Uiua

Takes about 3 seconds to solve both parts for live data, caused primarily by my terrible fill function in FieldCoords which repeatedly refills and dedups already discovered cells. I promised myself when I wrote it that I would revisit it, but I really can't be bothered right now. Sorry Kai.

LATE EDIT: Thanks to Quant for the inspiration to revisit this. With his code snippet and the realisation that I should normalise all fields to remove wasted space, runtime is now down to 55ms.

Data ← ⊜∘⊸≠@\n "AAAA\nBBCD\nBBCC\nEEEC"
N₄     ← [¯1_0 1_0 0_¯1 0_1]               # Four orthogonal neighbours.
Fences ← /+/+=0≡(⬚0⊡+N₄¤)⊙¤⊚.°⊚            # Fences for a field, by looking for edges.
Cs     ← [0 1 1 0 1 0 2 1 1 2 0 1 0 1 1 0] # Number of corners keyed by bitarray of 2x2 grid.
Sides  ← /+♭⬚0⧈(⊡:Cs°⋯♭)2_2°⊚              # Add border, look for corners in 2x2 windows.

# Use `classify` to find fields, then normalise to 0_0.
Fields ← ≡⍚(-¤⊸/↧)⊜□:⇡△.+1⍜♭⊛Data # Thanks to Quant!

/+×≡◇⊃⧻Fences Fields
/+×≡◇⊃⧻Sides Fields

Data ← ⊜∘⊸≠@\n "AAAA\nBBCD\nBBCC\nEEEC"
N₄     ← [¯1_0 1_0 0_¯1 0_1]               # Four orthogonal neighbours.
Fences ← /+≡(/+=0⬚0⊡+N₄¤)⊙¤⊚.°⊚            # Fences for a field, by looking for edges.
Cs     ← [0 1 1 0 1 0 2 1 1 2 0 1 0 1 1 0] # Number of corners keyed by bitarray of 2x2 grid.
Sides  ← /+/+⧈(⊡:Cs°⋯♭)2_2⌝↘¯1_¯1⌝↘1_1°⊚   # Add border, look for corners in 2x2 windows.

Fields ← ⊜□:⇡△.+1⍜♭⊛Data

/+×≡◇⊃⧻Fences Fields
/+×≡◇⊃⧻Sides Fields

C

No big trouble today, just a bit of careful debugging of my part 2 logic for which I was greatly helped by some Redditor’s testcase 🙏

Happy to have gotten it all in the single flood fill function without any extra passes.

::: spoiler Code

#include "common.h"

#define GZ 144
static char g[GZ][GZ];
static char seen[GZ][GZ];

static void
count(char c, int x, int y, int *area, int *perim, int *sides)
{
	if (g[y][x] != c) { (*perim)++; return; }
	if (seen[y][x]) return;

	*area += 1;
	seen[y][x] = 1;

	/* count start of top/left edges, end of bottom/right edges */
	*sides += g[y-1][x]!=c && ((g[y-1][x-1]==c) || (g[y][x-1]!=c));
	*sides += g[y+1][x]!=c && ((g[y+1][x+1]==c) || (g[y][x+1]!=c));
	*sides += g[y][x-1]!=c && ((g[y-1][x-1]==c) || (g[y-1][x]!=c));
	*sides += g[y][x+1]!=c && ((g[y+1][x+1]==c) || (g[y+1][x]!=c));

	count(c, x, y-1, area, perim, sides);
	count(c, x, y+1, area, perim, sides);
	count(c, x-1, y, area, perim, sides);
	count(c, x+1, y, area, perim, sides);
}

int
main(int argc, char **argv)
{
	int p1=0,p2=0, x,y, area, perim, sides;

	if (argc > 1)
		DISCARD(freopen(argv[1], "r", stdin));

	for (y=1; fgets(g[y]+1, GZ-2, stdin); y++)
		assert(y+1 < GZ);

	for (y=1; y<GZ-1; y++)
	for (x=1; x<GZ-1; x++)
		if (isalpha(g[y][x]) && !seen[y][x]) {
			area  = perim = sides = 0;
			count(g[y][x], x, y, &area, &perim, &sides);
			p1 += area * perim;
			p2 += area * sides;
		}

	printf("12: %d %d\n", p1, p2);
}

:::

https://github.com/sjmulder/aoc/blob/master/2024/c/day12.c

Dart

Filling to find regions was easy. Counting areas was easy. Counting fences was okay. Counting sides caused me a lot of frustration as I tried and rejected a number of approaches, eventually arriving at a reasonably simple corner-counting approach. None of this was helped by all the examples lacking at least two important layouts, causing today to be the first day that I ran out of hints for wrong answers :-(.

(corners is where the magic happens)

::: spoiler 70 or so lines, half a second to run, so that's fine for today.

import 'dart:math';
import 'package:collection/collection.dart';
import 'package:more/more.dart';

const List<Point> n4 = [Point(0, 1), Point(0, -1), Point(1, 0), Point(-1, 0)];
List<Point> n8 = n4 + [Point(1, 1), Point(1, -1), Point(-1, 1), Point(-1, -1)];
const List<Point> c4 = [Point(0, 0), Point(0, 1), Point(1, 0), Point(1, 1)];

(Map<Point, String>, Map<Point, List<Point>>) parse(ls) {
  var nodes = {
    for (var y in 0.to(ls.length))
      for (var x in 0.to(ls.first.length)) Point<num>(x, y): ls[y][x] as String
  };
  var nexts = Map.fromEntries(nodes.keys.map((n) => MapEntry(
      n,
      n4
          .map((d) => n + d)
          .where((d) => (nodes[d] ?? '') == nodes[n]!)
          .toList())));
  return (nodes, nexts);
}

(int, Set<Point>) survey(
    Point here, String target, Map<Point<num>, List<Point>> nexts,
    [Set sofar = const {}]) {
  seen.add(here);
  var fences = 4 - nexts[here]!.length;
  var area = {here};
  for (var f in nexts[here]!.where((e) => !seen.contains(e))) {
    var (fs, a) = survey(f, target, nexts, sofar.toSet()..add(f));
    fences += fs;
    area.addAll(a);
  }
  return (fences, area);
}

late Set<Point> seen;
List<(int, Set<Point<num>>)> costs(List<String> lines) {
  seen = {};
  var ret = <(int, Set<Point<num>>)>[];
  var (nodes, nexts) = parse(lines);
  var toVisit = nodes.keys.toSet();
  while (toVisit.isNotEmpty) {
    var here = toVisit.first;
    toVisit.remove(here);
    ret.add(survey(here, nodes[here]!, nexts));
    toVisit.removeAll(seen);
  }
  return ret;
}

