# Module Name: LanguageTokenizerGPTX

The `LanguageTokenizerGPTX` is an embedding utility tailored for the "EleutherAI/gpt-neox-20b" transformer model. This class allows for seamless tokenization and decoding operations, abstracting away the underlying complexity of the chosen transformer's tokenizer.

## Introduction:
Language tokenization is a crucial step in natural language processing tasks. This module provides an interface to tokenize and decode text using the GPT-Neox-20b transformer from the EleutherAI project. With the ability to manage end-of-string tokens, padding tokens, and a fixed model length, `LanguageTokenizerGPTX` serves as a convenient wrapper for the actual tokenizer from the transformers library.

## Class Definition:

```python
class LanguageTokenizerGPTX:
    def __init__(self):
        ...
    def tokenize_texts(self, texts: str) -> torch.Tensor:
        ...
    def decode(self, texts: torch.Tensor) -> str:
        ...
    def __len__(self) -> int:
        ...
```

### Parameters:
The class does not take any parameters upon instantiation. It uses predefined parameters internally to load the tokenizer.

### Methods:

#### 1. `__init__(self) -> None`:
Initializes the `LanguageTokenizerGPTX` object. This method loads the `AutoTokenizer` with predefined parameters.

#### 2. `tokenize_texts(self, texts: str) -> torch.Tensor`:
Tokenizes a given text or list of texts.

- **texts** (str): The input text(s) to tokenize.
  
  **Returns**:
  - A torch Tensor of token IDs representing the input text(s).

#### 3. `decode(self, texts: torch.Tensor) -> str`:
Decodes a given tensor of token IDs back to text.

- **texts** (torch.Tensor): The tensor of token IDs to decode.
  
  **Returns**:
  - A string representing the decoded text.

#### 4. `__len__(self) -> int`:
Provides the total number of tokens in the tokenizer's vocabulary.

  **Returns**:
  - An integer representing the total number of tokens.

## Usage Examples:

```python
from zeta import LanguageTokenizerGPTX
import torch

# Initialize the tokenizer
tokenizer = LanguageTokenizerGPTX()

# Example 1: Tokenize a single text
text = "Hello, world!"
tokenized_text = tokenizer.tokenize_texts(text)
print(tokenized_text)

# Example 2: Decode a tokenized text
decoded_text = tokenizer.decode(tokenized_text)
print(decoded_text)

# Example 3: Get the number of tokens in the tokenizer's vocabulary
num_tokens = len(tokenizer)
print(f"The tokenizer has {num_tokens} tokens.")
```

## Mathematical Formulation:

Given a text \( t \) and a vocabulary \( V \) from the GPT-Neox-20b model, tokenization maps \( t \) to a sequence of token IDs \( T \) where each token ID \( t_i \) corresponds to a token in \( V \). Decoding reverses this process.

\[ t \xrightarrow{\text{tokenize}} T \]
\[ T \xrightarrow{\text{decode}} t \]

## Additional Information:

The GPT-Neox-20b model is part of the EleutherAI project. It's a variant of the GPT architecture with tweaks in terms of model size and training. Utilizing such models require an understanding of tokenization and decoding, which this module aims to simplify.

## References:

- [Transformers Library by Hugging Face](https://huggingface.co/transformers/)
- [EleutherAI GPT-Neox](https://github.com/EleutherAI/gpt-neox)

Note: Ensure you have the necessary packages and dependencies installed, particularly the transformers library from Hugging Face.