When doing functional programming, you can't really do loops (because of referential transparency, you can't update iterators or indices). However, recursion still works.

I can prove the opposite for you. The assumption that Gobbel2000 describes is wrong in general. For example, take

L

A -> B, X
B -> C, X
C -> Z, X
Z -> C, X
X -> X, X

the first Z is reached after 3 steps, but then every cycle only takes 2 steps.

The matches are still at consistent intervals, but you can easily find a counterexample for that as well:

L

A -> 1Z, X
1Z -> 2Z, X
2Z -> A, X
X -> X, X

now the intervals will be 2, 1, 2, 1, ...

However, it is easy to prove that there will be a loop of finite length, and that the intervals will behave somewhat nicely:

Identify a "position" by a node you are at, and your current index in the LRL instruction sequence. If you ever repeat a position P, you will repeat the exact path away from the position you took the last time, and the last time you later reached P, so you will keep reaching P again and again. There are finitely many positions, so you can't keep not repeating any forever, you will run out.

Walking in circles along this loop you eventually find yourself in, the intervals between Zs you see will definitely be a repeating sequence (as you will keep seeing not just same-length intervals, but in fact the exact same paths between Zs).

So in total, you will see some finite list of prefix-intervals, and then a repeating cycle of loop-intervals. I have no idea if this can be exploited to compute the answer efficiently, but see my solution-comment for something that only assumes that only one Z will be encountered each cycle.

Scala3

this is still not 100% general, as it assumes there is only one Z-node along the path for each start. But it doesn't assume anything else, I think. If you brute force combinations of entries of the zs-arrays, this assumption can be trivially removed, but if multiple paths see lots of Z-nodes, then the runtime will grow exponentially. I don't know if it's possible to do this any faster.

def parse(a: List[String]): (List[Char], Map[String, Map[Char, String]]) =
    def parseNodes(n: List[String]) =
        n.flatMap{case s"$from = ($l, $r)" => Some(from -> Map('L'->l, 'R'->r)); case _ => None}.toMap
    a match{case instr::""::nodes => ( instr.toList, parseNodes(nodes) ); case _ => (List(), Map())}

def task1(a: List[String]): Long =
    val (instr, nodes) = parse(a)
    def go(i: Stream[Char], pos: String, n: Long): Long =
        if pos != "ZZZ" then go(i.tail, nodes(pos)(i.head), n+1) else n
    go(instr.cycle, "AAA", 0)

// ok so i originally thought this was going to be difficult, so
// we parse a lot of info we won't use
case class PathInfo(zs: List[Long], loopFrom: Long, loopTo: Long):
    def stride: Long = loopTo - loopFrom

type Cycle = Long

def getInfo(instr: List[Char], isEnd: String => Boolean, map: Map[String, Map[Char, String]], start: String): PathInfo =
    def go(i: Cycle, pos: String, is: List[Char], seen: Map[(Long, String), Cycle], acc: List[Long]): PathInfo =
        val current: (Long, String) = (is.size % instr.size, pos)
        val nis = if is.isEmpty then instr else is
        val nacc = if isEnd(pos) then i::acc else acc
        seen.get(current) match
            case Some(l) => PathInfo(acc, l, i)
            case _ => go(i + 1, map(pos)(nis.head), nis.tail, seen + (current -> i), nacc)
    go(0, start, instr, Map(), List())

// turns out we don't need to check all the different positions
// in each cycle where we are on a Z position, as a) every start
// walks into unique cycles with b) exactly one Z position,
// and c) the length of the cycle is equivalent to the steps to first
// encounter a Z (so a->b->c->Z->c->Z->... is already more complicated, 
// as the cycle length is 2 but the first encounter is after 3 steps)

// anyway let's do some math

// this is stolen code
def gcd(a: Long, b: Long): Long =
    if(b ==0) a else gcd(b, a%b)

def primePowers(x: Long): Map[Long, Long] =
    // for each prime p for which p^k divides x: p->p^k
    def go(r: Long, t: Long, acc: Map[Long, Long]): Map[Long, Long] =
        if r == 1 then acc else
            if r % t == 0 then
                go(r/t, t, acc + (t -> acc.getOrElse(t, 1L)*t))
            else
                go(r, t+1, acc)
    go(x, 2, Map())

// this algorithm is stolen, but I scalafied the impl
def vanillaCRT(congruences: Map[Long, Long]): Long = 
    val N = congruences.keys.product
    val x = congruences.map((n, y) => y*(N/n)*((N/n) % n)).sum
    if x <= 0 then x + N else x

def generalizedHardcoreCRTThatCouldHaveBeenAnLCMBecauseTheInputIsVeryConvenientlyTrivialButWeWantToDoThisRight(ys: List[Long], xs: List[Long]): Option[Long] =
    // finds the smallest k s.t. k === y_i  (mod x_i) for each i
    // even when stuff is not nice

    // pre-check if everything is sufficiently coprime
    // https://math.stackexchange.com/questions/1644677/what-to-do-if-the-modulus-is-not-coprime-in-the-chinese-remainder-theorem
    val vars = for
        ((y1, n1), i1) <- ys.zip(xs).zipWithIndex
        ((y2, n2), i2) <- ys.zip(xs).zipWithIndex
        if i2 > i1
    yield 
        val g = gcd(n1, n2)
        y1 % g == y2 % g
    
    if !vars.forall(a => a) then
        None
    else
        // we need to split k === y (mod mn) into k === y (mod m) and k === y (mod n) for m, n coprime
        val congruences = for
            (x, y) <- xs.zip(ys)
            (p, pl) <- primePowers(x)
        yield
            p -> (y % pl -> pl)
        
