How should I practice Autoregressive Token Generation?

Start by reading the full statement, solve the visible examples, then compare the pattern against related Ambience Healthcare phone screen problems in FastPrep.

Is Autoregressive Token Generation associated with Ambience Healthcare?

FastPrep tags this as a Ambience Healthcare phone screen coding practice problem when the problem catalog has that company and stage context.

Autoregressive Token Generation

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Problem statement

An autoregressive model starts with inputTokens. Its deterministic next-token calls are represented by predictedTokens in call order.

Append predictions until the sequence reaches maxTotalTokens, a generated token equals stopToken, or no supplied prediction remains. Include a generated stop token in the result.

Function

generateTokens(inputTokens: int[], predictedTokens: int[], maxTotalTokens: int, stopToken: int) → int[]

Examples

Example 1

inputTokens = [1,2]predictedTokens = [3,4,9,5]maxTotalTokens = 6stopToken = 9return = [1,2,3,4,9]

Generation includes the stop token and ends before token 5.

Example 2

inputTokens = [7]predictedTokens = [8,9,10]maxTotalTokens = 3stopToken = 99return = [7,8,9]

The maximum total length stops generation.

Example 3

inputTokens = [4,5]predictedTokens = []maxTotalTokens = 5stopToken = 0return = [4,5]

The supplied generator has no more predictions.

Constraints

1 <= inputTokens.length <= maxTotalTokens <= 100000.
0 <= predictedTokens.length <= 100000.
Tokens and stopToken are integers.