Function eq = const ListEquality().equals;
int corners(Set<Point> points) {
  var border = points.map((e) => n8.map((n) => n + e)).flattenedToSet
    ..addAll(points);
  // A corner is where a 2x2 grid contains one/three in-shape points, or
  // two diagonally-opposite cells
  var corners = 0;
  for (var cell in border) {
    var count = c4.map((e) => points.contains(e + cell)).toList();
    if (count.count((e) => e) % 2 == 1) {
      corners += 1;
    } else {
      if (eq(count, [true, false, false, true]) ||
          eq(count, [false, true, true, false])) {
        corners += 2;
      }
    }
  }
  return corners;
}

part1(lines) => costs(lines).map((e) => e.first * e.last.length).sum;
part2(lines) => costs(lines).map((e) => corners(e.last) * e.last.length).sum;

:::

Nim

Runtime: 7ms 3.18 ms

Part 1: I use flood fill to count all grouped plants and keep track of each border I see.
Part 2: I use an algorithm similar to "merge overlapping ranges" to count spans of borders (border orientation matters) in each row and column, for each group. Resulting code (hidden under spoiler) is a little messy and not very DRY (it's completely soaked).

Edit: refactored solution, removed some very stupid code.

::: spoiler proc groupSpans()

proc groupSpans(borders: seq[(Vec2, Dir)]): int =
  ## returns number of continuous groups of cells with same Direction
  ## and on the same row or column
  var borders = borders
  var horiz = borders.filterIt(it[1] in {U, D})
  while horiz.len > 0:
    var sameYandDir = @[horiz.pop()]
    var curY = sameYandDir[^1][0].y
    var curDir = sameYandDir[^1][1]
    for i in countDown(horiz.high, 0):
      if horiz[i][0].y == curY and horiz[i][1] == curDir:
        sameYandDir.add horiz[i]
        horiz.del i
    sameYandDir.sort((a,b)=>cmp(a[0].x, b[0].x), Descending)

    var cnt = 1
    for i, (p,d) in sameYandDir.toOpenArray(1, sameYandDir.high):
      if sameYandDir[i][0].x - p.x  != 1: inc cnt
    result += cnt

  var vert = borders.filterIt(it[1] in {L, R})
  while vert.len > 0:
    var sameXandDir = @[vert.pop()]
    var curX = sameXandDir[^1][0].x
    var curDir = sameXandDir[^1][1]
    for i in countDown(vert.high, 0):
      if vert[i][0].x == curX and vert[i][1] == curDir:
        sameXandDir.add vert[i]
        vert.del i
    sameXandDir.sort((a,b)=>cmp(a[0].y, b[0].y), Descending)

    var cnt = 1
    for i, (p,d) in sameXandDir.toOpenArray(1, sameXandDir.high):
      if sameXandDir[i][0].y - p.y  != 1: inc cnt
    result += cnt

:::

type
  Dir = enum L,R,U,D
  Vec2 = tuple[x,y: int]
  GroupData = object
    plantCount: int
    borders: seq[(Vec2, Dir)]

const Adjacent: array[4, Vec2] = [(-1,0),(1,0),(0,-1),(0,1)]

proc solve(input: string): AOCSolution[int, int] =
  let grid = input.splitLines()
  var visited = newSeqWith(grid.len, newSeq[bool](grid[0].len))
  var groups: seq[GroupData]

  proc floodFill(pos: Vec2, plant: char, groupId: int) =
    visited[pos.y][pos.x] = true
    inc groups[groupId].plantCount
    for di, d in Adjacent:
      let pd: Vec2 = (pos.x+d.x, pos.y+d.y)
      if pd.x < 0 or pd.y < 0 or pd.x > grid[0].high or pd.y > grid.high or
        grid[pd.y][pd.x] != plant:
        groups[groupId].borders.add (pd, Dir(di))
        continue
      if visited[pd.y][pd.x]: continue
      floodFill(pd, plant, groupId)

  for y in 0..grid.high:
    for x in 0..grid[0].high:
      if visited[y][x]: continue
      groups.add GroupData()
      floodFill((x,y), grid[y][x], groups.high)

  for gid, group in groups:
    result.part1 += group.plantCount * group.borders.len
    result.part2 += group.plantCount * group.borders.groupSpans()

---

Codeberg repo

Rust

I essentially used flood fill to collect each region. Part 1 was then relatively easy: for each point, check how many neighbors are outside of the region.

Part 2 took me forever, and I ended up looking for hints online, where I discovered that an easy way to count the number of sides is to instead count the number of corners. Doing this for "normal" corners (e.g. in a square) was relatively easy, but "reverse corners" took me a long time. Corners like here what we see in the NE corner of the first C in the third row here:

....
..C.
..CC
...C

I'm more or less happy with my solution, but my brain is now totally fried.

https://gitlab.com/bricka/advent-of-code-2024-rust/-/blob/main/src/days/day12.rs?ref_type=heads

use std::collections::HashSet;

use crate::grid::{Coordinate, Direction, Grid};
use crate::solver::DaySolver;

fn perimeter_score(c: Coordinate, grid: &MyGrid) -> usize {
    let plant_type = grid[c];

    Direction::orthogonal_iter()
        .map(|d| grid.neighbor_in_direction(c, d))
        .map(|c_opt| match c_opt {
            None => 1,
            Some(c) => if grid[c] == plant_type {
                0
            } else {
                1
            }
        })
        .sum()
}

type MyGrid = Grid<char>;

struct Region {
    #[allow(dead_code)]
    plant_type: char,
    coordinates: HashSet<Coordinate>,
}

impl Region {
    fn new(plant_type: char, coordinates: HashSet<Coordinate>) -> Region {
        Region { plant_type, coordinates }
    }

    fn iter(&self) -> impl Iterator<Item = &Coordinate> {
        self.coordinates.iter()
    }

    fn part1_score(&self, grid: &MyGrid) -> usize {
        self.coordinates.len() * self.coordinates.iter().map(|c| perimeter_score(*c, grid)).sum::<usize>()
    }

    fn part2_score(&self, grid: &MyGrid) -> usize {
        let area = self.coordinates.len();
        let sides = self.number_of_corners(grid);

        area * sides
    }

    fn number_of_corners(&self, grid: &MyGrid) -> usize {
        self.coordinates.iter().cloned()
            .map(|coordinate| {
                // How many corners do we have from here?
                // println!("Checking {}", border_coordinate);

                let corner_count = Direction::diagonal_iter()
                    .filter(|corner_direction| {
                        // Either:
                        // 1) Both neighbor directions are not 100% in the region
                        // 2) Both neighbors are in the region, but the corner itself isn't