        // now we eliminate redundant congruences
        // since our input is trivial, this is essentially
        // the step in which we solve the task by 
        // accidentaly computing an lcm
        val r = congruences.groupMap(_._1)(_._2).mapValues(l => l.map(_._2).max -> l.head._1).values.toMap
        
        // ok now we meet the preconditions
        // for doing this
        Some(vanillaCRT(r))

def task2(a: List[String]): Long =
    val (instr, nodes) = parse(a)
    val infos = nodes.keySet.filter(_.endsWith("A")).map(s => getInfo(instr, _.endsWith("Z"), nodes, s))
    generalizedHardcoreCRTThatCouldHaveBeenAnLCMBecauseTheInputIsVeryConvenientlyTrivialButWeWantToDoThisRight(
        infos.map(_.zs.head).toList, infos.map(_.stride).toList
    ).getOrElse(0)

@main def main: Unit =
  val data = readlines("/home/tim/test/aoc23/aoc23/inputs/day8/task1.txt")
  for f <- List(
      task1,
      task2,
    ).map(_ andThen println)
  do f(data)

Scala3

type Grid = List[List[Char]]

def tiltUp(a: Grid): Grid = 
    @tailrec def go(c: List[Char], acc: List[Char]): List[Char] =
        def shifted(c: List[Char]) = 
            val (h, t) = c.splitAt(c.count(_ == 'O'))
            h.map(_ => 'O') ++ t.map(_ => '.') ++ acc
        val d = c.indexOf('#')
        if d == -1 then shifted(c) else go(c.slice(d + 1, c.size), '#'::shifted(c.slice(0, d)))
        
    a.map(go(_, List()).reverse)

def weight(a: Grid): Long = a.map(d => d.zipWithIndex.filter((c, _) => c == 'O').map(1 + _._2).sum).sum
def rotateNeg90(a: Grid): Grid = a.reverse.transpose
def runCycle = Seq.fill(4)(tiltUp andThen rotateNeg90).reduceLeft(_ andThen _)

def stateAt(target: Long, a: Grid): Grid =
    @tailrec def go(cycle: Int, state: Grid, seen: Map[Grid, Int]): Grid =
        seen.get(state) match
            case Some(i) => if (target - cycle) % (cycle - i) == 0 then state else go(cycle + 1, runCycle(state), seen)
            case None => go(cycle + 1, runCycle(state), seen + (state -> cycle))
    
    go(0, a, Map())

def toColMajorGrid(a: List[String]): Grid = rotateNeg90(a.map(_.toList))

def task1(a: List[String]): Long = weight(tiltUp(toColMajorGrid(a)))
def task2(a: List[String]): Long = weight(stateAt(1_000_000_000, toColMajorGrid(a)))

Agreed, i get annoyed when I can't actually solve the problem. I would be ok if the inputs are trivial special cases, as long as feasible (but harder) generalized solutions still existed.

Scala3

// i is like
//  # # # # #
//   1 2 3 4
def smudgesAround(i: Int, s: List[List[Char]]): Long =
    val toEdge = math.min(i, s.size - i)
    (0 until toEdge).map(e => s(i - e - 1).lazyZip(s(i + e)).count(_ != _)).sum

def symmetries(g: List[List[Char]], smudges: Int) =
    val rows = (1 until g.size).filter(smudgesAround(_, g) == smudges)
    val g2 = g.transpose
    val cols = (1 until g2.size).filter(smudgesAround(_, g2) == smudges)
    100*rows.sum + cols.sum

def task1(a: List[String]): Long = a.chunk(_ == "").map(g => symmetries(g.map(_.toList), 0)).sum
def task2(a: List[String]): Long = a.chunk(_ == "").map(g => symmetries(g.map(_.toList), 1)).sum

Scala3

all done!

def parseLine(a: String): List[UnDiEdge[String]] = a match
    case s"$n: $r" => r.split(" ").map(_ ~ n).toList
    case _ => List()

def removeShortestPath(g: Graph[String, UnDiEdge[String]], ns: List[String]) =
    g.removedAll(g.get(ns(0)).shortestPathTo(g.get(ns(1))).map(_.edges.map(_.outer)).getOrElse(List()))

def removeTriple(g: Graph[String, UnDiEdge[String]], ns: List[String]) =
    List.fill(3)(ns).foldLeft(g)(removeShortestPath)

def division(g: Graph[String, UnDiEdge[String]]): Option[Long] =
    val t = g.componentTraverser()
    Option.when(t.size == 2)(t.map(_.nodes.size).product)

def task1(a: List[String]): Long = 
    val g = Graph() ++ a.flatMap(parseLine)
    g.nodes.toList.combinations(2).map(a => removeTriple(g, a.map(_.outer))).flatMap(division).take(1).toList.head

