use static memory for parser table generation

[ehwan]

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[gemini-code-assist]

Summary of Changes

Hello @ehwan, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request refactors the parser table generation within the rusty_lr_parser crate to leverage static memory allocation for key data structures like terminal sets and rulesets. By switching from dynamic Vecs to static arrays, the change aims to optimize memory usage and potentially improve runtime performance by avoiding heap allocations for these immutable, compile-time generated tables. Additionally, it includes minor improvements to the generated reduce_map initialization and a naming convention update for generated terminal set variables.

Highlights

• Static Memory Allocation for Parser Tables: The core change involves switching the generation of parser tables, specifically terminal sets and rulesets, from dynamic Vec allocations to static arrays. This means these data structures are now allocated at compile time, residing in the executable's data segment rather than on the heap.
• Optimized reduce_map Generation: The code generation for reduce_map now includes an optimization where Default::default() is used directly when the map would otherwise be empty, leading to cleaner and potentially more efficient generated code.
• Naming Convention Update: Generated identifiers for terminal sets have been updated from __rustylr_tset{index} to __RUSTYLR_TSET{index}, adopting an uppercase prefix for consistency or improved clarity.

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[gemini-code-assist]

Code Review

This pull request introduces a valuable optimization by moving parser table data from runtime allocations to static memory. Using static arrays for terminal sets and rule set components reduces allocations during parser initialization, improving performance and memory usage. The changes in rusty_lr_parser/src/emit.rs are well-implemented, and the generated code in the examples correctly reflects these improvements. The code is now clearer and more efficient. I've reviewed the changes and found no issues. Great work!