Following this instruction :
1. Strictly adhere to PEP-8 standards, ensuring code is clean, well-structured, and follows Python's style guide for readability.
2. Use appropriate modules like `typing` for type hints, ensuring code clarity, maintainability, and leveraging Python's standard libraries effectively.
3. Focus on time, space, robustness, scalability; write optimized, efficient code that can handle large datasets and scale seamlessly.
4. Implement comprehensive error handling, catching exceptions gracefully, providing meaningful error messages, and ensuring code robustness under unexpected conditions.
5. Ensure the developer agent writes advanced, maintainable Python code, balancing performance and readability while adhering to best practices.

TASK : Generalised robustness tokenization  Pipeline , 

Certainly! Let's go through a **basic step-by-step explanation** of how the **BPE (Byte-Pair Encoding)** tokenization process works from **data preparation** to **encoding and decoding**, while introducing key concepts like **padding**, **truncation**, and **special tokenization techniques**. We'll also discuss **input IDs**, **masked inputs**, and the process of decoding a sequence of integers back into tokens.

For simplicity, I'll break down each step and explain the process in a **modular** fashion.

---

## 1. Corpus Preparation

### **Corpus Definition**
A **corpus** is just a collection of text that you want the algorithm to learn from. Ultimately, the goal is to tokenize the text so that it can be processed by a machine (like a neural network).

#### Example:

Let’s assume we want to tokenize the following text:

```
"banana apple banana orange"
```

This is our **corpus**, which we’ll need to clean and prepare for tokenization.

### **Cleaning the Data**
Before we apply any tokenization algorithm, we need to clean the text. Basic cleaning steps may involve:

1. **Lowercasing**: Ensures uniformity.
2. **Removing Punctuation**: Unless punctuation is important for the task.
3. **Tokenization into words**: Breaking down the text into words or subwords.

#### Example (clean text):
Lowercase the text:
```
"banana apple banana orange"
```
No punctuation removal necessary here since it’s already clean.

---

## 2. Algorithm Development (Pseudo-Code)

Now, let’s begin by explaining the BPE tokenization algorithm at a very basic level with pseudo-code.

### **Steps in BPE Algorithm**

1. **Start with individual characters**: Split each word into its characters.
2. **Create a frequency table**: Count the frequency of each pair of characters.
3. **Merge the most frequent pair**: Replace the most frequent pair with a new subword token.
4. **Repeat**: Continue merging until your vocabulary size is reached or all pairs are exhausted.

### **Pseudo-Code to Develop BPE Algorithm**

We'll write a simplified version of the pseudo-code to give you an idea of the steps involved in building the BPE tokenization algorithm.

#### Step-by-Step Pseudo-Code:
```plaintext
1. Input: List of words in the corpus
2. Initialize a vocabulary with all unique characters in the corpus
3. Repeat the following steps until a desired vocabulary size or number of merges:

   3.1. Count the frequency of all consecutive character pairs in the corpus.
   
   3.2. Identify the pair with the highest frequency.
   
   3.3. Merge the frequent pair into a new token (subword).
   
   3.4. Update the corpus by replacing occurrences of the pair with the new token.
   
4. Output: The new vocabulary of subword tokens.
```

#### Simple Example:

```
Corpus: "banana apple banana orange"
Step 1:  ["b", "a", "n", "a", "n", "a"], ["a", "p", "p", "l", "e"], ["b", "a", "n", "a", "n", "a"], ["o", "r", "a", "n", "g", "e"]
Step 2: Count pairs, e.g., ("b", "a"), ("a", "n"), etc.
Step 3: Merge most frequent pair, e.g., merge "a" and "n" to "an"
Step 4: Continue merging until the vocabulary meets the target size.
```

---

## 3. Training BPE and Building Vocabulary

The BPE algorithm continues merging pairs until a desired vocabulary size (such as 10k tokens) is reached. At this point, all words in the corpus are either represented as complete tokens or subword tokens.