Scala3

def hash(s: String): Long = s.foldLeft(0)((h, c) => (h + c)*17 % 256)

extension [A] (a: List[A])
    def mapAtIndex(idx: Long, f: A => A): List[A] =
        a.zipWithIndex.map((e, i) => if i == idx then f(e) else e)

def runProcedure(steps: List[String]): Long =
    @tailrec def go(boxes: List[List[(String, Int)]], steps: List[String]): List[List[(String, Int)]] =
        steps match
            case s"$label-" :: t =>
                go(boxes.mapAtIndex(hash(label), _.filter(_._1 != label)), t)
            case s"$label=$f" :: t =>
                go(boxes.mapAtIndex(hash(label), b => 
                    val slot = b.map(_._1).indexOf(label)
                    if slot != -1 then b.mapAtIndex(slot, (l, _) => (l, f.toInt)) else (label, f.toInt) :: b
                ), t)
            case _ => boxes
    
    go(List.fill(256)(List()), steps).zipWithIndex.map((b, i) =>
        b.zipWithIndex.map((lens, ilens) => (1 + i) * (b.size - ilens) * lens._2).sum
    ).sum

def task1(a: List[String]): Long = a.head.split(",").map(hash).sum
def task2(a: List[String]): Long = runProcedure(a.head.split(",").toList)

Scala3

def diffs(a: Seq[Long]): List[Long] =
    a.drop(1).zip(a).map(_ - _).toList

def predictNext(a: Seq[Long], combine: (Seq[Long], Long) => Long): Long =
    if a.forall(_ == 0) then 0 else combine(a, predictNext(diffs(a), combine))

def predictAllNexts(a: List[String], combine: (Seq[Long], Long) => Long): Long = 
    a.map(l => predictNext(l.split(raw"\s+").map(_.toLong), combine)).sum

def task1(a: List[String]): Long = predictAllNexts(a, _.last + _)
def task2(a: List[String]): Long = predictAllNexts(a, _.head - _)

C++, kind of

Ok so this is a little weird. My code for task1 is attached to this comment, but I actually solved task2 by hand. After checking that bruteforce indeed takes longer than a second, I plotted the graph just to see what was going on, and you can immediately tell that the result is the least common multiple of four numbers, which can easily be obtained by running task1 with a debugger, and maybe read directly from the graph as well. I also pre-broke my include statements, so hopefully the XSS protection isn't completely removing them again.

My graph: https://files.catbox.moe/1u4daw.png

blue is the broadcaster/button, yellows are flipflops, purples are nand gates and green is the output gate.

Also I abandoned scala again, because there is so much state modification going on.

#include fstream>
#include memory>
#include algorithm>
#include optional>
#include stdexcept>
#include set>
#include vector>
#include map>
#include deque>
#include unordered_map>

#include fmt/format.h>
#include fmt/ranges.h>
#include flux.hpp>
#include scn/all.h>
#include scn/scan/list.h>

enum Pulse { Low=0, High };

struct Module {
    std::string name;
    Module(std::string _name) : name(std::move(_name)) {}
    virtual std::optional handle(Module const& from, Pulse type) = 0;
    virtual ~Module() = default;
};

struct FlipFlop : public Module {
    using Module::Module;
    bool on = false;
    std::optional handle([[maybe_unused]] Module const& from, Pulse type) override {
        if(type == Low) {
            on = !on;
            return on ? High : Low;
        }
        return {};
    }
    virtual ~FlipFlop() = default;
};

struct Nand : public Module {
    using Module::Module;
    std::unordered_map last;
    std::optional handle(Module const& from, Pulse type) override {
        last[from.name] = type;

        for(auto& [k, v] : last) {
            if (v == Low) {
                return High;
            }
        }
        return Low;
    }
    virtual ~Nand() = default;
};

struct Broadcaster : public Module {
    using Module::Module;
    std::optional handle([[maybe_unused]] Module const& from, Pulse type) override {
        return type;
    }
    virtual ~Broadcaster() = default;
};

struct Sink : public Module {
    using Module::Module;
    std::optional handle([[maybe_unused]] Module const& from, Pulse type) override {
        return {};
    }
    virtual ~Sink() = default;
};

struct Button : public Module {
    using Module::Module;
    std::optional handle([[maybe_unused]] Module const& from, Pulse type) override {
        throw std::runtime_error{"Button should never recv signal"};
    }
    virtual ~Button() = default;
};

void run(Module* button, std::map> connections, long& lows, long& highs) {
    std::deque> pending;
    pending.push_back({button, Low});

    while(!pending.empty()) {
        auto [m, p] = pending.front();
        pending.pop_front();

        for(auto& m2 : connections.at(m->name)) {
            ++(p == Low ? lows : highs);
            fmt::println("{} -{}-> {}", m->name, p == Low ? "low":"high", m2->name);
            if(auto p2 = m2->handle(*m, p)) {
                pending.push_back({m2, *p2});
            }
        }
    }
}

struct Setup {
    std::vector> modules;
    std::map by_name;
    std::map> connections;
};