                        let corner_in_region = match grid.neighbor_in_direction(coordinate, *corner_direction) {
                            None => false,
                            Some(c) => self.coordinates.contains(&c),
                        };

                        let both_neighbors_not_in_region = corner_direction.neighbor_directions().iter()
                            .all(|direction| match grid.neighbor_in_direction(coordinate, *direction) {
                                None => true,
                                Some(c) => !self.coordinates.contains(&c),
                            });

                        let both_neighbors_in_region = corner_direction.neighbor_directions().iter()
                            .all(|direction| match grid.neighbor_in_direction(coordinate, *direction) {
                                None => false,
                                Some(c) => self.coordinates.contains(&c),
                            });

                        both_neighbors_not_in_region || (both_neighbors_in_region && !corner_in_region)
                    })
                    .count();
                // println!("Corner count = {}", corner_count);
                corner_count
            })
            .sum()
    }
}

fn parse_input(input: String) -> MyGrid {
    input.lines()
        .map(|line| line.chars().collect())
        .collect::<Vec<Vec<char>>>()
        .into()
}

fn find_region_at(grid: &MyGrid, start: Coordinate) -> Region {
    let plant_type = grid[start];
    let mut coordinates = HashSet::new();
    let mut frontier = vec![start];

    while let Some(coordinate) = frontier.pop() {
        if grid[coordinate] == plant_type  && !coordinates.contains(&coordinate) {
            coordinates.insert(coordinate);
            frontier.extend(grid.orthogonal_neighbors_iter(coordinate));
        }
    }

    Region::new(plant_type, coordinates)
}

fn find_regions(grid: &MyGrid) -> Vec<Region> {
    let mut visited_coordinates: HashSet<Coordinate> = HashSet::new();
    let mut regions = vec![];

    for coordinate in grid.coordinates_iter() {
        if !visited_coordinates.contains(&coordinate) {
            let region = find_region_at(grid, coordinate);
            visited_coordinates.extend(region.iter().cloned());
            regions.push(region);
        }
    }

    regions
}

pub struct Day12Solver;

impl DaySolver for Day12Solver {
    fn part1(&self, input: String) -> usize {
        let grid = parse_input(input);
        let regions = find_regions(&grid);

        regions.into_iter()
            .map(|region| region.part1_score(&grid))
            .sum()
    }

    fn part2(&self, input: String) -> usize {
        let grid = parse_input(input);
        let regions = find_regions(&grid);

        regions.into_iter()
            .map(|region| region.part2_score(&grid))
            .sum()
    }
}

AAAAAA
AAABBA
AAABBA
ABBAAA
ABBAAA
AAAAAA

Haskell

This was a bit of a fiddly one. There's probably scope for golfing it down some more, but I've had enough for today :3

::: spoiler Solution

import Control.Arrow
import Data.List
import Data.Map (Map)
import Data.Map qualified as Map
import Data.Set (Set)
import Data.Set qualified as Set

readInput :: String -> Map (Int, Int) Char
readInput s = Map.fromList [((i, j), c) | (i, l) <- zip [0 ..] (lines s), (j, c) <- zip [0 ..] l]

(i1, j1) .+. (i2, j2) = (i1 + i2, j1 + j2)

(i1, j1) .-. (i2, j2) = (i1 - i2, j1 - j2)

directions = [(0, 1), (1, 0), (0, -1), (-1, 0)] :: [(Int, Int)]

edges = zip ps (drop 1 ps) :: [((Int, Int), (Int, Int))]
  where
    ps = [(0, 1), (1, 1), (1, 0), (0, 0), (0, 1)]

regions :: Map (Int, Int) Char -> [Set (Int, Int)]
regions = unfoldr (fmap (uncurry removeRegion) . Map.minViewWithKey)
  where
    removeRegion (p, t) = go Set.empty (Set.singleton p)
      where
        go r ps plots
          | Set.null ps = (r, plots)
          | otherwise =
              let ps' =
                    Set.filter (\p -> plots Map.!? p == Just t) $
                      Set.fromList (concatMap adjacent ps) Set.\\ ps
               in go (Set.union r ps) ps' (Map.withoutKeys plots ps')
        adjacent = (`map` directions) . (.+.)

boundary :: Set (Int, Int) -> Set ((Int, Int), (Int, Int))
boundary region =
  Set.fromList $
    [ (p .+. e1, p .+. e2)
      | p <- Set.elems region,
        (d, (e1, e2)) <- zip directions edges,
        p .+. d `Set.notMember` region
    ]

perimeter :: Set (Int, Int) -> [[(Int, Int)]]
perimeter = unfoldr (fmap (uncurry removeChain) . Set.minView) . boundary
  where
    removeChain e@(e1, e2) es = first (e1 :) $ go [] e es
    go c e@(e1, e2) es =
      case find ((== e2) . fst) es of
        Nothing -> (e1 : c, es)
        Just e' -> go (e1 : c) e' (Set.delete e' es)

countSides :: [(Int, Int)] -> Int
countSides ps = length $ group $ zipWith (.-.) (drop 1 ps) ps

main = do
  input <- readInput <$> readFile "input12"
  let rs = map (Set.size &&& perimeter) $ regions input
  print . sum $ map (\(a, p) -> a * sum (map (subtract 1 . length) p)) rs
  print . sum $ map (\(a, p) -> a * sum (map countSides p)) rs

:::

Haskell

Detecting regions is a floodfill. For Part 2, I select all adjacent tiles that are not part of a region and group them by the direction relative to the closest region tile, then group adjacent tiles with the same direction again and count.

