Hash Frequency Map: Mental Model

Start with the idea

Begin with the concrete question: Return true when the two lowercase words have identical character counts. For the visible input rust; tsur, the stated result is true. Before reading code, name the smallest piece of information that must survive from one step to the next. A key-derived representation lets later work reuse an earlier fact. It may be a set, a map from keys to state, compressed ranks, or a rolling fingerprint; name which one this pattern uses. That is the mental model for Hash Frequency Map; it gives you a reason for each update instead of a line of code to memorise.

The core mechanism is this: Map each key to its number of occurrences in one pass. Treat it as a claim to test after every operation. Write down the state before the first operation, update it once, and ask which part of the claim is still true. If an item is skipped, inserted, or removed, explain why that action cannot destroy an answer that should be retained. A useful trace has columns for the current input position, the state before the step, the action, and the state afterward. Use the visible sample until the expected result becomes inevitable rather than merely plausible.

Now inspect the Rust sketch, not just its final expression. The supplied Rust sketch is a starting point.