Setup parse(std::string path) {
    std::ifstream in(path);
    Setup res;
    auto lines = flux::getlines(in).to>();

    std::map> pre_connections;

    for(const auto& line : lines) {
        std::string name;
        if(auto r = scn::scan(line, "{} -> ", name)) {
            if(name == "broadcaster") {
                res.modules.push_back(std::make_unique(name));
            } 
            else if(name.starts_with('%')) {
                name = name.substr(1);
                res.modules.push_back(std::make_unique(name));
            }
            else if(name.starts_with('&')) {
                name = name.substr(1);
                res.modules.push_back(std::make_unique(name));
            }

            res.by_name[name] = res.modules.back().get();

            std::vector cons;
            if(auto r2 = scn::scan_list_ex(r.range(), cons, scn::list_separator(','))) {
                for(auto& c : cons) if(c.ends_with(',')) c.pop_back();
                fmt::println("name={}, rest={}", name, cons);
                pre_connections[name] = cons;
            } else {
                throw std::runtime_error{r.error().msg()};
            }
        } else {
            throw std::runtime_error{r.error().msg()};
        }
    }

    res.modules.push_back(std::make_unique("sink"));

    for(auto& [k, v] : pre_connections) {
        res.connections[k] = flux::from(std::move(v)).map([&](std::string s) { 
                try {
                    return res.by_name.at(s); 
                } catch(std::out_of_range const& e) {
                    fmt::print("out of range at {}\n", s);
                    return res.modules.back().get();
                }}).to>();
    }

    res.modules.push_back(std::make_unique("button"));
    res.connections["button"] = {res.by_name.at("broadcaster")};
    res.connections["sink"] = {};

    for(auto& [m, cs] : res.connections) {
        for(auto& m2 : cs) {
            if(auto nand = dynamic_cast(m2)) {
                nand->last[m] = Low;
            }
        }
    }

    return res;
}

int main(int argc, char* argv[]) {
    auto setup = parse(argc > 1 ? argv[1] : "../task1.txt");
    long lows{}, highs{};
    for(int i = 0; i < 1000; ++i)
        run(setup.modules.back().get(), setup.connections, lows, highs);

    fmt::println("task1: low={} high={} p={}", lows, highs, lows*highs);
}

My graph: https://files.catbox.moe/1u4daw.png

blue is the broadcaster/button, yellows are flipflops, purples are nand gates and green is the output gate.

Scala3

// math.floor(i) == i if i.isWhole, but we want i-1
def hardFloor(d: Double): Long = (math.floor(math.nextAfter(d, Double.NegativeInfinity))).toLong
def hardCeil(d: Double): Long = (math.ceil(math.nextAfter(d, Double.PositiveInfinity))).toLong

def wins(t: Long, d: Long): Long =
    val det = math.sqrt(t*t/4.0 - d)
    val high = hardFloor(t/2.0 + det)
    val low = hardCeil(t/2.0 - det)
    (low to high).size

def task1(a: List[String]): Long = 
    def readLongs(s: String) = s.split(raw"\s+").drop(1).map(_.toLong)
    a match
        case List(s"Time: $time", s"Distance: $dist") => readLongs(time).zip(readLongs(dist)).map(wins).product
        case _ => 0L

def task2(a: List[String]): Long =
    def readLong(s: String) = s.replaceAll(raw"\s+", "").toLong
    a match
        case List(s"Time: $time", s"Distance: $dist") => wins(readLong(time), readLong(dist))
        case _ => 0L

Scala3

def countDyn(a: List[Char], b: List[Int]): Long =
    // Simple dynamic programming approach
    // We fill a table T, where
    //  T[ ai, bi ] -> number of ways to place b[bi..] in a[ai..]
    //  T[ ai, bi ] = 0 if an-ai >= b[bi..].sum + bn-bi
    //  T[ ai, bi ] = 1 if bi == b.size - 1 && ai == a.size - b[bi] - 1
    //  T[ ai, bi ] = 
    //   (place) T [ ai + b[bi], bi + 1]   if ? or # 
    //   (skip)  T [ ai + 1, bi ]          if ? or .
    // 
    def t(ai: Int, bi: Int, tbl: Map[(Int, Int), Long]): Long =
        if ai >= a.size then
            if bi >= b.size then 1L else 0L 
        else
            val place = Option.when(
                bi < b.size && // need to have piece left
                ai + b(bi) <= a.size && // piece needs to fit
                a.slice(ai, ai + b(bi)).forall(_ != '.') && // must be able to put piece there
                (ai + b(bi) == a.size || a(ai + b(bi)) != '#') // piece needs to actually end
            )((ai + b(bi) + 1, bi + 1)).flatMap(tbl.get).getOrElse(0L)
            val skip = Option.when(a(ai) != '#')((ai + 1, bi)).flatMap(tbl.get).getOrElse(0L)
            place + skip

    @tailrec def go(ai: Int, tbl: Map[(Int, Int), Long]): Long =
        if ai == 0 then t(ai, 0, tbl) else go(ai - 1, tbl ++ b.indices.inclusive.map(bi => (ai, bi) -> t(ai, bi, tbl)).toMap)

    go(a.indices.inclusive.last + 1, Map())

def countLinePossibilities(repeat: Int)(a: String): Long =
    a match
        case s"$pattern $counts" => 
            val p2 = List.fill(repeat)(pattern).mkString("?")
            val c2 = List.fill(repeat)(counts).mkString(",")
            countDyn(p2.toList, c2.split(",").map(_.toInt).toList)
        case _ => 0L


def task1(a: List[String]): Long = a.map(countLinePossibilities(1)).sum
def task2(a: List[String]): Long = a.map(countLinePossibilities(5)).sum

(Edit: fixed mangling of &<)

