Add basic parser-driven masking utilities

[brandonwillard]

This PR introduces utilities for parser-driven masking: i.e. a parser is run alongside a language model's (LM) token sampling step and used to filter for grammar-valid next tokens. lark was chosen as the underlying parser library because I have previous experience using it, its design is fairly straightforward, it's pure Python, and it supports arbitrary EBNF-like grammars.

The current approach is very experimental and is essentially driven by some patches to lark's LALR parser. Those patches allow for incremental additions to a lexed/parsed string, and, more importantly, they allow for partial token matches during scanning. Partial tokens arise when the string being scanned cuts off without completely constructing/finalizing a token.

For example, the source string "def fo" would normally be scanned into two tokens def and fo; however, if the next LM-sampled token is "o(", the complete sampled source string would be "def foo(", and the parsed result would be the tokens def, foo, (. These two scan (or parse) results conflict, because the first one ends by assuming that fo is a completed name token, but the next result—when starting from the beginning—contains no such token. In other words, we would need to backtrack in order to find the "correct" name token (i.e. foo).

The approach currently used in this PR doesn't immediately accept (i.e. advance the parse state) scan results from tokens with ambiguities like that.

[brandonwillard]

The iterative parsing has been usable for a while (e.g. see the full Python demo) and the tools for partial parsing, pre-parsing, and indexing/caching vocabulary tokens are in place. I'll merge this as-is so that we can split the remaining work (e.g. bullet points in the description) into separate issues/PRs and focus on them independently.