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import itertools
import json
import math
import os
import random
import re
import sys
from dataclasses import dataclass
from functools import lru_cache
from typing import Any, Dict, Iterable, List, Optional, Sequence
import numpy as np
import torch
CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
PARENT_DIR = os.path.dirname(CURRENT_DIR)
if PARENT_DIR not in sys.path:
sys.path.insert(0, PARENT_DIR)
from aligned_cell_policy.shared_cell_policy import CellExample
from formatting_icon import is_consistent_pair
def all_remaining_empties_have_legal_value(grid: np.ndarray) -> bool:
g = np.asarray(grid, dtype=int).reshape(9, 9)
for r in range(9):
for c in range(9):
if int(g[r, c]) != 0:
continue
cell = r * 9 + c
has_legal = any(is_consistent_pair(g, cell=cell, value=v, t=3, n=9) for v in range(1, 10))
if not has_legal:
return False
return True
@dataclass(frozen=True)
class ParsedValues:
values: tuple[int, ...]
parse_ok: bool
strict_canonical: bool
def all_digit_values() -> List[int]:
return list(range(1, 10))
def make_solved_grid_from_row(row: Dict[str, Any]) -> np.ndarray:
grid = parse_grid_from_tuple_prompt(str(row["prompt"]))
solved = np.asarray(grid, dtype=int).copy()
triples = row.get("metadata", {}).get("target_triples_1based", [])
for rr, cc, value in triples:
solved[int(rr) - 1, int(cc) - 1] = int(value)
return solved
def _grid_state_key(grid: np.ndarray) -> tuple[int, ...]:
return tuple(int(v) for v in np.asarray(grid, dtype=int).reshape(-1))
def _legal_values_for_cell(state: tuple[int, ...], cell: int) -> tuple[int, ...]:
rr, cc = divmod(int(cell), 9)
if int(state[cell]) != 0:
return tuple()
g = np.asarray(state, dtype=int).reshape(9, 9)
return tuple(
int(value)
for value in all_digit_values()
if is_consistent_pair(g, cell=int(cell), value=int(value), t=3, n=9)
)
@lru_cache(maxsize=200000)
def _stage_i_consistent_values_for_grid(state: tuple[int, ...], stage_i: int) -> tuple[tuple[int, ...], ...]:
stage_i = max(1, int(stage_i))
out: List[tuple[int, ...]] = [tuple() for _ in range(81)]
for cell in range(81):
legal_values = _legal_values_for_cell(state, cell)
if not legal_values:
continue
if stage_i <= 1:
out[cell] = legal_values
continue
consistent_values: List[int] = []
for value in legal_values:
child = list(state)
child[cell] = int(value)
child_state = tuple(child)
child_stage_values = _stage_i_consistent_values_for_grid(child_state, stage_i - 1)
if all(int(child_state[idx]) != 0 or len(child_stage_values[idx]) > 0 for idx in range(81)):
consistent_values.append(int(value))
out[cell] = tuple(consistent_values)
return tuple(out)
def stage_i_consistent_values(
grid: np.ndarray,
*,
target_cell: tuple[int, int],
stage_i: int,
) -> List[int]:
g = np.asarray(grid, dtype=int).reshape(9, 9)
rr, cc = int(target_cell[0]), int(target_cell[1])
if int(g[rr, cc]) != 0:
return []
cell = rr * 9 + cc
stage_values = _stage_i_consistent_values_for_grid(_grid_state_key(g), int(stage_i))
return [int(value) for value in stage_values[cell]]
def canonicalize_values(values: Iterable[int]) -> List[int]:
out: List[int] = []
seen = set()
for value in values:
if isinstance(value, bool):
raise ValueError("Boolean values are not allowed.")
vv = int(value)
if vv < 1 or vv > 9:
raise ValueError(f"Value must be in [1,9], got {vv}.")
