Build a next-word frequency predictor

You are given a sequence of tokens (words) representing a corpus, e.g.: `tokens = [w0, w1, w2, ..., wK-1]` You need to build a **next-word predictor** that supports queries of the form: - `predict(word) -> next_word` Where `next_word` is the word that **most frequently follows** `word` in the corpus. More precisely: - For every adjacent pair `(tokens[i], tokens[i+1])`, count it as one occurrence of “`tokens[i+1]` follows `tokens[i]`”. - For a query `word = x`, return the `y` that maximizes `count(x, y)`. - If `x` never appears or never has a following word (e.g., it only appears as the last token), return a sentinel such as `null` / empty string. - If there is a tie for max frequency, you may return any tied word (or specify a deterministic tie-break, e.g., lexicographically smallest). ### Task Design the data structures and implement: 1. A one-time `build(tokens)` preprocessing step. 2. A `predict(word)` query. ### Constraints / expectations - The corpus is processed once (batch/offline build), then many queries may follow. - Aim for efficient preprocessing and fast queries.