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17 changed files with 7 additions and 6839 deletions

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@ -1,58 +1,7 @@
from typing import Any, Iterator
from ..base import BaseSolver
from ..tools import graphs
class Solver(BaseSolver):
def print_grid(self, grid: list[tuple[int, int]], n_rows: int, n_cols: int):
values = set(grid)
if self.files:
self.files.create(
"graph.txt",
"\n".join(
"".join(
"#" if (row, col) in values else "." for col in range(n_cols)
)
for row in range(n_rows)
).encode(),
text=True,
)
else:
for row in range(n_rows):
self.logger.info(
"".join(
"#" if (row, col) in values else "." for col in range(n_cols)
)
)
def dijkstra(self, corrupted: list[tuple[int, int]], n_rows: int, n_cols: int):
return graphs.dijkstra(
(0, 0),
(n_rows - 1, n_cols - 1),
graphs.make_neighbors_grid_fn(n_rows, n_cols, set(corrupted)),
)
def solve(self, input: str) -> Iterator[Any]:
values = [
(int(p[0]), int(p[1])) for r in input.splitlines() if (p := r.split(","))
]
_is_test = len(values) < 100
n_rows, n_cols, n_bytes_p1 = (7, 7, 12) if _is_test else (71, 71, 1024)
bytes_p1 = values[:n_bytes_p1]
self.print_grid(bytes_p1, n_rows, n_cols)
path_p1, cost_p1 = self.dijkstra(bytes_p1, n_rows, n_cols) or ((), -1)
yield cost_p1
path = path_p1
for b in range(n_bytes_p1, len(values)):
if values[b] not in path:
continue
path, _ = self.dijkstra(values[: b + 1], n_rows, n_cols) or (None, -1)
if path is None:
yield ",".join(map(str, values[b]))
break
def solve(self, input: str) -> Iterator[Any]: ...

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@ -1,42 +1,7 @@
from functools import cache
from typing import Any, Iterator
from ..base import BaseSolver
@cache
def is_valid(design: str, towels: tuple[str, ...]) -> bool:
if not design:
return True
return any(
design.startswith(towel) and is_valid(design[len(towel) :], towels)
for towel in towels
)
@cache
def count_valid(design: str, towels: tuple[str, ...]) -> int:
if not design:
return 1
return sum(
design.startswith(towel) and count_valid(design[len(towel) :], towels)
for towel in towels
)
class Solver(BaseSolver):
def solve(self, input: str) -> Iterator[Any]:
towels_s, designs_s = input.split("\n\n")
towels = tuple(s.strip() for s in towels_s.split(","))
designs = [
design
for design in self.progress.wrap(designs_s.splitlines())
if is_valid(design, towels)
]
yield len(designs)
yield sum(count_valid(design, towels) for design in self.progress.wrap(designs))
def solve(self, input: str) -> Iterator[Any]: ...

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@ -1,95 +1,7 @@
import itertools
from collections import Counter
from typing import Any, Callable, Iterable, Iterator, Sequence, TypeAlias
from typing import Any, Iterator
from ..base import BaseSolver
from ..tools.graphs import dijkstra, make_neighbors_grid_fn
Node: TypeAlias = tuple[int, int]
def make_neighbors_fn(grid: list[str], cheat_length: int):
n_rows, n_cols = len(grid), len(grid[0])
def _fn(node: Node):
row, col = node
return (
((row_n, col_n), abs(row_n - row) + abs(col_n - col))
for row_d in range(-cheat_length, cheat_length + 1)
for col_d in range(
-cheat_length + abs(row_d), cheat_length - abs(row_d) + 1
)
if 0 <= (row_n := row + row_d) < n_rows
and 0 <= (col_n := col + col_d) < n_cols
and grid[row_n][col_n] != "#"
)
return _fn
class Solver(BaseSolver):
def find_cheats(
self,
path: Sequence[Node],
cost: float,
costs_to_target: dict[Node, float],
neighbors_fn: Callable[[Node], Iterable[tuple[Node, float]]],
):
cheats: dict[tuple[tuple[int, int], tuple[int, int]], float] = {}
for i_node, node in enumerate(self.progress.wrap(path)):
for reach_node, reach_cost in neighbors_fn(node):
n_cost = (
i_node + reach_cost + costs_to_target.get(reach_node, float("inf"))
)
if n_cost < cost:
cheats[node, reach_node] = cost - n_cost
return cheats
def solve(self, input: str) -> Iterator[Any]:
grid = input.splitlines()
n_rows, n_cols = len(grid), len(grid[0])
start = next(
(i, j) for i in range(n_rows) for j in range(n_cols) if grid[i][j] == "S"
)
target = next(
(i, j) for i in range(n_rows) for j in range(n_cols) if grid[i][j] == "E"
)
reachable = dijkstra(
target,
None,
make_neighbors_grid_fn(
n_rows,
n_cols,
excluded=(
(i, j)
for i in range(n_rows)
for j in range(n_cols)
if grid[i][j] == "#"
),
),
)
# note: path is inverted here
path, cost = reachable[start]
costs_to_target = {k: c for k, (_, c) in reachable.items()}
self.logger.info(f"found past from start to target with cost {cost}")
for cheat_length in (2, 20):
cheats = self.find_cheats(
list(reversed(path)),
cost,
costs_to_target,
make_neighbors_fn(grid, cheat_length),
)
for saving, count in sorted(Counter(cheats.values()).items()):
self.logger.debug(
f"There are {count} cheats that save {saving} picoseconds."
)
target_saving = 100 if len(grid) > 20 else 50
yield sum(saving >= target_saving for saving in cheats.values())
def solve(self, input: str) -> Iterator[Any]: ...