Edit:

Takes 0.06s

::: spoiler Reveal Code

import Control.Arrow

import Data.Array.Unboxed (UArray)
import Data.Set (Set)
import Data.Map (Map)

import qualified Data.List as List
import qualified Data.Set as Set
import qualified Data.Map as Map
import qualified Data.Array.Unboxed as UArray

parse :: String -> UArray (Int, Int) Char
parse s = UArray.listArray ((1, 1), (n, m)) . filter (/= '\n') $ s
        where
                n = takeWhile (/= '\n') >>> length $ s
                m = filter (== '\n') >>> length >>> pred $ s

neighborCoordinates (p1, p2) = [(p1-1, p2), (p1, p2-1), (p1, p2+1), (p1+1, p2)]

allNeighbors p a = neighborCoordinates
        >>> filter (UArray.inRange (UArray.bounds a))
        $ p

regionNeighbors p a = allNeighbors p
        >>> filter ((a UArray.!) >>> (== pTile))
        $ a
        where
                pTile = a UArray.! p

floodArea :: Set (Int, Int) -> Set (Int, Int) -> UArray (Int, Int) Char -> Set (Int, Int)
floodArea e o a
        | Set.null o = e
        | otherwise  = floodArea e' o' a
        where
                e' = Set.union e o
                o' = Set.fold (Set.union . Set.fromDistinctAscList .  (filter (`Set.notMember` e')) . (flip regionNeighbors a)) Set.empty o

findRegions garden = findRegions' (Set.fromList . UArray.indices $ garden) garden

findRegions' remainingIndices garden
        | Set.null remainingIndices = []
        | otherwise = removedIndices : findRegions' remainingIndices' garden
        where
                removedIndices = floodArea Set.empty (Set.singleton . Set.findMin $ remainingIndices) garden
                remainingIndices' = Set.difference remainingIndices removedIndices

perimeter region = Set.fold ((+) . length . filter (`Set.notMember` region) . neighborCoordinates) 0 region

part1 rs = map (Set.size &&& perimeter)
        >>> map (uncurry (*))
        >>> sum
        $ rs

turnLeft ( 0, 1) = (-1, 0) -- right
turnLeft ( 0,-1) = ( 1, 0) -- left
turnLeft ( 1, 0) = ( 0, 1) -- down
turnLeft (-1, 0) = ( 0,-1) -- up

turnRight = turnLeft . turnLeft . turnLeft

move (py, px) (dy, dx) = (py + dy, px + dx)

tupleDelta (y1, x1) (y2, x2) = (y1-y2, x1-x2)

isRegionInner region p = all (`Set.member` region) (neighborCoordinates p)

groupEdges d ps
        | Set.null ps = []
        | otherwise   = collectedEdge : groupEdges d ps'
        where
                ps' = Set.difference ps collectedEdge
                collectedEdge = Set.union leftPoints rightPoints
                leftPoints = iterate (move dl)
                        >>> takeWhile (`Set.member` ps)
                        >>> Set.fromList
                        $ currentPoint
                rightPoints = iterate (move dr)
                        >>> takeWhile (`Set.member` ps)
                        >>> Set.fromList
                        $ currentPoint
                currentPoint = Set.findMin ps
                dr = turnRight d
                dl = turnLeft  d

linearPerimeter region = Map.foldr ((+) . length) 0 $ groupedEdges
        where 
                edgeTiles = Set.filter (not . isRegionInner region) region
                regionNeighbors = List.concatMap (\ p -> map (p,). filter (`Set.notMember` region) . neighborCoordinates $ p) . Set.toList $ region
                groupedNeighbors = List.map (uncurry tupleDelta &&& Set.singleton . snd)
                        >>> Map.fromListWith (Set.union)
                        $ regionNeighbors
                groupedEdges = Map.mapWithKey groupEdges
                        $ groupedNeighbors

part2 rs = map (Set.size &&& linearPerimeter)
        >>> map (uncurry (*))
        >>> sum
        $ rs

main = getContents
        >>= print
        . (part1 &&& part2)
        . findRegions
        . parse

:::

Ended up oversleeping somewhat, so I did the first part on the way to work using flood fills over a global visited set, and now that work's over I've sat down to expand that solution to do corner counting for part two as well.

::: spoiler C#

char[] map = new char[0];
(int X, int Y) size = (0, 0);

public void Input(IEnumerable<string> lines)
{
  map = string.Concat(lines).ToCharArray();
  size = (lines.First().Length, lines.Count());
}

Dictionary<HashSet<(int,int)>,int> areas = new Dictionary<HashSet<(int,int)>,int>();
public void PreCalc()
{
  HashSet<(int, int)> visited = new HashSet<(int, int)>();
  for (int y = 0; y < size.Y; ++y)
    for (int x = 0; x < size.X; ++x)
    {
      var at = (x, y);
      if (visited.Contains(at))
        continue;

      var area = Flood((x, y), visited);
      areas[area.Area] = area.Perim;
    }
}

public void Part1()
{
  int sum = areas.Select(kv => kv.Key.Count * kv.Value).Sum();

  Console.WriteLine($"Fencing total: {sum}");
}
public void Part2()
{
  int sum = areas.Select(kv => kv.Key.Count * countCorners(kv.Key)).Sum();

  Console.WriteLine($"Fencing total: {sum}");
}

readonly (int dX, int dY)[] links = new[] { (1, 0), (0, 1), (-1, 0), (0, -1) };
(HashSet<(int,int)> Area, int Perim) Flood((int X, int Y) from, HashSet<(int X, int Y)> visited)
{
  char at = map[from.Y * size.X + from.X];

  (HashSet<(int,int)> Area, int Perim) ret = (new HashSet<(int,int)>(), 0);
  visited.Add(from);
  ret.Area.Add(from);

  foreach (var link in links)
  {
    (int X, int Y) newAt = (from.X + link.dX, from.Y + link.dY);
    char offset ;
    if (newAt.X < 0 || newAt.X >= size.X || newAt.Y < 0 || newAt.Y >= size.Y)
      offset = '\0';
    else
      offset = map[newAt.Y * size.X + newAt.X];

    if (offset == at)
    {
      if (visited.Contains(newAt))
        continue;

      var nextArea = Flood(newAt, visited);
      ret.Area.UnionWith(nextArea.Area);
      ret.Perim += nextArea.Perim;
    }
    else
    {
      ret.Perim += 1;
    }
  }

