semvec/pypi-packages huggingface dataset:
ykgen 1.0.0
⚠️ SECURITY WARNING: This package was auto-generated to prevent dependency confusion attacks
License: MIT
Requires Python: >=3.6
Keywords: security dependency-confusion vulnerability-research vibehat

# ⚠️ Security Warning: ykgen

## 🚨 Dependency Confusion Vulnerability Detected

This package was **automatically created** by VibeHat security research to prevent potential dependency confusion attacks.

### What happened?
- Your code referenced a package called `ykgen` 
- This package didn't exist in the PyPI registry
- An attacker could have published a malicious package with this name
- We published this educational warning package to protect you

### Source
This package was referenced in: `main.py`

### What should you do?
1. **Review your code** - Make sure `ykgen` is the correct package name
2. **Check for typos** - This might be a misspelled legitimate package
3. **Use private indexes** - Consider using private PyPI indexes for internal packages
4. **Contact us** if this is a legitimate internal package name

### Learn More
Visit [https://vibehat.dev/dependency-confusion](https://vibehat.dev/dependency-confusion) to understand dependency confusion vulnerabilities.

### Package Ownership
If you are the rightful owner of this package name, please contact us through our website.

---
*This package was created by VibeHat Security Research to demonstrate and prevent dependency confusion vulnerabilities.*
stackformer 0.0.1
Modular transformer blocks built in PyTorch
License: MIT
Requires Python: >=3.9

## 🧱 Stackformer

**Stackformer** is a modular transformer-building framework written entirely in PyTorch. It is designed primarily for experimentation, providing various transformer blocks such as attention mechanisms, normalization layers, feed-forward networks, and a simple model architecture. The project is a work-in-progress with plans for further enhancements and expansions.

---

## 📖 About Me

My name is **Gurumurthy**, and I am a final-year Bachelor of Engineering student from India. I created this library as my own size project to showcase my skills and knowledge in deep learning and transformer architectures.

I am also interested and free to work with others on different projects for knowledge sharing and building connections.

---

## 🌟 Features

- Multiple attention mechanisms including multi-head, group query, linear, local, and KV cache variants  
- Token embedding via `tiktoken`  
- Absolute and sinusoidal positional embeddings  
- Normalization layers like LayerNorm and RMSNorm  
- Several feed-forward network variants with activations such as ReLU, GELU, SiLU, LeakyReLU, and Sigmoid  
- A simple GPT-style transformer model implementation  

---

## 📁 Project Structure

stackformer/ \
|-- modules/ \
|   |-- tokenizer.py            # Token embedding using tiktoken \
|   |-- position_embedding.py   # Absolute and sinusoidal embeddings \
|   |-- Attention.py            # Attention mechanisms \
|   |-- Normalization.py        # LayerNorm and RMSNorm \
|   |-- Feed_forward.py         # Feed-forward layers with various activations \
|-- models/ \
|   -- GPT_2.py               # GPT-style transformer stack model \
-- trainer.py                 # Training loop and utilities \

---

## 💻 Installation

Clone the repository and install in development mode:

```bash
git clone https://github.com/Gurumurthy30/Stackformer
cd transformers
pip install -e .
```

---

## 🚀 Future Plans
Currently, I am working on improving and optimizing the existing components while fixing known bugs and issues. After stabilizing the current modules, I plan to add more advanced blocks like Mixture of Experts (MoE), mask handling, and other essential transformer components. Eventually, I will expand the library by developing more comprehensive model architectures.
redblacktree 1.0.3
A pure python3 red black tree implementation
License: MIT

Pure python3 implementation of a red black tree can be used as a set or a dictionary or both at the same time. It is tested and stable.
If you need any feature implemented or god forbid find a bug just open an issue on https://github.com/leryss/py-redblacktree 

# Installation

    pip install redblacktree
    
# Examples
```py
>>> from redblacktree import rbtree

# Can initialize using lists, dicts or other iterables
>>> rbtree([1,2,3,4,5,6,7,8])
>>> Depth=4
           ┌───────────────4───────────────┐
   ┌───────2───────┐               ┌───────6───────┐
   1               3               5               7───┐
                                                       8
# You can store values with keys or not or both
>>> rbtree([1,2,(3,'three'),4,5,(6,'six'),7,8])
>>> Depth=4
           ┌───────────────4───────────────┐
   ┌───────2───────┐               ┌─────6:six─────┐
   1            3:three            5               7───┐
                                                       8
```

```py
# Inserting
tree.insert(5) # Inserts a key with no value
tree[5] = None # Same as above
tree.insert(5, 'five')
tree[5] = 'five'

# Removing
tree.remove(5)
del tree[5]

# Query
x = tree[5]
if 5 in tree:
	print('5 is in the tree')

# Slicing also supported
tree[5:10] # Returns (key, value) pairs of all keys >= 5 and <= 10
tree[:5]   # All (key, value) pairs for keys <= 5
# Beware slicing with a step for example tree[1:100:-1] wont work

# Simple iteration:
for k, v in tree:
    print(k, v)

# Iterate in reverse order
for k, v in reversed(tree):
    print(k, v)
```