You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: determinize.rs
    path of file: ./repos/regex/regex-automata/src/dfa
    
    the code of the file until where you have to start completion: use alloc::{collections::BTreeMap, vec::Vec};

use crate::{
    dfa::{
        dense::{self, BuildError},
        DEAD,
    },
    nfa::thompson,
    util::{
        self,
        alphabet::{self, ByteSet},
        determinize::{State, StateBuilderEmpty, StateBuilderNFA},
        primitives::{PatternID, StateID},
        search::{Anchored, MatchKind},
        sparse_set::SparseSets,
        start::Start,
    },
};

/// A builder for configuring and running a DFA determinizer.
#[derive(Clone, Debug)]
pub(crate) struct Config {
    match_kind: MatchKind,
    quit: ByteSet,
    dfa_size_limit: Option<usize>,
    determinize_size_limit: Option<usize>,
}

impl Config {
    /// Create a new default config for a determinizer. The determinizer may be
    /// configured 
    
    fn run(mut self) -> Result<(), BuildError> {
        if self.nfa.look_set_any().contains_word_unicode()
            && !self.config.quit.contains_range(0x80, 0xFF)
        {
            return Err(BuildError::unsupported_dfa_word_boundary_unicode());
        }

        // A sequence of "representative" bytes drawn from each equivalence
        // class. These representative bytes are fed to the NFA to compute
        // state transitions. This allows us to avoid re-computing state
        // transitions for bytes that are guaranteed to produce identical
        // results. Since computing the representatives needs to do a little
        // work, we do it once here because we'll be iterating over them a lot.
        let representatives: Vec<alphabet::Unit> =
            self.dfa.byte_classes().representatives(..).collect();
        // The set of all DFA state IDs that still need to have their
        // transitions set. We start by seeding this with all starting states.
        let mut uncompiled = alloc::vec![];
        self.add_all_starts(&mut uncompiled)?;
        while let Some(dfa_id) = uncompiled.pop() {
            for &unit in &representatives {
                if unit.as_u8().map_or(false, |b| self.config.quit.contains(b))
                {
                    continue;
                }
                // In many cases, the state we transition to has already been
                // computed. 'cached_state' will do the minimal amount of work
                // to check this, and if it exists, immediately return an
                // already existing state ID.
                let (next_dfa_id, is_new) = self.cached_state(dfa_id, unit)?;
                self.dfa.set_transition(dfa_id, unit, next_dfa_id);
                // If the state ID we got back is newly created, then we need
                // to compile it, so add it to our uncompiled frontier.
                if is_new {
                    uncompiled.push(next_dfa_id);
                }
            }
        }
        debug!(
            "determinization complete, memory usage: {}, \
             dense DFA size: {}, \
             is reverse? {}",
            self.memory_usage(),
            self.dfa.memory_usage(),
            self.nfa.is_reverse(),
        );

        // A map from DFA state ID to one or more NFA match IDs. Each NFA match
        // ID corresponds to a distinct regex pattern that matches in the state
        // corresponding to the key.
        let mut matches: BTreeMap<StateID, Vec<PatternID>> = BTreeMap::new();
        self.cache.clear();
        #[cfg(feature = "logging")]
        let mut total_pat_len = 0;
        for (i, state) in self.builder_states.into_iter().enumerate() {
            if let Some(pat_ids) = state.match_pattern_ids() {
                let id = self.dfa.to_state_id(i);
                log! {
                    total_pat_len += pat_ids.len();
                }
                matches.insert(id, pat_ids);
            }
        }
        log! {
            use core::mem::size_of;
            let per_elem = size_of::<StateID>() + size_of::<Vec<PatternID>>();
            let pats = total_pat_len * size_of::<PatternID>();
            let mem = (matches.len() * per_elem) + pats;
            log::debug!("matches map built, memory usage: {}", mem);
        }
        // At this point, we shuffle the "special" states in the final DFA.
        // This permits a DFA's match loop to detect a match condition (among
        // other things) by merely inspecting the current state's identifier,
        // and avoids the need for any additional auxiliary storage.
        self.dfa.shuffle(matches)?;
        Ok(())
    }