  return ret;
}

readonly (int dX, int dY)[] cornerPoints = new[] { (0, 0), (1, 0), (1, 1), (0, 1) };
readonly int[] diagonalValues = new[] { (2 << 0) + (2 << 2), (2 << 1) + (2 << 3) };
int countCorners(HashSet<(int X, int Y)> points)
{
  int corners = 0;
  var bounds = findBounds(points);
  for (int y = bounds.minY - 1; y < bounds.maxY + 1; ++y)
    for (int x = bounds.minX - 1; x < bounds.maxX + 1; ++x)
    {
      var atPoint = cornerPoints.Select(c => points.Contains((x + c.dX, y + c.dY)));
      var before = corners;
      if (atPoint.Where(c => c).Count() % 2 == 1)
        corners++;
      else if (diagonalValues.Contains(atPoint.Select((c, i) => c ? (2 << i) : 0).Sum()))
        corners += 2;
    }

  return corners;
}

(int minX, int minY, int maxX, int maxY) findBounds(HashSet<(int X, int Y)> points)
{
  (int minX, int minY, int maxX, int maxY) ret = (int.MaxValue, int.MaxValue, int.MinValue, int.MinValue);
  foreach (var point in points)
  {
    ret.minX = Math.Min(ret.minX, point.X);
    ret.minY = Math.Min(ret.minY, point.Y);
    ret.maxX = Math.Max(ret.maxX, point.X);
    ret.maxY = Math.Max(ret.maxY, point.Y);
  }

  return ret;
}

:::

Python

Part 1: Simple DFS counting up the cells and exposed edges

Part 2: Still DFS, however I chose to keep track of all segments of the area, merging them if two segments connected. In the end, number of non-overlapping, non-intersecting segments is equal to number of sides. Not the most efficient solution, but it works.

import os
from collections import defaultdict

# paths
here = os.path.dirname(os.path.abspath(__file__))
filepath = os.path.join(here, "input.txt")

# read input
with open(filepath, mode="r", encoding="utf8") as f:
    data = f.read()
# setup input vars
garden = data.splitlines()
m, n = len(garden), len(garden[0])


def part1():
    visited = set()

    def calcFenceCostFrom(i, j):
        """Calculates the fencing cost of the region starting from coords (i, j)"""
        global garden, m, n

        plant_type = garden[i][j]
        stack = [(i, j)]
        area, perimeter = 0, 0

        while stack:
            ci, cj = stack.pop()
            if (ci, cj) in visited:
                continue
            visited.add((ci, cj))

            # add cell to area
            area += 1

            # check top cell
            if ci > 0 and garden[ci - 1][cj] == plant_type:
                stack.append((ci - 1, cj))
            else:
                # if no top cell, add the edge to perimeter
                perimeter += 1

            # check left cell
            if cj > 0 and garden[ci][cj - 1] == plant_type:
                stack.append((ci, cj - 1))
            else:
                # if no left cell, add the edge to perimeter
                perimeter += 1

            # check bottom cell
            if ci < m - 1 and garden[ci + 1][cj] == plant_type:
                stack.append((ci + 1, cj))
            else:
                # if no bottom cell, add the edge to perimeter
                perimeter += 1

            # check right cell
            if cj < n - 1 and garden[ci][cj + 1] == plant_type:
                stack.append((ci, cj + 1))
            else:
                # if no right cell, add the edge to perimeter
                perimeter += 1

        return area * perimeter

    # calculate fencing cost for every region
    fencing_cost = 0
    for i in range(m):
        for j in range(n):
            if (i, j) in visited:
                continue
            fencing_cost += calcFenceCostFrom(i, j)

    print(fencing_cost)


def part2():
    visited = set()

    def calcFenceCostFrom(i, j):
        """Calculates the fencing cost of the region starting from coords (i, j)"""
        global garden, m, n

        plant_type = garden[i][j]
        stack = [(i, j)]
        area = 0

        # keep track of all distinct, non-intersecting horizontal and vertical segments
        segments = {
            "H": defaultdict(set),
            "V": defaultdict(set)
        }  # fmt: skip

        while stack:
            ci, cj = stack.pop()
            if (ci, cj) in visited:
                continue
            visited.add((ci, cj))

            # add cell to area
            area += 1

            # check top cell
            if ci > 0 and garden[ci - 1][cj] == plant_type:
                stack.append((ci - 1, cj))
            else:
                # record edge segment
                ei = ci - 0.25  # push out the horizontal segment
                segment_set = segments["H"][ei]
                ej_from, ej_to = cj - 0.5, cj + 0.5  # extend the segment to connect with neighbors
                merge_segments(segment_set, ej_from, ej_to)  # merge with current segment set

            # check left cell
            if cj > 0 and garden[ci][cj - 1] == plant_type:
                stack.append((ci, cj - 1))
            else:
                # record edge segment
                ej = cj - 0.25  # push out the vertical segment
                segment_set = segments["V"][ej]
                ei_from, ei_to = ci - 0.5, ci + 0.5  # extend the segment to connect with neighbors
                merge_segments(segment_set, ei_from, ei_to)  # merge with current segment set

            # check bottom cell
            if ci < m - 1 and garden[ci + 1][cj] == plant_type:
                stack.append((ci + 1, cj))
            else:
                # record edge segment
                ei = ci + 0.25  # push out the horizontal segment
                segment_set = segments["H"][ei]
                ej_from, ej_to = cj - 0.5, cj + 0.5  # extend the segment to connect with neighbors
                merge_segments(segment_set, ej_from, ej_to)  # merge with current segment set

            # check right cell
            if cj < n - 1 and garden[ci][cj + 1] == plant_type:
                stack.append((ci, cj + 1))
            else:
                # record edge segment
                ej = cj + 0.25  # push out the vertical segment
                segment_set = segments["V"][ej]
                ei_from, ei_to = ci - 0.5, ci + 0.5  # extend the segment to connect with neighbors
                merge_segments(segment_set, ei_from, ei_to)  # merge with current segment set

        # number of distinct segments == number of sides
        sides = sum(len(segment_set) for seg_dict in segments.values() for segment_set in seg_dict.values())
        return area * sides

    def merge_segments(segment_set: set, idx_from: int, idx_to: int):
        """Merge segment into existing segment set"""
        # find any overlapping / intersecting segments before and after current
        former_segment, latter_segment = None, None
        for s_from, s_to in segment_set:
            if s_from < idx_from and s_to >= idx_from:
                former_segment = (s_from, s_to)
            if s_to > idx_to and s_from <= idx_to:
                latter_segment = (s_from, s_to)

        if former_segment is None and latter_segment is None:
            # there is no overlapping segment
            segment_set.add((idx_from, idx_to))
        elif former_segment == latter_segment:
            # the overlap segment contains our full segment
            pass
        elif former_segment is None:
            # there is a latter segment only
            segment_set.remove(latter_segment)
            segment_set.add((idx_from, latter_segment[1]))
        elif latter_segment is None:
            # there is a former segment only
            segment_set.remove(former_segment)
            segment_set.add((former_segment[0], idx_to))
        else:
            # both are disconnected segments
            segment_set.remove(former_segment)
            segment_set.remove(latter_segment)
            segment_set.add((former_segment[0], latter_segment[1]))

    fencing_cost = 0
    for i in range(m):
        for j in range(n):
            if (i, j) in visited:
                continue
            fencing_cost += calcFenceCostFrom(i, j)

    print(fencing_cost)


part1()
part2()

