install -e '.[dev]'
```

### Enabling logging

To enable logging in pylsp:

    pylsp -v --log-file /tmp/pylsp.log

### Enabling tcp mode

Normally, editors communicate with language servers through standard
input/output. Optionally, you can run pylsp in tcp mode if you want to be able
to use the standard input/output, for example so you can use IPython or pudb,
using the --tcp flag:

    pylsp -v --log-file /tmp/pylsp.log --tcp --port 7090

Consult your lsp client documentation on how to connect to tcp-mode language
server, but if it doesn't support connecting to a language server via TCP, then
usually can configure `netcat`/`nc` to be your "language server" that will
proxy requests to a tcp-mode language server:

    nc localhost 7090

TODO: document how to connect to pylsp via pylsp from LSP clients.



## Credits

This package was created with
[Cookiecutter](https://github.com/audreyr/cookiecutter) from 
[python-lsp/cookiecutter-pylsp-plugin](https://github.com/python-lsp/cookiecutter-pylsp-plugin)
project template.
simba-torch 0.0.5
Paper - Pytorch
License: MIT
Requires Python: <4.0,>=3.6
Keywords: artificial intelligence, deep learning, optimizers, Prompt Engineering


# Simba
![graph](graph.png)
A simpler Pytorch + Zeta Implementation of the paper: "SiMBA: Simplified Mamba-based Architecture for Vision and Multivariate Time series"


## install
`$ pip install simba-torch`

## usage
```python

import torch 
from simba_torch.main import Simba

# Forward pass with images
img = torch.randn(1, 3, 224, 224)

# Create model
model = Simba(
    dim = 4,                # Dimension of the transformer
    dropout = 0.1,          # Dropout rate for regularization
    d_state=64,             # Dimension of the transformer state
    d_conv=64,              # Dimension of the convolutional layers
    num_classes=64,         # Number of output classes
    depth=8,                # Number of transformer layers
    patch_size=16,          # Size of the image patches
    image_size=224,         # Size of the input image
    channels=3,             # Number of input channels
    # use_pos_emb=True # If you want
)

# Forward pass
out = model(img)
print(out.shape)

```


# License
MIT

# Todo
- [ ] Add paper link
- [ ] Add citation bibtex
- [ ] cleanup