reasoning-gym/reasoning_gym/code/bf.py

140 lines
4.4 KiB
Python

from dataclasses import dataclass
from random import Random
from typing import Any, Optional
import bfi
from ..data.wordle_words import wordle_words
from ..factory import ProceduralDataset, register_dataset
from .contrib.bfit.Compiler import Compiler, Minify
@dataclass
class BFConfig:
"""Configuration for BF task generation"""
seed: Optional[int] = None
size: int = 500
difficulty: int = 1
def validate(self) -> None:
"""Validate configuration parameters"""
assert self.difficulty > 0, "difficulty must be greater than 0"
assert self.difficulty < 4, "difficulty must be less than 4"
class BFDataset(ProceduralDataset):
"""Generates BF tasks"""
def __init__(self, config: BFConfig):
self._prompt_templates = [
"This is a BF (Brainf*ck) computer program. What is the output?\n\n{bf_program}\n\nRespond only with the exact output of the program.",
"Consider the following BF (Brainf*ck) code. What would it output?\n\n{bf_program}\n\nProvide only the exact output of the code.",
]
super().__init__(config=config, seed=config.seed, size=config.size)
def __getitem__(self, idx: int) -> dict:
"""Generate a single BF task
Returns:
dict with keys:
- question: str, the task description with BF program
- answer: str, the result of this BF program BFI execution
- metadata: dict with generation parameters
"""
rng = Random(self.seed + idx)
bfit_code = self.generate_bfit_code(self.config.difficulty, rng)
bf_program = self.compile_bfit_code_to_bf(bfit_code)
result = bfi.interpret(bf_program, buffer_output=True)
return {
"question": rng.choice(self._prompt_templates).format(bf_program=bf_program),
"answer": result,
"metadata": {"bfit_code": bfit_code, "bf_program": bf_program},
}
def generate_bfit_code(self, difficulty, rng: Random) -> str:
if difficulty == 1:
word = rng.choice(wordle_words)
bfit_template = f"""
int main() {{
print("{word}");
}}
"""
elif difficulty == 2:
x = rng.randint(1, 4)
y = rng.randint(1, 5)
target = x * y * rng.randint(1, 9) + rng.randint(1, 9)
bfit_template = f"""
int main() {{
int acc = 0;
int target = {target};
int x = {x};
int y = {y};
while (acc < target) {{
acc = acc + x;
acc = acc + y;
}}
printint(acc);
}}
"""
elif difficulty == 3:
x = rng.randint(1, 7)
y = rng.randint(1, 9)
target = x * y * rng.randint(1, 9) + rng.randint(1, 9) + 50
conditional = target - rng.randint(1, 40)
bfit_template = f"""
int main() {{
int acc = 0;
int target = {target};
int x = {x};
int y = {y};
while (acc < target) {{
acc = acc + x;
if (acc > {conditional}) {{
acc = acc + y;
}}
}}
printint(acc);
}}
"""
rendered_bfit = bfit_template
return rendered_bfit
def compile_bfit_code_to_bf(self, bfit: str) -> str:
bf = Compiler.compile(bfit, optimize_code=True)
# bf = Minify.minify(bf) # Is this necessary?
return bf
def score_answer(self, answer: Optional[str], entry: dict[str, Any]) -> float:
"""Determine if the solution provided solves the BF task.
The function awards 1.0 for a correct answer.
Args:
answer (Optional[str]): The user's answer.
entry (dict[str, Any]): The original dataset entry containing the correct answer.
Returns:
float: The computed score between 0.0 and 1.0.
"""
if answer == None:
return 0.0
if answer != entry["answer"]:
if entry["answer"] in answer.splitlines():
# We can be quite confident that the correct answer was given
# It was likely just given alongside an explanation
return max(0.9 * len(answer) / len(entry["answer"]), 0.1)
if entry["answer"] in answer:
# Since answers are English words, some risk of the response coincidentally containing the answer
return max(0.5 * len(answer) / len(entry["answer"]), 0.1)
return 0.01
else:
return 1.0 # Yay
# Register the dataset
register_dataset("bf", BFDataset, BFConfig)