My first instinct was to just check every vocab token at every position, which is obviously O(n * vocab_size) and they basically said as much before I finished explaining it.
Start by clarifying the problem constraints and vocabulary characteristics, then propose a trie-based solution that scans the input once, using the trie to find the longest match at each position. Discuss time and space complexity, and mention potential optimizations like caching or Aho-Corasick for multiple patterns.
Pro tip: Emphasize that the trie approach gives O(n * L) worst-case time where L is max token length, but with a small constant factor; also mention that for very large vocabularies, a double-array trie or finite state transducer can reduce memory and improve cache performance.
Ask about vocabulary size, token length distribution, character set, and whether the tokenizer needs to handle streaming input. Confirm that the fallback is a single character and that overlapping matches should be resolved by longest match.
Propose a trie (prefix tree) built from the vocabulary, where each node represents a character and terminal nodes mark valid tokens. Explain why a trie enables efficient longest-match lookup.
Scan the input from left to right. At each position, traverse the trie as far as possible, keeping track of the last terminal node encountered. Emit that token and advance the position by its length; if no match, emit the single character and advance by one.
State that time complexity is O(n * L) where n is input length and L is maximum token length, and space is O(V) for the trie where V is total characters in vocabulary. Discuss optimizations like using arrays for children, or Aho-Corasick for multi-pattern matching.
Consider empty input, tokens longer than remaining input, overlapping tokens, and Unicode characters. Suggest testing with a small vocabulary and large input to verify performance.
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