Scala3

val tiers = List(List(1, 1, 1, 1, 1), List(1, 1, 1, 2), List(1, 2, 2), List(1, 1, 3), List(2, 3), List(1, 4), List(5))
val cards = List('2', '3', '4', '5', '6', '7', '8', '9', 'T', 'J', 'Q', 'K', 'A')

def cardValue(base: Long, a: List[Char], cards: List[Char]): Long =
    a.foldLeft(base)(cards.size * _ + cards.indexOf(_))

def hand(a: List[Char]): List[Int] =
    a.groupMapReduce(s => s)(_ => 1)(_ + _).values.toList.sorted

def power(a: List[Char]): Long =
  cardValue(tiers.indexOf(hand(a)), a, cards)

def power3(a: List[Char]): Long = 
  val x = hand(a.filter(_ != 'J'))
  val t = tiers.lastIndexWhere(x.zipAll(_, 0, 0).forall(_ <= _))
  cardValue(t, a, 'J'::cards)

def win(a: List[String], pow: List[Char] => Long) =
    a.flatMap{case s"$hand $bid" => Some((pow(hand.toList), bid.toLong)); case _ => None}
        .sorted.map(_._2).zipWithIndex.map(_ * _.+(1)).sum

def task1(a: List[String]): Long = win(a, power)
def task2(a: List[String]): Long = win(a, power3)

Scala3

Learning about scala-graph yesterday seems to have paid off already. This explicitly constructs the entire graph of allowed moves, and then uses a naive dijkstra run. This works, and I don't have to write a lot of code, but it is fairly inefficient.

import day10._
import day10.Dir._
import day11.Grid

// standing on cell p, having entered from d
case class Node(p: Pos, d: Dir)

def connect(p: Pos, d: Dir, g: Grid[Int], dists: Range) = 
    val from = Seq(-1, 1).map(i => Dir.from(d.n + i)).map(Node(p, _))
    val ends = List.iterate(p, dists.last + 1)(walk(_, d)).filter(g.inBounds)
    val costs = ends.drop(1).scanLeft(0)(_ + g(_))
    from.flatMap(f => ends.zip(costs).drop(dists.start).map((dest, c) => WDiEdge(f, Node(dest, d), c)))

def parseGrid(a: List[List[Char]], dists: Range) =
    val g = Grid(a.map(_.map(_.getNumericValue)))
    Graph() ++ g.indices.flatMap(p => Dir.all.flatMap(d => connect(p, d, g, dists)))

def compute(a: List[String], dists: Range): Long =
    val g = parseGrid(a.map(_.toList), dists)
    val source = Node(Pos(-1, -1), Right)
    val sink = Node(Pos(-2, -2), Right)
    val start = Seq(Down, Right).map(d => Node(Pos(0, 0), d)).map(WDiEdge(source, _, 0))
    val end = Seq(Down, Right).map(d => Node(Pos(a(0).size - 1, a.size - 1), d)).map(WDiEdge(_, sink, 0))
    val g2 = g ++ start ++ end
    g2.get(source).shortestPathTo(g2.get(sink)).map(_.weight).getOrElse(-1.0).toLong

def task1(a: List[String]): Long = compute(a, 1 to 3)
def task2(a: List[String]): Long = compute(a, 4 to 10)

Scala3

kind of convoluted, but purely functional

import scala.collection.immutable.NumericRange.Exclusive
import math.max
import math.min

extension [A] (l: List[A])
  def chunk(pred: A => Boolean): List[List[A]] =
    def go(l: List[A], partial_acc: List[A], acc: List[List[A]]): List[List[A]] =
      l match
        case (h :: t) if pred(h) => go(t, List(), partial_acc.reverse :: acc)
        case (h :: t) => go(t, h :: partial_acc, acc)
        case _ => partial_acc.reverse :: acc
    
    go(l, List(), List()).reverse

type R = Exclusive[Long]

def intersectTranslate(r: R, c: R, t: Long): R =
    (t + max(r.start, c.start) - c.start) until (t + min(r.end, c.end) - c.start)

case class MappingEntry(from: R, to: Long)
case class Mapping(entries: List[MappingEntry], produces: String):
    def resolveRange(in: R): List[R] =
        entries.map(e => intersectTranslate(in, e.from, e.to)).filter(!_.isEmpty)

def completeEntries(a: List[MappingEntry]): List[MappingEntry] = 
    a ++ ((0L until 0L) +: a.map(_.from).sorted :+ (Long.MaxValue until Long.MaxValue)).sliding(2).flatMap{ case List(a, b) => Some(MappingEntry(a.end until b.start, a.end)); case _ => None}.toList

def parse(a: List[String], init: List[Long] => List[R]): (List[R], Map[String, Mapping]) =
    def parseEntry(s: String): MappingEntry =
        s match
            case s"$end $start $range" => MappingEntry(start.toLong until start.toLong + range.toLong, end.toLong)

    a.chunk(_ == "") match
        case List(s"seeds: $seeds") :: maps => 
            init(seeds.split(raw"\s+").map(_.toLong).toList) -> (maps.flatMap{ case s"$from-to-$to map:" :: entries => Some(from -> Mapping(completeEntries(entries.map(parseEntry)), to)); case _ => None }).toMap
        case _ => (List(), Map()).ensuring(false)

def singletons(a: List[Long]): List[R] = a.map(s => s until s + 1)
def paired(a: List[Long]): List[R] = a.grouped(2).flatMap{ case List(x, y) => Some(x until x+y); case _ => None }.toList

def chase(d: (List[R], Map[String, Mapping]), initial: String, target: String) =
    val (init, m) = d
    def go(a: List[R], s: String): List[R] =
        if trace(s) == target then a else
            val x = m(s)
            go(a.flatMap(x.resolveRange), x.produces)
    go(trace(init), initial)

def task1(a: List[String]): Long = 
    chase(parse(a, singletons), "seed", "location").min.start

def task2(a: List[String]): Long =
    chase(parse(a, paired), "seed", "location").min.start