C#

There is probably a more efficient way of finding the sides, but this way was what came to me.

using System.Diagnostics;
using Common;

namespace Day12;

static class Program
{
    static void Main()
    {
        var start = Stopwatch.GetTimestamp();

        var sampleInput = Input.ParseInput("sample.txt");
        var programInput = Input.ParseInput("input.txt");

        var (samplePart1, samplePart2) = Solve(sampleInput);
        Console.WriteLine($"Part 1 sample: {samplePart1}");
        Console.WriteLine($"Part 1 input: {samplePart2}");

        var (inputPart1, inputPart2) = Solve(programInput);
        Console.WriteLine($"Part 2 sample: {inputPart1}");
        Console.WriteLine($"Part 2 input: {inputPart2}");

        Console.WriteLine($"That took about {Stopwatch.GetElapsedTime(start)}");
    }

    static (int part1, int part2) Solve(Input i)
    {
        var retail = 0;
        var bulk = 0;
        var allPlotPoints = new Dictionary<char, HashSet<Point>>();
        foreach (var p in Grid.EnumerateAllPoints(i.Bounds))
        {
            var plant = i.ElementAt(p);

            if (!allPlotPoints.TryGetValue(plant, out var previousPlotPoints))
            {
                previousPlotPoints = new();
                allPlotPoints[plant] = previousPlotPoints;
            }
            else if (previousPlotPoints.Contains(p)) continue;

            var plotPoints = new HashSet<Point>();
            var perimeter = SearchPlot(i, plotPoints, plant, p);
            var area = plotPoints.Count;
            var sides = CountSides(plotPoints);
            retail += area * perimeter;
            bulk += area * sides;

            previousPlotPoints.AddRange(plotPoints);
        }

        return (retail, bulk);
    }

    static int CountSides(HashSet<Point> plot)
    {
        var sides = 0;

        // Track the points we've visited searching for sides
        HashSet<Point> visitedDownRight = new(),
            visitedDownLeft = new(),
            visitedRightDown = new(),
            visitedRightUp = new();

        // Sort the points in the plot from upper-left to lower-right, so we can
        // go through them in reading order
        foreach (var p in plot.OrderBy(p => (p.Row * 10000) + p.Col))
        {
            // Move right while looking up
            sides += GetSideLength(p, plot, visitedRightUp, Direction.Right, Direction.Up) > 0 ? 1 : 0;
            
            // Move right while looking down
            sides += GetSideLength(p, plot, visitedRightDown, Direction.Right, Direction.Down) > 0 ? 1 : 0;
            
            // Move down while looking right
            sides += GetSideLength(p, plot, visitedDownRight, Direction.Down, Direction.Right) > 0 ? 1 : 0;
            
            // Move down while looking left
            sides += GetSideLength(p, plot, visitedDownLeft, Direction.Down, Direction.Left) > 0 ? 1 : 0;
        }

        return sides;
    }

    static int GetSideLength(Point p, HashSet<Point> plotPoints, HashSet<Point> visited, Direction move, Direction look)
    {
        if (!plotPoints.Contains(p)) return 0;
        if (!visited.Add(p)) return 0;
        if (plotPoints.Contains(p.Move(look))) return 0;
        return 1 + GetSideLength(p.Move(move), plotPoints, visited, move, look);
    }

    static int SearchPlot(Input i, HashSet<Point> plotPoints, char plant, Point p)
    {
        if (!plotPoints.Add(p)) return 0;
        return p
            .GetCardinalMoves()
            .Select(move =>
            {
                if (!i.IsInBounds(move) || (i.ElementAt(move) != plant)) return 1;
                return SearchPlot(i, plotPoints, plant, move);
            })
            .Sum();
    }
}

public class Input
{
    public required string[] Map { get; init; }
    
    public Point Bounds => new Point(this.Map.Length, this.Map[0].Length);
    public char ElementAt(Point p) => this.Map[p.Row][p.Col];
    public bool IsInBounds(Point p) => p.IsInBounds(this.Map.Length, this.Map[0].Length);
    
    public static Input ParseInput(string file) => new Input()
    {
        Map = File.ReadAllLines(file),
    };
}

Rust

Areas are found by flooding, in the meantime whenever the adjacent plot would be outside the region (or out of bounds) the edge (inside plot, outside plot) is saved in a perimeter list. Part 1 takes just the size of that list, in part 2 we remove fence parts and all entries directly next to it on both sides.

::: spoiler Solution

use std::collections::{HashSet, VecDeque};

use euclid::{default::*, point2, vec2};

type Fences = HashSet<(Point2D<i32>, Point2D<i32>)>;
const DIRS: [Vector2D<i32>; 4] = [vec2(0, -1), vec2(1, 0), vec2(0, 1), vec2(-1, 0)];

fn parse(input: &str) -> Vec<&[u8]> {
    input.lines().map(|l| l.as_bytes()).collect()
}

fn price(field: &[&[u8]], start: (usize, usize), visited: &mut [Vec<bool>]) -> (u32, Fences) {
    let crop = field[start.1][start.0];
    let width = field[0].len();
    let height = field.len();
    let mut area_visited = vec![vec![false; width]; height];
    let mut area = 0;
    let mut fences: Fences = HashSet::new();

    area_visited[start.1][start.0] = true;
    visited[start.1][start.0] = true;
    let start = point2(start.0 as i32, start.1 as i32);
    let bounds = Rect::new(Point2D::origin(), Size2D::new(width, height).to_i32());
    let mut frontier = VecDeque::from([start]);
    while let Some(p) = frontier.pop_front() {
        area += 1;
        for dir in DIRS {
            let next = p + dir;
            if bounds.contains(next) {
                let next_u = next.to_usize();
                if area_visited[next_u.y][next_u.x] {
                    continue;
                }
                if field[next_u.y][next_u.x] == crop {
                    visited[next_u.y][next_u.x] = true;
                    area_visited[next_u.y][next_u.x] = true;
                    frontier.push_back(next);
                    continue;
                }
            }
            fences.insert((p, next));
        }
    }
    (area, fences)
}