Once trained, a **vocabulary file** is created that lists all the subword tokens. For example:
```
Vocabulary: ["b", "a", "n", "an", "na", "p", "pl", "e", "or", "ange"]
```

### **Special Tokens**
Many tokenization methods also include **special tokens** such as:

- `[CLS]`: A special token for indicating the start of a sequence.
- `[PAD]`: For padding sequences to make them uniform in length.
- `[MASK]`: For masked language modeling (e.g., in BERT).
- `[SEP]`: To separate two sequences (like in question answering tasks).

---

## 4. Encoding Text: List of Strings → List of Integers

Once the vocabulary is built, we can now **encode** the text. Encoding is the process of converting a list of strings (words or subword tokens) into a list of **integer IDs** that can be fed into a model.

### **Steps:**
1. **Tokenize** the text using the BPE vocabulary (break the text into subword tokens).
   - Example: "banana apple banana orange" → \[ "ban", "ana", "apple", "orange" \]
   
2. **Convert tokens to IDs** using the vocabulary’s lookup table.
   - For each subword token, replace it with its unique ID.
   
   Example:
   ```
   Tokens: ["ban", "ana", "apple", "orange"]
   IDs:    [23, 45, 67, 89]  # Example integer IDs corresponding to each token
   ```
   
   These IDs will then be fed into the neural network.

### **Padding**
In many NLP tasks, the sequences of tokens may have different lengths. To ensure uniform input sizes, we often apply **padding**.

- **Padding** adds `[PAD]` tokens to shorter sequences to match the length of the longest sequence (or a predefined maximum length).
  
   Example:
   ```
   Sequence: [23, 45, 67]
   After padding to max length of 6: [23, 45, 67, 0, 0, 0]  # Assuming 0 is the pad token ID
   ```

### **Truncation**
If the input sequence exceeds the predefined **max_length**, we can cut off extra tokens using **truncation**.

   Example:
   ```
   Tokens: [23, 45, 67, 89, 12, 34, 56]
   Max length = 5, so we truncate to [23, 45, 67, 89, 12]
   ```

### **Example of Encoding a List of Strings to List of Integers**
```python
# Example word-to-id mapping from vocabulary
vocab = {"ban": 23, "ana": 45, "apple": 67, "orange": 89}
special_tokens = {"[PAD]": 0, "[CLS]": 1, "[SEP]": 2}

# Text to encode
text = ["ban", "ana", "apple", "orange"]

# Encode tokens into integer IDs using vocabulary
input_ids = [vocab[tok] for tok in text]  # input_ids = [23, 45, 67, 89]

# Add special tokens
input_ids = [special_tokens["[CLS]"]] + input_ids + [special_tokens["[SEP]"]]
# input_ids becomes: [1, 23, 45, 67, 89, 2]
```

---

## 5. Special Tokenization Techniques: Masking, Input IDs, and Attention Masks

In certain tasks (like pretraining BERT), we use **masked input sequences**.

### **Masked Inputs:**
- We randomly replace some tokens with a `[MASK]` token to train the model to predict the masked tokens.
  
   Example:
   ```
   Text:       "banana apple banana orange"
   Masked:     "banana [MASK] banana orange"
   Input IDs:  [23, 45, 67, 89]  →  [23, 103, 67, 89] (assuming 103 is the [MASK] ID)
   ```

### **Attention Masks:**
When padding is applied, we also generate an **attention mask** that tells the model which tokens are real and which are padding:
- Example attention mask: `[1, 1, 1, 0, 0, 0]` (1 for real tokens, 0 for `[PAD]` tokens).

---

## 6. Decoding: List of Integers → List of Strings

After a model processes the inputs, the output will often be a sequence of **integers (token IDs)**. We can **decode** these integers back into readable tokens using the vocabulary.

### **Steps for Decoding:**
1. **Take the list of integer IDs**.
2. **Look up each ID** in the reverse vocabulary to get the corresponding token.
3. **Concatenate tokens** to form the original text.

### **Example:**
```python
# Reverse lookup dictionary (id -> word)
id_to_word = {23: "ban", 45: "ana", 67: "apple", 89: "orange"}

# Decoded integer sequence
encoded_ids = [23, 45, 67, 89]

# Decoding back into string tokens
decoded_tokens = [id_to_word[id] for id in encoded_ids]
# decoded_tokens = ["ban", "ana", "apple", "orange"]
```

---

## Summary

1. **Corpus Preparation**: Clean