Scala3

Not much to say about this, very straightforward implementation that was still fast enough

case class Pos3(x: Int, y: Int, z: Int)
case class Brick(blocks: List[Pos3]):
    def dropBy(z: Int) = Brick(blocks.map(b => b.copy(z = b.z - z)))
    def isSupportedBy(other: Brick) = ???

def parseBrick(a: String): Brick = a match
    case s"$x1,$y1,$z1~$x2,$y2,$z2" => Brick((for x <- x1.toInt to x2.toInt; y <- y1.toInt to y2.toInt; z <- z1.toInt to z2.toInt yield Pos3(x, y, z)).toList)

def dropOn(bricks: List[Brick], brick: Brick): (List[Brick], List[Brick]) =
    val occupied = bricks.flatMap(d => d.blocks.map(_ -> d)).toMap

    @tailrec def go(d: Int): (Int, List[Brick]) =
        val dropped = brick.dropBy(d).blocks.toSet
        if dropped.intersect(occupied.keySet).isEmpty && !dropped.exists(_.z <= 0) then
            go(d + 1)
        else
            (d - 1, occupied.filter((p, b) => dropped.contains(p)).map(_._2).toSet.toList)
    
    val (d, supp) = go(0)
    (brick.dropBy(d) :: bricks, supp)

def buildSupportGraph(bricks: List[Brick]): Graph[Brick, DiEdge[Brick]] =
    val (bs, edges) = bricks.foldLeft((List[Brick](), List[DiEdge[Brick]]()))((l, b) => 
        val (bs, supp) = dropOn(l._1, b)
        (bs, supp.map(_ ~> bs.head) ++ l._2)
    )
    Graph() ++ (bs, edges)

def parseSupportGraph(a: List[String]): Graph[Brick, DiEdge[Brick]] =
    buildSupportGraph(a.map(parseBrick).sortBy(_.blocks.map(_.z).min))

def wouldDrop(g: Graph[Brick, DiEdge[Brick]], b: g.NodeT): Long =
    @tailrec def go(shaking: List[g.NodeT], falling: Set[g.NodeT]): List[g.NodeT] = 
        shaking match
            case h :: t => 
                if h.diPredecessors.forall(falling.contains(_)) then
                    go(h.diSuccessors.toList ++ t, falling + h)
                else
                    go(t, falling)
            case _ => falling.toList
    
    go(b.diSuccessors.toList, Set(b)).size

def task1(a: List[String]): Long = parseSupportGraph(a).nodes.filter(n => n.diSuccessors.forall(_.inDegree > 1)).size
def task2(a: List[String]): Long = 
    val graph = parseSupportGraph(a)
    graph.nodes.toList.map(wouldDrop(graph, _) - 1).sum

On your example, my code produces 301, which matches your 300 ignoring column symmetry (and 0 for task2, as there is no way to smudge a single cell to create symmetry).

Let me explain the code real quick: What smudgesAround does is compute the number of tiles you would need to change to create a symmetry around index i. First, toEdge is the number of tiles to the nearest edge, everything after that will be reflected outside the grid and won't matter. Then, for each actually relevant distance, we compare the rows/columns this distance from i. By zipping them, we create an Iterator that first yields the pair of first entries, then the pair of second entries, and so on. On each pair we apply a comparison, to count the number of entries which did not match. This is the number of smudges we need to fix to get these rows/columns to match. Summing over all relevant pairs of rows/columns, we get the total number of smudges to make i a mirror line. In symmetries, we just check each i, for task1 we want a mirror line without smudges and for task2 we want a mirror line for exactly one

You can also make this quite a bit shorter, if you want to sacrifice some more readability:

def compute(a: List[String], sm: Int) =
    a.chunk(_ == "").map(_.map(_.toList)).map(g => Seq(g, g.transpose).map(s => (1 until s.size).filter(i => (0 until math.min(i, s.size - i)).map(e => s(i - e - 1).zip(s(i + e)).count(_ != _)).sum == sm).sum).zip(Seq(100, 1)).map(_ * _).sum).sum

def task1(a: List[String]): Long = compute(a, 0)
def task2(a: List[String]): Long = compute(a, 1)