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@ -1,121 +1,7 @@
import heapq
from dataclasses import dataclass
from typing import Any, Iterator, Literal, Sequence, TypeAlias, cast
from typing import Any, Iterator
from ..base import BaseSolver
Action: TypeAlias = Literal[">", "<", "v", "^", "A"]
NUM_PAD = ((7, 8, 9), (4, 5, 6), (1, 2, 3), (None, 0, "A"))
MOV_PAD: tuple[tuple[Action | None, ...], ...] = ((None, "^", "A"), ("<", "v", ">"))
@dataclass(frozen=True, order=True)
class Node:
robot_1: tuple[int, int] = (0, 2)
robot_2: tuple[int, int] = (0, 2)
robot_3: tuple[int, int] = (3, 2)
code: str = ""
def apply_action(
robot: tuple[int, int],
action: Action,
pad: tuple[tuple[int | str | None, ...], ...],
):
d_row, d_col = {"^": (-1, 0), "v": (1, 0), ">": (0, 1), "<": (0, -1)}[action]
row, col = robot[0] + d_row, robot[1] + d_col
if 0 <= row < len(pad) and 0 <= col < len(pad[row]) and pad[row][col] is not None:
return (row, col)
return None
def create_node(node: Node, action: Action) -> Node | None:
# main pad moves -> move first robot
if action != "A":
robot = apply_action(node.robot_1, action, MOV_PAD)
if robot is not None:
return Node(
robot_1=robot,
robot_2=node.robot_2,
robot_3=node.robot_3,
code=node.code,
)
return None
# activate pad 1 -> action on robot 1
robot_1_action = MOV_PAD[node.robot_1[0]][node.robot_1[1]]
assert robot_1_action is not None
if robot_1_action != "A":
robot2 = apply_action(node.robot_2, robot_1_action, MOV_PAD)
if robot2 is not None:
return Node(
robot_1=node.robot_1,
robot_2=robot2,
robot_3=node.robot_3,
code=node.code,
)
return None
# activate pad 2 -> action on robot 2
robot_2_action = MOV_PAD[node.robot_2[0]][node.robot_2[1]]
assert robot_2_action is not None
if robot_2_action != "A":
robot3 = apply_action(node.robot_3, robot_2_action, NUM_PAD)
if robot3 is not None:
return Node(
robot_1=node.robot_1,
robot_2=node.robot_2,
robot_3=robot3,
code=node.code,
)
return None
value = NUM_PAD[node.robot_3[0]][node.robot_3[1]]
assert value is not None
return Node(
robot_1=node.robot_1,
robot_2=node.robot_2,
robot_3=node.robot_3,
code=node.code + str(value),
)
class Solver(BaseSolver):
def dijkstra_for_code(self, target: str):
queue: list[tuple[float, Node, tuple[str, ...]]] = [(0, Node(), ())]
preds: dict[Node, tuple[str, ...]] = {}
while queue:
dis, node, path = heapq.heappop(queue)
if not target.startswith(node.code):
continue
if node in preds:
continue
preds[node] = path
if node.code == target:
self.logger.info(f"found [{target}]: {''.join(path)} ({len(path)})")
return path
for action in cast(Sequence[Action], "A^v<>"):
node_2 = create_node(node, action)
if node_2:
heapq.heappush(queue, (dis + 1, node_2, path + (action,)))
return None
def solve(self, input: str) -> Iterator[Any]:
yield sum(
len(self.dijkstra_for_code(code) or ()) * int(code[:-1], 10)
for code in input.splitlines()
)
def solve(self, input: str) -> Iterator[Any]: ...

