1518. Implement Byte-Pair Encoding (BPE) Subword Tokenizer (Part 15)
LLM Prompting & NLPLinear Algebra & Numpy
### Problem #1518: Implement Byte-Pair Encoding (BPE) Subword Tokenizer (Part 15)
**Domain:** `Machine Learning & AI` | **Topic Focus:** `LLM Prompting & NLP, Linear Algebra & Numpy`
Implement an optimized, production-grade solution for **Implement Byte-Pair Encoding (BPE) Subword Tokenizer (Part 15)**.
### Requirements:
1. Your code must handle boundary inputs, edge cases, and maintain optimal time & space complexity.
2. In production, this logic scales to high-throughput environments.
3. Return the exact computed result.
Example 1:
Input: Sample Input #1518
Output: Output 1
Explanation: Demonstrates correct execution for LLM Prompting & NLP in Machine Learning & AI.
Constraints:
- Time Complexity: Optimal for standard production loads.
- Space Complexity: O(1) auxiliary or O(N) linear storage.
- Ensure memory safety, clean error propagation, and zero race conditions.
Integrity: 100%
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Input Arguments:
Test Case 1 for #1518
Expected Output:
Output 1