Scala3

forgot to post this

import Area.*
import Dir.*

enum Dir(num: Int, diff: (Int, Int)):
    val n = num
    val d = diff
    case Up extends Dir(3, (0, -1))
    case Down extends Dir(1, (0, 1))
    case Left extends Dir(2, (-1, 0))
    case Right extends Dir(0, (1, 0))
    def opposite = Dir.from(n + 2)

object Dir:
    def from(n: Int): Dir = Dir.all.filter(_.n == n % 4).ensuring(_.size == 1).head
    def all = List(Up, Down, Left, Right)

enum Area:
    case Inside, Outside, Loop

case class Pos(x: Int, y: Int)
type Landscape = Map[Pos, Pipe]
type Loop = Map[Pos, LoopPiece]

def walk(p: Pos, d: Dir): Pos = Pos(p.x + d.d._1, p.y + d.d._2)

val pipeMap = Map('|' -> List(Up, Down), '-' -> List(Left, Right), 'L' -> List(Up, Right), 'J' -> List(Up, Left), 'F' -> List(Right, Down), '7' -> List(Left, Down))

case class Pipe(neighbors: List[Dir])
case class LoopPiece(from: Dir, to: Dir):
    def left: List[Dir] = ((from.n + 1) until (if to.n < from.n then to.n + 4 else to.n)).map(Dir.from).toList
    def right: List[Dir] = LoopPiece(to, from).left

def parse(a: List[String]): (Pos, Landscape) =
    val pipes = for (r, y) <- a.zipWithIndex; (v, x) <- r.zipWithIndex; p <- pipeMap.get(v) yield Pos(x, y) -> Pipe(p) 
    val start = for (r, y) <- a.zipWithIndex; (v, x) <- r.zipWithIndex if v == 'S' yield Pos(x, y)
    (start.head, pipes.toMap)

def walkLoop(start: Pos, l: Landscape): Loop =
    @tailrec def go(pos: Pos, last_dir: Dir, acc: Loop): Loop =
        if pos == start then acc else
            val dir = l(pos).neighbors.filter(_ != last_dir.opposite).ensuring(_.size == 1).head
            go(walk(pos, dir), dir, acc + (pos -> LoopPiece(last_dir.opposite, dir)))

    Dir.all.filter(d => l.get(walk(start, d)).exists(p => p.neighbors.contains(d.opposite))) match
        case List(start_dir, return_dir) => go(walk(start, start_dir), start_dir, Map(start -> LoopPiece(return_dir, start_dir)))
        case _ => Map()

def task1(a: List[String]): Long =
    walkLoop.tupled(parse(a)).size.ensuring(_ % 2 == 0) / 2

def task2(a: List[String]): Long =
    val loop = walkLoop.tupled(parse(a))

    val ys = a.indices
    val xs = a.head.indices
    val points = (for x <- xs; y <- ys yield Pos(x, y)).toSet
    
    // floodfill
    @tailrec def go(outside: Set[Pos], q: List[Pos]): Set[Pos] =
        if q.isEmpty then outside else
            val nbs = Dir.all.map(walk(q.head, _)).filter(points.contains(_)).filter(!outside.contains(_))
            go(outside ++ nbs, nbs ++ q.tail)

    // start by floodfilling from the known outside: beyond the array bounds
    val boundary = ys.flatMap(y => List(Pos(-1, y), Pos(xs.end, y))) ++ xs.flatMap(x => List(Pos(x, -1), Pos(x, ys.end)))
    val r = go(boundary.toSet ++ loop.keySet, boundary.toList)

    // check on which side of the pipe the outside is, then continue floodfill from there
    val xsl = List[LoopPiece => List[Dir]](_.left, _.right).map(side => loop.flatMap((p, l) => side(l).map(d => walk(p, d))).filter(!loop.contains(_)).toSet).map(a => a -> a.intersect(r).size).ensuring(_.exists(_._2 == 0)).filter(_._2 != 0).head._1
    (points -- go(r ++ xsl, xsl.toList)).size

Scala3

task2 is extremely disgusting code, but I was drawing an ugly picture of the situation and just wrote it down. Somehow, this worked first try.

import day10._
import day10.Dir._
import day11.Grid

extension (p: Pos) def parity = (p.x + p.y) % 2

def connect(p: Pos, d: Dir, g: Grid[Char]) = 
    val to = walk(p, d)
    Option.when(g.inBounds(to) && g.inBounds(p) && g(to) != '#' && g(p) != '#')(DiEdge(p, to))

def parseGrid(a: List[List[Char]]) =
    val g = Grid(a)
    Graph() ++ g.indices.flatMap(p => Dir.all.flatMap(d => connect(p, d, g)))

def reachableIn(n: Int, g: Graph[Pos, DiEdge[Pos]], start: g.NodeT) =
    @tailrec def go(q: List[(Int, g.NodeT)], depths: Map[Pos, Int]): Map[Pos, Int] =
        q match
            case (d, n) :: t =>
                if depths.contains(n) then go(t, depths) else
                    val successors = n.outNeighbors.map(d + 1 -> _)
                    go(t ++ successors, depths + (n.outer -> d))
            case _ =>
                depths

    go(List(0 -> start), Map()).filter((_, d) => d <= n).keys.toList

def compute(a: List[String], n: Int): Long =
    val grid = Grid(a.map(_.toList))
    val g = parseGrid(a.map(_.toList))
    val start = g.get(grid.indexWhere(_ == 'S').head)
    reachableIn(n, g, start).filter(_.parity == start.parity).size

def task1(a: List[String]): Long = compute(a, 64)
def task2(a: List[String]): Long = 
    // this only works for inputs where the following assertions holds
    val steps = 26501365
    assert((steps - a.size/2) % a.size == 0)
    assert(steps % 2 == 1 && a.size % 2 == 1)

    val d = steps/a.size
    val k = (2 * d + 1)
    val k1 = k*k/2

    def sq(x: Long) = x * x
    val grid = Grid(a.map(_.toList))
    val g = parseGrid(a.map(_.toList))
    val start = g.get(grid.indexWhere(_ == 'S').head)
    val center = reachableIn(a.size/2, g, start)

