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 metrics Pipeline , 

NOTE : using the only torch module,  we have implement below scratch 

Problem Statement:




SubwordTokenization: Techniques like Byte Pair Encoding (BPE) , helpful for handling out-of-vocabulary words.


Generalised , scable, robustness, 

Byte Pair Encoding (BPE) is a subword tokenization technique used in natural language processing to address the issue of out-of-vocabulary (OOV) words and improve the efficiency of language models. Here’s a detailed step-by-step explanation of how BPE works:

Step 1: Initial Tokenization
Input Text: Start with a dataset of text. For example, consider the sentence "low lower newest."
Character-Level Tokenization: Tokenize the text at the character level. The initial tokens would look like this:
Tokens: ['l', 'o', 'w', ' ', 'l', 'o', 'w', 'e', 'r', ' ', 'n', 'e', 'w', 'e', 's', 't']
Step 2: Count Pair Frequencies
Pair Frequency Count: Count all adjacent pairs of characters (bigrams) in the dataset. For example, from the tokens above:
Pairs: ('l', 'o'), ('o', 'w'), ('w', ' '), (' ', 'l'), ...
Count how many times each pair appears.
Step 3: Merge the Most Frequent Pair
Identify the Most Frequent Pair: Find the pair of characters that occurs most frequently. For example, if ('l', 'o') is the most common pair.
Merge the Pair: Replace all occurrences of that pair with a new token representing the merged characters. For example:
Before: ['l', 'o', 'w', ' ', 'l', 'o', 'w', 'e', 'r', ' ', 'n', 'e', 'w', 'e', 's', 't']
After merging ('l', 'o'): ['lo', 'w', ' ', 'lo', 'w', 'e', 'r', ' ', 'n', 'e', 'w', 'e', 's', 't']
Step 4: Repeat Merging
Repeat Steps 2 and 3: Continue counting pairs and merging the most frequent pairs until a predetermined number of merges is reached or no more pairs can be merged.
Continue this process iteratively. After several iterations, you might get tokens like ['lo', 'w', ' ', 'low', 'e', 'r', ' ', 'new', 'est'].
Step 5: Final Vocabulary Creation
Create Subword Vocabulary: The final vocabulary consists of all the unique tokens created through the merging process, including single characters and merged subwords.
Example vocabulary: ['l', 'o', 'w', ' ', 'low', 'e', 'r', 'n', 'e', 'w', 'e', 's', 't'].
Step 6: Tokenization of New Words
Handle OOV Words: When encountering a new word that wasn't seen during training, break it down into subwords from the BPE vocabulary.
For instance, a new word like "lowercase" can be split into ['low', 'er', 'case'], thus reducing OOV occurrences.
Step 7: Encoding and Decoding
Encoding: When processing input text, convert it to the corresponding BPE tokens based on the vocabulary.
Decoding: Convert BPE tokens back into the original text or a form that can be processed by downstream tasks.
Advantages of BPE
Reduced OOV: Handles rare and compound words effectively.
Efficiency: Reduces the size of the vocabulary, making models more efficient in terms of memory and computation.
Flexibility: Allows models to understand and generate text for new words by breaking them down into familiar subword components.