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@ -3,57 +3,5 @@ from typing import Any, Iterator
from ..base import BaseSolver
def mix(secret: int, value: int) -> int:
return secret ^ value
def prune(secret: int) -> int:
return secret % 16777216
def next_number(secret: int) -> int:
# Calculate the result of multiplying the secret number by 64. Then, mix this
# result into the secret number. Finally, prune the secret number.
secret = prune(mix(secret, secret * 64))
# Calculate the result of dividing the secret number by 32. Round the result down
# to the nearest integer. Then, mix this result into the secret number. Finally,
# prune the secret number.
secret = prune(mix(secret, secret // 32))
# Calculate the result of multiplying the secret number by 2048. Then, mix this
# result into the secret number. Finally, prune the secret number.
secret = prune(mix(secret, secret * 2048))
return secret
class Solver(BaseSolver):
def solve(self, input: str) -> Iterator[Any]:
starts = [int(r) for r in input.splitlines()]
ends: list[int] = []
prices: list[int] = [0 for _ in range(2**16)]
for secret in self.progress.wrap(starts):
checked: list[bool] = [False] * len(prices)
hashed: int = 0
for i in range(2000):
last = secret % 10
secret = next_number(secret)
next = secret % 10
hashed = ((hashed << 4) & 0xFFFF) | ((last - next) & 0xF)
if i >= 3 and not checked[hashed]:
checked[hashed] = True
prices[hashed] += next
ends.append(secret)
for start, end in zip(starts, ends, strict=True):
self.logger.info(f"{start}: {end}")
yield sum(ends)
yield max(prices)
def solve(self, input: str) -> Iterator[Any]: ...

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@ -1,402 +0,0 @@
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import heapq
from typing import Callable, Iterable, TypeVar, overload
_Node = TypeVar("_Node")
def make_neighbors_grid_fn(
rows: int | Iterable[int],
cols: int | Iterable[int],
excluded: Iterable[tuple[int, int]] = set(),
diagonals: bool = False,
):
"""
Create a neighbors function suitable for graph function for a simple grid.
Args:
rows: Rows of the grid. If an int is specified, the rows are assumed to be
numbered from 0 to rows - 1, otherwise the iterable should contain the list
of valid rows.
cols: Columns of the grid. If an int is specified, the columns are assumed to be
numbered from 0 to cols - 1, otherwise the iterable should contain the list
of valid columns.
excluded: Cells of the grid that cannot be used as valid nodes for the graph.
diagonals: If True, neighbors will include diagonal cells, otherwise, only
horizontal and vertical neighbors will be included.
"""
ds = ((-1, 0), (0, 1), (1, 0), (0, -1))
if diagonals:
ds = ds + ((-1, -1), (-1, 1), (1, -1), (1, 1))
if isinstance(rows, int):
rows = range(rows)
elif not isinstance(rows, range):
rows = set(rows)
if isinstance(cols, int):
cols = range(cols)
elif not isinstance(cols, range):
cols = set(cols)
excluded = set(excluded)
def _fn(node: tuple[int, int]):
return (
((row_n, col_n), 1)
for dr, dc in ds
if (row_n := node[0] + dr) in rows
and (col_n := node[1] + dc) in cols
and (row_n, col_n) not in excluded
)
return _fn
@overload
def dijkstra(
start: _Node,
target: None,
neighbors: Callable[[_Node], Iterable[tuple[_Node, float]]],
) -> dict[_Node, tuple[tuple[_Node, ...], float]]: ...
@overload
def dijkstra(
start: _Node,
target: _Node,
neighbors: Callable[[_Node], Iterable[tuple[_Node, float]]],
) -> tuple[tuple[_Node, ...], float] | None: ...
def dijkstra(
start: _Node,
target: _Node | None,
neighbors: Callable[[_Node], Iterable[tuple[_Node, float]]],
) -> (
dict[_Node, tuple[tuple[_Node, ...], float]]
| tuple[tuple[_Node, ...], float]
| None
):
"""
Solve shortest-path problem using simple Dijkstra algorithm from start to target,
using the given neighbors function.
Args:
start: Starting node of the path.
target: Target node for the path.
neighbors: Function that should return, for a given node, the list of
its neighbors with the cost to go from the node to the neighbor.
Returns:
One of the shortest-path from start to target with its associated cost, if one
is found, otherwise None.
"""
queue: list[tuple[float, _Node, tuple[_Node, ...]]] = [(0, start, (start,))]
preds: dict[_Node, tuple[tuple[_Node, ...], float]] = {}
while queue:
dis, node, path = heapq.heappop(queue)
if node in preds:
continue
preds[node] = (path, dis)
if node == target:
break
for neighbor, cost in neighbors(node):
if neighbor in preds:
continue
heapq.heappush(queue, (dis + cost, neighbor, path + (neighbor,)))
if target is None:
return preds
return preds.get(target, None)