    // If you stare at the input enough, one can see that
    // for certain values of steps, the total area is covered
    // by some copies of the center diamond, and some copies
    // of the remaining triangle shapes.
    // 
    // In some repetitions, the parity of the location of S is
    // the same as the parity of the original S.
    // d0 counts the cells reachable in a center diamond where
    // this holds, dn0 counts the cells reachable in a center diamond
    // where the parity is flipped.
    // The triangular shapes are counted by dr and dnr, respectively.
    //
    // The weird naming scheme is taken directly from the weird diagram
    // I drew in order to avoid further confusing myself.
    val d0 = center.count(_.parity != start.parity)
    val dr = g.nodes.count(_.parity != start.parity) - d0
    val dn0 = center.size - d0
    val dnr = dr + d0 - dn0

    // these are the counts of how often each type of area appears
    val r = sq(2 * d + 1) / 2
    val (rplus, rminus) = (r/2, r/2)
    val z = sq(2 * d + 1) / 2 + 1
    val zplus = sq(1 + 2*(d/2))
    val zminus = z - zplus

    // calc result
    zplus * d0 + zminus * dn0 + rplus * dr + rminus * dnr

Scala3

case class Part(x: Range, m: Range, a: Range, s: Range):
    def rating: Int = x.start + m.start + a.start + s.start
    def combinations: Long = x.size.toLong * m.size.toLong * a.size.toLong * s.size.toLong

type ActionFunc = Part => (Option[(Part, String)], Option[Part])

case class Workflow(ops: List[ActionFunc]):
    def process(p: Part): List[(Part, String)] =
        @tailrec def go(p: Part, ops: List[ActionFunc], acc: List[(Part, String)]): List[(Part, String)] =
            ops match
                case o :: t => o(p) match
                    case (Some(branch), Some(fwd)) => go(fwd, t, branch::acc)
                    case (None, Some(fwd)) => go(fwd, t, acc)
                    case (Some(branch), None) => branch::acc
                    case (None, None) => acc
                case _ => acc
        go(p, ops, List())

def run(parts: List[Part], workflows: Map[String, Workflow]) =
    @tailrec def go(parts: List[(Part, String)], accepted: List[Part]): List[Part] =
        parts match
            case (p, wf) :: t => 
                val res = workflows(wf).process(p)
                val (acc, rest) = res.partition((_, w) => w == "A")
                val (rej, todo) = rest.partition((_, w) => w == "R")
                go(todo ++ t, acc.map(_._1) ++ accepted)
            case _ => accepted
    go(parts.map(_ -> "in"), List())

def parseWorkflows(a: List[String]): Map[String, Workflow] =
    def generateActionGt(n: Int, s: String, accessor: Part => Range, setter: (Part, Range) => Part): ActionFunc = p => 
        val r = accessor(p)
        (Option.when(r.end > n + 1)((setter(p, math.max(r.start, n + 1) until r.end), s)), Option.unless(r.start > n)(setter(p, r.start until math.min(r.end, n + 1))))
    def generateAction(n: Int, s: String, accessor: Part => Range, setter: (Part, Range) => Part): ActionFunc = p => 
        val r = accessor(p)
        (Option.when(r.start < n)((setter(p, r.start until math.min(r.end, n)), s)), Option.unless(r.end <= n)(setter(p, math.max(r.start, n) until r.end)))
    
    val accessors = Map("x"->((p:Part) => p.x), "m"->((p:Part) => p.m), "a"->((p:Part) => p.a), "s"->((p:Part) => p.s))
    val setters = Map("x"->((p:Part, v:Range) => p.copy(x=v)), "m"->((p:Part, v:Range) => p.copy(m=v)), "a"->((p:Part, v:Range) => p.copy(a=v)), "s"->((p:Part, v:Range) => p.copy(s=v)))

    def parseAction(a: String): ActionFunc =
        a match
            case s"$v<$n:$s" => generateAction(n.toInt, s, accessors(v), setters(v))
            case s"$v>$n:$s" => generateActionGt(n.toInt, s, accessors(v), setters(v))
            case s => p => (Some((p, s)), None)

    a.map(_ match{ case s"$name{$items}" => name -> Workflow(items.split(",").map(parseAction).toList) }).toMap

def parsePart(a: String): Option[Part] =
    a match
        case s"{x=$x,m=$m,a=$a,s=$s}" => Some(Part(x.toInt until 1+x.toInt, m.toInt until 1+m.toInt, a.toInt until 1+a.toInt, s.toInt until 1+s.toInt))
        case _ => None

def task1(a: List[String]): Long = 
    val in = a.chunk(_ == "")
    val wfs = parseWorkflows(in(0))
    val parts = in(1).flatMap(parsePart)
    run(parts, wfs).map(_.rating).sum

def task2(a: List[String]): Long =
    val wfs = parseWorkflows(a.chunk(_ == "").head)
    val parts = List(Part(1 until 4001, 1 until 4001, 1 until 4001, 1 until 4001))
    run(parts, wfs).map(_.combinations).sum