fn part1(input: String) {
    let field = parse(&input);
    let width = field[0].len();
    let height = field.len();
    let mut visited = vec![vec![false; width]; height];
    let mut total_price = 0;
    for y in 0..height {
        for x in 0..width {
            if !visited[y][x] {
                let (area, fences) = price(&field, (x, y), &mut visited);
                total_price += area * fences.len() as u32;
            }
        }
    }
    println!("{total_price}");
}

fn count_perimeter(mut fences: Fences) -> u32 {
    let list: Vec<_> = fences.iter().copied().collect();
    let mut perimeter = 0;
    for (v, w) in list {
        if fences.contains(&(v, w)) {
            perimeter += 1;
            let dir = w - v;
            let orth = dir.yx();
            let mut next = v + orth;
            while fences.remove(&(next, next + dir)) {
                next += orth;
            }
            let mut next = v - orth;
            while fences.remove(&(next, next + dir)) {
                next -= orth;
            }
        }
    }
    perimeter
}

fn part2(input: String) {
    let field = parse(&input);
    let width = field[0].len();
    let height = field.len();
    let mut visited = vec![vec![false; width]; height];
    let mut total_price = 0;
    for y in 0..height {
        for x in 0..width {
            if !visited[y][x] {
                let (area, fences) = price(&field, (x, y), &mut visited);
                total_price += area * count_perimeter(fences);
            }
        }
    }
    println!("{total_price}");
}

util::aoc_main!();

:::

Also on github

Python

Had to rely on an external polygon library for this one. Part 1 could have been easily done without it but part 2 would be diffucult (you can even use the simplify function to count the number of straight edges in internal and external boundaries modulo checking the collinearity of the start and end of the boundary)

import numpy as np
from pathlib import Path
from shapely import box, union, MultiPolygon, Polygon, MultiLineString
cwd = Path(__file__).parent

def parse_input(file_path):
  with file_path.open("r") as fp:
    garden = list(map(list, fp.read().splitlines()))

  return np.array(garden)

def get_polygon(plant, garden):
  coords = list(map(tuple, list(np.argwhere(garden==plant))))
  for indc,coord in enumerate(coords):

    box_next = box(xmin=coord[0], ymin=coord[1], xmax=coord[0]+1,
                   ymax=coord[1]+1)

    if indc==0:
      poly = box_next
    else:
      poly = union(poly, box_next)

  if isinstance(poly, Polygon):
    poly = MultiPolygon([poly])

  return poly

def are_collinear(coords, tol=None):
    coords = np.array(coords, dtype=float)
    coords -= coords[0]
    return np.linalg.matrix_rank(coords, tol=tol)==1

def simplify_boundary(boundary):

  # if the object has internal and external boundaries then split them
  # and recurse
  if isinstance(boundary, MultiLineString):
    coordinates = []
    for b in boundary.geoms:
      coordinates.append(simplify_boundary(b))
    return list(np.concat(coordinates, axis=0))

  simple_boundary = boundary.simplify(0)
  coords = [np.array(x) for x in list(simple_boundary.coords)[:-1]]
  resolved = False

  while not resolved:

    end_side=\
    np.concat([x[:,None] for x in [coords[-1], coords[0], coords[1]]], axis=1)

    if  are_collinear(end_side.T):
      coords = coords[1:]
    else:
      resolved = True

  return coords

def solve_problem(file_name):

  garden = parse_input(Path(cwd, file_name))
  unique_plants = set(garden.flatten())
  total_price = 0
  discounted_total_price = 0

  for plant in unique_plants:

    polygon = get_polygon(plant, garden)

    for geom in polygon.geoms:
      coordinates = simplify_boundary(geom.boundary)
      total_price += geom.area*geom.length
      discounted_total_price += geom.area*len(coordinates)

  return int(total_price), int(discounted_total_price)

I know I'm late, but it's still fun and I'm sure no-one will see this.

Part 2 took me way too long to get right. I was initially only returning the relative point to which a plot needed a fence. I ran into issues of knowing if it was a valid fence or not by my method of counting (later). I eventually went with returning a tuple of the plot and an enum flag of the sides that it has fences on.

For counting I grouped the points by one axis then sorted on the other and counted the number of times the transition between two wasn't contiguous.

It could by done in parallel, but the original traversal would need de-duping, which I didn't feel like doing. After that things are done on a region basis, which could be parallel.

I also can't help but notice mine is by far the longest ( > . < )

F#

::: spoiler Tap for spoiler

type Plant = char
type Plot = Plant * Point2I
type Region = Plot list

let area region = List.length region

let perimeter (region:Region) (farm:Grid<Plant>) =
    (0, region)
    ||> List.fold (fun sum plot ->
        movements4 (snd plot) farm
        |> List.fold (fun acc po ->
            acc + match po with
                  | ValueNone -> 1
                  | ValueSome pother ->
                      get pother farm
                      |> fun x ->
                          match x with
                          | ValueSome v when v <> fst plot -> 1
                          | _ -> 0
            ) 0
        |> (fun x -> x + sum)
        )

let movements (plot:Plot) g (visited:HashSet<Point2I>) =
    let plant, point = plot
    movements4 point g
    |> List.choosev (fun px ->
        let vx = get px g |> ifNoneFail "couldn't get point"
        struct(px,vx))
    |> List.filter (fun plotx ->
        let struct(px, vx) = plotx
        vx = plant && not (visited.Contains px))

// visited is needed because I'm using similar logic to the trails, but no stopping point, so I
// need to make sure that it doesn't retrace itself
let rec traverse grid plot (visited:HashSet<Point2I>) =
    let plant, point = plot
    if visited.Contains(point) then []
    else
        visited.Add(point) |> ignore
        let path =
            movements plot grid visited
            |> List.filter (fun struct(newPoint, newPlant) -> newPlant = plant && visited.Contains(newPoint) |> not )
            |> List.map(fun struct(newPoint, newPlant) -> traverse grid (newPlant, newPoint) visited)
            |> List.concat
            |> List.distinct
        plot :: path