if vv not in seen:
seen.add(vv)
out.append(vv)
return out
def values_json_text(values: Iterable[int]) -> str:
return json.dumps({"values": canonicalize_values(values)}, separators=(",", ":"))
def parse_values_json(text: str) -> ParsedValues:
raw = str(text).strip()
if not raw:
return ParsedValues(values=tuple(), parse_ok=False, strict_canonical=False)
try:
obj = json.loads(raw)
except Exception:
return ParsedValues(values=tuple(), parse_ok=False, strict_canonical=False)
if not isinstance(obj, dict):
return ParsedValues(values=tuple(), parse_ok=False, strict_canonical=False)
if set(obj.keys()) != {"values"}:
return ParsedValues(values=tuple(), parse_ok=False, strict_canonical=False)
values_obj = obj.get("values")
if not isinstance(values_obj, list):
return ParsedValues(values=tuple(), parse_ok=False, strict_canonical=False)
try:
values = canonicalize_values(values_obj)
except Exception:
return ParsedValues(values=tuple(), parse_ok=False, strict_canonical=False)
if len(values) != len(values_obj):
return ParsedValues(values=tuple(), parse_ok=False, strict_canonical=False)
canonical = values_json_text(values)
return ParsedValues(values=tuple(values), parse_ok=True, strict_canonical=(canonical == raw))
def compute_set_precision_recall(pred_values: Sequence[int], target_values: Sequence[int]) -> tuple[float, float]:
pred = set(int(v) for v in pred_values)
target = set(int(v) for v in target_values)
precision = 0.0 if not pred else float(len(pred & target) / max(1, len(pred)))
recall = 1.0 if not target else float(len(pred & target) / max(1, len(target)))
return precision, recall
def completion_ce_loss(
model: torch.nn.Module,
tokenizer: Any,
prompt_text: str,
completion_text: str,
device: torch.device,
) -> torch.Tensor:
prompt_ids = tokenizer(prompt_text, return_tensors="pt", add_special_tokens=False).input_ids.to(device)
all_ids = tokenizer(prompt_text + completion_text, return_tensors="pt", add_special_tokens=False).input_ids.to(
device
)
labels = all_ids.clone()
labels[:, : int(prompt_ids.shape[1])] = -100
out = model(input_ids=all_ids, labels=labels)
return out.loss
def batched_completion_ce_loss(
model: torch.nn.Module,
tokenizer: Any,
prompt_texts: Sequence[str],
completion_texts: Sequence[str],
device: torch.device,
) -> torch.Tensor:
if len(prompt_texts) != len(completion_texts):
raise ValueError("prompt_texts and completion_texts must have the same length")
if not prompt_texts:
raise ValueError("batched_completion_ce_loss requires at least one example")
full_texts = [str(p) + str(c) for p, c in zip(prompt_texts, completion_texts, strict=True)]
batch = tokenizer(full_texts, return_tensors="pt", add_special_tokens=False, padding=True)
prompt_batch = tokenizer(list(prompt_texts), return_tensors="pt", add_special_tokens=False, padding=True)
input_ids = batch["input_ids"].to(device)
attention_mask = batch["attention_mask"].to(device)
prompt_attention = prompt_batch["attention_mask"]
prompt_lengths = prompt_attention.sum(dim=1).tolist()
labels = input_ids.clone()
labels[attention_mask == 0] = -100
for row_idx, prompt_len in enumerate(prompt_lengths):
labels[row_idx, : int(prompt_len)] = -100
out = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)
return out.loss
def completion_logprob(
model: torch.nn.Module,
tokenizer: Any,
prompt_text: str,
completion_text: str,
device: torch.device,
) -> torch.Tensor:
prompt_ids = tokenizer(prompt_text, return_tensors="pt", add_special_tokens=False).input_ids.to(device)
all_ids = tokenizer(prompt_text + completion_text, return_tensors="pt", add_special_tokens=False).input_ids.to(
device
)
labels = all_ids.clone()
labels[:, : int(prompt_ids.shape[1])] = -100
out = model(input_ids=all_ids, labels=labels)
num_completion_tokens = int((labels != -100).sum().item())
return -out.loss * max(1, num_completion_tokens)
def enumerate_value_permutations(
values: Sequence[int],
*,
max_permutations: int,
rng: Optional[random.Random] = None,
) -> List[tuple[int, ...]]:
uniq = tuple(canonicalize_values(values))
if len(uniq) <= 1:
return [uniq]
total = math.factorial(len(uniq))
if total <= max(1, int(max_permutations)):
return [tuple(p) for p in itertools.permutations(uniq)]
rr = rng or random.Random(0)
perms = set()
base = list(uniq)
max_needed = max(1, int(max_permutations))
while len(perms) < max_needed:
shuffled = list(base)
rr.shuffle(shuffled)
perms.add(tuple(shuffled))
return list(perms)
def permutation_invariant_json_ce_loss(
model: torch.nn.Module,
tokenizer: Any,
prompt_text: str,
values: Sequence[int],
device: torch.device,
*,
max_permutations: int,
rng: Optional[random.Random] = None,
) -> torch.Tensor:
permutations = enumerate_value_permutations(values, max_permutations=max_permutations, rng=rng)
logps = [
completion_logprob(model, tokenizer, prompt_text, values_json_text(perm), device) for perm in permutations
]
stacked = torch.stack(logps, dim=0)
return -(torch.logsumexp(stacked, dim=0) - math.log(float(len(permutations))))
def build_supervised_completion(
ex: CellExample,
*,
stage_i: int,
rng: Optional[random.Random] = None,
randomize_order: bool = False,
) -> str:
values = stage_i_consistent_values(ex.grid, target_cell=ex.target_cell, stage_i=stage_i)
if randomize_order and len(values) > 1:
shuffled = list(values)
(rng or random).shuffle(shuffled)
values = shuffled
return values_json_text(values)
def summarize_values(values: Iterable[int]) -> str:
return "[" + ", ".join(str(int(v)) for v in values) + "]"
_TUPLE_PROMPT_RE = re.compile(r"\((\d+),(\d+),(\d+)\)")
def parse_grid_from_tuple_prompt(prompt_text: str) -> np.ndarray:
triples = _TUPLE_PROMPT_RE.findall(str(prompt_text))
if len(triples) < 81:
raise ValueError("Could not recover 81 (row,col,value) tuples from prompt.")
grid = np.zeros((9, 9), dtype=int)
for rr, cc, vv in triples[:81]:
r = int(rr) - 1
c = int(cc) - 1
grid[r, c] = int(vv)
return grid
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