/// Returns the list of plots walked and a new grid with the traversed plots set to '.'
let walk (plot:Plot) (farm:Grid<Plant>) : Region * Grid<Plant> =
    let foundMovements = HashSet()
    let region:Region = traverse farm plot foundMovements
    let updatedGrid =
        let arr = Array2D.copy farm
        for _, p in region do
            set p arr '.' |> ignore
        arr
    (region, updatedGrid)
    
let rec findRegions (farm:Grid<Plant>) : Region list=
    farm
    |> List.unfold (fun arr ->
        match (findFirst (fun struct(_,_,v) -> v <> '.' )) arr with
        | Some value ->
            let struct(x,y,v) = value
            let point = {X = x; Y = y}
            let plot : Plot = (v, point)
            let region, newFarm = walk plot arr
            Some (region, newFarm)
        | None -> None
    )
    
let part1 input =
    (readGrid2D input (fun _ c -> c))
    |> fun grid ->
        findRegions grid
        |> List.sumBy (fun region ->
            let a = area region
            let p = perimeter region grid
            a * p)

// Part 2 ---------------------------------------------
[<Flags>]
type Side = None = 0 | Top = 1 | Right = 2| Left = 4 | Bottom = 8
type PlotWithFence = Plot * Side 

type SideAndNextPoint = Side * Point2I

// I couldn't use my original movement function because it filters out off-grid positions, so I modified it here
let getSides plot grid =
    let toPlot (side:Side) (voption:Point2I voption) =
        match voption with | ValueNone -> None, side | ValueSome point -> (Some point, side)
    [
        l (snd plot) |> toPlot Side.Left
        r (snd plot) grid |> toPlot Side.Right
        u (snd plot) |> toPlot Side.Top
        d (snd plot) grid |> toPlot Side.Bottom
    ]


let rec findEdges grid (plot:Plot) (visited:HashSet<Point2I>) =
    
    // this attempt that works is to attach the side information to each point
    // I didn't see a great way to use the existing movements function, so I copied it and added a mapping
    // so that I know in what direction the movement options are
    let sidesWithFence = getSides plot grid

    let plant, point = plot
    if visited.Add(point) |> not then Side.None
    else
        // use the movements to find edges
        let edges = 
            sidesWithFence
            |> List.fold (fun sides next ->
                match next with
                | None, side ->  sides ||| side
                | Some point, side when (get point grid >>= fun v -> v <> plant) |> ifNone false -> sides ||| side
                | _ -> sides
            ) Side.None
            
        edges

let getSideCount2 (plotFences:(Point2I * Side) list) =
    // ok, now I have information on each SIDE that a fence is one... omg I suck at this.
    // I'll try to do the whole compare horizontally and vertically thing again, but this time
    // within each I'll have to check the top and bottom, or I'll iterate it twice
    
    let getX (pf:Point2I * Side) : int = (fst pf) |> _.X
    let getY (pf:Point2I * Side) : int = (fst pf) |> _.Y
    
    let countContiguous side group sort =
        plotFences
        |> Seq.filter (fun pf -> (snd pf) &&& side = side)
        |> Seq.groupBy group 
        |> Seq.map(fun (key, g) -> g |> Seq.sortBy sort)
        // |> Array.ofSeq // debugging code
        |> Seq.sumBy(fun grouping ->
            let total =
                grouping
                |> Seq.splitByComparison (fun last current -> 
// splitByComparison is an implementation I took from ChatGPT for a function that I commonly use in C# as part of the MoreLinq library that does the same thing. Here, I'm splitting whenever there is a difference. 
// in hindsight, it probably could have been a window
                    let l = sort last 
                    let r = sort current
                    l <> (r - 1)
                )
                |> Seq.length
            total)
    
    let topCounts = countContiguous Side.Top getY getX
    let bottomCounts = countContiguous Side.Bottom getY getX
    let leftCounts = countContiguous Side.Left getX getY
    let rightCounts = countContiguous Side.Right getX getY
    
    topCounts + bottomCounts + leftCounts + rightCounts


let part2 input =
    (readGrid2D input (fun _ c -> c))
    |> fun grid ->
        findRegions grid
        // |> List.take 1
        |> List.sumBy(fun region ->
            let (plotCount, edgecount) =
                let visited = HashSet()
                let regionCounts =
                    region
                    |> List.map(fun p -> (snd p), findEdges grid p visited)
                    |> getSideCount2
                (visited.Count, regionCounts)
            
            let area = plotCount
            area * edgecount
            )

:::

region.GroupBy(YAxis) // row
.Select(group => 
    group.Sort(g => g.XAxis) // column
        .Window(a,b => a != b - 1 ? 1 : 0).Sum()
.Sum()

Uiua

I spent a while thinking about how to best do a flood fill in Uiua when I saw that ⊜ (partition) works beautifully with multidimensional markers: "Groups are formed from markers that are adjacent along any axis.", meaning I just had to convert all letters into numbers and I'd get all indices belonging to a field into an array.
For part 2, I cheated a bit by coming here and reading that you only need to count the edges. To my surprise, the second part is actually a bit faster than part 1. Takes less than 0.2 seconds each though :D

Run with example input here

$ RRRRIICCFF
$ RRRRIICCCF
$ VVRRRCCFFF
$ VVRCCCJFFF
$ VVVVCJJCFE
$ VVIVCCJJEE
$ VVIIICJJEE
$ MIIIIIJJEE
$ MIIISIJEEE
$ MMMISSJEEE
.
N     ← +[0_¯1 0_1 ¯1_0 1_0]
Areas ← ⊜□:⇡△.+1⍜♭⊛
Peri  ← -/+≡(/+∊N¤)⟜¤⟜(×4⧻)
Sides ← (
  ⊙(-¤)↯:▽⊙0×°⊟.+2⌵⊸-+1⊃⊣⊢⊸⍜⍉≡⍆
  ⧻⊚⊸∊1_3⧈(/+/+)2_2.⍜⊡=₀+1:
  +⊙(×2/+/+⧈(∊[[1_0 0_1][0_1 1_0]])2_2◌)
)
Cost! ← /+≡◇(×^0⟜⧻)

PartOne ← (
  # &rs ∞ &fo "input-12.txt"
  ⊜∘≠@\n.
  Cost!Peri Areas
)

PartTwo ← (
  # &rs ∞ &fo "input-12.txt"
  ⊜∘≠@\n.
  Cost!Sides Areas
)

&p "Day 12:"
&pf "Part 1: "
&p PartOne
&pf "Part 2: "
&p PartTwo